17

Evaluating 

ASSIST

A Blueprint for Understanding

State-level Tobacco Control

NCI TOBACCO CONTROL MONOGRAPH SERIES

U.S. DEPARTMENT 

OF HEALTH AND 

HUMAN SERVICES

National Institutes 

of Health

National Cancer Institute
ii

Other NCI Tobacco Control Monographs

Strategies to Control Tobacco Use in the United States: A Blueprint for Public Health Action in the 

1990’s. Smoking and Tobacco Control Monograph No. 1. NIH Pub. No. 92-3316, December 1991.

Smokeless Tobacco or Health: An International Perspective. Smoking and Tobacco Control 

Monograph No. 2. NIH Pub. No. 92-3461, September 1992.

Major Local Tobacco Control Ordinances in the United States. Smoking and Tobacco Control 

Monograph No. 3. NIH Pub. No. 93-3532, May 1993.

Respiratory Health Effects of Passive Smoking: Lung Cancer and Other Disorders. Smoking 

and Tobacco Control Monograph No. 4. NIH Pub. No. 93-3605, August 1993.

Tobacco and the Clinician: Interventions for Medical and Dental Practice. Smoking and 

Tobacco Control Monograph No. 5. NIH Pub. No. 94-3693, January 1994.

Community-based Interventions for Smokers: The COMMIT Field Experience. Smoking and 

Tobacco Control Monograph No. 6. NIH Pub. No. 95-4028, August 1995.

The FTC Cigarette Test Method for Determining Tar, Nicotine, and Carbon Monoxide Yields 

of U.S. Cigarettes. Report of the NCI Expert Committee. Smoking and Tobacco Control 

Monograph No. 7. NIH Pub. No. 96-4028, August 1996.

Changes in Cigarette Related Disease Risks and Their Implications for Prevention and Control.

Smoking and Tobacco Control Monograph No. 8. NIH Pub. No. 97-4213, February 1997.

Cigars: Health Effects and Trends. Smoking and Tobacco Control Monograph No. 9. NIH Pub. 

No. 98-4302, February 1998.

Health Effects of Exposure to Environmental Tobacco Smoke. Smoking and Tobacco Control 

Monograph No. 10. NIH Pub. No. 99-4645, August 1999.

State and Local Legislative Action to Reduce Tobacco Use. Smoking and Tobacco Control 

Monograph No. 11. NIH Pub. No. 00-4804, August 2000.

Population Based Smoking Cessation. Smoking and Tobacco Control Monograph No. 12. NIH 

Pub. No. 00-4892, November 2000.

Risks Associated with Smoking Cigarettes with Low Machine-Measured Yields of Tar and Nicotine.

Smoking and Tobacco Control Monograph No. 13. NIH Pub. No. 02-5047, October 2001.

Changing Adolescent Smoking Prevalence. Smoking and Tobacco Control Monograph No. 14. 

NIH Pub. No. 02-5086, November 2001.

Those Who Continue to Smoke. Smoking and Tobacco Control Monograph No. 15. NIH Pub. 

No. 03-5370, September 2003.

ASSIST: Shaping the Future of Tobacco Prevention and Control. Tobacco Control Monograph 

No. 16. NIH Pub. No. 05-5645, May 2005.

Note, when citing this monograph in other works, please use the following format:

National Cancer Institute. Evaluating ASSIST: A Blueprint for Understanding State-level Tobacco Control.

Tobacco Control Monograph No. 17. Bethesda, MD: U.S. Department of Health and Human Services, 

National Institutes of Health, National Cancer Institute. NIH Pub. No. 06-6058, October 2006.
iii

Dedication

I wish to acknowledge the invaluable contributions of time and intellect that 

were provided to the ASSIST evaluation by the many dedicated researchers whose 

efforts and ingenuity helped make the evaluation come to fruition. Although far 

too many individuals were involved to name here, this volume is dedicated to all 

of you. Especially helpful was the technical expertise provided by Lois Biener, 

Frank Chaloupka, Mike Cummings, Betsy Gilpin, Stan Glantz, Larry Kincaid, 

David Murray, John Pierce, Jon Samet, and Bill Trochim. Their insight and 

commitment were essential for completing this enormous undertaking. In addition, my 

statistical team—Anne Hartman and Barry Graubard—provided invaluable input. I 

also want to thank my co-editor for this volume, Carol Schmitt, who provided endless 

support through the development of this monograph. 

The ASSIST evaluation would not have been possible without the support of 

former and current staff of the National Cancer Institute. Barbara Rimer, Bob Hiatt, 

and Bob Croyle recognized the contribution this project had to offer to advance 

understanding of population-level tobacco control efforts as well as how the ASSIST 

evaluation methods and measures could apply to the larger arena of cancer control. 

Performing this evaluation was a difficult task. Many new approaches were 

necessary; many different challenges had to be overcome; and many opinions had to be 

satisfied. We are truly fortunate that much was learned and much was accomplished. 

For this I am sincerely grateful. Albert Einstein said, “In the middle of every difficulty 

lies opportunity.” The ASSIST evaluation was such an opportunity, both intellectually 

and personally. Most of all, this evaluation was an opportunity to lend credibility to the 

ground-breaking work accomplished by all those involved in ASSIST. 

Frances A. Stillman

October 2006
iv

A Note from the Series Editor

With this volume, the National Cancer Institute (NCI) presents the 17th monograph 

of the Tobacco Control Monograph series. 

This monograph documents the evaluation of a groundbreaking NCI program. 

The American Stop Smoking Intervention Study for Cancer Prevention, known as 

ASSIST, put into practice NCI’s commitment to prevent and reduce tobacco use 

across all populations and age groups. ASSIST took evidenced-based interventions 

from controlled studies and implemented them in the larger community of 17 states. 

Its underlying rationale—that significant decreases in tobacco use could be realized 

only with interventions that changed the social environment such that smoking was 

non-normative—was a significant departure from previous tobacco control programs 

and in the vanguard of the “new” public health. Prior to ASSIST, few states addressed 

tobacco use at the population level. The ASSIST legacy remains today in the tobacco 

control professionals whose work continues to reduce the burden of disability and death 

caused by tobacco. 

ASSIST raised significant conceptual and practical challenges for its evaluation 

team. These challenges included context-dependent implementation and the diffusion 

of ASSIST and ASSIST-like interventions into non-ASSIST states. In addition, 

the evaluation did not begin until several years after ASSIST was implemented, 

and its budget was limited. What had been envisioned as a simple evaluation of a 

demonstration project became a complex evaluation effort that engaged a diverse group 

of scientists and practitioners and required numerous sources of data. The resulting 

evaluation successfully documented the effectiveness of ASSIST. It also validated the 

causal pathway described in NCI’s 1991 Smoking and Tobacco Control Monograph 1: 

Strategies to Control Tobacco Use in the United States: A Blueprint for Public 

Health Action in the 1990’s—that comprehensive interventions can change the social 

environment of tobacco use and subsequently result in decreased tobacco use.

This monograph stands alone as a documentation of the ASSIST evaluation and 

describes the challenges met in evaluating a program that was influenced by numerous 

forces outside the program’s control. However, this monograph may also be viewed 

as a companion to NCI Tobacco Control Monograph 16, which reviews the ASSIST 

program in detail. Together these two monographs provide a detailed history and 

evidence base that document the success of an NCI initiative that began with a series of 

research hypotheses, tested those hypotheses with community-based interventions, and 

ultimately fielded a demonstration program that fundamentally changed tobacco use 

prevention and control in the United States.

It has been seven years since ASSIST ended and all states became funded by the 

National Tobacco Control Program at the Centers for Disease Control and Prevention. 

At this writing, it is no longer considered normative for children to become smokers; 
v

laws and policies that restrict smoking in public places protect more Americans each 

year; and state tax increases have resulted in cigarette prices that are high enough to 

reduce consumption and prevalence. We have come far, but there is more work to be 

done before tobacco use is no longer the leading cause of death and disability in the 

United States.

This volume and several future volumes in the Tobacco Control Monograph Series 

have important implications for research, practice, and policy in tobacco control as 

well as in other areas of public health. Lessons learned from tobacco prevention and 

control can be applied to a variety of public health issues, including physical activity, 

diet and nutrition, overweight and obesity, and substance abuse. NCI is committed 

to disseminating this cross-cutting knowledge to the widest possible audience so 

that others can benefit from the experience of the tobacco prevention and control 

community. By so doing, NCI is increasing the evidence base for effective public health 

interventions and improving the translation of research to practice and policy.

Stephen E. Marcus, Ph.D.

Monograph Series Editor

Epidemiologist

Tobacco Control Research Branch

Behavioral Research Program

Division of Cancer Control and Population Sciences

October 2006

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T
vi

Foreword

This monograph, like so many others in the National Cancer Institute’s (NCI’s) 

Tobacco Control Monograph series, is an important document. At a time when 

“Big Science” is being supported to advance knowledge of society’s most pressing 

biomedical and public health problems, scientists are also being challenged to 

demonstrate what has been accomplished for the investment made. There are few 

guides as to how to evaluate large-scale science. This is one of them.

The American Stop Smoking Intervention Study for Cancer Prevention (ASSIST) 

was the first “demonstration” project that put into practice the ultimate phase of NCI’s 

Five Phases of Cancer Control Research advanced by Peter Greenwald (NCI’s Director 

of the Division of Cancer Prevention and Control) and Joseph W. Cullen (Deputy 

Director of the same division) in 1984. The ASSIST program followed the orderly 

and sequential progression of tobacco control research in the earlier four phases from 

public education in the 1960s, to individual-level interventions, to community-level 

and then population-level interventions in the 1970s and 1980s. The Community 

Intervention Trial for Smoking Cessation (COMMIT; 1986–92), which immediately 

preceded ASSIST, was a model for the application of a randomized controlled trial to 

community research. ASSIST was the next logical step and a serious federal investment 

designed to apply the evidence gained from COMMIT and the large body of other 

previous research to policy interventions in 17 states. However, at ASSIST’s outset, no 

evaluation was planned. Only after ASSIST was in progress did the need for some way 

to assess its impact become apparent. This monograph is a testament to the ingenuity 

and perseverance of the evaluation team that took on that challenge and saw the 

evaluation to its successful completion. Coincidently, like the number of states that had 

ASSIST contracts, this NCI monograph on the evaluation of ASSIST is number 17 in 

the Tobacco Control Monograph series.

The evaluation process is completely described in this monograph. It required the 

development of an overall design strategy that took into account the separate and 

unplanned impacts of other state-based initiatives supported by the Robert Wood 

Johnson Foundation and the Centers for Disease Control and Prevention (chapter 1). It 

required the development of metrics that assessed the power of state efforts in tobacco 

control as well as the countervailing efforts of the tobacco industry to negate these policy 

initiatives. The Strength of Tobacco Control Index (SoTC) was developed to answer this 

need after careful study of what information was available and reliable enough to be 

included in such an index (chapter 2). The evaluators also included metrics that captured 

changes in state and local clean indoor air laws (chapter 3) and developed metrics to 

repeatedly assess the initial and intermediate effects of the interventions (chapter 4). 

Finally, the evaluation took into account the differences among states in their tobacco 

Greenwald, P. G., and J. W. Cullen. 1984. The scientific approach to cancer control. CA-A Cancer Journal 

for Clinicians 34 (6): 330–31.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

vii

growing and production practices due to concerns about the influence of regional 

commercial interests on receptivity to the ASSIST program (chapters 5 and 6). 

All of these approaches to evaluation were novel and required a substantial amount 

of creativity on the part of the evaluation team and their technical advisors. At the time, 

the structure and implementation of the evaluation strategy were truly challenging, and 

painstaking effort was invested in testing and validation. In fact, the evaluation evolved 

over time. This process of evolution is covered as well as two aspects of the evaluation, 

the database of newspaper print media coverage (chapter 7) and the study of tobacco 

industry counter-measures (chapter 8), which did not figure in the final statistical analysis. 

The inclusion of these aspects in the monograph reflects the thoroughness of the team 

efforts to report on all aspects of this enormous undertaking, even the false starts.

The need for evaluation of other large-scale NCI-supported cancer research 

initiatives is now well recognized. These initiatives include the Transdisciplinary 

Tobacco Use Research Centers (TTURCs), the Centers for Excellence in Cancer 

Communication, and the Centers for Population Health and Health Disparities. None 

of these are state-based initiatives, yet each is a large and complex transdisciplinary 

research enterprise that has required a major public investment. The ASSIST evaluation 

stands at the vanguard of these efforts, and the reader will learn much about the critical 

role of such assessments in moving research into practice, in this case into practice 

against the nation’s number one cause of premature death and disability.

Robert A. Hiatt, M.D., Ph.D.

Director of Population Sciences and Deputy Director 

UCSF Comprehensive Cancer Center 

Professor, Epidemiology and Biostatistics 

University of California, San Francisco
viii

Preface

Capturing the scope of an ecological process such as the evaluation of the American 

Stop Smoking Intervention Study for Cancer Prevention (ASSIST) in monograph 

form presents a daunting task because the evaluation’s scope and methodology were 

constantly evolving. Numerous challenges arose in the writing of this monograph: 

(1) Some factors and covariates in the evaluation analysis consisted of relatively 

simple measurements, whereas others required entire sections or chapters to describe 

in adequate detail. (2) Demonstration projects were undertaken, such as the creation of 

a print newspaper database of tobacco coverage, which were ultimately not included 

in the overall ASSIST evaluation analysis but still have relevance to future research 

efforts. (3) Fundamental assumptions of the original evaluation, such as original design 

of a direct comparison between ASSIST and non-ASSIST states, were changed as 

ASSIST-type interventions were introduced in other states.

At the same time, the full story of the ASSIST evaluation is an important one to 

tell, because in the richness and complexity of its evolution, it serves as a guide for the 

future of evaluating large-scale population-level public health projects. Older evaluation 

methodologies were simply not adequate for a project of the scope of ASSIST and, in 

turn, the methods presented here will undoubtedly evolve further to meet the growing 

scope of future public health efforts.

Figure 1 outlines a framework for the content presented in this monograph. The 

monograph tells a procedural story rather than a chronological one, tracing the 

development of the ASSIST evaluation conceptual model, examining its assumptions in 

detail, discussing related projects, and finally reviewing the evaluation results in detail.

This framework provides a basis for presenting an overview of the ASSIST evaluation 

and its design, a detailed discussion of its evaluation components, a summary of related 

projects, and a discussion of the evaluation outcomes. The chapters are as follows:

Chapter 1. The ASSIST Evaluation Project: An Overview. This chapter presents an 

overview of the ASSIST evaluation and its historical context. It reviews the key points 

of the ASSIST project and describes the conceptual model that guided the ASSIST 

evaluation as well as the key constructs of the conceptual model, the rationales for 

their inclusion, and the research questions that established the linkages between these 

conceptual constructs.

Chapter 2. The Strength of Tobacco Control Index. The Strength of Tobacco Control 

index is a composite measure of the core components of a state-level tobacco control 

program, developed to assess which components of ASSIST or ASSIST-like programs 

might be related to a specified outcome or a trend of lower smoking prevalence or 

cigarette consumption. The Strength of Tobacco Control index assesses three major 

constructs: tobacco control resources, capacity, and program efforts focused on policy 

and environmental change. The Strength of Tobacco Control index was developed for the 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

ix

ASSIST evaluation but has demonstrated applicability for other evaluations, such as the 

Robert Wood Johnson Foundation’s SmokeLess States National Tobacco Policy Initiative. 

The Strength of Tobacco Control index is now being used to compare tobacco control 

programs across the United States. This chapter describes the creation of the Strength of 

Tobacco Control index, details its descriptive characteristics, and provides examples of 

how it may be used to assess and improve state-based tobacco control programs. 

Chapter 3. Measuring Policy and Legislative Changes. The implementation of 

legislative changes that would promote a tobacco-free social norm and environments 

was a critical objective of the ASSIST project. The ASSIST evaluation assessed 

changes in state and local clean indoor air laws as a component of the Initial Outcomes 

Index. This chapter describes the methods used to track and measure these legislative 

changes. A longitudinal comparison of clean indoor air legislation in ASSIST and nonASSIST states is also provided.

Chapter 4. Initial Outcomes Index. Changes in policy occur and can be measured 

before changes in individual behavior. An Initial Outcomes Index was developed to 

assess the policy outcomes of the states’ tobacco control efforts. This chapter describes 

the variables used in this index and the methods used to create it.

Figure 1. A Framework for the ASSIST Evaluation Monograph

Project scope and definition

� Overview of the ASSIST evaluation

Key evaluation metrics

� Strength of Tobacco Control (SoTC)

� Initial Outcomes Index (IOI)

− Policy and legislative changes

Covariates and initial conditions

� State conditions

− Impact of tobacco on state economies

Other efforts

� Print media database

� Tobacco industry interference tactics study

Methods and results

� Analytic methods and results

� Cost-effectiveness

� Summary

Project

scope and

definition

Key

evaluation

metrics

Covariates

and initial

conditions

Other

efforts

Methods

and

results
x

Chapter 5. State Facilitating Conditions and Barriers to Implementation of Tobacco 

Control Programs. Tobacco growing and production can profoundly affect the adoption 

of policy-based tobacco control interventions, as evidenced by substantial differences 

between tobacco-producing states and nonproducing states in areas such as tobacco 

taxes and the adoption of tobacco control policies. This chapter outlines the issues 

and assumptions leading to the development of a state-level variable representing the 

economic dependence of states on tobacco growing and manufacturing, for use as a 

covariate in the ASSIST evaluation regression analyses as part of the measures of state 

conditions.

Chapter 6. Measuring the Impact of Tobacco on State Economies. State differences 

in population demographics, and economic, political, social, cultural, and geographic 

factors can affect the likely acceptance, implementation, and outcomes of a state 

tobacco control program. This chapter describes these factors and how they might 

affect an evaluation of a tobacco control program, together with a discussion of which 

factors were used as part of the ASSIST evaluation. In addition, potential measurement 

techniques for other factors are suggested for use in future evaluations. 

Chapter 7. The ASSIST Newspaper Tracking System. Media advocacy was one of the 

three principal interventions of the ASSIST model. An analysis of the amount and type 

of newspaper media coverage potentially attributable to media advocacy interventions 

was one approach used to evaluate their success. This chapter describes a state-level 

index to measure newspaper coverage of the four ASSIST priority policy areas—

clean indoor air, restrictions on minors’ access to tobacco, excise tax increases, and 

restrictions on tobacco advertising and promotion. Although this index was not used in 

the ASSIST evaluation, the chapter demonstrates its potential value for future analyses.

Chapter 8. Evaluating Tobacco Industry Tactics as a Counterforce to ASSIST.

Tobacco industry efforts to counter tobacco control initiatives are not typically taken 

into account when tobacco control programs are evaluated, even though counterefforts 

constitute a strong force that impedes achievement of tobacco control intervention 

objectives. In this chapter, examples of tobacco industry efforts to counter ASSIST and 

other state tobacco control programs are provided from the peer-reviewed literature and 

tobacco industry documents. While the inherent difficulty of measuring these counterefforts ultimately precluded their use in the ASSIST evaluation model, the chapter 

discusses the impediments to creating a tobacco industry effort measure and potential 

solutions for overcoming these impediments.

Chapter 9. Final Outcomes: Analytical Methods and Results. The final outcomes 

of the ASSIST evaluation are cigarette smoking prevalence and per capita cigarette 

consumption at the end of the intervention. This chapter describes the methodology 

and statistical techniques used to assess the final outcomes and discusses the evaluation 

results.

Chapter 10. Cost-effectiveness of ASSIST. This chapter uses standard econometric 

techniques to assess the cost-effectiveness of the ASSIST project. The standard 

P r e f a c e
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

xi

econometric techniques enable comparisons of the cost-effectiveness of ASSIST with 

other types of tobacco control interventions and with other large-scale public health 

initiatives.

Chapter 11. The ASSIST Evaluation: Contributions to Evaluation of Complex Public 

Health Initiatives. The closing chapter summarizes the major findings of the ASSIST 

study and discusses how this evaluation effort can serve as a model for evaluating 

large-scale, complex public health initiatives. The chapter discusses the broader 

issues in evaluation of large-scale initiatives, such as addressing complexity in public 

health projects and the need to go beyond existing “black box” approaches. It also 

discusses the programmatic and evaluation challenges, such as tobacco industry efforts 

to undermine or counter public health initiatives, and the potential for generalizing 

evaluation efforts such as ASSIST.

The ASSIST evaluation represents a successful attempt to measure the effectiveness 

of upstream tobacco control interventions in an environment where these interventions 

were widely adopted beyond the states funded by the National Cancer Institute. Using 

a rigorously developed and validated model, the evaluation shows a positive correlation 

between these interventions and a decline in tobacco usage and, more important, 

provides a model for how complex public health issues can be evaluated at broad levels 

of the population. In this sense, efforts such as the ASSIST evaluation represent part 

of the future of public health as this field moves beyond individuals and communities 

to address the fundamental sociopolitical issues that will reduce society’s burden of 

disease and preventable death.

Frances A. Stillman

Senior Scientific Editor

Co-Director, Institute for Global Tobacco Control

Associate Professor, Department of Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Carol L. Schmitt

Senior Scientific Editor

Senior Health Research Scientist

Battelle Centers for Public Health Research & Evaluation

Baltimore, MD 
xii

The American Stop Smoking Intervention Study for 

Cancer Prevention (ASSIST)

This monograph, Evaluating ASSIST: A Blueprint for Understanding State-level 

Tobacco Control (NCI Tobacco Control Monograph 17), and the preceding one in 

this series, Monograph 16, ASSIST: Shaping the Future of Tobacco Prevention and 

Control, are designed as companion documents. Whereas Monograph 17 addresses 

the evaluation framework, the details of the ASSIST evaluation, and the results 

of this effort, Monograph 16 focuses on the processes and interventions used to 

implement ASSIST, lessons learned and insights, and the transition of ASSIST from 

a demonstration project to the National Tobacco Control Program supported by 

the Centers for Disease Control and Prevention. (Where appropriate, reference to 

Monograph 16 is provided; complete citation information for Monograph 16 can be 

found on page ii of this volume.) Following is a brief overview of Monograph 16, 

which was published in May 2005.

Monograph 16. ASSIST: Shaping the Future of Tobacco Prevention and Control

ASSIST was an 8-year, nonrandomized demonstration project for tobacco use 

prevention and control conducted by the National Cancer Institute, the American 

Cancer Society, and 17 state health departments. The goal of ASSIST was to change the 

social, cultural, economic, and environmental factors that promote tobacco use by using 

policy, mass media, and program services interventions. The four policy strategies were 

as follows:

■	 Raising excise taxes to increase the price of tobacco products

■	 Eliminating exposure to environmental tobacco smoke

■	 Limiting tobacco advertising and promotion

■	 Reducing minors’ access to tobacco products

The strategies for ASSIST were developed and implemented by state and local 

tobacco control coalitions using population-based research, public health practices, 

policy development, and media advocacy. The concepts of building on a strong 

evidence base; designing interventions with broad population impacts; changing social 

norms in pursuit of greater justice; developing strong partnerships based on common 

goals and mutual respect; maintaining a determination not to be swayed or pushed off 

target by one’s adversaries; and ensuring a serious commitment to evaluation, selfreflection, and midcourse correction were crucial components of ASSIST.

Monograph 16 provides in-depth descriptions of intervention processes, examples of 

materials and best practices, and resource lists and guidance for activities such as media 

advocacy campaigns. Numerous case studies are presented, not in the form of formal 

social research, but as stories and vignettes from state and local public health staff 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

xiii

and volunteers who describe their efforts, the barriers they encountered, the lessons 

they learned, and insights they gained. These case studies show ASSIST as it was 

experienced by the many committed and diverse people responsible for its success.

Below are the major topics addressed in Monograph 16:

■	 The historical context and conceptual framework of ASSIST

■	 The national partners and state agencies and their respective roles, and 

communication linkages among all the structural units that promoted collaborative 

decision making and were essential for the program to function

■	 National, state, and local capacity building by mobilizing communities, establishing 

coalitions, promoting participatory planning, and providing training and technical 

assistance

■	 Descriptions of strategies and intervention methods, insights, and lessons learned 

for the three ASSIST intervention channels—policy development, mass media and 

media advocacy, and program services

■	 The tobacco industry challenge to ASSIST and the ASSIST response

■	 Strategic planning for a national tobacco use prevention and control program

■	 The processes and challenges in maintaining capacity built by the ASSIST 

demonstration project, disseminating best practices, and building a comprehensive 

national tobacco use prevention and control program

■	 Contributions of ASSIST to tobacco use prevention and control and to other 

behavioral health programs

The insights and lessons learned from ASSIST have advanced our understanding of 

how research studies can be successfully translated and disseminated as demonstration 

projects, while illustrating how sustained funding builds effective tobacco use 

prevention and control programs. The ASSIST legacy endures in the infrastructure 

that continues to support tobacco use prevention and control interventions. As the first 

major public health intervention grounded in ecological theory, ASSIST remains an 

exemplar for modern systems-level public health programs.
xiv

Acronyms List

ACS American Cancer Society

ANRF American Nonsmokers’ Rights Foundation

ASSIST American Stop Smoking Intervention Study for Cancer Prevention

BRFSS Behavioral Risk Factor Surveillance System

CDC Centers for Disease Control and Prevention

COMMIT Community Intervention Trial for Smoking Cessation

CPS Current Population Survey

DHHS Department of Health and Human Services

ETS environmental tobacco smoke

FDA U.S. Food and Drug Administration

FOIA Freedom of Information Act

GDP gross domestic product

GSP gross state domestic product

IMPACT Initiatives to Mobilize for the Prevention and Control of Tobacco Use

IOI Initial Outcomes Index

ISIS Initiative for the Study and Implementation of Systems

MAV media advocacy variable 

MDS multidimensional scaling

MSA Master Settlement Agreement

NCI National Cancer Institute

NIH National Institutes of Health

NTCP National Tobacco Control Program

PDV present discounted value

PM Philip Morris

QALYs quality-adjusted life-years

RWJF Robert Wood Johnson Foundation

SCLD State Cancer Legislative Database

SoTC Strength of Tobacco Control

TUS-CPS Tobacco Use Supplement to the Current Population Survey

USDA United States Department of Agriculture
xv

This monograph was developed by the National Cancer Institute under the editorial 

direction of Senior Scientific Editors Frances A. Stillman and Carol L. Schmitt. 

Individual chapters of this monograph and the entire volume were subjected to 

extensive peer review and revision.

Acknowledgments

Authors and Contributors

Chapter 1

Authors

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Carol L. Schmitt, Ph.D.

Senior Health Research Scientist

Battelle Centers for Public Health 

Research & Evaluation

Baltimore, MD 

Chapter 2

Authors

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Carol L. Schmitt, Ph.D.

Senior Health Research Scientist

Battelle Centers for Public Health 

Research & Evaluation

Baltimore, MD

Pamela I. Clark, Ph.D.

Senior Research Scientist

Battelle Centers for Public Health 

Research & Evaluation

Baltimore, MD

William M. K. Trochim, Ph.D.

Professor

Department of Policy Analysis and 

Management

Cornell University

Ithaca, NY 

Stephen E. Marcus, Ph.D.

Monograph Series Editor

Epidemiologist

Tobacco Control Research Branch

Behavioral Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

Contributors

Warren Strauss, M.S.

Research Leader, Statistics and 

Information Analysis 

Battelle Memorial Institute

Columbus, OH

Jyothi Nagaraja, M.S.

Research Scientist

Battelle Memorial Institute

Columbus, OH
xvi

Chapter 3

Authors

Jamie Chriqui, Ph.D., M.H.S.

Technical Vice-President

Center for Health Policy and Legislative 

Analysis

The MayaTech Corporation

Silver Spring, MD 

Marcy Frosh, J.D.

Consultant

The MayaTech Corporation

Silver Spring, MD

Ross C. Brownson, Ph.D.

Professor, Community Health in 

Epidemiology

Saint Louis University School of Public 

Health

St. Louis, MO

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Chapter 4

Authors

Elizabeth A. Gilpin, M.S.

Clinical Professor of Biostatistics 

Department of Family & Preventive 

Medicine

University of California, San Diego

La Jolla, CA

Anne M. Hartman, M.S., M.A.

Biostatistician

Risk Factor Monitoring and Methods 

Branch

Applied Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Chapter 5

Author

Elizabeth A. Gilpin, M.S.

Clinical Professor of Biostatistics

Department of Family & Preventive 

Medicine

University of California, San Diego

La Jolla, CA

Contributors

Sandy J. Slater, Ph.D.

Research Specialist

Institute for Health Research and Policy

University of Illinois at Chicago

Chicago, IL

Anne M. Hartman, M.S., M.A.

Biostatistician

Risk Factor Monitoring and Methods 

Branch

Applied Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

A c k n o w l e d g m e n t s
xvii

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Chapter 6

Authors

Lan Liang, Ph.D.

Senior Economist

Center for Financing, Access, and Cost 

Trends

Agency for Health Care Research and 

Quality

Rockville, MD

Frank J. Chaloupka, Ph.D.

Professor of Economics

College of Business Administration

University of Illinois at Chicago

Chicago, IL

Kathryn Ierulli, Ph.D.

Adjunct Assistant Professor

University of Chicago Graduate School of 

Business

Chicago, IL

Chapter 7

Authors

W. Douglas Evans, Ph.D.

Vice President

Center for Health Promotion Research

Research Triangle Institute (RTI) 

International

Washington, DC

Alec Ulasevich, Ph.D.

Senior Research Analyst

Health Program

American Institutes for Research 

Silver Spring, MD

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Contributor

K. Viswanath, Ph.D. 

Department of Medical Oncology 

Dana Farber Cancer Institute

Associate Professor

Department of Society, Human 

Development, and Health

Harvard School of Public Health

Harvard University

Boston, MA

Chapter 8

Authors

Brion J. Fox, J.D.

Associate Scientist

Comprehensive Cancer Center

University of Wisconsin

Madison, WI

Stella Aguinaga Bialous, Dr.Ph., R.N., 

M.S.C.N.

President

Tobacco Policy International

San Francisco, CA

William M. K. Trochim, Ph.D.

Professor

Department of Policy Analysis and 

Management

Cornell University

Ithaca, NY 

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Carol L. Schmitt, Ph.D.

Senior Health Research Scientist

Battelle Centers for Public Health 

Research & Evaluation

Baltimore, MD 
xviii

Chapter 9

Authors

Barry I. Graubard, Ph.D.

Senior Investigator

Division of Cancer Epidemiology and 

Genetics

National Cancer Institute

Bethesda, MD

Anne M. Hartman, M.S., M.A.

Biostatistician

Risk Factor Monitoring and Methods 

Branch

Applied Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

Elizabeth A. Gilpin, M.S.

Clinical Professor of Biostatistics

Department of Family & Preventive 

Medicine

University of California, San Diego

La Jolla, CA

David M. Murray, Ph.D.

Professor and Chair

Division of Epidemiology and Biostatistics

School of Public Health

The Ohio State University

Columbus, OH

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Contributors

James T. Gibson 

Senior Programmer/Systems Analyst

Information Management Services

Silver Spring, MD

William Davis, Ph.D.

Mathematical Statistician

Statistical Research & Applications 

Branch

Division of Cancer Control and Population 

Sciences

National Cancer Institute, NIH

Bethesda, MD

Chapter 10

Authors

Richard M. Peck, Ph.D.

Associate Professor

Department of Economics

University of Illinois at Chicago

Chicago, IL

Mary Ellen Wewers, Ph.D., M.P.H.

Professor

School of Public Health

The Ohio State University

Columbus, OH

Contributors

Anne M. Hartman, M.S., M.A.

Biostatistician

Risk Factor Monitoring and Methods 

Branch

Applied Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

Barry I. Graubard, Ph.D.

Senior Investigator

Division of Cancer Epidemiology and 

Genetics

National Cancer Institute

Bethesda, MD

A c k n o w l e d g m e n t s
xix

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Chapter 11

Authors

Frances A. Stillman, Ed.D.

Co-Director, Institute for Global Tobacco 

Control

Associate Professor, Department of 

Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Carol L. Schmitt, Ph.D.

Senior Health Research Scientist

Battelle Centers for Public Health 

Research & Evaluation

Baltimore, MD

Reviewers

Chapter Reviewers

Lois Biener, Ph.D.

Senior Research Fellow

Center for Survey Research

University of Massachusetts Boston

Boston, MA

Michele Bloch, M.D., Ph.D.

Medical Officer

Tobacco Control Research Branch

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

David M. Burns, M.D.

Professor of Family and Preventive 

Medicine

Professor of Medicine

School of Medicine

University of California, San Diego

San Diego, CA

Carol Celebucki, Ph.D.

Associate Professor

Department of Psychology

University of Rhode Island, Providence 

Campus

Providence, RI

Gregory Connolly, D.M.D., M.P.H.

Director, Massachusetts Tobacco Control 

Program

Massachusetts Department of Public 

Health

Boston, MA

Richard A. Daynard, J.D., Ph.D.

Professor of Law

Northeastern University School of Law

Boston, MA

Ellen Feighery, R.N., M.S.

Principal Investigator

Public Health Institute

Half Moon Bay, CA

Fred Gale, Ph.D.

Senior Economist

Economic Research Service

United States Department of Agriculture

Washington, DC

Stanton A. Glantz, Ph.D.

Professor of Medicine and Director

Center for Tobacco Control Research and 

Education

Comprehensive Cancer Center

University of California, San Francisco

San Francisco, CA

Thomas Glynn, Ph.D.

Director, Cancer Science & Trends

Director, International Tobacco Programs

American Cancer Society

Washington, DC

Ellen J. Hahn, D.N.S., R.N.

Associate Professor

College of Nursing

University of Kentucky

Lexington, KY
xx

Teh-wei Hu, Ph.D.

Professor Emeritus of Health Economics

Division of Health Policy and 

Management 

School of Public Health

University of California, Berkeley

Berkeley, CA

Donald S. Kenkel, Ph.D.

Professor

Policy Analysis and Management

College of Human Ecology

Cornell University

Ithaca, NY

Douglas Luke, Ph.D.

Associate Professor of Community Health

School of Public Health

Saint Louis University

St. Louis, MO

Jonathan M. Samet, M.D., M.S. 

Professor and Chairman

Department of Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD 

Brenda Seals, Ph.D., M.A., M.P.H.

Director, Masters in Public Health 

Program

Practice and Research Coordinator

Department of Public Health

Temple University

Philadelphia, PA

Donald R. Shopland

United States Public Health Service, 

Retired

Ringgold, GA

Robert E. Vollinger Jr., M.S.P.H.

Program Director, Public Health Advisor

Tobacco Control Research Branch

Behavioral Research Program 

Division of Cancer Control & Population 

Sciences

National Cancer Institute

Bethesda, MD

Melanie Wakefield, Ph.D.

Director and NHMRC Principal Research 

Fellow

Centre for Behavioural Research in Cancer

Cancer Control Research Institute

The Cancer Council Victoria

Carlton, Victoria 

Australia

Kenneth E. Warner, Ph.D.

Dean 

School of Public Health

University of Michigan

Ann Arbor, MI

Senior Peer Reviewers

Michele Bloch, M.D., Ph.D.

Medical Officer

Tobacco Control Research Branch

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

Thomas Glynn, Ph.D.

Director, Cancer Science & Trends

Director, International Tobacco Programs

American Cancer Society

Washington, DC

Scott Leischow, Ph.D.

Deputy Director 

Arizona Cancer Center 

The University of Arizona 

Tucson, AZ

Jonathan M. Samet, M.D., M.S.

Professor and Chairman

Department of Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD 

A c k n o w l e d g m e n t s
xxi

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Robert E. Vollinger Jr., M.S.P.H.

Program Director, Public Health Advisor

Tobacco Control Research Branch

Behavioral Research Program 

Division of Cancer Control & Population 

Sciences

National Cancer Institute

Bethesda, MD

ASSIST Evaluation Team

Technical Expert Panel

Lois Biener, Ph.D.

Senior Research Fellow

Center for Survey Research

University of Massachusetts Boston

Boston, MA

Frank J. Chaloupka, Ph.D. 

Professor of Economics

College of Business Administration

University of Illinois at Chicago

Chicago, IL

K. Michael Cummings, Ph.D., M.Ph.

Chair, Department of Health Behavior

Division of Cancer Prevention and 

Population Sciences

Roswell Park Cancer Institute

Buffalo, New York

Stanton Glantz, Ph.D.

Professor of Medicine and Director

Center for Tobacco Control Research and 

Education

Comprehensive Cancer Center

University of California, San Francisco

San Francisco, CA

D. Lawrence Kincaid, Ph.D.

Associate Scientist

Department of Health Behavior & Society

Johns Hopkins University

Baltimore, MD

John Pierce, Ph.D.

Professor, Family & Preventive Medicine

Cancer Prevention & Control Program

University of California, San Diego

San Diego, CA

Jonathan M. Samet, M.D., M.S. 

Professor and Chairman

Department of Epidemiology

Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD

Series Editor

Stephen E. Marcus, Ph.D.

Monograph Series Editor

Epidemiologist

Tobacco Control Research Branch

Behavioral Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

Bethesda, MD

Finally, the editors would like to 

acknowledge the contributions of the 

editorial consultants and the current and 

former staff members of the American 

Institutes for Research, who provided 

technical and editorial assistance in the 

preparation of this monograph:

Margot Raphael and Paige Jackson, 

Project Managers

Richard S. Gallagher, Technical Editor

Elizabeth Bruce and Laurene Jackson, 

Editors

Michael W. Rollins, Karen Ward, and 

Francesca R. Oneto, Graphic Designers

Matthew J. Koenig, Project Associate

xxiii

Contents

Chapter 1. The ASSIST Evaluation Project: An Overview..........................................1

Introduction........................................................................................................................................ 4

The ASSIST Evaluation...................................................................................................................... 13

Summary.......................................................................................................................................... 24

Conclusions...................................................................................................................................... 26

References....................................................................................................................................... 26

Chapter 2. The Strength of Tobacco Control Index................................................31

Introduction...................................................................................................................................... 33

Development of the Strength of Tobacco Control Index..................................................................... 34

Collection, Analysis, and Validation of SoTC Data............................................................................ 40

Results of SoTC................................................................................................................................ 44

Use of the SoTC to Evaluate Individual State Programs................................................................... 49

Summary.......................................................................................................................................... 54

Conclusions...................................................................................................................................... 54

References....................................................................................................................................... 86

Chapter 3. Measuring Policy and Legislative Changes..........................................87

Introduction...................................................................................................................................... 89

State and Local Government Action for Tobacco Use Prevention and Control ................................... 90

The Value of Monitoring Policy and Legislative Changes.................................................................. 91

Methods for Rating the Comprehensiveness of Tobacco Control Laws ............................................. 93

State and State+Local Rating Results........................................................................................... 100

Summary........................................................................................................................................ 104

Conclusions.................................................................................................................................... 105

References..................................................................................................................................... 106

Chapter 4. Initial Outcomes Index........................................................................111

Introduction.................................................................................................................................... 113

Tobacco Use Outcome Measures .................................................................................................... 114

Defining and Selecting Initial Outcomes........................................................................................ 116

Initial Outcomes Selected for the ASSIST Evaluation...................................................................... 119

The IOI for Policy............................................................................................................................. 129

Summary........................................................................................................................................ 135

Conclusions.................................................................................................................................... 137

References..................................................................................................................................... 138
xxiv

Chapter 5. State Facilitating Conditions and Barriers to Implementation of 

Tobacco Control Programs...................................................................................141

Introduction.................................................................................................................................... 143

Overview of Influences.................................................................................................................... 144

State Conditions Selected for the ASSIST Evaluation ..................................................................... 145

Data Sources.................................................................................................................................. 149

Other Potential Covariates............................................................................................................. 151

Summary........................................................................................................................................ 155

Conclusions.................................................................................................................................... 156

References..................................................................................................................................... 158

Chapter 6. Measuring the Impact of Tobacco on State Economies.....................163

Introduction.................................................................................................................................... 165

Background.................................................................................................................................... 166

Studies on the Economic Impact of Tobacco .................................................................................. 171

Measuring the Economic Impact of Tobacco for ASSIST ................................................................. 174

State Data and Trends.................................................................................................................... 175

Summary........................................................................................................................................ 181

Conclusions.................................................................................................................................... 182

References..................................................................................................................................... 182

Chapter 7. The ASSIST Newspaper Tracking System............................................185

Introduction.................................................................................................................................... 187

The Challenge of Measuring the Effect of Media Interventions ...................................................... 189

Tracking the Articles....................................................................................................................... 190

Trend Analyses............................................................................................................................... 195

Summary........................................................................................................................................ 201

Conclusions.................................................................................................................................... 204

References..................................................................................................................................... 211

Chapter 8. Evaluating Tobacco Industry Tactics as a Counterforce to ASSIST...213

Introduction.................................................................................................................................... 215

Expert-based Conceptual Map of Industry Tactics ......................................................................... 217

Review of the Published Literature on Tobacco Industry Tactics..................................................... 224

Review of Tobacco Industry Documents.......................................................................................... 236

Tobacco Industry Tactics: An Evaluation Challenge........................................................................ 254

Summary........................................................................................................................................ 255

Conclusions.................................................................................................................................... 256

References..................................................................................................................................... 257

C o n t e n t s
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

xxv

Chapter 9. Final Outcomes: Analytical Methods and Results ..............................265

Introduction.................................................................................................................................... 269

Overall Considerations for Analysis................................................................................................ 272

Adult Smoking Prevalence.............................................................................................................. 274

Per Capita Cigarette Consumption................................................................................................. 276

Primary Results.............................................................................................................................. 279

Secondary Results (SoTC and IOI Analyses) ................................................................................... 279

Summary........................................................................................................................................ 289

Conclusions.................................................................................................................................... 292

References..................................................................................................................................... 302

Chapter 10. Cost-effectiveness of ASSIST............................................................305

Introduction.................................................................................................................................... 307

A Cost-effectiveness Analysis: The Basic Approach ....................................................................... 308

Cost-effectiveness: ASSIST............................................................................................................. 310

Summary........................................................................................................................................ 322

Conclusions.................................................................................................................................... 323

References..................................................................................................................................... 323

Chapter 11. The ASSIST Evaluation: Contributions to Evaluation of Complex 

Public Health Initiatives........................................................................................325

Introduction.................................................................................................................................... 328

Challenges to Evaluating ASSIST................................................................................................... 328

Response to Evaluation Challenges............................................................................................... 329

Key Findings of the ASSIST Evaluation ........................................................................................... 331

What Do These Results Mean?........................................................................................................ 332

The Legacy of the ASSIST Evaluation.............................................................................................. 334

The Future of Tobacco Control ........................................................................................................ 336

Using the ASSIST Evaluation Approach for Other Public Health Initiatives..................................... 338

Summary........................................................................................................................................ 339

Conclusions.................................................................................................................................... 340

References..................................................................................................................................... 344

Index......................................................................................................................351

Case Study

Case Study 7.1. Editorials Promote a Stronger Youth Access Law in North Carolina ...................... 201
xxvi

Tables

Table 1.1. Comparison of COMMIT and ASSIST................................................................................. 12

Table 1.2. Key Constructs and Variables Initially Proposed for the ASSIST Evaluation...................... 19

Table 1.3. Research Questions Guiding the ASSIST Evaluation ........................................................ 21

Table 1.4. Final Constructs and Variables Used for the ASSIST Evaluation ...................................... 25

Table 2.1. The Constructs, Domains, and Measures of the Strength of Tobacco Control Index ......... 38

Table 2.2. Standardized Strength of Tobacco Control Index and Construct Scores, 

1999–2000, by State, Sorted by SoTC Scores................................................................................... 45

Table 2.3. Correlation of Legislative Scores (1999) with SoTC Scores (1999)................................... 48

Table 2.4. Correlation of Tobacco Use Prevalence (2000) with SoTC Scores (1999).......................... 48

Table 2.5. Inputs: Resources and Capacity, Sorted by Funds............................................................ 50

Table 2.6. Components of the Efforts Construct, Sorted by Efforts Score ......................................... 52

Table 2.E.1. Within-method Correlation Analysis of Domain-level Strength of Tobacco 

Control Variables.............................................................................................................................. 79

Table 2.E.2. Within-method Correlation Analysis among Three Constructs and Overall 

Strength of Tobacco Control for Full Model....................................................................................... 80

Table 2.E.3. Within-method Correlation Analysis among Three Constructs and Overall 

Strength of Tobacco Control for Reduced Model ............................................................................... 80

Table 2.E.4. Amount of Variability Explained by First Three Factor Loadings in Principal 

Components Analysis....................................................................................................................... 81

Table 3.1. Target Criteria Rated with 4 Points for Items in the Youth Access Policy Area ................. 94

Table 3.2. Target Criteria Rated with 4 Points for Items in the Clean Indoor Air Policy Area............. 95

Table 3.3. Summary Scores for Youth Access Legislation, All States, 1993–99.............................. 100

Table 3.4. Summary Scores for State Clean Indoor Air by Preemption Score Adjustment and 

Year, All States, 1993–99............................................................................................................... 101

Table 3.5. State+Local Clean Indoor Air Summary Scores by Year, 1993–98.................................. 103

Table 3.6. State+Local Summary Scores for Clean Indoor Air for ASSIST and 

Non-ASSIST States, 1993–98......................................................................................................... 103

Table 4.1. Real Price of Cigarettes, 1992–99................................................................................. 121

Table 4.2. Legislative Score Including Preemption and Local Adjustment...................................... 124

Table 4.3. Percentage of Indoor Workers with Smoke-free Workplaces............................................ 127

Table 4.4. Correlations among Initial Outcomes in 1992–93 and 1998–99 ................................... 129

Table 4.5. Initial Outcomes Index................................................................................................... 131

Table 4.6. Correlation of Initial Outcomes with Final Outcomes and Change in 

Initial Outcomes with Change in Final Outcomes........................................................................... 133

Table 5.1. Variables Included in the ASSIST Evaluation Analysis.................................................... 146

Table 5.2. Per Capita Funding ($/Person) for Tobacco Control in Each State, by Year .................... 154

Table 6.1. Percentages of Gross State Product from Tobacco Growing and Manufacturing ............ 177

C o n t e n t s
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

xxvii

Table 7.1. Examples of Key Words for Search Strategy.................................................................... 192

Table 7.2. Definitions of Coded Variables....................................................................................... 194

Table 7.3. Examples of Tobacco Topics........................................................................................... 194

Table 7.4. Average Rate Variables for Each Year Analyzed (District of Columbia Excluded) ........... 199

Table 8.1. Clusters and Statements in Descending Order, by Average Importance Rating.............. 221

Table 8.2. Lobbying and Legislative Strategy................................................................................. 226

Table 8.3. Legal and Economic Intimidation and Creating the Illusion of Support ......................... 228

Table 8.4. Usurping the Agenda ..................................................................................................... 228

Table 8.5. Harassment ................................................................................................................... 228

Table 8.6. Undermining Science..................................................................................................... 230

Table 8.7. Media Manipulation and Public Relations...................................................................... 231

Table 8.8. Industry Tactics to Oppose Specific Public Health Initiatives......................................... 232

Table 8.9. Tobacco Industry Behavior at State and Local Levels..................................................... 232

Table 9.1. Baseline Characteristics of ASSIST and Non-ASSIST States........................................... 270

Table 9.2. Covariates at Individual and State Levels ..................................................................... 275

Table 9.3. Results of Regression Analysis of Smoking Prevalence for ASSIST Status, 

by Sex and Age............................................................................................................................... 277

Table 9.4. Adjusted Differences in per Capita Cigarette Consumption between ASSIST and 

Non-ASSIST States during the Intervention Period ......................................................................... 281

Table 9.5. IOI Analyses of the Impact of SoTC, as a Main Effect, on IOI and 

the Components of IOI.................................................................................................................... 282

Table 9.6. Results from Regression Analysis of Smoking Prevalence for Strength of 

Tobacco Control ............................................................................................................................. 285

Table 9.7. Results from Regression Analysis of Smoking Prevalence for 

Initial Outcomes Index.................................................................................................................... 285

Table 9.8. Results from Regression Analysis of per Capita Cigarette Consumption for 

Strength of Tobacco Control ........................................................................................................... 286

Table 9.9. Results from Regression Analysis of per Capita Cigarette Consumption for 

Interactions of Strength of Tobacco Control and ASSIST Status ..................................................... 287

Table 9.10. Results from Regression Analysis of Temporal Pattern of per Capita 

Cigarette Consumption for Strength of Tobacco Control................................................................. 288

Table 9.11. Results from Regression Analysis of per Capita Cigarette Consumption for 

Initial Outcomes Index.................................................................................................................... 288

Table 9.12. Results from Regression Analysis of per Capita Cigarette Consumption for 

Interactions of Change in Initial Outcomes Index and Its Components with ASSIST Status ........... 289

Table 10.1. ASSIST Total State Expenditures................................................................................... 311

Table 10.2. ASSIST per Capita Expenditures (Adult Population, 18 Years Old and Older) ............... 312

Table 10.3. Life-years Gained Attributable to ASSIST: Women ........................................................ 313

Table 10.4. Cost-effectiveness with Various Recidivism Rates...................................................... 313

Table 10.5. Cost-effectiveness with Various Changes in Women’s Prevalence Rates..................... 314

Table 10.6. Fraction of Lifetime Smoker Quits Attributable to ASSIST ............................................ 314
xxviii

Figures

Figure 1.1. Trends over Time in Tobacco Control Projects and Their Evaluation.................................. 6

Figure 1.2. NCI’s Five Phases of Cancer Control Research.................................................................. 7

Figure 1.3. The ASSIST Evaluation Model.......................................................................................... 18

Figure 1.4. ASSIST Evaluation Timeline............................................................................................ 18

Figure 1.5. Final Conceptual Framework Used for ASSIST Evaluation............................................... 24

Figure 2.1. Heuristic Map of Components Proposed for Strength of Tobacco Control Index .............. 37

Figure 2.2. Strength of Tobacco Control Scores by State .................................................................. 46

Figure 2.3. Resources Construct Scores by State ............................................................................. 46

Figure 2.4. Efforts Construct Scores by State................................................................................... 47

Figure 2.5. Capacity Construct Scores by State................................................................................ 47

Figure 2.6. Change in Tobacco Control Funding (in Dollars), 1991–98 ............................................ 51

Figure 2.E.1. Factor Analysis for Full Model...................................................................................... 81

Figure 2.E.2. Strength of Tobacco Control—Full Model.................................................................... 83

Figure 2.E.3. Strength of Tobacco Control—Reduced Model ............................................................ 84

Figure 2.F.1. Basic Conceptual Framework....................................................................................... 85

Figure 3.1. Mean Youth Access Summary Scores by Year and Preemption Status .......................... 101

Figure 3.2. Mean Clean Indoor Air Summary Scores by Year and Preemption Status...................... 102

Figure 4.1. Real Price of Cigarettes Versus Adult Smoking Prevalence, 1992–93 .......................... 122

Figure 4.2. Real Price of Cigarettes Versus per Capita Cigarette Consumption.............................. 123

Figure 4.3. Legislative Score Versus Adult Smoking Prevalence..................................................... 125

Figure 4.4. Legislative Score Versus per Capita Cigarette Consumption ........................................ 126

Figure 4.5. Workplace Smoking Ban Versus Adult Smoking Prevalence.......................................... 128

Figure 4.6. Workplace Smoking Ban Versus per Capita Cigarette Consumption............................. 128

Figure 4.7. Initial Outcomes Index Versus Adult Smoking Prevalence............................................. 132

Figure 4.8. Initial Outcomes Index Versus per Capita Cigarette Consumption................................ 132

Figure 4.9. Change in Initial Outcomes Index Versus Change in Adult Smoking Prevalence........... 134

Figure 4.10. Change in Initial Outcomes Index Versus Change in per Capita 

Cigarette Consumption .................................................................................................................. 134

Figure 4.11. Trends in per Capita Cigarette Consumption for States in Each Tertile 

Group of Tobacco Control Initial Outcomes Index ........................................................................... 135

Figure 5.1. Interactions of Forces Acting to Influence Tobacco Use ................................................ 144

Figure 6.1. Tobacco Leaf Production............................................................................................... 167

Figure 6.2. Tobacco Leaf Production and Cigarette Production, Consumption, and Exports........... 169

Figure 6.3. Tobacco Leaf Production and Exports, and Imports’ Share in U.S. Use......................... 169

Figure 6.4. Tobacco Farming and Manufacturing as Share of Gross State Product, 2000 .............. 176

Figure 6.5. Tobacco Farming and Manufacturing as Share of Gross Domestic Product, 

United States................................................................................................................................. 178

Figure 6.6. Tobacco Farming and Manufacturing as Share of Gross State Product, 

North Carolina................................................................................................................................ 178

C o n t e n t s
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

xxix

Figure 6.7. Tobacco Growing and Manufacturing as Share of Gross State Product, 

Kentucky......................................................................................................................................... 179

Figure 6.8. Tobacco Farming and Manufacturing as Share of Gross State Product, 

Virginia .......................................................................................................................................... 179

Figure 6.9. Tobacco Farming and Manufacturing as Share of Gross State Product, 

Georgia........................................................................................................................................... 180

Figure 6.10. Tobacco Farming and Manufacturing as Share of Gross State Product, 

South Carolina ............................................................................................................................... 180

Figure 6.11. Tobacco Farming and Manufacturing as Share of Gross State Product, 

Tennessee....................................................................................................................................... 181

Figure 7.1. Article Type by Year, 1993–2000................................................................................... 197

Figure 7.2. Policy Topic by Year, 1993–2000................................................................................... 197

Figure 8.1. Point-cluster Map Showing the Multidimensional Scaling Arrangement of 

the 88 Statements with the Eight-cluster Solution Superimposed ................................................. 223

Figure 8.2. Concept Map Showing Clusters and Relative Importance Ratings............................... 224

Figure 8.3. Experts’ Interpretation of the Industry Tactics Concept Map ........................................ 225

Figure 9.1. Bimonthly per Capita Cigarette Consumption in ASSIST and 

Non-ASSIST States......................................................................................................................... 278

Figure 9.2. Crude Difference in Bimonthly per Capita Consumption of Cigarettes between 

ASSIST and Non-ASSIST States....................................................................................................... 280

Figure 9.3. Yearly Initial Outcomes Index Average for ASSIST and Non-ASSIST States.................... 284

Figure 9.B.1. Examples of States with Nonlinear Trends in Bimonthly per Capita 

Cigarette Consumption after 1993................................................................................................. 297

Figure 9.B.2. Random Effects Analysis with 95% Confidence Interval of Difference in 

per Capita Cigarette Consumption (Non-ASSIST Minus ASSIST)..................................................... 298

Appendices

Appendix 2.A. Assessment Rating of Variables................................................................................ 56

Appendix 2.B. Key Elements of Strength of Tobacco Control Efforts ................................................. 57

Appendix 2.C. Agency Worksheet...................................................................................................... 61

Appendix 2.D. Survey 2..................................................................................................................... 65

Appendix 2.E. Validation of the Strength of Tobacco Control Model.................................................. 77

Appendix 2.F. Construct Validation of Strength of Tobacco Control.................................................. 85

Appendix 5.A. Baseline Values for Variables Considered in the ASSIST Evaluation Analyses.......... 157

Appendix 7.A. Coding Topics........................................................................................................... 205

Appendix 7.B. Search Strategies.................................................................................................... 207

Appendix 9.A. Issues Related to Selection of Methods for Analyzing Smoking Prevalence.............. 293

Appendix 9.B. Approaches to Analyzing per Capita Cigarette Consumption Data .......................... 296
xxx

Appendix 9.C. Outcome Measures: Per Capita Cigarette Consumption (Packs/Month) and 

Adult (18 Years and Older) Smoking Prevalence (%) ..................................................................... 301

Appendix 11.A. Crude Prevalence, Initial Outcomes Index, and Strength of 

Tobacco Control Index, by State...................................................................................................... 342

C o n t e n t s
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

1

1. The ASSIST Evaluation Project: An Overview

Frances A. Stillman and Carol L. Schmitt

Contents

Introduction...................................................................................................................... 4

The ASSIST Evaluation: A Historical Context ..........................................................................5

The 1990s: A Turning Point for Tobacco Control..............................................................8

Tobacco Control Today..................................................................................................11

The ASSIST Evaluation.................................................................................................... 13

Conceptual Design...............................................................................................................16

The ASSIST Evaluation Model...............................................................................................16

Evolution ......................................................................................................................16

Key Constructs..............................................................................................................17

Outcome Measures.......................................................................................................20

Research Questions.............................................................................................................21

Analytic Challenges.............................................................................................................21

Diffusion, Contamination, and Secular Trends.............................................................22

Competing Factors and Forces.....................................................................................22

State Selection Bias.....................................................................................................23

Limited Number of Available Observations...................................................................23

Final Conceptual Framework................................................................................................23

Summary........................................................................................................................ 24

Conclusions.................................................................................................................... 26

References..................................................................................................................... 26

Tables and Figures

Figure 1.1. Trends over Time in Tobacco Control Projects and Their Evaluation.............................6

Figure 1.2. NCI’s Five Phases of Cancer Control Research.............................................................7

Table 1.1. Comparison of COMMIT and ASSIST............................................................................12

Figure 1.3. The ASSIST Evaluation Model.....................................................................................18

Figure 1.4. ASSIST Evaluation Timeline.......................................................................................18

Table 1.2. Key Constructs and Variables Initially Proposed for the ASSIST Evaluation.................19

Table 1.3. Research Questions Guiding the ASSIST Evaluation ...................................................21

Figure 1.5. Final Conceptual Framework Used for ASSIST Evaluation..........................................24

Table 1.4. Final Constructs and Variables Used for the ASSIST Evaluation .................................25

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

3

1. The ASSIST Evaluation Project: An Overview

This chapter presents an overview of the American Stop Smoking Intervention 

Study for Cancer Prevention (ASSIST)* evaluation and its historical context. It 

reviews the key points of ASSIST and describes the conceptual framework that 

guided the ASSIST evaluation, as well as the key constructs of the conceptual 

framework, the rationale for their inclusion, and the research questions that 

established the linkages between these conceptual constructs.

ASSIST presented a unique challenge for evaluating tobacco control† program 

effectiveness. The ASSIST program guidelines included a focus on broad social 

and environmental change and recommended that interventions be delivered at 

the highest structural level (i.e., state or region) to ensure the greatest impact on 

tobacco use (see Monograph 16, chapter 2, pp. 21–23). As a result, one of the aims 

of the ASSIST evaluation was to show that this approach to tobacco control would 

reduce cigarette consumption and smoking prevalence. In the past, tobacco control 

interventions were often delivered in isolation or were aimed at specific groups and 

tested under controlled circumstances. In contrast, ASSIST was a demonstration 

project that combined capacity building and policy-focused interventions to change 

how tobacco control was delivered in 17 states. This focus on capacity development 

and policy interventions represented a more upstream approach to tobacco control, 

and evaluating it required identifying constructs or components and measures that 

went beyond those used to assess more traditional interventions that focused on 

changing individual behavior.

The ASSIST evaluation team developed a conceptual framework around a set 

of constructs including state tobacco control functioning, policy development, 

and state-level demographics and conditions that were used to help understand 

the process of change resulting from statewide tobacco control efforts. The initial 

outcomes were changes in policy, and the final outcomes were changes in smoking 

prevalence and cigarette consumption. Additional components of this model, such as 

tobacco industry interference tactics and print media coverage, were also studied. In 

some cases, measures were developed but were not ultimately included in the

*The official name for ASSIST was the American Stop Smoking Intervention Study for Cancer Prevention. 

The title was often shortened to the American Stop Smoking Intervention Study, and it is this shortened 

form that is used in this monograph. For a more extensive description of the ASSIST conceptual 

framework, model, interventions, and case studies, and discussion of how ASSIST contributed to the 

development of a national tobacco control program, please see NCI Tobacco Control Monograph 16—

ASSIST: Shaping the Future of Tobacco Prevention and Control.

†The phrase “tobacco use prevention and control” was emphasized in the development and dissemination 

of ASSIST materials. In this monograph, the phrase has frequently been shortened to “tobacco control.”
4

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

Introduction

T

his chapter introduces the model used 

for the evaluation of ASSIST—one 

of the largest government-sponsored tobacco control initiatives ever undertaken. 

In addition, and perhaps more important, this chapter explores the historical 

context and trends that led to a unique 

and forward-thinking approach to evaluation. In this and subsequent chapters, 

the underlying theoretical perspective, 

the development and measurement of the 

evaluation components, and the analysis 

methods and outcomes are described.

While tobacco has played an important role in U.S. history, efforts to curtail 

its use have an equally long history. 

Thomas Jefferson noted that “[Tobacco] 

is a culture productive of infinite wretchedness. . . . The cultivation of wheat 

is the reverse in every circumstance.”1

However, the past half-century marks a 

unique period in which organized public 

health efforts, particularly at the policy 

level, have contributed to changes in 

social norms that have made cigarette 

smoking less socially acceptable to the 

public. This success is attributable to a 

complex and interdependent mosaic of 

interventions delivered through multiple 

channels.

Against this backdrop, ASSIST represented a major initiative to address 

tobacco use through high-level, policybased interventions delivered at the state 

and community levels. Unlike prior efforts, ASSIST was a demonstration project and not a randomized trial, focusing 

instead on multiple interventions, many 

with indirect long-term outcomes, without the benefit of randomized control 

groups. Moreover, ASSIST implemented 

interventions at the level of a broad 

population group, through means such as 

capacity building, policy advocacy, legislative change, and media interventions, 

rather than measures such as individual 

smoking cessation assistance.

The challenge of evaluating ASSIST 

resulted in a sophisticated and statistically validated model, developed with 

multidisciplinary input. The evaluation 

assessed not only the effectiveness of 

the ASSIST intervention in the 17 intervention states but also overall tobacco 

control efforts across all U.S. states and 

the District of Columbia. The evaluation 

introduced a new and more ecological 

final evaluation model. These measures are described in this monograph because 

they formed part of the knowledge base of population-level tobacco control that was 

developed for the overall ASSIST evaluation effort.

By developing and validating a conceptual framework that reflects the complexity 

inherent in tobacco control, and by developing measures that are strongly related 

to tobacco control outcomes, this effort serves as a model for evaluations of 

public health interventions with components that are diffused throughout an entire 

population. Moreover, such an approach fits a growing systems view of the world 

where the interrelationships and feedback across factors more closely mirror realworld behavior and outcomes.
5

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

approach, including an assessment of the 

upstream or more short-term indicators 

of tobacco control efforts and outcomes. 

The ultimate and long-term hypothesized 

outcomes were changes in smoking 

prevalence (the number of people who 

smoke) and per capita cigarette consumption. Change in prevalence across all 

states was assessed with multiple linear 

regression that adjusted for potential confounding factors. In addition, per capita 

consumption was examined using mixed 

effects linear modeling that accounted 

for the consumption rates in each state 

during the time when the ASSIST intervention began and incorporated the 

state factors associated with cigarette 

consumption and each state’s seasonal 

pattern of consumption. The evaluation 

effort demonstrated that ASSIST was 

a success, and both this project and its 

evaluation can serve as models for how 

large-scale public health efforts must 

continue to evolve in the future.

The ASSIST Evaluation: A Historical 

Context

The ASSIST evaluation presented a 

unique challenge, formed by the confluence of numerous trends within both 

tobacco control and public health in 

general—trends toward more complex 

interventions that were aimed at broader 

population groups and took place in 

complex environments that were increasingly less amenable to randomized trials 

or controls.

To put the ASSIST evaluation in its 

proper context, one should first look at 

the broader trends in tobacco control 

that framed this project. Half a century 

ago, cigarette smoking was an ingrained 

part of American culture, with an adult 

prevalence rate of nearly 60% for males 

and 44% overall, and a concomitant 

burden of premature disease and death.2

By 2004, overall tobacco prevalence had 

declined by nearly a factor of two from 

these levels, ranking as one of the great 

success stories of public health.3

Figure 1.1 depicts the evolution of 

tobacco control interventions and evaluation of those interventions between 1964 

and the ASSIST evaluation. The trajectory between these two points in time 

encompasses five general phases in the 

evolution of tobacco control efforts:

Phase 1: Education. The first surgeon 

general’s report on smoking and health,4

a massive school-based smoking prevention program, and extensive public 

service advertising and education about 

the dangers of smoking yielded a measurable reduction in tobacco prevalence and 

cigarette consumption. The first National 

Cancer Institute (NCI) tobacco control 

monograph, Strategies to Control Tobacco 

Use in the United States: A Blueprint for 

Public Health Action in the 1990’s, notes 

that despite this initial drop, it quickly became clear that information alone would 

not be sufficient to effect major changes in 

tobacco use.5(p ix)

Phase 2: Individual-level Intervention. 

In the years following the mid-1960s, 

numerous resources became available 

to promote smoking cessation among 

individuals. These resources included 

clinics and classes to help smokers quit 

smoking, self-help and behavioral strategies for smoking cessation, and interventions to educate the general population 

about the dangers of smoking. Most 
6

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

cessation strategies focused on teaching 

individual smokers how to quit smoking. Other cessation approaches included 

physician advice and counseling, mass 

media campaigns, and the beginnings of 

on-demand resources such as telephone 

quit lines.6

Phase 3: Community-level Intervention. From the 1970s through the 1980s, 

an era in which early gains in smoking 

cessation began to plateau, there was a 

growing recognition that tobacco use was 

a public health problem with epidemiological implications. This recognition 

led to community-based interventions to 

address tobacco use. Early communitybased intervention studies included the 

1972 Stanford Three Community Study7

and the North Karelia demonstration 

project in Finland.8 These interventions 

were followed by larger-scale projects 

such as the Community Intervention 

Trial for Smoking Cessation (COMMIT), 

funded by NCI from 1986 through 

1992.9–12 A randomized community trial 

comparing the effects of interventions in 

paired U.S. cities, COMMIT focused on 

areas such as cessation resources, education, and health-care interventions, and 

also on broader areas such as community 

mobilization and workplace smoking, 

laying the groundwork for a coalition 

model of tobacco control.

Phase 4: Population-level Intervention. 

Projects such as COMMIT began to sow 

the seeds of intervention through means 

Figure 1.1. Trends over Time in Tobacco Control Projects and Their Evaluation

System-level

Intervention

Trends in Tobacco Control Trends in Evaluation

Outcome

Measurement

Randomized

Clinical

Trials

Ecological

Assessment

Population-level

Intervention

Community-level

Intervention

Individual-level

Intervention

Public

Education

� Smaller populations

� Lower complexity

� More study controls

� Easier gains

� Larger populations

� Higher complexity

� Fewer study controls

� Harder gains
7

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

such as worksite smoking policies and 

community mobilization, which naturally led to efforts that addressed tobacco 

health issues through large-scale population-level interventions. By the close 

of the 1980s, numerous such initiatives 

took shape, ranging from efforts promoting clean air laws and increased taxation to media interventions—and social 

norms about smoking began to change. 

As a result of these early successes, the 

need for comprehensive approaches to 

tobacco control was recognized. A comprehensive approach required employing 

multiple channels and sectors, including 

political, economic, education, communication, health professional, and health 

voluntary sectors.5(p52) It was against 

this backdrop that the hypothesis behind 

ASSIST, that smoking behavior could be 

changed through sociopolitical means, 

was ultimately formed and tested.

Phase 5: System-level Intervention. 

Today, the epidemiological model of tobacco control continues to evolve toward 

a broader systems view that incorporates 

the multiplicity of factors and stakeholder 

groups behind patterns of tobacco use and 

public health. Recent initiatives in tobacco control, such as the NCI-funded Initiative for the Study and Implementation 

of Systems13 and the Global Tobacco 

Research Network,14 are now exploring 

tobacco control issues at systems and 

network levels, while broader efforts, 

such as the Syndemics initiative funded 

by the Centers for Disease Control and 

Prevention (CDC),15 show promise for 

examining the interrelationship of tobacco use prevention and control and other 

population-level health issues.

This progression represented more 

than just simple evolution—it was also 

part of a strategic objective on the part of 

NCI to implement population-level tobacco control on a framework of proven 

science. Figure 1.2 illustrates five phases 

of cancer control defined in the early 

1980s under the leadership of Dr. Peter 

Greenwald and Dr. Joseph Cullen, Director and Deputy Director, respectively, 

of NCI’s Division of Cancer Prevention 

and Control.

In practice, this framework helped 

guide the science from COMMIT, a 

randomized community trial aimed at 

Figure 1.2. NCI’s Five Phases of Cancer Control Research

Basic

Biomedical

Research

I

Hypothesis

Development

II

Methods

Development

III

Controlled

Intervention 

Trials

IV

Defined

Population

Studies

V

Demonstration

and

Implementation

Nationwide

Prevention 

and Health 

Services

Programs

Sources: Greenwald, P. G., and J. W. Cullen. 1984. The scientific approach to cancer control. CA-A Cancer 

Journal for Clinicians 34 (6): 328–332. National Cancer Institute. 1990. Smoking, tobacco, and cancer 

program: 1985-1989 status report (NIH publication no. 90-3107). Bethesda, MD: U.S. Department of Health 

and Human Services, Public Health Service, National Institutes of Health (p. vi).
8

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

defined community groups, to ASSIST, a 

demonstration project with much larger 

target groups and more complex interventions, while at the same time defining a 

clear trend toward larger-scale tobacco 

control efforts. This science-based model 

for tobacco control also helped lay the 

groundwork for a fundamental shift in 

tobacco control philosophy during the 

1990s, which in turn led to equally fundamental changes in how society as a whole 

viewed the use of tobacco products.

The 1990s: A Turning Point for Tobacco 

Control

Through the beginning of the 1990s, 

the story of modern tobacco control in the 

United States was marked by a transition 

from public education to one of individual 

and community-level interventions. While 

these methods did achieve substantial 

reductions in tobacco use, at a broader 

social level these gains took place in a 

society in which smoking remained an 

accepted part of the fabric of life. Cigarette advertising, smoke-filled bars, and a 

doctrine of personal choice all remained 

part of the landscape of public life, as had 

been the case for decades before.

By comparison, the decade that followed marked a critical juncture in how 

society viewed tobacco. By the beginning of the new millennium, cigarettes 

had become an increasingly expensive, 

legislated, and socially unacceptable 

product—and tobacco manufacturers 

began to be held much more accountable for the health consequences of 

their products. This environment was 

a direct result of policy-level interventions promoted by a broad coalition of 

government, health-care, and community 

stakeholders—guided by a strong voice 

from the population itself, as expressed 

through their elected officials.

ASSIST. ASSIST, launched in 1991, 

was a major policy-level tobacco control 

initiative that became a vanguard of the 

tobacco use prevention and control efforts that followed. During the same period as the COMMIT intervention, NCI 

published its first monograph on tobacco 

control, which became known as the 

“blueprint.”5 The blueprint synthesized 

40 years of research on effective tobacco 

control strategies. This document identified the need for comprehensive tobacco 

control interventions, primarily through 

policy-based approaches that could 

alter the sociopolitical environment of 

tobacco use. Along with the COMMIT 

findings, this document became the basis 

for ASSIST.

ASSIST was a macro-level policy approach to tobacco control.16,17 NCI made 

the first substantial monetary investment 

to accomplish its stated tobacco control 

objectives by releasing a Request for 

Proposal to fund state tobacco control 

programs. In 1991, NCI partnered with 

the American Cancer Society to implement ASSIST through contracts to 17 

state health departments; the contracts 

incorporated the recommendations that 

were in the blueprint. These 17 states 

were funded to implement upstream 

interventions in three core areas: policy, 

media, and program services, to be delivered across several population channels. 

(For a more extensive discussion of the 

ASSIST intervention areas, see Monograph 16, chapter 2, pp. 26–28.)
9

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

ASSIST was the first major federal 

investment in state tobacco control infrastructure, and its program standards 

formed the foundation of two other 

nationally-based programs, SmokeLess 

States and Initiatives to Mobilize for 

the Prevention and Control of Tobacco 

(IMPACT), during the 1990s.18 As the 

largest public-private partnership in tobacco control ever implemented, ASSIST 

invested about $22.5 million per year in 

tobacco control programs. Although this 

amount was substantial, it represented 

only about 0.03% of the $5.7 billion 

that the tobacco industry spent on average per year to market its products each 

year during the same period (1991–99). 

The NCI investment allowed states to 

establish strong infrastructures to support comprehensive state tobacco control 

programs. Moreover, ASSIST provided 

states with the guidance they needed to 

implement strong, evidence-based tobacco control practices.

SmokeLess States. During the same period, in 1993, the Robert Wood Johnson 

Foundation in partnership with the 

American Medical Association funded 

the SmokeLess States National Tobacco 

Policy Initiative.19 This complementary 

private-sector initiative initially funded 

coalitions in 19 states and a youthspecific project in Tucson, Arizona. Two 

years later, additional funding brought 

in 13 new grantees, and by the time the 

program ended in 2004, almost all of 

the states had been funded.20 Much like 

ASSIST, the SmokeLess States project 

focused on policy-level initiatives for 

tobacco control, concentrating on clean 

air ordinances, increasing tobacco taxes, 

and providing insurance coverage for 

tobacco dependence treatment. It also 

fostered a similar coalition model for the 

implementation of its interventions.

IMPACT. In 1994, through IMPACT, 

CDC funded the remaining 32 nonASSIST states and the District of 

Columbia (California had its own welldeveloped tobacco control program and 

was not included in IMPACT) to implement tobacco control programs, providing technical assistance with limited 

funding support (average annual awards 

were $360,000) to build the states’ capacity to sustain broad-based tobacco control 

programs. CDC provided technical assistance and training on planning, developing, implementing, and evaluating 

NCI Tobacco Control 

Monograph 16: ASSIST

Tobacco Control Monograph 16, ASSIST: 

Shaping the Future of Tobacco Prevention 

and Control, is a companion volume to this 

monograph. Monograph 16 provides the 

background and history of ASSIST. This 

history includes not only the program components but also a detailed look at how the 

initiative was implemented. The case studies 

and detailed descriptions of the “complexities, politics, and outright opposition encountered by the ASSIST team”a afford the reader 

a better understanding of state-level tobacco 

control programs and a recognition of how 

far we have come since the 1950s, when tobacco use was a well-accepted social behavior. Monograph 16 also leaves the reader with 

an appreciation for the challenges faced by 

the ASSIST evaluation team.

aNational Cancer Institute. 2005. ASSIST: 

Shaping the future of tobacco prevention and 

control (Tobacco control monograph no. 16, 

NIH pub. no. 05-5645). Bethesda, MD: National Cancer Institute (p. viii).
10

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

tobacco control programs.21 While 

SmokeLess States and IMPACT had very 

beneficial effects on national smoking 

policy, they also presented a challenge 

for the ASSIST evaluation: the ASSIST 

influence had now spread beyond the 17 

states under study, necessitating a fresh 

approach to the ASSIST evaluation.

Individual States. Concurrently, the efforts of individual states in the 1990s 

began to demonstrate the potential impact of policy initiatives. In California, 

Proposition 99 raised over $150 million 

for tobacco control education and research via the imposition of an additional 

tax of 25¢ per pack, and the resulting 

advertising and outreach efforts helped 

reduce California’s smoking prevalence 

from 26% to 18%.22 In Massachusetts, 

successive 25¢ cigarette tax increases in 

1992 and 1996 helped fund an aggressive campaign of advertising, education, 

and cessation resources within a coalition 

environment. As a result, smoking prevalence decreased from 23.5% to 19.4% 

during the 1990s, a decline almost four 

times the national average during this 

period.23 The successes of state-level programs like these furthered the scientific 

support for larger-scale initiatives such as 

ASSIST and SmokeLess States.

SmokeLess States Versus ASSIST

The SmokeLess States project differed from ASSIST in two important ways.

■ First, SmokeLess States funding did not go through state health departments as did the funding for 

ASSIST. Therefore, SmokeLess States grantees, who were mainly health voluntary agencies and 

other coalition partners, did not have to contend with state governmental restrictions and bureaucratic limitations. They were freer to engage in media and policy advocacy to promote specific policy 

changes, which was severely limited under the government funding of ASSIST. Funding from the 

Robert Wood Johnson Foundation could be used for advocacy (educating policy makers and the 

public about tobacco-related policies) but not for lobbying. However, funds for lobbying were provided through partnerships with voluntary agencies (American Cancer Society, American Heart Association, American Lung Association), which did allow SmokeLess States grantees to advocate for 

specific legislation, an activity in which state health departments could not engage.a

■ Second, while ASSIST was a demonstration project designed to employ policy interventions within 

17 specific funded states, SmokeLess States eventually funded 42 state coalitions. Although there 

was no a priori evaluation plan for SmokeLess States, it is currently being evaluated using the 

ASSIST evaluation framework.

Projects such as SmokeLess States also benefited from the knowledge base that evolved from ASSIST. 

A unique component of ASSIST was the ASSIST Coordinating Center, which provided technical assistance to the ASSIST states but also helped diffuse ASSIST-like interventions to other states. This 

dissemination was done primarily through a national tobacco control conference to which all states, 

not just the 17 ASSIST states, were invited.

aGerlach, K. K., and M. A. Larkin. 2005. The SmokeLess States Program. In The Robert Wood Johnson Foundation anthology: To improve health and health care, vol. 8, 29–46. San Francisco: JosseyBass. www.rwjf.org/files/publications/books/2005/chapter_02.pdf.
11

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Turning Point for the Tobacco Industry. 

The tobacco industry, whose marketing 

expenditures have always far outstripped 

the sums invested in tobacco control, responded to these measures with numerous counterefforts. These ranged from 

spending tens of millions of dollars on 

efforts to defeat policy initiatives such 

as the ones outlined above, to moments 

such as April 14, 1994, when the CEOs 

of seven major tobacco companies appeared before the U.S. House of Representatives Subcommittee on Health and 

the Environment chaired by Rep. Henry 

Waxman and claimed that nicotine was 

“not addictive.”24

However, the tide of both public opinion and legislation turned substantially 

against the tobacco industry during the 

1990s. In particular, a 1994 lawsuit by 

the state of Mississippi to recover the 

costs of treating sick smokers under 

Medicaid unleashed a flood of similar 

lawsuits from other states, culminating in settlements with four individual 

states and, ultimately, the $300+ billion 

Master Settlement Agreement between 

the tobacco industry and state attorneys 

general in 1998.25 This settlement, which 

provided monetary payments to states 

as well as funding for numerous tobacco 

cessation resources, put the industry in 

the unique position of subsidizing tobacco control efforts at the same time it 

was aggressively marketing its products. 

Of equal importance, this agreement also 

negotiated the conditions under which 

internal tobacco industry documents that 

revealed the scope of industry efforts to 

promote its products and to counter tobacco control efforts should be made and 

remain accessible to the public.

All of these factors combined to create both great progress and great challenges in tobacco control by the end of 

the 1990s. Tobacco use in the United 

States is now lower than it has been in 

over half a century, and there is a strong 

and growing evidence base that shows 

that population-based strategies are effective. In the process, the public’s perception of tobacco use has changed and 

is now viewed as a social as well as an 

individual problem.

Tobacco Control Today

On September 30, 1999, the ASSIST 

contracts ended and on October 1, 1999, 

CDC funding for the National Tobacco 

Control Program (NTCP) began. Chapter 

10 in Monograph 16 describes the transition from ASSIST and IMPACT to NTCP. 

As of 2005, the field of tobacco control 

encompassed a broad mosaic of efforts 

spanning the entire spectrum from the 

individual, to the community, to national 

and even global populations. The evolution of those efforts over time points to a 

number of trends that have influenced the 

direction of the evaluation of ASSIST:

■ Increasing complexity. A generation 

ago, tobacco control specialists looked 

at the effectiveness of individual 

interventions. Today, they are also 

likely to be examining interrelated 

social, political, and economic factors 

that relate to the root causes of 

tobacco use—interventions in which 

causes and effects must be quantified 

by increasingly sophisticated and 

often indirect means.

■ Larger sample sizes. There is a clear 

trend toward interventions aimed at 
12

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

larger populations, in keeping with a 

growing epidemiological and systems 

view of tobacco use and health issues. 

Factors behind this trend include the 

spread of policy interventions and 

dissemination of best practices to 

growing stakeholder networks. In a 

world where a highly competitive 

tobacco industry seeks growth 

in overseas markets and targets 

population groups, future efforts 

to reduce tobacco use will become 

global as well as national.

■ More stakeholders. Tobacco control 

has evolved over the past several 

decades from an ancillary public 

health issue to a field unto itself. 

Today, stakeholders range from 

practitioners and activists at the 

community level, to an extensive 

and transdisciplinary network of 

researchers, to thought leaders and 

organizations at the highest levels of 

government.

■ Tougher gains over time. As of 

2004, adult smoking prevalence 

rates over the preceding 15 years had 

declined at approximately half the rate 

of the 15 years following the release 

of the 1964 surgeon general’s report.26

While today’s continuing rates of 

decline remain a positive trend, it is 

clear that further gains in tobaccorelated health increasingly lie beyond 

simple interventions.

Trends such as these can be seen 

clearly by doing a side-by-side comparison of the two most recent large-scale 

government tobacco control efforts, 

COMMIT and ASSIST. Table 1.1 illustrates many of the factors that influenced 

the design of the ASSIST evaluation. 

(For a more extensive comparison of 

COMMIT and ASSIST, see Monograph 

16, chapter 1, p. 10.)

These differences underscore the natural evolution that occurred in tobacco 

control and, by corollary, other issues in 

public health. As a result, the ASSIST 

evaluation represents an important first 

Table 1.1. Comparison of COMMIT and ASSIST

COMMIT ASSIST

Focus on community-level interventions Focus on state- and community-level interventions

Interventions to directly change smoking behavior Interventions to change the social and cultural 

environment and attitudes toward smoking. 

These environmental changes, in turn, create an 

environment that changes tobacco use behavior.

Clinical trial model, tracking a cohort within city 

pairs with and without intervention

Ecological model applied to statewide populations

Focus on developing intervention channels Focus on policy change, program implementation, 

and capacity building

Focus on research and data collection with less 

funding to direct services or interventions

Demonstration project with less focus on research 

or evaluation and most funding directed toward 

interventions

Incorporated a community-level coalition model Incorporated a state-level coalition model
13

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

step in how to assess future populationlevel efforts that address tobacco use 

and, potentially, other behaviors that 

cause preventable death and disease. 

It represents a fundamental change in 

evaluation methodology, as well as a 

base from which future public health 

and evaluation efforts will continue to 

evolve.

The ASSIST Evaluation

Because ASSIST was a demonstration project, the original evaluation plan 

was for a very limited assessment, based 

on a comparison of final outcomes (e.g., 

tobacco use) between ASSIST and nonASSIST states. The rationale for this was 

that ASSIST interventions were based 

on known science, its influence was initially limited to specific states, and its 

focus was on implementation. However, 

as NCI efforts increasingly focused on 

identifying and disseminating evidencebased approaches into practice across 

the cancer control continuum to increase 

the likelihood of improved intervention 

outcomes,27 it became clear that evaluating ASSIST was crucial.

■ This project represented a rare 

opportunity to measure the 

effectiveness and cost-effectiveness of 

upstream interventions, particularly 

as they related to other accepted 

public health interventions (such as 

mammography, diet and exercise 

approaches to obesity prevention, and 

injury and violence prevention) and 

public education.

■ ASSIST interventions were expanding 

to other states, amidst other modalities 

for tobacco control, and a mechanism 

was needed to assess how effective 

these measures were at a population 

level.

■ The science of evaluation itself 

needed to evolve beyond the bounds 

of randomized clinical trials and 

single disciplines toward methods and 

measures to evaluate complex public 

health initiatives.

Fundamental differences between 

COMMIT and ASSIST precluded adapting the COMMIT evaluation methodology to ASSIST. COMMIT was a 

randomized community trial, and its 

purpose was to test the effectiveness of an 

intervention and the dissemination of successful strategies through a demonstration 

project. The protocol for COMMIT was 

fixed across all sites, whereas the protocol 

for ASSIST varied across sites. In addition, COMMIT was implemented only in 

communities whose populations ranged 

in size from 50,000 to 170,000, whereas 

ASSIST was implemented across entire 

states whose mean population size was 

approximately four million.

Evaluating ASSIST, therefore, required a new approach. ASSIST was a 

large-scale, multisite demonstration project (Phase V) designed to reduce smoking prevalence through the development 

and implementation of a comprehensive 

tobacco prevention and control intervention. It was a natural experiment rather 

than a randomized experiment and was 

not comprehensive in the scope of its 

interventions, which meant that ASSIST 

was not amenable to a standard evaluation of processes or outcomes. Instead, 

this effort required an evaluation paradigm that could measure the impact of 

program interventions on public health 
14

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

outcomes in an environment with substantial diffusion of these interventions.

What factors ultimately defined the 

methodology for evaluating ASSIST? Four 

key principles underscored the design and 

implementation of this evaluation:

Use an Ecological Approach. This evaluation was not a simple cause-and-­effect 

study, but rather an observation of numerous factors interacting toward an 

outcome. There were multiple levels of 

activity; these levels interacted synergistically over time; and they formed 

elements of an overall approach in which 

the sum of the parts was expected to be 

greater in terms of success than each individual program component alone.

Measure the Impact of Social Rather Than 

Individual Change. The classic randomized 

experiment measures the effectiveness 

of a single intervention on a defined outcome. By comparison, ASSIST sought to 

change the social environment surrounding tobacco use and, in turn, effect longterm changes in individual behavior.

Seek to Measure Capacity for Change. In 

the clinical model, an intervention has a 

specific effect. In the ecological model, 

interventions create capacity (in the form 

of resources, coalitions, and policy) that, 

in turn, creates environmental change 

and continues to adapt to the conditions 

of this environment.

In tobacco control, growing evidence 

shows the impact capacity has to change 

behaviors and outcomes: for example, 

according to recent CDC best practices, recommended levels of funding 

could have substantial positive impact 

on tobacco sales;28 however, successful 

implementation of these resources requires adequate infrastructure, such as 

numbers of staff and levels of staff experience, and the strength of agency and 

community coalitions.29,30 That infrastructure was conceptualized and subsequently quantified as capacity, a concept 

for which there are multiple models 

in the extant literature (W. Trochim, 

F. Stillman, P. Clark, and C. Schmitt, 

2003, “Empirically-Developed Conceptual Model,” unpublished work).

Focus on Intermediary as well as Final 

Outcomes. The ASSIST evaluation focused not only on the long-term goals 

of a tobacco control program—namely, 

reduced tobacco prevalence and cigarette 

consumption levels—but also identified, 

assessed, and in some cases measured 

the relationship between the intervention, interim outcomes, and long-term 

outcomes. A formal measure of some 

of these outcomes, the Initial Outcomes 

Index, was part of the ASSIST evaluation analysis, based on measures of 

total cigarette price, a rating of local and 

state clean indoor air policies, and the 

percentage of workers covered by 100% 

smoke-free workplaces.

Before ASSIST, no evaluation methodology had been developed to measure the outcomes of such a complex 

program. The ASSIST evaluation was 

designed to determine if multiple, community-based, statewide efforts could 

accelerate the reduction of smoking 

prevalence; the evaluation was not designed to compare any single tobacco 

control intervention or combination of 

interventions. Measures of program effectiveness included individual-level 

outcomes (e.g., reductions in cigarette 
15

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

consumption and smoking prevalence) 

as well as macro-level environmental 

changes (e.g., enactment of policies and 

legislation, and increase in the coverage 

of tobacco-related issues in the media). 

Because ASSIST was a demonstration 

project, the proportion of evaluation dollars to program dollars was quite low—

less than 5%. The rationale behind this 

lack of investment in a comprehensive 

evaluation of ASSIST was that as a 

Phase V project, ASSIST was supposed 

to implement strategies whose effectiveness had already been documented, not 

break new ground or test the effectiveness of new methods. Thus, the original 

plans to evaluate ASSIST relied on a 

very simple methodology that required 

little additional data collection—comparing tobacco use and environmental 

changes in ASSIST and non-ASSIST 

states.

However, the ASSIST evaluation 

evolved into an integrated and comprehensive analysis of ASSIST and of 

state-level tobacco control program 

effectiveness in general. The ASSIST 

evaluation compared changes in tobacco 

control policies, state per capita cigarette 

consumption, and adult smoking prevalence in ASSIST and non-ASSIST states 

and the District of Columbia. Smoking 

prevalence was obtained from adults 

interviewed in the NCI-sponsored Tobacco Use Supplement to the U.S. Census Bureau’s Current Population Survey 

(TUS-CPS) in 1992–93 and 1998–99. 

Per capita cigarette consumption was 

calculated every two months for each 

state from sales data for the total number 

of cigarette packs moved from wholesale 

warehouses, divided by the state’s adult 

population. This analysis represented a 

major advance in the evaluation of comprehensive state-level tobacco control 

programs and, by corollary, of complex 

multifactor public health interventions.

The development of the ASSIST 

evaluation conceptual framework helped 

redirect the evaluation effort to a more 

comprehensive look at overall tobacco 

control development and effectiveness. 

Randomized Clinical Trials Versus ASSIST

The requirements of the ASSIST evaluation 

were not unique to tobacco control. A growing evidence base to guide clinical practice, 

such as the Cochrane Collaboration, is being 

increasingly applied to public health interventions. However, clinical practices do not 

necessarily translate well to public health settings. For example, randomized clinical trials 

are often inappropriate or infeasible in public 

health settings, where it is often impossible 

or undesirable to limit interventions across 

population groups. In addition, randomized 

clinical trials frequently do not account for 

the complexity of effect modification of the 

interventions and comorbidity factors found 

in the real world—a fact illustrated by the 

growth of public health efforts that use a 

systems approach to model the interplay 

between linked epidemics and related phenomena.a Finally, randomized clinical trials 

may have limited generalizability outside the 

restricted interventions and populations used 

in the trials.

Using Cochrane-style meta-analysis efforts 

to drive future advances in evidence-based 

public health requires a fresh approach to 

program evaluation. The size and scope of the 

ASSIST effort made it an ideal test case for 

developing such an evaluation methodology.

aCenters for Disease Control and Prevention. 

2004. Syndemics Prevention Network. http://

www.cdc.gov/syndemics.
16

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

On the basis of this model, a series of 

research questions were formulated to 

establish linkages between the complex 

program components and outcomes. In 

addition to examining whether the 17 

ASSIST states achieved lower cigarette 

consumption and lower smoking prevalence than the other 33 states and the 

District of Columbia, the evaluation design provided for an in-depth evaluation 

of state tobacco control program components. The evaluation allowed a determination of whether states with more 

tobacco control resources and infrastructure and those that focused more effort 

on changing the policy environment produced greater change in tobacco-related 

policies (initial outcomes) and achieved 

lower tobacco prevalence and cigarette 

consumption rates (final outcomes).

Conceptual Design

ASSIST represents an ecological systems model (sometimes referred to as 

“the new public health”)—an approach 

that focuses on changing the social, 

cultural, economic, and physical environmental factors that influence health 

behaviors.31,32

The ASSIST evaluation model is 

based on the assumption that cigarette 

smoking is driven by a complex set of 

environmental factors and that changes in 

smoking that result from tobacco control 

policy initiatives occur incrementally 

and at a modest pace. Testing these assumptions required multiple outcome 

points (initial, intermediate, and final) 

to track change as it occurred over the 

8-year span of ASSIST. This span accommodated the expectation that a measurable reduction in smoking prevalence 

would lag behind changes in policy and 

social norms and would also lag behind 

reductions in cigarette consumption. 

Therefore, early signs of change, such as 

change in policy for states (for example, 

the amount of tax or new clean indoor air 

legislation), could serve as an initial indicator that the intervention had an effect.

The ASSIST Evaluation Model

Evolution

In 1992, an evaluation group was 

convened to develop and implement an 

evaluation methodology for ASSIST, as 

originally designed—as a simple comparison of smoking prevalence between 

ASSIST and non-ASSIST states. An early plan also included matching ASSIST 

states with non-ASSIST states. However, 

this methodology lacked adequate statistical power to assess change.

Some components of the early evaluation design included

■ Measures such as the TUS-CPS, an 

extensive tobacco use questionnaire, 

tied in with the U.S. Census and 

tobacco use information from the 

Behavioral Risk Factor Surveillance 

System (BRFSS) developed by NCI 

but implemented by the CDC.

■ An ASSIST Coalition Assessment 

designed as a qualitative measure 

of state-level tobacco control 

coalitions, in areas such as 

environmental, structural, and 

functional characteristics. This 

assessment, a case study approach 

based on document reviews, one-onone interviews, direct observations, 

and a written survey instrument, was 
17

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

pilot tested but never implemented 

across all ASSIST states. Ultimately, 

the Strength of Tobacco Control 

(SoTC) measure, discussed in more 

detail in chapter 2, was developed 

and implemented to gather data on 

program components and functioning 

across all U.S. states.

■ A rating system for the ASSIST 

evaluation using the State Cancer 

Legislative Database.

In the second phase of the ASSIST 

evaluation, a Technical Expert Panel 

was convened and the final conceptual 

framework was developed. This section 

describes its key constructs, assessment 

techniques, and the analytical methods 

used for prevalence and consumption 

analyses.

The ASSIST evaluation ultimately 

compared changes in tobacco control policies, state per capita cigarette consumption, and adult smoking prevalence in the 

17 ASSIST states with those in the 33 

non-ASSIST states and the District of Columbia. The evaluation also analyzed the 

effect of program components and tobacco control policies on smoking prevalence 

and per capita cigarette consumption. The 

development of the ASSIST evaluation 

conceptual framework and the research 

questions that sought to establish linkages 

between the program components and 

program outcomes provided a more comprehensive assessment of ASSIST effectiveness and tobacco control functioning 

across the United States.

Key Constructs

Figure 1.3 presents the conceptual 

framework for the ASSIST evaluation, 

illustrating the sequential process of 

change resulting from statewide tobacco 

control efforts. The model consists of 

key constructs that may impede or promote progress toward the final outcomes 

of reducing cigarette consumption and 

smoking prevalence, expressed as groupings of related variables used to index 

or measure the more abstract concepts 

behind them.

Figure 1.4 shows the timeline for 

data collection in the ASSIST evaluation. Per capita cigarette consumption 

data were collected every two months 

for each state from sales data for the 

total number of cigarette packs moved 

from wholesale warehouses, divided 

by the state’s adult population. Smoking prevalence was collected in the 

NCI-sponsored TUS-CPS in 1992–93 

and 1998–99. For the ASSIST evaluation, only data from baseline (1992–93) 

and final (1998–99) collections were 

used. Data for the SoTC measure were 

collected only once, at the end of the 

intervention phase, whereas data for the 

Initial Outcomes Index (IOI) were collected throughout the study. The measurement and computation of indirect 

indices such as SoTC and IOI required 

more sophisticated efforts, described in 

detail in chapters 2 and 4, respectively, 

in this monograph. Table 1.2 delineates 

the key constructs and the variables that 

were proposed for the evaluation.

The Strength of Tobacco Control 

(SoTC) index was developed to measure the components of ASSIST or 

ASSIST-like programs. The index is a 

multi-element measure consisting of 

three major components:
1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

18

Figure 1.4. ASSIST Evaluation Timeline

1988

Time (Years)

1990 1992 1994 1996 1998 2000

Planning Intervention Analysis

Baseline Consumption Active Monitoring of Consumption

Active Monitoring of IOI

Baseline

Prevalence

Follow-up

Prevalence

SOTC

Source: Stillman, F. A., A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. M. Murray, and J. T. Gibson. 2003. 

Evaluation of the American Stop Smoking Intervention Study (ASSIST): A report of outcomes. Journal of the 

National Cancer Institute 95 (22): 1682. Used by permission of Oxford University Press.

Figure 1.3. The ASSIST Evaluation Model

ASSIST

And

Other

Initiatives

Pro-Tobacco

Efforts

AntiTobacco

Efforts

Capacity

To

Implement

Tobacco

Control

Activities

Initial

Outcomes

of Tobacco

Control

Efforts

Intermediate

Outcomes

in Attitudes

and

Behavior

Final

Outcomes

in Prevalence

and

Consumption

Resources

State Conditions

SOTC

Source: Stillman, F. A., A. Hartman, B. Graubard, E. Gilpin, D. Chavis, J. Garcia, L. M. Wun, W. Lynn, and M. 

Manley. 1999. The American Stop Smoking Intervention Study: Conceptual framework and evaluation design. 

Evaluation Review 23 (3): 263. Used with permission.
19

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Table 1.2. Key Constructs and Variables Initially Proposed for the ASSIST Evaluation

Measure Variable

Key constructs

Resourcesa ■ Dollars expended for tobacco control

■ Source of funds for tobacco control

Capacity to implement tobacco 

control activitiesa

■ Number of state-level tobacco control personnel 

Capability of state organization to provide surveillance, training, 

and technical assistance

■

■ Number of state organizations involved in tobacco control

■ Frequency and type of contact between organizations

■ Linkages between state and local tobacco control

Antitobacco effortsa ■ Quality of state tobacco control plan

■ Comprehensiveness of state tobacco control plan

■ Type of tobacco control strategies

■ Comprehensiveness of state tobacco control effort

Protobacco efforts ■ Advertising dollars

■ Legislative activities

■ Other activities

State conditions Age, education, population size, poverty status, race/ethnicity, 

sex, urban/rural

■

Economic value of tobacco from agricultural, manufacturing, and 

processing (% of gross state product)

■

Outcome measures

Initial outcomes ■ Rating of local and state tobacco control policies

Percentage of workers covered by clean indoor air policies and 

workplace smoking bans

■

■ Media advocacy score

■ Cigarette price/tax

Intermediate outcomes ■ Behavior change 

■ Attitudes

Final outcomes ■ Prevalence

■ Consumption

Source: Stillman, F., A. Hartman, B. Graubard, E. Gilpin, D. Chavis, J. Garcia, L. M. Wun, W. Lynn, and M. Manley. 

1999. The American Stop Smoking Intervention Study (ASSIST): Conceptual framework and evaluation design. 

Evaluation Review 23 (3): 264. Used with permission.

a

Summarized to form the Strength of Tobacco Control (SoTC) index.
20

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

■ The first component of SoTC is 

resources committed to state tobacco 

control efforts. This construct includes 

state budgetary expenditures for 

tobacco control and the number of 

personnel working on tobacco control.

■ The second component is the 

capacity to implement tobacco 

control activities. This construct 

includes the number of statelevel agencies and local coalitions 

committed to tobacco control. This 

capacity construct also measures the 

extent to which specific structures 

and linkages have developed among 

key state agencies, coalitions, and 

advocacy groups. Studies have 

demonstrated that these linkages 

can be measured with quantitative 

indicators.33,34

■ The third component is tobacco 

control program efforts. This 

construct includes tobacco control 

program efforts that focus on 

socioenvironmental and policy 

interventions and efforts that focus on 

changing individual behavior.

These three variables (resources, capacity, and efforts) were summarized 

to form the overall exposure measure 

of tobacco control efforts at the state 

level—SoTC—which in turn served as 

an indirect measure of ASSIST.

Outcome Measures

Tobacco control efforts produce many 

types of change, as noted by the outcome 

measures listed in table 1.2. Initial outcomes could be measured at both the individual (micro) and state (macro) levels. 

For example, a workplace tobacco policy 

(a primary intervention objective) is an 

initial outcome. Workplace tobacco policies can be self-imposed by employers 

and measured by individuals responding 

to a survey, or they can be mandated by 

state or local legislation and measured 

by a rating of the state or local legislation. Intermediate outcomes include 

changes in smoking behavior (quit attempts) and changes in attitudes. Final 

outcomes include changes in consumption levels and prevalence rates as well 

as in initiation rates and quit ratios.

The analyses of multiple outcomes 

(e.g., cigarette consumption, quit ratios, 

initiation rates, delay in age of initiation, 

changes in workplace policies, and media exposure at their different levels—

initial, intermediate, final—in addition to 

smoking prevalence outcomes) are critical to understanding the relationships 

and timing of the various components 

of the tobacco control model. From 

the California experience, it is apparent that changes in cigarette consumption can be seen sooner than changes 

in prevalence.35 Changes in prevalence 

attributable to an intervention result 

from a complex mixture of changes in 

quitting and initiation, delays in the age 

of initiation, and changes in the ability 

to affect these in the entire population 

examined. Cigarette consumption may 

also change as a consequence of several 

factors, such as the number of people beginning to smoke, the number of people 

quitting completely, and the number of 

smokers cutting down the number of 

cigarettes smoked. However, cigarette 

consumption is a more sensitive measure 

of tobacco control outcomes than smoking prevalence because it is a continuous 

measure and is collected frequently over 
21

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

time, resulting in many more measurements with a better basis for estimating 

trends in a time-trend analysis.36

Research Questions

The ASSIST evaluation was guided 

by a series of research questions that are 

summarized in table 1.3. The initial question was whether the 17 ASSIST states 

would achieve lower cigarette consumption rates and lower smoking prevalence 

than all other states.37 However, the evaluation design included questions about 

the relationship between exposure to 

tobacco control efforts (i.e., as measured 

by SoTC) or initial outcomes and levels 

of cigarette consumption and prevalence 

across all 50 states and the District of 

Columbia. In other words, did states with 

higher SoTC scores or higher initial outcome scores have lower tobacco usage? 

The practices and approaches that were 

most likely associated with successful 

implementation of state-level tobacco 

control programs were also identified.

Analytic Challenges

When ASSIST began in 1991, the 

initial plan for its evaluation was a 

simple ASSIST/non-ASSIST comparison using responses from the BRFSS. 

However, it was determined that data 

from the BRFSS were not comparable 

across states: not all states were using the 

BRFSS at the beginning of the project; 

in addition, states used different methodologies, specifically varying sampling 

strategies. The evaluation ultimately used 

the TUS-CPS, which was developed by 

NCI for the ASSIST evaluation and was 

conducted by the Bureau of the Census. 

Baseline (1992–93), mid-project (1995–

96), and follow-up (1998–99) surveys 

of smoking and tobacco use prevalence 

were to be measured in all 50 states and 

the District of Columbia. The TUS-CPS 

Table 1.3. Research Questions Guiding the ASSIST Evaluation

What is the effect of ASSIST on cigarette consumption and smoking prevalence rates (final outcomes)?

What is the relationship between ASSIST and the Strength of Tobacco Control index (SoTC: resources, 

capacity, and antitobacco efforts)?

What is the relationship between SoTC and cigarette consumption and smoking prevalence rates?

What is ASSIST’s effect on initial outcomes (worksite smoking bans, legislative scores, media advocacy 

scores, cigarette prices)?

How are the initial outcomes related to the final outcomes?

What is the relationship between SoTC and the initial outcomes?

Did ASSIST modify the effects of the initial outcomes and/or SoTC’s effects on the final outcomes?

Source: Stillman, F., A. Hartman, B. Graubard, E. Gilpin, D. Chavis, J. Garcia, L. M. Wun, W. Lynn, and M. Manley. 

1999. The American Stop Smoking Intervention Study (ASSIST): Conceptual framework and evaluation design. 

Evaluation Review 23 (3): 267. Used with permission.

■

■

■

■

■

■

■
22

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

provided state-specific estimates as well 

as overall data on the U.S. population at 

large.

The primary endpoint planned for 

ASSIST was the prevalence of cigarette 

smoking and other tobacco use in the 

intervention sites. Smoking prevalence 

in the ASSIST states was to be compared with smoking prevalence in nonASSIST states. A simple comparison at 

that time seemed a rational approach because few state health departments had 

tobacco control programs and ASSIST 

was therefore relatively unique.

However, this simple evaluation plan 

could not be used. The size and complexity of this demonstration project 

resulted in a number of difficult analytic challenges, including diffusion of 

ASSIST-like activities to other states, 

variations in state conditions that could 

affect program implementation or outcomes, site selection bias, and statistical 

limitations related to the small number 

of observations (50 states plus the District of Columbia).

Diffusion, Contamination, and Secular 

Trends

ASSIST was designed as a catalyst for 

tobacco control efforts, and no effort was 

made throughout the project to inhibit or 

prevent the diffusion of tobacco control 

strategies from ASSIST to non-­ASSIST 

sites. Within the first few years of the 

project, non-ASSIST states adopted 

ASSIST program elements. In fact, the 

spread of activities from ASSIST to nonASSIST sites was considered a possible 

indicator of success, and substantial natural diffusion from parallel antitobacco 

activities was expected to occur throughout ASSIST. ASSIST was considered a 

precursor to a national tobacco control 

program with “sustained funding for all 

states and territories,”18(p446) and wide 

diffusion of ASSIST practice standards 

would make this transition easier. (As 

discussed previously, two key initiatives 

that helped spread the concepts of many 

ASSIST interventions to other states were 

the SmokeLess States National Tobacco 

Policy Initiative and the CDC IMPACT 

program.)

As a result, at the midpoint of the 

ASSIST intervention, all states had tobacco control programs. This situation 

was desirable from a public health perspective, but it made it difficult to characterize non-ASSIST states as control or 

no-treatment states. In addition, it was 

expected that it would take an extended 

period of time for the program to affect 

consumption and prevalence, making it 

difficult to separate secular trends in tobacco use from program effects.

Competing Factors and Forces

The evaluation was further complicated by the fact that ASSIST activities 

were not conducted in a vacuum. State 

conditions such as demographics (specifically, sex, age, race/ethnicity, poverty 

status, education, urban/rural, population size) and economic dependence 

on tobacco (the relative contribution of 

tobacco growing and manufacturing to 

each state’s economy) were expected to 

influence the success of tobacco control 

efforts. In addition, ASSIST represented 

a considerable economic threat to profits 

from sales of tobacco. Tobacco industry 

internal documents reveal that in 1989, 
23

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

immediately upon announcement of the 

ASSIST Request for Proposal, the industry began to develop a strategy to counter 

tobacco control activities in the ASSIST 

states (see Monograph 16, chapter 8, for 

more extensive discussion and details of 

the activities of the tobacco industry in 

countering ASSIST). The billions of dollars that the tobacco industry spent promoting their products each year between 

1991 and 1999 (from over $4 billion in 

1991 to over $8 billion in 1999)38 far exceeded the funding that states received in 

their contracts from NCI and the American Cancer Society.

State Selection Bias

Because ASSIST was a demonstration project and not a research study, 

the award of contracts was not based 

on random assignment but rather on 

other considerations that included 

the competitiveness of the states’ 

proposals.39,40 All 50 states and the 

District of Columbia were eligible to 

compete for the contracts; 35 states applied, and 23 states were deemed eligible 

for funding based on published selection 

criteria.39 In addition, an attempt was 

made to include states that were unlikely 

to be able to develop their own tobacco 

control programs and that were unlikely 

to reach the prevalence goals set without considerable assistance. Therefore, 

although the states chosen for ASSIST 

funding represented a wide range in ability and experience in developing and 

implementing tobacco control programs, 

they were a purposeful, not a randomly 

selected, sample.

At baseline, the average prevalence 

of adult smoking for ASSIST states 

was only slightly higher than for nonASSIST states (25.2% and 24.4%, respectively, p = .35). Among the ASSIST 

states, there were wide variations in state 

conditions, pre-intervention levels of 

tobacco control activities, and tobacco 

control policies. This meant that the 

evaluation would have to use covariates 

to control for the nonrandomization and 

baseline differences of the states and to 

reduce the variability of estimates. These 

differences are displayed and discussed 

in chapter 5 of this monograph.

Limited Number of Available Observations

Since the state was the basis of the 

ASSIST programs, the unit of analysis 

was the state. Many constructs in the 

tobacco control evaluation model were 

measured only at the state level. However, this provides a maximum of only 

50 states and the District of Columbia. 

As a result, quantitative analyses, such 

as regression models, were limited to 

relatively few variables in each analytic 

model. With only 51 observations, even 

a modest degree of random variation severely limits the power of the analysis to 

detect an effect.

Final Conceptual Framework

By the end of ASSIST, its evaluation 

director had streamlined the conceptual 

framework discussed earlier in this chapter and finalized the actual variables that 

would be used to measure all of the constructs. The final conceptual framework 

for the evaluation, as shown in figure 

1.5, aggregated the state-level tobacco 

control efforts into a single SoTC measure and sought to create a similar measure for the strength of tobacco industry 
24

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

counterefforts. Other measures tracked 

initial outcomes in policy, intermediate 

outcomes in attitudes and behavior, and 

final outcomes in tobacco prevalence and 

per capita consumption, subsequent to the 

implementation of ASSIST interventions.

Table 1.4 outlines the actual evaluation measures and variables resulting 

from this final conceptual framework. 

Compared with the original constructs 

and variables outlined in table 1.2, table 

1.4 reflects considerably greater aggregation of tobacco control measures, 

as well as a much broader range of 

state conditions that served as covariates and/or demographic criteria for the 

evaluation analyses.

Summary

T

he remainder of this monograph 

documents the component parts of 

the ASSIST evaluation project, starting 

with its core metrics, SoTC and IOI, 

as well as a detailed chapter examining policy and legislative changes that 

helped contribute to IOI. The monograph 

then discusses the state conditions that 

were covariates in the analysis, and state 

economic dependence on tobacco. Next, 

two ancillary efforts are discussed that 

did not yield evaluation metrics but provided valuable insights for future work: 

a database of print media coverage on 

tobacco and a study of tobacco industry 

countertactics. Finally, the evaluation 

Figure 1.5. Final Conceptual Framework Used for ASSIST Evaluation

State Conditions

ASSIST

& Other

Initiatives

Strength of

Industry CounterEfforts (SIC)

Strength of

Tobacco Control

(SoTC) Efforts

Initial

Outcomes

in Policy

Intermediate

Outcomes in

Attitudes and

Behavior

Final Outcomes

in Prevalence and

Consumption

Source: Stillman, F. A., A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. M. Murray, and J. T. Gibson. 2003. 

Evaluation of the American Stop Smoking Intervention Study (ASSIST): A report of outcomes. Journal of the 

National Cancer Institute 95 (22): 1682. Used by permission of Oxford University Press.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

25

Table 1.4. Final Constructs and Variables Used for the ASSIST Evaluation

Measure Variables

Intervention measures

ASSIST indicator ■ Identification of states as either ASSIST or non-ASSIST 

Strength of Tobacco Control 

(SoTC) index

■ Resources committed to tobacco control (staff and funds)

■ Capacity to deliver state-level tobacco control (infrastructure)

Program efforts focused on policy and socioenvironmental 

change

■

State conditions (controlled factors) ■ Age: 18–29, 30–49, 50–64, 65 years or older

Person-level (demographic factors) ■ Sex: male, female

Education: less than 9th grade, 9th–12th (no high school 

diploma), high school diploma, some college or associate’s 

degree, 4-year college degree or higher

■

■ Family income: in dollars

Race/ethnicity: black–non-Hispanic, Hispanic, white nonHispanic, other■

■ Household size: number of residents

■ Census region: Midwest, West, South, Northeast

■ Employment status: employed, unemployed

State-level (sociodemographic 

factors)

■ Sex: % female

■ Education: % above high school degree

■ Income: % below poverty level

■ Race/ethnicity: % black–non-Hispanic, % Hispanic

■ Metropolitan residency: % living in metropolitan area

■ Census region: Midwest, West, South, Northeast

■ State population: 18 years of age or older

Economic value of tobacco: fraction of gross state product 

from growing, manufacturing, and processing tobacco

■

Outcome measures

Initial Outcomes Index (IOI) ■ % of workers covered by 100% smoke-free workplace

■ Cigarette price (including tax)

■ Rating of local and state clean indoor air policies

Final outcomes ■ Adult smoking prevalence (18 years of age or older)

■ Per capita cigarette consumption

Source: Stillman, F. A., A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. M. Murray, and J. T. Gibson. 2003. 

Evaluation of the American Stop Smoking Intervention Study (ASSIST): A report of outcomes. Journal of the 

National Cancer Institute 95 (22): 1683. Used by permission of Oxford University Press.
26

1 . T h e A S S I S T E v a l u a t i o n P r o j e c t : A n O v e r v i e w

and cost-effectiveness of ASSIST are 

discussed.

The evaluation of ASSIST was an opportunity to generate invaluable information about the delivery and impact of the 

largest federal tobacco control initiative 

at that time. It was also a unique research 

opportunity to investigate the complex 

relationships inherent in a large-scale 

public health intervention. The new indices, databases, and analytical methods 

developed to address the challenges of 

the evaluation yielded a new model for 

state-level tobacco control evaluation. 

The lessons learned can be used to enhance tobacco control program development, as well as other initiatives that 

seek to change health behavior through a 

macro-level systems approach.

Conclusions

1. ASSIST was an ambitious public 

health effort to control tobacco use 

by building a sustainable, professional infrastructure for tobacco control and by implementing upstream, 

policy-level interventions. It was the 

natural extension of earlier interventions at the individual and community levels: an environmental approach 

to tobacco control that targeted the 

smoking behavior of populations.

2. The ASSIST evaluation created a 

conceptual framework that documented the fundamental components of the ASSIST environmental 

approach to tobacco control. This 

conceptual framework was used to 

develop new measures and methods 

that were used to document the outcomes of this project.

3. Key components of the ASSIST evaluation included intervention measures 

including the state-level Strength of 

Tobacco Control metric and demographic factors, and outcome measures including the Initial Outcomes 

Index, tobacco use prevalence, and 

per capita cigarette consumption.

4. The ASSIST evaluation faced numerous challenges, including the 

diffusion of its interventions to other 

states, competing factors such as 

demographics and the economic impact of tobacco on states, and limited 

state-level samples. Addressing these 

challenges ultimately led to a unique 

evaluation methodology with lessons 

for future efforts involving widely diffused, population-level public health 

interventions. Many population-based 

health interventions raise similar 

challenges to evaluation. Because the 

ASSIST evaluation successfully met 

those challenges, it remains an exemplar for future evaluations.

References

1. Jefferson, T. 1787. Notes on the State 

of Virginia, Query 20. Ed. W. Peden. 

167–68. Chapel Hill: University of 

North Carolina Press. http://etext.lib.

virginia.edu/toc/modeng/public/JefVirg.

html.

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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

31

2. The Strength of Tobacco Control Index

Frances A. Stillman, Carol L. Schmitt, Pamela I. Clark, 

William M. K. Trochim, and Stephen E. Marcus

Contributors: Warren Strauss and Jyothi Nagaraja

Contents

Introduction.................................................................................................................... 33

Development of the Strength of Tobacco Control Index................................................... 34

Defining State-level Tobacco Control Programs and Development of the Heuristic Map ......34

Description of the Constructs and Domains.........................................................................36

Development of the Data Collection Instruments and Analytic Plan ....................................39

Collection, Analysis, and Validation of SoTC Data.......................................................... 40

Data Collection ....................................................................................................................40

SoTC Respondents........................................................................................................41

Data Analysis.......................................................................................................................42

Validation of the Conceptual Model .....................................................................................43

Results of SoTC.............................................................................................................. 44

The Relationship between SoTC Scores and Other Indicators...............................................44

Limitations of the SoTC Index ..............................................................................................48

Use of the SoTC to Evaluate Individual State Programs................................................. 49

Inputs: Resources and Capacity ..........................................................................................49

Activities..............................................................................................................................51

Summary........................................................................................................................ 54

Conclusions.................................................................................................................... 54

References..................................................................................................................... 86

Tables and Figures

Figure 2.1. Heuristic Map of Components Proposed for Strength of Tobacco 

Control Index...............................................................................................................................37

Table 2.1. The Constructs, Domains, and Measures of the Strength of Tobacco 

Control Index...............................................................................................................................38

Table 2.2. Standardized Strength of Tobacco Control Index and Construct Scores, 

1999–2000, by State, Sorted by SoTC Scores..............................................................................45

Figure 2.2. Strength of Tobacco Control Scores by State .............................................................46
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

32

Figure 2.3. Resources Construct Scores by State ........................................................................46

Figure 2.4. Efforts Construct Scores by State..............................................................................47

Figure 2.5. Capacity Construct Scores by State...........................................................................47

Table 2.3. Correlation of Legislative Scores (1999) with SoTC Scores (1999)..............................48

Table 2.4. Correlation of Tobacco Use Prevalence (2000) with SoTC Scores (1999).....................48

Table 2.5. Inputs: Resources and Capacity, Sorted by Funds.......................................................50

Figure 2.6. Change in Tobacco Control Funding (in Dollars), 1991–98 .......................................51

Table 2.6. Components of the Efforts Construct, Sorted by Efforts Score ....................................52

Table 2.E.1. Within-method Correlation Analysis of Domain-level Strength of Tobacco 

Control Variables.........................................................................................................................79

Table 2.E.2. Within-method Correlation Analysis among Three Constructs and Overall 

Strength of Tobacco Control for Full Model..................................................................................80

Table 2.E.3. Within-method Correlation Analysis among Three Constructs and Overall 

Strength of Tobacco Control for Reduced Model ..........................................................................80

Table 2.E.4. Amount of Variability Explained by First Three Factor Loadings in Principal 

Components Analysis..................................................................................................................81

Figure 2.E.1. Factor Analysis for Full Model.................................................................................81

Figure 2.E.2. Strength of Tobacco Control—Full Model...............................................................83

Figure 2.E.3. Strength of Tobacco Control—Reduced Model .......................................................84

Figure 2.F.1. Basic Conceptual Framework..................................................................................85

Appendices

Appendix 2.A. Assessment Rating of Variables.............................................................. 56

Appendix 2.B. Key Elements of Strength of Tobacco Control Efforts ............................... 57

Appendix 2.C. Agency Worksheet.................................................................................... 61

Appendix 2.D. Survey 2................................................................................................... 65

Appendix 2.E. Validation of the Strength of Tobacco Control Model................................ 77

Appendix 2.F. Construct Validation of Strength of Tobacco Control................................. 85
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

33

2. The Strength of Tobacco Control Index

Introduction

T

his chapter discusses the development and implementation of the SoTC index—a 

state-level measure of tobacco control interventions—and provides the SoTC score 

and its component constructs for each state. Based on three key constructs—­resources, 

capacity, and efforts—the SoTC index represents a “dose-level” measurement of 

ASSIST interventions for the 17 states within the project and other states that benefited 

from the diffusion of these interventions through other initiatives.

The success of the ASSIST evaluation depended on identifying accurate metrics for 

assessing state-level performance in tobacco control outcomes. Moreover, this project set out to measure the impact of interventions that were being used far beyond the 

states originally funded by the ASSIST project. The SoTC index represents an indirect 

measure of state-level tobacco control performance, using aggregated results derived 

from its three constructs and their supporting data sources. The development process 

for the SoTC index serves as an example of participatory design, validation of realworld factors, and collection and analysis of data from multiple sources. Its values were 

correlated significantly with other constructs such as legislative policy scores and, as 

discussed in more detail in chapter 9 of this monograph, ultimately correlated with tobacco control outcomes at the state level.

The Strength of Tobacco Control (SoTC) index was created to measure the 

program effects of the American Stop Smoking Intervention Study (ASSIST) and 

to serve as an overall measure of tobacco control intensity at the state level. The 

measure comprises three constructs (resources, capacity, and efforts) that constitute 

the multiple facets and components of tobacco control. This chapter describes four 

key stages of the evolution and use of SoTC:

■ Development of the SoTC index around the three constructs (resources, capacity 

for state-level tobacco control, and program efforts focused on policy and socialenvironmental change); development of a survey methodology for measuring 

these constructs; and determination of how the level of these constructs in a 

specific state constitutes the SoTC for that state;

■ Collection and analysis of the SoTC data and validation of the SoTC heuristic 

map using factor analysis and structural equation modeling;

■ Results of SoTC, including comparison of SoTC results across states and analysis 

of how SoTC relates to intermediate and final outcome measures;

■ Use of SoTC to evaluate individual state programs: beyond ASSIST program 

evaluation, the SoTC measure is useful as a means for states to conduct a process 

analysis of their tobacco control programs.
34

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

Development of the Strength of 

Tobacco Control Index

T

he ASSIST evaluation presented a 

unique challenge with implications 

for the future of evidence-based public 

health. The challenge was to develop a 

measure that (1) could be used outside 

the bounds of a controlled trial and 

(2) could be related to public health 

outcomes. The SoTC index is a metric 

that measures the magnitude of a state’s 

tobacco control program. The index 

was based on a heuristic model that was 

internally and externally validated and 

was subsequently used to evaluate the 

effects of ASSIST interventions. Further, 

SoTC holds promise as a process evaluation measure that states can use to assess 

their tobacco control programs.

As described in chapter 1, ASSIST 

was implemented during a period when 

state-level tobacco control programs 

were instituted in all 50 states and the 

District of Columbia. State, federal, and 

foundation initiatives built varying levels 

of tobacco control infrastructures, and 

at the close of the twentieth century this 

infrastructure received additional funding from settlements of lawsuits against 

the tobacco industry. By the time of the 

ASSIST evaluation, every state had a 

functioning tobacco control program, and 

the ASSIST effects could not be easily 

disentangled from the effects of other 

initiatives. This meant that ASSIST could 

not be evaluated by simply comparing 

ASSIST states with non-ASSIST states. 

The ASSIST evaluation team agreed that 

an index quantifying each state’s tobacco 

control program was required for the 

evaluation statistical models and that this 

index should include a measure of program components (activities) and a measure of how tobacco control programs are 

organized to deliver those components 

(inputs). SoTC was developed to be this 

standard measure of state-level tobacco 

control programs.

The major challenge in constructing 

this metric was to develop and test a 

measure that adequately described the 

intensity of a state’s tobacco control 

program. In addition, coalition building 

was a core component of ASSIST, and 

a state’s tobacco control program could 

not be adequately measured unless all 

the organizations delivering tobacco control in that state were identified and their 

contributions measured. These challenges were addressed in the construction 

of the SoTC survey instruments, in the 

identification of respondents, and, subsequently, in the data-reduction strategy 

that produced the SoTC scores.

This chapter examines the development, validation, results, and future applicability of SoTC as a metric, both for 

the ASSIST program and for the future 

evaluation of state-based tobacco control 

programs.

Defining State-level Tobacco Control 

Programs and Development of the 

Heuristic Map

Within the ASSIST evaluation 

model,1 the SoTC index quantifies the 

state’s tobacco control program. The 

state tobacco control program includes 

the inputs (resources and capacity) that 

a state has available for tobacco control and the tobacco control activities 

(efforts) it performs. Another component 
35

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

of the overall evaluation framework, the 

Initial Outcomes Index, measured the 

initial policy outcomes produced by the 

program (see chapter 4). The components of the SoTC index were defined, 

constructed, and implemented in a logical and scientifically defensible manner. 

Potential index components were identified in an extensive literature review and 

analyzed for their parsimony, scientific 

support, and feasibility. A heuristic map 

for SoTC was developed. This heuristic 

map was used to develop the survey 

instrument, the data collection process, 

and the subsequent analytic plan.

An expert panel, the SoTC Workgroup, was convened to determine the 

components that constituted SoTC and 

to assess how those components could 

be validly and reliably measured. The 

workgroup began by reviewing the extant literature on state tobacco control 

programs and consulting tobacco control 

experts. On the basis of its initial review, 

the workgroup determined that a quality 

tobacco control program was based on 

the following three constructs:

■ Resources: assets for tobacco control

■ Capacity: ability (including 

infrastructure) to implement tobacco 

control activities, given sufficient 

resources

■ Efforts: the comprehensiveness 

of tobacco control activities, from 

policy-focused activities to program 

services

The workgroup subsequently identified 27 variables that they considered 

measures of these constructs. Each of the 

proposed 27 variables was then rated on 

the following criteria:

■ Parsimony was defined as the degree 

to which the variable centrally and 

simply described an ASSIST-like 

intervention expected to affect 

changes in policy and media, based 

on descriptions of ASSIST.2,3 Each 

variable was rated for parsimony on 

a scale ranging from 1 (no expected 

relationship to the ASSIST evaluation 

conceptual framework) to 5 (the 

strongest expected relationship to 

the ASSIST evaluation conceptual 

framework).

■ Scientific support was defined as 

demonstrated reliability and validity 

in peer-reviewed journals and other 

scientific publications. Scientific 

support was rated on a scale ranging 

from 1 (measure may have face 

validity, but operational definitions 

in the literature do not support 

construct validity or reliability), 

to 3 (an accepted measure used in 

several publications that have used a 

common measurement approach with 

slight variations), to 5 (a standardized 

measure with demonstrated reliability 

and validity that has been used in 

several different studies).

■ Feasibility was defined as data 

that could be collected within the 

allocated time frame (during 1999 to 

coincide with the Current Population 

Survey data collection)4 and at a 

reasonable cost. Feasibility was rated 

on a scale ranging from 1 (feasibility 

undetermined), to 3 (feasibility 

established and data for variable must 

be collected), to 5 (data are currently 

collected and available).

Two members of the workgroup reviewed the evidence on each variable. 
36

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

For cases in which the raters did not 

agree, the entire workgroup discussed 

the variable under consideration until 

they reached consensus. Variables with 

high ratings on all three criteria were 

retained. Variables that received low ratings on scientific support were retained 

only if they were deemed central to 

measuring a component of SoTC, and 

variables rated low on feasibility were 

eliminated. At the end of this process, 

14 variables remained in the SoTC 

index. For each variable selected, the 

workgroup provided sample items from 

the extant scientific literature and suggested potential informants or archival 

data sources. The original list of proposed indicators, their ratings, and recommendations for inclusion are included 

in appendix 2.A, and a list of the 14 variables with sample items and information 

sources is included as appendix 2.B.

Subsequently, a second workgroup 

was convened to examine whether the 

variables identified adequately and validly represented the three constructs 

(resources, capacity, and efforts). This 

workgroup examined the applicability 

of the variables to evaluating state-level 

tobacco control programs and corroborated these measures against applicable 

research literature. This expanded group 

included members with additional skill 

sets—psychometricians (to address validity and data-reduction considerations), 

evaluation researchers, multilevel 

analysts, tobacco epidemiologists, and 

survey researchers, along with several 

members of the original expert panel. In 

addition to refining and validating the 

criteria behind SoTC, this group helped 

to develop and refine the data-collection 

instruments behind the three SoTC constructs into their final form.

This process also resulted in a heuristic map (figure 2.1) that depicts a hierarchy of all components in the proposed 

SoTC index. As may be seen in this map, 

the SoTC index is composed of three 

constructs at the highest level: resources, 

capacity, and efforts. In turn, these constructs comprise several domains.

Description of the Constructs and 

Domains

The heuristic map was used to generate survey items from which an SoTC 

index score could be generated and to 

subsequently guide the analysis and 

interpretation of the data. Table 2.1 presents the constructs, indicators (domains), 

and associated measures. As the survey 

items were generated, a fuller description of the three constructs emerged.

■ The resources construct may be 

described as the “raw materials” 

a state needs to engage in tobacco 

control. The resources construct 

was defined as the amount of money 

allocated for a state’s tobacco control 

program and the number of full-time 

equivalent staff assigned to tobacco 

control in a state.

■ The capacity construct may be 

described as the “engine” or the 

potential ability a state has to perform 

tobacco control activities. This 

construct was originally defined 

by state leadership support for 

tobacco control, the character of 

relationships between state tobacco 

control agencies, the independence 

and power of the health department 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

37

Figure 2.1. Heuristic Map of Components Proposed for Strength of Tobacco Control Index

Resources Efforts

Conceptual Framework

Capacity

Funds State Facilitating Factors Social Environment

Individual Behaviors

Inter-Agency

Relationships

Health Department

Infrastructure

Statewide Coalitions

Surveillance & 

Evaluation

Staff

Program 

Administration Costs

Allocations

Dedicated TC

Overhead Utilities Brick & Mortar Communication Coverage

Process

Outcome

Membership

Program Evaluation Prevalence/Consumption

Level of Activity Staffing

Adults

Youth

Sub Populations

TC Plus Other Duties

Expenditures

Leadership

Position in

Organization

Line of

Authority

Scattered or

Consolidated

Budget

Line

Ed. Board

Briefings

Provide

Materials/

Interviews

Economics Media Culture

Media Message Target Groups

Media Advocacy

Developing Local Capacity

Policy Advocacy

Mass Media

Training & Technical

Assistance

Coalition

Building

Create &

Disseminate

Materials

Smoking

Cessation

Prevention

Programs

Health

Fairs

Public

Forums

Health Care

Provider

Training

Grants to

Local

Smoke-free

s

Schools

Clean

Indoor Air

Advertising

and Display PUP

Quantity Quality

Pass to Locals
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

38

Table 2.1. The Constructs, Domains, and Measures of the Strength of Tobacco Control 

(SoTC) Index

Construct/Domain Description of measures

Resources

Staff ■ Number of full-time staff dedicated to tobacco control

Funding ■ Amount of money received by the state health department and major agencies

Capacity

Leadership ■ Support of governor for tobacco control

■ Support of state representatives for tobacco control

■ Support of state senate for tobacco control

■ Support of state attorney general for tobacco control

■ Support of the chief health officer for tobacco control

Interagency 

relationships

■ Interaction with state health department as viewed by all other agencies

Frequency of contact with state health department as viewed by all other 

agencies

■

Perceived quality of interactions between all agencies BUT state health 

department as viewed by all other agencies

■

Perceived quantity of interactions between all agencies BUT state health 

department as viewed by all other agencies

■

Health department 

infrastructure

Level of involvement in deciding which tobacco-related programs the agency 

participates in

■

■ Level of involvement related to hiring decisions

Distance (inclusive) between the chief tobacco control person and the state’s 

chief health officer

■

Statewide 

coalitions

■ Does your coalition have any paid staff?

■ Proportion of state that is covered by local coalitions

Staff experience ■ Months at agency

■ Months in current position

■ Months involved with tobacco control

Efforts

Media advocacy ■ Does agency hold media editorial board briefings?

■ Does agency give press background information on smoking issues?

■ Does agency give interviews?

■ Has agency included media reps in tobacco control activities?

Mass media ■ Specific mass media (e.g., TV, radio) used by organizations

■ Specific targets of antitobacco message (e.g., demographics)

■ Was message used in mass media efforts focused on tobacco industry tactics?

Developing local 

capacity

Specific training and technical assistance activities an agency is performing at 

the local level

■

■ Specific coalition-building activities an agency is performing at the local level

■ Does agency give grants/contracts to local agencies?

Policy advocacy ■ Does agency do policy advocacy on smoke-free schools?

■ Does agency do policy advocacy on clean indoor air?

■ Does agency do policy advocacy to repeal or fight preemption laws?

■ Does agency do policy advocacy to restrict tobacco ads and displays?

■ Does agency do policy advocacy to increase tobacco taxes?

■ Does agency do policy advocacy to increase youth possession penalties?

Individual 

behaviors

■ Does agency disseminate materials for general public?

■ Does agency do cessation focused on specified target groups?

■ Does agency do school/youth prevention?

■ Does agency do health provider training?

■ Does agency do health fairs?

■ Does agency do public forums?
39

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

tobacco control program director, 

the composition and character 

of the state-level tobacco control 

coalition(s), and the experience level 

of state tobacco control professionals.

■ The efforts construct described the 

tobacco control activities that the state 

tobacco control program engaged in.*

These efforts were categorized into 

activities that focused on changing 

the social climate of tobacco use 

(e.g., media advocacy efforts to 

gain antitobacco coverage and an 

antitobacco editorial slant) and 

activities that focused on individual 

behavior change (e.g., education 

programs and cessation services).

Development of the Data Collection 

Instruments and Analytic Plan

The SoTC index measures were 

collected with two data-collection 

instruments:

1. A self-administered questionnaire 

(worksheet). The original SoTC 

workgroup recognized that some data 

collection could not be completed easily by telephone. For example, it might 

have been difficult for respondents to 

provide information accurately about 

funding amounts without consulting 

records or co-workers. The self-administered questionnaire asked respondents to list the amount of funding 

they received from such sources as 

the National Cancer Institute, Robert 

Wood Johnson Foundation, Centers 

for Disease Control and Prevention, 

and state-level sources, and to list the 

number of full-time equivalent staff 

members dedicated to tobacco control 

in their organizations. In addition, respondents estimated the percentages 

of time and money they spent on interventions aimed at changing the social 

environment, on interventions aimed at 

changing individual behaviors, and on 

administrative functions. This instrument is included as appendix 2.C.

2. A computer-assisted telephone 

interview. The remaining data were 

collected by telephone interview. 

This instrument is included as appendix 2.D.

Both instruments were tested in cognitive interviews in a laboratory environment. The interviews resulted in minor 

modifications in wording, particularly 

for item instructions and formatting of 

the self-administered questionnaire.

The original SoTC workgroup plan included an analytic strategy for the SoTC 

survey to be aggregated into an index 

using standard scaling techniques, including the use of z-score sums and principal 

components analysis. Once the survey 

instruments were tested, an expert panel 

was convened to finalize the analytic plan.

*A state tobacco control program was defined as the state health department and its state-level tobacco 

control partners. At the minimum, that partnership included the state health department, the three voluntary 

agencies (American Cancer Society, American Heart Association, American Lung Association), and any 

state-level tobacco control coalition(s).
40

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

Collection, Analysis, and 

Validation of SoTC Data

Once the conceptual model for the SoTC index was defined, its implementation involved a three-step process:

■ Collection: Participants were 

identified and data were collected 

using the survey instruments 

described in the previous section.

■ Analysis: Single SoTC scores for 

each state were derived from these 

data, using a heuristic map as a basis 

for interpreting and aggregating data 

for each of the three constructs, which 

were subsequently combined to form 

the single SoTC score.

■ Validation: Factor analysis and 

structural equation modeling were 

performed to assess the internal 

consistency of the original heuristic 

model. SoTC constructs were 

correlated with ratings from expert 

opinions to assess the construct 

validity of the overall index and its 

components.

The next sections describe the details 

of these three processes.

Data Collection

The data collection phase began with 

the identification of stakeholders in each 

state’s tobacco control community. Each 

person interviewed was asked to identify 

additional tobacco control professionals 

in their states—that is, a snowball sample 

of respondents. U.S. Office of Management and Budget clearance was obtained 

for this process. The responses to these 

interviews were used to calculate the 

SoTC index values for each state.

During ASSIST, NCI formed a strategic alliance with the American Cancer Society (ACS), which had already 

partnered with the American Lung Association (ALA) and American Heart 

Association (AHA) to form the Coalition on Smoking OR Health in 1982. 

This partnership allowed ASSIST to 

build on the ACS national structure. 

In addition, “as a nongovernmental 

organization, ACS could advocate for 

public policies and speak out against 

the tobacco industry in ways that a government agency was precluded from 

doing.”5(p48) Moreover, states were 

directed to form tobacco control coalitions that included voluntary agencies, 

advocacy groups, minority groups, and 

business leaders. These groups and organizations were to be recruited for a 

cohesive and comprehensive coalition 

that could work collaboratively and 

implement strategies and interventions 

that would promote strong tobacco 

control, including legislative and policy 

approaches.

The initial fixed-list respondents of 

the SoTC survey were defined as staff 

members of state health departments, 

statewide tobacco control coalitions, and 

state-level components of all three voluntary health organizations (American 

Cancer Society, American Lung Association, and American Heart Association). 

The exception was the District of Columbia, where the respondents were from 

city-level agencies and organizations. 

The degree to which other state-level 

organizations participated in tobacco 

control varied widely. Therefore, these 

organizations were identified through the 

snowball sample procedure.
41

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

The initial respondent list was compiled from several sources. The program 

offices for ASSIST (National Cancer 

Institute [NCI]), Initiatives to Mobilize for the Prevention and Control of 

Tobacco Use (IMPACT—CDC), and 

SmokeLess States (Robert Wood Johnson Foundation) provided lists of their 

state grantees, which included all state 

health departments and some voluntary 

agencies. Additional voluntary agency 

contacts were identified by their national 

offices and through searches of their Internet sites, telephone calls to state offices, or a combination of these strategies.

To develop the snowball sample, each 

fixed-list respondent was asked to identify other state-level entities that conducted tobacco control activities. Before they 

were interviewed, snowball-­identified 

entities were screened (either by telephone or, if available, via the Internet) to 

ensure that they were state-­level agencies active in tobacco control and that 

their organization had not previously 

completed this survey. Once interviewed, 

these respondents became an additional 

source of referrals. A state’s sample was 

considered complete when there were 

no new nominations from within that 

state. In a few cases, health department 

contacts were called to verify that the list 

of respondents interviewed in their state 

was inclusive.

All respondents answered the 

computer-assisted telephone interview. In addition, a subset of respondents completed the self-administered 

questionnaire.

The unit of measurement in the survey 

was the agency or organization. Only 

one computer-assisted telephone interview per entity was conducted, although 

more than one person in an agency could 

contribute to the interview. The instruments were constructed as modules, and 

lead-in screening items were constructed 

for each module. To complete a module, 

a respondent had to have self-referred 

into the module via the screening items. 

Self-referral thereby became the criterion for identifying the appropriate individual as the respondent for an entity.

SoTC Respondents

Staff from 372 agencies and organizations in 50 states and the District 

of Columbia completed the computerassisted telephone interview. This represented 100% of health departments, 

voluntary agencies, and state-level coalitions in each state, plus organizations 

identified through snowball sampling. 

Self-­administered questionnaires were 

completed by all 139 agencies that directly received federal, state, or foundation 

funds, or who had received funds from 

state lawsuits against the tobacco industry. The original data-­collection plan 

included self-administered questionnaire 

completion by all respondents. Despite 

follow-up telephone calls, however, the 

overall response rate for these questionnaires did not exceed 55%. The decision 

was then made to target questionnaire 

return from those agencies with identifiable and stable funding sources. These 

respondents included all state health department representatives (both ASSIST 

and IMPACT states), SmokeLess States 

grantees (identified by the SmokeLess 

States office), and recipients of tobacco 

industry settlement funds outside the 
42

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

Master Settlement Agreement (these respondents were identified by the health 

department respondents in those states). 

All self-administered questionnaires were 

obtained from these agencies.

Data Analysis

The objective of the data analysis 

was to derive a single SoTC score for 

each state as well as a score for each of 

the three major constructs (resources, 

capacity, and efforts). This goal was accomplished by using the heuristic map to 

sequentially assess each of the hierarchical groupings and subsequently combine 

the assessments. Figure 2.1 illustrates 

the hierarchical groupings of the SoTC 

construct, moving from domain and 

subdomains to the single SoTC rating. 

A later section of this chapter addresses 

the comparative importance of individual 

construct scores and the overall composite measure.

The utility of single performance 

scores has recently been questioned. The 

balanced scorecard approach is perhaps 

the best-known “dose measure” derived 

performance metric currently used in private industry. Performance on this metric 

requires that a program be assessed on 

four categories—financial, customer, 

internal business process, and innovation 

and learning. The criticisms of this approach are that unlike the SoTC index, 

the balanced scorecard is not based on 

a theoretical perspective and it does not 

Examples of the Self-referral Process

Interrelationships between state agencies module. This module required a respondent within each 

state agency who was most likely to have worked directly with other state-level organizations. If the 

fixed-list respondent was not the person directly in contact with the other agencies and organizations, 

that person’s subjective evaluation of the working relationship between the respondent’s agency and 

other entities could be misleading. To prevent this potential problem, each agency respondent was 

screened as follows: “We would like to ask some questions about the interrelationships of tobacco control organizations in your state. Are you the person in your organization who has the most contact with 

other tobacco control organizations?”

If the response was “yes,” the relationship module was completed by that person. If the response was 

“no,” an intra-agency snowball referral to the appropriate person was obtained, that part of the process 

was ended, and the rest of the interview was continued. The appropriate person within that agency was 

then contacted, and the screening question was asked again. The module would then be completed by 

that respondent only if he or she self-identified through the screening item. 

Health department infrastructure module. Because the state health department was the recipient of 

ASSIST and most other state-level tobacco control funding, the way in which the state health department was organized to implement tobacco control programs was an important element of the SoTC 

index. Only the highest-level tobacco control officer in the state health department answered the questions in the health department infrastructure module. The respondent was asked, “Would you describe 

yourself as the highest-level tobacco control specialist in your organization?”

If the response was “yes,” the infrastructure module was completed. If the response was “no,” the 

respondent was asked, “Who would you say is the highest-level tobacco control specialist in your 

organization?” The named official was then contacted, and the screening process was repeated until 

someone self-identified into the module.
43

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

incorporate stakeholder input.6 While the 

SoTC measure underwent an extensive 

validation process and was associated 

with lower cigarette consumption, analyses of specific state programs show a 

complex interplay among these construct 

values that is not completely reflected in 

the single score.

To ensure that all variables combined 

had the same measurement scale, all 

variables were standardized before being 

combined at any level of that hierarchy 

(e.g., survey question, subdomain, domain, or construct). The goal of the SoTC 

index was to provide a single measure 

that both explained strength of tobacco 

control at the state level and also captured 

the maximum variability in those survey 

measures that were consistent with the 

conceptual model. By using a hierarchical principal components approach to 

combine the survey variables at each level 

of hierarchy within the conceptual model 

(using weights from the first eigenvector), the maximum amount of variability 

among the questionnaire responses was 

captured. The model validation described 

in the next section (and detailed in appendix 2.E) suggested that the SoTC score 

better discriminated between states when 

several domains were omitted. Therefore, 

the final SoTC scores were based on this 

“reduced” model.

For example, respondents answered 

a series of questions about the use of 

mass media in their tobacco control efforts; these questions constitute the mass 

media subdomain. Each respondent’s answer to the survey questions in the mass 

media subdomain was standardized. 

Those scores were then entered into 

a principal components analysis. The 

principal components equation for that 

set of standardized scores was then 

solved, yielding one mass media subdomain score for each respondent. At 

the subdomain level, a mean state score 

was calculated from the principal components score. Subsequently, the mass 

media subdomain score was combined 

with the other subdomains (e.g., media 

advocacy, policy advocacy, developing 

local capacity) to compose the social 

environment domain, which is focused 

on changing the social environment of 

tobacco use. The social environment 

domain was then combined with the individual behavior efforts domain (e.g., 

efforts aimed at changing individual behaviors) to form the efforts construct.

Finally, the three constructs—

resources, capacity, and efforts—were 

combined using the same analytic technique (hierarchical principal components 

analysis). This process resulted in a single aggregate SoTC score for each state.

Validation of the Conceptual Model

Additional analyses explored whether 

the data supported the structure of 

relationships hypothesized by the analytic map—for example:

■ Did the data show that the efforts 

variable was truly made up of the 

individual behaviors and social 

environment domains?

■ Did the data show that these domains 

were more related to the efforts 

construct than to the capacity or 

resources constructs?
44

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

Factor analysis and structural equation modeling were used to answer these 

questions. These analyses indicated that 

the domains making up the efforts construct were significantly related to each 

other and not to domains within the resources or capacity constructs (appendix 

2.E). Likewise, the domains making up 

the resources construct were significantly 

related to each other and not to domains 

within the capacity or efforts constructs.

The relationship between the domains 

in the capacity construct was not as 

clear-cut, and additional analyses were 

performed to determine which domains 

yielded the best SoTC index model. 

When all 12 domains were included in 

the model, that model accounted for 50% 

of the variability in the correlation matrix. 

However, after removing three domains 

within the capacity construct—leadership, 

health department infrastructure, and staff 

experience—the model accounted for 

60% of the variability in the correlation 

matrix. The SoTC index scores used in 

the ASSIST evaluation therefore consisted of these nine domains. The model 

validation analysis and justification for 

the reduced model are described in more 

detail in appendix 2.E, and the participatory approach used to validate the SoTC 

criteria is described in appendix 2.F.

Results of SoTC

T

able 2.2 shows the SoTC index scores 

and the three construct scores (resources, efforts, and capacity) for the 50 

states and the District of Columbia, and 

figures 2.2 through 2.5 show maps of 

these results by state. ASSIST states did 

not differ significantly from non-ASSIST 

states on overall SoTC score or on any of 

the three constructs.

The Relationship between SoTC 

Scores and Other Indicators

The SoTC scores for all states and 

the District of Columbia were compared 

with the legislative score (described in 

chapter 3). The legislative score, a component of the Initial Outcomes Index, 

measures the strength of a state’s policies on clean indoor air and youth access 

to tobacco. Since these two policy areas 

were part of the focus of the ASSIST 

program, it was expected that a strong 

tobacco control program (as measured 

by the SoTC index) would be associated 

with higher levels of tobacco control 

policy. Table 2.3 shows the results of 

this analysis. The overall SoTC index 

score was significantly correlated with 

the legislative score and with the efforts 

construct.

The SoTC scores for all states and the 

District of Columbia were also compared 

with the prevalence of tobacco use measured at the state level in the Tobacco 

Use Supplement of the Current Population Survey.7 Table 2.4 shows the results 

of the analyses of state SoTC scores and 

construct scores for 1999, and the prevalence of tobacco use by state for 2000.

Prevalence of tobacco use was significantly correlated with the SoTC index 

score as well as the resources and capacity constructs but was not significantly 

correlated with the efforts construct. In 

addition, per capita adult cigarette consumption levels showed a correlation 

with both the SoTC index and its capacity construct.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

45

Table 2.2. Standardized Strength of Tobacco Control (SoTC) Index and Construct Scores, 

1999–2000, by State, Sorted by SoTC Scores

(Shading indicates ASSIST states.)

State SoTC Index Score Resources Efforts Capacity

AZ 4.03 4.85 1.13 1.76

CA 3.73 4.13 1.31 1.80

MN 1.74 3.54 –0.46 –0.11

FL 1.70 1.38 0.12 1.75

MS 1.28 1.83 1.63 –0.75

NJ 1.12 –0.11 0.87 1.68

RI 1.09 –0.54 2.35 0.95

MD 0.97 –0.36 2.42 0.46

HI 0.96 –0.27 1.22 1.27

MI 0.90 –0.17 1.37 0.93

OR 0.90 0.05 0.63 1.25

OK 0.84 –0.47 1.20 1.26

NY 0.69 –0.17 1.18 0.64

KS 0.47 –0.44 –0.21 1.59

MA 0.46 1.12 –0.30 –0.10

IA 0.41 –0.36 –0.16 1.33

CT 0.37 –0.50 1.43 0.18

GA 0.39 –0.39 0.89 0.41

AK 0.30 –0.44 1.69 –0.22

WA 0.23 –0.19 –1.35 1.71

ID 0.13 –0.55 0.01 0.85

AR 0.08 –0.20 –0.75 0.96

VA 0.07 –0.38 0.73 –0.01

WI –0.04 –0.21 –0.18 0.29

NC –0.14 –0.13 –0.52 0.26

AL –0.18 –0.14 1.07 –1.02

KY –0.19 –0.47 1.88 –1.30

UT –0.29 –0.38 –0.43 0.18

NE –0.31 –0.48 –1.16 0.80

CO –0.40 –0.12 –0.40 –0.36

NH –0.45 –0.50 1.23 –1.28

SC –0.48 –0.51 –1.82 1.02

NM –0.53 –0.40 –0.92 0.11

WV –0.53 –0.29 0.36 –1.01

TX –0.61 –0.11 –0.79 –0.49

PA –0.68 –0.33 0.15 –1.10

IL –0.71 –0.45 0.36 –1.19

MO –0.79 –0.37 0.78 –1.75

DC –0.87 –0.47 0.17 –1.32

WY –0.92 –0.53 –2.44 0.63

ND –0.93 –0.61 –1.90 0.30

OH –1.05 –0.32 –0.26 –1.52

DE –1.07 –0.52 –0.63 –1.05

IN –1.08 –0.29 –1.24 –0.88

SD –1.20 –0.50 –0.69 –1.30

ME –1.24 –0.32 –0.73 –1.56

TN –1.28 –0.61 –2.98 0.43

NV –1.42 –0.59 –2.56 –0.20

VT –1.50 –0.58 –0.43 –2.00

MT –1.60 –0.61 –1.27 –1.52

LA –2.30 –0.50 –1.59 –2.77

Overall

Mean 0.0 0.0 0.0 0.0

SD 1.20 1.58 1.26 1.60

ASSIST

Mean .11 .026 –.017 .24

SD .78 .97 1.11 1.10

Non-ASSIST

Mean –.05 –.01 .01 –.12

SD 1.32 1.25 1.35 1.29
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

46

Figure 2.2. Strength of Tobacco Control Scores by State

<-0.5

>0.5

-0.5 to 0.0

0.0 to 0.5

Figure 2.3. Resources Construct Scores by State

<-0.5

>0.5

-0.5 to 0.0

0.0 to 0.5
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

47

Figure 2.4. Efforts Construct Scores by State

<-0.5

>0.5

-0.5 to 0.0

0.0 to 0.5

Figure 2.5. Capacity Construct Scores by State

<-0.5

>0.5

-0.5 to 0.0

0.0 to 0.5
48

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

Although these correlations were 

statistically significant, one must interpret them with caution. The ASSIST 

states were not randomly selected, and 

the baseline prevalence of tobacco use 

was different in each. Baseline differences and other covariates had to be accounted for in the analyses. The degree 

to which SoTC index values related to 

these results is more fully explained in 

chapter 9, including a discussion of multivariate models that include the SoTC.

Limitations of the SoTC Index

The resultant SoTC scores performed 

well in the evaluation analyses. However, as with any measure, limitations 

in the conceptualization, measurement, 

and data-reduction strategies may have 

biased some individual state scores. For 

example, the heuristic model is based 

on the assumption that a high-scoring 

tobacco control program will have all 

the inputs and engage in all the activities 

measured. The heuristic model and 

subsequent survey may not have been 

comprehensive enough to capture all 

the components necessary to produce 

an effective tobacco control program. In 

addition, while the SoTC index captured 

whether a specific tobacco control activity was performed in a state, the “dose” 

of that activity was not measured. An 

organization that held one editorial board 

briefing in a year received the same 

score on that item as an organization that 

held weekly editorial board briefings, 

and this bias was included in the aggregate state score.

Other potential biases resulted from 

the respondents interviewed. Although 

great care was taken to ensure that all 

organizations engaged in tobacco control in a state were interviewed, some 

organizations may have been omitted. 

Each organization’s contribution to tobacco control was equally weighted, and 

this equal weighting may have yielded 

an inaccurate picture of an individual 

state tobacco control program. For example, while the health department may 

have received the bulk of money for tobacco control in a state, its activities did 

not carry greater weight within the SoTC 

Table 2.3. Correlation of Legislative Scores (1999) with SoTC Scores (1999)

Correlation/ 

Significance SoTC Resources Capacity Efforts

Pearson r .318 .129 .068 .336

p .023 .366 .633 .016

Table 2.4. Correlation of Tobacco Use Prevalence (2000) with SoTC Scores (1999)

Correlation/ 

Significance SoTC Resources Capacity Efforts

Pearson r –.404 –.323 –.313 –.180

p .003 .021 .025 .207
49

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

construct than did the activities of any 

other agency. These challenges and limitations should be acknowledged, and individual states can address them in light 

of their own environmental context. But 

for the purpose of the ASSIST evaluation, the aggregate scores were found to 

provide a valid measure of the program 

inputs and activities.

Use of the SoTC to Evaluate 

Individual State Programs

T

he first sections of this chapter describe why the SoTC index was needed for the ASSIST evaluation and how 

the index was constructed and tested, 

in addition to providing individual state 

scores and some of the index’s univariate relationships with other index scores 

in the evaluation. Chapter 9 reports a 

significant multivariate relationship 

between the SoTC index and tobacco 

consumption. In addition, while other 

researchers have reported a relationship 

between tobacco control outcomes and 

funding,8 the ASSIST evaluation demonstrated the relationship between another 

component of tobacco control—program 

capacity—and outcomes.

This section examines the domainlevel indicators within each of the three 

main constructs of SoTC at the state 

level. While the aggregate measures of 

SoTC and the three major constructs are 

well suited to between-state comparisons, the domain-level indicators are 

measures that may be more important 

for understanding how individual states 

meet unique environmental challenges. 

For example, in a state with high tobacco 

taxes, tobacco control advocates may be 

less likely to focus on legislation to increase them further. Alternatively, a state 

with a strong tobacco industry presence 

may expend high levels of effort without 

concomitant gains in tobacco control 

legislation. As such, the domain scores 

and their component measures may be 

better used as part of a state process 

evaluation that incorporates contextual 

factors such as state political climate and 

tobacco industry activities.

Inputs: Resources and Capacity

Funds allocated for tobacco control varied widely by states during the 

1990s. Table 5.2 in chapter 5 shows per 

capita funding by state throughout the 

decade and provides an overview of 

the change in absolute state funding for 

tobacco control during this period. This 

information provides some context for 

understanding why some states had more 

well-established tobacco control programs than others.

Table 2.5 shows the component domain scores for the resources and capacity constructs. This table is sorted 

by funds allocated to states for tobacco 

control. The resource construct of SoTC 

revolves significantly around funding, 

and many of the ASSIST states received 

tobacco control funding for the first time 

during the program. Figure 2.6 shows the 

level of increased tobacco control funding during this period. However, while 

well-funded tobacco control programs, 

such as those in Massachusetts and California, have yielded significant decreases 

in smoking prevalence,9,10 one of the 

more important findings of this study 

was that funding alone was a necessary 
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

50

Table 2.5. Inputs: Resources and Capacity, Sorted by Funds

(Shading indicates ASSIST states.)

State

Resources Capacity

Funds Staff Leadership

Interagency 

relationships

Health dept. 

infrastructure Coalition

Staff 

experience

CA 4.74 1.06 1.34 1.36 0.52 1.45 –0.32

MN 4.16 0.19 1.83 –0.43 –0.89 0.20 –0.89

MS 1.90 0.25 0.18 –0.62 –0.26 –0.56 0.70

FL 1.08 0.74 0.38 1.28 –0.89 1.45 –0.86

MA 1.07 1.52 1.78 –0.70 1.35 0.45 3.06

AZ 0.34 6.42 1.12 1.29 0.65 1.45 –0.75

OR –0.01 0.11 –0.75 0.42 –0.19 1.45 –1.61

NY –0.04 –0.37 –1.84 –0.02 –0.26 0.95 –0.37

ME –0.19 –0.25 2.53 –0.82 –0.05 –1.56 –1.34

MI –0.21 0.53 –2.59 1.66 –0.89 –0.05 2.60

WI –0.24 –0.23 –0.85 –0.03 0.45 0.45 –1.54

NJ –0.25 –0.04 0.51 1.16 0.52 1.45 0.95

WA –0.25 –0.08 1.95 1.82 –0.26 0.95 –0.45

MD –0.26 –0.25 –0.14 0.26 2.06 0.45 –1.30

NC –0.28 –0.18 –1.16 0.50 –1.66 –0.06 0.54

UT –0.28 –0.32 –1.14 0.98 2.06 –0.56 –0.39

CO –0.30 –0.23 –1.19 –0.55 0.65 –0.06 –1.71

IL –0.30 –0.16 –0.93 –1.08 –0.96 –0.81 0.07

IN –0.30 –0.24 0.31 –1.45 1.35 –0.06 1.84

MO –0.31 –0.21 –0.78 –1.75 –0.96 –1.06 –0.76

NM –0.31 0.07 –0.22 0.26 1.35 –0.06 1.13

RI –0.31 –0.35 0.89 2.00 –1.02 –0.30 0.34

SC –0.31 –0.24 –0.29 0.62 1.35 0.95 0.48

VA –0.31 –0.32 –1.14 0.64 2.06 –0.56 0.45

WV –0.31 –0.08 –0.83 –1.67 0.52 –0.06 –0.20

AK –0.32 –0.41 –0.23 2.08 –0.26 –2.06 –0.98

AL –0.32 –0.07 0.30 0.10 0.58 –1.56 5.17

DE –0.32 –0.39 1.41 0.66 –0.26 –2.06 –1.71

HI –0.32 –0.18 2.01 0.46 –0.96 1.45 1.71

ID –0.32 –0.31 0.16 0.93 –0.89 0.45 0.33

KY –0.32 –0.31 –0.73 –1.58 –0.12 –0.56 0.03

OH –0.32 –0.29 0.26 –1.06 1.35 –1.31 1.16

PA –0.32 –0.12 –1.18 –1.54 –0.26 –0.31 –0.18

TX –0.32 0.18 0.01 0.43 1.35 –1.06 –0.38

CT –0.33 –0.39 –1.99 –1.42 1.22 1.45 0.49

DC –0.33 –0.31 2.55 –1.02 –1.66 –1.06 –1.80

GA –0.33 –0.27 –0.93 1.37 1.35 –0.56 –0.63

IA –0.33 –0.17 –1.34 0.56 0.65 1.45 1.84

KS –0.33 –0.44 –2.14 1.60 –1.66 0.95 1.49

MT –0.33 –0.46 0.82 –1.96 –0.89 –0.56 –1.38

NE –0.33 –0.36 0.01 0.25 –0.19 0.95 0.14

NH –0.33 –0.27 1.26 –1.53 –0.83 –0.56 –1.09

OK –0.33 –0.31 0.22 0.43 –0.05 1.45 0.30

VT –0.33 –0.43 3.28 –0.99 –1.66 –2.06 –0.51

AR –0.34 0.60 3.01 1.12 –0.26 0.45 –1.38

LA –0.34 –0.35 0.43 –3.50 –0.19 –1.06 0.81

ND –0.34 –0.50 –3.07 0.57 –0.96 –0.05 0.61

NV –0.34 –0.46 –0.47 –0.28 –0.05 –0.05 –0.41

SD –0.34 –0.46 –2.71 –1.57 –3.14 –0.56 –1.54

TN –0.34 –0.50 –1.67 0.21 0.65 0.45 –1.71

WY –0.34 –0.35 1.75 0.55 0.52 0.45 –0.06

ASSIST

Mean 1.80 –0.03 –0.06 0.07 0.21 0.09 0.24

SD 1.10 0.46 1.46 1.17 1.07 0.75 1.38

Non-ASSIST

Mean 0.04 0.02 0.03 –0.04 –0.11 –0.05 0.12

SD 0.96 1.19 1.53 1.25 1.12 1.14 1.38
51

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

but not a sufficient factor for public 

health outcomes in tobacco control.

Funding has built capacity to deliver 

tobacco control in many states—particularly those states with longstanding 

programs—and capacity can be used to 

gain more funding. For example, table 

2.5 shows that most states had similar 

funds for tobacco control at the end of 

the ASSIST period. The only outlier 

states were those with well-established 

tobacco control programs (California 

and Massachusetts) in addition to states 

that had recently received lawsuit settlement funds from the tobacco industry 

(such as Minnesota, Mississippi, and 

Florida). Table 2.5 also illustrates the 

fact that states had different strengths in 

capacity. For instance, while some states 

had their highest scores in health department capacity, others had their highest 

scores in interagency relationships or coalitions. These data can be incorporated 

with information about a state’s environmental context as part of a process evaluation. In this way, state program staff 

can better understand how best to use 

the resources they have to build capacity 

and how that capacity enables or hinders 

their ability to perform tobacco control 

activities.

Activities

The components of the SoTC efforts 

construct allow individual states to 

measure their program activity focus. 

Table 2.6 presents the component domain scores for the efforts construct. As 

this table illustrates, states concentrated 

their efforts in different domain areas, 

presumably reflecting such factors as 

Figure 2.6. Change in Tobacco Control Funding (in Dollars), 1991–98

-17M to 0 

2M to 44M

0 to 1M

1M to 2M
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

52

Table 2.6. Components of the Efforts Construct, Sorted by Efforts Score

(Shading indicates ASSIST states.)

State

Social environment

Individual 

Overall behavior

Media 

advocacy Mass media

Policy 

advocacy Local capacity

MD 3.02 2.01 2.59 0.91 3.06 2.21

KY 2.98 2.34 1.66 2.16 2.36 1.06

AK 2.72 1.64 2.57 1.70 1.84 0.91

RI 2.19 0.55 2.49 2.09 1.05 2.92

NH 2.11 1.87 1.44 1.85 0.94 0.51

NY 2.09 1.34 1.93 2.03 0.69 0.44

HI 1.58 0.77 0.27 2.39 1.06 1.05

OK 1.56 1.81 0.48 0.73 1.50 1.03

CA 1.55 1.07 1.55 1.39 0.46 1.29

MI 1.25 2.46 0.37 0.36 0.58 1.71

OR 1.14 2.84 0.19 –0.53 0.99 0.20

GA 1.07 –1.28 1.71 1.23 1.15 0.86

CT 1.01 0.80 1.75 –0.16 0.51 2.10

NJ 0.94 0.15 –0.71 2.18 0.98 0.95

CO 0.88 –0.50 2.03 0.95 –0.05 –1.81

WI 0.76 1.01 1.30 –0.81 0.72 –1.20

VA 0.64 1.09 –0.31 –0.40 1.46 0.94

IL 0.58 0.39 1.86 –0.51 –0.07 0.20

WV 0.53 0.69 –0.30 1.38 –0.19 0.24

PA 0.48 0.70 –1.57 0.83 1.40 –0.16

DC 0.35 –1.22 1.35 0.88 –0.15 0.00

MO 0.33 1.00 –0.40 –0.74 1.14 1.37

MS 0.20 0.30 1.05 0.63 –1.32 3.39

NE 0.12 –0.07 –0.38 1.26 –0.44 –2.70

KS –0.09 –0.13 –0.48 0.14 0.19 –0.37

ID –0.11 –1.16 –0.65 0.46 0.88 0.14

AL –0.16 –1.04 0.65 0.27 –0.42 2.51

MN –0.17 0.92 –0.85 –0.75 0.29 –0.85

AZ –0.20 –0.73 –1.26 0.44 0.86 2.70

ME –0.24 0.86 –1.40 1.55 –1.52 –1.36

OH –0.25 –0.36 0.22 –0.37 –0.22 –0.32

IA –0.27 –0.44 –0.72 0.46 –0.09 –0.07

FL –0.28 –1.15 1.18 –2.02 1.01 0.56

VT –0.52 –1.43 –0.04 0.41 –0.55 –0.40

NM –0.56 –3.53 1.44 0.70 –0.55 –1.45

UT –0.61 –1.06 0.63 –1.46 0.04 –0.32

AR –0.67 1.42 –2.35 0.27 –1.01 –0.96

MA –1.08 0.16 –0.96 0.20 –2.32 0.46

TX –1.14 –1.68 0.33 –1.02 –0.98 –0.57

DE –1.32 0.12 –0.54 –0.53 –2.62 –0.02

WY –1.35 –1.18 –1.12 0.64 –2.16 –3.98

SD –1.35 0.16 –0.95 –3.17 0.16 –0.11

LA –1.41 –1.42 –0.60 –1.21 –0.86 –2.06

NC –1.43 –1.30 –1.56 –0.96 –0.34 0.35

IN –1.61 –0.82 –2.68 –1.52 0.40 –1.06

WA –1.62 –0.42 –1.75 –1.02 –1.36 –1.29

ND –1.77 1.81 –1.32 –1.61 –3.48 –2.36

MT –2.64 –2.30 –2.03 –3.74 0.38 –0.06

SC –2.97 –2.72 –0.72 –2.30 –2.77 –0.94

TN –3.03 –0.97 –3.08 –2.52 –1.96 –3.43

NV –3.24 –3.36 –2.29 –3.14 –0.66 –2.28

ASSIST

Mean 0.00 0.05 –0.12 0.17 –0.10 –0.03

SD 1.40 1.50 1.49 1.37 1.24 1.34

Non-ASSIST

Mean 0.00 –0.03 0.06 –0.09 0.05 0.02

SD 1.57 1.46 1.45 1.50 1.38 1.68
53

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

state support for tobacco control, tobacco industry activities, populations with 

unique needs, and tobacco control staff 

strengths and interests.

Domain-level scores can provide a 

more nuanced picture of tobacco control 

programs in individual states, as they 

show areas where states focused their 

efforts. For instance, in 1998–99 Rhode 

Island’s scores suggest that their focus 

was on mass media (2.49) and policy 

advocacy (2.09) efforts with a lower 

effort score in media advocacy (0.55). 

In contrast, Michigan’s scores suggest 

that their major focus was on media 

advocacy efforts (2.46) and that they focused a lesser amount of effort on mass 

media (0.37) and policy advocacy (0.58). 

Maryland’s highest effort score was in 

building local capacity (3.06), followed 

by mass media (2.59) and media advocacy (2.01). Maryland’s lowest effort 

score was policy advocacy (0.91). Where 

a state tobacco control program focuses 

its efforts and the degree to which these 

efforts yield intermediate and long-term 

outcomes are determined by whether the 

state has the funding to build and sustain 

a basic tobacco control infrastructure 

and by each state’s unique economic, 

political, and other contextual factors.

While the overall SoTC index and constructs (resources, efforts, and capacity) 

have been validated and are correlated 

with several important measures used in 

the ASSIST evaluation model, a deeper 

The Future of SoTC: Tracking Trends over Time

The SoTC data were collected at only one time point for the ASSIST evaluation and were used to create a single measure of exposure to tobacco control at the state level. However, the SoTC surveys generated rich descriptive information that has not yet been fully mined. The survey was repeated in 2002 

and again in early 2004 as part of the SmokeLess States evaluation and will continue to be collected in 

2006 and 2008 through funding by the Robert Wood Johnson Foundation. With this trend, data factors 

that emerged as being important for ASSIST can be examined from the perspective of how they have 

evolved over time. Moreover, initial analyses suggest that these data can be used to document changes 

in state tobacco control programs. Still, many questions remain—for example:

■ The distribution of SoTC scores suggests that while a few states were functioning at a high level and 

a few states were functioning at a low level, most were functioning at about the same level. In this 

case their relative rankings may not be particularly meaningful.

■ Until the ASSIST evaluation, the components of SoTC were not consistently measured for all states. 

Therefore, there is no way to document how state tobacco control programs evolved from mostly 

voluntary efforts to maintenance of effective programs in state health departments with capacity for 

continued effect on tobacco use.

■ Funds for tobacco control have recently decreased. The SoTC data collected for and after the 

ASSIST evaluation may be combined with case studies to document how these funding cuts have affected state tobacco control programs.

The SoTC is now available as a tool for states to use in measuring their own resources, capacity, and 

efforts. A baseline (1999–2000) measure for each state is available for measuring change over time, 

identifying strengths and weaknesses, and adapting efforts to regional conditions. By using these data 

as a basis for comparison over time, SoTC will be useful to tobacco control practitioners as they develop strategies to reduce the epidemic of tobacco-related addiction, disease, and death.
54

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

examination of these data at the state level 

suggests questions for planning and evaluating state tobacco control programs:

■ How does a state with high resources 

(e.g., funding) and lower capacity 

compare with a state with low resources 

and higher capacity, even though both 

have similar SoTC index scores?

■ Do extremes in one specific 

construct—or its subfactors—affect 

the overall effectiveness of SoTC as an 

evaluation metric for state programs?

■ What can we learn from “outlier” 

states (such as California) that have 

disproportionate levels of funding 

relative to outcomes, as well as 

other factors such as the maturity of 

those states’ existing tobacco control 

programs?

Questions like these represent promising areas for further study. Although a 

validated, composite metric represents 

an important step in program evaluation, 

these state results suggest that the SoTC 

data could help individual states identify 

their strengths and weaknesses and subsequently help them better adapt to the 

challenges they face.

Summary

Public health programs are implemented and evaluated at the state level, 

and this limits the number of observations available for statistical comparisons 

to 50 (or 51 if the District of Columbia is 

included). An analysis using only 50 observations in turn severely restricts how 

many factors can be included in a statistical analysis. Therefore, the construction 

of highly aggregated measures (such as 

the SoTC index) is required.

Many critical public health issues do 

not lend themselves to solutions through 

randomized clinical trials as used for 

therapeutic agents. It is not possible to 

selectively deny a public health intervention to specific population groups 

(as a control group) or to hold other comorbid social or environmental factors 

constant between them. While ASSIST 

was in progress, its interventions spread 

from ASSIST to non-ASSIST states. 

This meant that the ASSIST evaluation 

had no control states to which ASSIST 

states could be compared. Instead, the 

SoTC measure was constructed to assess the relative strength of ASSIST-like 

programs in every state, and this measure 

was subsequently correlated to outcomes.

Public health interventions are influenced by and interact with the multiple 

facets of their environment in a dynamic 

and complex fashion. Aggregate indexes 

such as SoTC allow analysis of the interrelationship between the multiple factors 

that affect a tobacco control program and 

the outcomes the program influences. 

Such an analysis promotes rigorous and 

valid process and outcome evaluations 

of what is an inherently multivariate 

system.

Conclusions

1. The Strength of Tobacco Control 

index measures a state’s overall 

tobacco control program. Survey instruments were constructed, tested, 

and applied with respondents from 
55

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

entities engaging in state-level tobacco control. Data analysis showed that 

its three latent variables constitute 

a valid map of what Strength of Tobacco Control can measure with good 

internal coherence.

2. The Strength of Tobacco Control 

index was derived from component 

metrics in the areas of resources, capacity, and efforts. The resource component addressed both financial and 

manpower assets devoted to tobacco 

control. Capacity addressed areas such 

as legislative support, coalitions, and 

public health infrastructure. Efforts 

incorporated areas of comprehensive 

activity such as policy-focused initiatives, education, and mass media.

3. State Strength of Tobacco Control 

values were correlated significantly 

with other data sources such as 

legislative policy scores and tobacco use prevalence. In addition, the 

Strength of Tobacco control index 

performed well in the evaluation analyses detailed in the final outcomes.

4. The Strength of Tobacco Control 

model can serve as a guide for future 

evaluations of state tobacco control 

programs and is a basis for identifying optimal practices for tobacco 

control. In addition, it represents 

an example of a “dose-level” measure that can be used for evaluating 

the effectiveness of future complex 

population-level public health interventions.

5. The tracking of trends in Strength of 

Tobacco Control results over time represents a promising area for research 

in evaluating the long-term effectiveness of tobacco control programs.
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

56

Appendix 2.A. Assessment Rating of Variables

Criteria

Variable Parsimony

Scientific 

support Feasibility Recommendation

Resources

Per capita dollars expended for tobacco 

control efforts 3 3 5 Y

Number of state-level personnel

Full Time Equivalents working on 

tobacco control 3 3 3 Y

Capacity

Organizational capacities of each of top 5–6 

state-level tobacco control organizations 3 1 2 Y

Frequency of contact among top 5–6 statelevel tobacco control organizations 5 3 3 Y

Type of contact among top 5–6 statelevel tobacco control organizations 5 3 3 Y

Total number of state organizations 

involved in tobacco control 5 3 3 Y

Percentage of organizations that actively 

participate in state coalition 5 3 3 Y

Number of local coalitions and tobacco 

control organizations 5 3 3 Y

Percentage of state covered by local 

coalitions 5 3 1 N

Perceived capacities of local 

coalitions/communities 3 1 2 Y

Dose strength of training and technical 

assistance provided from state-level 

organizations to local levels 3 3 2 Y

Antitobacco efforts

Quality of state tobacco control plan 5 3 4 Y

Percentage of efforts devoted to policy and 

media advocacy 5 3 4 Y

Comprehensiveness of tobacco control 

efforts (i.e., number of different strategies in 

“typology”) 3 3 4 Y

Focus of strategy of implementation (i.e., 

level at which funds are expended) 3 2 3 Y

Perceived potency of state policy change 

efforts 1 3 2 N

Perceived potency of private policy change 

efforts 1 3 2 N

Perceived potency of media change efforts 1 3 2 N
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

57

Appendix 2.B. Key Elements of Strength of Tobacco Control Efforts

Resources: Mobilization of resources committed to antitobacco effortsIndicatorsa,b Variables Sample itemsc FeasibilitydThe amount, types, and sources of money

Per capita dollars expended for tobacco control efforts

Does your organization receive any federal funds? If yes, how much do you receive from (name of federal source) in 1999? What are the funds used for? Does your organization spend this money itself or pass it to other organizations through sub-grants?Does your organization receive any state funds? If yes, how much do you receive from (name of state source) in 1999? What are the funds used for? Does your organization spend this money itself or pass it to other organizations through sub-grants?

■■

May be collected from

Key informants at state levelProgram records of

federal and state governmentvoluntary associationsnational and state foundations

■■

–––

aDowney, L.E., and J. Gardiner. 1996. Youth access to tobacco: A partial inventory of state initiatives. Chicago: University of Illinois at Chicago, Office of Social Science Research.bAssociation of State and Territorial Health Officials. 1991. State tobacco prevention and control activities: Results of the 1989–1990 Association of State and Territorial Health Officials (ASTHO) survey. Final report. Morbidity and Mortality Weekly Report 40 (RR-11): 1–41.cGardiner, J. 1998. Strength of state tobacco control efforts. Final report to the National Cancer Institute ASSIST program. University of Illinois at Chicago, Office of Social Science Research.dGold, M., L. Burnbauer, and K. Chu. 1995/96. How adequate are state data to support health reform or monitor health system change? Inquiry 32:468–75.
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

58Appendix 2.B. (continued)

Capacity: Knowledge, skills, and infrastructure for implementing antitobacco efforts

Indicatorse Variables Sample itemsf Feasibilityg

Personnel Number of state-level personnel How many people in your organization work on tobacco 

control and counting all the people, what would be the fulltime equivalent?

May be collected from 

Key informant interviews

■

State-level organizational capacity for tobacco control

Organizational capacities of each of top 5–6 state-level tobacco control organizations in:

Financial management Training and technical assistance for local levelImplementation of tobacco control programs Policy and media advocacy Institutionalizing programs Surveillance and evaluation

■■■■■■

For measuring surveillance and evaluation:

Does your organization conduct an annual survey on tobacco use?Does your organization monitor tobacco control legislation/policies?Does your organization have designated staff responsible for maintaining tobacco control related data?Does your organization have designated staff responsible for evaluation of tobacco control efforts?

■■■■

Key informant interviews

■

State-level interorganizational collaboration

Frequency of contact among top 5–6 state-level tobacco control organizationsType of contact among top 5–6 statelevel tobacco control organizations

■■

During the last 12 months, what have you been doing concerning tobacco control activities with personnel from (name appropriate state-level organization)Code for highest level:

0 = no contact1 = networking (exchanging information for mutual benefit)2 = coordinating (exchanging information and altering activities for mutual benefit and a common purpose)3 = cooperating (exchanging information, altering activities, and sharing resources for mutual benefit and a common purpose)4 = collaborating (exchanging information, altering activities, sharing resources, and enhancing each other’s capacity for mutual benefit and a common purpose)

Key informant interviews (network analysis)

■

eMeissner, H. I., L. Bergner, and K. M. Marconi. 1992. Developing cancer control capacity in state and local public health agencies. Public Health Reports 107:15–23.fHimmelman, A., M. Luxenberg, and C. Schmitz. 1995. Consortium Activities Diagnostic Survey: A tool to understand the ways that groups work together. Minneapolis: University of Minnesota, Center for Urban and Regional Affairs.gCalloway, M., J. Morrisey, and R. Paulson. 1993. Accuracy and reliability of self-reported data in interorganizational networks. Social Networks 15:377–98.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

59

Appendix 2.B. (continued)

Capacity: Knowledge, skills, and infrastructure for implementing antitobacco efforts

Indicatorsh,i Variables Sample itemsj Feasibility

State-level coalition capacity

Total number of state organizations involved in tobacco controlPercent of organizations that actively participate in state coalition

■■

Questions to key informants representing state coalitions:

How many organizations currently belong to your state-level tobacco control coalition?What is the number of regularly attended meetings in your coalition during the past 12 months?

Review of coalition records for membership lists and meeting attendance

■

––

■

Key informant interviews ASSIST, IMPACT, SmokeLess States progress reportsCoalition records

■■■

Local community capacity

Number of local coalitions and tobacco control organizationsPerceived capacities of local coalitions/communities

■■

When you think of all the counties across your state, in approximately how many of these counties are there:

a. organized tobacco control effortsb. tobacco control efforts that have successfully mobilized diverse constituenciesc. efforts that have developed strategic plans for tobacco controld. efforts that have marshaled their own local resources for tobacco controle. policy advocacy efforts for tobacco control on the local levelf. efforts that have been successful in changing local policies

Response format: none, just a few, about half, most, all, don’t know

National organization databases (e.g., Americans for Nonsmokers’ Rights, ASSIST Coordinating Center) Key informant interviews

■■

Supportive linkages between state and local levels

Dose strength of training and technical assistance provided from state-level organizations to local levels

Please indicate whether, in the past 12 months, (name of organization) has provided none, a little, some, or a lot of training and TA to local tobacco control efforts in each of the following areas:

a. How to mobilize (recruiting members/organizations)b. How to structure a local coalitionc. Building members’ knowledge and skillsd. How to collaborate with other organizations or local coalitions

Response format: none, a little, some, a lot, don’t know

Key informant interviews

■

hSchwartz, R., C. Smith, M. A. Speers, L. J. Dusenbury, F. Bright, S. Hedlund, F. Wheeler, and T. L. Schmid. 1993. Capacity building and resource needs of state health agencies to implement community-based cardiovascular disease programs. Journal of Public Health Policy 14 (4): 480–94.iGoodman, R., A. Steckler, and M. Alciati. 1997. A process evaluation of the National Cancer Institute’s Data-based Intervention Research Program: A study of organizational capacity building. Health Education Research: Theory and Practice 12:101–17.jGoodman, R., M. Speers, et al. in press. An attempt to identify and define the dimensions of community capacity to provide a basis for measurement. Health Education & Behavior.
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

60Appendix 2.B. (continued)

Antitobacco efforts: The quality of planning and the array of programs, policies, and practices used in tobacco control

Indicatorsk,l Variables Sample itemsm,n Feasibility

Quality of state plan for antitobacco strategies

Quality of state plan composed of expert rating for

Overall logic model described/impliedClarity and specificity of objectivesDuration and intensity of activitiesFeasibility of activitiesInnovativenessOverall qualityComprehensiveness of plan 

■■■■■■■

Clarity and specificity of information about Objectives:

1 = vague, grandiose terms (e.g., “promote cessation” ) with little detail3 = goals stated with enough clarity so that at least half of them could be quantified (e.g., “promote city ordinances banning cigarette vending machines”)5 = all or almost all goals have quantifiable levels of outcome specified (e.g., “increase the percentage of workplaces with smoke-free policies by 20%”) 

May be collected from 

State tobacco control plans

■

Typology of antitobacco strategies

Percentage of efforts devoted to policy and media advocacyComprehensiveness of tobacco control efforts (i.e., number of different strategies in typology) Focus of strategy implementation (i.e., the level [state, local] at which funds are expended)

■■■

Considering all the funds you receive from federal, state, and nongovernmental sources, can you estimate what percentage of funds currently goes into the following types of tobacco control programs?

Enforcement and inspectionsIndividual-oriented cessation and treatment programsSchool-based prevention programsPublic education/media campaignsCommunity-based approaches, including advocacy and coalition buildingEvaluation, surveillance, and researchOther tobacco control activities, including program administration

■■■■■■■

ASSIST, IMPACT, and SmokeLess States progress reports Key informants

■■

kCenter for Substance Abuse Prevention. 1995. National Evaluation of the Community Partnership Demonstration Grant Program: Executive Summary of the Fourth Annual Report. Rockville, MD: Department of Health and Human Services, Substance Abuse and Mental Health Services Administration.lKegler, M. C., A. Steckler, K. McLeroy, and S. H. Malek. 1998. Factors that contribute to effective community health promotion coalitions: A study of 10 project ASSIST coalitions in North Carolina. Health Education & Behavior 25 (3): 338–53.mButterfoos, F., R. Goodman, A. Wandersman, R. Valois, and M. Chinman. 1996. The Plan Quality Index: An empowerment evaluation tool for measuring and improving quality of plans. In Empowerment evaluation, ed. D. Fetterman, S. Kaftarian, and A. Wandersman. Thousand Oaks, CA: Sage Publications.nMitchell, R., J. Stevenson, and P. Florin. 1996. A typology of prevention activities: Applications to community coalitions. Journal of Primary Prevention 16(4): 413–36. 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

61

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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

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2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

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Appendix 2.C. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

65

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REC 01 

PROGRAMMER NOTE: DK=8, RF=9. START TIME................................................... : 

MODULE A: UNIVERSAL 

 

A1. In the past two years, has your organization 

participated in building, enhancing or maintaining local 

coalitions in your state? By local, we mean a coalition

that is formed to serve the needs of a region, county, 

or municipality within your state, but is not a statewide 

coalition.

YES...........................................................................................1 

NO..................................(SKIP TO A2)....................................2

A. In the past two years, has your organization 

provided any formal training for local coalitions? 

YES...........................................................................................1 

NO.............................................................................................2

B. In the past two years, have you assisted local 

coalitions in building or improving their capacity 

to communicate with their memberships? 

YES...........................................................................................1 

NO.............................................................................................2

 

C. In the past two years, has your organization 

assisted local coalitions to build their 

memberships?

YES...........................................................................................1 

NO.............................................................................................2

 

D. In the past two years, has your organization 

assisted local coalitions to conduct needs 

assessments? 

YES...........................................................................................1 

NO.............................................................................................2

 

E. In the past two years, has your organization 

helped local coalitions to evaluate their 

programs? 

YES...........................................................................................1 

NO.............................................................................................2

 

F. In the past two years, has your organization 

helped local coalitions to mobilize diverse 

constituencies, such as different ethnic or

socioeconomic groups? 

YES...........................................................................................1 

NO.............................................................................................2

 

G. In the past two years, has your organization 

helped local coalitions to generate local 

resources for tobacco control activities?

YES...........................................................................................1 

NO.............................................................................................2

 

H. In the past two years, has your organization 

provided staffing for local coalitions?

YES...........................................................................................1 

NO.............................................................................................2

 

I. In the past two years, has your organization 

supplied any other technical assistance to local 

coalitions? 

YES.................................. (SPECIFY) ......................................1 

NO.............................................................................................2

SPECIFY: _______________________________________________________________________________

 

      

      

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2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

66

Strength of Tobacco Control Survey 2 Page 2 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

A2. In the past two years, has your organization 

sponsored or participated in any activity designed to 

assist tobacco users to quit? 

YES...........................................................................................1 

NO.................................... (SKIP TO A3)..................................2

 

A. Who were the focus of your tobacco use 

cessation activities? Were they (READ

OPTIONS)? YES NO

1. Adults in general? ............................................... 1 2 

2. Youth?................................................................. 1 2 

3. Pregnant women?............................................... 1 2 

4. Any other special groups? .................................. 1

(SPECIFY UP TO 3) 

2

GROUP 1: ______________________________________________________________________________

GROUP 2: ______________________________________________________________________________

GROUP 3: ______________________________________________________________________________

 

A3. In the past two years, has your organization 

disseminated materials for use by the general public,

such as pamphlets, videos, or radio spots? 

YES...........................................................................................1 

NO.............................................................................................2

 

A4. In the past two years, has your organization created 

or produced your own pamphlets containing tobaccorelated materials? 

YES...........................................................................................1 

NO.............................................................................................2

 

A5. In the past two years, has your organization created 

or produced your own videos or radio spots on 

tobacco-related issues?

YES...........................................................................................1 

NO.............................................................................................2

 

A6. In the past two years, has your organization 

participated in any health fairs? 

YES...........................................................................................1 

NO.............................................................................................2

 

A7. In the past two years, has your organization provided 

or sponsored a telephone or internet help line for 

those who want to quit? 

YES...........................................................................................1 

NO.............................................................................................2

 

A8. In the past two years, has your organization provided 

tobacco use prevention programs for schools or youth 

groups? 

YES...........................................................................................1 

NO.............................................................................................2

 

A9. In the past two years, has your organization provided 

training for health care professionals about tobacco 

issues?

YES...........................................................................................1 

NO.............................................................................................2

 

A10. In the past two years, has your organization 

participated in any public forum, such as seminars or

workshops to educate the public about tobaccorelated issues? 

YES...........................................................................................1 

NO.............................................................................................2

Appendix 2.D. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

67

Strength of Tobacco Control Survey 2 Page 3 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

 

A11. In the past two years, has your organization 

purchased mass media, or had in-kind donations of 

mass media, to inform the public about tobaccorelated issues? 

YES...........................................................................................1 

NO..................................(SKIP TO A12)..................................2

 

A. We would like to ask a few questions about your 

purchase or donation of mass media. What media 

did you use? Did you use (READ OPTIONS)? YES NO

1. Newspapers ......................................................... 1 2 

2. Billboards ............................................................. 1 2 

3. Radio.................................................................... 1 2 

4. Television ............................................................. 1 2 

5. Other (SPECIFY)................................................... 1 2 

SPECIFY: _____________________________________________________________________

 

B. When you used media, what groups did you 

specifically target with your messages Did you target 

(READ OPTIONS)? YES NO

1. Youth.................................................................... 1 2 

2. Adults in general .................................................. 1 2 

3. Policy makers....................................................... 1 2 

4. Minority groups (SPECIFY) ................................... 1 2 

GROUP 1: _____________________________________________________________________

GROUP 2: _____________________________________________________________________

GROUP 3: _____________________________________________________________________

 

C. In the past two years, did you purchase media 

coverage that focused on tobacco industry efforts 

or tactics? 

YES...........................................................................................1 

NO.............................................................................................2

 

A12. In the past two years, has your organization 

participated in media advocacy activities? By media 

advocacy, we mean activities that are intended to get 

influential media representatives to understand and 

agree with anti-tobacco positions and policies. 

YES...........................................................................................1 

NO..................................(SKIP TO A13)..................................2

 

A. We would like to ask a few questions about your 

advocacy activities. In the past two years, has 

anyone from your organization attended a 

newspaper or magazine editorial board briefing? 

YES...........................................................................................1 

NO.............................................................................................2

 

B. In the past two years, has anyone from your 

organization provided the press with background 

materials on smoking issues? 

YES...........................................................................................1 

NO.............................................................................................2

 

C. In the past two years, has anyone from your 

organization responded to interview requests by

the media? 

YES...........................................................................................1 

NO.............................................................................................2

 

Appendix 2.D. (continued)
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

68

Strength of Tobacco Control Survey 2 Page 4 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

D. In the past two years, has your organization 

involved media representatives in your tobacco 

control activities? 

YES...........................................................................................1 

NO.............................................................................................2

 

A13. In the past two years, has your organization 

participated in policy advocacy activities, such as

working to change laws or policies regarding tobacco 

use, sales, or display in your state? 

YES...........................................................................................1 

NO..................................(SKIP TO A14)..................................2

 

A. We are interested in which public policy areas

you have been involved with. In the last two 

years, has your agency worked to promote clean 

indoor air? 

YES...........................................................................................1 

NO.............................................................................................2

 

B. In the past two years, has your agency worked to 

initiate or increase penalties for youth tobacco 

possession, use, or purchase? 

YES...........................................................................................1 

NO.............................................................................................2

 

C. What about working to prevent or repeal 

penalties for youth tobacco possession, use or

purchase? 

YES...........................................................................................1 

NO.............................................................................................2

 

D. In the past two years, has your organization 

worked to increase taxes on tobacco? 

YES...........................................................................................1 

NO.............................................................................................2

 

E. What about working to prevent or repeal 

preemption laws? 

YES...........................................................................................1 

NO.............................................................................................2

 

F. What about working on policies to limit how 

tobacco can be sold or displayed? 

YES...........................................................................................1 

NO.............................................................................................2

 

G. And what about work to change policy regarding 

tobacco use on school property or during school 

sponsored events? 

YES...........................................................................................1 

NO.............................................................................................2

 

A14. Does your organization have a designated legislative 

liaison for tobacco control policy to your state 

legislature? 

YES...........................................................................................1 

NO.............................................................................................2

 

A15. In the past two years, has any organization done a 

survey of tobacco use in your state? 

YES...........................................................................................1 

NO..................................(SKIP TO A16)..................................2 

DK ..................................(SKIP TO A16)..................................8

 

A. Did your agency sponsor or participate in this 

assessment?

YES...........................................................................................1 

NO..................................(SKIP TO A16)..................................2

 

 1. Did you survey tobacco use among youth? YES...........................................................................................1 

NO................................(SKIP TO A15 A2)...............................2

 a. Did you do the Youth Risk Behavior 

Survey?

YES...........................................................................................1 

NO.............................................................................................2

 b. Did you do the Youth Tobacco 

Survey?

YES...........................................................................................1 

NO.............................................................................................2

 c. Did you do a different survey? YES.................................. (SPECIFY) ......................................1 

NO.............................................................................................2

SPECIFY SURVEY 1: _________________________________________________________________

SPECIFY SURVEY 2: _________________________________________________________________

Appendix 2.D. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

69

Strength of Tobacco Control Survey 2 Page 5 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

 

 2. Did you survey tobacco use among adults? YES...........................................................................................1 

NO................................(SKIP TO A15 A3)...............................2

 a. Did you do the Behavioral Risk Factor 

Survey?

YES...........................................................................................1 

NO.............................................................................................2

 b. Did you do any other surveys? YES.................................. (SPECIFY) ......................................1 

NO.............................................................................................2

SPECIFY SURVEY 1: _________________________________________________________________

SPECIFY SURVEY 2: _________________________________________________________________

 

 3. (Was/Were) your survey(s) designed to 

provide estimates of tobacco use among 

any minority groups?

YES.................................. (SPECIFY) ......................................1 

NO.............................................................................................2

SPECIFY GROUP 1: __________________________________________________________________

SPECIFY GROUP 2: __________________________________________________________________

SPECIFY GROUP 3 __________________________________________________________________

 

 

A16. In the past 2 years, has your organization formally 

evaluated the implementation and effectiveness of 

your tobacco control efforts? 

YES...........................................................................................1 

NO..................................(SKIP TO A17)..................................2

 

A. Does your organization have a system to 

continually or periodically monitor your program

effectiveness?

YES...........................................................................................1 

NO.............................................................................................2

 

A17. Does your organization have a routine mechanism for 

updating best practices regarding tobacco control? 

YES...........................................................................................1 

NO.............................................................................................2

 

A18. In the past two years, has your organization awarded 

grants or contracts for tobacco control activities?

YES...........................................................................................1 

NO..................................(SKIP TO A19)..................................2

 

A. Does your organization require budget reports 

from the recipient organizations? 

YES...........................................................................................1 

NO.............................................................................................2

 

B. Does your organization monitor the expenditure 

of funds and the use of resources by the recipient 

organizations? 

YES...........................................................................................1 

NO.............................................................................................2

 

A19. Has your organization ever asked for a legal opinion 

from your State Attorney General’s Office on a 

tobacco-related issue? 

YES...........................................................................................1 

NO..................................(SKIP TO A20)..................................2 

DK ..................................(SKIP TO A20)..................................8

 

A. Did the office supply an opinion in a timely 

fashion? 

YES...........................................................................................1 

NO.............................................................................................2

 

A20. Does your State Attorney General’s Office have a 

designated tobacco specialist? 

YES...........................................................................................1 

NO..................................(SKIP TO A21)..................................2 

DK ..................................(SKIP TO A21)..................................8

 

A. Has that tobacco specialist, to the best of your 

knowledge, publicly campaigned in support of a 

tobacco control issue? 

YES...........................................................................................1 

NO.............................................................................................2

 

Appendix 2.D. (continued)
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

70

Strength of Tobacco Control Survey 2 Page 6 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

B. Has that tobacco specialist ever taken the lead 

on or initiated a tobacco control activity or

program within your state? 

YES...........................................................................................1 

NO.............................................................................................2

 

A21. In the past two years, has your organization 

participated in a state-level tobacco control coalition?

YES...........................................................................................1 

NO..................................(SKIP TO INSTRUCTION BOX) .......2

 

A. We would like to ask a few questions about the 

structure and activities of the state level coalition. 

These questions are best answered by the staff 

person who is responsible for running the 

coalition. Are you the best person to ask, or is 

there someone else, either in your agency or in a 

different agency or organization that we should 

talk to? 

SELF .....................................(DO COALITION MODULE B) ...1 

SOMEONE ELSE IN SAME AGENCY 

 (GET INTRA-AGENCY REFERRAL) ...2 

SOMEONE IN ANOTHER AGENCY 

 (GET INTER-AGENCY REFERRAL) ...3 

 

IF HEALTH DEPARTMENT RESPONDENT, DO HEALTH DEPARTMENT CAPACITY MODULE C.

IF NOT HEALTH DEPARTMENT, DO SUPPORT MODULE D.

ALL RESPONDENTS DO INTERAGENCY RELATIONSHIPS MODULE E.

 

A22. In addition to the American Heart Association, the 

American Lung Association, The American Cancer 

Society, and your State Health Department, are there

other state level organizations that you can refer us to 

that play a significant role in tobacco control in your 

state?

YES.................................... (ASK A).........................................1 

NO......................(SKIP TO INSTRUCTION BOX) ...................2

 

A. Please tell me the agency and the name and telephone number of a contact person there.

AGENCY #1: ________________________________________________________________________________

CONTACT PERSON: _________________________________________________________________________

PHONE NUMBER: - - 

AGENCY #2: ________________________________________________________________________________

CONTACT PERSON: _________________________________________________________________________

PHONE NUMBER: - - 

AGENCY #3: ________________________________________________________________________________

CONTACT PERSON: _________________________________________________________________________

PHONE NUMBER: - - 

 

INSTRUCTION BOX 

SKIP TO MODULE F: DEMOGRAPHICS

 

 END TIME....................................................... : 

Appendix 2.D. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

71

Strength of Tobacco Control Survey 2 Page 7 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

MODULE B: COALITION

B1. What is the name of your coalition? _____________________________________________________

B2. What is your position within the coalition? Are you 

(READ CATEGORIES)? CODE ONLY ONE. 

SPECIFY: _________________________________

A staff member providing support for the coalition ........1 

The president or chair of the coalition ...........................2 

Another elected or appointed leader of the 

coalition......................................................................3 

A representative of a member organization...................4 

Other position..................(SPECIFY) .............................5 

 

B3. What is the largest geopolitical boundary of your 

coalition’s responsibility? Would you say (READ

CATEGORIES)? CODE ONLY ONE.

A city, town or county ....................................................1 

A region within the state................................................2 

The state .......................................................................3 

A region encompassing more than one state................4 

 

B4. How large is your coalition in terms of member 

organizations? # OF ORGANIZATIONS ..........................................

 

A. How many individual members? # OF INDIVIDUALS .................................................

 

B5. In what year was your coalition formed? YEAR FORMED........................................................ 19 

 

B6. Does your coalition have any paid staff? YES.......................................................................................... 1 

NO...................................... (SKIP TO B7)............................... 2 

 

A. What is the source or sources of the staffs’ salary?

SOURCE #1: ___________________________________________________________________________

SOURCE #2: ___________________________________________________________________________

SOURCE #3: ___________________________________________________________________________

 

B7. Which of the following have any representation in your 

coalition? YES NO

a. Are voluntary health organizations represented?......... 1 2 

b. Is the Parent Teacher Association represented? ......... 1 2 

c. Are schools of medicine, public health or nursing 

represented?................................................................. 1 2 

d. Are other colleges or universities represented?........... 1 2 

e. Are law enforcement agencies represented?............... 1 2 

f Are prosecutors or district attorneys represented? ...... 1 2 

g. Are judges or magistrates represented? ...................... 1 2 

h. Are retail tobacco outlets represented?........................ 1 2 

i. Are public health officials represented? ....................... 1 2 

j. Is the state medical society represented?.................... 1 2 

k. Is the state dental society represented?....................... 1 2 

l. Are religious organizations or faith groups 

represented?................................................................. 1 2 

Appendix 2.D. (continued)
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

72

Strength of Tobacco Control Survey 2 Page 8 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

 

B8. Which statement best describes the nature of your 

coalition’s activities? READ CATEGORIES AND CODE 

ONLY ONE..

Primarily information-sharing.........................................1 

Primarily active participation in tobacco control

activities .....................................................................2

Both information-sharing and participation in 

tobacco control activities ............................................3 

 

B9. In the past year, has your coalition sponsored or taken

a lead role in any of the following activities? YES NO

a. Policy advocacy activities, such as working to 

change laws or policies concerning tobacco use, 

sale, or display in your state?....................................... 1 2 

b. Individual tobacco cessation programs, such as quit 

smoking classes or smokers’ hot lines? ....................... 1 2 

c. Using mass media, such as television or radio spots 

or billboards, to promote anti-tobacco positions?......... 1 2 

d. Media advocacy activities, such as making editorial 

board visits or producing background materials for 

the press?..................................................................... 1 2 

B10. In addition to your statewide coalition, can you 

estimate the proportion of your state that is covered 

by local coalitions? Would you say less than 10%, 

about a quarter, about half, about three fourths, or 

about all? 

<10%........................................................................................ 1 

ABOUT A QUARTER............................................................... 2 

ABOUT HALF .......................................................................... 3 

ABOUT THREE QUARTERS .................................................. 4 

ABOUT ALL ............................................................................. 5 

 

B11. About how many local coalitions are there in your 

state? # LOCAL COALITIONS ...........................................

 

Appendix 2.D. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

73

Strength of Tobacco Control Survey 2 Page 9 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

MODULE C: HEALTH DEPARTMENT CAPACITY 

PROGRAMMER NOTE: This module is asked only if the respondent represents the State Health Department.

 

C1. Would you describe yourself as the highest level 

tobacco control specialist in your organization? 

YES.......................................................................................... 1 

NO................(GET INTRA-AGENCY REFERRAL AND 

TERMINATE MODULE)....................... 2 

 

A. Who would you say is the highest level tobacco control specialist in your organization?

SPECIFY: ___________________________________________________________________________

 

C2. Which statement best describes your level of 

involvement in deciding which tobacco-related 

programs your agency participates in? CODE ONLY 

ONE.

I have very little input into decisions about which 

programs we participate in ............................................1 

I make recommendations regarding 

programmatic priorities that require a 

supervisor’s approval ....................................................2 

I have nearly complete autonomy in deciding my 

organization’s tobacco program priorities......................3 

 

C3. What about hiring decision? If a tobacco control 

position were to be created in your organization, 

which of the following statements best describes your

involvement in choosing whom to hire? 

I have very little input into hiring decisions ....................1 

I make recommendations regarding hiring 

decisions that require a supervisor’s approval ..............2 

I have nearly complete autonomy in making 

hiring decisions .............................................................3 

 

C4. Which of the following two statements best describes

how tobacco control is organized in your agency? 

There is a designated tobacco control unit with a 

person or person who do tobacco control 

activities as their major function in the 

organization.....................(END) ....................................1 

There is no designated tobacco control unit; the 

activities are done within other functioning units ...........2 

 

A. How many other units would you say are involved 

with tobacco control activities? # UNITS .........................................................................

 

B. Do any of these units which are doing tobacco 

control have a separate budget line for tobacco 

control efforts? 

YES.......................................................................................... 1 

NO............................................................................................ 2 

Appendix 2.D. (continued)
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

74

Strength of Tobacco Control Survey 2 Page 10 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

MODULE D: SUPPORT 

PROGRAMMER NOTE: This module is asked of all respondents except Health Department.

 

D1. I am going to read the names of several entities. I would like your opinion about how supportive each of 

the following has been regarding your tobacco control agenda over the past two years. For each entity, 

please tell me if in your opinion they have been not at all supportive, fairly supportive, quite a bit 

supportive, or extremely supportive.

 NOT AT ALL

SUPPORTIVE

FAIRLY 

SUPPORTIVE

QUITE A BIT 

SUPPORTIVE

EXTREMELY

SUPPORTIVE

a. The Governor............................................................ 1 2 3 4 

b. The State House of Representatives........................ 1 2 3 4 

c. The State Senate...................................................... 1 2 3 4 

d. The media................................................................. 1 2 3 4 

e. The State Attorney General...................................... 1 2 3 4 

f. The Chief Health Officer........................................... 1 2 3 4 

Appendix 2.D. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

75

Strength of Tobacco Control Survey 2 Page 11 MODULE E: INTERAGENCY RELATIONSHIPS E1. *?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

We would like to ask some questions about the interrelationships of tobacco control organizations in your 

state. Are you the person in your organization who has the most contact with other tobacco control organizations? 

YES.......................................................................................... 1 NO................(GET INTRA-AGENCY REFERRAL AND 

TERMINATE MODULE)....................... 2 

E3.

E2. Now I am going to ask you about several 

organizations in your state. Which of the following four statements best describes your organization’s interaction with the (READ

ITEM)? Would you say you have had no

contact, have tended to work at cross purposes (or had turf wars), have shared information with each other, have developed a plan to share responsibilities and to coordinate efforts, or have actually shared funding or staff? 

Had no contact

Tended to work at cross 

purposes/ turf wars 

Shared information 

We have a plan to 

coordinate efforts and 

share responsibilities 

Actually shared funding or 

staff 

During the past two years, how frequently did you have contact with (READ ITEMS)? Would you say you had rare contact, occasional contact, or extensive contact? 

ASK E3 RARE CONTACT OCCASIONAL CONTACT EXTENSIVE CONTACT 

A. Department of Health................................. 1 2 3 4 5 1 2 3 B. American Cancer Society.......................... 1 2 3 4 5 1 2 3 C. American Lung Association...................... 1 2 3 4 5 1 2 3 D. American Heart Association ..................... 1 2 3 4 5 1 2 3 E. State Tobacco Control Coalition .............. 1 2 3 4 5 1 2 3 F. State Attorney General’s Office................ 1 2 3 4 5 1 2 3 G. State Medical Society ................................ 1 2 3 4 5 1 2 3 H. Date Dental Society.................................... 1 2 3 4 5 1 2 3 I. Tobacco Control Researchers in your 

state ............................................................. 1 2 3 4 5 1 2 3 

PROGRAMMER NOTE: EXCLUDE FROM A-I RESPONDENT’S OWN AGENCY. 

Appendix 2.D. (continued)
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

76

Strength of Tobacco Control Survey 2 

*?&RQWUROOHG)LOHV?3'?)*?4&$7,?4&DWLYGRF

MODULE F: DEMOGRAPHICS 

F1. We would like to ask a few questions about you. What 

is your job title? 

TITLE: ___________________________________________

 

F2. How long have you worked for your present 

organization or agency? YEARS...........................................................................

MONTHS .......................................................................

 

F3. How long have you been in your current position? YEARS...........................................................................

MONTHS .......................................................................

 

F4. How long have you been involved in tobacco control? YEARS...........................................................................

MONTHS .......................................................................

 

Thank you very much for your time. We appreciate your expertise. 

Appendix 2.D. (continued)
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

77

Appendix 2.E. Validation of the Strength of Tobacco Control Model

This appendix discusses the analysis methods used to validate the SoTC model and 

the justification for using a reduced model in the calculation of the SoTC index score.

Validation of the Heuristic Model

The SoTC model was validated using principal components analysis, factor analysis, 

and structural equation modeling. The results of the structural equation modeling provided a measure of statistical significance associated with each pathway in the conceptual model and provided the estimated appropriate weighting factor (with error bounds) 

for combining the subdomains, domains, and constructs to summarize SoTC.

Correlation Analysis

A correlation analysis was performed across each variable in the SoTC hierarchy 

(subdomain) of the conceptual model as an exploratory tool. The purpose of this exercise was to determine how well the various different variables within the hierarchy 

interrelated. The expectation was that domain and subdomain variables from within the 

same construct would have stronger correlation coefficients than those that came from 

different constructs. The degree to which this could be established is the basis for the 

validation of the conceptual model.

Principal Components Analysis

The next step in analyzing each within-method correlation matrix was to perform 

a principal components analysis on a correlation matrix including all of the averaged 

variables at the subdomain and domain levels but not at the construct or SoTC levels. 

The purpose of this exercise was to demonstrate that a significant portion (greater than 

50%) of the variability in the 12-variable correlation matrix could be explained within 

the first three vectors of factor loadings. The measure of the amount of variability 

explained by each factor loading was summarized as a proportion by the eigenvalue 

associated with each vector of factor loadings. It was presumed that these first three 

vectors were associated with the three latent constructs (resources, capacity, and efforts) 

depicted in the conceptual model. This presumption was verified using a factor analysis 

as described below.

Factor Analysis. Factor analysis can be considered as an extension of principal components analysis. The goal of factor analysis is to describe the structure of a correlation 

matrix for a set of response variables by using a smaller number of factors (or latent 

variables). The idea is to separate the response variables into groups, such that variables 

within a group are highly correlated with each other but not correlated as much with 

variables in other groups, with an implicit goal that each group of variables represents a 

single underlying construct, or factor, that is responsible for the observed correlations. 
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

78

After an exploratory data analysis and the principal components analysis, it appeared 

as though the response variables in the correlation matrix could be separated into three 

groups that, in turn, were associated with one of the constructs in the conceptual model 

(resources, capacity, and efforts).

Essentially, the factor analysis allowed for use of an orthogonal transformation of 

the principal components analysis results to better visualize the separation between the 

three main constructs. Two algorithms for estimation were explored—principal components and maximum likelihood—and the results were summarized by plotting the 

resulting first three factor loadings in a three-dimensional plot to demonstrate how the 

different constructs separate from each other in describing SoTC. This plot was generated for the factor loadings before and after the orthogonal transformation was applied.

Structural Equations Model. A structural equations model was used to compare the 

relationships between observed variables from the SoTC survey and latent variables 

from the conceptual model, resulting in a covariance matrix with a certain structure that 

corresponded with the SoTC conceptual model. The model contains parameters that 

describe the contribution of each domain to its corresponding constructs and each construct to the overall measure of SoTC. These parameters were estimated with a structural equations model, using the covariance matrix of observed data as input to the model.

The results of the structural equations model provide a measure of statistical significance associated with each pathway in the conceptual model and the estimated appropriate weighting factor (with error bounds) for combining the subdomains, domains, 

and constructs to summarize SoTC. These weighting factors, quite naturally, are consistent with the eigenvalues from the principal components analysis conducted at each 

level of hierarchy within the conceptual model. The level of significance associated 

with each pathway within the structural equation model was then used to reduce and 

verify the conceptual model.

Results

Within-method Correlation Analysis

Within-method correlation matrices among domain-level variables are presented in 

table 2.E.1. The 12 × 12 domain-level correlation matrix or the reduced 9 × 9 correlation matrix that eliminates three of the variables related to the capacity construct, as 

appropriate, becomes the basis for all the remaining analyses (principal components 

analysis, factor analysis, and structural equations models). Subdomain variables from 

within the same construct were more highly correlated than variables that came from 

different constructs, exceptions being the leadership, health department infrastructure, 

and staff experience domains that contribute to the capacity construct. This supported 

the observed separation between the variables that contributed to the capacity construct 

in factor analysis figures.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

79

Table 2.E.1. Within-method Correlation Analysis of Domain-level Strength of Tobacco Control Variables

Variables Staff Funds Leadership

Interagency 

relationships

Health dept. 

infrastructure

Statewide 

coalition

Staff 

experience

Media 

advocacy

Mass 

media

Developing 

local 

capacity

Policy 

advocacy

Individual 

behaviors

Staff 1.00 .34 .18 .21 .11 .29 –.03 –.01 –.07 .11 .08 .33Funds .34 1.00 .25 .09 –.02 .22 –.03 .14 .09 .00 .05 .18Leadership .18 .25 1.00 –.00 –.04 –.14 –.12 –.14 –.11 –.14 .24 –.02Interagency relationships

.21 .09 –.00 1.00 .11 .34 .07 .06 .12 .02 .19 .14

Health dept. infrastructure

.11 –.02 –.04 .11 1.00 .13 .18 –.13 .10 –.01 .07 –.01

Statewide coalition

.29 .22 –.14 .34 .13 1.00 .08 .12 –.09 .14 .06 .01

Staff experience

–.03 –.03 –.12 .07 .18 .08 1.00 –.09 –.04 –.10 .15 .25

Media advocacy

–.01 .14 –.14 .06 –.13 .12 –.09 1.00 .29 .39 .45 .36

Mass media –.07 .09 –.11 .12 .10 –.09 –.04 .29 1.00 .48 .53 .50Developing local capacity

.11 .00 –.14 .02 –.01 .14 –.10 .39 .48 1.00 .37 .55

Policy advocacy

.08 .05 .24 .19 .07 .06 .15 .45 .53 .37 1.00 .37

Individual behaviors

.33 .18 –.02 .14 –.01 .01 .25 .36 .50 .55 .37 1.00
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

80

Tables 2.E.2 and 2.E.3 provide the correlation matrices among construct-level variables. As seen from these tables, each of the three main construct-level variables appeared to summarize different information, since none of them are highly correlated 

with each other. However, all three of these constructs make a significant contribution 

to the overall summary measure of SoTC.

Principal Components Analysis

Table 2.E.4 provides the summary of the amount of variability explained within the 

first three principal components, when analyzing the 12 × 12 correlation matrix among 

the domain-level variables included in the SoTC conceptual model and a reduced 

9 × 9 correlation matrix that eliminates three of the five domains related to capacity 

construct. As demonstrated in the table, approximately 50% of the variability in the 

12 × 12 (full model) correlation matrix and 60% of the variability in the 9 × 9 correlation matrix (reduced model) could be explained by the first three factor loadings.

Factor Analysis

Figure 2.E.1 summarizes the factor analysis that essentially rotates the first three 

principal components, allowing for graphic grouping of the “like” variables that 

contribute to each of the three main constructs. This analysis demonstrates that it is 

possible to group the variables in a manner that clearly separates them into the three 

construct groups. The figure also demonstrates that there was substantial separation 

Table 2.E.2. Within-method Correlation Analysis among Three Constructs and Overall Strength of 

Tobacco Control (SoTC) for Full Model

Variables Resources Capacity Efforts SoTC

Resources 1.00 .18 .22 .72

Capacity .18 1.00 .14 .62

Efforts .22 .14 1.00 .67

SoTC .72 .62 .67 1.00

Note: Full model consists of all 12 domain-level variables.

Table 2.E.3. Within-method Correlation Analysis among Three Constructs and Overall Strength of 

Tobacco Control (SoTC) for Reduced Model

Variables Resources Capacity Efforts SoTC

Resources 1.00 .30 .22 .78

Capacity .30 1.00 .12 .70

Efforts .22 .12 1.00 .59

SoTC .78 .70 .59 1.00

Note: Reduced model consists of only 9 of the 12 domain-level variables, eliminating 3 of the 5 variables related to 

capacity construct, based on the results of the structural equation model.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

81

between the five original variables that contributed to the capacity construct if viewed 

at the first three eigenvectors and suggests that reduced models should be investigated. 

The next section describes the results of the structural equation modeling analysis that 

was performed to accomplish this.

Table 2.E.4. Amount of Variability Explained by First Three Factor Loadings in Principal 

Components Analysis

Model Factor loadings Cumulative eigenvalues

Fulla 1 0.24

2 0.38

3 0.50

Reducedb 1 0.32

2 0.50

3 0.62

aFull model consists of all 12 domain-level variables. bReduced model consists of only 9 of the 12 domain-level variables, eliminating 3 of the 5 variables related to capacity construct, based on the results of structural equation models.

Figure 2.E.1. Factor Analysis for Full Model

Leadership Funds

Staffing

Inter Agency

Policy Advocacy

Mass Media

Health Dept.

Infrastructure

Staff

Experience

Individual Behaviors

Developing Local Capacity

Coalitions
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

82

Structural Equation Modeling

The results of a structural equation model fit to the full conceptual model, in which 

all 12 domain-level variables were entered as manifest variables in the analysis, are 

summarized in figures 2.E.2 and 2.E.3 for full and reduced models.

Each of these 12 variables contributed to one of three latent variables (constructs) 

in the SoTC conceptual model. The strength of these relationships is provided along 

the arrows (with correlation coefficients, associated standard errors, and p values). The 

error left unexplained is also provided (F2), and since this analysis was based on the 

analysis of a correlation matrix, R-squared for each structural relationship can be calculated as 1–variance. In addition, the p values for an overall model chi-square test and 

goodness-of-fit index statistic are also provided.

Note that in 9 of the 12 cases a significant amount of the variability was explained 

by the pathways in the conceptual model, the exceptions being the leadership, health 

department infrastructure, and staff experience domains that contribute to the capacity construct. Correlations between the three latent variables (at the construct level) are 

provided in these figures as well.

The model itself (as fitted) was not particularly well suited to assess the contributions to an overall SoTC score because it would be based on combining three variables 

that were already latent. However, the strength of these relationships was estimated 

independently of the structural equation modeling (as seen in tables 2.E.2 and 2.E.3); 

these estimates are seen in figures 2.E.2 and 2.E.3. Due to the weak relationships observed for three of the five capacity construct variables, the overall fit of the full model 

was not particularly good. However, figure 2.E.3 represents a reduced model (eliminating these three variables from the conceptual model) that fits the data quite well, based 

on the overall model chi-square test and goodness-of-fit index statistic.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

83

Figure 2.E.2. Strength of Tobacco Control—Full Model

σ2

σ2 σ2

σ2

σ2

σ2

σ2

σ2 σ2

σ2σ

σ

2

2 R = 0.133

se = 0.200

p = 0.511

R = 0.707

se = 0.253

p = 0.006

R = 0.483

se = 0.201

p = 0.018

R = 0.083

se = 0.176

p = 0.681 R = 0.468 se = 0.196

p = 0.018

R = 0.530

se = 0.146

P < 0.001 R = 0.696

se = 0.139

p < 0.001

R = 0.718

se = 0.138

R = 0.70

p < 0.001

0

se = 0.139

p < 0.001

R = 0.623

se = 0.142

p < 0.001

R = 0.196

se = 0.177

p = 0.270

R = 0.750

se = 0.253

p = 0.004

R = 0.117

se = 0.176

p = 0.508

R = 0.539

se = 0.248

p = 0.032

R = 0.233

se = 0.208

p = 0.265

GFI = 0.845 Pr > Chi-Square = 0.080

= 0.50 = 0.77

= 0.52

= 0.48= 0.61

= 0.51

= 0.72 = 0.99

= 0.78

= 0.96

= 0.44

= 0.99

Efforts Capacity

Resources

Individual

Behaviors

Policy

Advocacy

Inter-Agency

Relationships

Health Dept.

Infrastructure

Statewide

Conditions

Staff

Experience

Funds Staffing

Media

Advocacy

Mass Media

Leadership

Developing

Local Capacity

SOTC
2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

84 Figure 2.E.3. Strength of Tobacco Control—Reduced Model

σ2 σ2 σ2

σ2

σ2 σ2

σ2

σ

σ

2

2

Efforts Capacity

Resources

Individual

Behaviors

Policy

Advocacy

Inter-Agency

Relationships

Funds Staffing

Media

Advocacy

Mass Media

Leadership

Developing

Local Capacity

SOTC

R = 0.141

se = 0.209

p = 0.500

R = 0.591

se = 0.259

p = 0.027

R = 0.236

se = 0.209

p = 0.264

R = 0.692

se = 0.240

p = 0.006

R = 0.493

se = 0.198

p = 0.016

R = 0.483

se = 0.204

p = 0.022 R = 0.710 se = 0.259

p = 0.008

R = 0.531

se = 0.146

p = 0.001 R = 0.696 se = 0.139

p < 0.001

R = 0.718

se = 0.138

R = 0.69

p < 0.001

9

se = 0.139

p < 0.001

R = 0.623

se = 0.142

p < 0.001

GFI = 0.902 Pr > Chi-Square = 0.265

= 0.52 = 0.76

= 0.52

= 0.51

= 0.61= 0.49

= 0.72 = 0.77

= 0.50
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

85

Appendix 2.F. Construct Validation of Strength of Tobacco Control

Concept mapping (more fully described in chapter 8, pages 217–23) was used to 

validate the SoTC construct. Forty-three key stakeholders in the tobacco control and 

prevention field (state and local, frontline and research, experienced and relative newcomers) were asked to identify an initial set of tobacco control program components. 

Components were brainstormed over the World Wide Web, and the following focus 

statement was used: “One specific component of a strong tobacco control program is...” 

The statements provided by the respondents were subsequently revised and refined into 

a final set of 73 components that were sorted by 41 of the original 43 respondents and 

rated for whether they were a local, state, or mixed responsibility. Concept mapping was 

used to analyze the sorting and rating data and to generate the conceptual framework. 

The results provide a summary of what key stakeholders in the tobacco control field 

identify as the components of a strong tobacco control program—components that are 

congruent with the SoTC. The basic conceptual framework categorizes the 73 specific 

components into 12 categories that, in turn, are grouped into four major areas (management, processes, programs and services, and outcomes) that suggest a natural progression, or logic model (see figure 2.F.1). The framework also shows that strong tobacco 

control efforts address both systemic and individual change, with respondents indicating that states should have greater responsibility for systematic change, while local 

communities should have greater responsibility for individual behavior change.

Figure 2.F.1. Basic Conceptual Framework

Outcome

Surveillance

Policy Interventions

Advocacy

Youth Policy

& Programs Infrastructure

Mobilization

Collaboration

& TA

Public Education

Media

Budget

Cessation

Services

Monitoring

Industry

Management Processes Programs & Services Outcomes

Systemic Change

Individual Change
86

2 . T h e S t r e n g t h o f T o b a c c o C o n t r o l I n d e x

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MD: National Cancer Institute.

6. Kenny, G. 2003. Balanced scorecard: 

Why it isn’t working. New Zealand 

Management (March): 32–34.

7. National Cancer Institute. 2005. What 

is the TUS-CPS? http://riskfactor.

cancer.gov/studies/tus-cps.

8. Farrelly, M. C., T. F. Pechacek, and 

F. J. Chaloupka. 2003. The impact of 

tobacco control program expenditures 

on aggregate cigarette sales: 1981–

2000. Journal of Health Economics 22 

(5): 843–59.

9. Pierce, J. P., E. A. Gilpin, S. L. Emery, 

M. M. White, B. Rosbrook, C. C. Berry, 

and A. J. Farkas. 1998. Has the 

California tobacco control program 

reduced smoking? JAMA: The Journal 

of the American Medical Association

280 (10): 893–99.

10. Koh, H. K., C. M. Judge, H. Robbins, 

C. C. Celebucki, D. K. Walker, and 

G. N. Connolly. 2005. The first decade 

of the Massachusetts Tobacco Control 

Program. Public Health Reports 120 

(5): 482–95.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

87

3. Measuring Policy and Legislative Changes

Jamie Chriqui, Marcy Frosh, Ross C. Brownson, and Frances A. Stillman

Contents

Introduction.................................................................................................................... 89

State and Local Government Action for Tobacco Use Prevention and Control ................. 90

The Value of Monitoring Policy and Legislative Changes................................................ 91

Methods for Rating the Comprehensiveness of Tobacco Control Laws ........................... 93

Rating State Tobacco Control Laws......................................................................................93

Limitations..........................................................................................................................96

Rating Local Tobacco Control Laws—The Case of Clean Indoor Air.....................................97

Challenges in Developing the Local Rating System .............................................................99

State and State+Local Rating Results......................................................................... 100

Youth Access Ratings........................................................................................................100

Clean Indoor Air Ratings....................................................................................................100

State+Local Clean Indoor Air Ratings................................................................................102

Summary...................................................................................................................... 104

Conclusions.................................................................................................................. 105

References................................................................................................................... 106

Tables and Figures

Table 3.1. Target Criteria Rated with 4 Points for Items in the Youth Access Policy Area ............94

Table 3.2. Target Criteria Rated with 4 Points for Items in the Clean Indoor Air Policy Area........95

Table 3.3. Summary Scores for Youth Access Legislation, All States, 1993–99.........................100

Figure 3.1. Mean Youth Access Summary Scores by Year and Preemption Status .....................101

Table 3.4. Summary Scores for State Clean Indoor Air by Preemption Score Adjustment 

and Year, All States, 1993–99...................................................................................................101

Figure 3.2. Mean Clean Indoor Air Summary Scores by Year and Preemption Status.................102

Table 3.5. State+Local Clean Indoor Air Summary Scores by Year, 1993–98.............................103

Table 3.6. State+Local Summary Scores for Clean Indoor Air for ASSIST and 

Non-ASSIST States, 1993–98....................................................................................................103

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

89

3. Measuring Policy and Legislative Changes

Introduction

T

his chapter examines a tool for measuring legislative changes related to tobacco use 

prevention and control, based on state and aggregated local data sources, as well as 

trends in these data over the duration of ASSIST. Focusing on key areas of youth access to tobacco products and clean indoor air policies (eliminating exposure to environmental tobacco smoke), this tool ranked the extensiveness of specific legislative items 

to create a summary legislative score for each of these two policy areas. The resulting 

summary score for clean indoor air was used as part of the Initial Outcomes Index (IOI) 

created as part of the ASSIST evaluation analysis.

This chapter describes measures and methods developed for the evaluation of the 

American Stop Smoking Intervention Study (ASSIST) for assessment of state and 

local legislative changes in youth access laws and clean indoor air laws. Although 

only the clean indoor air measure was subsequently used in the overall analysis, 

these measures have broader applicability in the analysis of changes in state and 

local laws for specific tobacco control policy objectives.

On the basis of state legislative data from the National Cancer Institute’s (NCI’s) 

State Cancer Legislative Database (SCLD) and local data from the American 

Nonsmokers’ Rights Foundation (ANRF), state-level summary scores were created 

for youth access and clean indoor air policy, encompassing state legislation as well 

as the incremental effect of stronger local ordinances. State scores were based on a 

nine-category quantitative rating in each area, subject to a substantive numerical 

penalty where preemption laws forbade the implementation of stronger local 

legislation. An incremental local component to the clean indoor air scores was also 

created for subareas where local laws were equivalent to or stronger than state 

legislation, weighted by the percentage of the state’s population represented in the 

community.

Analysis of these data showed that state summary scores for youth access 

legislation increased over time from a mean of 7.20 to 11.57 accounting for 

preemption, and from 8.35 to 15.59 without preemption, during the period studied 

(1993–98). Summary scores for clean indoor air exhibited a smaller increase over 

this period, from a mean of 7.16 to 8.02 with preemption and 8.71 to 10.98 without. 

The composite state+local scores for clean indoor air also increased during this 

period from a mean of 7.71 to 8.64 accounting for preemption, and perhaps more 

important, there was a significant difference in these scores between ASSIST (M = 

10.56) and non-ASSIST (M = 7.68) states.
90

3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

Governments at all levels in the 

United States have enacted legislation 

addressing the public health effects of 

smoking. Increasingly, legislative restrictions are viewed as a critical component 

of strategies that may also include tax 

measures, media interventions, community programs, and other efforts. 

ASSIST directed intervention efforts at 

four policy areas: eliminating exposure 

to environmental tobacco smoke, promoting higher taxes for tobacco, limiting 

tobacco advertising and promotions, and 

reducing minors’ access to tobacco products. This chapter describes the system 

developed to measure state and local 

legislative changes in the United States. 

Of these policy areas, the ASSIST evaluation focused on state and local clean 

indoor air laws as a variable for the IOI 

because there were available data sources for this measure. A measure did not 

have to be developed for state taxes on 

tobacco because these data were available. The system used data from NCI’s 

SCLD, data from the ANRF database 

on local legislation, and policy priorities 

identified for ASSIST. Raw data from 

the clean indoor air model are presented 

here. This chapter also reviews how the 

system applies to state laws designed to 

restrict minors’ access to tobacco products. Although originally developed for 

use in the ASSIST evaluation, the tools 

presented in this chapter will enable the 

tobacco control and research communities to monitor progress toward specific 

policy markers based on changes in state 

and local laws.

State and Local Government 

Action for Tobacco Use 

Prevention and Control

T

he volume of state and local laws on 

clean indoor air and youth access to 

tobacco is one indicator that legislators have responded to a health policy 

approach that goes beyond individual 

health risks to target broad sectors of the 

population.1,2 Much of the state-level 

activity for clean indoor air legislation 

began in the 1980s.3 Notably, 1986 was a 

watershed year for scientific knowledge 

about environmental tobacco smoke, 

which was summarized in reports by the 

surgeon general and the National Research Council. These reports made the 

scientific case for enacting policies to 

protect the public from the effects of involuntary smoking, and states responded 

with laws restricting smoking in public 

places.3–5 In 1993, the Environmental 

Protection Agency released its risk assessment report on the health consequences of involuntary smoking, and 

state legislators’ attention to the clean 

indoor air issue continued to evolve in 

state legislatures.6 States seeking to enact new requirements for clean indoor air 

found many prototypes in strong local 

ordinances that had been enacted and 

implemented in preceding years.7,8

New state laws on youth access to 

tobacco followed federal activity aimed 

specifically at the youth cohort.9 The 

Synar amendment required states to 

adopt and implement sales restrictions to 
91

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

minors or risk losing certain block grant 

funds and, in response, by 2002 all states 

had enacted laws prohibiting the sale of 

tobacco to minors.10 (See NCI Monograph 16, chapters 6 and 9.) Additionally, the Food and Drug Administration 

promulgated regulations (later invalidated by the Supreme Court) restricting 

minors’ access to tobacco.

Data from NCI’s SCLD for 1993 

through 1999 indicate that states were active in passing clean indoor air laws from 

1993 to 1995 but that the level of activity 

flattened out in the late 1990s.11 Data on 

clean indoor air and youth access laws 

and regulations by local governments, collected for more than two decades by the 

ANRF, indicate that for the period 1980 

to 1998, the number of local clean indoor 

air laws and regulations enacted annually 

in the United States peaked in 1993.12

For the same period, the annual number 

of laws enacted to restrict youth access 

to tobacco lagged behind clean indoor air 

ordinances until 1994. Local activity on 

youth access has slightly outpaced clean 

indoor air provisions since 1994, but passage of new local ordinances in both of 

these areas has slowed.12 At the time of 

the ASSIST evaluation, over 1,500 communities had enacted some type of clean 

indoor air ordinance, and over 1,300 communities had enacted some type of youth 

access to tobacco ordinance.13*

The trend toward adoption of preemption language related to state clean 

indoor air laws (and youth access to 

tobacco laws) is well discussed in the 

literature.14,15 At the time of the ASSIST 

evaluation, 27 states included preemption 

provisions in connection with tobacco 

control laws.16 As defined generally, state 

preemption prohibits lower level jurisdictions from enacting laws more stringent 

than, or different from, the higher level 

law.17 Both the Department of Health and 

Human Services and the American Public 

Health Association have issued statements opposing state preemption of local 

tobacco control ordinances.18,19

The Value of Monitoring Policy 

and Legislative Changes

T

he usefulness of surveillance of tobacco control policy change is well recognized, and monitoring systems are now an 

important part of tobacco control efforts 

in the United States. Former U.S. Surgeon General David Satcher emphasized 

the importance of data collection and 

data analysis to identify tobacco control 

problems and to make progress in solving 

these problems, and he called for the replication of such systems worldwide.20

The NCI system for rating selected 

tobacco control laws is a benchmark 

tool: The model offers data comparing 

the laws in all 50 states and the District 

of Columbia to well-established public 

health goals.11,21 The value of longitudinal monitoring of this kind is also made 

clear in Stillman et al.,22 1999, wherein 

the ratings serve as a key variable in the 

ASSIST IOI (along with cigarette prices 

and the percentage of workers covered 

by a 100% smoke-free workplace). 

*Information about the ANRF database of community ordinances is located at 

www.no-smoke.org/document.php?id=313.
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3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

The rating system was not designed to 

predict the effect of laws on behavior; 

instead, it was expected that the relationship between the rating data and other 

variables such as prevalence and consumption could be tested as it was in the 

ASSIST evaluation.

The NCI rating system establishes a 

numerical rating for every state based 

on the extensiveness of the state’s youth 

access and clean indoor air laws. The 

system measures changes in these laws, 

establishes a firm baseline, uses verifiable data based directly on state laws, 

and rates the same item for every state 

(with a high level of interrater agreement) based on established public health 

objectives.11,21 The system thereby offers 

a high degree of measurability for the 

ASSIST evaluation and other research.

In the ASSIST evaluation, the unit of 

measure is the state; therefore, an index 

to assess states based on their changes 

in tobacco control policy is particularly 

useful in at least two important respects. 

The clean indoor air ratings serve as one 

variable in the IOI and thereby as a measure of the effect of ASSIST on policy 

outcomes. Specifically, for purposes of 

its IOI, the ASSIST evaluation used a 

combined state+local clean indoor air 

rating. As noted below, only local measures that were as restrictive or more 

restrictive than the state law were included in the combined rating. ASSIST 

states had higher policy scores than 

non-ASSIST states prior to 1995, and 

the early baseline environment in these 

states may account for the new clean 

indoor air laws that were enacted in later 

years. In a related aspect of the ASSIST 

evaluation, the combined state+local 

clean indoor ratings became a variable 

in the analysis of whether the initial outcomes affected smoking prevalence and 

consumption rates.22

In another example, the NCI rating 

system was also analyzed with the NCI 

Tobacco Use Supplement to the U.S. 

Census Bureau’s Current Population 

Survey.23 The analysis revealed a differential of more than 30 percentage points 

among the states in the proportion of the 

workforce with smoke-free policies.24 As 

new findings show that there are significant risks in even short-term exposure 

to secondhand smoke,25,26 data that help 

states, cities, and countries evaluate the 

extensiveness of their clean indoor air 

laws will become increasingly important.

Generating quantitative indicators 

based on state and local laws can help 

inform decision makers about whether 

specific aspects of their tobacco control 

policy are in the best interests of public 

health. Measures of tobacco control inputs are important in evaluating the comprehensiveness and strength of tobacco 

control policies by (1) providing target 

goals by which states can monitor progress, (2) facilitating comparison among 

states and counties, (3) enabling longitudinal tracking of changes in policy 

actions over time, and (4) measuring the 

effect of the inputs on outputs or behavioral and other changes.27
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Methods for Rating the 

Comprehensiveness of 

Tobacco Control Laws

T

he methods used in the ASSIST evaluation for rating state tobacco control 

laws have been published in detail.11,21

This section summarizes those methods 

and their limitations and presents an additional approach used in the evaluation 

for adding a local-level component to the 

system for rating state clean indoor air 

legislation.

Rating State Tobacco Control Laws

In 1995, NCI convened a technical 

advisory committee composed of governmental and nongovernmental tobacco 

control specialists to develop a system for 

rating state tobacco control laws in the 

SCLD. With this effort, the NCI program 

personnel and the ASSIST evaluation team 

hoped to create a tool to monitor changes 

in tobacco control policy in all states.

The rating system developed by the 

committee included information on state 

laws only; executive orders, regulations, 

and nongovernmental policies were not 

captured in the system. In one instance 

(Maryland, 1995–99), proxy scores were 

used to complete categories of a state 

clean indoor air rating for which a narrower statutory provision connoted a broad restriction upheld by the state’s highest court 

in regulation form. The committee recognized that providing data on state laws 

alone would not reflect overall tobacco 

control policy for states. Nevertheless, a 

tracking system for state laws had the benefit of providing consistent, reliable data 

on a critical component of state tobacco 

control policy, and such data were viewed 

as a potentially valuable research tool.

To identify the variables to include in 

the rating system, the committee reviewed 

major provisions of state laws, ASSIST 

policy priorities,28,29 and reports of scientific research. The committee identified 

nine legislative items to rate in the youth 

access and the clean indoor air legislative 

areas (tables 3.1 and 3.2). For the youth 

access area, six items specifically address 

restrictions aimed at limiting minors’ access to tobacco products, and three items 

emphasize the importance of enforcement 

efforts. Similarly, for the clean indoor 

air area, seven legislative items address 

specific location restrictions that can affect a large number of persons, and two 

items address enforcement of the location 

restriction laws. The ratings reflect the cumulation of each state’s law over time, so 

that all amendments to and repeals of the 

law are incorporated in the annual scores.

Decision criteria are applied to each 

legislative item to determine its rating by 

number of points. The item is described 

according to four or five criteria representing possible levels of requirements 

Rating System for State Laws

■ The system has two policy areas: clean indoor air and youth access.

■ In each policy area, nine legislative items 

are rated.

■ Four or five decision criteria rate the extensiveness of each item.

■ Within each policy area, the sum of the individual ratings for each of the nine items 

is the summary score assigned to the legislative (policy) area for the specified state.
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3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

in the item. In each instance, a score of 

four points reflects the target score from 

a public health policy perspective. For 

example, for a law in the area of clean 

indoor air, the first item applies to government workplaces, and the target criterion 

is that 100% of government worksites are 

100% smoke free. An additional point is 

assigned if the law specifies that government worksites and grounds are 100% 

smoke free. The five decision criteria for 

government worksites describe incrementally the requirements in the law that 

will lead to that outcome and have rating 

points from 0 to 5 accordingly. The more 

comprehensive the requirement, the more 

points are assigned. See sidebar for an example of the decision criteria for ratings.

The criteria used for rating the youth 

access and clean indoor air areas were 

devised to depict the degree of comprehensiveness and stringency of the provisions in the laws. The highest rating for 

some items is +5, for others the rating 

is +4, and it describes an ideal situation, usually with tobacco restrictions 

and population coverage at 100%. The 

Table 3.1. Target Criteria Rated with 4 Points for Items in the Youth Access Policy Area

Item Target criteria rated with 4 points

1 Minimum age Prohibits the sale or distribution of any tobacco products to persons 

under 18 years of age through any sales or distribution outlet, and a 

warning sign is required at point of purchase with specific penalty for 

failing to post a sign

2 Packaging Prohibits all cigarette sales other than in a sealed package conforming 

to federal labeling requirements

3 Clerk intervention Prohibits access to or purchase of tobacco products without the 

intervention of a sales clerk

4 Photographic identification Requires merchants to request photographic identification for people 

who appear to be under 21 years of age

5 Vending machines Total ban on sale of all tobacco products through vending machines 

in all locations

6 Free distribution Total ban on distribution of free tobacco samples, coupons for free 

samples, or rebates

7 Graduated penalties Establishes a system of graduated penalties or fines applicable to 

all youth access laws, to be levied within 3 years, plus possibility 

of suspension or revocation of a required tobacco retail license for 

repeated sales to minors

8 Random inspections Establishes random, unannounced inspections of retailers as part of 

the enforcement mechanism, using underage buyers for the purpose 

of identifying violators, and does not prohibit other use of minors to 

test compliance

9 Statewide enforcement Establishes a clearly designated statewide enforcement authority for 

sales

Source: Alciati, M. H., M. Frosh, S. B. Green, R. C. Brownson, P. H. Fisher, R. Hobart, A. Roman, R. C. Sciandra, and 

D. M. Shelton. 1998. State laws on youth access to tobacco in the United States: Measuring their extensiveness with a 

new rating system. Tobacco Control 7:345–52. Reproduced with permission of the BMJ Publishing Group.
95

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

descending criteria reflect where on the 

per-item rating scale the provisions qualify in relation to the ideal. The criteria also 

take into account features of the laws that 

narrow their application—for example, 

exclusions or explicit exemptions. For 

each item, if a state law preempts stronger 

local ordinances, the rating for the specific item is reduced by 2 (–2) points (with a 

minimum score of 0 on each item).

A summary score, which is the measure of the comprehensiveness of the 

laws, is calculated for the legislative 

area for a state by adding the rating 

points for all nine items for the area. 

For calculating the summary score, 

individual items are considered of equal 

weight across the rating area. For example, for calculation of a summary 

score for clean indoor air, restrictions 

on government or private worksites are 

weighted equally with restrictions on 

retail stores.

The maximum possible summary 

score is 39 points for the youth access 

area and 42 points for the clean indoor 

air area. Because of the –2-point penalty 

for an item that is preempted, the summary score for each policy area could be 

reduced by up to 18 points. The rating 

reduction for preemption was recognized from the outset as a heavy penalty. However, the committee deemed it 

important to identify the specific items 

that included preemptions rather than 

to create a separate or 10th item to account for preemption because it would 

be impossible to then account for how 

many individual items were affected by 

preemption.

Table 3.2. Target Criteria Rated with 4 Points for Items in the Clean Indoor Air Policy Area

Item Target criteria rated with 4 points

1 Government worksites Government worksites are 100% smoke free, no exemptions

2 Private worksites Private worksites are 100% smoke free, no exemptions

3 Schools No smoking permitted in schools during school hours or while school 

activities are being conducted

4 Childcare facilities No smoking permitted during operating hours in childcare facilities 

(explicitly including licensed home-based facilities)

5 Restaurants Restaurants (explicitly including bar areas of restaurants) are 100% 

smoke free

6 Retail stores Retail stores or retail businesses open to the public are 100% smoke 

free

7 Recreational/cultural 

facilities

Recreational and cultural facilities are 100% smoke free

8 Penalties Penalties or fines, applicable to smokers and to proprietors/employers, 

for any violation of clean indoor air legislation

9 Enforcement Enforcement authority designated for clean indoor air legislation, and 

sign posting is required

Source: Chriqui, J. F., M. Frosh, R. C. Brownson, D. M. Shelton, R. C. Sciandra, R. Hobart, P. H. Fisher, R. el Arculli, 

and M. H. Alciati. 2002. Application of a rating system to state clean indoor air laws (USA). Tobacco Control 11 (1): 

26–34. Reproduced with permission of the BMJ Publishing Group.
96

3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

Limitations

As developed in 1995, the rating system included state laws only: reliable 

sources for data on executive orders, 

regulations, and nongovernmental policies were too limited for inclusion at 

The Rating Process in Detail

The following equations help to illustrate the rating process and the effect of the preemption reduction 

on the individual item ratings and summary scores for a given state, s, at time t. In these equations, 

Sst represents the summary score for state s at time t across each of the nine items; Sstp represents the 

state summary score with the preemption reduction; p represents the 2-point preemption reduction applied to each item, i, as appropriate; and ixst represents each of the nine items (denoted by x) in both the 

youth access and clean indoor air areas for a given state, s, at time t.

The state summary score without the preemption reduction is calculated as follows:

Sst = i1st + i2st + i3st + i4st + i5st + i6st + i7st + i8st + i9st (3.1)

12 = 4 + 4 + 4 + 0 + 0 + 0 + 0 + 0 + 0.

The state summary score with the preemption reduction is calculated as follows:

Sstp = (i1st – p) + (i2st – p) + (i3st – p) + (i4st – p) + (i5st – p) + 

(i6st – p) + (i7st – p) + (i8st – p) + (i9st – p) (3.2)

6 = (4 – 2) + (4 – 2) + (4 – 2) + 0 + 0 + 0 + 0 + 0 + 0.

Item 1: Government worksites are 100% smoke free.

Points Decision Criteria

+5 100% of government worksites and grounds (or a specified distance from entries/exits) are 

smoke free, no exemptions

+4 Government worksites are 100% smoke free, no exemptions 

+3 No smoking permitted in government worksites unless restricted to enclosed, separately ventilated designated smoking areas or government worksites are 100% smoke free, with a minimal 

exemption, for example, worksites with five or fewer employees, privately enclosed offices used 

exclusively by smokers, or other narrow exemption (for example, based on smoker density)

+2 Smoking in government worksites restricted to designated smoking areas that are separate 

and enclosed or to enclosed, separately ventilated designated smoking areas, with a minimal 

exemption

+1 Smoking in government worksites restricted only to designated smoking areas; or to designated smoking areas that are separate and enclosed, with a minimal exemption; or any stricter 

requirement that applies to some but not all types of worksites (for example, warehouses exempted) and/or includes more than a minimal exemption

0 No restrictions, or requirement(s) that smoking be permitted

Source: Chriqui, J. F., M. Frosh, R. C. Brownson, D. M. Shelton, R. C. Sciandra, R. Hobart, P. H. Fisher, R. El Arculli, and M. H. Alciati. 2002. Application of a rating system to state clean indoor air laws 

(USA). Tobacco Control 11 (1): 26–34. Reproduced with permission of the BMJ Publishing Group.
97

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

that time. (In the instance of Maryland’s 

scores for 1995 through 1999, proxy 

scores were used to complete categories 

of the state clean indoor air rating for 

which a narrower statutory provision 

connoted a broad restriction upheld by 

the state’s highest court in regulation 

form.) State laws alone could not reflect 

overall tobacco control policy for states; 

nevertheless, the system has the benefit 

of providing consistent, reliable data on 

a critical component of state tobacco 

control policy.

Moreover, the state rating system was 

not designed as a stand-alone measure; 

rather, it was intended to serve with 

other variables as a measure of a state’s 

overall tobacco control policy on initial 

and long-term outcomes. The limitations 

of the system and the decision rules applied in its creation should be considered 

in light of this goal. An assessment of 

the effect of the state youth access laws 

on youth smoking behavior has been 

published.30 Data on the effect of clean 

indoor air laws11 as part of the IOI are 

presented in chapter 4 of this monograph.

The rating system was not intended 

to produce predictive scales for measuring the effect of laws on behavioral and 

other outcomes. Rather it was intended 

to evaluate the extent to which state laws 

met specified health policy goals and to 

document changes in those laws over 

time. For these reasons, no attempt was 

made to give different weights to individual items within the rating scale. Analyses to test the construct validity, which 

might be appropriate for psychological 

and behavioral research, were not applicable here for a number of reasons. 

These reasons are related not only to the 

obvious limitations of the sample size 

(fixed at 51), but also to the nature of 

the data involved. Any attempt to relate 

extensiveness of the laws to subsequent 

tobacco consumption would require adjustments for other variables, in addition 

to information about changes in laws and 

tobacco consumption over time. Many 

potentially mediating variables are relevant here, including the implementation 

and enforcement of state laws. It was anticipated that the usefulness of the rating 

system would be tested in its application 

as a covariate or intervening variable in 

subsequent research. For further discussion of such variables, see chapter 5 on 

state facilitating conditions.

Rating Local Tobacco Control Laws—

The Case of Clean Indoor Air

Recognizing the importance of local 

policy activity related to clean indoor air, 

a working group of the ASSIST Evaluation Technical Expert Panel was convened to adapt the state clean indoor air 

rating method for use in measuring local 

clean indoor air ordinances. To adapt the 

method, the working group first needed 

to identify the best available source of 

information on local tobacco control 

ordinances as a basis for understanding 

the extent to which a local rating method 

could be developed. Local ordinance 

data available as of the end of 1998 (the 

most recent data available at the time of 

the ASSIST evaluation) were obtained 

from the ANRF for this purpose. The 

ANRF data were deemed to be the best 

available source of local ordinance tobacco data across the states at the time; 

however, the data were not entirely 
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3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

complete because of difficulties in collecting ordinance information from local 

governments. Therefore, the working 

group decided to use the ANRF data as a 

proxy for local ordinances in the states.

The local rating criteria and points 

were devised to correspond, to the extent 

possible, with the state rating categories 

and points. Limitations in the ANRF 

data precluded rating four of the nine 

items in the state method. Accordingly, 

the following five items were used to 

rate the local ordinances:

1. Private worksites

2. Restaurants

3. Recreational and cultural facilities

4. Enforcement

5. Penalties

The categories of government worksites, schools, childcare facilities, and 

retail stores were omitted.

Each community received a rating 

for each of the five items. The summary 

score for the legislative area (clean indoor air) for a community was the sum of 

the five per-item scores. A series of comparisons were made to adjust the local 

scores for each community on each of the 

five provisions to reflect whether the local score was greater than (equation 3.3), 

less than (equation 3.4), or the same as 

(equations 3.5 and 3.6) the state score.

In the following equations, ixst represents the per-item (x) score for state s at 

time t; ixlot represents the per-item score 

for the individual local community lo at 

time t; aixlot represents the adjusted peritem score for the individual community 

lo at time t. If a local score equaled a 

state score, the local community was 

given 0.5 points to indicate that the local community’s ordinance was at 

least as strong as the state’s ordinance 

(equation 3.5).

When the local item is stronger than the 

state item,

ixlot > ixst ➝ aixlot = ixlot – ixst (3.3)

When the local item is weaker than the 

state item,

ixlot < ixst ➝ aixlot = 0 (3.4)

When the local item is as strong as the 

state item,

ixlot = ixst ➝ aixlot = 0.5 (3.5)

When the local item and the state item 

both equal 0,

ixlot = 0; ixst = 0 ➝ aixlot = 0 (3.6)

Separate adjusted local item scores 

were created for each community represented in the data set. Once the adjusted 

local per-item scores were computed, 

each score was then weighted by the 

percentage of the state’s population 

(perpop) represented in the community 

(equation 3.7):

Weighted adjusted local per-item 

score = aixlot × perpoplo (3.7)

Population estimates as of July 1, 

1996, were used as a proxy for the median community-level population across 

the years of interest for the ratings. The 

population estimates were obtained 

from the U.S. Census Bureau.31 To account for possible jurisdictional overlap, 

the population figures for a county accounted only for the unincorporated 

portions of the county. For example, the 

population of the city of Rockville, an 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

incorporated city within Montgomery 

County, Maryland, was not included in 

the county’s population score.

The weighted adjusted local peritem ratings were used to calculate 

the summary scores for each community within a state, and those were then 

summed to create a total local rating for 

each state. The combined state+local 

score per item was calculated by adding 

the state legislative rating score (incorporating the preemption reduction) to the 

state’s local rating for each year, 1993 

through 1998.

Challenges in Developing the Local 

Rating System

Developing the local legislative rating methodology presented three unique 

challenges. First, at the time of the 

ASSIST evaluation there was no central 

repository to which local governments 

sent information about the tobacco control measures they had passed. Instead, 

ANRF tracks and collects information 

on local tobacco control policy activity, 

and this database was used as a proxy 

measure of local ordinance activity.

Second, local ordinances, in and of 

themselves, must be examined within 

their appropriate jurisdictional contexts. In other words, if a county has 

an ordinance that restricts smoking in 

restaurants to separately enclosed areas and a city within the county has an 

ordinance requiring that the separately 

enclosed areas also contain separate 

ventilation, which ordinance would apply to restaurants in the city? From our 

legal research to resolve this issue, we 

determined that the predominant scheme 

emerging in regard to jurisdictional hierarchy is that of a dominant municipality 

whereby incorporated areas are accorded jurisdictional precedence limited 

only by state law in a given policy area. 

In other words, incorporated cities’ ordinances take precedence over county ordinances (when the city is incorporated 

within the county).

Third, we had to account for the fact 

that, in many instances, state law preempts stronger local laws. To account for 

this when creating a combined state+local 

rating measure, we used the state clean 

indoor air score that incorporated the 

preemption reduction plus the local score, 

which was weighted for the percentage 

of the population covered by the local 

ordinances. The working group chose not 

to exclude those ordinance provisions 

that might have been preempted because 

excluding them would have counted the 

preemption effect twice: The state score 

had already been reduced by two points 

for each preempted item. In addition, the 

state scores that incorporate the preemption reduction were used to account for 

the effect of preemption on the state’s 

ability to encourage policy making and 

enforcement. An alternative approach to 

account for the preemption effect would 

have been to use the state scores without 

the preemption reduction and then to omit 

local scores for items that had been preempted by state law. The working group 

decided against the latter approach because the local ordinance information was 

proxy data and the local scores, in and of 

themselves, might not accurately capture 

the preemption effect.
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3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

State and State+Local Rating 

Results

T

he following discussion summarizes 

the results of the state ratings as well 

as the results of the state+local clean 

indoor air ratings.* In all instances, the 

overall summary scores were low and 

indicated that the states (and localities 

in the case of clean indoor air) were far 

from meeting key public health targets 

in the youth access and clean indoor air 

topic areas. These results have been published in detail.10,11,21

Youth Access Ratings

The summary scores for youth access 

legislation increased over time (table 

3.3). Without the preemption reduction, the youth access summary ratings 

ranged from 0 to 26 points for 1993 and 

from 0 to 30 points for 1999. With the 

preemption reduction applied, the scores 

ranged from 0 to 18 points for 1993 and 

from 0 to 30 points for 1999. The states 

with the highest summary scores for 

1997 through 1999 did not include any 

preemptive provisions in their laws.

The mean youth access summary 

score without the preemption reduction increased by more than 7 points for 

1993 through 1999. With the preemption 

reduction applied, the mean youth access summary rating increased only 4.37 

points. As table 3.3 and figure 3.1 show, 

an increasing number of state laws preempted local youth access provisions in 

the later years. This finding is consistent 

with other studies on the prevalence of 

state youth access preemption provisions 

during the 1990s.15,17

Clean Indoor Air Ratings

The change in summary scores over 

time was smaller for clean indoor air 

Table 3.3. Summary Scores for Youth Access Legislation, All States, 1993–99

Score 1993 1994 1995 1996 1997 1998 1999

Score reduced for preemption

Low 0 2 1 1 2 3 3

High 18 21 21 21 29 30 30

Mean 7.20 7.94 8.16 9.06 10.96 11.24 11.57

SD 4.03 4.39 4.48 4.77 6.29 6.71 6.57

Score not reduced for preemption

Low 0 3 3 3 3 3 3

High 26 26 26 26 29 30 30

Mean 8.35 10.22 10.80 12.16 14.39 15.08 15.59

SD 4.99 5.80 5.93 5.85 6.15 6.23 6.25

Note: The maximum possible score is 39 points.

*Individual state scores for clean indoor air and youth access are presented in chapter 4.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

than for youth access (table 3.4). The 

high scores (both with and without the 

preemption reduction) did not change 

after 1995. Without the preemption reduction, the scores ranged from 0 to 20 

points for 1993 and from 0 to 31 points 

for 1999. With the preemption reduction 

applied, the scores ranged from 0 to 18 

points for 1993 and from 0 to 23 points 

for 1999.

The mean summary scores without 

the preemption reduction did not change 

Figure 3.1. Mean Youth Access Summary Scores by Year and Preemption Status

Mean youth access summary score

1993 1994 1995 1996 1997 1998

0

2

4

6

8

10

12

14

16 Without preemption

With preemption

Year

Table 3.4. Summary Scores for State Clean Indoor Air by Preemption Score Adjustment and Year, 

All States, 1993–99

Score 1993 1994 1995 1996 1997 1998 1999

Score reduced for preemption

Low 0 0 0 0 0 0 0

High 18 19 23 23 23 23 23

Mean 7.16 7.43 7.86 7.71 7.71 7.84 8.02

SD 5.54 5.45 5.92 5.94 5.94 5.98 6.02

Score not reduced for preemption

Low 0 0 0 0 0 0 0

High 20 31 31 31 31 31 31

Mean 8.71 10.02 10.67 10.67 10.67 10.80 10.98

SD 5.18 6.06 6.11 6.11 6.11 6.08 6.03

Note: The maximum score is 42 points.
102

3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

between 1995 and 1997, and the mean 

scores with the preemption reduction decreased from 1995 to 1996 and remained 

the same through 1997. In both cases, 

the difference between the mean scores 

remained stable at 2.96 points for 1996 

through 1999 (figure 3.2). Thus, the 

clean indoor air summary scores were 

continuously affected by preemption 

during the 1990s.

State+Local Clean Indoor Air Ratings

As noted above, for the purpose of the 

ASSIST evaluation, a measure was constructed that could be used to examine the 

combined effect of state+local laws on 

initial and later outcomes. Also, the data for 

this analysis covered 1993 through 1998 

and reflect the state clean indoor air score 

(with preemption) plus the additional local 

score weighted for the percentage of the 

population covered by the local ordinances.

A comparison of the summary 

scores for the state+local clean indoor 

air legislation for 1993 through 1998 

(table 3.5) with the state clean indoor 

air scores with preemption (table 3.4) 

shows that, for the most part, the addition of the local score increased the 

clean indoor air rating over time. The 

addition of the local ratings to the state 

ratings increased the mean total scores 

consistently over time from 0.55 points 

for 1993 to 0.77 points for 1997, but 

the mean scores decreased between 

1997 and 1998 to 0.62 points (tables 

3.4 and 3.5). The variance in the scores 

(as measured by the standard deviation) 

also decreased with the addition of the 

local scores. By the end of 1998, the 

means of the combined scores for states 

with preemption increased by .9 points, 

but the combined scores continued to 

reveal how far both states and localities were from meeting tobacco control 

Figure 3.2. Mean Clean Indoor Air Summary Scores by Year and Preemption Status

Mean clean indoor air summary score

1993 1994 1995 1996 1997 1998

2

4

6

8

10

12

Without preemption

With preemption

Year
103

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

policy targets in restricting exposure to 

environmental tobacco smoke.

Not accounting for other state conditions or factors, the scores for ASSIST 

state state+local clean indoor air scores 

were greater than the scores for nonASSIST states (table 3.6). Across all 

years, minimum and mean scores were 

greater for the ASSIST states than for 

the non-ASSIST states and indicated that 

the ASSIST states may have started out 

with stronger laws. Although the highest

score among the non-ASSIST states was 

greater than for any of the ASSIST states, 

the deviation between the scores within 

the ASSIST group was smaller and possibly indicated that local governments were 

more active in the ASSIST states than in 

the non-ASSIST states. (See chapter 4 for 

a further discussion of the adjustments 

that were made to the state+local scores 

for inclusion in the ASSIST IOI, and for 

a discussion of the significance of the 

scores by ASSIST state status.)

Table 3.5. State+Local Clean Indoor Air Summary Scores by Year, 1993–98

Score 1993 1994 1995 1996 1997 1998

Low 0 0 0 0 0 0

Higha 18.00 19.98 24.10 24.10 24.10 24.10

Mean 7.71 8.08 8.55 8.43 8.48 8.64

SD 5.32 5.19 5.73 5.77 5.80 5.83

Note: The maximum score is 42 points. The state score reflects the state score adjusted for preemption.

a

Maryland was the outlier in all years, due to passage of the Maryland Occupational and Safety Health (MOSH) 

regulation prohibiting workplace smoking, along with the lack of preemption legislation.

Table 3.6. State+Local Summary Scores for Clean Indoor Air for ASSIST and Non-ASSIST States, 

1993–98

Score 1993 1994 1995 1996 1997 1998

Non-ASSIST

Low 0 0 0 0 0 0

High 18.00 18.00 24.10 24.10 24.10 24.10

Mean 6.80 6.95 7.59 7.60 7.66 7.68

SD 5.49 5.05 5.99 5.99 6.04 6.06

ASSIST

Low 1.86 3.57 3.44 3.06 3.06 3.06

High 16.33 19.98 20.18 20.39 20.47 20.31

Mean 9.54 10.32 10.47 10.07 10.13 10.56

SD 4.58 4.83 4.75 5.07 5.06 4.96

Note: The maximum score is 42 points.
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3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

Summary

T

obacco use prevention and control are 

public health issues that have been addressed by federal, state, and local governments through laws and other policy 

instruments, and longitudinal monitoring 

of policy and legislative changes is fundamental to tobacco control. Enactment 

of tobacco control laws, however, establishes only a framework for preventing 

and controlling tobacco use. Enforcement 

of these laws is equally—if not more—

challenging than getting the laws passed.

Tobacco control policies are also accomplished through means other than 

state laws. While there is no comprehensive database for the various inputs 

involved, there are some data that move 

in this direction—for example, data on 

trends in smoking policies for workers 

and occupations due to mandated and 

voluntary actions32 and data on hospitalbased smoking bans.33 In addition, there 

is a need for data on intermediate indicators related to tobacco control policies 

and laws, specifically, changes in knowledge of health consequences and knowledge of codified laws.34

Former U.S. Surgeon General Satcher 

characterized data monitoring and analysis as critical public health tools.20 In 

line with the former surgeon general’s 

global thinking on this issue, a worldwide tracking system of comprehensive 

measures for change in tobacco control 

policy would be an invaluable tool. Current databases that capture state (and 

local) tobacco control legislation in the 

United States can help lay the groundwork for such an effort.

The ASSIST evaluation used data 

from NCI’s SCLD (www.scld-nci.net) 

for state youth access and clean indoor 

air laws. NCI’s SCLD program has 

monitored state tobacco control laws 

since 1993 and makes data available to 

the research and public health communities. In addition, the SCLD Updates Index, a searchable quarterly summary of a 

wide range of cancer-related legislation, 

provides current information on tobacco 

laws. Information on tobacco-related 

state legislation is also available from 

three other sources:

■ The State Tobacco Activities Tracking 

and Evaluation System (STATE; 

http://apps.nccd.cdc.gov/statesystem), 

is a CDC database that monitors state 

tobacco control laws and executive 

orders.

■ State Legislated Actions on Tobacco 

Issues (SLATI) of the American Lung 

Association (www.lungusa.org) is an 

advocacy-based reporting service on 

state tobacco control measures.

■ The Campaign for Tobacco-Free 

Kids (www.tobaccofreekids.org) is a 

nonprofit, nongovernment initiative 

that provides tobacco control-related 

information such as state and federal 

tobacco tax rates, and the current 

status of tobacco-relevant legislation.

A number of state legislatures have 

searchable data on tobacco control laws 

available on their Web sites; however, 

the state legislative information available 

varies greatly by state in terms of the frequency of updating. In addition, the state 

legislatures often note that the material 

provided on the Web site does not reflect 

an “official” version of the law and that 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

it must be obtained from hard copy volumes produced by the legislature.

For legislation on local clean indoor 

air, the ASSIST evaluation used the database of the ANRF (www.no-smoke.

org), a nonprofit organization that has 

tracked local tobacco control ordinances 

and health regulations since 1985. Some 

state and local governments and research organizations, such as the Robert 

Wood Johnson Foundation-supported 

ImpacTeen project (www.impacteen.org) 

at the University of Illinois at Chicago, 

also collect local tobacco control ordinance information for use in research 

and policy efforts. Given the difficulty 

of monitoring legislative changes at the 

local level, developing a coordinated and 

comprehensive approach to collecting 

data will be important.

Uniform data on local tobacco control 

laws are more difficult to collect and 

analyze than state laws, but the complement of state+local data offers considerable potential for refining and specifying 

changes in tobacco control policy nationwide. As local tobacco policy information becomes more readily available, 

new measures will be needed that can 

be used to evaluate the effect of policy 

on initial and later behavioral outcomes. 

Tools such as the IOI can be applied 

more extensively to tobacco control 

measures once more data are available.

Finally, researchers face both the opportunity and the challenge of linking 

data and data analyses of governmental 

action on tobacco use prevention and 

control to other relevant data sets. The 

ASSIST evaluation broke new ground 

in measuring policy outcomes with the 

IOI, which includes state and local clean 

indoor air scores and other variables. Research that builds on available and new 

measurement tools will have important 

benefits for long-term tobacco control. 

Some researchers are already using 

tobacco control policy markers for research in this promising direction.24,30

Conclusions

1. As part of the ASSIST evaluation, a 

measure of legislative changes was 

developed in two areas: youth access to tobacco products and clean 

indoor air. The resulting clean indoor 

air score became a component of the 

Initial Outcomes Index used in the 

overall analysis.

2. The methodology for the measurement of legislative policy change 

involved a rating scale applied to nine 

target criteria within each policy area, 

based on state-level data compiled 

from the National Cancer Institute’s 

State Cancer Legislative Database 

and aggregated local data from the 

American Nonsmokers’ Rights Foundation. Penalty values were applied 

to states with preemption laws, while 

population-adjusted incremental 

values were added in cases where 

stronger local laws existed.

3. Target criteria for youth access to 

tobacco included minimum age, packaging, clerk intervention, photographic identification, vending machines, 

free distribution, graduated penalties, 

random inspections, and statewide 

enforcement. Target criteria for clean 
106

3 . M e a s u r i n g P o l i c y a n d L e g i s l a t i v e C h a n g e s

indoor air included government 

worksites, private worksites, schools, 

childcare facilities, restaurants, retail 

stores, recreational/cultural facilities, 

penalties, and enforcement.

4. During the period of study from 1993 

to 1999, mean summary scores for 

youth access legislation increased 

4.37 points from 7.20 to 11.57 when 

adjusted for preemption, and 7.24 

points from 8.35 to 15.59 without 

this adjustment. Similarly, mean 

summary scores for clean indoor air 

legislation increased 0.86 points from 

7.16 to 8.02 when adjusted for preemption, and 2.27 points from 8.71 to 

10.98 without this adjustment.

5. Beyond the immediate use as outcome metrics within the ASSIST 

evaluation, this effort was a valuable 

test case for the quantitative measurement of legislative policy outcomes 

for a broad range of future tobacco 

use prevention and control issues.

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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

111

Chapter 4. Initial Outcomes Index

Elizabeth A. Gilpin, Anne M. Hartman, and Frances A. Stillman

Contents

Introduction.................................................................................................................. 113

Tobacco Use Outcome Measures .................................................................................. 114

Adult Smoking Prevalence.................................................................................................114

Per Capita Cigarette Consumption ....................................................................................115

Defining and Selecting Initial Outcomes...................................................................... 116

Initial and Intermediate Outcomes Versus Other Forces of Change....................................116

Criteria for Selecting Initial Outcomes in Evaluations of Tobacco Control Efforts..............118

Initial Outcomes Selected for the ASSIST Evaluation.................................................... 119

Cigarette Price...................................................................................................................120

Strength of Clean Indoor Air Legislation ............................................................................122

Smoke-free Workplaces......................................................................................................123

The IOI for Policy........................................................................................................... 129

Construction of the IOI.......................................................................................................129

Correlation of the IOI with Final Outcomes.........................................................................130

Summary...................................................................................................................... 135

Conclusions.................................................................................................................. 137

References................................................................................................................... 138

Tables and Figures

Table 4.1. Real Price of Cigarettes, 1992–99............................................................................121

Figure 4.1. Real Price of Cigarettes Versus Adult Smoking Prevalence, 1992–93 .....................122

Figure 4.2. Real Price of Cigarettes Versus per Capita Cigarette Consumption.........................123

Table 4.2. Legislative Score Including Preemption and Local Adjustment.................................124

Figure 4.3. Legislative Score Versus Adult Smoking Prevalence................................................125

Figure 4.4. Legislative Score Versus per Capita Cigarette Consumption ...................................126

Table 4.3. Percentage of Indoor Workers with Smoke-free Workplaces.......................................127

Figure 4.5. Workplace Smoking Ban Versus Adult Smoking Prevalence.....................................128

Figure 4.6. Workplace Smoking Ban Versus per Capita Cigarette Consumption........................128

Table 4.4. Correlations among Initial Outcomes in 1992–93 and 1998–99 ..............................129

Table 4.5. Initial Outcomes Index..............................................................................................131
4 . I n i t i a l O u t c o m e s I n d e x

112

Figure 4.7. Initial Outcomes Index Versus Adult Smoking Prevalence........................................132

Figure 4.8. Initial Outcomes Index Versus per Capita Cigarette Consumption...........................132

Table 4.6. Correlation of Initial Outcomes with Final Outcomes and Change in Initial 

Outcomes with Change in Final Outcomes................................................................................133

Figure 4.9. Change in Initial Outcomes Index Versus Change in Adult Smoking Prevalence......134

Figure 4.10. Change in Initial Outcomes Index Versus Change in per Capita Cigarette 

Consumption.............................................................................................................................134

Figure 4.11. Trends in per Capita Cigarette Consumption for States in Each Tertile 

Group of Tobacco Control Initial Outcomes Index ......................................................................135
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

113

4. Initial Outcomes Index

Introduction

T

his chapter discusses the process of defining initial outcomes for ASSIST and the 

criteria used to ultimately define the IOI used in the ASSIST evaluation analysis.1

As discussed in more detail in chapter 9, part of this analysis examined the relationship 

between these initial outcomes and other evaluation metrics as well as final outcomes 

such as tobacco prevalence and per capita consumption. This IOI was designed to serve 

as a near-term measure for the effectiveness of ASSIST interventions.

In an effective tobacco control program, it is reasonable to expect that early, or 

initial, outcomes of the interventions would relate to later outcomes, usually defined 

as declines in tobacco use. However, changing the smoking behavior of a population 

does not necessarily occur immediately subsequent to a tobacco control program. For 

Some outcomes of a tobacco control program, such as tobacco use prevalence, 

may become clear only over a period of many years. Therefore, initial and 

intermediate outcome measures, which in turn can be related to final outcomes such 

as smoking prevalence, represent an important tool for program evaluation. This 

chapter discusses criteria for measurement of initial and intermediate outcomes 

from tobacco control programs, the development of a specific metric (the Initial 

Outcomes Index; IOI) for the American Stop Smoking Intervention Study (ASSIST) 

evaluation, and the IOI’s association with final outcomes such as tobacco use 

prevalence and consumption.

The IOI was formed from three initial outcomes, each of which was significantly 

correlated with reduced prevalence and consumption levels at baseline in 1993: 

total cigarette price, a rating of local and state clean indoor air policies, and the 

percentage of workers covered by 100% smoke-free workplaces. The IOI index 

value was formed from equal weightings of these three factors, normalized at 

baseline, because the results of a principal components analysis showed very similar 

loadings.

Over the period of study from 1992 through 1999, 65% of ASSIST states showed 

an increase in IOI greater than the mean for all states, versus only 32% of nonASSIST states. In a comparison of IOI results with final outcomes, IOI showed a 

significant association with prevalence and consumption levels throughout the study 

period, whereas the only IOI factor that showed a significant association between 

changes over time and changes in outcomes was a strong relationship between 

cigarette price and per capita consumption. Nonetheless, the data produced from 

this evaluation serve as an important baseline for future efforts to track initial 

outcomes that relate tobacco control program effects to long-term outcomes.
114

4 . I n i t i a l O u t c o m e s I n d e x

example, increased tobacco taxes are 

associated with decreased experimental 

and established smoking among youth,2

but this effect would not be reflected 

in smoking prevalence rates for several 

years. Therefore, policy makers need 

more proximal measures of program effects to protect and advocate for program 

funding as well as to evaluate program 

progress. This chapter explains the concept and importance of measuring initial 

outcomes and presents criteria for evaluators to consider in defining and selecting those outcomes in their evaluations 

of program effects, followed by a discussion of the specific initial outcomes selected for the ASSIST evaluation.

For ASSIST, the units of observation 

for these outcomes were the 50 states 

of the United States and the District of 

Columbia. Because only 51 units of observation were available for the ASSIST 

evaluation, for statistical purposes it was 

necessary to combine the selected initial outcomes into a single measure, the 

IOI. The methodology for constructing 

the ASSIST IOI and its relation to the 

measures describing the ASSIST final 

outcomes (reduced adult smoking prevalence and lower adult cigarette consumption rates) are described below. Data for 

each initial outcome are also described 

and tabulated.

Tobacco Use Outcome 

Measures

T

he two most widely used and generally accepted indicators of population 

tobacco use are smoking prevalence and 

per capita cigarette consumption.3 These 

measures are used in this chapter and in 

chapter 9, which provides a comprehensive analysis of final outcomes as part of 

the evaluation of the ASSIST intervention. This section gives a brief description 

of the data sources for these measures as 

used in the ASSIST evaluation.

Adult Smoking Prevalence

Adult (18 years old and older) smoking prevalence estimates for each state 

and for the District of Columbia were 

obtained from the Tobacco Use Supplement to the Current Population Survey 

(TUS-CPS).4 The baseline prevalence 

estimates were from the September 

1992, January 1993, and May 1993 

TUS-CPS, and the estimates at the end 

of the intervention period were from the 

September 1998, January 1999, and May 

1999 TUS-CPS.4

The CPS is a nationwide population 

survey (civilian, noninstitutionalized 

population, 15 years old and older) 

conducted continuously by the U.S. 

Bureau of the Census for labor force 

monitoring.5 Briefly, the CPS includes 

a probability sample based on a stratified sampling scheme of clusters of four 

neighboring households identified from 

the most recent decennial census, updated building permits, and other sources. 

All strata are defined within state boundaries, and the sample is allocated among 

the states so that state-specific estimates 

can be computed. National Cancer Institute (NCI) staff developed the TUS, and 

trained Bureau of Census interviewers 

pretested it prior to its implementation.

For the TUS-CPS, all household 

members 15 years old and older are 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

asked to answer two questions that determine their current smoking status: 

(1) whether they have smoked at least 

100 cigarettes in their entire life and (2) 

whether they now smoke cigarettes every 

day, some days, or not at all. To be considered a current smoker, respondents 

must answer yes to the first question and 

every day or some days to the second. If 

one or more household members are not 

present at the time of the initial household interview, they are called later and 

administered the TUS-CPS by telephone. 

Even so, not all household members answer the TUS-CPS. (The response rate 

is between 75% and 85%.) The answers 

on smoking status are obtained from a 

proxy respondent for each household 

member not present at the time of the 

household interview and are replaced 

with self-response data following the 

administration of the TUS-CPS. For the 

present analyses, both proxy and selfreport data on smoking status were used. 

Smoking prevalence was computed as 

the percentage of all adult respondents 

who were identified as current smokers.

Per Capita Cigarette Consumption

Until late 1998 when it was disbanded, the Tobacco Institute compiled 

cigarette sales data monthly in each 

state for federal tax reporting purposes.6

Since then, individuals from the institute 

formed a consulting firm, and now they 

produce the same data as part of the economic consulting firm Orzechowski and 

Walker, with support from the tobacco 

industry.7 These data are reported annually (November 1 to October 31) to 

the Federal Trade Commission, which 

publishes the new data each year along 

with those from past years.

As these data are from wholesale 

warehouse removals, there is considerable variation from one month to the 

next—in particular, the levels of removals in the last month of any quarter are 

strongly correlated with the removals 

in the first month of the next quarter. 

This variation has little to do with actual 

consumption and likely reflects business 

practice. To remove this source of variability, data were combined into twomonth intervals with December/January, 

February/March, and so on treated as 

single intervals. To convert the sales data 

to per capita cigarette consumption, the 

mean number of packs removed from 

warehouses in a given interval was divided by the total population of adults aged 

18 years old and older in each state at 

each bimonthly time point. Annual population totals are available from the U.S. 

Bureau of the Census,8–10 and these were 

interpolated to obtain the population at 

given times (interval midpoints) during 

the year. For the analyses described in 

chapter 9, the bimonthly values were 

analyzed. For this chapter, generally the 

data were aggregated for the periods 

August/September through April/May 

to correspond to the period when adult 

smoking prevalence estimates were obtained from the TUS-CPS.

Although a decline in smoking prevalence is generally reflected in a decline 

in per capita cigarette consumption, the 

opposite may not occur. Current smokers 

could choose to smoke less instead of 

quitting, or new, younger smokers could 

reach adulthood with a lower level of 
116

4 . I n i t i a l O u t c o m e s I n d e x

consumption than the level of consumption of smokers in previous cohorts. 

In these situations, prevalence would 

remain constant, but per capita consumption would decline.

Defining and Selecting Initial 

Outcomes

Once a tobacco control program is under way, early intervention strategies, 

if effective, should begin to foster societal changes that will ultimately result in 

lower smoking prevalence and per capita 

cigarette consumption. These two results 

are the final outcomes of the program, 

and they, in turn, will lead to reductions 

in smoking-related morbidity and mortality, the public health goals of tobacco 

control. Comprehensive tobacco control 

programs with appropriate monetary and 

human resources undertake a variety of 

efforts aimed at influencing the social 

environment, such as the following:

■ Educating the public

■ Advocating for the enactment of new 

legislation or policies

■ Promoting smoking cessation through 

clinics and telephone helplines

■ Advocating for increased enforcement 

of laws restricting smoking or 

restricting sales of tobacco products to 

minors

■ Forming coalitions of advocates to 

conduct tobacco control efforts11,12

Although the effects of each of these 

efforts on smoking prevalence or cigarette consumption may not be known 

for years, early effects or reactions in 

the 0- to 2-year aftermath can be defined 

and tracked. These effects are initial 

outcomes. For example, an initial outcome may be the passage of a law that 

prohibits smoking in public buildings 

or the adoption by businesses of smokefree workplace policies. These initial 

policy outcomes might lead directly 

to the final outcomes. (For example, a 

smoker in a smoke-free workplace might 

smoke less or quit altogether.) However, 

another outcome, an intermediate outcome, such as enforcement of the law or 

policy, might be necessary for the final 

outcome to occur. For example, unless 

clean indoor air policy is enforced, it 

may not have an effect on smoking behavior. Whether an outcome is initial or 

intermediate is usually determined from 

the strategic plan of the tobacco control 

program, which sets forth the specific 

tactics and outcomes expected from 

those tactics.

Initial and Intermediate Outcomes 

Versus Other Forces of Change

It is not always clear whether an 

outcome is an initial or an intermediate 

outcome or whether it is directly attributable to an intervention’s specific 

activities or to a change that is already 

occurring in society. While it is generally easier to link an initial outcome to 

a specific intervention, the link for an 

intermediate outcome may be somewhat 

more difficult to establish. These points 

are illustrated in the examples below.

New legislation pertaining to restricting smoking in the workplace is clearly 

an initial outcome. However, increased 

levels of workers’ reports of workplace 

smoking restrictions, more of an intermediate outcome, might come about 

through means other than just legislation. 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

For instance, some large corporations, 

subject to smoke-free workplace laws in 

some states, extend such policies to cover all of their facilities nationwide. Also, 

smoke-free policies in some locales are 

a result of health department regulation 

rather than legislation. Thus, workers’ 

reports of a 100% smoke-free workplace might be a measure of the reach or 

strength of legislation or regulation, or 

the reports might reflect prevailing and 

expanding social norms.

A little further downstream from the 

initiation of a tobacco control policy, 

and more clearly an intermediate outcome, is the level of report of smoke-free 

homes. Increased population knowledge 

about the dangers of secondhand smoke 

would be the initial outcome result from 

a tobacco control program’s media campaign (intervention) to educate the public 

about the dangers of secondhand smoke. 

Armed with this knowledge, people then 

may increasingly decide to restrict or 

Examples of Initial Outcomes

Examples of initial outcomes for the types of efforts mentioned in this section include the following:

■ Awareness of media campaigns. Media campaigns are one tool that tobacco control programs can 

use to educate the public. Such campaigns have focused on the health risks that cigarette smoke 

poses both to smokers and to nonsmokers, especially children. Some campaigns have been primarily 

informational about the dangers of smoking; others have directly attacked the tobacco industry. A 

measurable initial outcome of such efforts could be the rate of recall that the public has of specific 

media campaigns, assessed through surveys. Another outcome might be the change in the population’s smoking-related knowledge or attitudes.

■ News coverage. Calling journalists’ attention to important issues related to tobacco (e.g., newly 

documented adverse health effects, the benefits of new policy initiatives) is a strategy for encouraging the public and policy makers to support the enactment of tobacco control policies. A measurable 

initial outcome of these media advocacy efforts could be the number of published news stories and 

editorials that present the issue favorably to tobacco control. With resultant increased public support, 

an intermediate outcome might be passing legislation that has been associated with quitting smoking 

(e.g., clean indoor air laws) or better enforcement of current legislation.

■ Use of cessation assistance. Many comprehensive programs offer smoking cessation assistance to 

smokers through local programs or statewide telephone helplines. A measurable initial outcome of 

these programs could be the number of individuals attending the cessation programs or calling the 

helpline.

■ Enforcement of laws forbidding sales to minors. At the time a tobacco prevention and control program is implemented, a law might already be on the books that prohibits sales of tobacco to minors, 

but it is not being enforced. A strategy of the program might be to increase enforcement by working 

with the local agency that has responsibility for enforcement or to increase compliance by educating 

merchants about the law and the benefits of enforcing the law. A measure of the initial outcome of 

these efforts could be the results of sales checks in which minors attempt to buy cigarettes.

■ Formation of coalitions. As soon as possible in the start-up of a community-based program, all 

members of the community—usually represented by organizations—who would have an interest in 

preventing and controlling tobacco use should be invited and involved in planning and conducting 

the strategies. The initial outcome is the formation of a coalition, which can be measured by the 

number of members and the type of community representation in the coalition.
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4 . I n i t i a l O u t c o m e s I n d e x

ban smoking altogether in their homes. 

However, tracking that decision to an intervention is not necessarily straightforward; other program elements, including 

workplace smoking restrictions, may 

have influenced or facilitated the decision to have a smoke-free home.

Thus, in selecting outcomes to measure, it is important that program planners and evaluators at least in theory be 

able to relate the activities of the intervention to the outcome to be measured 

and recognize that other factors might 

also bring about the initial or intermediate outcome of interest.

Criteria for Selecting Initial Outcomes 

in Evaluations of Tobacco Control 

Efforts

The four criteria for selecting initial 

outcomes to include in an evaluation are 

the following:

1. The outcome must be consistently 

measurable across all units of the 

evaluation and over time. While 

states are a logical unit for evaluation, a local government may also 

want to track its progress over time 

and institute surveillance systems for 

this purpose. Evaluating a particular 

tobacco control program over time, or 

comparing different tobacco control 

programs (e.g., states) at a given point 

in time, requires standardized means 

of collecting information and constructing the appropriate measures. If 

the nature of the measure (e.g., how 

data are gathered or reduced) changes 

even slightly, it will be impossible 

to determine whether any changes in 

the resulting values are from actual 

changes in the population or are simply an artifact of the new measure. 

One mechanism for gathering consistent and comprehensive data is a 

national population survey. With standard sampling plans and methodologies and a standard set of questions, 

population surveys can serve as a surveillance system for initial, intermediate, and final outcomes expected from 

a tobacco control program.

2. There must be sufficient variability in 

the measure across the units of observation and/or over time. If little variability exists, it would not be possible 

to meaningfully rank the units according to the level of the particular initial 

outcome. And if all units changed in 

lockstep, there would be no basis for 

comparing trends over time.

3. The initial outcome should be a potentially caused result of an intervention undertaken as part of a tobacco 

control program. For example, cigarette prices will increase if the legislature passes a new excise tax. Sales 

of cigarettes to minors should decline 

if enforcement of youth access laws 

is increased. In these examples, 

cigarette price and test-buy data are 

the initial outcomes. It is not always 

possible a priori to know how the initial outcomes will change over time. 

Change may occur so slowly that 

there is little to analyze, or so rapidly 

and in lockstep that attribution to the 

intervention is difficult. Evaluators 

should learn from the experiences of 

others regarding which initial outcomes might provide the best indicator of an intervention’s effect.

4. Either preexisting evidence should 

indicate an association, or in its 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

absence there should be at least a 

potential link between the initial outcome and the ultimate outcomes. As 

an example, the level of media attention given to tobacco-related issues 

should raise public awareness and 

knowledge as well as influence policy 

makers. This in turn could influence 

social norms, which ultimately may 

discourage smoking initiation and 

increase smoking cessation, leading 

to reduced per capita cigarette consumption and smoking prevalence. 

However, the level of media attention 

may not actually correlate significantly with the level of per capita 

cigarette consumption or smoking 

prevalence until a number of years 

have passed. A lag would be expected 

between the initial outcome and its 

effect on smoking behavior.

Initial Outcomes Selected for 

the ASSIST Evaluation

F

or the evaluation of ASSIST, three 

initial outcomes were selected: (1) 

cigarette price (including tax), (2) rating of local and state clean indoor air 

policies, and (3) percentage of workers 

covered by 100% smoke-free workplaces. The discussion in this section 

focuses on the rationale for selecting the 

initial outcomes for the ASSIST evaluation, variability of the initial outcomes 

across the states (the unit of measure for 

the ASSIST evaluation), and the relation of the initial outcomes to the final 

outcomes. In addition to the criteria 

for selecting a useful initial outcome 

(described in the preceding section), 

the ASSIST evaluation required that 

baseline data (just prior to the onset of 

the program) for each initial outcome 

measured be available at several points 

through the end of the program.

A central component of the ASSIST 

model is the use of policy to change 

physical environments and influence 

social norms that in turn help shape 

health-related behavior. To achieve these 

objectives, the ASSIST program guidelines required the states to implement 

interventions in four policy areas: clean 

indoor air, restricted tobacco advertising 

and promotion, reduced access to tobacco products by minors, and price 

ASSIST Policy Areas

Following the ASSIST framework, the 17 

ASSIST states promoted interventions in four 

policy areas, expressed as objectives in the 

“ASSIST Program Guidelines for TobaccoFree Communities”:

Eliminate environmental tobacco smoke in 

all areas where others may face involuntary 

exposure and the serious health risks associated with inhalation of other people’s 

tobacco smoke.

Eliminate all tobacco product advertising and promotion, other than point-ofsale and objective product information 

advertising.

Reduce access to and availability of tobacco products, particularly to persons under 

the legal age of purchase.

Reduce consumption of cigarettes and other tobacco products through price increases 

using increased taxes and other costs imposed on tobacco products.

Source: ASSIST Coordinating Center. 1991. 

Overview. ASSIST program guidelines 

for tobacco-free communities. Internal 

document, ASSIST Coordinating Center, 

Rockville, MD (p. 12).
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4 . I n i t i a l O u t c o m e s I n d e x

increases of tobacco products. The initial 

outcomes measured in the evaluation 

derive from these policy areas and interventions. However, because the actual 

initial outcomes to be monitored were 

not selected until after the ASSIST intervention began, appropriate data related 

to restriction of advertising and promotions and to reduced access to tobacco 

products were not included as part of the 

data collection at baseline. Thus, initial 

outcomes related to these two interventions could not be assessed.

Cigarette Price

The cost of a pack of cigarettes to 

the consumer reflects the selling price, 

federal and state excise taxes, and state 

and local sales taxes. Research has established that smokers are sensitive to 

the cost of cigarettes; smokers change 

the amount they purchase in accordance 

with the price they have to pay. Studies consistently estimate the adult price 

elasticity of demand13 to be about –0.4. 

This means that for every 10% increase 

in cigarette prices, demand for cigarettes 

should fall by 4%.14,15

While the amount of state excise tax 

could also be considered an initial outcome, the ASSIST evaluation uses the 

total price of a pack of cigarettes because 

the tobacco industry sometimes temporarily changes the price of cigarettes to 

counteract a tax increase. It is the total 

cost of cigarettes to the consumer that 

affects consumption; therefore, price, not 

tax, was the initial outcome selected for 

the evaluation.

Because the ASSIST evaluation examined trends over time, the average 

cigarette price for each state and for each 

year was adjusted to the baseline period 

using the consumer price index. The adjusted prices are referred to as the “real” 

prices of cigarettes in each year.

Table 4.1 shows the real cigarette 

prices for each state from baseline to 

the end of the ASSIST program (as of 

November of each year). Considerable 

variability is apparent in the real price of 

cigarettes among the states in any given 

year. At baseline (1992–93), the price 

of cigarettes ranged from $1.53/pack in 

Kentucky to $2.32/pack in Hawaii. Over 

the course of ASSIST, many states raised 

their tobacco excise taxes, and the tobacco industry raised cigarette prices in 

1998 after the Master Settlement Agreement was final. Thus, the average price 

per pack over all states increased from 

$1.90/pack at baseline to $2.15/pack at 

the end of the program (1998–99). The 

pack price decreased slightly over the 

ASSIST period in Minnesota but increased by $0.80 in Alaska.

The relationship between the real 

price of cigarettes at baseline and adult 

smoking prevalence16 for each state is 

shown in figure 4.1 (r = –.39, p < .01). 

In this figure and in subsequent figures, 

it should be noted that values at the extremes can influence the magnitude of 

the correlation coefficients. However, 

even after omitting extreme values, the 

weaker correlations were still statistically different from zero (p < .05).

Figure 4.2 shows the correlations at 

baseline between real cigarette price and 

adult per capita cigarette consumption. 

The values for per capita cigarette 

consumption are the average of the 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

121

Table 4.1. Real Price of Cigarettes, 1992–99

(Shading indicates ASSIST states.)

State 1992–93 1993 1994 1995 1996 1997 1998 1998–99 Change

AK 2.161 1.963 1.918 1.969 1.929 2.617 2.845 2.963 0.802

AL 1.800 1.570 1.534 1.522 1.569 1.661 1.768 1.898 0.098

AR 1.892 1.731 1.670 1.622 1.703 1.730 1.883 2.002 0.110

AZ 1.887 1.662 1.628 2.019 2.096 2.132 2.284 2.394 0.507

CA 2.183 1.997 1.992 1.963 1.928 1.963 2.092 2.539 0.356

CO 1.661 1.497 1.564 1.571 1.569 1.637 1.773 1.901 0.240

CT 2.162 2.127 2.071 2.023 1.993 2.035 2.175 2.309 0.147

DC 2.233 2.210 2.237 2.281 2.190 2.207 2.330 2.448 0.215

DE 1.812 1.488 1.531 1.567 1.546 1.579 1.722 1.852 0.040

FL 2.018 1.775 1.746 1.768 1.742 1.787 1.985 2.104 0.086

GA 1.693 1.493 1.513 1.493 1.474 1.531 1.684 1.799 0.106

HI 2.318 2.190 2.263 2.224 2.265 2.382 2.724 2.843 0.525

IA 1.963 1.763 1.791 1.786 1.793 1.796 1.945 2.069 0.106

ID 1.767 1.549 1.634 1.719 1.740 1.728 1.916 2.029 0.262

IL 1.962 1.887 1.869 1.859 1.895 1.897 2.218 2.341 0.379

IN 1.646 1.511 1.470 1.443 1.478 1.540 1.728 1.851 0.205

KS 1.813 1.638 1.655 1.628 1.613 1.703 1.884 1.993 0.180

KY 1.526 1.376 1.360 1.369 1.391 1.451 1.651 1.758 0.232

LA 1.843 1.607 1.551 1.596 1.564 1.700 1.876 2.002 0.159

MA 2.053 1.963 2.054 2.016 2.308 2.392 2.572 2.689 0.636

MD 1.998 1.782 1.765 1.731 1.805 1.827 2.001 2.111 0.113

ME 2.025 1.850 1.866 1.834 1.810 2.050 2.456 2.543 0.518

MI 1.886 1.665 2.260 2.254 2.229 2.273 2.408 2.531 0.645

MN 2.263 2.044 2.050 2.053 2.076 2.062 2.105 2.243 –0.020

MO 1.625 1.493 1.489 1.545 1.533 1.593 1.780 1.884 0.259

MS 1.863 1.641 1.571 1.595 1.625 1.655 1.854 1.979 0.116

MT 1.746 1.479 1.480 1.453 1.483 1.546 1.704 1.808 0.062

NC 1.571 1.377 1.413 1.405 1.421 1.539 1.691 1.808 0.237

ND 1.868 1.868 1.815 1.795 1.854 1.914 2.054 2.187 0.319

NE 1.842 1.746 1.712 1.709 1.743 1.803 1.950 2.074 0.232

NH 1.769 1.542 1.561 1.562 1.589 1.736 1.935 2.057 0.288

NJ 2.137 1.905 1.903 1.911 1.858 1.950 2.511 2.624 0.487

NM 1.778 1.626 1.634 1.657 1.664 1.699 1.839 1.977 0.199

NV 2.099 1.987 1.937 1.927 1.903 1.882 2.082 2.222 0.123

NY 2.106 2.108 2.096 2.059 2.082 2.100 2.289 2.411 0.305

OH 1.726 1.556 1.558 1.554 1.572 1.603 1.764 1.886 0.160

OK 1.875 1.665 1.601 1.572 1.619 1.688 1.844 1.963 0.088

OR 1.887 1.744 1.806 1.768 1.778 2.063 2.243 2.346 0.459

PA 1.904 1.643 1.635 1.662 1.687 1.779 1.884 2.021 0.117

RI 2.049 1.768 2.130 2.130 2.087 2.187 2.342 2.462 0.413

SC 1.681 1.428 1.400 1.450 1.456 1.527 1.646 1.772 0.091

SD 1.825 1.568 1.601 1.651 1.697 1.769 1.875 1.989 0.164

TN 1.812 1.606 1.581 1.565 1.566 1.601 1.784 1.908 0.096

TX 2.120 1.864 1.876 1.857 1.815 1.893 2.075 2.189 0.069

UT 1.860 1.656 1.684 1.695 1.756 2.059 2.220 2.333 0.473

VA 1.725 1.598 1.567 1.544 1.499 1.491 1.665 1.784 0.059

VT 1.802 1.638 1.601 1.912 1.906 1.982 2.133 2.245 0.443

WA 2.167 2.285 2.294 2.435 2.538 2.561 2.654 2.780 0.613

WI 2.027 1.834 1.876 1.950 1.895 1.946 2.218 2.331 0.304

WV 1.739 1.555 1.579 1.541 1.537 1.605 1.775 1.874 0.135

WY 1.638 1.441 1.457 1.506 1.476 1.487 1.646 1.775 0.137

Overall

Mean 1.898 1.725 1.742 1.759 1.771 1.850 2.029 2.155 0.257

SD 0.191 0.227 0.248 0.252 0.260 0.283 0.305 0.309 0.187

ASSIST

Mean 1.890 1.736 1.803 1.812 1.826 1.891 2.085 2.204 0.313

SD 0.224 0.262 0.306 0.317 0.344 0.340 0.363 0.361 0.206

Non-ASSIST

Mean 1.902 1.719 1.712 1.733 1.744 1.829 2.001 2.130 0.229

SD 0.176 0.211 0.212 0.214 0.206 0.253 0.273 0.281 0.173

Source: Orzechowski, W., and R. C. Walker. 2000. The tax burden on tobacco: Historical compilation 1999. Monthly 

state cigarette tax reports, Vol. 34. Arlington, VA: Orzechowski & Walker. 
122

4 . I n i t i a l O u t c o m e s I n d e x

bimonthly values for the 10-month period from August/September 1992 through 

April/May 1993, which correspond to 

the same months for which prevalence 

was computed from the TUS-CPS. The 

correlation between the real price of 

cigarettes and per capita cigarette consumption was –.67 (p < .0001).

Strength of Clean Indoor Air 

Legislation

Public health concerns about the 

harmful effects of secondhand smoke and 

about the right of nonsmokers to breathe 

clean air have translated into community ordinances and state or local laws 

to protect nonsmokers.17–20 Restrictions 

on smoking protect nonsmokers from 

secondhand smoke in government and 

private worksites, schools, childcare facilities, restaurants, retail stores, and recreational and cultural facilities.

The strength of the legislation is a 

score that reflects both the strictness 

and the coverage of clean air ordinances 

within each state and should reflect the 

success of advocacy efforts within each 

state. The score includes a preemption 

penalty and a further adjustment for local ordinance strength. (See chapter 3.) 

Table 4.2 presents the legislative scores 

for each state for each year from 1993 to 

1998. The maximum possible score for 

a state with top ratings across the board 

is 42. (See chapter 3 for a more thorough 

explanation of how the legislative scores 

were obtained and adjusted for strength 

of local laws and preemption.) In 1993, 

Figure 4.1. Real Price of Cigarettes Versus Adult Smoking Prevalence, 1992–93

Prevalence (%)

1.50 1.75 2.00 2.25 2.50

5

10

15

20

25

30

35

Real price ($)

r = -.39

Note: Smoking prevalence data are from U.S. Department of Commerce, Bureau of the Census. September 

1992, January 1993, May 1993. Tobacco Use Supplement to the Current Population Survey. Washington, DC: 

U.S. Department of Commerce.
123

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

the legislative scores ranged from a low 

of zero for Iowa, Mississippi, and Oklahoma to a high of 18 for Alaska. Iowa 

and Mississippi remained at zero through 

1998 and were joined by Kentucky in 

1994. Because of preemptive laws, some 

state scores declined over the period, 

others remained the same because no 

new legislation was enacted, and some 

improved. For instance, California declined by more than 8 points after preemption, whereas Maryland increased 

its score by more than 19 points. The 

mean clean indoor air legislative score 

did, however, increase slightly over time, 

from 7.71 points in 1993 to 8.64 points 

in 1998.

Figures 4.3 and 4.4 show the correlations of this legislative score with adult 

smoking prevalence (r = –.37, p < .01) 

and per capita cigarette consumption 

(r = –.30, p < .05), respectively, at baseline in 1993.

Smoke-free Workplaces

Considerable evidence indicates 

that smoking restrictions lead to smokers modifying their smoking behavior 

by reducing consumption or quitting 

altogether.21–27 The inconveniences that 

smokers experience in having to leave 

their work areas to smoke, combined 

with their awareness of the smoke-free 

social norm, are incentives for them to 

reduce or quit smoking. Accordingly, 

the percentage of indoor workers reporting that their workplace is completely 

smoke-free is an important early outcome 

Figure 4.2. Real Price of Cigarettes Versus per Capita Cigarette Consumption

1.50 1.75 2.00 2.25 2.50

2

4

6

8

10

12

14

16

18

20

Consumption (packs/month)

Real price ($)

r = -.67

Note: Per capita cigarette consumption data are from U.S. Department of Commerce, Bureau of the Census. 

September 1992, January 1993, May 1993. Tobacco Use Supplement to the Current Population Survey.

Washington, DC: U.S. Department of Commerce.
4 . I n i t i a l O u t c o m e s I n d e x

124

Table 4.2. Legislative Score Including Preemption and Local Adjustment

(Shading indicates ASSIST states.)

State 1993 1994 1995 1996 1997 1998 Change

AK 18.00 18.00 18.00 18.00 18.00 18.07 0.07

AL 1.48 1.48 1.53 1.53 1.53 1.53 0.05

AR 5.03 5.03 5.03 5.03 5.03 5.03 0.00

AZ 7.67 7.89 7.89 8.35 8.37 8.37 0.70

CA 15.62 6.93 6.93 6.96 6.97 6.98 –8.64

CO 5.28 6.43 6.24 6.24 6.24 6.24 0.96

CT 2.00 2.00 2.00 2.00 2.00 2.00 0.00

DC 12.00 12.00 12.00 12.00 12.00 12.00 0.00

DE 0.83 5.37 5.08 5.08 5.08 5.08 4.25

FL 7.00 7.00 7.00 7.00 7.00 7.00 0.00

GA 2.82 7.04 7.18 7.20 7.20 7.17 4.35

HI 12.42 12.06 12.06 12.06 13.67 14.26 1.84

IA 0.00 0.00 0.00 0.00 0.00 0.00 0.00

ID 9.00 9.00 9.00 9.00 9.00 9.00 0.00

IL 3.94 3.94 6.94 6.92 6.92 6.92 2.98

IN 5.12 5.12 5.18 5.18 5.18 9.23 4.11

KS 13.27 14.47 14.47 14.47 14.47 14.47 1.20

KY 3.00 0.00 0.00 0.00 0.00 0.00 –3.00

LA 2.99 7.83 8.11 8.11 8.11 8.11 5.12

MA 11.17 11.89 12.49 12.64 12.82 13.01 1.84

MD 4.91 4.94 24.10 24.10 24.10 24.10 19.19

ME 9.00 9.00 9.00 9.00 9.00 9.10 0.10

MI 16.33 16.33 16.34 16.34 16.35 16.35 0.02

MN 13.01 13.01 13.00 13.00 13.00 13.00 –0.01

MO 14.49 14.49 14.49 14.49 14.49 14.49 0.00

MS 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MT 12.00 12.00 12.00 12.00 12.00 12.00 0.00

NC 3.41 3.57 3.44 3.44 3.44 3.44 0.03

ND 14.00 14.00 14.00 14.00 14.00 14.00 0.00

NE 9.00 9.00 9.00 9.00 9.00 9.00 0.00

NH 15.00 15.00 15.00 15.00 15.00 15.00 0.00

NJ 11.02 11.02 11.02 11.04 11.05 14.06 3.04

NM 6.49 6.54 6.72 6.72 6.85 6.85 0.36

NV 4.00 4.00 4.00 4.00 4.00 4.00 0.00

NY 16.33 19.98 20.18 20.39 20.47 20.31 3.98

OH 7.29 10.28 10.28 10.28 10.32 10.32 3.03

OK 0.00 1.00 1.00 1.00 1.00 1.00 1.00

OR 9.06 9.06 9.06 9.06 9.26 9.26 0.20

PA 0.13 0.13 0.13 0.13 0.13 0.13 0.00

RI 13.00 13.00 13.00 13.00 13.03 13.05 0.05

SC 9.04 11.04 11.04 3.06 3.06 3.06 –5.98

SD 4.08 5.08 0.31 0.31 0.31 0.31 –3.77

TN 0.47 0.47 0.47 0.47 0.47 0.47 0.00

TX 7.12 7.45 7.47 7.47 7.56 7.58 0.46

UT 17.00 14.00 14.00 14.00 14.00 14.00 –3.00

VA 1.86 3.86 3.86 3.86 3.86 3.86 2.00

VT 9.13 9.13 13.13 13.13 13.13 13.13 4.00

WA 11.16 11.16 11.16 11.16 11.16 11.16 0.00

WI 12.04 15.04 15.04 15.04 15.04 15.04 3.00

WV 3.41 4.00 5.76 6.62 7.10 7.28 3.87

WY 0.84 0.84 0.84 0.84 0.84 0.84 0.00

Overall

Mean 7.71 8.08 8.55 8.43 8.48 8.64 0.93

SD 5.32 5.19 5.73 5.77 5.80 5.83 3.59

ASSIST

Mean 9.54 10.32 10.47 10.07 10.13 10.56 1.02

SD 4.58 4.83 4.75 5.07 5.06 4.96 2.40

Non-ASSIST

Mean 6.80 6.95 7.59 7.60 7.66 7.68 0.88

SD 5.49 5.05 5.99 5.99 6.04 6.06 4.09
125

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

and reflects to some degree the strength 

and scope of clean indoor air legislation. 

Because some private workplaces have 

a smoke-free policy even though there 

is no law requiring it, and because some 

workplaces may not comply with state or 

local laws requiring no-smoking restrictions in public or work areas, workers’ 

reports of the policies provide additional 

relevant information beyond that captured in the legislative score.

The TUS-CPS included questions 

to identify indoor workers and to assess the level of smoking restrictions 

in the workplace. The supplements 

asked, “Which of these best describes 

the area in which you work most of the 

time?”28(p9-15) A response indicating that 

the person worked indoors and outside a 

home and was not self-employed led to 

a further question: “Does your place of 

work have an official policy that restricts 

smoking in any way?”28(p9-16) Those 

persons who answered “yes” were then 

asked, “Which of these best describes 

your place of work’s smoking policy for 

indoor public or common areas?”28(p9-16)

and “Which of these best describes your 

place of work’s smoking policy for work 

areas?”28(p9-17) Those persons who reported that smoking was not allowed in 

any work areas and in any public and 

common areas were considered to have a 

smoke-free workplace.

Table 4.3 presents the results for the 

percentage of indoor workers reporting 

smoke-free workplaces from baseline 

to the end of ASSIST. The values in the 

Figure 4.3. Legislative Score Versus Adult Smoking Prevalence

0 5 10 15 20

5

10

15

20

25

30

35

Prevalence (%)

Legislative score

r = -.37

0

Note: Per capita cigarette consumption data are from U.S. Department of Commerce, Bureau of the Census. 

September 1992, January 1993, May 1993. Tobacco Use Supplement to the Current Population Survey. 

Washington, DC: U.S. Department of Commerce.
126

4 . I n i t i a l O u t c o m e s I n d e x

table were interpolated/extrapolated 

from the results of the 1992–93, 1995–

96, and 1998–1999 TUS-CPS. (For example, the slope of the line between the 

1992–93 and 1995–96 values was used 

to determine how much to add to the 

1992–93 value to obtain values for 1994 

and 1995.) Again, there was considerable variability in this measure among 

the states at baseline: Kentucky and 

Washington State were the states with 

the lowest percentage (29.1%) and highest percentage (66.8%), respectively, of 

indoor workers covered by smoke-free 

workplace policies. All states improved 

over time, with the mean percentage 

of indoor workers protected increasing 

from 46.4% at baseline to 68.5% at the 

end of ASSIST. The state showing the 

least improvement was Oregon, which 

started at a relatively high level in 1993, 

and the state improving the most was 

Arkansas, which started at a relatively 

low level.

The correlations between percentage of workers reporting a smoke-free 

workplace and smoking prevalence (r = 

–.57, p < .0001) and per capita cigarette 

consumption (r = –.51, p < .0001) at 

baseline, respectively, are shown in figures 4.5 and 4.6.

Figure 4.4. Legislative Score Versus per Capita Cigarette Consumption

0 5 10 15 20

2

4

6

8

10

12

14

Consumption (packs/month)

Legislative score

r = -.30

16

18

20

Note: Per capita cigarette consumption data are from U.S. Department of Commerce, Bureau of the Census. 

September 1992, January 1993, May 1993. Tobacco Use Supplement to the Current Population Survey. 

Washington, DC: U.S.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

127

Table 4.3. Percentage of Indoor Workers with Smoke-free Workplaces

(Shading indicates ASSIST states.)

State 1992–93 1993 1994 1995 1996 1997 1998 1998–99 Change

AK 57.4 60.7 64.7 68.7 70.3 71.5 72.7 72.9 15.5

AL 38.2 43.0 48.8 54.5 57.9 60.9 63.8 64.3 26.1

AR 31.9 36.5 42.0 47.5 52.6 57.6 62.5 63.4 31.5

AZ 56.2 58.4 61.1 63.8 65.4 66.8 68.2 68.5 12.3

CA 57.5 62.4 68.3 74.1 75.6 76.3 77.0 77.1 19.6

CO 53.4 58.3 64.2 70.1 71.6 72.1 72.6 72.6 19.2

CT 47.5 52.8 59.2 65.6 68.6 71.0 73.4 73.8 26.3

DC 51.1 57.5 65.2 72.9 74.1 74.1 74.0 74.0 22.9

DE 50.1 54.4 59.4 64.5 66.9 68.7 70.5 70.8 20.7

FL 52.8 56.5 60.8 65.1 66.7 67.8 68.8 69.0 16.2

GA 47.4 50.1 53.3 56.5 59.8 63.1 66.3 66.9 19.5

HI 46.5 50.6 55.4 60.2 63.9 67.4 70.8 71.4 24.9

IA 45.2 49.8 55.3 60.8 64.0 66.9 69.7 70.2 25.0

ID 59.2 62.3 66.1 69.8 70.7 71.0 71.2 71.3 12.1

IL 39.8 45.5 52.4 59.3 62.4 64.9 67.3 67.7 27.9

IN 34.7 39.4 45.0 50.6 53.5 55.8 58.0 58.4 23.7

KS 49.3 53.1 57.6 62.1 65.8 69.4 73.0 73.6 24.3

KY 29.1 34.9 41.8 48.7 51.5 53.5 55.5 55.9 26.8

LA 39.3 44.0 49.7 55.4 58.5 61.0 63.6 64.0 24.7

MA 48.1 54.3 61.8 69.3 72.3 74.4 76.5 76.9 28.8

MD 52.1 60.5 70.6 80.7 82.2 82.0 81.7 81.7 29.6

ME 54.8 59.7 65.6 71.5 73.3 74.3 75.2 75.4 20.6

MI 39.5 43.3 47.8 52.4 55.4 58.1 60.8 61.3 21.8

MN 54.4 58.2 62.7 67.1 69.6 71.6 73.6 73.9 19.5

MO 38.9 44.3 50.8 57.3 60.3 62.7 65.0 65.4 26.5

MS 39.9 43.7 48.2 52.8 55.9 58.7 61.4 61.9 22.0

MT 42.9 46.9 51.8 56.7 60.6 64.3 68.0 68.6 25.7

NC 31.3 37.9 45.8 53.7 56.7 58.7 60.8 61.1 29.8

ND 47.0 50.9 55.5 60.1 62.5 64.5 66.5 66.8 19.8

NE 44.0 49.3 55.6 62.0 64.3 65.9 67.5 67.7 23.7

NH 52.5 57.9 64.3 70.8 72.6 73.5 74.3 74.5 22.0

NJ 46.8 52.5 59.4 66.3 68.7 70.2 71.7 72.0 25.2

NM 55.4 58.1 61.3 64.5 65.8 66.7 67.6 67.7 12.3

NV 33.5 35.2 37.3 39.4 42.2 45.2 48.2 48.7 15.2

NY 41.8 48.1 55.6 63.1 66.6 69.3 71.9 72.4 30.6

OH 37.8 43.1 49.3 55.6 58.6 60.9 63.2 63.6 25.8

OK 41.5 46.1 51.7 57.2 60.6 63.6 66.6 67.1 25.6

OR 59.3 61.3 63.6 66.0 66.5 66.6 66.6 66.7 7.4

PA 42.2 47.1 52.9 58.7 62.2 65.2 68.3 68.8 26.6

RI 44.8 51.6 59.7 67.9 70.0 70.9 71.8 71.9 27.1

SC 37.5 43.3 50.3 57.3 60.0 61.8 63.6 63.9 26.4

SD 43.5 48.7 54.9 61.0 61.7 61.3 60.9 60.9 17.4

TN 35.3 40.4 46.4 52.5 56.1 59.2 62.3 62.8 27.5

TX 50.6 54.4 59.0 63.5 64.8 65.4 66.0 66.1 15.5

UT 65.0 70.1 76.2 82.3 83.5 83.8 84.0 84.0 19.0

VA 43.2 48.4 54.6 60.8 64.3 67.1 70.0 70.5 27.3

VT 58.0 63.5 70.1 76.8 77.7 77.5 77.3 77.2 19.2

WA 66.8 68.3 70.0 71.7 72.6 73.4 74.1 74.2 7.4

WI 43.9 48.8 54.8 60.8 62.6 63.5 64.5 64.7 20.8

WV 39.1 44.8 51.6 58.5 60.7 62.0 63.4 63.6 24.5

WY 47.6 51.3 55.7 60.1 62.3 64.1 65.8 66.1 18.5

Overall

Mean 46.4 51.0 56.6 62.1 64.6 66.4 68.2 68.5 22.1

SD 8.71 8.37 8.24 8.42 7.79 7.06 6.48 6.39 5.70

ASSIST

Mean 45.6 50.5 56.5 62.5 64.9 66.6 68.3 68.6 23.0

SD 9.07 8.17 7.32 6.78 6.38 6.03 5.73 5.69 6.11

Non-ASSIST

Mean 46.8 51.3 56.6 61.9 64.4 66.3 68.1 68.5 21.7

SD 8.65 8.58 8.77 9.22 8.50 7.63 6.91 6.79 5.52

Source: U.S. Department of Commerce, Bureau of the Census. September 1992, January 1993, May 1993; September 1995, January 

1996, May 1996; September 1998, January 1999, and May 1999. Tobacco Use Supplement to the Current Population Survey. 

Washington, DC: U.S. Bureau of the Census. 
4 . I n i t i a l O u t c o m e s I n d e x

128

Figure 4.6. Workplace Smoking Ban Versus per Capita Cigarette Consumption

30 40 50 60 70

Consumption (packs/month)

Indoor workers with ban (%)

r = -.51

2

4

6

8

10

12

14

16

18

20

Source: National Cancer Institute. 2005. What is the TUS-CPS? http://riskfactor.cancer.gov/studies/tus-cps.

Figure 4.5. Workplace Smoking Ban Versus Adult Smoking Prevalence

30 40 50 60 70

5

10

15

20

25

Prevalence (%)

Indoor workers with ban (%)

r = -.57

30

35

Source: National Cancer Institute. 2005. What is the TUS-CPS? http://riskfactor.cancer.gov/studies/tus-cps.
129

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

The IOI for Policy

Construction of the IOI

Each of the three initial outcomes chosen for the ASSIST evaluation was measured consistently among the states and 

over time, varied considerably among 

the states, was conceptually related to 

the program interventions, and was significantly correlated with adult smoking 

prevalence and per capita cigarette consumption. Because the state is the unit of 

analysis for the ASSIST evaluation, with 

only 51 units of observation and a number of other variables to consider (see 

chapter 9), all three outcomes could not 

be included separately in the statistical 

analyses of the final outcomes (smoking prevalence and per capita cigarette 

consumption). Thus, the individual initial 

outcomes were combined into an index.

For individual outcomes to be combined into an index, there should be 

some indication of a consistent relationship or correlation among those outcomes; as one changes, the others should 

also change. Table 4.4 shows the intercorrelations among the initial outcomes 

for 1993 and 1998. The correlations 

were stronger among the initial outcomes in 1998 than they were in 1993. 

Although the purpose of constructing 

the IOI for the ASSIST analysis was to 

economize on the number of factors that 

would need to be considered in the final 

analysis (see chapter 9), another reason 

to combine factors would be to avoid 

multicollinearity. A well-constructed 

IOI could be useful in evaluating total 

program effect if it comprised initial outcomes related to each program component. The correlations among the initial 

outcomes in 1998–99 are strong enough 

that the use of the index for the ASSIST 

evaluation is warranted.

Creating an index implies combining 

the individual initial outcomes, either by 

summing them directly or by weighting 

them in some way. One way of determining appropriate weighting factors is 

from a principal components analysis of 

the standardized variables. Because the 

first principal component is the linear 

combination of the variables (initial 

outcomes), which explains the greatest 

portion of the variance, it provides an alternative to equal weighting of the three 

initial outcomes. However, preliminary 

results indicated that the weights for 

the three variables (on the first principal 

component) were very nearly equal. 

Thus, it was decided to simply sum the 

individual initial outcomes.

Table 4.4. Correlations among Initial Outcomes in 1992–93 and 1998–99

Initial outcome

Correlation

1992–93 1998–99

Cigarette price vs. legislative score .374* .525***

Cigarette price vs. smoke-free workplace .385* .442*

Legislative score vs. smoke-free workplacea .427* .503**

a

1993 and 1998 data.

*p < .01. **p < .001. ***p < .0001.
130

4 . I n i t i a l O u t c o m e s I n d e x

Since the unit of measurement for each 

initial outcome was different, the values 

for each initial outcome at baseline were 

standardized (mean = 0, standard deviation = 1), and the resulting z scores were 

summed to obtain the IOI at baseline. 

Subsequent values for each year from 

1993 through 1998 were obtained by subtracting each state’s score in a given year 

from the baseline mean and dividing by 

the baseline standard deviation to obtain 

a normalized difference score with the 

baseline value as the reference. The normalized z scores for each initial outcome 

were added to obtain the index values for 

1993 through 1998. Table 4.5 shows the 

IOI value for each of the states in each 

year. At baseline, the IOI values ranged 

from a low of –4.81 in Kentucky to a 

high of 4.57 in Alaska. All states showed 

an improvement, with Alaska improving the most. Of the ASSIST states, 65% 

(11/17) showed improvement at or greater 

than the mean increase from baseline to 

the end of the intervention period for all 

states, whereas only 32% (11/34) of the 

non-ASSIST states showed this degree of 

improvement.

Simple univariate tests comparing 

means for ASSIST states with those for 

non-ASSIST states at baseline and at the 

end of the intervention period showed 

no statistically significant differences 

(two-tailed z tests and p < .05) for the 

IOI or for any of the IOI components. 

However, the ASSIST states showed 

marginally significantly higher mean 

legislative clean indoor air scores at 

both times (p < .10). Without appropriate adjustment for important covariates, 

these analyses are problematic; a more 

appropriate and thorough analysis is presented in chapter 9.

Correlation of the IOI with Final 

Outcomes

The correlation of the IOI with adult 

smoking prevalence at baseline (r = –.57, 

p < .0001) is shown in figure 4.7. The 

correlation of the IOI with per capita 

cigarette consumption (r = –.64, p < 

.0001) is shown in figure 4.8.

Table 4.6 presents the correlations 

of each initial outcome with each final 

outcome at baseline (1992–93) and at the 

1995–96 and 1998–99 time frames. The 

“change” columns show the correlations 

of change for each initial outcome with 

the change in final outcomes over the 

entire ASSIST intervention period. The 

correlation of each initial outcome and 

the overall IOI with adult smoking prevalence and per capita cigarette consumption was statistically significant within 

each time frame. However, none of the 

1993 to 1999 changes in initial outcomes 

was correlated at all with the change in 

adult smoking prevalence; all the correlations were less than .11 in magnitude. 

The correlation of change in the IOI with 

change in per capita cigarette consumption was significant, but this was due 

entirely to the highly significant relationship between change in cigarette price 

and change in per capita consumption. 

In contrast to cigarette price, the other 

two initial outcomes—legislative score 

and percentage of indoor workers with 

smoke-free workplaces—were not significantly correlated with change in per 

capita cigarette consumption.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

131

Table 4.5. Initial Outcomes Index

(Shading indicates ASSIST states.)

State 1992–93 1993 1994 1995 1996 1997 1998 1998–99 Change

AK 4.57 3.91 4.14 4.86 4.84 8.58 9.92 10.55 5.98

AL –2.62 –3.28 –2.80 –2.20 –1.56 –0.74 0.15 0.89 3.51

AR –2.20 –2.52 –2.20 –1.82 –0.81 –0.10 1.27 1.99 4.19

AZ 1.06 0.14 0.31 2.66 3.34 3.69 4.65 5.25 4.19

CA 4.25 3.84 2.85 3.37 3.37 3.63 4.38 6.74 2.49

CO –0.90 –1.19 0.06 0.74 0.89 1.30 2.07 2.75 3.65

CT 0.44 0.86 1.30 1.78 1.98 2.47 3.47 4.22 3.78

DC 3.10 3.71 4.73 5.85 5.51 5.60 6.24 6.85 3.75

DE –1.31 –2.52 –0.86 –0.15 0.01 0.39 1.35 2.07 3.38

FL 1.23 0.38 0.72 1.34 1.38 1.74 2.89 3.53 2.30

GA –1.87 –2.61 –1.35 –1.06 –0.78 –0.10 1.07 1.73 3.60

HI 3.10 2.89 3.76 4.11 4.75 6.06 8.36 9.04 5.94

IA –1.24 –1.77 –0.99 –0.39 0.03 0.37 1.47 2.17 3.41

ID 1.02 0.24 1.12 1.99 2.20 2.17 3.18 3.78 2.76

IL –1.13 –0.87 –0.17 1.13 1.68 1.97 3.92 4.61 5.74

IN –3.14 –3.31 –2.88 –2.37 –1.86 –1.28 0.73 1.42 4.56

KS 0.93 0.45 1.28 1.66 2.01 2.89 4.25 4.89 3.96

KY –4.81 –4.94 –4.79 –3.95 –3.51 –2.97 –1.69 –1.09 3.72

LA –1.99 –2.68 –1.41 –0.47 –0.29 0.72 1.93 2.64 4.63

MA 1.66 1.90 3.37 4.14 6.04 6.75 7.98 8.63 6.97

MD 0.65 0.49 1.56 6.14 6.70 6.79 7.67 8.24 7.59

ME 1.87 1.52 2.28 2.79 2.87 4.23 6.49 6.96 5.09

MI 0.76 0.04 3.68 4.17 4.39 4.93 5.95 6.64 5.88

MN 3.83 3.11 3.65 4.18 4.58 4.74 5.20 5.96 2.13

MO –1.01 –1.08 –0.36 0.67 0.96 1.54 2.79 3.38 4.39

MS –2.37 –3.10 –2.95 –2.30 –1.79 –1.31 0.05 0.76 3.13

MT –0.40 –1.33 –0.76 –0.34 0.26 1.02 2.27 2.88 3.28

NC –4.26 –4.51 –3.39 –2.54 –2.12 –1.27 –0.24 0.41 4.67

ND 1.10 1.54 1.79 2.21 2.80 3.34 4.30 5.04 3.94

NE –0.33 –0.23 0.33 1.04 1.49 1.98 2.93 3.61 3.94

NH 1.39 0.82 1.66 2.41 2.76 3.62 4.77 5.42 4.03

NJ 1.92 1.36 2.14 2.97 2.97 3.63 7.31 7.93 6.01

NM 0.17 –0.31 0.11 0.63 0.82 1.12 1.96 2.70 2.53

NV –1.12 –1.51 –1.54 –1.35 –1.15 –0.92 0.48 1.27 2.39

NY 2.18 2.91 4.40 5.10 5.66 6.08 7.34 8.03 5.85

OH –1.96 –2.25 –0.96 –0.26 0.17 0.61 1.71 2.40 4.36

OK –2.13 –2.70 –2.21 –1.73 –1.09 –0.38 0.78 1.46 3.59

OR 1.68 1.15 1.75 1.82 1.93 3.47 4.42 4.96 3.28

PA –1.87 –2.68 –2.06 –1.25 –0.71 0.12 1.01 1.79 3.66

RI 1.60 0.91 3.74 4.67 4.69 5.32 6.23 6.88 5.28

SC –1.90 –2.56 –1.53 –0.47 –1.63 –1.05 –0.22 0.47 2.37

SD –1.40 –2.15 –1.08 –1.00 –0.68 –0.35 0.16 0.75 2.15

TN –3.08 –3.58 –3.02 –2.41 –1.99 –1.44 –0.13 0.58 3.66

TX 1.54 0.63 1.28 1.70 1.63 2.12 3.15 3.75 2.21

UT 3.68 3.20 3.48 4.24 4.70 6.31 7.18 7.77 4.09

VA –2.37 –2.44 –1.51 –0.92 –0.76 –0.48 0.76 1.44 3.81

VT 1.09 0.87 1.43 4.57 4.65 5.02 5.79 6.37 5.28

WA 4.40 5.18 5.43 6.36 7.00 7.21 7.78 8.45 4.05

WI 1.20 0.76 2.23 3.30 3.22 3.59 5.13 5.74 4.54

WV –2.48 –2.79 –1.77 –0.85 –0.45 0.15 1.22 1.77 4.25

WY –2.51 –3.12 –2.53 –1.77 –1.68 –1.42 –0.38 0.33 2.84

Overall

Mean 0.00 –0.38 0.42 1.24 1.56 2.19 3.36 4.05 4.05

SD 2.32 2.44 2.48 2.66 2.67 2.79 2.84 2.84 1.26

ASSIST

Mean 0.21 –0.03 1.16 1.91 2.19 2.74 4.03 4.68 4.47

SD 2.47 2.60 2.75 2.75 2.95 2.92 3.00 2.99 1.34

Non-ASSIST

Mean –0.10 –0.55 0.05 0.90 1.23 1.91 3.03 3.74 3.85

SD 2.27 2.37 2.29 2.58 2.50 2.72 2.73 2.75 1.18
4 . I n i t i a l O u t c o m e s I n d e x

132

Figure 4.7. Initial Outcomes Index Versus Adult Smoking Prevalence

-6 -4 -2 0 2

Prevalence (%)

Initial Outcomes Index (IOI)

r = -.57

5

10

15

20

30

35

4 6

25

Note: Source for prevalence data: National Cancer Institute. 2005. What is the TUS-CPS? http://riskfactor.

cancer.gov/studies/tus-cps.

Figure 4.8. Initial Outcomes Index Versus per Capita Cigarette Consumption

-10 -5 0

Consumption (packs/month)

Initial Outcomes Index (IOI)

r = -.64

5 10

4

2

6

8

12

10

16

14

20

18

Note: Source for per capita consumption data: National Cancer Institute. 2005. What is the TUS-CPS? http://

riskfactor.cancer.gov/studies/tus-cps.
133

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

For each state, figure 4.9 plots change 

in the IOI against change in adult smoking prevalence. Figure 4.10 plots change 

in the IOI against per capita cigarette 

consumption between baseline and the 

end of the program.

While these results are not completely 

convincing in the context of the ASSIST 

evaluation, they do not mean that the approach would not be useful in another 

program evaluation. As illustrated earlier in this chapter, other measures of 

tobacco control activity could have been 

included in the IOI measure for ASSIST 

that perhaps would have made it more 

sensitive to program effects. As it stands, 

the description of the IOI provided in 

this chapter should be taken as a model 

of how to combine such measures into 

an index for further analysis.

In addition, the 50 states and the 

District of Columbia were divided into 

three groups of 17 based on their IOI at 

baseline. To examine trends in per capita 

cigarette consumption in each group, the 

bimonthly consumption values were averaged over the states in each group from 

1983 to early 2000. The results are shown 

in figure 4.11. A smoothed trend line was 

computed for each group.29 In the preASSIST period, the trends in per capita 

cigarette consumption for the three groups 

were nearly parallel and diverged from 

one another only slightly. After mid-1993, 

the low IOI group showed a general increase in per capita cigarette consumption 

that did not turn down again until 1999, 

and the medium and high IOI groups 

showed greater separation than previously. 

Although both continued to decline, the 

rate of decline was somewhat slower than 

earlier, except that the high IOI group 

appeared to gain momentum again in 

1997. In April 1993, the price of premium 

brands of cigarettes decreased,30 and particularly in the low IOI group, it appeared 

that tobacco control efforts were not sufficient to completely counteract the influence of the price drop on consumption.

Table 4.6. Correlation of Initial Outcomes with Final Outcomes and Change in Initial Outcomes 

with Change in Final Outcomes

1992–93 1995–96 1998–99 Change

Cigarette price

Adult smoking prevalence

Per capita cigarette consumption

–.392**

–.671 

–.391** 

–.705****

–.366**

–.675 

–.073

–.683** **

Legislative score

Adult smoking prevalence

Per capita cigarette consumption

–.366**

–.299*

–.277*

–.348*

–.320*

–.385**

–.092

.018

Smoke-free workplaces

Adult smoking prevalence

Per capita cigarette consumption

–.571 

–.516** **

–.687 

–.465** *

–.633****

–.517 

.108

.207

IOI

Adult smoking prevalence

Per capita cigarette consumption

–.574** **

–.641 

–.557** **

–.661 

–.495** *

–.666****

–.051

–.425**

Note: IOI indicates Initial Outcomes Index.

*p < .05. **p < .01. ***p < .001. ****p < .0001.
4 . I n i t i a l O u t c o m e s I n d e x

134

Figure 4.9. Change in Initial Outcomes Index Versus Change in Adult Smoking Prevalence

0 2 4 8

Prevalence (%)

Change in IOI

r = -.05

-5

-6

-4

-3

-2

0

-1

2

1

6

Figure 4.10. Change in Initial Outcomes Index Versus Change in per Capita Cigarette 

Consumption

0 2 4 6 8

Change in consumption (packs/months)

Change in IOI

r = -.42

-3

-4

-2

0

-1

1

2

4

3
135

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Summary

An initial outcome is an early indication of whether the efforts of a tobacco control program are having an effect. 

An early indication of program effectiveness is sometimes necessary to convince 

legislators that the program is having an 

effect even before the program would 

be expected to influence the smoking 

behavior of a population. Increases in the 

levels of initial outcomes should eventually discourage tobacco use and thereby 

improve public health. To be useful in 

evaluating tobacco control programs, an 

initial outcome should (1) be measured 

consistently over time and among the 

units of observation (e.g., states for the 

ASSIST evaluation), (2) show variability among the units of observation, (3) 

reflect successful implementation of tobacco control program efforts, and (4) be 

related at least logically to the final outcome measures—smoking prevalence 

and per capita cigarette consumption.

Many outcomes could be selected as 

initial outcomes. The choice of outcomes 

will vary among programs and will depend on individual program emphases 

and the availability of measures. The 

initial outcomes selected for the ASSIST 

evaluation were cigarette price, a score 

(or rating) of local and state clean indoor 

air policies, and the percentage of indoor 

workers who reported that they worked 

in a 100% smoke-free environment. 

Each of these initial outcomes (1) could 

be measured consistently among states 

and over time, (2) showed a high degree 

of variability among states, (3) was 

related to a specific ASSIST program 

area, and (4) was significantly correlated 

with both adult smoking prevalence and 

Figure 4.11. Trends in per Capita Cigarette Consumption for States in Each Tertile Group of 

Tobacco Control Initial Outcomes Index

6

8

10

12

14

16

18

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Year

High

Medium

Low

Per capita packs/month
136

4 . I n i t i a l O u t c o m e s I n d e x

per capita cigarette consumption. The 

changes over time were noticeable, and 

ASSIST states appeared to change more 

than non-ASSIST states. However, the 

differences in change between the two 

groups of states were not large. It might 

be that for large changes to occur, states 

might have to achieve a certain threshold 

of public support. Once this “tipping 

point” is reached, legislatures might 

more readily enact legislation regarding 

both taxation and protection from secondhand smoke.

Because of the limited number of units 

of observation available for the ASSIST 

evaluation, an IOI was created for use in 

the analyses of the final outcomes. Such 

an index may also be necessary for the 

evaluation of state programs if the unit of 

observation is a limited number of jurisdictional levels (e.g., counties, regions) of 

program delivery and assessment. However, if the program has only one or two 

program areas of emphasis or if little is 

gained by combining the initial program 

measures, it would be best to evaluate the 

initial outcomes separately.

The three initial outcome measures 

for the ASSIST evaluation were not 

selected until after ASSIST program 

implementation. It would have been optimal to have had more discussion during 

the planning phases of the intervention 

regarding which initial outcomes would 

be assessed. Such discussions should 

include the methodology for repeatedly 

assessing the initial outcomes over the 

units of observation at different points in 

time, before, during, and after the intervention period. It turned out that no initial outcome measures were available for 

two of the ASSIST program areas.

Another limitation of the initial 

outcomes selected for ASSIST is the 

overlap between the legislative score 

and the percentage of indoor workers 

reporting smoke-free workplaces. In addition, as mentioned above, readiness 

to enact legislation regarding taxation 

might also be associated with readiness 

to enact clean indoor air laws. Nevertheless, the ASSIST IOI was correlated 

with the final outcomes measured at 

baseline (1992–93), during the program 

(1995–96), and at the end of the program (1998–99). Change in the IOI was 

significantly correlated with changes in 

per capita cigarette consumption, mainly 

because of the strong correlation of cigarette price and this outcome, but change 

in the IOI was not correlated with 

change in adult smoking prevalence.

During the ASSIST intervention 

period, tobacco control efforts were 

gathering momentum in non-­ASSIST 

states, which complicated the evaluation of ASSIST. (See chapters 1 and 2.) 

For example, a tobacco control program had been ongoing in California 

(a non-­ASSIST state) since 1990 (see 

Monograph 16, chapter 5, pp. 146–147, 

and chapter 11, pp. 497–498). Also, the 

Centers for Disease Control and Prevention and the Robert Wood Johnson 

Foundation provided modest funding 

for tobacco control activities, including Initiatives to Mobilize for the 

Prevention and Control of Tobacco 

Use (IMPACT), the SmokeLess States 

National Policy Initiative, and the Campaign for Tobacco-Free Kids, in many 

other states. The Robert Wood Johnson 

Foundation’s SmokeLess States initiative 

faced fewer political and bureaucratic 
137

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

barriers than did ASSIST and IMPACT. 

Rather than working directly with state 

agencies, SmokeLess States employed 

voluntary health organizations or other 

nongovernmental organizations, such as 

state tobacco control coalitions, as their 

lead agencies. As shown in the analyses 

described in this chapter, many states 

made significant progress in (1) increasing cigarette prices, (2) improving their 

legislative clean indoor air scores, and 

(3) increasing the percentages of indoor 

workers in smoke-free workplaces.

The methodology described in this 

chapter for specifying and combining 

initial outcome measures was illustrated 

in the context of the ASSIST evaluation 

but could be adapted for evaluations of 

other tobacco control programs at the 

state and local levels. There are lessons 

to be learned from the ASSIST experience with regard to selection of initial 

outcomes. For example, if a prevention 

program plans to focus efforts on enforcing youth access laws, there should be a 

metric in place to evaluate whether such 

enforcement has occurred. This metric 

could be derived from sales checks or 

could rely on an adolescent smokers 

population survey that asks whether 

the adolescent was asked for identification the last time he or she tried to buy 

cigarettes. Presumably this metric would 

show variability in levels over the units 

of observation and should be related ultimately to measures of youth smoking 

behavior (e.g., prevalence of smoking in 

the past 30 days, daily smoking). Ideally, 

appropriate measures for the initial 

outcomes expected from each program 

component should be designed before 

a comprehensive program is initiated. 

However, useful information can still be 

provided even if not all program areas 

have initial outcomes that can be assessed. As mentioned above, an index of 

these initial outcomes may be necessary 

if the initial outcomes are many and the 

observational units are few.

Conclusions

1. It may take many years for tobacco 

control programs to demonstrate 

desired changes in tobacco use. Measurements such as the change in policy assessed by the Initial Outcomes 

Index represent a promising way to 

assess program effectiveness within a 

shorter term.

2. Criteria for selecting initial outcomes 

for the ASSIST evaluation included 

continuous measurability, sufficient 

variability over time or other units 

of observation, a relationship with a 

tobacco control intervention, and evidence indicating a relationship with 

tobacco-related health outcomes.

3. The Initial Outcomes Index used in 

the ASSIST evaluation analysis was 

defined as an equal weighting of 

three factors that were initially correlated with lower tobacco use: total 

cigarette price, a rating of local and 

state clean indoor air policies, and 

the percentage of workers covered by 

100% smoke-free workplaces.

4. Overall, ASSIST states showed a 

greater increase in the Initial Outcomes Index than non-ASSIST 

states. The Initial Outcomes Index 

was also significantly related to tobacco prevalence and consumption. 

However, the only Initial Outcomes 
138

4 . I n i t i a l O u t c o m e s I n d e x

Index component related to changes 

in outcomes was the relationship between cigarette price and per capita 

consumption. This metric represented 

an important first step in establishing 

the relationship between initial policy 

outcomes for tobacco control and 

long-term program outcomes.

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13. National Cancer Institute. 1993. The 

impact of cigarette excise taxes on 

smoking among children and adults: 

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25. Farrelly, M. C., W. N. Evans, and 

A. E. Sfekas. 1999. The impact of 

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E. Kahkonen, and K. Reijula. 2001. The 

short-term impact of national smokefree workplace legislation on passive 

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27. Gower, K. B., D. M. Burns, 

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2000. Workplace smoking restrictions, 

rules about smoking in the home and 

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in public places. In State and local 

legislative action to reduce tobacco 

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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

141

5. State Facilitating Conditions and Barriers to 

Implementation of Tobacco Control Programs

Elizabeth A. Gilpin

Contributors: Sandy J. Slater and Anne M. Hartman

Contents

Introduction.................................................................................................................. 143

Overview of Influences.................................................................................................. 144

State Conditions Selected for the ASSIST Evaluation ................................................... 145

Gender, Age, Race/Ethnicity, Education, Socioeconomic Status, and Geography................146

Economic Dependence on Tobacco.....................................................................................148

Data Sources................................................................................................................ 149

Demographics....................................................................................................................149

Economic Dependence on Tobacco.....................................................................................150

Other Potential Covariates........................................................................................... 151

Family................................................................................................................................151

Religious Community.........................................................................................................151

Local Government..............................................................................................................152

Dominant State Political Philosophy..................................................................................152

State Wealth ......................................................................................................................153

Summary...................................................................................................................... 155

Conclusions.................................................................................................................. 156

References................................................................................................................... 158

Tables and Figure

Figure 5.1. Interactions of Forces Acting to Influence Tobacco Use ...........................................144

Table 5.1. Variables Included in the ASSIST Evaluation Analysis...............................................146

Table 5.2. Per Capita Funding ($/Person) for Tobacco Control in Each State, by Year ...............154

Appendix

Appendix 5.A. Baseline Values for Variables Considered in the ASSIST 

Evaluation Analyses .................................................................................................................157

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

143

5. State Facilitating Conditions and Barriers to Implementation of 

Tobacco Control Programs

Introduction

T

his chapter explores the individual and state conditions that could have potentially influenced the implementation and outcomes of a state tobacco use prevention and control program. States varied widely on these factors. First, an overview of multiple-level 

factors that affect tobacco use is provided, followed by review and description of the state 

factors included as covariates in the ASSIST evaluation and how they were measured. The 

covariates do not represent an exhaustive list of factors that might have affected the evaluUnderlying state conditions can have an important impact on the success of 

tobacco control interventions such as the American Stop Smoking Intervention 

Study (ASSIST). Factors such as population-level smoking patterns; the economic 

influence of the tobacco industry; and the overall social, cultural, and policy 

environment can potentially affect the implementation of tobacco control programs 

and individual smoking behavior.

This chapter describes the measures of state conditions—demographic factors 

and economic dependence on tobacco—used as covariates in the ASSIST evaluation 

analysis. The demographic measures were obtained from census and population 

survey data and included gender, age, race and ethnicity, education, income, state 

population, metropolitan area representation, and region. The measure of state 

dependence on tobacco growing and manufacturing is described more fully in 

chapter 6 of this monograph.

Other factors that the extant literature suggests might affect tobacco control 

interventions and outcomes are discussed, from individual and environmental factors 

such as family, religion, community organizations, and local government, to statelevel factors such as per capita wealth and dominant political affiliation. Although 

examining these relationships was outside the scope of the ASSIST evaluation, they 

are promising areas for future study.

The state-level demographic covariates described in this chapter were not 

significantly related to smoking prevalence in the evaluation analysis described in 

chapter 9 because in this analysis they had already been used as covariates at the 

individual level to adjust state-level smoking prevalence rates. However, several 

variables described in this chapter contributed significantly to state-level differences 

in per capita cigarette consumption, including percentage of the population that 

was Hispanic, percentage with incomes below poverty level, and percentage of gross 

state product derived from tobacco growing and manufacturing.
144

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

ation; therefore, other factors that might 

be considered for inclusion in future analyses are also reviewed.

Overview of Influences

Reducing the number of smokers in the population, a key goal of tobacco control efforts, is accomplished 

through youth prevention and adult cessation. If fewer youths become smokers 

and more smokers quit, the impact of 

smoking-related diseases on the public 

health is reduced. Thus, it is important to 

understand those factors that influence 

individual smoking behavior (affecting 

initiation or cessation). Figure 5.1 presents a simplified view of these influences 

and how they might interact.

Family, close friends, and even acquaintances can influence how an individual views tobacco use and whether 

that individual becomes and remains a 

smoker. The larger community, schools, 

the workplace, churches, and other 

organizations to which the individual 

belongs may also influence his or her 

perceptions and beliefs about tobacco 

use. Finally, the prevalence of smoking 

within a community will reflect and, in 

turn, affect these perceived norms.

Local, state, and federal legislative 

bodies can influence the community 

environment through enactment of laws 

(e.g., smoke-free workplace laws, youth 

access laws) and antitobacco media campaigns. The community environment, in 

turn, affects individuals both directly and 

indirectly. For example, laws that restrict 

workplace smoking have the potential 

to change both community norms and 

individual behavior. When a community 

passes and enforces laws that restrict 

workplace smoking, that community creates an environment where smoking is 

a marginalized, non-normative activity. 

Workplace smoking restrictions also directly affect the individual. For example, 

a worker might find that he or she can no 

longer smoke inside at work.

Figure 5.1. Interactions of Forces Acting to Influence Tobacco Use

Individual

Family and Friends

Community

Structural Changes

(Laws)

Tobacco

Industry
145

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

The tobacco industry works as a counterforce to enactment of tobacco control 

laws (see chapter 8), but it can also be the 

target of action by governments. For example, the 1998 Master Settlement Agreement 

between 46 states and the tobacco industry 

placed restrictions on how that industry 

can advertise and promote its products, 

particularly the practices that appeared 

to be influential in promoting smoking 

among children and adolescents.1 Tobacco 

advertising and promotions seek to portray 

tobacco use as a normative behavior—acceptable to the community and glamorous, 

sophisticated, or “cool” for the individual. 

These images directly contradict the 

ASSIST goals of creating a social environment in which smoking is non-normative.

To the extent that individuals understand 

the dangers of tobacco use and appreciate 

the utility of having laws related to it, they, 

or the organizations that they form and 

work through, may lobby governments to 

take action to pass such laws. Ultimately, 

by no longer purchasing tobacco products, 

individuals will have a profound influence 

on the tobacco industry.

No single factor or group of factors 

determines whether a youth becomes a 

smoker or an adult quits smoking. Rather, 

the individual is influenced by his or her 

environment and can act to change this 

environment at many levels: (1) within the 

family; (2) through organizations within the 

community; and ultimately (3) through new 

laws at the local, state, and national levels. 

Such laws frame the culture in the community with respect to tobacco use. At the 

same time, the tobacco industry is working 

diligently to counter any such efforts and to 

promote smoking within the population.

State Conditions Selected for 

the ASSIST Evaluation

Many factors could have been associated with tobacco control outcomes, and many were considered for 

the ASSIST evaluation. However, only a 

limited number could be included in the 

evaluation because there were only 51 

units of observation (the 50 states and 

the District of Columbia). In addition, 

data needed to be available for every 

state, and those data had to be collected 

in a uniform manner. This requirement 

also limited the factors that could be 

considered for inclusion. For example, 

no consistent data were available on the 

resources the tobacco industry devoted 

to efforts against tobacco control in each 

state, so this important factor could not 

be included.

Data were aggregated into indices to 

reduce the number of covariates included 

in the statistical analysis. Chapter 2 describes the Strength of Tobacco Control 

(SoTC) measure, which included subscales for resources, capacity, and efforts. 

An index for the strength of clean indoor 

air legislation was developed (chapter 

3), which was a component of the Initial 

Outcomes Index (IOI; chapter 4), along 

with cigarette price and the percentage 

of indoor workers who reported that their 

workplace was smoke free (chapter 4). It 

was hypothesized (chapters 2 and 9) that 

SoTC would reflect a state’s implementation of tobacco control and that initial 

outcomes (IOI measures) would affect a 

state’s likelihood of reducing tobacco use 

behavior, the main outcomes of interest 

(chapter 9).
146

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

The state condition variables included 

in the ASSIST evaluation analyses were 

demographic factors and a measure of 

state economic dependence on tobacco. 

These measures are presented in table 

5.1. The sections that follow review in 

more detail the association of these measures with tobacco use and the sources 

of these data.

Gender, Age, Race/Ethnicity, 

Education, Socioeconomic Status, and 

Geography

Smoking prevalence differs by gender, age, race/ethnicity, educational 

attainment, socioeconomic status, and 

geography.2 A state’s demographic profile could potentially affect the implementation and subsequent outcomes of a 

tobacco control program. For example, 

older age groups show lower prevalence 

than younger groups3 partly because 

many smokers quit (or die) as they age. 

In this case a state with a significant 

number of older residents might have a 

lower smoking prevalence rate than other 

states. Alternatively, if tobacco control efforts prove consistently effective 

among adolescents, as some evidence 

indicates,4–7 fewer young people will 

mature to adulthood as smokers, and 

over time adult smoking prevalence will 

fall as a result. In this example, states 

that have younger populations might 

show more rapid reductions in smoking 

prevalence.

The California experience provides evidence that state tobacco control programs 

can affect youth initiation in this way. 

Nationally, smoking prevalence among 

youth rose between 1991 and 1997, when 

it began to decline again.8 In contrast, 

although youth prevalence rates rose in 

parallel with the national average, they 

remained lower in California. In addition, 

the downward trend in youth prevalence 

began two years earlier in California (in 

1995) than in the nation as a whole.9 This 

Table 5.1. Variables Included in the ASSIST Evaluation Analysis

Variable Data source

Demographic

Women (%) Census

Median age Census

African American (%) Census

Hispanic (%) Census

Education above high school level (%) CPS

Household income below poverty level (%) Census

State population Census

Living in a metropolitan area (%) CPS

Indicator variables for region of country (Midwest, West, South, or Northeast) CPS

Economic dependence on tobacco

Gross state product derived from tobacco growing and manufacture (%) See chapter 6

Notes: Demographic variables were for persons aged 18 years and older. CPS indicates Current Population Survey.
147

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

difference has been attributed to the California tobacco control program, which 

began prior to ASSIST but shared many 

of the same goals and components.

A measure of the state’s Hispanic and 

African American populations was included in the analysis. Hispanic populations show overall lower rates of smoking 

because the low smoking rates among 

women in these groups more than offset 

high rates among men,10,11 and states 

with large populations of Hispanics may

have lower prevalence rates. In the past, 

African Americans exhibited higher 

prevalence rates than most other minority groups except Native Americans,11

but the gap between African Americans 

and non-Hispanic whites has narrowed 

in recent years: in 2001, adult prevalence 

for African Americans fell below that for 

non-Hispanic whites.12 Additionally, African American youth have shown lower 

rates of initiation in recent years10 and 

perhaps are now maturing to adulthood 

Demographics and Smoking Cessation

Smokers are increasingly concentrated in lower socioeconomic groups,a but it is unknown whether this 

is a result of poorer access to cessation interventions or increased resistance to quitting. A number of 

studies summarized in Women and Smoking: A Report of the Surgeon Generalb suggest that women 

may have more difficulty successfully quitting than men, but ASSIST appeared to affect quitting 

among women more than men (see chapter 9). Historically, African Americans have also had a more 

difficult time successfully quitting.c,d,e Greater difficulty in quitting for this subpopulation may result 

in slower declines in prevalence for states with large African American populations than would be expected from reduced youth initiation. Finally, some demographic groups (e.g., females) may also not 

benefit to the extent that others do from pharmaceutical aids for cessation.f

aBurns, D. M., and K. E. Warner. 2003. Smokers who have not quit: Is cessation more difficult and 

should we change our strategies? In Those who continue to smoke (Smoking and tobacco control 

monograph no. 15, NIH publication no. 03-5370), 11–31. Bethesda, MD: National Cancer Institute.

bU.S. Department of Health and Human Services. 2001. Women and smoking: A report of the surgeon 

general. Washington, DC: U.S. Department of Health and Human Services, Public Health Service, 

Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and 

Health Promotion, Office on Smoking and Health.

cRoyce, J. M., N. Hymowitz, K. Corbett, T. D. Hartwell, and M. A. Orlandi. 1993. Smoking cessation 

factors among African Americans and whites. COMMIT Research Group. American Journal of Public 

Health 83 (2): 220–26.

dAhluwalia, J. S. 1996. Smoking cessation in African-Americans. American Journal of Health Behavior 20 (5): 312–18.

eU.S. Department of Health and Human Services. 1998. Tobacco use among U.S. racial/ethnic minority groups—African Americans, American Indians and Alaska Natives, Asian Americans and Pacific Islanders, and Hispanics: A report of the surgeon general. Washington, DC: U.S. Department of Health 

and Human Services, Public Health Service, Centers for Disease Control and Prevention, National 

Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health.

f

Gonzales, D., W. Bjornson, M. J. Durcan, J. D. White, J. A. Johnston, A. S. Buist, D. P. Sachs, et al. 

2002. Effects of gender on relapse prevention in smokers treated with bupropion SR. American Journal 

of Public Health 22 (4): 234–39.
148

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

as confirmed never smokers. Previous 

research has suggested that some African 

Americans delayed smoking initiation 

into the young adult years.8 Thus, states 

with large populations of African Americans may show relatively more rapid 

declines in prevalence than other states, 

because fewer African Americans are becoming smokers.

Individuals with higher levels of educational attainment are less likely to ever 

initiate smoking and are more likely to 

quit than are individuals who are less 

well educated.2,13–15 In general, lower 

socioeconomic groups have higher rates 

of smoking than other socioeconomic 

groups.16,17 However, some studies have 

shown that after accounting for educational attainment as an indicator of socioeconomic status, many of the racial/ethnic 

effects described above are considerably diminished.14,16,18 Midwestern and 

southern states, compared with eastern 

and western states, tend to have higher 

smoking prevalence.12 If these rates are 

attributable to socioeconomic rather than 

to racial/ethnic indicators, different conclusions about the potential effects on a 

state’s population will be reached.

Economic Dependence on Tobacco

The extent to which tobacco growing and manufacturing contribute to a 

state’s economy may play a role in that 

state’s culture regarding tobacco use and 

its political will to undertake tobacco 

control. When tobacco is part of a state’s 

history and identity, residents may be 

less inclined to recognize its dangers and 

may be more supportive of smoking. If 

a significant proportion of the workforce 

is engaged in tobacco production or 

manufacturing, people may not support 

tobacco control for fear of job loss. Also, 

the state government would want to protect its revenue stream gained from this 

industry and protect its workers.

Research suggests that states that have 

economies that are highly dependent on 

tobacco are less likely to adopt strong 

tobacco control measures. For example, 

in 2002, the average of state cigarette 

excise tax rates in the seven largest 

tobacco-growing states (Georgia, Kentucky, North Carolina, South Carolina, 

Tennessee, West Virginia, and Virginia) 

was 9.5¢ per pack, whereas the average 

in the remaining states and the District 

of Columbia was more than 69¢ per 

pack, a differential that has grown significantly over the past several decades.19

Similarly, Chaloupka and Saffer,20 using 

data from 1975 through 1985, found that 

states with greater per capita production 

of tobacco were less likely to adopt laws 

restricting cigarette smoking in a variety 

of public places, including restaurants, 

as well as in private workplaces, with the 

latter effect statistically significant. Likewise, Ohsfeldt, Boyle, and Capilouto21

found that the per capita value of state 

tobacco production was negatively associated with strength of smoking restrictions and cigarette excise tax rates.

The degree to which tobacco growing and manufacturing affect state 

economies is controversial. Much of 

this research has been sponsored by the 

tobacco industry, and critics of these 

studies argue that they are overestimates. 

For example, Warner and Fulton22 argue 

that the multiplier effect significantly 

overstates the economic impact of 

tobacco, since it implicitly assumes that 
149

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

the money spent on tobacco would not 

be spent elsewhere. When allowing for a 

redistribution of money spent on tobacco 

to spending on other goods and services 

in the absence of tobacco, Warner et al.23

found that most of the states would actually see increased employment in the 

absence of tobacco. Tobacco-related jobs 

from growing, wholesaling, and retailing 

would be replaced by similar or better 

jobs related to other goods and services 

in the absence of tobacco. Thus, it appears that the tobacco industry tries to 

overstate the economy’s dependence on 

tobacco in order to dissuade individuals 

and governments from taking any action 

to control tobacco use.

Data Sources

Demographics

Individual-level data within each 

state were from the 1992–93, 1995–96, 

and 1998–99 Current Population Surveys (CPS),24 conducted in September, 

January, and May of each period. The 

U.S. Bureau of the Census continuously 

conducts these surveys to monitor the 

labor force, covering the civilian, noninstitutionalized population of persons 

aged 15 years and older in the United 

States.24 These household surveys select 

a stratified probability sample of clusters 

of households identified from the Census 

Bureau and other sources. The survey 

design calls for surveying about 56,000 

households per month, and each household is part of a panel that is interviewed 

eight times over a 16-month period. However, all respondents in the above months 

were interviewed only once, in one of the 

three months listed above. The surveys 

from these months included a special 

Tobacco Use Supplement (TUS-CPS) 

sponsored by the National Cancer Institute specifically for the ASSIST evaluation.25 About a quarter of the interviews 

were conducted in person (first or fifth 

time in a panel); the remainder were 

conducted by telephone (second, third, 

fourth, sixth, seventh, or eighth time in a 

panel). One household resident provided 

the demographic information for all 

household residents. The individual-level 

demographic data were used in the first 

stage of the two-stage analysis of adult 

smoking prevalence (see chapter 9).

The data on smoking prevalence and 

the percentage of indoor workers with 

smoke-free workplaces were aggregated 

by state from the TUS-CPS (see chapters 

3 and 4). The main part of the CPS was 

the source of data for one individuallevel variable (percentage with above a 

high school education) and two statelevel demographic variables (percentage 

living in a metropolitan area and region 

of the country); again, data were summarized for persons aged 18 years and older 

within each state. The other demographic variables were from the U.S. Bureau 

of the Census (the economic dependence 

on tobacco variable is described briefly 

below and more fully in chapter 6).

The baseline mean values of the variables listed in table 5.1 as well as others 

(e.g., smoking prevalence) are compared 

between the ASSIST and non-ASSIST 

states in table 9.1 (see chapter 9). The 

baseline values of each variable in 

table 5.1 for each state are presented in 

appendix 5.A. Appendix 9.C (see chapter 9) presents adult (aged 18 years and 
150

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

older) smoking prevalence obtained from 

the TUS-CPS for each state. It also presents the per capita cigarette consumption from sales data aggregated over the 

months of the CPS (September through 

May) for each TUS-CPS period. This 

aggregation allowed per capita cigarette 

consumption to be examined in the 

same time periods as tobacco use prevalence. The actual bimonthly per capita 

consumption data are available on the 

University of California Social Science 

Data Collection Web site.26 Prevalence 

and consumption, in addition to being 

the outcomes of interest, are indicators 

of the tobacco use culture in each state 

prior to the ASSIST intervention.

Economic Dependence on Tobacco

Each state’s economic dependence on 

tobacco was computed using a procedure 

that is fully described in chapter 6. During the period covered by the evaluation, 

16 states grew appreciable amounts of 

tobacco, with 6 states (North Carolina, 

Kentucky, Tennessee, South Carolina, Virginia, and Georgia) accounting for most 

of the total. Appreciable levels of cigarette 

and other tobacco product manufacturing 

occurred in 24 states. Seven states (North 

Carolina, Virginia, Kentucky, Georgia, 

Florida, New York, and Tennessee) accounted for the majority of employment 

in tobacco product manufacturing, with 

5 others (Alabama, Connecticut, Illinois, 

Pennsylvania, and West Virginia) accounting for much of the remaining employment. Four of the ASSIST states (North 

Carolina, South Carolina, Virginia, and 

West Virginia) had significant economic 

dependence on tobacco.

The measure used in the ASSIST 

evaluation was obtained by summing 

the estimates for employment/compensation and for tobacco manufacturing 

for each year of interest for each state. 

Some states had a zero for one or both 

measures. The final estimates were 

scaled so that the sum of state estimates 

equaled published estimates for the 

entire United States for each year. Finally, the estimates were divided by the 

total gross state product, and the results 

were summed to form the tobacco-dependence measures used in the ASSIST 

evaluation analyses.

As a preview to chapter 9, which 

presents the analysis of the final outcomes (state smoking prevalence and 

per capita consumption), none of the 

state-level conditions were significantly 

associated with smoking prevalence. 

This occurred because the data on smoking prevalence had been adjusted at the 

individual level for the demographic 

variables before the state-level analysis 

was conducted. Thus, when adjustment 

was made for the relationship between 

demographics and smoking status at 

the individual level, these factors were 

not important in explaining variation in 

prevalence at the state level. The analysis of per capita cigarette consumption 

from tobacco sales data could be conducted only at the state level. In these 

analyses, several variables contributed 

significantly to the observed differences 

among states: percentage Hispanic, percentage with incomes below poverty, 

and percentage of gross state product 

derived from tobacco growing and 

manufacturing.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Other Potential Covariates

I

n this section, other factors that could 

potentially affect tobacco control efforts and outcomes are discussed. Since 

it is the individual who uses tobacco,

individual characteristics that have been 

differentially associated with tobacco 

use are first described. For example, the 

potential impact of a tobacco control 

program may be limited if a state’s population has a disproportionate number 

of difficult-to-affect individuals (e.g., a 

large fraction of smokers who are highly 

nicotine dependent). Next, factors that 

influence the individual in the relatively 

immediate environment are summarized, 

and a section follows that describes 

conditions at the state level, other than 

population composition, that could also 

modify a state’s ability to adopt and 

carry out tobacco control policy.

Family

Families share genes and environments, both of which can affect smoking behavior. There is evidence that 

genetic characteristics may play a role 

in determining which individuals become dependent smokers.27,28 Genetic 

characteristics may also contribute to 

increased difficulty in quitting for some 

smokers29,30 or may modify the potential 

effectiveness of pharmaceutical aids for 

smoking cessation.31 It is currently unknown whether genetic factors co-vary 

with any particular population subgroups 

in such a way as to influence tobacco 

control success.

Although the influence of genetic 

characteristics on smoking dependence 

requires further investigation, current 

data clearly indicate that the culture 

within the family plays a role in smoking 

behavior. Parental smoking is an important determinant of adolescent smoking 

uptake.32,33 Furthermore, in families 

in which the parents had quit smoking 

before children were born or when the 

children were very young, those children 

are less likely to smoke as adolescents.34

Parents are important role models for 

smoking, and when they smoke their 

behavior conveys to children that smoking is acceptable and has certain benefits 

(e.g., relaxation, relief from stress).

In addition to influencing smoking uptake, having other smokers in the family 

may make it more difficult for someone 

to quit35,36 by providing both a cue to 

smoke and a ready source of cigarettes.37

Religious Community

Religious beliefs and practices affect 

tobacco use. Abstinence from tobacco 

is one of the fundamental beliefs of the 

Seventh-Day Adventist Church38 and the 

Church of Jesus Christ of the Latter-Day 

Saints (Mormons).39 At the individual, 

family, and community levels, these 

religious communities directly affect 

smoking behavior. Research indicates 

that strict religious and moral prohibitions on risky activities such as smoking 

are congruent with the philosophy of “the 

body is a temple,” which leads to fewer 

people engaging in such behaviors.40

There is also evidence that young adults 

who attend religious services relatively 

frequently have lower smoking rates than 

those who do not attend such services.41

Where particular religious communities dominate, they can influence state 
152

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

smoking prevalence rates. For example, 

although there are more Seventh-Day 

Adventists on the West Coast and in the 

southern United States than in any other 

region of the United States, they do not 

predominate in any one state,42 and their 

numbers are relatively small. In contrast, 

over 70% of Utah residents were Mormon 

during ASSIST43 and, not surprisingly, 

Utah was the state with the lowest smoking prevalence in the late 1990s.12

Local Government

As mentioned earlier, the more supportive a population is of tobacco control measures, the more likely it is that 

local tobacco control policy will be 

enacted. In California, momentum to 

enact smoke-free policies was created 

and propagated at the local level. The 

key players in this movement were the 

voluntary organizations, together with 

other coalitions of health-conscious citizens. This resulted in a state law being 

enacted in 1995 that prohibited smoking 

in nearly all indoor workplaces, which 

was extended in 1998 to include bars and 

gaming rooms.

Such activity at the local level is 

feared by the tobacco industry because 

it cannot effectively lobby local governments in every community, both small 

and large, throughout the nation.44 The 

tobacco industry response has been 

to lobby for legislation and to support 

candidates at the state level in an effort 

to see that laws are passed that preempt 

stronger laws from being passed at the 

local level.44–46 However, voluntary 

health organizations such as the American Heart Association, the American 

Lung Association, and the American 

Cancer Society continue to be active at 

both the local and state levels working 

for laws that can further tobacco control 

and improve public health.

The various individual and immediate 

environmental characteristics summarized above could affect tobacco control 

success at the state level, depending on 

whether the state’s populations consist of 

relatively larger or smaller proportions 

of these demographic groups. States 

with high smoking prevalence rates may 

have higher percentages of families with 

multiple smokers, which make smoking 

more culturally accepted and raise barriers to quitting. Alternatively, in states 

with relatively low prevalence, the social 

pressure on the remaining smokers to 

quit might be considerable. Despite considerable research, there is little evidence 

that the remaining smokers are those 

who are more addicted and therefore less 

able to quit.3,47

Dominant State Political Philosophy

Ideology, party politics, and political 

participation are closely tied in the various states.48,49 Political ideology is often 

focused on whether the responsibility 

for health belongs with the individual or 

with society.50–52 Other political ideologies characterize governments within the 

context of conservative or liberal tendencies or positions that historically have 

been affiliated with political parties.48

The political party dominating a state 

may affect whether tobacco control legislation is implemented. For example, 

Ohsfeldt, Boyle, and Capilouto21 found 

a significant relationship between political liberalism, the strength of a state’s 
153

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

restrictions on cigarette smoking, and the 

level of cigarette excise taxes. Chriqui53

found that Republican-controlled state 

legislatures were significantly less likely 

to enact laws designed to restrict minors’ 

access to tobacco products than were 

states whose legislatures were controlled 

by the Democratic Party or were divided 

between the parties.

It is possible that when efforts to enact tobacco control legislation are successful in a few states, other states will 

be encouraged to work toward similar 

measures. For example, Weller54 notes 

that the success of tobacco tax initiatives 

in Massachusetts, California, and Arizona encouraged tobacco control advocates 

and legislators to pursue a similar strategy in Oregon. This spillover effect may 

also explain why increasing numbers of 

states have recently been able to enact 

comprehensive smoke-free workplace 

legislation (including bars and clubs). 

California was the first and, for several 

years, the only state with such a law, 

but by early 2006, ten additional states 

(Connecticut, Delaware, Maine, Massachusetts, Montana, New Jersey, New 

York, Rhode Island, Vermont, and Washington) had put such laws in place, and a 

number of other states are in the process 

of enacting such legislation.

State Wealth

A few studies have investigated state 

resources and finances as determinants 

of state tobacco control efforts, although 

the measures used have not been consistent. Using state per capita total government expenditures as a global measure 

of the state’s ability to support tobacco 

control efforts, Ohsfeldt, Boyle, and 

Capilouto21 found that states that spent 

more per capita were more likely to 

adopt stronger restrictions on smoking as 

well as have higher cigarette excise taxes. Other studies focused specifically on 

resources available for tobacco control 

activities. Wakefield and Chaloupka55

showed that program success in reducing 

youth smoking was dependent on the extent of implementation and the degree to 

which such efforts were undermined by 

the tobacco industry and by other state 

funding priorities.

Funding for tobacco control was one 

component of the SoTC measure described in chapter 2. Table 5.2 shows the 

amount of money spent per capita on 

tobacco control in each state from 1991 

through 1998, which spans the ASSIST 

intervention period. In 1991, only California showed appreciable spending for 

tobacco control. By 1993, the ASSIST 

Politically Active Citizens and 

Tobacco Control

One study suggests that a politically active 

citizenry may be associated with support for 

tobacco control, irrespective of political affiliation. Chaloupka and Saffera used a measure 

of the political participation of state residents 

in their analysis of the determinants of state 

restrictions on smoking. They found that the 

probability of adopting relatively stringent 

restrictions on smoking was significantly 

higher in states where a greater percentage of 

the state population was politically active, as 

measured by the percentage of the state population voting in elections for the U.S. House 

of Representatives.

aChaloupka, F. J., and H. Saffer. 1992. Clean 

indoor air laws and the demand for cigarettes. 

Contemporary Policy Issues 10:72–83.
5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

154

Table 5.2. Per Capita Funding ($/Person) for Tobacco Control in Each State, by Year

(Shading indicates ASSIST states.)

State 1991 1992 1993 1994 1995 1996 1997 1998

Change 

1991–98

AK 0.00 0.00 0.00 0.88 1.06 1.02 1.00 1.09 1.09

AL 0.01 0.00 0.01 0.06 0.06 0.06 0.04 0.11 0.10

AR 0.01 0.00 0.00 0.05 0.05 0.05 0.05 0.14 0.13

AZ 0.02 0.03 0.01 0.07 0.16 3.05 5.12 7.49 7.47

CA 6.43 2.81 3.90 2.85 2.17 2.12 4.94 5.91 −0.52

CO 0.19 0.18 0.39 0.54 0.53 0.48 0.47 0.43 0.24

CT 0.00 0.01 0.01 0.05 0.03 0.03 0.10 0.18 0.18

DC 0.02 0.04 0.02 0.53 0.44 0.00 0.34 0.84 0.82

DE 0.01 0.02 0.01 0.12 0.15 0.14 0.37 0.76 0.75

FL 0.00 0.00 0.00 0.04 0.04 0.04 0.04 0.04 0.04

GA 0.00 0.01 0.00 0.02 0.03 0.02 0.04 0.09 0.09

HI 0.06 0.09 0.12 0.40 0.22 0.21 0.41 0.64 0.58

IA 0.01 0.01 0.00 0.08 0.08 0.08 0.14 0.19 0.18

ID 0.24 0.21 0.18 0.33 0.16 0.16 0.15 0.30 0.06

IL 0.00 0.00 0.00 0.04 0.05 0.05 0.09 0.16 0.16

IN 0.11 0.11 0.13 0.18 0.25 0.21 0.24 0.25 0.14

KS 0.00 0.00 0.00 0.18 0.26 0.25 0.25 0.21 0.21

KY 0.00 0.00 0.00 0.04 0.05 0.07 0.11 0.20 0.20

LA 0.00 0.00 0.00 0.06 0.05 0.05 0.05 0.07 0.07

MA 0.12 0.15 0.15 17.42 14.77 14.16 12.70 7.25 7.13

MD 0.00 0.00 0.00 0.06 0.07 0.08 0.09 0.13 0.13

ME 0.30 0.29 0.70 0.83 0.81 0.80 0.80 0.80 0.50

MI 0.16 0.21 0.30 0.35 0.20 0.20 0.20 0.20 0.04

MN 0.46 0.44 0.63 0.60 0.85 0.69 0.67 0.99 0.53

MO 0.10 0.09 0.21 0.29 0.28 0.25 0.28 0.29 0.19

MS 0.00 0.00 0.00 0.04 0.04 0.04 0.03 0.15 0.15

MT 0.00 0.00 0.00 0.37 0.31 0.30 0.29 0.50 0.50

NC 0.12 0.12 0.27 0.34 0.25 0.25 0.29 0.29 0.17

ND 0.02 0.02 0.00 0.52 0.41 0.40 0.39 0.66 0.64

NE 0.00 0.00 0.00 0.25 0.28 0.31 0.34 0.41 0.41

NH 0.00 0.00 0.00 0.27 0.27 0.26 0.25 0.35 0.35

NJ 0.09 0.08 0.16 0.22 0.20 0.23 0.25 0.12 0.03

NM 0.36 0.33 0.62 0.75 0.68 0.67 0.66 0.66 0.30

NV 0.00 0.00 0.00 0.11 0.15 0.11 0.06 0.20 0.20

NY 0.07 0.06 0.13 0.15 0.13 0.12 0.14 0.14 0.07

OH 0.00 0.00 0.00 0.04 0.03 0.03 0.05 0.09 0.09

OK 0.00 0.00 0.00 0.06 0.05 0.05 0.05 0.15 0.15

OR 0.01 0.02 0.03 0.09 0.08 0.13 0.16 0.26 0.25

PA 0.05 0.00 0.00 0.03 0.01 0.03 0.03 0.05 0.00

RI 0.48 0.45 0.83 1.07 0.93 1.06 1.11 1.33 0.85

SC 0.32 0.37 0.39 0.35 0.26 0.32 0.32 0.31 –0.01

SD 0.02 0.04 0.03 0.15 0.13 0.12 0.12 0.48 0.46

TN 0.00 0.00 0.00 0.02 0.02 0.01 0.01 0.06 0.06

TX 0.02 0.03 0.03 0.05 0.02 0.02 0.02 0.04 0.02

UT 0.27 0.34 0.32 0.42 0.14 0.13 0.21 0.30 0.03

VA 0.07 0.07 0.16 0.21 0.21 0.22 0.23 0.19 0.12

VT 0.02 0.00 0.00 0.30 0.38 0.37 0.35 0.77 0.75

WA 0.14 0.13 0.28 0.33 0.34 0.31 0.32 0.31 0.17

WI 0.12 0.12 0.30 0.39 0.30 0.28 0.31 0.32 0.20

WV 0.23 0.22 0.46 0.64 0.73 0.58 0.64 0.58 0.35

WY 0.00 0.00 0.00 0.28 0.17 0.16 0.16 0.62 0.62

Sources: National Cancer Institute, ASSIST Program Office. Farrelly, M. C., T. F. Pechacek, and F. J. Chaloupka. 

2003. The impact of tobacco control program expenditures on aggregate cigarette sales: 1981–2000. Journal of Health 

Economics 22 (5): 843–59.
155

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

states began to show increases in funding. However, by the end of the period, 

all states were spending something, 

and a number of non-ASSIST states 

had increased their spending, some to 

approximately the same level as that 

of some ASSIST states. Arizona in 

particular showed a huge increase. In 

1998, the mean per capita expenditure 

for tobacco control in ASSIST states 

was $0.85 (SE = 0.47), compared with 

$0.70 (SE = 0.30) in non-ASSIST states, 

which represents a change from 1991 of 

$0.65 (SE = 0.47) and $0.48 (SE = 0.21), 

respectively.

The information outlined in this section indicates that a state’s political environment and wealth can influence its 

ability to enact tobacco control policy. 

Some states tend to support tobacco 

control, probably because they recognize 

a role for government in this area. Alternatively, other states may be more likely 

to view smoking as an individual choice 

and may be more influenced by tobacco 

industry efforts working against tobacco 

control. States with a high prevalence 

of persons of low socioeconomic status 

may take in less revenue and therefore have fewer resources to devote to 

tobacco control. Unfortunately, low 

socioeconomic status is associated with 

high smoking rates. Finally, states with 

greater economic dependence on tobacco 

may perceive that their interests lie in 

protecting tobacco rather than instituting 

tobacco control policies, and the tobacco 

industry endeavors to reinforce this view.

Summary

T

his chapter describes how characteristics of individuals, population 

composition, and conditions at the state 

level can mediate the ultimate effectiveness of tobacco control efforts. States 

with relatively greater representation in 

their populations of certain demographic 

groups more resistant to smoking cessation or susceptible to smoking uptake 

may not reduce prevalence to the same 

extent as other states, even with the same 

level of resources available for tobacco 

control.

However, resources for tobacco control differ by state and are part of the underlying state conditions that dictate how 

fast progress can be made. A state’s political climate, general wealth, economic 

dependence on tobacco, and normative 

outlook regarding the acceptability of 

smoking all potentially influence that 

state’s ability to reduce tobacco use. 

Another factor affecting tobacco control 

progress is the extent of counteractivity 

from the tobacco industry. An adequate 

interpretation of the findings from 

evaluations of tobacco control programs 

requires an appreciation of the implications related to all of these factors with 

respect to tobacco control success.

While appropriate measures of many 

important state conditions are lacking, 

the ASSIST analysis (chapter 9) was 

able to adjust for differing state demographic profiles (gender, age, race/ethnicity, education, poverty level, etc.) and 

included a composite variable for each 

state’s economic dependence on tobacco 

(see chapter 6).

Program evaluators need to be aware 

of the issues raised in this chapter and 

look for success or lack of success in 

subgroups within the population before 
156

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

concluding that a program had little or 

no effect. This evaluation process can 

reveal new areas where improved or 

tailored interventions are necessary and 

thus inform the design of future tobacco 

control strategies.

Conclusions

1. Economic and demographic factors 

are important state conditions that 

may affect tobacco control interventions. Demographic factors and state 

economic dependence on tobacco 

were measured and were used as covariates for the ASSIST evaluation 

analysis.

2. State demographic factors, developed 

from census and population survey 

data, included gender, age, race and 

ethnicity, education, income, state 

population, metropolitan area representation, and region. Because such 

factors have been historically related to 

differences in smoking prevalence and 

consumption levels, they can affect 

long-term changes in the outcomes of 

tobacco control interventions.

3. State economic dependence on tobacco represented a gross state product derived from tobacco growing 

and manufacturing. During the timeframe of ASSIST, the influence of 

this dependence could be observed 

in areas such as the lower level of tobacco control policy outcomes—for 

example, increases in cigarette taxes 

and enactment of clean indoor air 

laws—in major tobacco-growing 

states.

4. Several of the state conditions used 

in the ASSIST evaluation analysis 

did show a significant relationship 

with levels of per capita cigarette 

consumption. Because demographic 

factors were used to adjust state-level 

prevalence rates, these factors were 

already accounted for in the evaluation analysis.

5. Other state-level conditions, such as 

family, religion, wealth, and political 

affiliation, represent promising future 

areas for exploring the relationship 

between population demographics 

and health outcomes relating to the 

use of tobacco.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

157

Appendix 5.A. Baseline Values for Variables Considered in the 

ASSIST Evaluation Analyses

(Shading indicates ASSIST states.)

State

Female 

(%)

Median 

age

African 

American 

(%)

Hispanic 

(%)

Education 

at or above 

high school 

(%)

Below 

poverty 

(%)

State 

population

Metropolitan 

(%)

Index of 

Economic 

Dependence 

on Tobacco

AK 46.7 38 3.8 3.0 55.2 9.1 409,021 48.0 0

AL 53.2 42 22.9 0.6 36.7 17.4 3,124,244 63.8 0.342722

AR 52.9 43 13.8 0.8 34.2 20.0 1,788,260 40.0 0

AZ 51.3 41 2.7 16.6 51.9 15.4 2,895,613 79.3 0

CA 50.4 39 6.7 23.6 49.8 18.2 22,610,825 96.0 0.002424

CO 51.1 40 3.7 11.4 56.7 9.9 2,615,886 83.3 0

CT 52.4 42 7.3 5.7 49.0 8.5 2,492,818 93.5 1.107191

DC 54.1 40 61.8 5.5 50.5 26.4 461,735 100.0 0

DE 52.3 41 15.6 2.2 45.2 10.2 525,876 65.4 0

FL 52.4 44 11.3 12.3 45.5 17.8 10,507,267 91.1 0.298797

GA 52.4 40 24.9 1.8 42.8 13.5 5,045,636 66.9 11.13065

HI 49.5 41 2.5 6.3 50.5 8.0 863,260 72.3 0

IA 52.4 43 1.5 1.1 43.2 10.3 2,095,129 47.7 0

ID 50.9 41 0.3 4.8 48.2 13.1 769,924 23.5 0

IL 52.3 41 13.4 7.2 46.1 13.6 8,674,831 83.1 0.197492

IN 52.4 41 7.1 1.6 34.6 12.2 4,240,393 65.4 0.32914

KS 51.7 42 5.2 3.4 48.9 13.1 1,865,017 61.1 0

KY 52.6 41 6.6 0.6 35.4 20.4 2,829,299 43.5 34.32137

LA 53.0 41 28.0 2.4 40.0 26.4 3,059,288 77.4 0

MA 52.9 41 4.2 4.1 51.6 10.7 4,609,469 91.0 0.060287

MD 52.3 40 23.9 2.7 46.1 9.7 3,699,137 98.1 0.175933

ME 52.2 42 0.4 0.5 42.0 15.4 931,807 34.9 0

MI 52.4 41 12.9 1.9 44.7 15.4 7,021,665 80.1 0

MN 51.7 41 1.9 1.1 48.2 11.6 3,298,907 69.3 0

MO 52.8 42 9.8 1.1 41.9 16.1 3,878,349 69.6 0.071814

MS 53.5 41 32.0 0.6 39.7 24.7 1,886,630 28.9 0

MT 51.1 43 0.3 1.3 48.7 14.9 606,971 23.6 0

NC 52.3 41 20.1 1.1 41.8 14.4 5,229,560 54.1 65.14254

ND 50.7 42 0.5 0.6 49.9 11.2 466,205 43.6 0

NE 52.1 42 3.2 2.3 46.2 10.3 1,175,012 46.3 0

NH 51.8 40 0.5 0.9 51.0 9.9 835,095 59.6 0

NJ 52.6 42 12.1 9.4 46.1 10.9 5,963,048 100.0 0.030058

NM 51.5 41 1.8 35.8 46.5 17.4 1,132,096 54.2 0

NV 49.3 41 5.8 9.8 46.4 9.8 1,024,116 88.1 0

NY 53.0 41 13.4 11.6 45.9 16.4 13,674,145 91.3 1.104069

OH 52.8 42 9.9 1.2 40.5 13.0 8,234,828 79.3 0.17161

OK 52.2 42 6.7 2.4 44.4 19.9 2,363,718 59.3 0

OR 51.5 42 1.4 3.6 53.7 11.8 2,260,603 73.0 0

PA 53.1 43 8.4 1.7 37.0 13.2 9,145,540 83.2 0.215757

RI 53.0 42 3.1 4.2 44.5 11.2 762,522 92.2 0

SC 52.5 41 27.1 0.9 38.7 18.7 2,696,448 65.6 2.839496

SD 51.5 42 0.5 0.7 44.8 14.2 510,020 17.9 0

TN 52.9 42 14.4 0.7 35.8 19.6 3,813,635 65.2 4.249443

TX 51.5 39 11.0 23.5 45.1 17.4 12,812,537 84.4 0.016489

UT 51.2 38 0.7 4.8 54.6 10.7 1,214,648 87.0 0

VA 51.7 40 17.8 2.6 47.2 9.7 4,881,939 72.3 29.20964

VT 51.8 41 0.3 0.7 46.0 10.0 428,251 24.0 0

WA 51.0 41 2.8 3.9 56.4 12.1 3,866,788 77.3 0

WI 51.9 41 4.2 1.6 43.8 12.6 3,721,995 57.6 0.200908

WV 53.0 44 2.9 0.5 28.0 22.2 1,384,643 40.8 0.899024

WY 50.3 41 0.7 5.0 49.8 13.3 332,679 19.6 0

Note: Demographic factors are for those aged 18 years and older.
158

5 . S t a t e F a c i l i t a t i n g C o n d i t i o n s a n d B a r r i e r s

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163

6. Measuring the Impact of Tobacco on State 

Economies

Lan Liang, Frank J. Chaloupka, and Kathryn Ierulli

Contents

Introduction.................................................................................................................. 165

Background.................................................................................................................. 166

Tobacco Growing................................................................................................................166

Tobacco Manufacturing .....................................................................................................170

Studies on the Economic Impact of Tobacco ................................................................ 171

Tobacco-Industry-Sponsored Studies.................................................................................171

Non–Tobacco-Industry-Sponsored Studies.........................................................................172

Measuring the Economic Impact of Tobacco for ASSIST ............................................... 174

State Data and Trends.................................................................................................. 175

Summary...................................................................................................................... 181

Conclusions.................................................................................................................. 182

References................................................................................................................... 182

Table and Figures

Figure 6.1. Tobacco Leaf Production..........................................................................................167

Figure 6.2. Tobacco Leaf Production and Cigarette Production, Consumption, and Exports......169

Figure 6.3. Tobacco Leaf Production and Exports, and Imports’ Share in U.S. Use....................169

Figure 6.4. Tobacco Farming and Manufacturing as Share of Gross State Product, 2000 .........176

Table 6.1. Percentages of Gross State Product from Tobacco Growing and Manufacturing .......177

Figure 6.5. Tobacco Farming and Manufacturing as Share of Gross Domestic Product, 

United States............................................................................................................................178

Figure 6.6. Tobacco Farming and Manufacturing as Share of Gross State Product, 

North Carolina...........................................................................................................................178

Figure 6.7. Tobacco Growing and Manufacturing as Share of Gross State Product, 

Kentucky....................................................................................................................................179

Figure 6.8. Tobacco Farming and Manufacturing as Share of Gross State Product, 

Virginia .....................................................................................................................................179

Figure 6.9. Tobacco Farming and Manufacturing as Share of Gross State Product, 

Georgia......................................................................................................................................180
6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

164

Figure 6.10. Tobacco Farming and Manufacturing as Share of Gross State Product, 

South Carolina ..........................................................................................................................180

Figure 6.11. Tobacco Farming and Manufacturing as Share of Gross State Product, 

Tennessee..................................................................................................................................181
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

165

6. Measuring the Impact of Tobacco on State Economies

Introduction

T

his chapter describes the development of a measure for state economic dependence 

on tobacco, as a covariate factor in the analyses performed for the ASSIST evaluation model.1 It contains some basic background information on tobacco growing and 

manufacturing in the United States, describes studies by the tobacco industry and others 

that assess the economic contribution of tobacco to the national economy and the economies of individual states, reviews the methods used to construct state-level measures 

of the economic impact of tobacco for use in the ASSIST evaluation, and discusses 

these data and associated trends.

Tobacco has played an important role in the economy of the United States since the 

colonial era. American Indians presented Christopher Columbus with gifts of tobacco 

upon his arrival in 1492, and he introduced tobacco to Europe upon his return there. 

As demand rose in Europe, tobacco became the most important American agricultural 

export of the late eighteenth century.2 High tobacco tariffs in England helped lead to the 

American Revolutionary War. Subsequently, the tobacco industry contributed significantly to the economic growth of the United States through much of the nineteenth and 

twentieth centuries.

Although the consumption of tobacco products has declined in recent years, tobacco 

growing and manufacturing continue to be important parts of several state economies, 

with a potential impact on the implementation of upstream, policy-based tobacco control 

On the basis of available evidence, states’ economic dependence on tobacco 

can be shown to be a factor countervailing the efforts of upstream, policy-based 

interventions such as those of the American Stop Smoking Intervention Study 

(ASSIST). In areas such as tobacco excise taxes and the adoption of tobacco control 

policies, there are substantial differences between national averages and those of 

tobacco-producing states. Therefore, a state-level variable representing the state’s 

economic dependence on tobacco farming and manufacturing was developed for use 

as a covariate in the ASSIST evaluation regression analyses.

This chapter outlines the issues and assumptions leading to the development of 

this economic dependence variable, which was based on the economic contribution 

of tobacco growing and manufacturing, within a broader context including factors 

such as tobacco consumption, exporting, and importing. The chapter also examines 

research on the state-level economic impact of tobacco, the wide divergence in 

assumptions and outcomes between industry-sponsored and non–industry-sponsored 

studies, and trends toward a continued diminishing impact of tobacco on state 

economies over time.
166

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

interventions in these states. Evidence 

such as the following suggests that the 

economic importance of tobacco plays a 

key role in shaping state tobacco control 

policies and activities, social norms about 

tobacco use, and, consequently, tobacco 

use itself:

■ As of January 1, 2004, the four states 

with the lowest cigarette excise 

taxes per pack were Virginia (2.5¢), 

Kentucky (3¢), North Carolina (5¢), 

and South Carolina (7¢), all among the 

top tobacco-growing states. Since then, 

Virginia, Kentucky, and North Carolina 

have all significantly increased their 

cigarette excise taxes, while South 

Carolina still ranks among the lowest.

■ The average cigarette excise tax in the 

six leading tobacco-growing states 

(Kentucky, North Carolina, Georgia, 

South Carolina, Tennessee, and 

Virginia) is currently 25.7¢ per pack, 

while the average in non–tobaccogrowing states is 100.5¢ per pack. The 

overall average excise tax for all states 

as of February 2006 is 91.7¢.

■ The strength of state clean indoor air 

laws in non–tobacco-growing states 

was nearly five times that in the six 

leading tobacco-growing states, as 

measured by a comprehensive index 

that accounts for state preemption in 

1999.3

Limited empirical evidence supports 

the notion that the economic impact of 

tobacco on a state’s economy can act 

as a barrier to the adoption of effective 

tobacco control policies. Chaloupka and 

Saffer,4 for example, found that states 

with greater per capita production of 

tobacco were less likely to adopt laws 

restricting smoking, with a statistically 

significant effect on the adoption of only 

the most comprehensive restrictions. 

Similarly, Ohsfeldt and his colleagues5

found that the per capita value of state 

tobacco production had a negative impact on the strength of state restrictions 

on smoking and on state cigarette excise 

taxes. As a result of findings such as 

these, a study was undertaken as part of 

the ASSIST evaluation that ultimately 

led to state-level economic dependence 

on tobacco being quantified as a covariate in the evaluation analysis.

Background

Tobacco Growing

The 2002 Census of Agriculture indicates that there were 56,977 farms 

that grew tobacco in the United States 

in that year, just under 2.7% of all U.S. 

farms.6 These farms used 428,631 acres 

to grow tobacco—less than 0.5% of all 

farm acreage in the United States. This 

percentage was down sharply from the 

93,530 farms and 837,363 acres reported 

in the 1997 Census of Agriculture. Tobacco farming generally has been quite 

profitable, with the typical farm that 

grows tobacco generating nearly fourfifths of its gross income from tobacco.7

While these figures indicate a significant reduction in the number of tobacco 

farms and acreage devoted to tobacco 

growing, there has been a sharp rise in 

the average tobacco acreage per tobacco 

farm, with the average acreage per farm 

increasing by nearly two-thirds from 

1987 to 2002.
167

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Tobacco growing in the United States 

has been declining for several decades, 

and the rate of decline has been accelerating in recent years (see figure 6.1). 

Overall tobacco leaf production peaked 

at more than 2.3 billion pounds in 1963 

and then declined gradually from 1963 

through 1990. From 1990 through 1998, 

production was relatively stable, averaging about 1.6 billion pounds per year. 

Since 1998, however, overall leaf production has dropped sharply, falling by 

nearly 50%.

Several factors have contributed to the 

decline in overall tobacco leaf production in the United States (see figure 6.2):

■ Declining U.S. cigarette production.

U.S. cigarette production peaked at 

just over 750 billion cigarettes in 

1996 and has declined sharply since 

then, falling to just under 500 billion 

cigarettes by 2003.

■ Reduced consumption. U.S. 

cigarette consumption peaked at 640 

billion cigarettes in 1981 and then 

declined steadily through the early 

1990s before leveling off for several 

years. Since 1998, overall cigarette 

consumption has further declined 

by about 8%. Part of this decline is 

explained by tax and price increases, 

stronger tobacco control policies, 

and increased investments in tobacco 

control programs.

■ Increased cigarette imports.

Cigarette imports have risen over the 

past several years, from just under 3 

billion cigarettes in 1996 to more than 

23 billion cigarettes in 2003.

Figure 6.1. Tobacco Leaf Production

1975 2003

800

1,200

1,600

2,000

Year

1995

Pounds (millions)

1980 1985 1990 2000

Source: USDA. 2003. Tobacco situation and outlook yearbook. Washington, DC: USDA, Market and Trade 

Economics Division, Economic Research Service; USDA. 2004. Tobacco outlook. Washington, DC: USDA, 

Economic Research Service.
168

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

■ Decreased cigarette exports.

Much more of the recent decline in 

production is the result of the decline 

in cigarettes exported from the 

United States. Total exports peaked 

at nearly 244 billion cigarettes in 

1996 but had fallen by more than half 

(to just over 121 billion cigarettes) 

in 2003. Much of this decline can be 

attributed to leading U.S. cigarette 

manufacturers’ shifting production 

overseas in recent years.

Changes in the global markets for 

tobacco leaf have also contributed to the 

decline in U.S. tobacco leaf production 

(see figure 6.3). One such change is 

the sharp increase in U.S. imports of 

foreign-grown tobacco leaf. Imports of 

flue-cured tobacco grew from an average of 15.6 million pounds per year in 

the early 1970s to an average of 192.6 

million pounds per year for the most 

recently available 5 years (1998 through 

2002); similar patterns exist for imports 

of burley tobacco (average of 19.0 million pounds per year in the early 1970s 

and 202.4 million pounds per year for 

1998–2002).8

Consequently, the share of imported 

tobacco leaf used in U.S. cigarette 

A Small and Shrinking Fraternity: Tobacco Growing by the Numbers

Beyond the fact that tobacco farming has declined as a percentage of state revenue, the impact of these 

declines has been tightly concentrated. Although some form of tobacco is grown in nearly half of the 

U.S. states, the vast majority of tobacco leaf comes from a small number (6) of states, and the economies of those states are, in turn, affected disproportionately by the recent declines.

■ Based on cash receipts from tobacco in 2002, the states most dependent on tobacco farming and 

manufacturing include (share of total cash receipts in parentheses) North Carolina (37.1%), Kentucky (26.3%), Tennessee (9.1%), Virginia (7.3%), South Carolina (6.2%), and Georgia (6.0%).a

Other states with cash receipts from tobacco of at least $4 million in 2002 include Florida, Ohio, 

Indiana, Connecticut, Pennsylvania, Massachusetts, Wisconsin, Missouri, Maryland, and West 

Virginia. In total, this second group of states grows less than 8% of the tobacco crop in the United 

States.

■ Despite the fact that 6 states account for the largest share of the overall U.S. tobacco crop, cash 

receipts from tobacco typically account for a relatively small percentage of receipts from all farm 

commodities; in 2002, the only state in which tobacco accounted for more than 10% of receipts 

from all farm commodities was Kentucky (at 14.4%).a

■ Regional tobacco varieties represent a very small share of the overall market, including Southern 

Maryland, Virginia dark fire-cured, Kentucky-Tennessee fire-cured, Virginia sun-cured, Green River, 

One Sucker, Pennsylvania seedleaf filler, Connecticut Valley broadleaf binder, Wisconsin binder, 

and Connecticut Valley cigar wrapper. The vast majority of the tobacco grown in the United States is 

flue-cured and burley tobacco, accounting for approximately 61% and 33%, respectively, of tobacco 

leaf production in 2003.b

aU.S. Department of Agriculture. 2003. Tobacco situation and outlook yearbook. Washington, DC: 

Market and Trade Economics Division, Economic Research Service, U.S. Department of Agriculture.

bCapehart, T. 2004. Tobacco outlook (TBS-257, 09.17.04 Summary). Washington, DC: U.S. Department of Agriculture, Economic Research Service. 

http://usda.mannlib.cornell.edu/reports/erssor/specialty/tbs-bb/2004/tbs257.pdf.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

169

Figure 6.2. Tobacco Leaf Production and Cigarette Production, Consumption, and Exports

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

Tobacco leaf 

production

U.S. cigarette consumption 

Total cigarette production

Cigarette exports

Pounds of tobacco (millions)

Cigarettes (billions)

1,000

800

1,200

1,400

1,600

1,800

2,200

2,000

1975 2003

Year

1980 1985 1990 1995 2000

Source: USDA. 2003. Tobacco situation and outlook yearbook. Washington, DC: USDA, Market and Trade 

Economics Division, Economic Research Service; USDA. 2004. Tobacco outlook. Washington, DC: USDA, 

Economic Research Service.

Figure 6.3. Tobacco Leaf Production and Exports, and Imports’ Share in U.S. Use

300

900

1,300

1,900

500

700

1,100

1,500

1,700

2,100

0.0

5.0

10.0

15.0

30.0

35.0

40.0

45.0

Imports (%)

25.0

20.0

50.0

Tobacco leaf production

Share of imports in production

Tobacco leaf exports

Pounds of tobacco (millions)

1975 1979 1983 1987 1991 1995 1999 2003

Year

Source: USDA. 2003. Tobacco situation and outlook yearbook. Washington, DC: USDA, Market and Trade 

Economics Division, Economic Research Service; USDA. 2004. Tobacco outlook. Washington, DC: USDA, 

Economic Research Service; USDA. 2004. U.S. tobacco import update. Washington, DC: USDA, Economic 

Research Service.
170

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

production rose from less than 1% in the 

early 1970s to 46.3% in 2002.8 At the 

same time, exports of U.S.-grown tobacco leaf have fallen by nearly half over 

the past 25 years, from a peak of 765 

million pounds in 1978 to 384 million 

pounds in 2002.8 The significant growth 

in tobacco farming in other countries and 

reductions in barriers to trade in tobacco 

leaf have greatly increased competition 

in the global tobacco markets, resulting in lower-priced and better-quality 

tobacco leaf. The relatively high U.S. tobacco leaf prices (in large part the result 

of the price support system in the United 

States) have contributed to the declines 

in both domestic and foreign demand for 

U.S.-grown tobacco leaf.

Tobacco Manufacturing

While tobacco leaf is the primary 

ingredient in tobacco products, it accounts for a very small share of the 

overall value of tobacco products. Gale 

and his colleagues7 estimated that in 

1997, domestically grown tobacco leaf 

accounted for about 2.3% of the total 

value of the tobacco products consumed 

in the United States. The largest share of 

this total, 43%, was the value added in 

tobacco product manufacturing—defined 

as the final value of the tobacco products 

produced minus the costs of the raw materials and intermediate products used in 

producing tobacco, such as tobacco leaf, 

papers, filters, and packaging materials. 

A relatively small share of this percentage 

was labor costs (just over 6%), whereas a 

much larger share went to advertising (almost 20%). Capital costs (including profits) accounted for much of the remainder 

of the manufacturing share, while 

wholesale and retail value added and federal, state, and local taxes accounted for 

the rest of the overall value.

There are three primary types of 

manufacturing related to tobacco: stemming and redrying, manufacturing 

cigarettes, and manufacturing other tobacco products. In contrast to the large 

number of tobacco farms, relatively 

few establishments are involved in tobacco manufacturing. On the basis of 

the most recently available data from 

the Census of Manufacturers, in 1997, 

25 establishments owned by 14 companies were involved in tobacco stemming and redrying,9 13 establishments 

were owned by 9 companies involved in 

manufacturing cigarettes,10 and 63 establishments were owned by 52 companies 

involved in manufacturing other tobacco 

products. Collectively, these establishments employed 33,620 persons, with 

cigarette manufacturing accounting for 

nearly two-thirds of the total.9–11 Although tobacco product manufacturing 

involves relatively few people, the value 

added by manufacturers is significant. 

In 1997, the value added in production 

was $29.3 billion for manufacturing 

cigarettes, $2.7 billion for manufacturing 

other tobacco products, and $0.7 billion 

for stemming and redrying.

In contrast to tobacco farming, tobacco manufacturing takes place in a 

small number of states. As with tobacco 

farming, however, most of the economic 

contribution of tobacco manufacturing 

is concentrated in even fewer states. 

Almost two-thirds of the value added 

from tobacco manufacturing in 1997 

came from manufacturing in North 

Carolina (34.3%), Virginia (21.4%), and 
171

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Kentucky (9.8%). Other states in which 

tobacco products are manufactured 

include Alabama, Florida, Georgia, Illinois, Pennsylvania, Tennessee, and West 

Virginia.

Overall employment in tobacco manufacturing has been falling for several 

decades. In 1977, almost 61,000 persons 

were employed in tobacco manufacturing; by 2000, employment had fallen 

by more than half to just over 28,000 

persons. As with the declines in tobacco 

leaf production, part of this decline in 

employment can be attributed to declines 

in overall U.S. cigarette production and 

consumption. In addition, much of the 

decline in employment is the result of 

tobacco industry actions, including increased automation of production processes (a longstanding trend)12 and the 

shifting of production from the United 

States to other countries (particularly 

important in more recent years). Major 

factors behind the shift to overseas production include reductions in trade and 

investment barriers, the opening of previously closed markets (particularly in Asia 

and Central/Eastern Europe), lower labor 

and other operating expenses, and an interest in locating in expanding markets.

Studies on the Economic 

Impact of Tobacco

Tobacco-Industry-Sponsored Studies

The earliest studies on the impact of 

tobacco growing, manufacturing, and 

related activities on the U.S. and state 

economies were produced by the tobacco industry, and comparable studies 

have been produced in numerous other 

countries. Over the past 25 years, industry-commissioned studies in the United 

States have estimated the industry’s 

contributions to employment, income, 

and tax revenues.13–18 These studies 

have often been used in efforts to influence legislators in debates over tobacco 

control policies by arguing that stronger 

tobacco control policies and the resulting 

reductions in tobacco use would lead to 

significant job losses and reductions in 

income and tax revenues. More recently, 

reductions in industry settlement payments to the states have been added to 

the list of potential negative economic 

consequences of tobacco control policies 

(see the discussion on the Philip Morris 

USA Web site about the impact of state 

cigarette tax increases on Master Settlement Agreement payments).19,20

The tobacco-industry-sponsored 

studies typically conclude that tobacco makes a significant contribution 

to virtually every state economy. The 

methodologies used in these studies are 

similar. For example, the 1996 American 

Economics Group18 study describes the 

economic impact of tobacco in multiple 

sectors: the core sector, the supplier sector, and the expenditure-induced sector.

■ The core sector includes not only 

the growing of tobacco and the 

manufacturing of tobacco products 

but also the wholesale and retail 

distribution of tobacco products.

■ The supplier sector is defined by 

the industries that are involved in 

producing goods and services that 

are used by those in the core sector, 

including those supplying paper 

products; fertilizer for tobacco 
172

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

farmers; and gas, water, and electricity 

used in farming, manufacturing, and 

distribution.

■ The expenditure-induced sector

reflects the “multiplier” effects 

associated with the spending resulting 

from the incomes generated by those 

working in the core and supplier 

sectors, as well as effects resulting 

from government spending of excise 

and sales tax revenues from tobacco 

products, and personal and corporate 

income taxes and FICA taxes from 

those in the core and supplier sectors.

The 1996 American Economics 

Group report, the most recent industrysponsored, publicly available report, 

describes the economic impact of tobacco in 1994. This report concludes that 

tobacco generated over 1.8 million jobs 

that produced $54.3 billion in wages and 

benefits, while total taxes generated from 

tobacco were almost $36 billion. Most of 

tobacco’s economic impact comes from 

the supplier and expenditure-­induced 

sectors rather than the core sector. For 

example, jobs in the core sector accounted for less than one-quarter of the 

total, whereas incomes earned in the 

core sector accounted for just over onesixth of the total. Similarly, according to 

American Economics Group estimates, 

less than half of the taxes generated by 

tobacco come from sales and excise 

taxes on tobacco products, with the majority coming from personal, FICA, and 

corporate income taxes. Within the core 

sector, the jobs most clearly dependent 

on tobacco (those in tobacco growing, 

auction warehousing and distribution, 

and manufacturing) and the incomes 

they generate account for a small share 

of the core sector totals (43.3% and 

32.6%, respectively).

Non–Tobacco-Industry-Sponsored 

Studies

In recent years, the tobaccoindustry-sponsored studies have been 

increasingly scrutinized, and several 

recent studies have concluded that the 

estimates produced by the tobaccoindustry-­sponsored studies significantly 

overstate the impact of tobacco on the 

U.S. and state economies.21–25 The 

key difference between these studies 

and the tobacco-­industry-sponsored 

studies results from the non–tobaccoindustry-sponsored studies’ focus on 

the net rather than gross economic 

impact of tobacco. Specifically, the 

non–tobacco-industry-sponsored studies explicitly model the alternative 

economic activity that would result if 

resources used for tobacco were used 

for other economic activity. This perspective is given little attention in the 

tobacco-industry-sponsored studies; for 

example, in the 1985 Chase Econometrics study,14 the authors acknowledge 

that money not spent on tobacco would 

be reallocated to other spending and that 

there would be virtually no difference at 

the national level between economic activity with and without tobacco.

Warner and Fulton22 were the first to 

formally address this issue in their analysis of the economic impact of tobacco on 

the Michigan economy. Using a relatively sophisticated macroeconomic model 

(the REMI model developed by Regional 

Economic Models, Inc.) adapted for 

Michigan, Warner and Fulton forecast 

the effects on employment and income 
173

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

under alternative scenarios ranging from 

accelerations in the rate of decline of 

tobacco use to the complete elimination 

of tobacco consumption. They then compared these forecasts with those assuming a continuation of the current trend in 

tobacco consumption. In each scenario, 

the money that would have otherwise 

been spent on tobacco was redistributed 

to spending on other goods and services 

on the basis of the typical spending patterns of Michigan consumers. Assuming a doubling in the rate of decline in 

tobacco use, Warner and Fulton22 predict 

relatively modest gains in employment 

and incomes in Michigan. Under the 

more extreme assumption that tobacco 

consumption would be eliminated, they 

estimate an overall increase of about 

5,600 jobs in Michigan and an increase 

in Michigan incomes of $226 million. 

These gains reflect the fact that Michigan is an importer of tobacco products 

and that, in the absence of tobacco, funds 

once spent on tobacco would be more 

likely to be spent on goods and services 

produced in Michigan, producing more 

jobs and higher incomes in Michigan.

As in Warner and Fulton’s 1994 

study,22 Warner et al.24 reallocated the 

money that would have been spent on 

tobacco to spending on other goods 

and services on the basis of regional 

consumption patterns. These estimates 

were compared with those generated by 

assuming that existing trends in tobacco 

use would continue. On the basis of their 

findings, Warner et al. conclude that 

industry claims about large job losses 

resulting from stronger tobacco control 

policies and programs and resulting declines in tobacco use are significantly 

overstated and that the real economic 

impact of tobacco is relatively small.

These forecasts are consistent with 

observed economic activity in major 

tobacco-growing regions, as described 

by the United States Department of Agriculture (USDA) in 1997.25 In its review, 

the USDA concludes that the declines 

The Regional Impact of 

Tobacco Economics

Warner and his colleaguesa examined 

tobacco’s net economic impact on regional 

economies, based on the eight regions defined by the U.S. Department of Commerce’s 

Bureau of Economic Analysis, with the 

bureau’s southeastern region divided into 

non–­tobacco-growing and tobacco-growing/

producing states. Modeling a period between 

1993 and 2000, Warner et al. predict that

■ A doubling of the downward trend in tobacco use would lead to a loss of 36,600 

jobs in the southeastern tobacco region by 

2000—only 0.2% of total employment in 

the region—with offsetting increases in the 

rest of the country.

■ Even a total elimination of tobacco use 

would stabilize at slightly more than 1% 

of the employment in this region, while 

producing a net gain of jobs at a national 

level. Warner and colleagues predict a loss 

of about 303,000 jobs in the southeastern 

tobacco region in this case, stabilizing 

to 222,000 jobs by 2000 as the regional 

economy adjusted, but the number of jobs 

gained in other regions would rise to produce an overall increase of 133,000 jobs 

nationally.

aWarner, K. E., G. A. Fulton, P. Nicolas, and 

D. R. Grimes. 1996. Employment implications of declining tobacco product sales for 

the regional economies of the United States. 

JAMA: The Journal of the American Medical 

Association 275 (16): 1241–46.
174

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

in tobacco production in the 1980s had 

relatively little impact on the economies 

of major tobacco-growing regions, with 

inflation-adjusted income rising by 28% 

on average in all U.S. tobacco-growing 

counties from 1979 through 1989. The 

USDA study attributes this result to the 

fact that tobacco growing accounted for 

less than 1%, on average, of total incomes in these counties.

Measuring the Economic 

Impact of Tobacco for ASSIST

On the basis of the studies discussed in the previous section, the measure 

of the contribution of tobacco to state 

economies developed for the ASSIST 

evaluation focuses on the core activities that are directly related to tobacco. 

Specifically, this measure was intended 

to focus on tobacco growing, warehousing, and manufacturing; the measure 

excludes the wholesale and retail distribution of tobacco products, the supplier 

sector, and the expenditure-induced sector, given that economic activity related 

to tobacco in these sectors would almost 

certainly be replaced by economic activity related to other goods and services in 

the absence of tobacco and, hence, does 

not depend on tobacco.

An initial examination of the data 

available from published sources as well 

as data presented in the tobacco-industrysponsored studies16,18 clearly indicated 

that tobacco warehousing was a relatively 

minor activity, with tobacco growing and 

tobacco product manufacturing accounting for 95% or more of direct tobacco 

employment and an even higher share 

of income. Moreover, the correlations 

between the warehousing and tobaccogrowing measures for both employment 

and income were very high (.94), 

indicating that tobacco growing served 

as an effective measure of both activities. Given the relatively minor economic 

contribution of tobacco warehousing, its 

high correlation with tobacco growing, 

and the lack of reliable state-level data on 

it, the measure developed for the ASSIST 

evaluation focused on tobacco growing 

and tobacco manufacturing.

Industry-sponsored studies make a 

variety of somewhat arbitrary assumptions (e.g., assuming that each tobaccofarming-related job is a half-time job 

and that the number of unpaid workers 

relative to paid workers is the same for 

all farms18) to produce estimates of the 

number of full-time-equivalent farmers 

growing tobacco and the incomes they 

earn from tobacco farming. For this 

evaluation, rather than adopting these 

arbitrary assumptions and the likely error that would be introduced into the 

economic impact measure, an alternative 

measure was used that more directly 

reflects the value of tobacco farming. 

Specifically, state-level cash receipts 

from tobacco, available from the USDA, 

were used to capture the contribution of 

tobacco farming to state economies.

Similarly, several alternative measures 

of the economic contribution from tobacco manufacturing were considered, 

including total employment in tobacco 

product manufacturing, total compensation in tobacco product manufacturing, and the value added from tobacco 

product manufacturing. Published data 

on these measures were available from 
175

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

multiple sources, including the Bureau 

of Labor Statistics (the ES-202 reports), 

the Bureau of Economic Analysis (state 

annual personal income tables), and the 

U.S. Census Bureau (County Business 

Patterns reports). All data obtained were 

for Standard Industrial Classification 21, 

which includes the following activities: 

multiple aspects of tobacco processing; 

production of cigarettes, cigars, and 

other tobacco products; tobacco thrashing, stemming, and redrying; and related 

activities. Varying amounts of data are 

contained in these published reports, 

with some data unreported for confidentiality reasons (e.g., for confidentiality 

reasons, the BLS ES-202 reports do 

not contain information on employment when 70% or more of the total in 

the state is controlled by one firm). The 

published data were supplemented with 

unpublished information from a variety 

of federal and state agencies, and, on 

the basis of historical trends and relative 

shares, some imputation was done to 

obtain estimates of employment in and 

compensation from tobacco manufacturing for each state in each year, with the 

imputation done so that the sum of state 

estimates equaled reported estimates for 

the United States.

To provide an understanding of the 

relative importance of tobacco to the 

overall state economy, all measures 

were divided by an appropriate measure 

of total economic activity in the state. 

Specifically, the dollar-denominated 

measures were divided by gross state 

domestic product (GSP), and the employment measures were divided by total 

employment in the state (both obtained 

from the Bureau of Economic Analysis). 

Finally, a single variable reflecting the 

combined economic contribution of tobacco growing and manufacturing was 

obtained by adding the measures of the 

value of crop receipts from tobacco as a 

share of GSP and the total compensation 

for tobacco manufacturing as a share of 

GSP. This variable was ultimately used 

as a covariate as part of state conditions 

for the regression analyses described in 

other chapters.

State Data and Trends

Several interesting observations emerge from the data on the contribution of tobacco to state economies. First, 

as illustrated in figure 6.4 (the data in 

figures 6.4–6.11 were calculated by the 

authors) and table 6.1, while tobacco 

growing and manufacturing do contribThe Problem of Quantifying Tobacco Labor

Alternative metrics were considered in the 

development of the measure of the impact 

of tobacco on state economies, including 

measures based on employment and wages. 

However, quantifying employment and wages 

for tobacco farming is a difficult task, given 

that few farmers grow tobacco exclusively 

(indeed, on farms that grow tobacco, only 

6% of the land, on average, is used for tobaccoa); many working on farms are unpaid 

(e.g., owner-operators and family labor); and 

employment is seasonal and includes many 

temporary, short-term laborers.

aGale, H. F. Jr., L. Foreman, and T. Capehart. 

2000. Tobacco and the economy: Farms, jobs 

and communities (Agricultural Economic 

Report No. 789). Washington, DC: U.S. Department of Agriculture, Economic Research 

Service.
176

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

ute to economic activity in a number of 

states, this contribution is relatively minor in most of these states. In 2000, for 

example, the share of GSP accounted for 

by tobacco farming and manufacturing 

was just over 4% in North Carolina, just 

over 2% in Kentucky and Virginia, and 

slightly less than 1% in Georgia. Over 

the period from 1979 through 2000, the 

share of GSP accounted for by tobacco 

growing and manufacturing exceeded 

0.2% in any year in just two other 

states—South Carolina and Tennessee.

Second, as illustrated in figure 6.5, the 

economic contribution of tobacco to the 

national economy has been declining for 

most of the period from 1979 through 

2000. Tobacco farming contributed just 

over 0.1% of gross domestic product 

(GDP) in the early 1980s; by 2000, this

was down to less than 0.03% of GDP. 

Tobacco manufacturing’s contribution to 

GDP has been somewhat more stable but 

has been generally declining since the 

early 1990s. In general, the value of tobacco manufacturing depends heavily on 

the price of tobacco products, as can be 

seen by some of the larger changes in the 

share of GDP accounted for by tobacco 

manufacturing over time. For example, 

the “Marlboro Friday” reductions in the 

prices of leading cigarette brands in 1993 

contributed to a significant decline in the 

economic impact of tobacco manufacturing, whereas the settlement-related price 

increases of the late 1990s contributed to 

the increase in the economic impact of 

tobacco manufacturing at the end of the 

period. Recent declines in the production 

of tobacco products have almost certainly 

Figure 6.4. Tobacco Farming and Manufacturing as Share of Gross State Product (GSP), 2000

W

WI

V

VA

O

OK

PA

SC

TNTX

H

NY

N

NV

J

N

NE

C

MS

M

MI

O

MDMA

K

LA

IL

IN

Y

G

FL

A

DC

A

CA

CT

L

0.0

1.5

2.5

4.0

0.5

1.0

2.0

3.0

3.5

4.5

Percentage of GSP

State

Tobacco farming

Tobacco manufacturing

Tobacco farming and manufacturing

Note: Tobacco farming and manufacturing do not contribute to GSP in Alaska, Arkansas, Colorado, Hawaii, Iowa, 

Idaho, Maine, Minnesota, Mississippi, Montana, North Dakota, New Hampshire, New Mexico, Oregon, Rhode 

Island, South Dakota, Utah, Vermont, Washington, and Wyoming.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

177

Table 6.1. Percentages of Gross State Product from Tobacco Growing and Manufacturing

(Shading indicates ASSIST states.)

State 1992 1993 1994 1995 1996 1997 1998 1999

AK 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

AL 0.03 0.02 0.02 0.03 0.03 0.03 0.03 0.03

AR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

AZ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CT 0.11 0.07 0.05 0.06 0.07 0.07 0.07 0.06

DC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

DE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FL 0.03 0.02 0.02 0.03 0.03 0.03 0.03 0.03

GA 1.11 0.90 0.87 1.13 1.15 1.15 1.08 1.01

HI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

IA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

ID 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

IL 0.02 0.01 0.01 0.02 0.02 0.02 0.01 0.01

IN 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.01

KS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

KY 3.43 3.24 3.25 2.94 2.99 2.61 2.31 2.03

LA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MA 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MD 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.01

ME 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MO 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

MS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NC 6.51 4.81 4.53 4.48 4.11 4.07 3.54 3.04

ND 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NH 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NM 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NV 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.03

NY 0.11 0.11 0.14 0.15 0.19 0.18 0.19 0.19

OH 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01

OK 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

OR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

PA 0.02 0.02 0.01 0.01 0.02 0.02 0.02 0.01

RI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

SC 0.28 0.26 0.23 0.22 0.24 0.23 0.19 0.16

SD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

TN 0.42 0.38 0.33 0.31 0.34 0.30 0.26 0.22

TX 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

UT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

VA 2.93 2.50 2.50 2.48 2.44 2.26 2.04 1.83

VT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

WA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

WI 0.02 0.02 0.01 0.01 0.01 0.01 0.00 0.00

WV 0.09 0.08 0.09 0.09 0.08 0.08 0.08 0.07

WY 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

178

Figure 6.5. Tobacco Farming and Manufacturing as Share of Gross Domestic Product (GDP), 

United States

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Year

Percentage of U.S. GDP

1980 1985 1990 1995 2000

Tobacco farming and manufacturing

Tobacco manufacturing

Tobacco farming 

Figure 6.6. Tobacco Farming and Manufacturing as Share of Gross State Product (GSP), 

North Carolina

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Year

Percentage of GSP, North Carolina

Tobacco farming and manufacturing

Tobacco manufacturing

Tobacco farming 

1980 1985 1990 1995 2000
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

179

Figure 6.7. Tobacco Growing and Manufacturing as Share of Gross State Product (GSP), 

Kentucky

0.00

1.00

2.00

3.00

4.00

5.00

6.00

Year

Percentage of GSP, Kentucky

Tobacco farming and manufacturing

Tobacco manufacturing

Tobacco farming 

1980 1985 1990 1995 2000

Figure 6.8. Tobacco Farming and Manufacturing as Share of Gross State Product (GSP), 

Virginia

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Year

Percentage of GSP, Virginia

Tobacco farming and manufacturing

Tobacco 

manufacturing

Tobacco farming 

1980 1985 1990 1995 2000
6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

180

Figure 6.9. Tobacco Farming and Manufacturing as Share of Gross State Product (GSP), 

Georgia

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Year

Percentage of GSP, Georgia

Tobacco farming and 

manufacturing

Tobacco 

manufacturing

Tobacco farming 

1980 1985 1990 1995 2000

Figure 6.10. Tobacco Farming and Manufacturing as Share of Gross State Product (GSP), 

South Carolina

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Year

Percentage of GSP, South Carolina

Tobacco farming and manufacturing

Tobacco 

farming

Tobacco manufacturing

1980 1985 1990 1995 2000
181

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

led to a renewal of the downward trend 

in the economic impact of tobacco manufacturing on the U.S. economy.

Finally, even in the states where tobacco growing and manufacturing have had 

a significant impact on the state economy, 

their importance has diminished over time 

(see figures 6.6 through 6.11). For example, in North Carolina and Kentucky, 

the states where tobacco has historically 

had the greatest economic impact, the 

share of GSP accounted for by tobacco 

growing and manufacturing fell by nearly 

60% from 1979 through 1999. Similarly, 

tobacco’s share of GSP in Virginia fell by 

more than 40% during this period. The 

only exception to this trend is Georgia, 

which experienced an increase in tobacco 

manufacturing’s contribution to its GSP 

in the early 1980s, followed by relative 

stability; recent trends in Georgia, however, suggest that the economic impact of 

tobacco is beginning to fall there as well.

Summary

T

obacco growing and manufacturing 

have played important roles in the 

development and growth of the U.S. 

economy for many years. While tobacco 

growing and manufacturing take place in 

more than half of U.S. states, the economic impact of these activities is concentrated in a small number of states, most 

notably North Carolina, Kentucky, and 

Virginia. Moreover, the economic contribution of tobacco to state economies has 

been falling for many years, the result 

of declines in tobacco use in the United 

States, increased use of foreign-grown 

Figure 6.11. Tobacco Farming and Manufacturing as Share of Gross State Product (GSP), 

Tennessee

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Year

Percentage of GSP, Tennessee

Tobacco farming and manufacturing

Tobacco farming

Tobacco manufacturing

1980 1985 1990 1995 2000
182

6 . M e a s u r i n g t h e I m p a c t o f T o b a c c o o n S t a t e E c o n o m i e s

tobacco, reduced exports of tobacco leaf 

and tobacco products, increased automation of tobacco product production 

processes, and the shift of production facilities to overseas locations.

While the economic influence of tobacco continues to decline, there is still 

evidence to support its impact on upstream tobacco control interventions such 

as taxes and legislation—therefore, an 

accurate evaluation of such interventions 

must take these economic factors into account. By developing a measure that accounts for tobacco-related state economic 

conditions, we can provide a more accurate picture of the impact of state-level 

programs such as ASSIST relative to the 

environment of the states themselves.

Conclusions

1. Anecdotal and empirical evidence 

indicates that state restrictions on 

smoking, and cigarette and other tobacco product excise taxes are lower 

in states that have relatively more 

visible sectors of tobacco growing 

and/or manufacturing.

2. While substantial gaps exist between 

the tobacco industry’s and the public 

health community’s interpretations 

of the economic impact of tobacco, 

studies that incorporate redistribution of tobacco spending have shown 

negligible employment impact at an 

overall national level.

3. Despite the limited and declining 

economic impact of tobacco, arguments about the importance of tobacco to state economies have created 

barriers to the adoption of effective 

and comprehensive tobacco control 

policies in many states.

4. To factor the potential state-level 

economic impact of tobacco into 

the impact of ASSIST on policy 

outcomes and smoking behavior, a 

quantitative variable was developed 

for the ASSIST evaluation analyses, 

based on key indicators of tobacco 

growing and manufacturing relative 

to a state’s gross domestic product. 

This variable, which was subsequently integrated as part of baselevel state conditions in the analyses, 

helped model the relative impact of 

these economic dependence factors 

on the kinds of upstream, policybased interventions studied as part of 

ASSIST.

References

1. Stillman, F., A. Hartman, B. Graubard, 

E. Gilpin, D. Chavis, J. Garcia, 

L. M. Wun, W. Lynn, and M. Manley. 

1999. The American Stop Smoking 

Intervention Study: Conceptual 

framework and evaluation design. 

Evaluation Review 23 (3): 259–80.

2. Johnson, P. R. 1984. The economics 

of the tobacco industry. New York: 

Praeger.

3. Chriqui, J. F., M. Frosh, 

R. C. Brownson, D. M. Shelton, 

R. C. Sciandra, R. Hobart, P. H. Fisher, 

R. El Arculli, and M. H. Alciati. 2002. 

Application of a rating system to state 

clean indoor air laws (USA). Tobacco 

Control 11 (1): 26–34.

4. Chaloupka, F. J., and H. Saffer. 1992. 

Clean indoor air laws and the demand 

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5. Ohsfeldt, R. L., R. G. Boyle, and 

E. I. Capilouto. 1999. Tobacco taxes, 

smoking restrictions, and tobacco use. 

In The economic analysis of substance 

use and abuse: An integration of 

econometric and behavioral economic 

research, ed. F. J. Chaloupka, 

M. Grossman, W. K. Bickel, and 

H. Saffer, 15–29. Chicago: Univ. of 

Chicago Press.

6. U.S. Department of Agriculture. 2004. 

2002 census of agriculture: United 

States summary and state data; Vol. 

1. Geographic Area Series, Part 51.

Washington, DC: U.S. Department 

of Agriculture, National Agricultural 

Statistics Service.

7. Gale, H. F. Jr., L. Foreman, and 

T. Capehart. 2000. Tobacco and the 

economy: Farms, jobs and communities

(Agricultural Economic Report 

No. 789). Washington, DC: U.S. 

Department of Agriculture, Economic 

Research Service.

8. Capehart, T. 2004. U.S. tobacco import 

update (TBS-2003-01, 02.13.04). 

Washington, DC: U.S. Department of 

Agriculture, Economic Research Service. 

http://www.ers.usda.gov/publications/so/

view.asp?f=specialty/tbs-bb.

9. U.S. Bureau of the Census. 1999. 

Tobacco stemming and redrying: 1997 

Economic Census; Manufacturing.

Industry Series. Washington, DC: U.S. 

Department of Commerce, Economics 

and Statistics Administration, U.S. 

Census Bureau.

10. U.S. Bureau of the Census. 1999. 

Cigarette manufacturing: 1997 

Economic Census; Manufacturing.

Industry Series. Washington, DC: U.S. 

Department of Commerce, Economics 

and Statistics Administration, U.S. 

Census Bureau.

11. U.S. Bureau of the Census. 1999. Other 

tobacco product manufacturing: 1997 

Economic Census; Manufacturing.

Industry Series. Washington, DC: U.S. 

Department of Commerce, Economics 

and Statistics Administration, U.S. 

Census Bureau.

12. Virginia Department of Business 

Assistance. 2001. Manufacturing in 

Virginia: SIC 21; Tobacco products.

Richmond: Virginia Department of 

Business Assistance.

13. Wharton Applied Research Center. 

1979. A study of the tobacco industry’s 

economic contribution to the nation, 

its fifty states, and the District of 

Columbia. Philadelphia: Wharton 

Applied Research Center and Wharton 

Econometric Forecasting Associates, 

Inc., University of Pennsylvania. 

http://legacy.library.ucsf.edu/tid/

hvh54c00.

14. Chase Econometrics. 1985. The 

economic impact of the tobacco 

industry on the United States economy 

in 1983: Vol. 1; The impacts on the 

national economy. Bala Cynwyd, PA: 

Chase Econometrics.

15. Price Waterhouse. 1990. The economic 

impact of the tobacco industry on the 

United States economy. Arlington, VA: 

Price Waterhouse. http://legacy.library.

ucsf.edu/tid/wgd55c00.

16. Price Waterhouse. 1992. The economic 

impact of the tobacco industry on the 

United States in 1990. Arlington, VA: 

Price Waterhouse. http://legacy.library.

ucsf.edu/tid/kid42d00.

17. Tobacco Merchants Association. 

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the national economy (1980-1995).

Princeton, NJ: Tobacco Merchants 

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ucsf.edu/tid/gvu11c00.
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18. American Economics Group. 1996. 

The U.S. tobacco industry in 1994: 

Its economic impact on the states.

Washington, DC: American Economics 

Group.

19. Philip Morris. n.d. Policies, practices & 

positions: Excise tax increases. http://

www.pmusa.com/en/policies_practices/

legislation_regulation/excise_taxes.asp.

20. Schroeder, S. A. 2004. Tobacco 

control in the wake of the 1998 Master 

Settlement Agreement. New England 

Journal of Medicine 350 (3): 293–301.

21. Arthur Andersen Economic Consulting. 

1993. Tobacco industry employment: 

A review of the Price Waterhouse 

Economic Impact Report and Tobacco 

Institute estimates of “economic losses 

from increasing the federal excise 

tax.” Los Angeles: Arthur Andersen 

Economic Consulting. 

http://tobaccodocuments.org/lor/

89735021-5033.html.

22. Warner, K. E., and G. A. Fulton. 1994. 

The economic implications of tobacco 

product sales in a nontobacco state. 

JAMA: The Journal of the American 

Medical Association 271 (10): 771–76.

23. Warner, K. E., and G. A. Fulton. 1995. 

Importance of tobacco to a country’s 

economy: An appraisal of the tobacco 

industry’s economic argument. Tobacco 

Control 4: 180–83.

24. Warner, K. E., G. A. Fulton, P. Nicolas, 

and D. R. Grimes. 1996. Employment 

implications of declining tobacco 

product sales for the regional 

economies of the United States. JAMA: 

The Journal of the American Medical 

Association 275 (16): 1241–46.

25. U.S. Department of Agriculture. 1997. 

Tobacco situation and outlook.

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view.asp?f=specialty/tbs-bb.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

185

7. The ASSIST Newspaper Tracking System

W. Douglas Evans, Alec Ulasevich, and Frances A. Stillman

Contributor: K. Viswanath

Contents

Introduction.................................................................................................................. 187

The Challenge of Measuring the Effect of Media Interventions .................................... 189

Tracking the Articles..................................................................................................... 190

Collecting the Articles........................................................................................................191

Determining the Relevance of the Articles.........................................................................191

Coding the Articles and Maintaining a Database..............................................................192

Developing Indices from the Database..............................................................................193

Trend Analyses............................................................................................................. 195

Trend Analyses of Types of Articles.....................................................................................196

Analyses of ASSIST Versus Non-ASSIST States...................................................................199

A Case Study of Newspaper Coverage in North Carolina ....................................................200

Summary...................................................................................................................... 201

Limitations........................................................................................................................202

Future Directions...............................................................................................................203

Conclusions.................................................................................................................. 204

References................................................................................................................... 211

Case Study

Case Study 7.1. Editorials Promote a Stronger Youth Access Law in North Carolina .................201

Tables and Figures

Table 7.1. Examples of Key Words for Search Strategy...............................................................192

Table 7.2. Definitions of Coded Variables..................................................................................194

Table 7.3. Examples of Tobacco Topics......................................................................................194

Figure 7.1. Article Type by Year, 1993–2000..............................................................................197

Figure 7.2. Policy Topic by Year, 1993–2000..............................................................................197

Table 7.4. Average Rate Variables for Each Year Analyzed (District of Columbia Excluded) ......199
7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

186

Appendices

Appendix 7.A. Coding Topics......................................................................................... 205

Appendix 7.B. Search Strategies.................................................................................. 207
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

187

7. The ASSIST Newspaper Tracking System

Introduction

T

his chapter describes (1) the database created to track newspaper coverage of 

ASSIST’s four priority policy areas during the project, (2) the methods developed to 

code and analyze the data, and (3) a state-level scoring system created to characterize 

the newspaper coverage. Several analyses are presented to demonstrate the score’s potential uses, including

■ Trend analyses of coverage by topic, to highlight the potential use of the scoring 

system to determine whether media interventions are stimulating adequate 

newspaper coverage to engender support for the proposed policies;

To determine whether media interventions and media advocacy influenced 

newspaper coverage of tobacco control issues, the American Stop Smoking 

Intervention Study (ASSIST) evaluation analyzed newspaper coverage of ASSIST 

priority policy areas. The ASSIST Newspaper Tracking System, a comprehensive 

database of newspaper coverage of tobacco control efforts in the United States from 

1993 to 2000, compiled over 124,000 articles from newspapers across the country 

with the assistance of a national newspaper clipping service. This database was 

subsequently used to describe trends in media coverage for ASSIST policy areas over 

time as well as differences in news coverage between ASSIST and non-ASSIST states.

Articles were judged for relevance on the basis of a three-tiered key-word search 

of tobacco-, legislative restriction-, and policy-related terms as well as a subjective 

content review. These articles were then coded for subsequent analysis along indices 

such as policy type, topic, circulation, article type, and point of view. Four state-level 

metrics were produced from this scoring system: raw frequency of articles; relative 

(percentage) frequency; a rate variable comparing number of articles to the number 

of newspapers per state; and a media advocacy variable (MAV), a per capita index 

of prohealth coverage computed from multiple factors.

Although a subsequent trend analysis of these scoring data could not establish 

evidence of changes in coverage over time, this study provided evidence that ASSIST 

media intervention efforts had an effect on newspaper coverage of tobacco control 

policy activities. The ASSIST Initial Outcomes Index was used to control for baseline 

differences between states, revealing a significant positive difference in the rate 

variable between ASSIST states and non-ASSIST states, with a plausible explanation 

being the presence of ASSIST media interventions. This database and its associated 

study serve as a valuable basis for future research efforts involving media outcomes 

in tobacco control and how to quantify them relative to policy intervention efforts.
188

7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

■ Comparison of newspaper coverage in 

ASSIST states with coverage in nonASSIST states;

■ A case study of newspaper coverage 

in one specific state, North Carolina, 

related to the successful promotion of 

stronger youth tobacco access laws.

Although this database was not 

ultimately used as part of the formal 

ASSIST evaluation analysis, it represents a valuable first effort in measuring 

the results of media advocacy efforts for 

tobacco control.

In a social-environmental public 

health approach, interventions that 

change public and private tobacco use 

policies are among the most powerful 

interventions available to tobacco control advocates. Policies that promote 

nonsmoking behavior lead to the development of a social norm that holds tobacco use unacceptable. One of the three 

principal interventions of the ASSIST 

model was media intervention, including 

media advocacy, a highly effective tool 

for promoting policies as part of a public 

health agenda. The ASSIST Newspaper 

Tracking System represented an effort 

to determine the success of the ASSIST 

media interventions by analyzing the 

amount and type of newspaper coverage 

Media Advocacy

“‘Media advocacy is the strategic use of mass media as a resource for advancing a social or public 

policy initiative.’a Media advocacy stimulates community involvement in defining policy initiatives 

that influence the social environment in which individuals make choices—for example, choices about 

tobacco use. Media advocates react to unexpected events and breaking news and create events to draw 

media attention and coverage to an issue.b When traditional media relations and interventions—for 

example, publicizing special events, marking health observances, and publicizing research results—are 

used strategically, not just informatively, they are tactics in the approach of media advocacy. In all the 

ASSIST states, ASSIST staff and volunteers were trained to use all media interventions in ways that 

were strategic and community based. In this way, ASSIST advanced the state of the art in media advocacy for tobacco control.

“Media advocates must know the relevant policy issues, know how to frame an issue for public debate, 

and know how the media function—what types of stories are deemed newsworthy, how editors decide 

what stories get covered, and what deadlines and logistic issues might influence coverage. Therefore, ASSIST conducted media advocacy training to impart knowledge and skills to advocates and 

to encourage and empower their involvement in tobacco control. A communication network among 

advocates for sharing information on local and national activities helped ASSIST advocates implement 

media advocacy efforts. Newsletters, listservs, and computer newsgroups supported timely communication and creativity among the national, state, and local advocates.”

Source: National Cancer Institute. 2005. ASSIST: Shaping the future of tobacco prevention and control

(Tobacco control monograph no. 16, NIH publication no. 05-5645). Bethesda, MD: National Cancer 

Institute (pp. 127, 129).

aNational Cancer Institute. 1989. Media strategies for smoking control: Guidelines (NIH publication 

no. 89-3013). Bethesda, MD: National Institutes of Health (p. 8).

bWallack, L., K. Woodruff, L. Dorfman, and I. Diaz. 1999. News for a change: An advocate’s guide to 

working with the media. Thousand Oaks, CA: Sage.
189

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

of the policy areas promoted by the 

ASSIST intervention: clean indoor air, 

restrictions on minors’ access to tobacco, 

excise tax increases, and restrictions on 

tobacco advertising and promotion (see 

Monograph 16, chapter 5, pp. 149–152).

The Challenge of Measuring 

the Effect of Media 

Interventions

T

he idea that news media coverage 

brings issues to the attention of the 

public and of policy makers may seem 

intuitive, if not obvious. The challenge 

in evaluation, however, is demonstrating that news media coverage does in 

fact influence the thinking, decisions, 

and behavior of the public and of policy 

makers. Although determining such a 

cause-and-effect relationship for some 

very focused and geographically limited 

topics might be possible, researchers in 

the field of evaluation are still grappling 

with how to do so for wide-scale public 

health interventions. When ASSIST was 

initiated in 1991, there was little published literature on methods for evaluating the effects of media interventions,1

which are important interventions of the 

ASSIST model. Therefore, designing 

a method of evaluation was essential. 

Evaluating media intervention effects 

would entail determining actual news 

media coverage, tracking coverage 

trends, and comparing those trends with 

levels or types of media intervention efforts. National Cancer Institute (NCI) 

and ASSIST Coordinating Center staff 

members developed methods that not 

only measured the effects of ASSIST but 

also contributed to advancing the field of 

evaluation in this regard.

Though an appropriate method to 

evaluate ASSIST media interventions 

was not available, the development of 

the newspaper tracking system was 

influenced by the work in media studies that documented the relationship 

between news media coverage of social 

problems and the effects of that coverage on audience perceptions of those 

problems. Early research was on agenda 

setting, and that research posited that 

the public’s attention, over time, would 

be focused on issues occupying time or 

space in the mass media.2 In the words 

of Bernard Cohen, “The press may not 

be successful much of the time in telling 

people what to think, but it is strikingly 

successful in telling readers what to 

think about.”3(p13) However, subsequent 

work in media studies revealed that 

the model described in Wallack et al.2

was too limited and suggested that by 

presenting social problems as such, the 

media do help individuals to think about 

a problem in a certain way. The media 

provide arguments for and against social 

issues and thereby define the terms in 

which the topics are considered.4(p231)

This study and more recent studies suggest that the public agenda, news media 

coverage, and public policy are mutually 

influential and, of course, multifaceted.5

If so, media advocacy interventions 

should be capable of influencing news 

media coverage. Recently and since the 

end of ASSIST, researchers documented 

the interrelationships among media, 

advocacy, and health promotion. They 

found that during the 1980s organized 

groups and institutions that sought to 
190

7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

place cardiovascular disease issues on 

the public agenda stimulated an increase 

in news media coverage of heart disease 

as a social problem.6

ASSIST conducted media advocacy 

for many of its media interventions. 

Media advocacy grew out of the work of 

social movement organizations in many 

arenas, but only recently has it become a 

formal element of approaches for affecting public health policy. The objective of 

media advocacy is to influence how the 

media present issues, and the goal is to 

thereby shape policy agendas and regulatory or legislative actions. (For a full discussion of these concepts and how they 

were applied in ASSIST, see Monograph 

16, chapter 5.)

The steps in this first stage of developing an evaluation approach to the media interventions of a wide-scale public 

health intervention were the following:

■ Establish a tracking system for 

identifying and collecting newspaper 

articles about ASSIST-relevant 

tobacco control topics.

■ Code the articles for characteristics 

that could be used for meaningful 

analyses.

■ Maintain a database of the coded data.

■ Create indices from the database for 

use in analyses.

■ Conduct trend analyses for the entire 

ASSIST implementation phase 

(1993–99), comparing ASSIST states 

with non-ASSIST states.

■ Provide the 17 ASSIST states with 

trend analyses of newspaper coverage 

on a quarterly basis as feedback on 

their media intervention efforts.

These efforts are described in the subsequent sections of this chapter.

The newspaper tracking effort was 

unprecedented in scope, though it tracked 

only newspaper coverage. The omission of broadcast and electronic media 

(television, radio, and the World Wide 

Web, which was in its infancy when data 

collection began) does not detract from 

the overarching purpose of the tracking 

system. Newspaper coverage has been 

found to parallel coverage in the electronic 

media.7 In fact, newspaper coverage of 

issues, especially in the elite press (a term 

used in the industry to refer to influential 

agenda-setting media), has been shown to 

be the source of much of what is covered 

by the electronic media. Research has also 

documented the importance of newspaper 

coverage in the decision-making process 

for policy makers and legislators in many 

areas of public policy.5 Thus, the ASSIST 

Newspaper Tracking System is an important resource for additional kinds of 

analyses and for further hypothesis testing 

about tracking and evaluating news media 

coverage to determine the effectiveness of 

tobacco control interventions designed to 

promote a tobacco-free social norm.

Tracking the Articles

F

rom October 1993 through December 

2000, the ASSIST Coordinating Center tracked all daily newspapers in the 

United States for articles covering the 

tobacco control policy areas established 

in the ASSIST model: clean indoor air, 

restrictions on minors’ access to tobacco, 

excise tax increases, and restrictions on 

tobacco advertising and promotion.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Collecting the Articles

Following a pilot study to examine 

the feasibility and specificity of tracking 

newspaper coverage of tobacco control 

issues in three states,8 a national newspaper-clipping service was selected as the 

data collection agent. This service monitors 17,247 different publications and 

claims an estimated capture ratio of 80%, 

meaning that 80% of all articles germane 

to a particular search strategy will be 

identified and clipped by a reader.

Between October 1, 1993, and December 31, 2000, this service monitored all 

daily newspapers in the United States using a search strategy devised by ASSIST 

Coordinating Center researchers. In December 2000, 1,766 newspapers were in 

the sampling frame, but this number had 

varied over time due to newspaper openings, closings, and mergers.

National newspapers—such as USA 

Today, the Wall Street Journal, and the 

Christian Science Monitor—were excluded from the search strategy because 

determining their readership in each 

state would be difficult. The New York 

Times and the Washington Post were 

included in the search, even though they 

have national circulations, because they 

are primarily city newspapers with a 

section that covers only local news. Advertisements, movie reviews, restaurant 

reviews, and obituaries that had tobaccorelated content were excluded from 

the search. Syndicated columns were 

counted as one article (identified by the 

newspaper of origin and attributed to that 

state) to capture an important source of 

editorial opinion, to credit its influence, 

and to limit redundancy.

The newspaper-clipping service obtained potentially relevant articles from 

the universe of daily newspapers. Potentially relevant news and feature articles, 

letters to the editor, and editorials about 

tobacco control policies were identified 

with a three-tiered search strategy. The 

first tier consisted of tobacco key words, 

the second tier consisted of legislative key 

words, and the third of policy-related key 

words. Examples of key words are listed 

in table 7.1. See appendix 7.A for a list of 

the coding topics; see appendix 7.B for a 

complete list of search strategies.

To enhance the likelihood of identifying the most relevant articles, this strategy required that one word from each tier 

appear in the article or headline in order 

for it to be clipped. For example, appearance of the word tobacco by itself did 

not qualify an article for selection—the 

article could have been about tobacco 

farming and not about tobacco control 

policy. However, the appearance of the 

words tobacco and bill and tax would 

qualify an article for selection. The chief 

advantages of the search strategy were 

its simplicity and replicability. No decisions about the content of the article 

were necessary other than to note the 

appearance of the key words. The above 

criteria could not be applied to singleparagraph articles, such as letters to the 

editor. In these cases, a key word from 

two of the three tiers was sufficient for 

selection.

Determining the Relevance of the 

Articles

The clipping service identified each 

clipping by the newspaper’s name, cir-
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7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

culation, and date of publication. The 

service shipped all clipped articles in 

bulk to the ASSIST Coordinating Center 

on a monthly basis. Each article was then 

reviewed to ensure that it met the search 

criteria and that it was indeed relevant. 

Trained research staff of the ASSIST 

Coordinating Center performed this task. 

First, each article was screened to verify 

that a key word from each tier was used 

in it. This criterion, however, was not 

sufficient to determine relevance because 

even though an article might include the 

key words, it might be only tangentially 

related to the topic of tobacco control. 

For example, a story about a politician’s 

personal life might mention his or her 

efforts in tobacco control but might not 

be about tobacco control policy. Articles 

that were determined to be relevant were 

entered into the database. See Stillman 

and colleagues9 for additional details.

Coding the Articles and Maintaining a 

Database

The coding process began once an 

article was accepted as relevant and was 

added to the database. The challenge 

posed by the coding process was to characterize the information from each article sufficiently to clearly represent how 

the tobacco control issues were treated. 

The data from the ASSIST Newspaper 

Tracking System were most suitable for 

identifying what tobacco control issues 

were discussed across the United States. 

The data were less suitable for revealing 

details about the discussion, about the 

quality of the discussion, and about the 

approach to the discussion. The sheer 

volume of articles made it necessary to 

choose between two very different types 

of coding: conducting a surface analysis 

of the entire scope of newspaper coverage of tobacco control policy, or coding 

Table 7.1. Examples of Key Words for Search Strategy

Tier 1: Tobacco Tier 2: Legislative restrictions Tier 3: Policy

ASSIST Ban/banned/banning Advertising Pharmacy

Cigarettes Bill Airport Promotion

Nicotine Law/lawsuit Arena Public places

Smoking Legislation/legislative/legislator Billboard Restaurants

Smoke-free Ordinance Bowling alley School(s) 

Snuff Policy Buildings Stores

Tobacco Prohibit/prohibition Children Tax

Regulation/regulatory Coliseum Vending machines

Restrictions Jail Workplace(s) 

Mall Youth

Minor(s) 
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and analyzing a small percentage of the 

clippings in depth.

As an example of the coding process, 

all hard news stories were coded as neutral, whereas editorials, letters to the editor, and editorial cartoons were coded as 

either neutral, prohealth, or protobacco. 

Such articles were coded as neutral when 

the author addressed both sides of an issue and expressed no dominant theme or 

position. The argument could be made 

that hard news stories express a bias 

even though they should be neutral, in 

accordance with journalistic principles; 

however, assessing bias in news stories 

would have required far greater staff 

resources, monetary resources, and indepth textual analysis than were possible 

for this study.

The ASSIST Coordinating Center 

developed a codebook to provide background and instructions for coders. New 

tobacco control topics that appeared in 

the newspaper clippings and that had implications for the coding were addressed 

at bimonthly meetings of coders and 

project administrators. Addenda to the 

coding manual were written as needed. 

Coders were in a training period until 

they achieved an intercoder reliability 

rate of 100%. Every month thereafter, 

for quality control, the coding supervisor 

made spot reliability checks of 20 clips 

from each coder. Any clips that did not 

meet coding standards were recoded to 

meet the standards. The reliability scores 

remained high, at 95%–99%, throughout 

the project.

Each article was coded on six variables: policy type, topic code, circulation of source newspaper, type of 

article, front page, and origin of story. 

Articles that were editorial in nature 

were coded on a seventh variable, point 

of view. These variables are described in 

table 7.2.

Table 7.3 provides examples of the 

tobacco policy topics coded by the tracking system. The final coded database 

consists of 124,401 articles. The record 

of each article contains a set of coded 

variables and the article’s identifiers—

newspaper’s name, circulation, and date 

of publication.

Developing Indices from the Database

To render the data useful for trend 

analyses, a number of indices were 

created: raw frequency, relative frequency, rate variable, and media advocacy 

variable. The purpose of these indices 

was to reduce complex data to manageable constructs for meaningful analysis 

and to test theoretical assumptions about 

the relationship between news media 

coverage and social-environmental 

outcomes. For example, agenda-­setting 

theory proposes that the quantity and 

specific characteristics of coverage influence the public debate about policy 

issues.1 To test such assumptions parsimoniously, an index that captured these 

characteristics was necessary.

The first index of the articles was 

raw frequency, simply the number of 

articles designated by one value of a 

specific variable (e.g., the number of 

articles published during 1994). The 

second index was relative frequency, 

the percentage of articles with a given 

characteristic, such as the percentage 

of stories on the front page in a year. 
7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

194

Table 7.2. Definitions of Coded Variables

Variable Operational definition

Policy type Four policy areas were coded to correspond with ASSIST objectives: clean indoor 

air, restrictions on minors’ access to tobacco, excise tax increases, and restrictions 

on tobacco advertising and promotion. A fifth category, referred to as miscellaneous, 

includes lawsuits against the tobacco industry, national settlement talks, and proposed 

regulation of nicotine by the U.S. Food and Drug Administration (FDA).

Topic code Since 1996, specific topic codes were entered to specify content of articles (e.g., state 

excise tax) beyond policy type.

Circulation This variable refers to the number of copies of the publication that are distributed daily. 

(For the New York Times and the Washington Post, these were further divided into 

specific circulation figures for each adjacent state—New York/New Jersey/Connecticut, 

and Virginia/Maryland/District of Columbia, respectively.)

Type of 

article

Articles were classified as being one of three types: news story (a factual account of an 

event or issue), editorial (an opinion of an event or issue written by newspaper staff), or 

letter to the editor (usually written to the newspaper by a member of the community).

Front page This variable was created in an attempt to identify the visibility of a specific article in the 

particular newspaper.

Point of view The points of view of editorials, letters to the editor, and editorial cartoons were coded as 

neutral, prohealth, or protobacco. Hard news stories were coded as neutral.

Origin of 

story

To assess the salience of the tobacco control policy issue, the articles were coded as 

either national or local in focus. Stories from a national wire service (e.g., Associated 

Press, United Press International, Reuters) were coded as national; stories from a local 

journalist (whose name was stated in the byline) were coded as local. When the media 

analysis first started, this variable was not included in the coding protocol. This variable 

was added after 6 months of coding—when it became apparent that many articles in local 

papers were covering national tobacco policy issues taken from national wire services.

Table 7.3. Examples of Tobacco Topics

Clean indoor air

Minors’ access 

to tobacco

Excise tax 

increases 

Advertising and 

promotion Miscellaneous

Smoke-free 

bowling alley

Licensing vendors Federal excise 

tax

Banning billboards 

in a state

Cigarette package 

labeling

Sports arena 

restricts smoking

Youth purchase 

attempts

State excise tax Removing billboards 

from sports arenas

FDA regulation of 

nicotine content

Restricting 

smoking in schools

Banning vending 

machines

FDA regulation of 

cigarette advertising

Lawsuits

Note: FDA indicates U.S. Food and Drug Administration.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Relative frequency enables comparisons 

among variables over time and from year 

to year.

Neither the raw frequency nor the relative frequency can be the basis for valid 

comparisons between states because the 

number of newspapers published varies by state. The number of newspapers 

would affect the number of articles 

published (i.e., the more newspapers in 

a state, the greater the number of articles 

published). Yet, comparisons between 

states are important because the unit of 

analysis in ASSIST is the state.

To address this problem, a third index 

was calculated, a rate variable: the number of articles clipped in a state divided 

by the number of newspapers in that 

state. This quotient yielded the rate variable of tobacco control articles per newspaper per state. The rate variable could 

be calculated for specific time units—for 

example, a rate per month, per quarter, 

or per year. Since the newspaper business is volatile (subject to new starts, 

mergers, and closures), the clipping service provided the number of newspapers 

included in the monthly set of clippings.

The fourth index was the media advocacy variable (MAV), which was created 

as an index of prohealth coverage. This 

index included the amount, prominence, 

and point of view of the coverage. MAV 

was adjusted for a state’s population; 

thus, it could be used to make state-tostate comparisons of prohealth coverage. 

MAV was calculated as follows:

type × point of view ×

front page × (circulation × 2.2)

state population

where

type: 0 = cartoons; 

1 = all other articles

point of view: −2 = protobacco; 

1 = neutral; 2 = prohealth

front page: 2 = yes; 1 = no

circulation × 2.2: This product is an 

estimate of the circulated paper’s 

total readership.

Weighting of the MAV components 

was based on previous media advocacy 

research methods10 and expert recommendations. MAV has been applied in 

modeling the overall effects of ASSIST 

on hypothesized long-term health outcomes (such as change in tobacco use 

prevalence) and in modeling associations 

between ASSIST and tobacco control 

policy outcomes. Although MAV has 

not been incorporated into analyses presented in this monograph, it can be used 

to measure overall population exposure 

to prohealth coverage. It has significant 

potential for application in future news 

media analyses.

Trend Analyses

T

he ASSIST Newspaper Tracking System is the most comprehensive available record of newspaper coverage of 

tobacco control from 1993 to 2000. The 

database can be used for various types 

of analyses—for example, for overall 

news coverage trend analyses that assess 

changes in the quantity and characteristics of coverage over time or for detailed 

content analyses of news articles focused 

on understanding the coverage of a specific topic or set of topics.
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7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

In general, the analyses reported here 

were based on the frequency of articles 

within each of the coded variables (table 

7.2). For descriptive analyses, a chi-square 

analysis was used to compare frequencies 

of articles within each of the categories, 

and each observation was assumed to be 

independent. In addition, articles grouped 

by years of publication were considered to 

be independent samples. In other words, 

the appearance of an article about a particular tobacco control policy or topic at 

a single point in time was considered independently of the appearance of another 

article during subsequent time periods. 

The tracking system did not track articles 

appearing in specified newspapers over 

time. The assumption of independence is 

consistent with defining the article as the 

unit of observation.

The next sections present three examples of analyses conducted during the 

ASSIST evaluation: (1) trend analyses 

of types of articles, (2) comparison of 

trends in ASSIST and non-ASSIST 

states, and (3) a case study of a specific 

policy initiative in an ASSIST state.

Trend Analyses of Types of Articles

Because the sampling frame of the ASSIST Newspaper Tracking System included all daily newspapers, the data can be 

used to assess the characteristics of coverage during the data collection period. 

Researchers from NCI and the ASSIST 

Coordinating Center conducted a series 

of descriptive analyses resulting in an 

overall characterization of trends in coverage during the implementation phase 

of ASSIST. Trends were characterized as 

changes in quantities, rates, or characteristics of coverage over time.

From October 1, 1993, through December 31, 2000, 124,401 articles were coded 

and entered into the database. Of those 

articles, 67% were news stories, 15% were 

letters to the editor, 17% were editorials, and 1% consisted of cartoons. Some 

variations in the relative frequencies of 

the types of articles occurred throughout 

the years of observation (figure 7.1). For 

example, the percentage of news stories 

gradually rose over time, whereas the percentage of letters to the editor fell. News 

stories constituted 58% of all articles in 

1993, 71% in 1997, and 76% in 2000. 

Similarly, the relative frequency of editorials increased from 13% in 1993 to 21% 

in 1998 but declined to 16% in 1999 and 

to 14% in 2000. In contrast, the relative 

frequency of letters to the editor declined 

from 28% in 1993 to 10% in 1997. After a 

small rebound to 15% in 1998, the relative 

frequency of letters to editors declined to 

12% in 1999 and to only 9% in 2000.

The largest proportion of newspaper 

articles across the data collection period 

concerned clean indoor air policies (40%), 

followed by minors’ access issues (15%), 

tobacco excise taxes (10%), and tobacco 

advertising and promotion policies (6%). 

The miscellaneous category accounted for 

more than 29% of all coded articles in the 

database because this category included 

the high-attention topics of lawsuits 

against the tobacco industry and the Master Settlement Agreement, which affected 

all states (see figure 7.2), and Food and 

Drug Administration (FDA) regulations. 

The percentage of articles about each policy area varied over time. In 1993, clean indoor air accounted for 63% of all articles; 

this number dropped to 24% in 1996 and 

rose to 40% in 1999 and 2000.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

197

Figure 7.1. Article Type by Year, 1993–2000

0

10

20

30

40

50

60

70

80

Articles in database (%)

90

Year

Cartoons

Hard news

Editorials

Letters

1993 1994 1995 1996 1997 1998 1999 2000

Figure 7.2. Policy Topic by Year, 1993–2000

0

10

20

30

40

50

60

Articles in distribution (%)

70

Year

Excise taxes

Clean indoor air

Miscellaneous

Minors’ access

Advertising and promotion

1993 1994 1995 1996 1997 1998 1999 2000
198

7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

The relative frequency of articles 

on excise taxes dropped from a high 

of 25% in 1993 to 5% in 1995. Coverage of minors’ access issues peaked in 

1996. The relative frequency of articles 

in the miscellaneous category steadily 

increased and peaked in 1998 and represented 40% of all articles in 2000.

Overall, the proportion of news stories was relatively consistent among the 

four policy types and ranged from 67% 

for clean indoor air to 71% for youth 

access, with advertising and promotion 

restrictions and miscellaneous each at 

70%. However, for the excise tax issue, 

only 49% of coverage was news stories. 

The distribution of type of stories by 

topic was similar for all years.

In the overall database, editorials, 

letters to the editor, and cartoons were 

coded for point of view. In the following analysis, we examined only 

editorials and excluded those with a 

neutral point of view (i.e., percentage 

comparisons are between prohealth 

and protobacco editorials only). The 

prohealth point of view outweighed 

the protobacco point of view by nearly 

a 2:1 margin (22,282 to 12,441, with 

3,087 neutral). The data also show 

year-by-year variations. In 1993, 58% 

of all editorials expressing an opinion 

were prohealth, and 42% were protobacco, with prohealth peaking at 76% 

in 1996 and dropping to 60% in 1998. 

All the comparisons between prohealth 

and protobacco percentages within 

individual years were statistically significant (p < .001). The percentages 

of articles voicing prohealth points of 

view remained relatively unchanged 

from 1998 to 2000.

Regarding tax issues, the prohealth 

point of view peaked at 73% in 1996, but 

in 1998, editorials expressing protobacco 

views outnumbered the prohealth position by almost 2:1 (64% versus 36%). For 

clean indoor air issues, the percentage of 

protobacco editorials ranged from 62% 

to 72% in 1993–97, dropped to 56% in 

1998, but climbed to 70% in 2000. Editorials on the issue of youth access were 

consistently prohealth over time. In the 

miscellaneous category, the percentage 

of editorials on advertising and promotion policies expressing prohealth views 

decreased from 82% in 1994 to 59% in 

1996 and rose to 71% in 1998, to 80% in 

1999, and to 84% in 2000.

Readiness for Media Interventions

“For ASSIST to conduct media interventions successfully, three elements had to be 

in place:

An infrastructure of organizational units 

clearly responsible for the interventions

A system of communication throughout 

the infrastructure that would enable timely 

implementation of media interventions

Technical assistance to equip ASSIST 

personnel and coalition volunteers with 

needed skills and to provide them continued support in planning and implementing 

media interventions

“These three elements were developed and 

established during ASSIST’s 2-year planning 

phase. . . . By mid-1993, the 17 states were 

ready to implement media intervention strategies described in their annual action plans.” 

Source: National Cancer Institute. 2005. 

ASSIST: Shaping the future of tobacco prevention and control (Tobacco control monograph no. 16, NIH publication no. 05-5645). 

Bethesda, MD: National Cancer Institute 

(pp. 123–24).

1.

2.

3.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

In terms of the visibility of the policy 

types, minors’ access to tobacco products was most frequently found on the 

front page of the newspapers (17.7% 

versus 13.2% for all others, p < .001).

Analyses of ASSIST Versus Non-ASSIST 

States

The ASSIST Newspaper Tracking 

System was designed in part to assess 

whether newspaper coverage would follow ASSIST efforts to implement media 

interventions. One research question was 

whether newspaper coverage of tobacco 

control issues would be greater and more 

favorable to tobacco control in ASSIST 

states than in non-ASSIST states.

The ASSIST states were not selected 

randomly; rather, they were selected according to the criteria of a competitive 

contract review process. Thus, the analyses were based on a quasi-experimental 

design that would compare newspaper 

coverage of tobacco control in ASSIST 

and non-ASSIST states prior to the 

intervention and then at several points 

afterward (i.e., a pretest, multiposttest 

design). However, the ASSIST Newspaper Tracking System did not include 

data prior to the ASSIST implementation phase, and attempts to reconstruct 

a baseline using electronic data sources 

(e.g., Lexis-Nexis) proved unsuccessful.9

Given these limitations, a two-step 

approach was used to (1) control for 

between-groups differences at baseline 

using a construct that accounted for relevant tobacco control policy variables and 

(2) test for an ASSIST-by-year interaction. 

Step 2 was based on the hypothesis of an 

increasing intervention effect over time: 

as the project progressed, ASSIST staff 

would gain experience in media advocacy 

and consequently would become more 

successful at media interventions.

The outcome measure used to control 

for baseline differences was the ASSIST 

Initial Outcomes Index. (See chapter 4 for 

details.) The Initial Outcomes Index is a 

summary measure of three tobacco control variables: the percentage of workers 

covered by 100% smoke-free workplaces, 

cigarette price, and rating of local and 

state clean indoor air policies. The Initial 

Outcomes Index measured at the baseline 

also predicted the volume of newspaper 

coverage of tobacco control issues.

The dependent variable was the rate 

variable, defined as the number of articles 

published in each state during the year 

divided by the number of newspapers. 

Table 7.4 presents the average rates for 

Table 7.4. Average Rate Variables for Each Year Analyzed (District of Columbia Excluded)

States 1994 1995 1996 1997 1998

ASSIST 

Mean 18.31 10.80 10.95 14.73 13.78

SD 9.43 7.81 8.02 9.74 7.11

Non-ASSIST 

Mean 12.13 7.64 7.31 11.10 10.25

SD 8.05 5.57 4.91 9.25 9.16
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7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

each year analyzed. Data from the District of Columbia were considered outliers and were omitted from these analyses 

because it is the home of The Washington Post, which is not only a “­local” 

newspaper, but is one of the premier 

national newspapers. In addition, the District of Columbia is not a state.

The analytic methods were as follows 

(see Stillman and colleagues9 for additional details):

■ Conduct a repeated-measures analysis 

to account for the presence of 

correlations between the observations 

for the same state over time.

■ Use the PROC MIXED procedure 

in the statistical computer package 

SAS11 for mixed linear regression 

equations. (Mixed models are 

more general than standard linear 

regression models in that they allow 

for the modeling of the variances and 

covariance of the observations and the 

means.)

■ Perform a log transformation of the 

dependent variable, and calculate an 

average article rate for each state to 

normalize the error terms.

■ Enter ASSIST status, Initial Outcomes 

Index rate, year, and interactions 

between these terms into the model.

The primary results of these analyses 

were as follows:

■ Main effects for the Initial Outcomes 

Index (p < .0003) and year (p < .0003)

■ No observed ASSIST-by-year 

interaction, contrary to what was 

hypothesized

■ An observed main effect for ASSIST 

(p < .0007), indicating greater news 

coverage in ASSIST states than in 

non-ASSIST states

The hypothesis of a change in coverage over time was not confirmed, but the 

analyses provided evidence that ASSIST 

media intervention efforts had an effect 

on newspaper coverage of tobacco control. While the quasi-experimental design does not allow causal conclusions, 

a plausible explanation of the greater 

newspaper coverage of tobacco control 

efforts in ASSIST states is the presence 

of the ASSIST media interventions. See 

Stillman and colleagues9 for details on 

methods and findings.

A Case Study of Newspaper Coverage 

in North Carolina

The data in the ASSIST Newspaper 

Tracking System database can be analyzed at the individual state level with a 

case study approach. Data on the coverage of a specific event or sequence of 

events can be combined with firsthand 

accounts of tobacco control activities to 

explain the observed patterns of coverage. Reports of such analyses to program 

managers can help them determine the 

effectiveness of specific media interventions and media advocacy efforts and can 

guide them in developing their annual 

strategic action plans. Case study 7.1, 

from North Carolina, illustrates how 

newspaper coverage can be related to 

specific events and issues. The frequencies of editorials on youth access policy 

were assessed for 90 days before and 90 

days after the passage of two youth access laws by the North Carolina General 

Assembly, the first in 1995 and the second in 1997.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Summary

T

he ASSIST Newspaper Tracking System is a rich source of data on tobacco 

control newspaper coverage and can 

serve as a resource for future quantitative 

and qualitative analyses. Analyses of the 

data have numerous applications—for 

example, to reveal needs and opportunities for program improvement, to help 

program planners develop more effective 

strategic plans, and to identify trends in 

coverage of specific issues that might 

require counteractions. In addition, 

the system is an important step toward 

Case Study 7.1

Editorials Promote a Stronger Youth Access Law in North Carolina

In April 1995, North Carolina passed a relatively weak law to restrict the sale of tobacco products to 

minors. The law contained the word knowingly and thereby gave retailers an escape hatch if they were 

caught selling to minors. At this point, ASSIST had been in its implementation phase in North Carolina 

for about 18 months. Media advocacy activities were in their early developmental phase at the time. 

The data show that eight editorials appeared in newspapers across the state in the 6-month period 

surrounding the passage of the law. One editorial appeared before the passage of the law, and seven 

appeared after passage. The point of view of the editorials was overwhelmingly prohealth. Only one 

editorial written after passage of the law was protobacco.

Anecdotal evidence from tobacco control advocates in the state indicates that health groups in North 

Carolina were outraged at the passage of such a weak youth protection law. Tobacco control activists 

met with the editorial boards of several North Carolina newspapers to inform them about the dangers 

of allowing the word knowingly to remain in the law. These activities may account for the high percentage of prohealth editorials that appeared after the law was passed.

Nearly 2 years later, in February 1997, tobacco control advocates in North Carolina were successful 

in getting the legislature to pass a youth access bill that was stronger than the 1995 measure. The 1997 

bill removed the word knowingly and required retailers to check the identification of anyone appearing 

to be younger than 18 years of age. By this time, ASSIST had been in its implementation phase for almost 4 years, and media advocacy training and practice were in full swing throughout the state.

During the 90 days before and after the passage of the second bill, 42 editorials were published—

21 before and 21 after the law was passed. Before the law was passed, the editorials were overwhelmingly prohealth (91%). After the law was passed, the point of view of the editorials was almost evenly 

split: 52% prohealth and 48% protobacco. 

Tobacco control advocates reported that the tobacco industry and the retail merchants’ lobby tried to 

influence the language of this stronger law. The industry framed the issue of youth access as an equal 

responsibility between retailers and minors. Thus, they sought to include language making it illegal for 

minors to possess tobacco products as well as to purchase or attempt to purchase them. Not only would 

retailers be legally responsible for selling tobacco products to minors, but also minors would be legally 

responsible for attempting to purchase those products. Additional analyses could determine whether the 

prohealth editorials after the passage of the law reflected these lobbying efforts of the tobacco industry.

Source: Ulasevich, A., and W. D. Evans. 2001. Tale of two laws: Case study approach using the 

ASSIST Media Analysis System. Poster presented at the 129th annual meeting of the American Public 

Health Association, Atlanta.
202

7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

developing methodologies that can be 

applied by other public health programs 

relying on changing social norms about 

health behaviors.

The ASSIST Newspaper Tracking 

System also is a major contribution to the 

field of news media analysis. In addition 

to content analysis, this system offers the 

opportunity to evaluate correlations between news media coverage and changes 

in the social environment surrounding 

a public health issue. A major contribution of the system is a demonstration 

of how to monitor news coverage on a 

continuous basis and in tandem with the 

implementation of a large-scale public 

health intervention. The system now 

serves as a model that can be expanded 

further or revised, based on insights from 

its applications to ASSIST. Building on 

the model, new initiatives might seek to 

extend the system to capture additional 

variables on important concepts12 and to 

test previously held assumptions about 

the need for census (or near census) sampling and coding methods.13 The ASSIST 

newspaper tracking system was the first 

such tracking system in which newspaper 

coverage of tobacco control efforts was 

systematically collected, analyzed, and 

used as part of an evaluation effort. As a 

result, analysis of newspaper coverage is 

now a component of other tobacco control program evaluations, including the 

current evaluation of the Robert Wood 

Johnson Foundation’s SmokeLess States 

National Tobacco Policy Initiative.14

Limitations

In advancing to the next development 

in methods for tracking and evaluating media, it is important to take into 

consideration the limitations of the 

ASSIST Newspaper Tracking System and 

changing context. For example, limited 

resources required a somewhat superficial 

classification of the article variables rather 

than an in-depth assessment of the content. In particular, the purpose of providing ongoing feedback to activists would 

be served better if analyses could be made 

of the arguments in editorial materials. 

Also, fine-tuning the coding categories 

might be considered. Although recent evidence suggests that newspaper-­clipping 

services appear to miss a significant 

proportion of relevant articles,15 during 

the intervention period there was high 

consistency between the national clipping 

service reports and ASSIST state reports 

of newspaper coverage.

The goal of this project was to capture 

the universe of newspaper articles on 

ASSIST-relevant tobacco issues across 

all daily newspapers in the United States 

and thereby establish a database from 

which analyses could be conducted to 

identify trends in coverage and correlations to ASSIST media interventions. 

Because newspaper editorial boards 

control which stories are actually published, newspaper editorial policies are 

an important variable in a full evaluation of the data. For example, one might 

argue that editorial policy is a mediating variable capable of explaining some 

portion of a variance between media 

advocacy efforts (independent variable) 

and amount or characteristics of newspaper coverage (dependent variable). 

The ASSIST Newspaper Tracking System does not contain an editorial policy 

variable, so no analysis of the effect of 

editorial policy has been conducted.
203

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Future Directions

Challenges for future analyses include 

the following:

■ How can editorial policy be captured? 

Are there regional or state variations 

in media bias? Could these variations 

be coded?

■ How can one assess whether policy 

makers read the newspaper coverage 

and whether the articles influence 

their thinking and policy decisions on 

tobacco control issues?

■ Can the readership of specific 

newspapers be measured and 

coded by meaningful demographic 

characteristics?

■ How can program intervention 

activities be captured for a later 

analysis of their relationship to media 

output?

■ How can coding terms for articles 

be related to program intervention 

activities?

■ Can the quality of program media 

interventions be assessed and coded, 

and can an analysis be conducted to 

determine whether certain types of 

interventions yield more newspaper 

coverage of the desired sort? In 

Indiana, a study synchronizing the 

process evaluation tracking system of 

a program with a news media tracking 

system is under way.

■ Is there a theoretical base on which 

to build and test a model of the 

relationship between newspaper 

coverage and policy change?

■ What would be the measure of 

an appropriate time lag between 

newspaper coverage and expected 

policy change?

Strategic Elements for Success

Through media advocacy and other media 

interventions, the 17 ASSIST states made 

tobacco and health an issue of public priority. The media interventions brought media 

attention to the four priority policy areas of 

the ASSIST model: clean indoor air, restrictions on minors’ access to tobacco, excise tax 

increases, and restrictions on tobacco advertising and promotion. “Although no single 

strategy worked in every state, the following 

important elements were in place throughout 

the project and contributed to the success of 

the media interventions:

■ Technical assistance and training in media 

communications

■ Strategic communication plans

■ Mechanisms for sharing information, 

ideas, and successes

■ Communications with the ASSIST Coordinating Center, which provided a national 

perspective on tobacco issues

■ A dedicated media staff person at the local 

level

■ Access to national experts

■ Access to materials that could be adapted 

locally for news stories, editorials, press 

releases, and other formats

■ A clear understanding of the audiences to 

be reached

■ Familiarity with the media markets

■ Well-established media relations

■ Skills in media advocacy”

Source: National Cancer Institute. 2005. 

ASSIST: Shaping the future of tobacco prevention and control (Tobacco control monograph no. 16, NIH publication no. 05-5645). 

Bethesda, MD: National Cancer Institute 

(p. 152).
204

7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

■ Are there elements of the newspaper 

tracking system that can be 

extrapolated to a model for other 

types of media (broadcast and 

Internet)?

■ Is there a direct relationship between 

the amount of newspaper coverage of 

tobacco control issues and reported 

awareness of those issues?

■ To what extent do changes in the 

amount of news media coverage of 

tobacco control lead to changes in 

knowledge, attitudes, or beliefs about 

specific tobacco control issues?

Conclusions

1. The ASSIST Newspaper Tracking System established a method to 

systematically identify and collect 

newspaper coverage from all daily 

newspapers across the United States 

about ASSIST-relevant tobacco control policy topics. These articles were 

subsequently coded and entered into 

a database.

2. Newspaper articles were coded for 

policy type covered, topic covered, 

newspaper circulation, article type, 

front page story location, point of 

view, and origin.

3. An assessment of changes in media 

coverage over time reflected changing interest in various tobacco control 

policy areas, such as clean indoor air, 

taxation, and advertising, as well as 

tobacco industry lawsuits, the Master Settlement Agreement, and U.S. 

Food and Drug Administration regulations.

4. Additional research conducted as 

part of the ASSIST evaluation found 

that ASSIST media advocacy efforts 

were associated with higher levels 

of newspaper coverage and coverage 

that was more positive toward tobacco control.

5. While the newspaper tracking system data were not a part of the final 

ASSIST evaluation model, such data 

served important functions. The data 

provided valuable information to the 

ASSIST states that allowed them to 

track their own progress in obtaining positive media coverage for their 

tobacco control policy initiatives. 

In addition, the data documented 

the entire history of tobacco control 

policy coverage from 1993 through 

1999. This leaves a legacy for future 

researchers to delve into more deeply 

and analyze the complex factors associated with newspaper coverage.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

205

Appendix 7.A. Coding Topics

Policy 1

1. Ban smoking in worksites/public buildings/prisons

2. Ban smoking in bars/gaming clubs/parlors

3. Ban smoking in recreation buildings

4. Ban smoking in airports

5. Ban smoking on transportation/metro

6. Ban smoking in malls

7. Ban smoking in parks/beaches/playgrounds

8. Ban smoking in restaurants

9. Ban smoking in schools/daycare centers

10. Ban smoking in stadiums/arenas

11. Ban smoking around entrances/on grounds and outdoors in general

12. Secondhand smoke/lawsuits due to secondhand smoke (i.e., flight attendants or 

prisoners)

13. Secondhand smoke in multiple family dwellings/nursing homes

Policy 2

1. ASSIST mention (coded as policy 5)

2. Access to minors regulation

3. Sting operations/compliance checks (includes smokeless)

4. Student fines/suspensions/policies for possession by a minor

5. Banning vending machines

6. Regulating the licensing of vendors/sales/violations/Internet sales

7. Banning the selling of “loosies” single cigarettes

8. “Underage Sales Prohibited” labels

9. Sales banned near schools

10. Lawsuits dealing w/selling tobacco to minors (California...)

11. Banning sales of cigars to minors

Policy 3

1. Federal tax on tobacco

2. State tax on tobacco

Policy 4

1. Banning billboards/advertising in a state or metropolitan area

2. Banning billboards/advertising from sports arenas

3. Banning billboards around schools/playgrounds/parks

4. Restricting tobacco company sponsorship of sport/entertainment events

5. Banning advertising on race cars
7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

206

6. Banning tobacco advertising on clothes

7. FDA regulating advertising of cigarettes/voluntary

8. Banning cigarette displays in stores/self-service/window ads

9. Regulation of cyber (Internet) tobacco ads

10. Banning free samples

11. Banning advertising ads in magazines, newspapers, TV, radio (any media)/transportation

12. Banning Joe Camel/Marlboro Man/cartoon characters (including lawsuits against 

these characters)

13. Foreign banning or phase out of advertising overseas

Policy 5

1. Regulating tobacco package labeling/ingredient disclosure law

2. FDA regulating/banning nicotine content/as a drug delivery system/prohibition/prohibit retail sales/tobacco legislation mentions/National Tobacco Policy mentions

3. Lawsuits dealing with states/insurance companies [i.e., Medicare/Medicaid suing 

tobacco companies] as well as class action lawsuits

4. Lawsuit dealing with discrimination [employers not hiring smokers]/firing 

employees/law prohibiting employees to smoke at all

5. Pharmacies and stores banning tobacco products

6. Banning smokeless tobacco products

7. Cigarette price increases by manufacturers/government tobacco bills to increase 

prices (no mention of it as a tax)

8. Tobacco industry contributions to state politicians

9. Tobacco industry contributions to national (House and Senate) politicians

10. National Center/Campaign for Tobacco-Free Kids

11. Centers for Disease Control/IMPACT

12. Centers for Disease Control/Office on Smoking and Health

13. Overseas/International tobacco marketing/promotion

14. Settlement talks

15. Cigar/cigarette disposal laws

16. International lawsuits

17. Supreme Court debates/decisions on tobacco

Subjects Not Included in the Search

Articles related to tobacco farming

Financial position of tobacco companies

Cessation counseling

Tobacco tax revenues and their use (e.g., tobacco tax used for cancer research)

Lawsuits involving tobacco companies suing another party (e.g., ABC, EPA)

How Settlement money is to be spent

Lawyers’ fees lawsuits/discussions

Veterans’ issue with VA
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

207

Appendix 7.B. Search Strategies

ASSIST Media Analysis

All references from all dailies. Exclude radio and television.

ASSIST (Amer. Stop Smoking Intervention Study)

Amer. Stop Smoking Intervention Study

Original features from all dailies. Confine syndication to the 10 largest cities. Include all references from dailies (no exclusions) in connection with ASSIST (Amer. 

Stop Smoking Intervention Study). Exclude radio, television, photos without stories, 

syndicated columns, syndicated Sunday supplements, police reports, foreign publications, Christian Science Monitor, USA Today, Wall Street Journal.

ATF (Bureau of Alcohol, Tobacco & Firearms) in connection with tobacco control laws 

& policies re: sting operations, licensing of retail stores, or youth buying operations

BATF (Bureau of Alcohol, Tobacco & Firearms) in connection with tobacco control laws 

& policies re: sting operations, licensing of retail stores, or youth buying operations

Bureau of Alcohol, Tobacco & Firearms in connection with tobacco control laws & 

policies re: sting operations, licensing of retail stores, or youth buying operations

Cigarettes in connection with tobacco control laws re: sting operations, licensing of 

retail stores, or youth buying operations

FBI (Fed. Bureau of Investigation) in connection with tobacco control laws re: sting 

operations, licensing of retail stores, or youth buying operations

Law enforcement in connection with tobacco control laws re: sting operations, 

licensing of retail stores, or youth buying operations

Nicotine in connection with tobacco control laws re: sting operations, licensing of 

retail stores, or youth buying operations

Police in connection with tobacco control laws re: sting operations, licensing of retail stores, or youth buying operations

Smokeless tobacco in connection with tobacco control laws re: sting operations, 

licensing of retail stores, or youth buying operations

Smoking in connection with tobacco control laws re: sting operations, licensing of 

retail stores, or youth buying operations

Snuff in connection with tobacco control laws re: sting operations, licensing of retail 

stores, or youth buying operations

Tobacco in connection with tobacco control laws re: sting operations, licensing of 

retail stores, or youth buying operations

Tobacco, smokeless in connection with tobacco control laws re: sting operations, 

licensing of retail stores, or youth buying operations
7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

208

Original features from all dailies. Confine syndication to the 10 largest cities. Include all references from dailies (no exclusions) in connection with ASSIST (Amer. 

Stop Smoking Intervention Study). Exclude radio, television, photos without stories, 

syndicated columns, syndicated Sunday supplements, movie reviews, obituaries, weddings, police reports, restaurant reviews, foreign publications, Christian Science Monitor, USA Today, Wall Street Journal.

Advertising in connection with bans re: cigarettes, nicotine, smoking, snuff, or tobacco

Cigarettes in connection with advertising bans

Cigarettes in connection with bans re: public places (i.e., airports, restaurants)

Cigarettes in connection with lawsuits

Cigarettes in connection with legislation

Cigarettes in connection with limiting availability to minors (i.e., vending machines, 

retail stores)

Cigarettes in connection with prisons re: banning of

Cigarettes in connection with prohibitions

Cigarettes in connection with taxes

Nicotine in connection with advertising bans

Nicotine in connection with bans re: public places (i.e., airports, restaurants)

Nicotine in connection with lawsuits

Nicotine in connection with legislation

Nicotine in connection with limiting availability to minors (i.e., vending machines, 

retail stores)

Nicotine in connection with prisons re: banning of

Nicotine in connection with prohibitions

Nicotine in connection with taxes

Prisons in connection with cigarettes, nicotine, smoking, snuff, or tobacco re: banning of

Prisons in connection with smokeless tobacco re: banning of

Smokeless tobacco in connection with advertising bans

Smokeless tobacco in connection with bans re: public places (i.e., airports, restaurants)

Smokeless tobacco in connection with lawsuits

Smokeless tobacco in connection with legislation

Smokeless tobacco in connection with limiting availability to minors (i.e., vending 

machines, retail stores)

Smokeless tobacco in connection with prisons re: banning of

Smokeless tobacco in connection with prohibitions

Smokeless tobacco in connection with taxes
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

209

Smokeless tobacco in connection with workplace re: banning of

Smoking in connection with advertising bans

Smoking in connection with bans re: public places (i.e., airports, restaurants)

Smoking in connection with lawsuits

Smoking in connection with legislation

Smoking in connection with limiting availability to minors (i.e., vending machines, 

retail stores)

Smoking in connection with prisons re: banning of

Smoking in connection with prohibitions

Smoking in connection with taxes

Snuff in connection with advertising bans

Snuff in connection with bans re: public places (i.e., airports, restaurants)

Snuff in connection with lawsuits

Snuff in connection with legislation

Snuff in connection with limiting availability to minors (i.e., vending machines, 

retail stores)

Snuff in connection with prisons re: banning of

Snuff in connection with prohibitions

Snuff in connection with taxes

Taxes in connection with cigarettes, nicotine, smoking, snuff, or tobacco

Taxes in connection with smokeless tobacco

Teenagers in connection with limiting availability of tobacco prods. to minors

Tobacco in connection with advertising bans

Tobacco in connection with bans re: public places (i.e., airports, restaurants)

Tobacco in connection with lawsuits

Tobacco in connection with legislation

Tobacco in connection with limiting availability to minors (i.e., vending machines, 

retail stores)

Tobacco in connection with prisons re: banning of

Tobacco in connection with prohibitions

Tobacco in connection with taxes

Tobacco, smokeless in connection with advertising bans

Tobacco, smokeless in connection with bans re: public places (i.e., airports, restaurants)

Tobacco, smokeless in connection with lawsuits

Tobacco, smokeless in connection with legislation
7 . T h e A S S I S T N e w s p a p e r T r a c k i n g S y s t e m

210

Tobacco, smokeless in connection with limiting availability to minors (i.e., vending 

machines, retail stores)

Tobacco, smokeless in connection with prisons re: banning of

Tobacco, smokeless in connection with prohibitions

Tobacco, smokeless in connection with taxes

Tobacco, smokeless in connection with workplace

Workplace in connection with smokeless tobacco re: bans

Original features from all dailies. Confine syndication to the 10 largest cities. Include all references from dailies (no exclusions) in connection with ASSIST (Amer. 

Stop Smoking Intervention Study). Exclude radio, television, photos without stories, 

syndicated columns, syndicated Sunday supplements, movie reviews, police reports, 

restaurant reviews, foreign publications, Christian Science Monitor, USA Today, Wall 

Street Journal.

Americans With Disabilities Act—fed. legis. in connection with tobacco control 

laws and policies

Camel Cigarettes in connection with Joe Camel—advertising character re: 

advertising ban

Camel, Joe—advertising character in connection with cigarettes re: advertising ban

Cigarettes in connection with bans re: public transportation

Joe Camel—advertising character in connection with cigarettes re: advertising ban

Nicotine in connection with bans re: public transportation

Public transportation in connection with bans re: smoking, snuff, tobacco, cigarettes, 

nicotine, or smokeless tobacco

Smokeless tobacco in connection with bans re: public transportation

Smoking in connection with bans re: public transportation

Snuff in connection with bans re: public transportation

Tobacco in connection with bans re: public transportation

Tobacco in connection with restrictions re: indoor air quality

Tobacco companies in connection with sponsorship of sporting or cultural events re: 

advertising ban

Tobacco, smokeless in connection with bans re: public transportation

Transportation, public in connection with bans re: smoking, snuff, tobacco, 

cigarettes, nicotine, or smokeless tobacco
211

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

References

1. Dearing, J. W., and E. M. Rogers. 1996. 

Communication concepts 6: Agenda 

setting. Thousand Oaks, CA: Sage.

2. Wallack, L., L. Dorfman, D. Jernigan, 

and M. Themba-Nixon. 1993. Media 

advocacy and public health: Power for 

prevention. Thousand Oaks, CA: Sage.

3. Cohen, B. C. 1963. The press and 

foreign policy. Princeton, NJ: Princeton 

University Press.

4. Fiske, S. T., and S. E. Taylor. 1984. 

Social cognition: Topics in social 

psychology. Reading, MA: AddisonWesley.

5. Rogers, E. M., and J. W. Dearing. 

1988. Agenda-setting research: Where 

has it been, where is it going? In 

Communication Yearbook 11, 555–94. 

Newbury Park, CA: Sage.

6. Viswanath, K., and J. R. Finnegan Jr. 

2002. Reflections on community health 

campaigns: Secular trends and the 

capacity to change. In Public health 

communication: Evidence for behavior 

change effect, 289–312. Mahwah, NJ: 

Lawrence Erlbaum.

7. Moynihan, R., L. Bero, 

D. Ross-Degnan, D. Henry, K. 

Lee, J. Watkins, C. Mah, and 

S. B. Soumerai. 2000. Coverage by the 

news media of the benefits and risks of 

medications. New England Journal of 

Medicine 342 (22): 1645–50.

8. The COMMIT Research Group. 

1995. Community Intervention Trial 

for Smoking Cessation (COMMIT): 

I. Cohort results from a four-year 

community intervention. American 

Journal of Public Health 85 (2): 183–92.

9. Stillman, F. A., K. A. Cronin, 

W. D. Evans, and A. Ulasevich. 

2001. Can media advocacy influence 

newspaper coverage of tobacco: 

Measuring the effectiveness of the 

American Stop Smoking Intervention 

Study’s (ASSIST) media advocacy 

strategies. Tobacco Control 10 (2): 

137–44.

10. Schooler, C., S. S. Sundar, and J. Flora. 

1996. Effects of the Stanford Five-City 

Project Media Advocacy Program. 

Health Education Quarterly 23 (3): 

346–64.

11. SAS Institute, Inc. 1989. SAS/STAT 

user’s guide. Version 6, 4th ed., Vol. 1. 

Cary, NC: SAS Institute, Inc.

12. Smith, K., M. Wakefield, C. Siebel, 

G. Szczypka, S. Slater, 

Y. Terry-McElrath, S. Emery, and 

F. J. Chaloupka. 2002. Coding 

the news: The development of a 

methodological framework for coding 

and analyzing newspaper coverage of 

tobacco issues (ImpacTeen Research 

Paper Series No. 21). Chicago: 

University of Illinois at Chicago.

13. Evans, W. D., and A. Ulasevich. 

2005. News media tracking of 

tobacco control: A review of sampling 

methodologies. Journal of Health 

Communication 10 (5): 403–17.

14. Smith, K. C., Y. Terry-McElrath, M. 

Wakefield, and R. Durrant. 2005. Media 

advocacy and newspaper coverage of 

tobacco issues: a comparative analysis of 

1 year’s print news in the United States 

and Australia. Nicotine and Tobacco 

Research 7 (2): 289–99.

15. Ribisl, K., C. Patterson, B. Hooten, 

S. Slazas, and A. O. Goldstein. 

2002. Assessment of the accuracy 

of newspaper clipping services. In 

Incorporating newspaper coverage of 

tobacco issues into tobacco control 

program evaluations. Oral presentation 

at the National Conference on Tobacco 

or Health, San Francisco, November 

19–21.

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

213

8. Evaluating Tobacco Industry Tactics as a 

Counterforce to ASSIST

Brion J. Fox, Stella Aguinaga Bialous, William M. K. Trochim, 

Frances A. Stillman, and Carol L. Schmitt

Contents

Introduction.................................................................................................................. 215

Background .......................................................................................................................216

Expert-based Conceptual Map of Industry Tactics ....................................................... 217

Methods.............................................................................................................................217

Participants................................................................................................................217

Procedures..................................................................................................................217

Results..............................................................................................................................219

Lessons from the Concept Map..........................................................................................220

Review of the Published Literature on Tobacco Industry Tactics................................... 224

Methods.............................................................................................................................225

Results..............................................................................................................................226

Lobbying and Legislative Strategy..............................................................................226

Legal and Economic Intimidation and Creating the Illusion of Support......................227

Usurping the Agenda..................................................................................................227

Harassment................................................................................................................227

Undermining Science..................................................................................................229

Media Manipulation and Public Relations..................................................................229

Case Studies that Examine Tobacco Industry Tactics.........................................................230

General State-level Industry Behavior ........................................................................231

Gaps in the Literature........................................................................................................233

Lessons from the Literature Review...................................................................................233

Review of Tobacco Industry Documents........................................................................ 236

Methods for Document Search and Analysis......................................................................236

Results..............................................................................................................................237

Lobbying and Legislative Strategy..............................................................................238

Legal and Economic Intimidation...............................................................................240

Usurping the Agenda..................................................................................................243

Creating Illusion of Support........................................................................................245

Harassment................................................................................................................247

Undermining Science..................................................................................................250
8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

214

Media Manipulation....................................................................................................251

Public Relations.........................................................................................................253

Tobacco Industry Tactics: An Evaluation Challenge...................................................... 254

Approaching Limitations and Challenges to Measurement................................................255

Summary...................................................................................................................... 255

Conclusions.................................................................................................................. 256

References................................................................................................................... 257

Tables and Figures

Table 8.1. Clusters and Statements in Descending Order, by Average Importance Rating.........221

Figure 8.1. Point-cluster Map Showing the Multidimensional Scaling Arrangement 

of the 88 Statements with the Eight-cluster Solution Superimposed ........................................223

Figure 8.2. Concept Map Showing Clusters and Relative Importance Ratings..........................224

Figure 8.3. Experts’ Interpretation of the Industry Tactics Concept Map ...................................225

Table 8.2. Lobbying and Legislative Strategy............................................................................226

Table 8.3. Legal and Economic Intimidation and Creating the Illusion of Support ....................228

Table 8.4. Usurping the Agenda ................................................................................................228

Table 8.5. Harassment ..............................................................................................................228

Table 8.6. Undermining Science................................................................................................230

Table 8.7. Media Manipulation and Public Relations.................................................................231

Table 8.8. Industry Tactics to Oppose Specific Public Health Initiatives....................................232

Table 8.9. Tobacco Industry Behavior at State and Local Levels................................................232
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

215

8. Evaluating Tobacco Industry Tactics as a Counterforce to ASSIST

Introduction

I

n the ASSIST evaluation, the Strength of Tobacco Control index (chapter 2) was developed to represent the public health effort. The original evaluation model also called 

for an index to represent tobacco industry* efforts, a countervailing force to state tobacco control efforts.1 White and Bero (Monograph 16, chapter 8) provide in-depth documentation that the tobacco industry did work to counter ASSIST, perceived ASSIST 

as a significant threat, made and executed plans to counter it, and even evaluated their 

own efforts. Unfortunately, this analysis could not be used as the basis for a measure 

of tobacco industry efforts because its purpose was limited to a description of tobacco 

Tobacco use prevention and control faces a challenge common to several public 

health issues: the existence of a profit-making industry that actively works to counter 

its efforts. Consequently, one of the original goals of the evaluation of the American 

Stop Smoking Intervention Study (ASSIST) was to define an index of protobacco 

activity as part of its analytic database. Formal efforts toward this goal included 

a concept-mapping process among key stakeholders, a review of the published 

literature on tobacco industry tactics, and a review of tobacco industry documents. 

The concept mapping process yielded eight categories of tobacco industry tactics 

used to counter tobacco control: lobbying and legislative strategies, legal and 

economic intimidation, creating the illusion of support, usurping the agenda, 

harassment, undermining science, media manipulation, and public relations. A 

review of the published literature and of the tobacco industry documents provided 

evidence of these tactics.

Significant issues remain for identifying data sources to measure these 

activities and designing systematic methods to collect data. A quantifiable index 

of protobacco activity was not completed for the ASSIST evaluation, but progress 

was made toward developing such an index. While the ASSIST concept-mapping 

exercise yielded potential categories and weighting data for future metrics efforts 

and correlated well with the core areas identified above, the development of 

dynamic quantitative measures of protobacco efforts remains a goal for future 

tobacco control assessment efforts.

*The term tobacco industry in this chapter refers to a group of private corporations in the business of 

selling tobacco products and to their affiliates, who share an incentive to promote the overall use of 

tobacco. It is not necessarily intended to imply that these companies are working in concert, or that any 

particular company engages in the use of any particular tactic, or that the tactics represent illegal behavior. 
216

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

industry responses to ASSIST and it was 

performed after the ASSIST evaluation 

had been completed.

The goal of the analyses described in 

this chapter was to create the tobacco 

industry counterforce measure described 

in the ASSIST evaluation conceptual model,1 a counterforce that “had a 

dampening effect on the program” (see 

Monograph 16, chapter 8, page 371). 

The approach to creating this measure 

was a “top down” one. That is, a concept mapping study was used to create 

categories of tobacco industry efforts a 

priori. Subsequently, these categories 

were investigated to assess whether they 

could encompass the extant literature 

and the tobacco industry documents 

available during the time in which the 

search was conducted (1999–2000) and, 

additionally, whether these sources could 

become a source of data for the measure 

of protobacco forces metric described by 

Stillman and colleagues.1 Challenges to 

creating and measuring this index were 

not surmounted during the period of the 

ASSIST evaluation, and the evaluation 

was not able to account for the tobacco 

industry’s countervailing efforts. However, the work described here will serve as 

a foundation for those who continue to 

work on developing a valid and reliable 

measure of tobacco industry tactics.

Background

At the time of the ASSIST evaluation, 

it was unknown whether the publicly 

available tobacco industry documents 

provided a comprehensive and representative sample of all the actions the industry 

took to counter tobacco control efforts. 

Therefore, other sources of information 

were looked for to use as a conceptual 

framework of tobacco industry tactics. 

A review of the literature yielded two 

reviews of the tobacco industry’s national 

and international tactics. Both reviews 

organize individual tobacco industry efforts against tobacco control into larger 

categories of tactics. Sweda and Daynard2

analyzed how the size, concentration, and 

wealth of the tobacco industry allowed 

it to use numerous strategies to interfere 

with public health. The strategies Sweda 

and Daynard identify were influencing 

legislative bodies through political contributions, threatening lawsuits against 

whistle-blowing media, using front 

groups, reframing the public debate from 

the health issue to economic and personal 

freedoms issues, and attempting to confuse the scientific record.

Saloojee and Dagli3 describe various 

methods used by the tobacco industry 

to counter tobacco control efforts and 

discuss these tactics within the context of 

the ongoing globalization of tobacco use. 

Saloojee and Dagli identify nine focal 

points of the industry’s efforts: engineering consent, mobilizing corporate resources, manufacturing doubt, protecting 

corporate rights, gathering intelligence, 

controlling the agenda, peddling influence, promoting voluntary codes and preemptive legislation, and opening markets 

through trade sanctions and corruption.

These two reviews were useful for 

understanding the scope of the tobacco 

industry tactics and validating the underlying principle that protobacco forces 

exist to disrupt public health programming. Neither review, however, was 

conducted with an eye toward creating 

a comprehensive and measurable metric 
217

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

of industry activity. In addition, on the 

basis of the tobacco industry documents 

available at that time, it was impossible 

to determine how well those tobacco 

industry efforts represented all that was 

occurring. Therefore, the experiential 

knowledge of tobacco control professionals was used to guide development 

of an index variable for industry tactics.

Expert-based Conceptual Map 

of Industry Tactics

T

he first method used to identify tobacco control counterefforts was 

concept mapping. This mapping project 

involved a Web-based collaboration 

that engaged tobacco control experts 

from across the country; they provided 

input on the various tobacco industry 

tactics. Participant input was analyzed 

with a sequence of multivariate statistical analyses, and the resulting output, 

including a variety of conceptual maps, 

was interpreted by a group of experts. 

This section describes the methods used 

to develop the conceptual framework 

and presents the basic results.

Methods

Concept mapping was used to develop 

the conceptual model of tobacco industry tactics. Concept mapping is a participatory mixed-methods approach that 

integrates group process activities with 

several multivariate statistical analyses 

to yield both a statistical and a visual 

representation of a conceptual domain. 

Concept mapping was first described by 

Trochim and Linton.4 Trochim5 presents 

a wide range of example projects.

Participants

Participants in the concept mapping 

process were those who fulfilled the 

following criteria: had encounters with 

tobacco industry resistance to tobacco 

control programming; had an understanding of tobacco industry challenges 

to tobacco control; or had published 

research on tobacco industry documents 

or behavior. A panel of 34 tobacco control experts was selected to participate. 

Tobacco industry representatives were 

not included because their participation 

could be deemed contrary to the interests of the tobacco companies, making 

it difficult to confirm the reliability of 

their responses.

Procedures

The procedure for concept mapping 

is described in detail by Trochim.6 The 

concept-mapping process took place 

from July 2000 through September 2000. 

All data were collected from the participants over the World Wide Web. There 

were four distinct phases in the process: 

brainstorming, sorting and rating, data 

analyses and generation of the maps, and 

expert panel interpretation of the maps.

Brainstorming. Each of the 34 experts 

logged on to a private Web page at least 

once during a 4-week period and responded to the following prompt: “One 

specific activity/tactic the tobacco industry uses to oppose tobacco control 

is . . . ” Each participant could make as 

many response statements to the prompt 

as warranted. The statements were 

compiled in a list as entered, sequentially. The process generated a total of 226 

statements from the 34 participants.
218

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

Sorting and Rating. In preparation for 

the sorting and rating task, similar statements were consolidated into a single 

representative statement. The objective 

was to have a final set of mutually exclusive statements, with only one main idea 

in each, and with no loss of content from 

the original list. The 226 statements were 

edited into a final set of 88 statements.

Participants were asked to log on to 

another Web page for the sorting and rating tasks. Twenty-two of the 34 experts 

participated in this phase. Each participant 

conducted an unstructured pile sorting 

of the statements by grouping the brainstormed statements into piles in a way that 

made sense to him or her.7–9 Three restrictions were applied to this sorting task:

1. No statement could be its own pile 

(each pile had to contain at least two 

statements).

2. There could not be a pile consisting 

of all the statements.

3. There could be no pile called “miscellaneous” containing unrelated 

statements.

Each participant was asked to provide 

a brief label that summarized the contents of each pile. Each participant was 

asked to rate the 88 statements according 

to the following instructions:

Rate each statement on a 1-to-5 

scale for its relative importance in 

undermining tobacco control efforts. 

Use a 1 if the statement is relatively 

unimportant (compared to the rest 

of the statements) in undermining 

tobacco control efforts; use a 5 if it 

is extremely important. Although 

every statement probably has some 

importance (or it wouldn’t have been 

brainstormed), try to spread out your 

ratings and use each of the 5 rating 

values at least several times.

Data Analyses and Generation of the 

Maps. The analysis* began with construction of a binary square symmetric matrix 

of similarities for each sort. For any two 

statements, a 1 was assigned to the corresponding cell if the participant placed 

them in the same pile; otherwise, a 0 was 

entered.8 A total similarity matrix was 

obtained by summing across the separate sort matrices. Thus, any cell in this 

total matrix could have integer values 

between 0 and 22 (the number of people 

who sorted the statements), where the 

value indicates the number of people 

who placed the pair in the same pile. In 

addition, in this analysis the final matrix 

was filtered by changing any matrix values of 1 to a 0. In effect, this means that, 

for the two statements to be considered 

at all similar, at least two participants 

had to have placed the two statements together. This filtering helps minimize the 

effects of any sorting errors or spuriousness in sorting on the final results.

The total similarity matrix was analyzed with nonmetric multidimensional 

scaling (MDS) analysis10 with a twodimensional solution. The solution was 

limited to two dimensions primarily 

for ease of use considerations11 and because it was used as the basis for cluster 

analysis.

The x,y MDS configuration was the 

input for the agglomerative hierarchical 

cluster analysis using Ward’s algorithm 

*All analyses were made with The Concept System© software, version 1.75 (www.conceptsystems.com).
219

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

as the basis for defining a cluster.12 Using the MDS configuration as input to 

the cluster analysis in effect forces the 

cluster analysis to partition the MDS 

configuration into non-overlapping clusters in two-dimensional space. There 

is no simple mathematical criterion by 

which a final number of clusters can be 

selected. The procedure followed here 

was to examine an initial cluster solution 

that was the maximum that was felt to be 

desirable for interpretation in this context. Then successively lower cluster solutions were examined, with a judgment 

made at each level regarding whether 

the merger seemed substantively reasonable. The suitability of different cluster 

solutions was examined and resulted in 

acceptance of the eight-cluster solution 

as the pattern that both preserved the 

most detail and yielded substantively interpretable clusters of statements.

The MDS configuration of the statement points was graphed in two dimensions. This point map shows the location 

of all the brainstormed statements; the 

statements closer to one another are 

generally expected to be more similar 

in meaning. A cluster map was also 

generated. It shows the original statement points enclosed by polygon-shaped 

boundaries for the eight clusters.

The 1-to-5 importance rating variable 

was averaged across persons for each 

statement and across statements for each 

cluster. Two graphs were developed to 

display the rating information. The first 

depicted is a point rating map, which 

shows the original point map with the 

average rating per item displayed as 

vertical columns in the third dimension. 

The second is a cluster rating map, 

which shows the cluster average rating in 

the third dimension.

Expert Panel Interpretation of the Maps. 

Thirteen of the original panel of participants were convened for a face-to-face 

meeting to review and interpret the results of the mapping process. The interpretation session followed a structured 

process described in detail by Trochim.6

The participants examined the maps to 

determine whether they made intuitive 

and rational sense and to discuss what 

the maps might imply about tobacco 

industry tactics to disrupt public health 

programs. They discussed each cluster 

until a consensus was reached on an 

acceptable cluster label. The participants 

then examined the labeled cluster map 

to determine if any clusters could be 

grouped into regions. These were discussed, and partitions were drawn on 

the map to indicate the different regions. 

Just as in labeling the clusters, the group 

then arrived at a consensus label for each 

of the identified regions.

This step-by-step interpretation culminated in a discussion of the overall 

meaning of the various maps and representations. This discussion resulted in 

the articulation of the conceptual model 

of tobacco industry tactics; this model is 

described below.

Results

In MDS analyses, the stress value is 

the statistic that is commonly reported to 

indicate the goodness-of-fit of the twodimensional configuration to the original 

similarity matrix. A lower stress value 
220

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

indicates a better fit. In a study of the 

reliability of concept mapping, Trochim 

reports that the average stress value 

across 33 projects was .285 with a range 

from .155 to .352.13 The stress value in 

our analysis was .237, which is better 

(i.e., lower) than that average.

In the analyses, an eight-cluster solution best fit the data. The 88 statements 

and their rankings by perceived importance within clusters are listed in table 

8.1. The point-cluster map in figure 8.1 

shows all of the tobacco industry tactics 

statements (points) in relation to one another. Figure 8.2 shows the cluster-­rating 

map, where the layers of each cluster depict the average importance rating, with 

more layers indicating higher importance. Note that the average represented 

by the layers in the map is actually a 

double averaging—across all of the 

participants and across all of the factors in each cluster. Consequently, even 

slight differences in averages between 

clusters are likely to be meaningful. The 

map clearly shows that, in general, the 

clusters along the bottom were judged 

to be more important for undermining 

antitobacco efforts.

The 13 participants interpreted the 

map and table in terms of several patterns. The four clusters across the top 

were thought to describe the tactics that 

the tobacco industry uses to control 

tobacco-related messages and issues. 

These include attempts to undermine 

legitimate messages from scientific studies (“Undermining Science”), attempts 

to manipulate the media (“Media Manipulation”), the industry’s public relations efforts (“Public Relations”), and 

activities to gain control of the popular 

agenda (“Usurping the Agenda”). The 

four clusters across the bottom describe 

industry actions—what the tobacco 

industry does. This includes lobbying efforts (“Lobbying and Legislative 

Strategy”), the use of front groups and 

artificially created grassroots movements 

(“Creating the Illusion of Support”), 

intimidation (“Legal and Economic Intimidation”), and harassment of tobacco 

control professionals (“Harassment”).

The participants also interpreted a horizontal dimension. Toward the left on the 

map are clusters that represent tactics that 

are more hidden or covert in nature. On 

the right are tactics that tend to be more 

overt or public in nature. The participants 

also suggested that the two dimensions 

can be viewed as forming four quadrants 

based on the 2 × 2 combination of these 

dimensions, and they provided a short 

label for each quadrant:

Issue Framing = Public + Messages

Lobbying Tactics = Public + Action

Science Public Relations = Covert + 

Messages

Harassment = Covert + Action

Last, the expert panel agreed on a final labeling for all areas of the map (see 

figure 8.3).

Lessons from the Concept Map

The concept map represents an empirically derived consensus of a panel of 

tobacco control experts and may serve 

as the basis for subsequent measurement 

development. For example, the individual 

statements generated by the expert panel 

may serve as the basis for individual 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

221

Table 8.1. Clusters and Statements in Descending Order, by Average Importance Rating

Item # Statement Mean

Lobbying and legislative strategies 3.71

85 Writing and pushing preemptive legislation at state level 4.67

8 Creating loopholes in laws and agreements (e.g., the MSA) to allow business as usual 4.57

26 Contributing funds to political groups at federal, state, and local levels to support industry goals 4.43

53 Using clout to influence introduction, advancement, modification, or suppression of bills in 

legislative bodies

4.38

87 Lobbying to ensure that funds directed to tobacco control are diverted to nontobacco control initiatives 4.33

27 Using clout to limit powers of regulatory agencies (jurisdiction, procedures, budgets) 4.29

63 Providing legislators with contributions, gifts, and other perks 4.10

44 Promoting partial or weak measures as an alternative to effective measures 4.10

52 Inserting limiting language in legislation, such as “knowingly” sell tobacco to minors 4.05

13 Writing weak tobacco control legislation, then arguing that tobacco control measures are ineffective 3.86

17 Ghost writing nontobacco bills (e.g., sewage) with clauses that if enacted, would bring 

preemption via the backdoor

3.71

7 Lobbying government officials to set unrealistic tobacco control goals to ensure program failure 3.67

61 Using political and/or monetary clout to delay funding of tobacco control programs 3.67

36 Lobbying to ensure that funds are diverted to ineffective tobacco control activities 3.67

62 Working against campaign finance reform to maintain influence 3.62

21 Working against strengthening campaign and lobbying disclosure laws 3.57

19 Promoting tort reform 3.38

41 Using clout to assign tobacco control programs to hostile/apathetic agencies for implementation 3.19

76 Conducting “briefings” of members of Congress, allies, and consultants to sway opinion on an issue 3.14

1 Promoting smokers’ rights legislation 3.05

29 Using tobacco companies’ subsidiaries (i.e., Miller and Kraft) in political opposition to tobacco 

control legislation

3.05

10 Ensuring supportive legislators will lob soft questions during testimony 2.38

2 Using tobacco employees to lobby against legislation with the excuse that it threatens their job 

security

2.38

Legal and economic intimidation 3.46

16 Devoting considerable resources to legal fights 4.76

65 Creating and funding front groups 3.81

46 Ensuring that court battles are fought in favorable jurisdictions 3.76

64 Infiltrating official and de facto regulatory organizations (like ASHRAE) 3.43

58 Filtering documentation through their attorneys in order to hide behind attorney work product 

[privilege]

3.29

9 Encouraging (or failing to discourage) smuggling as a way to counter tax hikes 3.10

4 Countering tax increases with promotions and cents off 3.05

48 Threatening to withdraw support from credible groups to control [them] 2.48

Usurping the agenda 3.39

42 Developing alliances with retailers, vendors, and the hospitality industry in opposition to public 

health policies

3.90

40 Usurping the public health process, such as creating their own youth tobacco prevention programs 3.33

22 Avoiding regulatory and legislative interventions by establishing their own programs, such as 

“We Card”

3.24

66 Promoting a tobacco-control focus that is limited to youth issues 3.24

35 Shifting blame to the victims (e.g., passing youth possession laws to punish youths) 3.24

Creating illusion of support 3.27

54 Using legal and constitutional challenges to undermine federal, state, and local legislative and 

regulatory initiatives

4.52

81 Using antilobbying legislation to suppress tobacco control advocacy 3.57

68 Flying in cadre of “experts” to fight local/state legislation 3.43

39 Creating the illusion of a protobacco grassroots movement through direct mail database and paidfor petition names3.19

60 Using international activities to avoid domestic rules on ads, taxation, etc. 3.05

33 Entering false testimony and false data into the public record 2.95

75 Tying states’ MSA money to increases/decreases of smoking prevalence 2.95

59 Using employees and their families to make campaign contibutions that are difficult to track 2.52
8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

222

Item # Statement Mean

Harassment 3.26

43 Intimidating opponents with overwhelming resources 4.38

32 Using the courts and threats of legal action to silence opponents 4.19

37 Harassing tobacco control workers via letters, FOIAs, and legal action 3.43

56 Silencing industry insiders 3.19

23 Hassling prominent tobacco control scientists for their advocacy work 3.00

3 Infiltrating tobacco prevention and control groups 2.81

25 Trying to undermine those selling effective cessation products 1.81

Undermining science 3.26

11 Creating doubt about the credibility of science by paying scientists to disseminate protobacco 

information

3.76

18 Sowing confusion about the meaning of statistical significance and research methods 3.57

38 Creating scientific forums to get protobacco information into the scientific literature 3.33

5 Influencing scientific publication by paying journal editors to write editorials opposing tobacco 

restrictions

3.10

71 Creating doubt about the credibility of science by paying scientists to provide expert testimony 3.10

80 Creating doubt about the credibility of legitimate science by paying scientists to conduct research 3.05

86 Conducting studies that, by design, cannot achieve a significant result 2.90

Media manipulation 2.91

77 Using advertising dollars to control content of media 3.71

34 Putting own “spin” on the issues by manufacturing information sources 3.43

67 Taking advantage of the “balanced reporting” concept to get equal time for junk science 2.86

69 Ghost writing protobacco articles 2.76

6 Avoiding the key health questions by saying they are not experts and then not agreeing with the 

experts

2.71

84 Misrepresenting facts in situations where there is no time to verify 2.67

74 Publicly acknowledging the risk of tobacco use, but minimizing the magnitude 2.67

30 Publicizing research into “safe cigarettes” 2.48

Public relations 2.85

12 Using philanthropy to link their public image with positive causes 4.00

28 Using philanthropy to build a constituency of support among credible groups 3.62

73 Diverting attention from the health issues by focusing attention on the economic issues 3.48

51 Distracting attention from the real issues with alternative stances such as accommodation and 

ventilation

3.38

88 Asserting that restrictions on tobacco could lead to restrictions on other industries and products 3.38

14 Minimizing importance of misdeeds in the past by claiming they’ve changed 3.24

20 Arguing that tobacco control policies are antibusiness 3.19

72 Maintaining that the tobacco industry is of critical importance to the economy 3.19

45 Portraying themselves as “responsible,” “reasonable,” and willing to engage in a “dialogue” 2.90

78 Misrepresenting legal issues to naive reporters and stock analysts 2.86

79 Feeding protobacco information to market analysts who are predisposed to accepting and 

transmitting it

2.86

15 Representing people as “antismoker” instead of antismoking 2.81

82 Developing protobacco media content, such as videos and press releases 2.67

83 Painting tobacco control activists as extremists 2.67

55 Pretending that the “real” tobacco control agenda is prohibition 2.57

57 Casting tobacco control as a civil rights threat 2.52

49 Portraying tobacco control as a class struggle against poor and minority groups 2.48

24 Providing extensive media training for executives who will be in the public eye 2.43

70 Shifting attention toward lawyers’ monetary gains and away from tobacco litigation 2.38

47 Avoiding losing public debates by overcomplicating simple issues 2.29

31 Blaming it on “fall-guys” (past or rogue employees) when the industry is caught misbehaving 2.00

50 Refusing or avoiding media debates where they think they will do poorly 1.71

Note: MSA indicates Master Settlement Agreement. ASHRAE indicates American Society of Heating, Refrigerating 

and Air-Conditioning Engineers. FOIAs indicates requests made under the Freedom of Information Act.

Table 8.1. (continued)
223

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

measures. It is anticipated that a protobacco index might be aggregated from the 

structural units of the map. For instance, 

it may make sense to aggregate subindex 

scores for the clusters “Public Relations 

and Usurping the Agenda” into a total 

score that represents “­Issue Framing.” 

Carrying this notion up the hierarchy, one 

might then aggregate the fourfold index 

scores that represent the quadrants into an 

overall index of tobacco industry tactics 

in a manner analogous to the hierarchical 

index construction used in the Strength of 

Tobacco Control index (see chapter 2).

However, additional empirical work 

is needed to validate the results and to 

explore the classifications inherent in the 

map. The next sections of this chapter 

describe two sources of data used to 

validate the categories identified in the 

concept map—a review of the literature covering tobacco industry actions 

against tobacco control efforts and a review of the tobacco industry documents. 

These reviews were performed to determine whether the categories of tactics 

described by concept mapping can also 

be identified from these sources.

Figure 8.1. Point-cluster Map Showing the Multidimensional Scaling Arrangement of the 88 

Statements with the Eight-cluster Solution Superimposed

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17, 85

18

19

20

21

23 22

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30 31

32

33

34

35

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37

38

39

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41

42

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47

48

50 49

51

52

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68

69

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73

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77

78

79

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81

82

83

84

86

87

88

Source: Trochim, W. M. K., F. A. Stillman, P. I. Clark, and C. L. Schmitt. 2003. Development of a model of the 

tobacco industry’s interference with tobacco control programmes. Tobacco Control 12: 144. Reproduced with 

permission from the BMJ Publishing Group.
224

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

Review of the Published 

Literature on Tobacco Industry 

Tactics

T

he concept mapping provided a basis for measuring tobacco industry 

strategies. Subsequently, the published 

scientific literature on tobacco industry 

activities was reviewed to determine 

whether the tobacco industry actually used these or similar tactics when 

confronting public health initiatives 

at the local, state, and national levels 

and whether the tactics described in 

the literature could be subsumed under 

the categorization scheme described 

by the concept mapping project. While 

these eight categories did effectively 

represent those described in the extant 

literature, we found no existing source 

of data from which a valid quantitative 

measure could be constructed for every 

state.

The first part of this section provides 

a brief description of the method used to 

identify and select articles for inclusion 

in the review. It subsequently presents 

articles illustrative of each category of 

tobacco industry tactic described by 

the concept mapping analysis. Finally, 

studies are presented that illustrate that 

the tobacco industry employed a combination of tactics to counter specific public health initiatives.

Figure 8.2. Concept Map Showing Clusters and Relative Importance Ratings

Lobbying & Legislative Strategy

Creating Illusion of Support

Harassment

Legal & Economic Intimidation

Undermining Science Media Manipulation

Public Relations

Usurping the Agenda

Layer Value

1 2.85 to 3.02

2 3.02 to 3.19

3 3.19 to 3.36

4 3.36 to 3.53

5 3.53 to 3.71

Rate each statement for how important you think it is in

undermining tobacco control efforts, where:

1 = Relatively Unimportant 2 = Somewhat Important

3 = Moderately Important

4 = Very Important 5 = Extremely Important

Source: Trochim, W. M. K., F. A. Stillman, P. I. Clark, and C. L. Schmitt. 2003. Development of a model of the 

tobacco industry’s interference with tobacco control programmes. Tobacco Control 12: 145. Reproduced with 

permission from the BMJ Publishing Group.
225

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Methods

The peer-reviewed literature was 

searched for articles examining the tactics the tobacco industry has used to 

oppose tobacco control. A broad search 

strategy was used to capture articles 

published from 1955 through October 2001. The search was limited to 

English-language and human studies. The 

MEDLINE database was queried with 

multiple search term combinations with 

Boolean operators. The general search 

category tobacco industry was combined 

with a series of search terms related to 

possible tobacco industry tactics. Terms 

included harassment, public relations, intimidation, illusion, legislat**, lobby**, 

media, science, undermin**, agenda, 

support, legal, illegal, econom**, NCI, 

ASSIST, interfer**, and strategy.

In an initial screening of the search 

results, the first screener (KK) identified 

173 articles for potential inclusion in the 

review. Two additional screeners (BF, SB) 

reviewed the search protocol and list of 

captured articles for completeness and 

relevance. The articles were then reviewed 

for relevance to the question posed, and 

any clearly irrelevant articles were excluded from further analysis. Fifty-one articles 

Figure 8.3. Experts’ Interpretation of the Industry Tactics Concept Map

Lobbying & 

Legislative 

Strategy

Creating 

Illusion of

Support

Harassment

Legal & 

Economic

Intimidation

Undermining Science

Media Manipulation

Public Relations

Usurping

the Agenda

Messages

Action

Covert

Public

Lobbying Tactics

Harassment

Science PR

Issue Framing

Source: Trochim, W. M. K., F. A. Stillman, P. I. Clark, and C. L. Schmitt. 2003. Development of a model of the 

tobacco industry’s interference with tobacco control programmes. Tobacco Control 12: 145. Reproduced with 

permission from the BMJ Publishing Group.
226

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

were retained for the review after a consensus of the three screeners determined 

that those articles were likely to describe 

tobacco industry tactics.

For the analysis, the articles were categorized by the themes described in the 

concept map. Articles that were deemed 

redundant for a particular theme are not 

included in this discussion.

Results

Lobbying and Legislative Strategy

This first concept map domain describes how contributing funds to 

political candidates, incumbents, parties, committees, and interest groups 

allows access and influence over these 

groups. This construct includes the 

use of campaign contributions, general 

Table 8.2. Lobbying and Legislative Strategy

Authors Short description

Begay, Traynor, and 

Glantz14

The industry contributed more to the California legislature from 1985 to 

1992 than to the U.S. Congress, most notably in the four years following the 

1988 enactment of Proposition 99.

Glantz and Begay15 In the 1991–92 California legislative session, there was a positive correlation 

between tobacco industry campaign contributions and support of industry 

positions among legislators. From 1988 to 1994, tobacco lobbying 

expenditures correlated negatively with state tobacco control expenditures.

Moore, Wolfe, Lindes, 

and Douglas16

Federal Election Commission records of personal contributions and those to 

10 tobacco political action committees were investigated during the 102nd 

and 103rd sessions of the U.S. Congress. Money received from the tobacco 

industry was the best predictor of a proindustry position.

Siegel, Carol, Jordan, 

Hobart, Schoenmarklin, 

DuMelle, and Fisher17

State preemption laws passed between 1985 and 1995 were reviewed. 

Three major trends in tobacco industry efforts were identified: preexisting 

tobacco control bills were amended by industry-friendly legislators, 

“superpreemption” bills seeking to limit all local tobacco control efforts 

were promoted, and an attempt was made by the industry to use the Synar 

amendment as a federal preemption of the enforcement of youth access laws.

Monardi and Glantz18 In 1993–94 legislative sessions in California, Colorado, Ohio, Pennsylvania, 

New Jersey, and Washington State, the relationship between tobacco 

industry donations to specific legislators and the level of their support for 

tobacco industry positions was positive. The analysis controlled for personal 

party affiliation and party control of the legislature.

DiFranza and Rigotti19 The tobacco industry attempted to impede implementation and enforcement 

of youth access laws in Massachusetts. Health department officials 

concluded that the outcome of these efforts included inadequate budgets 

allocated for enforcement, retailer-based court disputes over citations, and 

political pressure over using older youths in compliance checks.
227

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

lobbying, and other efforts to affect legislation passage, implementation, and 

enforcement (see table 8.2).

It is clear from the studies reviewed 

that the tobacco industry engages in 

tactics identified as Lobbying and 

Legislative Strategies. They are able to 

influence legislation, because their monetary contributions to politicians give them 

access to the legislative process. The high 

importance ratings that the concept mapping participants assigned to this strategy’s role in undermining tobacco control 

is supported by the published literature.

Legal and Economic Intimidation and 

Creating the Illusion of Support

The tobacco industry devotes considerable resources to policy battles and 

seeks to portray substantial support by 

the community at large for its positions. 

This support can exert political pressure 

on lawmakers, who mistake it for a true 

grassroots movement.

The two concept map domains Legal 

and Economic Intimidation and Creating the Illusion of Support are combined 

here because of shared aspects of tobacco industry association with front groups 

and engineered constituencies. While 

the major focus of Legal and Economic 

Intimidation is disempowering groups 

that promote tobacco control through 

financing and/or creating adversarial 

front groups, such as retailer alliances, 

Creating the Illusion of Support strategies focus on empowering groups that 

support the tobacco industry agenda (see 

table 8.3).

As the public has become aware and 

resistant to the tobacco industry’s direct 

tactics, the industry has employed tactics 

that minimize their own public exposure. 

Many of these less direct tactics are 

those included in the Legal and Economic Intimidation and Creating Illusion 

of Support constructs.

Usurping the Agenda

This concept map domain encompasses tactics characterized by the tobacco industry’s apparent championship 

of another group’s cause. For example, 

their widely publicized youth smoking prevention programs and support of 

laws that punish youth for the purchase, 

use, and possession of tobacco products 

appear to serve the public health agenda. 

In reality, however, the tobacco industry’s active participation in these issues 

allows them to control the agenda, rather 

than be controlled by it, and divert attention from issues they cannot directly address or counter (see table 8.4).

The Usurping the Agenda tactics most 

frequently identified are the use of industry 

alliances to present a protobacco position 

and the creation of youth access programs, 

where it has been particularly successful. 

This group of tactics allows the tobacco 

industry to control the public health agenda 

instead of being controlled by it.

Harassment

The tobacco industry uses other tactics 

to frighten and overwhelm opponents. 

The tactics include threatening to sue 

or actually suing tobacco control advocates. Alternatively, the industry makes 

burdensome requests for information 

under the Freedom of Information Act 

(FOIA). As a result, people targeted by 
8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

228

Table 8.5. Harassment

Authors Short description

Aguinaga and 

Glantz25

The tobacco industry filed burdensome FOIA requests with public health agencies to 

slow agency action.

Nicholl26 The tobacco industry accused tobacco-control advocates and program managers of 

illegal lobbying to further inflate the perception that the industry position was the 

public’s position.

Bialous, Fox, 

and Glantz27

Managers from the 17 ASSIST states were interviewed about formal complaints from 

the industry alleging illegal lobbying activities. Managers in ASSIST states that had 

not been targets of these industry complaints reported limiting their tobacco control 

interventions because they feared they would also be accused of illegal lobbying.

Note: FOIA indicates Freedom of Information Act.

Table 8.3. Legal and Economic Intimidation and Creating the Illusion of Support

Authors Short description

Traynor, 

Begay, and 

Glantz20

During 1991–92 in California, the tobacco industry used front groups and links to 

restaurant and merchant associations to conceal direct lobbying efforts in local policy 

debates. These groups monitored legislation and organized local initiative campaigns 

against tobacco control regulations. Telephone hotlines and public relations firms kept 

track of local policy developments and organized consumer lobbying efforts.

Ellis, Hobart, 

and Reed21

In the late 1980s and early 1990s in Contra Costa County, CA, industry associations 

used direct mailing campaigns, front groups, public relations firms, and lobbying for 

“smokers’ rights” to create the illusion of a genuine protobacco grassroots movement.

Table 8.4. Usurping the Agenda

Authors Short description

Bidell, Furlong, 

Dunn, and 

Koegler22 

In Santa Barbara County, CA, the tobacco industry, retailers, and industry associations 

worked together to attempt to defeat local legislation banning self-service tobacco 

displays. When the bill passed, those groups worked to delay the enactment and 

impede the enforcement of the bans.

Ritch and 

Begay23

The tobacco industry made alliances with retailers, vendors, restaurateurs, tavern 

proprietors, and the general hospitality industry. A trade association advocating 

protobacco-industry policies claimed that proposed clean indoor air legislation would 

have negative economic consequences.

DiFranza and 

Godshall24

The tobacco industry created a “model” state bill for underage tobacco sales that 

would undermine enforcement efforts.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

these tactics may be afraid to pursue an 

aggressive tobacco control agenda, or 

an agency may become so overburdened 

complying with information requests that 

its employees cannot accomplish their tobacco control duties (see table 8.5).

This tactic not only compels agencies 

to devote limited resources to a response, 

but it also creates an atmosphere in 

which all tobacco control managers, 

even those who have never been the direct target of these tactics, must become 

hypervigilant about their involvement in 

any policy interventions.

Undermining Science

The tobacco industry has made extraordinary efforts to confuse the public 

about the science of the health risks of 

smoking and environmental tobacco 

smoke. While industry executives and 

scientists have known about and accepted the scientific evidence about health 

risks from tobacco use and secondhand 

smoke for decades,28 they have actively 

resisted the public disclosure of this 

information. In addition, they have initiated and supported a research agenda 

designed to counter legitimate negative 

findings about tobacco’s health effects. 

Outcomes from this research agenda 

are, in turn, finely tuned to minimize 

or negate negative findings and are 

released to the public by forums allegedly unrelated to the industry. Tobacco 

company efforts to control the focus of 

tobacco research, counter already-published negative research, and actively 

work to confuse the public are well represented in the industry documents and 

subsequently in the published literature 

(see table 8.6).

The tobacco industry’s use of tactics 

under this domain is well documented. 

These tactics included designing studies 

to yield results that support tobacco 

industry positions, paying scientists 

to perform these studies, and creating 

quasi-legitimate forums to publicize 

those results. Lawyers with an eye to potential litigation at every level controlled 

research agendas. The tobacco industry 

successfully kept information from the 

public for many years. When this information became public knowledge, they 

attacked its veracity.

Media Manipulation and Public Relations

The Media Manipulation and Public 

Relations domains use similar tactics 

and will therefore be discussed together. 

The primary difference is that media manipulation tactics are more likely to be 

indirect and public relations tactics are 

more likely to be direct. For example, 

media manipulation tactics include situations in which the tobacco industry 

does not pay for positive media attention 

but can direct messages by threatening 

to withhold advertising revenues from a 

magazine that runs antitobacco stories. 

Public relationships tactics, in comparison, distract the public from health issues by focusing attention on alternative 

issues. Threats to the tobacco industry 

are reframed as threats to the economy 

of the state or nation, other businesses, 

or minority citizens. The common theme 

that connects the two domains is that 

they both involve presenting a manufactured image of the tobacco industry to 

the public (see table 8.7).

Media manipulation and public relations tactics are well used by the tobacco 
230

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

industry. With these tactics, the industry 

shapes public opinion. Public opinion, 

in turn, affects whether legislation favorable to the tobacco industry is enacted 

and enforced.

Case Studies that Examine Tobacco 

Industry Tactics

The research reviewed above corresponds largely, though not precisely, 

to the concept map headings. The extant 

Table 8.6. Undermining Science

Authors Short description

Glantz, Barnes, Bero, 

Hanauer, and Slade29

In the mid-1970s, Brown & Williamson scientists determined that secondhand 

smoke causes irritation, contains toxins, and is carcinogenic, but those 

scientists did not release the information. They also confirmed that nicotine 

is addictive and that tobacco use causes disease. Company scientists were not 

allowed to pursue research that might make the company subject to litigation.

Bero, Barnes, Hanauer, 

Slade, and Glantz30

Brown & Williamson funded external research projects for their potential 

public relations value, for potential use in undermining legitimate research, 

or to support industry objectives relating to such areas as ventilation and 

accommodation.

Barnes, Hanauer, Slade, 

Bero, and Glantz31

Brown & Williamson funded research intended to prove that environmental 

tobacco smoke is not harmful.

Hanauer, Slade, Barnes, 

Bero, and Glantz32

Internal research by Brown & Williamson that would have been 

disadvantageous in liability lawsuits was kept confidential. Internal 

documentation of practices and research was reviewed by attorneys in order to 

invoke attorney work product privilege in discovery.

Barnes and Bero33 The Center for Indoor Air Research was established and funded by three U.S. 

tobacco companies to research indoor air, including secondhand smoke.

Hirschhorn34 Programs to fund and disseminate research intended to prove that secondhand 

smoke is safe were initiated by Philip Morris, R.J. Reynolds, and British 

American Tobacco in Germany, influencing policy in that nation and 

elsewhere in Europe.

Ong and Glantz35 Philip Morris developed research and dissemination strategies to combat the 

results of a study conducted by the International Agency for Research on 

Cancer that connected secondhand smoke with lung cancer.

Drope and Chapman36 U.S. tobacco companies retained scientists to create and publicize doubt 

about the health risks of secondhand smoke. Scientists were organized and 

forums for dissemination were sponsored. Unfavorable results were kept from 

public release, and all studies contained contractual clauses requiring attorney 

review.

Muggli, Forster, Hurt, 

and Repace37

Studies were explicitly designed to refute the Environmental Protection 

Agency report connecting secondhand smoke and lung cancer. Resulting 

information was disseminated at industry-sponsored forums and in popular 

print media.

Ong and Glantz38 Philip Morris used the concept of “sound science” to attack the Environmental 

Protection Agency report implicating secondhand smoke in lung cancer. It 

also funded research to prove that secondhand smoke is not harmful.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

literature also includes case studies that 

focus on tobacco industry responses to 

particular legislative or regulatory efforts. These studies cannot be sorted and 

reviewed under separate concept map 

headings because each one describes 

multiple tactics the industry used to 

achieve their goals. These efforts are 

therefore grouped loosely under two 

categories: constellations of tactics that 

the industry uses to oppose a specific 

public health program and constellations 

of tactics the industry uses at the state 

and local levels to oppose tobacco control (see table 8.8).

General State-level Industry Behavior

Four papers have examined tobacco 

industry behavior at the state level and 

one paper examined industry behavior 

at the local level. These studies provide 

a general overview of the tactics the 

industry employs as it faces increasing 

challenges in the business environment. 

These reviews are based on industry 

documents, public records, and interviews (see table 8.9).

Taken together, these case studies 

show that the tobacco industry relies on 

direct and indirect political pressure to 

counter tobacco control efforts at the 

state and local levels. Direct pressure is 

exerted through lobbying efforts, and 

access to politicians is ensured through 

generous political contributions. Indirect pressure is applied through the 

manipulation of public opinion. A combination of strategies, including legislative and legal intimidation, usurping the 

agenda, creating an illusion of support, 

and harassment, harness other individuals and groups to present protobacco 

Table 8.7. Media Manipulation and Public Relations

Authors Short description

Richards, Tye, and 

Fischer39

A disparity was found between the tobacco industry’s pronouncements and 

practices regarding advertising. As broadcast advertising was restricted 

to reduce youth consumption, funds were moved to billboard and print 

advertisements. These advertisements also appeared to target youths despite 

industry statements to the contrary.

Hurt and Robertson40 The industry marketed low-nicotine and low-tar cigarettes as healthier for 

smokers than traditional cigarettes despite evidence to the contrary. As a 

result, the public became confused about the overall dangers of tobacco use.

Kennedy and Bero41 Newspapers and periodicals in 1981–94 characterized the health effects 

of secondhand smoke as controversial despite few research citations 

supporting this claim. A positive correlation was found between the amount 

of tobacco advertisements in a given magazine and the likelihood that the 

magazine would frame tobacco issues as controversial.

Mangurian and Bero42 Unfavorable media coverage of a smoke-free workplace ordinance 

increased after speakers supporting the industry spoke at public hearings.

Magzamen, Charlesworth, 

and Glantz43

Disproportionate coverage of industry-promoted topics, including economic 

cost/benefit analyses, ventilation utility, and smokers’ rights, was found 

in news and opinion pieces on passage of a smoke-free workplace law in 

California in 1997–98.
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232

Table 8.9. Tobacco Industry Behavior at State and Local Levels

Authors Short description

Nicholl26 This study identifies tobacco industry tactics in eight statewide tobacco tax 

initiative campaigns (California, Massachusetts, Arizona, Oregon, Montana, 

Nebraska, Arkansas, and Colorado). These tactics were characterized by the 

ability to apply direct and indirect pressure on the legislature through the use of 

front groups, manufactured grassroots constituents, and media saturation. Other 

tactics, such as hiring professional signature gatherers at higher than market 

wages, were used to defeat petitioning on the part of tobacco control coalitions. 

Jacobson, 

Wasserman, and 

Raube50; Jacobson 

and Wasserman51

The tobacco industry favored preemption clauses in six states (Arizona, 

Florida, Illinois, Minnesota, New York, and Texas), in part because they could 

weaken coalition support for the proposed legislation. Monetary contributions 

to politicians, combined with direct lobbying efforts, promoted the tobacco 

industry’s interests in the legislature. After tobacco control legislation was 

passed, the industry continued its efforts to impede implementation and 

enforcement of the laws.

Givel and Glantz52 In the 1990s, tobacco industry tactics included contributions and gift giving to 

legislators, creation and maintenance of front groups, affiliation with business 

associations and smokers’ rights groups, and accusations of illegal lobbying 

directed at public health workers. Statewide preemption clauses were often 

the tobacco industry’s objective, and the industry focused lobbying efforts and 

financial contributions on influencing committee processes and vote scheduling. 

Samuels and Glantz53 The industry used multiple tactics against tobacco control ordinances as policy 

debates moved to the municipal level in the late 1980s.

Table 8.8. Industry Tactics to Oppose Specific Public Health Initiatives

Authors Short description

Samuels, Begay, Hazan, 

and Glantz44

In response to Pittsburgh’s proposed health policies in 1987, the tobacco 

industry formed alliances with business and labor organizations to oppose 

a proposed ordinance that would restrict smoking in some restaurants, 

workplaces, and public areas. In addition, a public debate on accommodation, 

spearheaded by Philip Morris, served to distract attention from the public 

health issues of clean indoor air. 

MacDonald and 

Glantz45 (see also 

Magzamen and 

Glantz46)

The tobacco industry relied on multiple tactics as it opposed the smoking 

restrictions in California Assembly Bill 13 and its subsequent proposed 

extension to bars, while supporting preemption amendments and weaker 

alternative legislation. 

Goldman and Glantz47 The tobacco industry used multiple strategies to oppose Oregon’s Measure 44 

in 1996. 

Koh48 The tobacco industry used multiple strategies to oppose the 1992 

Massachusetts Question 1 tobacco tax initiative.

Traynor and Glantz49 The tobacco industry used a variety of tactics, including front groups, legal 

challenges, government lobbying, and media saturation, to halt, delay, or 

subvert passage of California’s Proposition 99 in 1989.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

arguments using public relations tactics. 

At the same time that political pressure 

is being applied, other lobbying and 

legislative strategies, such as clauses 

including preemption and support for 

smokers’ rights, are employed to weaken coalition support for proposed legislation and make that legislation more 

congruent with tobacco industry goals. 

These tactics continue during the implementation and enforcement phase of any 

legislation adopted.

Gaps in the Literature

The gaps in the literature researching tobacco industry tactics are mostly 

describable via the concept map. Some 

map clusters, such as Lobbying and 

Legislative Strategy and Undermining 

Science, are well represented in the literature. Others, such as Media Manipulation, are not. Generally, tactics that are 

either quantifiable via dollars spent or 

well described within the context of a 

particular policy debate are better represented. In contrast, tactics that can be inferred from a source, but not necessarily 

documented, are less well represented. 

For example, when manufacturing 

information sources or using advertising dollars to control content of the 

media, the tobacco industry takes great 

pains to hide their footprints. Likewise, 

tactics that are more diffuse and tend 

to be standard business practices, like 

front groups, mobilization of grassroots 

support, or public-relations-aided issue framing, are not as well researched. 

There are disparities within the clusters 

as well. While the lobbying and legislative efforts of the industry have been 

well studied, particularly in the area 

of state initiative campaigns, research 

focusing on local or national debates or 

campaigns is needed. In addition, as the 

current literature suggests, the industry 

continues to develop new strategies and 

combinations of strategies, and these 

should be monitored.

Lessons from the Literature Review

The review of the published scientific 

literature verifies that the tobacco industry uses tactics to disrupt public health 

initiatives. This review further validates 

the inclusion of protobacco forces in the 

ASSIST evaluation design and for the 

use of the concept map as a basis for this 

metric. However, the review does not 

identify a data set that would easily lead 

to the development of an index. The primary data sources used in the underlying 

literature, such as public records and 

interviews, provide valid information 

for case studies but pose challenges to a 

systematic and reliable data-collection 

effort. For example, since states have 

different reporting requirements for lobbyists and lobbying expenses, state-level 

comparisons on this measure are not 

possible, although these data could be 

used to track these tobacco-related activities within a state. Similarly, corporate 

advertising expenditures are not reported 

in sufficient detail to inform an index. In 

addition, some of the described tactics, 

particularly those that are more implicit 

or covert (such as attempting to confuse 

and reframe policy debates and construct 

the illusion of widespread popular support), are by definition difficult to document, measure, and include in a metric.
8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

234

Quantifying Tobacco Industry Policy-related Activity

While tobacco industry efforts range from advocacy to advertising and promotion, policy-related activities represent an important area for metrics of protobacco activities. According to Stanton A. Glantz, 

University of California professor and co-author of The Tobacco Wara and The Cigarette Papers,b channels for the tobacco industry to exercise influence over policy-making efforts include the following:c

Campaign contributions to individual politicians. Relationships between campaign contributions 

and legislative behavior have been demonstrated at both the state and federal levels.d,e,f While public 

disclosure is generally required for campaign contributions, accessibility of these data varies from 

state to state, and in the wake of reduced contribution limits, many contributions originate from employees and business partners who cannot be directly traced to tobacco interests.

Campaign contributions to political parties and “friends” committees. These contributions are frequently exempt from contribution limits and make it more difficult to track the flow of money and its 

concomitant influence. Such contributions also benefit legislators while not putting them on record as 

directly accepting tobacco industry money.

Lobbying. Tobacco companies have conducted lobbying efforts through numerous trade organizations 

as well as their own individual efforts. Firms have also channeled lobbying activities through secondary organizations such as nontobacco subsidiaries and supported affinity groups.e Tracking this activity 

is prone to much of the same state-to-state variability as tracking campaign contributions, and secondary lobbying poses additional difficulties for meaningful data collection.

Referenda and initiative campaigns. A common legal strategy of tobacco companies is to support a 

referendum opposing tobacco control legislation in those states that allow citizen-initiated challenges 

to passed legislation. Similarly, tobacco companies often oppose citizen-based initiatives to propose 

and vote on laws outside of normal legislative channels, a mechanism commonly used by tobacco control advocates. These efforts are frequently unsuccessful.g,h,i,j,k,l,m,n,o

“Smokers’ rights,” “accommodation,” and related public relations campaigns. Indirect policyrelated efforts by the tobacco industry include (1) forming “smokers’ rights” groups, often organized 

through public relations firms; (2) forming coalitions with the hospitality and restaurant industry 

aimed at accommodating smokers and challenging smoke-free ordinances; and (3) disseminating 

industry-developed “educational” programs designed to supplant public health efforts in schools.h,i,p,q,r

Litigation. The tobacco industry frequently engages in legal challenges to tobacco control measures 

and their advocates, particularly at the local level, often with “concerned citizens” rather than the tobacco companies themselves as plaintiffs.k,s,t

While Glantzc echoes the view of the authors of this chapter that much of these data may be difficult or 

impossible to accurately quantify, particularly given state variations and the quality of data sources, a 

potential future direction lies in indirect measures of these activities.d,e,n,u,v

■ Because disclosure requirements have remained relatively constant, the potential exists to track 

changes in industry activity over time, using existing disclosure channels—particularly regarding 

within-state behavior.

■ Several studies of individual states have shown a relationship between industry expenditures and 

the efforts of tobacco control activists, providing an important covariate relationship that should be 

tracked and measured over time. 

■ There is a consistent relationship between campaign contribution levels and legislative behavior—

and legislative behavior can be measured as a matter of public record. To quantify the records of 

specific legislators, Glantzc has used a “tobacco policy score,” representing a 0 through 10 ranking 

of the level of protobacco legislative activity.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

235

These factors all point toward the possibility of quantifying tobacco industry political activity based on 

metrics, which, in turn, may ultimately represent indicators of the many tacit activities that currently 

elude direct measurement.

aGlantz, S. A., and E. D. Balbach. 2000. The tobacco war. Los Angeles: Univ. of California Press. bGlantz, S. A., J. Slade, L. A. Bero, P. Hanauer, and D. E. Barnes. 1996. The cigarette papers. Los Angeles: Univ. of California Press.

cGlantz, S. A. 2004. Measuring tobacco industry policy-related activity (Report to the National Cancer 

Institute). Internal document, ASSIST Coordinating Center, Rockville, MD.

dGlantz, S. A., and M. E. Begay. 1994. Tobacco industry campaign contributions are affecting tobacco 

control policymaking in California. Journal of the American Medical Association 272 (15): 1176–82. eMonardi, F., and S. A. Glantz. 1998. Are tobacco industry campaign contributions influencing state 

legislative behavior? American Journal of Public Health 88 (6): 918–23. f

Moore, S., S. M. Wolfe, D. Lindes, and C. E. Douglas. 1994. Epidemiology of failed tobacco control 

legislation. Journal of the American Medical Association 272 (15): 1171–75. gHanauer, P. 1985. Proposition P: Anatomy of a nonsmokers’ rights ordinance. New York State Journal 

of Medicine 85 (7): 369–74. hSamuels, B., and S. A. Glantz. 1991. The politics of local tobacco control. Journal of the American 

Medical Association 266 (15): 2110–17. i

Traynor, M. P., M. E. Begay, and S. A. Glantz. 1993. New tobacco industry strategy to prevent local 

tobacco control. Journal of the American Medical Association 270 (4): 479–86.

j

Traynor, M. P., and S. A. Glantz. 1996. California’s tobacco tax initiative: The development and passage of Proposition 99. Journal of Health Politics, Policy and Law 21 (3): 543–85. kMonardi, F., and S. A. Glantz. 1996. Tobacco industry political activity and tobacco control policymaking in Washington: 1983–1996 (Center for Tobacco Control Research and Education, Report 

WA1996). San Francisco: University of California San Francisco, Institute for Health Policy Studies. 

http://www.library.ucsf.edu/tobacco/wa. 

l

Aguinaga Bialous, S., and S. A. Glantz. 1997. Tobacco control in Arizona, 1973–1997 (Center for 

Tobacco Control Research and Education, Report AZ1997). San Francisco: University of California at 

San Francisco, Institute for Health Policy Studies. http://www.library.ucsf.edu/tobacco/az. 

mGoldman, L. K., and S. A. Glantz. 1998. Industry political expenditures and tobacco policy making 

in Oregon: 1985–1997 (Center for Tobacco Control Research and Education, Report OR1998). San 

Francisco: University of California at San Francisco, Institute for Health Policy Studies. http://www.

library.ucsf.edu/tobacco/or.

nBegay, M. E., and S. A. Glantz. 1997. Question 1 tobacco education expenditures in Massachusetts. 

Tobacco Control 6 (3): 213–18. oKoh, H. K. 1996. An analysis of the successful 1992 Massachusetts tobacco tax initiative. Tobacco 

Control 5 (3): 220–25. pSamuels, B. E., M. E. Begay, A. R. Hazan, and S. A. Glantz. 1992. Philip Morris’s failed experiment 

in Pittsburgh. Journal of Health Politics, Policy and Law 17 (2): 329–51. qCardador, M., A. R. Hazan, and S. A. Glantz. 1995. Tobacco industry smokers’ rights publications: A 

content analysis. American Journal of Public Health 85 (9): 1212–17. r

Stauber, J., and S. Rampton. 1995. Toxic sludge is good for you. Monroe, ME: Common Courage 

Press.

s

Monardi, F., A. O’Neill, and S. A. Glantz. 1996. Tobacco industry political activity and tobacco 

control policymaking in Colorado: 1979–1995 (Center for Tobacco Control Research and Education, 

Report CO1996). San Francisco: University of California San Francisco, Institute for Health Policy 

Studies. http://repositories.cdlib.org/ctcre/tcpmus/CO1996. 

t

Monardi, F. M., and S. A. Glantz. 1997. Tobacco industry political activity and tobacco control policymaking in New Jersey: 1982–1995 (Center for Tobacco Control Research and Education, Report 
236

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

Review of Tobacco Industry 

Documents

I

n this section, tobacco industry 

documents are presented to determine whether the specific tactics that 

the tobacco industry used to counter 

ASSIST can be organized by the concept mapping framework. Millions of 

pages of previously confidential industry 

documents came into the public domain 

with the settlement of the lawsuit filed 

by the state of Minnesota against several 

tobacco companies54(Bates no. 106035476/5504)

and with the Master Settlement Agreement in 1998 between 46 state attorneys 

general and the tobacco companies.55

Analyses of these documents have provided researchers with a better understanding of tobacco industry goals and 

strategies. Because of the breadth of tobacco industry tactics and the industry’s 

particular focus on ASSIST, unique 

searches were conducted rather than relying solely on the documents described 

in the peer-­reviewed literature.

Since the end of the data collection 

for this chapter, White and Bero56 published a comprehensive analysis of internal tobacco industry documents related 

to countering ASSIST. Their findings, although presented under a different organizational framework, confirmed that the 

tobacco industry perceived ASSIST as a 

threat and developed coordinated efforts 

on many fronts to minimize the impact 

of ASSIST, both in the states involved in 

the project and in non-ASSIST states (to 

avoid a domino effect of tobacco control 

policies). The strategies used by the tobacco industry had been previously used, 

together and separately, in other industry 

attempts to derail public health efforts. 

White and Bero reached the following 

conclusions:

[The tobacco industry] coordinated 

resources to aggressively monitor, 

audit, and infiltrate ASSIST coalitions; 

pursue legal actions; preempt local 

tobacco control initiatives; generate 

negative publicity about ASSIST; 

and use its political and other allies 

to attack ASSIST at every level of 

government. Furthermore, the tobacco 

industry attempted to hide its efforts by 

working through third parties.56(p246)

This section discusses many of these 

tactics in more detail and places them 

within the concept mapping framework.

Methods for Document Search and 

Analysis

Tobacco industry documents were located via electronic searches of publicly 

available collections on the Internet, 

including tobacco industry Web sites 

NJ1997). San Francisco: University of California San Francisco, Institute for Health Policy Studies. 

http://galen.library.ucsf.edu/tobacco/nj. 

uAguinaga, S., H. MacDonald, M. Traynor, M. E. Begay, and S. A. Glantz. 1995. Undermining popular government: Tobacco industry political expenditures in California 1993–1994 (Center for Tobacco 

Control Research and Education, Report CA1995). San Francisco: University of California at San 

Francisco, Institute for Health Policy Studies. http://repositories.cdlib.org/ctcre/tcpmus/CA1995.

vBegay, M. E., M. Traynor, and S. A. Glantz. 1993. The tobacco industry, state politics, and tobacco 

education in California. American Journal of Public Health 83 (9): 1214–21.
237

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

(for example, www.tobaccoarchives.

com). The documents were accessed 

between September 2001 and July 2002. 

The search strategy was to progress 

from broad searches to narrow ones; for 

example, an initial search with a broad 

term, such as NCI, was narrowed by 

adding another term to the search string 

(for example, NCI & ASSIST). Search 

terms included NCI, ASSIST, the names 

of the ASSIST states, and a variety of 

combinations. Other search terms related 

to theoretically derived tactics (lobby**, 

allies, alliances, strategi**, tactic**, 

budget, campaign, taxation, public relations, smokers’ rights, legislat**, FOIA, 

media) were used separately and in 

combination with the above terms. Additional searches were conducted for the 

names of key players and organizations 

identified in initial searches.

Documents were selected for detailed 

review if they met one of two criteria: 

relevance for overall tobacco industry 

strategy or relevance to any state-­specific 

strategy, mainly but not exclusively 

ASSIST states. Because the goal of the 

analysis was to identify tactics generally, 

documents were not excluded simply 

because they addressed non-ASSIST 

states. The search and selection focused 

on documents dated from 1987 and 

later, with a few exceptions (e.g., earlier 

documents that were used for historical context). Documents were excluded 

that were exact duplicates and copies 

of documents that were obtained by the 

tobacco industry through FOIA requests 

from state health departments or the 

National Cancer Institute (NCI). Most 

of the documents retrieved came from 

either Philip Morris or the now extinct 

Tobacco Institute.

Efforts were made to identify the most 

significant documents as determined by 

the closeness of the match to the search 

items. The sheer volume of documents 

made it unfeasible to review all documents that mentioned ASSIST, and the 

fact that the documents were treated as 

exemplars made it unnecessary. Since 

the goal was not to have a comprehensive review of how the industry countered ASSIST but to identify potential 

constructs for measurement, omissions 

were not problematic for the analysis. 

The difficulties in searching the documents and the limitations within the 

dataset have been well documented.57

Nonetheless, the documents provide 

public health professionals with insights 

into the processes of this industry and its 

strategies to preempt or counter tobacco 

control efforts. These insights may assist 

with the efforts to define a construct of 

tobacco industry tactics.

Results

From the documents reviewed, it 

was concluded that many of the tobacco 

industry strategies identified through 

the concept mapping analysis and in the 

extant literature were used to counter 

tobacco control generally and to counter 

ASSIST specifically. As in the previous section, the tobacco industry efforts 

identified are organized by the same categories identified in the concept mapping 

analysis and subsequently used to organize the literature review.
238

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

Lobbying and Legislative Strategy

The documents provide a vast amount 

of evidence of the industry’s lobbying 

and legislative strategies, both before 

and after the creation of ASSIST.

A 1988 Tobacco Institute presentation 

describes successful lobbying efforts at 

the local level:

First, our efforts to repeal, modify 

and roll back existing legislation. 

... The city of Aurora, CO, is in the 

final stages of repealing workplace 

smoking restrictions enacted 2 years 

ago ... Success was achieved through a 

program involving the local lobbyists, 

field staff and the business community. 

... Anchorage, Alaska presents an 

opportunity for us to roll back a total

smoking ban in city buildings imposed 

in early 1987. ... So, by conditioning 

the entire political, legislative and 

public climate in Anchorage, we 

may be successful in rolling back 

the current ordinance. [emphasis in 

original]58(Bates no. TI01770261,TI01770262, 

TI01770263)

Another activity in the legislative 

tactics was to use company employees 

as lobbying agents, as described in this 

Brown & Williamson letter to its employees urging them to become involved 

in legislative issues at the local level:

we are asking you to become a 

monitor of tobacco issues and help 

prevent your local governmental 

jurisdictions from implementing 

restrictive regulations that would 

result in lost sales and profits. We are 

asking you to become the industry’s 

“eyes and ears” by monitoring your 

boards of health and town councils. 

We need you to watch for upcoming 

public hearings or any activity related 

to tobacco regulation. The sooner we 

hear about a proposed regulation, the 

sooner we can mobilize a grassroots 

effort to protect the industry. As 

stated, we support efforts to keep 

tobacco out of the hands of children, 

but the proposals being offered in 

New Jersey are unreasonable. ... To 

combat the growing number of local 

government tobacco regulations, we 

need you to urge your local customers 

to get involved. ... [Urge them] to 

attend public meetings and voice their 

opposition.59(Bates no. 640572304–2305)

Another 1988 document gives an 

example of the strategy to introduce tobacco industry-friendly legislation that 

supersedes public health efforts:

The second approach is that of 

ventilation ... or IAQ [Indoor Air 

Quality] legislation that you have 

heard about for some time. ... 

Ventilation legislation should, in many 

cases, make smoking restrictions 

redundant. Beverly Hills, for instance, 

was the first test of the ventilation 

approach. You will recall that the city’s 

total ban on restaurant smoking was 

modified to accommodate smokers and 

nonsmokers.

The third and final approach of the 

legislative program is smokers’ 

rights legislation... our experience is 

beginning to suggest that there is a 

growing perception among smokers 

... and some non-smokers ... that 

the anti-tobacco zealots have gone 

too far ... Language in bills being 

considered right now in New York 

and Pennsylvania would do just that 

[ensure fair treatment of smokers 

in society].58(Bates no. TI01770265–

0266,TI01770268–0269)
239

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

A 1988 Tobacco Institute document 

presents another tobacco industry legislative strategy—to support legislation 

that will benefit the industry in an indirect manner:

Clearly, the most intriguing work this 

year on the advertising front comes to 

us from Massachusetts and the Boston 

area MBTA. As you recall, tobacco 

advertising was banned from transit 

facilities last year. ... This year, we 

have worked to change that situation 

in a roundabout manner. We have 

offered a measure that requires that the 

MBTA to [sic] maximize advertising 

revenues from all legal sources and 

channel those funds to help the elderly 

and handicapped. This “white hat” 

proposal would supercede [sic] the ban 

on tobacco advertising and, in theory 

at least, require the MBTA to use 

all legal sources for this worthwhile 

project ... [added handwritten note] 

including tobacco advertising.60(Bates no. 

TIOK0019093)

To stop the increasing number of clean 

indoor air laws and regulations passing 

throughout the country, Philip Morris 

(PM) created the Accommodation program as a preemptive measure, since they 

knew that smoke-free environments lead 

to a decrease in cigarette consumption. 

Preemption is described in this presentation by Philip Morris’s Tina Walls:

Today we want to discuss one of PM 

USA’s most important priorities for 

1994 and 1995–accommodation/preemption. Our goal, simply stated, is 

to see some form of accommodation/

pre-emption legislation passed in 

all 50 states. The achievement of 

universal accommodation/pre-emption 

is imperative ... if our consumers 

have fewer opportunities to enjoy 

our products, they will use them 

less frequently and the result will 

be an adverse impact on our bottom 

line.61(Bates no. 2041183752)

A 1992 letter from Spearman Management, Inc., to the Tobacco Institute 

provides an example of the tobacco 

industry’s contributions to legislators:

I am quite distressed at a telephone 

call that I received the other day from 

former Florida State Senator Lincoln 

Diaz-Balart, who was just elected to 

the U.S. House of Representatives, 

and his brother Mario, who is a state 

representative here and is running a 

highly successful campaign for the 

Senate. ... Lincoln has been a great 

friend to our issues throughout his 

career in the Florida Legislature. He 

NEVER has voted against us. Lincoln 

met with the Tobacco Institute’s 

Washington lobbyists ... asking for 

the financial support. ... Lincoln now 

advises us that he did not receive a 

single contribution from the tobacco 

industry during his campaign. ... This 

is especially distressing to me because 

of the message it sends to his brother 

and the entire Cuban Caucus, who we 

always have counted on as a block vote 

for our key issues. ... At a time when 

our issues increasingly are coming 

under fire ... [we] cannot afford to 

alienate any of our friends.62(Bates no. 

TIFL0066675–6676)

The documents also support the fact 

that the tobacco industry intended to 

use lobbying tactics specifically against 

ASSIST. A 1990 R.J. Reynolds letter 

discussed North Carolina’s intention to 

apply for an ASSIST contract and the 

industry’s early reactions to that:
240

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

A task force ... is in the final stages of 

drafting an application for grant money. 

Governor Martin has talked with the 

Department Secretary about the grant 

application and they are keeping it very 

low key. ... the Governor felt that North 

Carolina might be able to divert some 

of these funds from other states and use 

them for more constructive purposes like 

infant mortality studies. The Tobacco 

Institute, Philip Morris and RJR have 

done nothing to influence the state’s 

decision on applying for NCI funds. 

Everyone has viewed this as a no-win 

situation. ... I recommend that we 

continue to monitor the grant drafting 

process and urge Governor Martin to 

stay closely in touch with the situation. 

Further assessment will need to be made 

if and when the proposal is funded.63(Bates 

no. 507720040–0041)

A 1991 R.J. Reynolds memorandum 

presents some “potential courses of action” to address the ASSIST program at 

both the federal and state levels:

We will continue monitoring ASSIST 

activity in the 17 states selected for 

initial participation. ... Potential 

courses of action being studied to 

manage the situation include:

■ Restrict or limit how the funds are 

used through the state appropriations 

process and contacts with executive 

branch officials. ...

■ In states where legislatures have or 

are considering the use of state funds 

for tobacco counter-advertising, make 

key legislative and executive branch 

officials aware that federal funds are 

already being used for that purpose. ...

■ Work with the tobacco-land 

Congressional delegation to eliminate 

ASSIST funds in future appropriation 

bills; alternatively, seek restrictions on 

how the funds can be used. ...64(Bates no. 

507770176)

In addition, a 1995 Lorillard letter asks employees to join in counterASSIST measures:

Coalitions throughout these [ASSIST] 

states, funded with your tax dollar, 

are working to put us out of business. 

... The purpose of this letter is to 

ask you to join me and the rest of the 

Wisconsin Sales team in telling our 

elected officials that we do not want 

our tax dollars funding programs like 

ASSIST.65(Bates no. 94561863)

[The letter, which apparently had attachments, was also forwarded to Lorillard employees in NJ]:66(Bates no. 94561862)

The time is now to support the 

industry that supports you and your 

family!67(Bates no. 94549615)

Legal and Economic Intimidation

The tactics under this cluster include 

tobacco industry activities to use its 

legal and financial power to prevent and 

oppose the adoption of tobacco control 

measures at the policy level. This cluster 

includes the funding of front groups to 

defend the industry’s interests, threats 

and filing of legal suits, and infiltrating and influencing through regulatory 

and semi-regulatory bodies at the national and state level in order to promote 

industry-friendly recommendations, 

guidelines, and policies. The cluster 

also includes the promotion of smokers’ rights legislation and measures. A 

1988 Tobacco Institute presentation, 

when addressing some of the strategies 
241

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

the industry was to use for promoting 

ventilation for the purpose of avoiding 

public smoking restrictions, states that

In Maryland and Connecticut, we 

will have opportunities ... to explore 

another angle in the ventilation issue 

... to get experts appointed to IAQ 

commissions ... Recommendations 

from these groups are often translated 

into legislative proposals. So it 

is important to be included at the 

table.58(Bates no. TI01770267)

A 1988 Tobacco Institute memorandum states that among several new strategies of the public smoking programs is

a more aggressive legal program to 

establish the precedent to protect 

smokers’ rights, to act as a deterrent 

to smoker discrimination, and to 

brief representatives from organized 

labor and minority bar associations 

on smokers’ rights issues.68(Bates no. 

2021195610)

The creation and funding of smokers’ 

rights groups is described in a 1989 Tobacco Institute document:

. . . not all smokers appear willing to 

defend smokers’ rights. The industry is 

held responsible for projects it funds. 

Therefore, words spoken or written by 

sponsored smokers’ rights groups must 

be consistent with industry positions. 

... [Strategies, goals, and tactics] 

Encourage aggressive and efficient 

operation of smokers’ rights groups in 

states with most significant anti-tobacco 

activity, to augment efforts to motivate 

individual smokers. ... Provide 

financial, technical and legal support to 

each group to develop their own packets 

of information on specific smokers’ 

rights issues.69(Bates no. TIMN0366820–6821)

A Tobacco Institute 1995 review discusses several legislative issues of that 

year as well as some regulatory challenges faced by the industry and how 

the industry attempted to counter the 

progress of the Occupational Safety and 

Health Administration’s (OSHA) proposed regulation on indoor smoking:

Six months of public hearings on 

OSHA’s proposed smoking ban and 

indoor air quality regulation finally 

closed in March 1995. In these 

hearings, the Institute’s representatives 

ensured that scientific evidence on the 

issues of environmental tobacco smoke 

was fully presented, that unfounded 

assertions were questioned, and that 

the perspectives of the businesses 

being regulated by OSHA were bought 

[sic] to the forefront.

Industry action did not end with 

the hearing, but instead focused 

on written submissions, responses 

and legal analyses. The Institute 

will ensure that the best available 

information is presented to OSHA as 

the hearing record is compiled and a 

course of action determined by the 

agency. The Institute continues to 

assist Members of Congress in their 

attempts to present information at 

OSHA. ... [On the FDA proposal 

to regulate tobacco] Preparing long 

before the actual issuance of the 

rulemaking, the Institute and industry 

allies were poised and armed with 

legal challenges to FDA jurisdiction, 

and lawsuits were filed even before 

the FDA’s regulatory language was 

printed in the Federal Register. The 

response from Congress was close 

behind. ... The rulemaking process 

for both the FDA and OSHA will 

enter a new stage in January 1996, 
242

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

when comment periods close on both 

proposed regulations. The Institute 

will be ready.70(Bates no. 88028786–8787)

The documents show that the tactics 

categorized under this domain were also 

brought to bear against ASSIST. A 1991 

document describes how the industry 

intended to use its financial and legal 

power to counter the ASSIST program:

■ State Activities’ regional staff will 

include plans to counter use of 

ASSIST funds for legislative activities 

within their planning for other 

administrative challenges stemming 

from EPA and OSHA activity on 

environmental tobacco smoke.

■ Such plans may include limiting state 

health department’s authority to fund 

community coalitions which pursue 

adoption of legislation or regulations.

■ Such plans may also include limiting 

state funding of anti-smoking 

programs by amounts received under 

federal program.71(Bates no. 512715489)

Threats of lawsuits are also another 

consideration. A 1991 Tobacco Institute 

memorandum discussing the issue of 

state initiatives states:

[For Massachusetts] Our counsel is 

recommending that we proceed with a 

lawsuit aimed at declaring the initiative 

unconstitutional ... This approach has 

some merit and there is little case law 

in existence to guide us on our chance 

for success; however, I believe it is 

worth pursuing. ... Finally, you may 

recall that we sought approval of an 

initial $120,000 to launch our effort in 

Massachusetts, including the necessary 

legal work that resulted in filing 

appropriate challenges to the measure’s 

constitutionality and to the ballot and 

petition language.72(Bates no. 91815022)

A follow-up memo states:

We have retained the services of two 

attorneys with substantial initiative 

experience in the State. ... They have 

helped us greatly on our issue so 

far. ... We first attempted to present 

legal arguments about the initiative’s 

constitutionality to the Attorney 

General prior to his certification of 

the issue for circulation. ... A second 

stage of legal pursuits is to take our 

constitutionality arguments to the 

State Supreme Judicial Court.73(Bates no. 

91815008–5009)

A 1993 document describes the utilization of legal tactics related to several 

ASSIST-based state initiatives:

[In Colorado] A new, ASSIST funded 

coalition has been established to work 

on a variety of anti-tobacco issues. 

Among the coalition’s priorities is the 

passage of a tobacco tax increase for 

a variety of anti-tobacco purposes. 

... Goal: increase coalition partners, 

maintain current initiative laws, 

and defend/deter/delay potential 

anti-tobacco initiative. ... [Tactics] 

Fund legal challenge and signature 

verification challenge. ...Track ASSIST 

funding to determine potential violation 

of laws governing use of federal and 

state funds. ... Increase difficulty for 

opposition to retain various professional 

signature gathering firms to assist in 

their efforts. ... Form alliance with 

tolerance campaign. Contribute toward 

legal work being done for coalition 

aimed at overturning law [an anti-gay 

rights legislation].74(Bates no. 92758356–8357)
243

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Usurping the Agenda

This cluster includes the industry’s 

efforts to undermine both the public 

and the legislative agenda in the area 

of tobacco control by imposing its own 

agenda. Activities include the development of alliances with groups, such as 

retailers, in order to broaden the support 

base for its agenda, mainly in the area of 

youth smoking and public smoking.

A 1988 Tobacco Institute presentation 

on the legislative challenges and plans 

to address the issue of public smoking 

states:

... there are opportunities to put the 

anti-tobacco groups on the defensive 

... in their own strongholds ... 

places like California, Colorado and 

Massachusetts ... places for us to go 

on the offensive ... We are excited 

about the opportunities presented by 

the new approaches ... of repealing/

modifying/rolling back existing 

legislation ... of promoting ventilation 

... or IAQ [Indoor Air Quality] 

legislation ... and smokers’ rights 

legislation. ... By implementing these 

concepts, we can set the legislative 

agenda ... [emphasis in original]58(Bates 

no. TI01770259–0260)

To gather support from the hospitality industry against growing smoking 

restrictions, the industry developed a 

program to present the smoker as customer and to assist hospitality venues in 

accommodating both smokers and nonsmokers.

Subjected to heavy anti-smoker 

pressure, the hospitality and 

travel industries are beginning to 

soften—and in some instances 

abandon—their traditional opposition 

to anti-smoking initiatives. Many are 

being encouraged to restrict smoking 

to protect the public’s health, to 

reduce overhead expenses and thereby 

increase profits. ... [Strategies, goals 

and tactics include] Maintain a list of 

individuals within the hospitality and 

travel companies, who are responsible 

for developing and implementing 

anti-smoker policies. ... Promote 

improved indoor air quality as an 

alternative to smoking restrictions. 

Promote indoor air quality consultant 

speakers at national, state and local 

hospitality association meetings. ... 

Encourage state and local hospitality 

associations to produce guides 

on smoking restrictions laws to 

assure reasonable interpretation and 

protection of smokers’ rights.69(Bates no. 

TIMN0366822–6824)

A 1990 Tobacco Institute memorandum refers to a model bill that could be 

introduced at state level:

. . . a state-level model bill addressing 

industry issues that fall within the scope 

of the “minors” debate ... The industry 

model addresses key issues such as 

vending sales, sampling, licensing and 

preemption. It is important to note that 

this is a generic bill ... [it] is unlikely

that the bill would be utilized in its 

entirety in any situation, since some 

states already have laws regulating one 

or more of its provisions. ... this model 

“minors” bill was designed to be used 

either defensively, or in a proactive or 

preemptive manner in priority states 

where these issues are most likely to 

surface. [emphasis in original]75(Bates no. 

947035576)
244

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

The 1990 Tobacco Institute plan, 

when addressing the issues related to 

fighting advertising restrictions, states:

Demonstrate that the industry does not 

want children to use its products and 

has taken positive steps to discourage 

such use.

Goals and Tactics:

1. Determine the feasibility of three new 

programs to:

■ Create a new industry advertising 

and promotion code that would 

cover all sales and promotions 

practices that become controversial 

because of exposure to youth, 

black, Hispanic or other allegedly 

vulnerable audiences.

■ Publicly support enactment of 

smoking age laws in the few states 

which do not have legislation on the 

books.

■ Develop a cooperative program with 

retailers, especially convenience 

stores, encouraging compliance 

with smoking age laws via pointof-purchase and other information 

material.76(Bates no. TIMN0361600)

A Tobacco Institute 1997 document 

demonstrates that the industry continued 

to be concerned with setting the agenda 

for state activity:

legislative assemblies are our last line 

of defense ... In keeping with this, it is 

surprising to read that projects relating 

to youth smoking, fire prevention and 

voluntary solutions to workplace and 

restaurant smoking have little or no 

value. We judge these to be examples 

of the bona fides of the cigarette 

industry in its urgings of private rather 

than public policy solutions to major 

issues. Our files contain innumerable 

communications to support this 

view. ...77(Bates no. TIDN0016871)

These tactics also apply to industry efforts in response to ASSIST. A 

1992 Philip Morris memo on countering ASSIST states and the potential for 

ASSIST to create marketing restrictions 

describes how its youth access program 

can serve as a preemptive tactic:

Also, a major goal of ASSIST is to 

reduce youth incidence, the tobacco 

industry could also offer our own 

youth initiatives with Tobacco Helping 

Youth Say no, and the COURSE 

Consortium, (once a curriculum 

is developed) and suggest that 

further Federal or state funding is 

not needed for youth anti-smoking 

campaigns.78(Bates no. 2023916867)

Retailers’ associations also played a 

prominent role in the industry’s attack 

on ASSIST in Minnesota. A series of 

1995 and 1996 “status reports” from the 

Minnesota Coalition of Responsible Retailers discussed strategies to deal with 

potential sales and marketing restrictions, many in direct response to ASSIST initiatives. For example,

[Detroit Lakes] ... An ordinance 

committee consisting of ASSIST 

group members, retailers and a city 

council member will meet to discuss 

what kind of ordinance should be 

adopted regarding tobacco products. 

Rather than waiting for the ordinance 

committee to act, Tom Briant will be 

coordinating a pro-active ordinance 

sponsored by the retailers that will 

adopt reasonable regulations on 

tobacco products based on certain 

ordinances adopted in other Minnesota 

cities. A lead retailer who knows all of 

the city council members personally 

will be working on the pro-active 

ordinance.79(Bates no. 94004146)
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Creating Illusion of Support

This concept map cluster includes the 

well-described industry tactics of funding front groups either in entirety or as 

a major sponsor. The industry goal is to 

give the impression that there is broadbased, grassroots support for its proposals and strategies. The companies would 

also use their own employees in this 

grassroots efforts.

When discussing the plan to stop 

tobacco control measures for 1988 and 

beyond, a Tobacco Institute document 

describes how the industry desired to be 

perceived as a local interest:

We remain an alien corporate entity, 

not a voter. This will change. A 

legislator must identify the tobacco 

industry as a local interest group: a 

tobacco wholesaler, a tobacco retailer, 

a vendor, a member company sales 

representative, a convenience store 

manager or even a bowling center 

proprietor. Unless a legislator can 

identify with our industry in the form 

of an individual, it is very easy for 

that legislator to vote against the 

industry without any fear of local 

accountability.80(Bates no. TIMN0014204)

The document proceeds to describe 

how the tobacco industry is going to “recruit” people to represent them, how it 

will develop “grassroots education seminars,” and continue with the phone banks 

and direct mail, and

The identification of local legislative 

activists and state legislators to be 

targeted is the cornerstone of this 

plan. This “name and face” contact 

system must be supported by the 

mechanical resources that have been 

effectively executed in the past. We 

have been putting the cart before the 

horse ... now we have the horse: the 

local activist. ...These programs, in 

support of local activists’ personal 

contacts, are designed to drive 

home the economic realities of tax 

increases, advertising bans and 

restrictive smoking legislation. This 

is a flexible support plan that can 

be tailor made to any legislative 

district depending on the issue or 

the specific committee or floor 

vote.80(Bates no. TIMN0014209)

The Tobacco Institute also discussed, 

in its 1988 budget and plans, how broadbased coalitions could advance the tobacco industry agenda.

In dealing with public issues, the 

industry has come to rely more and 

more heavily on development of 

effective coalitions to complement 

and supplement its communications 

activities. ... Our allies’ greatest 

strength – independence – remains 

a limit on the usefulness of these 

coalitions. Allies may not agree or 

even have an interest in all industry 

issues, and may not be willing or 

able to assist in all ways requested. 

... [The objective is to] establish 

and maintain working relations with 

other groups and individuals for 

the purpose of demonstrating broad 

support for industry positions and 

initiatives. [One of the strategies is 

to] broaden relationships with nontobacco groups with which we are now 

working, establish relationships with 

new groups. [These groups included: 

labor unions, AFL-CIO, publishers’ 

groups, women’s and minority groups, 

Asian retailer communities.]81(Bates no. 

TIDN0018062–8063,TIDN0018066)
246

8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

Indeed, the industry considered its 

ability to create the illusion of support as 

one of its greatest strengths, as described 

in this 1989 Strategic Plan for the Tobacco Institute:

... The Institute’s ability to mobilize 

coalitions and third-party allies.

The Institute has been successful in its 

effort to develop relationships with the 

business community, minority groups, 

farmers, labor unions, veterans, active 

military personnel and low-income 

groups in an effort to fight excise 

taxes, smoking restrictions and other 

proposals. ... [The Institute] has 

utilized its coalition building strengths 

to find allies in the advertising 

industry, as well as among cultural 

and sports organizations who rely on 

promotional funding from tobacco 

manufacturers.82(Bates no. 87649084)

A 1994 Brown & Williamson draft 

document describes a proposal for the 

creation of the company’s grassroots political action program, “The Minutemen 

Project”:

... This proposal calls for bringing 

grassroots pressure on elected officials 

to resist the unwarranted regulation 

and the taxing of the tobacco industry. 

Government officials must be made 

to understand that anti-tobacco 

activities have a high political risk. 

... To develop a targeted grassroots 

process that will be make it possible 

for B&W to place political pressure on 

specific elected officials at the federal, 

state or local levels and to expand the 

program to a broader scale, reflecting 

a pro-active governmental affairs 

process. ... Such constituents would 

consist of individuals and groups 

that support the concept of freedom 

from governmental intervention, 

including smokers, members of the 

trade, suppliers, wholesalers, retailers, 

growers and others. [Coalition partners 

would include, among others] grocers 

associations, restaurant association, 

bingo parlors, anti-tax groups, ... [the 

strategy would include] phone banks 

... direct mail, ... personal visits, ... 

media [training], testimony ... survey 

and polls, ... petitions, ... resolutions. 

... Activating third parties offers 

insulation to the company from direct 

criticism. Even though the third parties 

are sought out and activated through 

company funded efforts, it will be 

the third parties actually doing the 

lobbying. ... The potential downside 

is that the company will be accused of 

fraud.83(Bates no. 533250094–0095,533250098)

However, the creation of grassroots 

efforts was not always without problems. 

A 1987 document about a campaign in 

Minnesota describes in detail how the 

grassroots effort was mounted, with 

phone banks, several waves of mailings to tobacco retailers, letter writing 

campaigns, and a petition drive. When 

assessing the problem with the missing 

human component, it states:

Even though we blanketed the state 

with industry-generated, professionally 

prepared, targeted and timed grassroots 

activity, the missing factor was 

the human component. That is, we 

failed to zero in on persons in each 

legislative district who contribute to 

their legislator, regularly communicate 

with their legislator, socialize with 

their legislator and are active in that 

legislator’s reelection bids. ... The 

missing component allowed the 

legislator to follow the line of least 

resistance, thereby circumventing 
247

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

any real accountability to his or her 

constituency and supporters.80(Bates no. 

TIMN0014202)

This ability to create the illusion of 

support would be used to counter the 

ASSIST program as well, as described 

in this 1992–96 Philip Morris Corporate 

Affairs Plan:

Counter ASSIST Program in 17 states: 

Work with grass roots organizations 

to divert state health department 

funds, equivalent to the amount of 

ASSIST funding, to support other 

health programs (pre-natal care, 

half-way houses, etc.) ... Develop 

retailer mobilization program to aid 

in identifying and fighting local sales 

restrictions – particularly vending 

and free standing display bans or 

restrictions. ... Coordinate with 

minority business organizations 

to demonstrate the economic 

benefits of the industry on minority 

communities. ... [To oppose smoking 

restrictions/bans] Develop and 

market workplace and service venue 

accommodation programs with: 

National Federation of Independent 

Businesses, American Manufacturers 

Association, International Council 

of Shopping Centers, Hospitality/

Service Associations.84(Bates no. 

2025869600,2025869602)

A 1993 report reinforces the concept 

of using “grassroots,” in addition to lobbying, to counter ASSIST, describing 

the utilization of allies and front groups 

financed by the tobacco industry and 

discussing how important for the future 

of the industry it is to maintain funding 

for these activities.85 A 1994 document 

discussing some of the main political 

challenges in the northeast region gives 

another example of how the tobacco industry used front groups in both ASSIST 

and non-ASSIST states:

A major tax increase will be proposed 

next session in Maine to pay for health 

care reform. We will be pulling out all 

stops to show how a steep tax increase 

will hurt Maine’s economy. ... We 

will also be working with allies such 

as the Maine Grocers Association 

to encourage opposition to any tax 

increase. Maine is an ASSIST state, 

which means it gets federal funds to 

reduce the incidence of smoking. ... 

ASSIST funding gives the antis in 

Maine and other states deep pockets 

to lobby for smoking and marketing 

restrictions at the local level. The 

presence of ASSIST makes enacting 

smoking accommodation and 

marketing pre-emption priorities for us 

in Maine during 1995. ... Rhode Island 

is an ASSIST state, and we expect 

a proposal to ban public smoking in 

Rhode Island next year. Our goals in 

Rhode Island in 1995 will be to work 

with others to oppose any proposed 

smoking ban while working to help 

enact smoking accommodation and 

marketing pre-emption.86(Bates no. 

2040236694–6695,2040236698)

Harassment

This cluster describes the industry activities to undermine public health efforts 

to the point where public health activities would be ineffective. It encompasses 

harassment and intimidation such as the 

use of the industry resources to file encumbering requests for documents with 

the Freedom of Information Act, threats 

of legal challenges, and the infiltration of 

tobacco control groups and coalitions.
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8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

The industry kept close track of the 

developments in the tobacco control 

community. This 1990 Tobacco Institute 

memorandum describes attendance at 

a Stop Teenage Addiction to Tobacco 

(STAT) conference:

These [conference] recommendations 

include what we believe to be an [sic] 

comprehensive list of the anti-tobacco 

movement’s goals for the coming 

year. ... The industry should give 

consideration to these activities in 

developing its plans for 1991.87(Bates no. 

947187672)

The following document was written 

by a tobacco industry person who attended a coalition meeting in Fort Collins, CO, in 1992, making use of open 

meeting policies:

They seem to comprise the Coalition 

for a Tobacco-Free Colorado. ... 

despite my effort to remain invisible, 

ended up seated at the head of the 

table. I signed in as a student... 

[the] “close quarters” inhibited my 

notetaking somewhat. ... would advise 

future “plants” to arrive late and leave 

early, avoiding the awkward small talk 

with other attendees that might create 

suspicion.88(Bates no. 2023667420,2023667422)

The tactic was broadly used with 

ASSIST, where the industry kept trying to accuse ASSIST states of illegally 

using federal funds for non-allowed 

lobbying activities. Apparently, shortly 

after the announcement of the ASSIST 

project, Peter Greenwald, Director of the 

NCI Division of Cancer Prevention and 

Control, received a letter from Senator 

Malcolm Wallop (R-WY) and other 

legislators expressing concern that “Government funds might be used to support 

lobbying at the State and local levels.” 

Greenwald responded that it was “neither the design not the intent of ASSIST 

to use Federal money for such purposes” 

but emphasis would be on policy and 

dissemination of information to policy 

makers.89(Bates no. 512545942)

A 1991 letter from R.J. Reynolds to 

Representative Rick Boucher also brings 

up the issue of federal funds being used 

for lobbying through ASSIST:

ASSIST raises serious policy and 

legal questions about the role of NCI 

... it appears that the NCI research 

concentrated on the use of media 

events, ... and lobbying for increased 

cigarette prices, clean indoor air, and 

restrictions on tobacco promotions as 

means to stop tobacco use. ...These 

issues are important enough to merit 

appropriate Congressional oversight. 

Your role as Chairman of the Science 

Subcommittee makes you an ideal 

candidate to exercise that oversight 

responsibility.90(Bates no. 512546018–6019)

Another 1991 memorandum describes 

the actions the industry will take against 

ASSIST:

... the following actions may be 

undertaken to counter potential state 

and local legislative action resulting 

from [ASSIST]

■ Public Affairs Division will obtain 

technical proposals submitted ... under 

the Freedom of Information Act.

■ Proposals will be reviewed ... for 

“public policy” activities that could 

spark state or local legislation on 

tobacco issues.

■ Federal Division will alert key 

Members of Congress to protest 

use of federal taxpayer dollars (a) 

for activities that could impact 
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tobacco-producing states’ economies, 

and (b) to interfere in state and local 

legislative issues.

■ Federal Division will attempt to 

amend [NCI’s] next authorization or 

appropriation bill (a) to prohibit use 

of grant funds for influencing state or 

local legislation, ballot initiatives, or 

other regulatory activities, and (b) to 

require detailed auditing and reporting 

of grant expenditures. ...

■ State Activities’ regional staff will 

identify local business and labor 

interests in 17 grant states who could 

gain representation in communitybased ASSIST coalitions.71(Bates no. 

512715488–5489)

A 1992 Philip Morris memo discusses 

the industry “opportunities to disrupt 

ASSIST funding.”78(Bates no. 2023916866)

1) Congressional Investigation ... A 

more thorough investigation should 

be launched, particularly in terms of 

the NCI/ACS relationship and the use 

of federal funds for state and local 

lobbying purposes. With the current 

budget debate in Washington, this 

would be a good time to launch an 

investigation....78(Bates no. 2023916866)

Prior to this Congressional Investigation plan the industry attempted to 

achieve similar results, through allegations of using federal funds for lobbying activities, with letters from Senators 

Wallop (R-WY), Hatch (R-UT), and 

McConnell (R-KY) to U.S. Department 

of Health and Human Services Secretary 

Louis Sullivan. However, no widespread 

investigation resulted from these 

letters.91,92

Another document lists the utilization 

of FOIA as a counter-ASSIST measure:

In response to our Freedom of 

Information Act request, the National 

Cancer Institute sent us the proposals 

submitted by states that did not receive 

ASSIST awards in addition to the 17 

that did. ... Though these states have 

not received ASSIST funds, they’ll 

probably be using their proposals as 

blueprints for at least limited antismoking activities anyway, having 

gone through the trouble of developing 

them. So, a review of these proposals 

at some point would probably be 

useful.93(Bates no. TCAL0314212)

FOIA tactics were also used at the 

state level. For example, in Minnesota, 

a 1993 memorandum describes how 

requests were filed with the state health 

department:

Tom Briant, esq., of the Minnesota 

Wholesalers Association has obtained 

over 500 pages of documents from a 

recently conducted FOIA of the state’s 

ASSIST program. Tom developed the 

attached chart from his review of the 

documents. ... I have the documents 

from Tom’s first FOIA and will 

receive updates from a second FOIA 

that will soon be submitted to the state 

health department. ... Tom’s work 

reaffirms the value of conducting 

FOIAs in ASSIST states. As we 

discussed, it is important that we 

finalize our selection of states for this 

activity.94(Bates no. 2023763792)

Another January 1993 ASSIST/FOIA 

titled “ASSIST Program Issue Alternative 

Course of Corrective Action” discusses 

the potential for allegations of illegal lobbying by ASSIST grantees and suggests 

that grant applications for all 17 states 

should be obtained through FOIA requests and then using the information as
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8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

... prima facie evidence of a lobbying 

restricting violation. Additionally, 

we should determine whether similar 

requests have been made at the state 

level for funding requests from local 

organizations...95(Bates no. 2023667197)

A series of documents, mainly from 

the Minnesota Wholesale Marketers Association, Inc., discuss the challenges 

of the use of ASSIST funds brought up 

by the Minnesota Grocers Association, 

following extensive FOIA requests, the 

decision of the Ethical Practices Boards, 

and the denial of the grant application of 

the Minnesota Grocers Association for 

ASSIST funds. For example,

Other options still under consideration 

are requesting an investigation of 

the Minnesota ASSIST Project by 

the Inspector General of HHS and 

asking the Minnesota Legislative 

Auditor to commence investigation 

which the Auditor had placed on hold 

pending the Ethical Practices Board 

Investigation.96(Bates no. 2047234478)

Documents relating to the use of 

FOIA as a tactic against ASSIST in 

Colorado97,98 and Washington also 

exist.99,100

Undermining Science

This concept map cluster represents 

the tobacco industry’s decades-long tactics of challenging evidence about the 

hazards of tobacco use. For many years, 

the industry denied the harmful effects of 

smoking. However, it now concedes that 

smoking may be associated with some 

diseases, such as lung cancer, emphysema, and heart disease, and with other 

serious diseases. This tactic includes the 

industry’s use of consultants and public 

relations efforts in an attempt to create 

controversy with the public and policy 

makers, where no scientific controversy 

exists. This tactic is exemplified in 

the tobacco industry’s approach to the 

research about the negative health effects 

of secondhand smoke. The industry created research funding centers as part of 

this effort.

For example, a 1988 Tobacco Institute 

presentation discussing the industry’s 

scientific program states:

[CIAR] members are in London at 

a symposium which hopefully will 

produce some immediate ammunition. 

We hope the Center comes up with 

some science ... and soon ... because 

frankly nothing else could have the 

same effect.58(Bates no. TI01770275)

Another 1988 Tobacco Institute document states that as part of the plan to 

address the growing public concern over 

secondhand smoke, one of the tactics 

would be

Through the Center for Indoor Air 

Research, encourage scientific research 

and publication of articles that point 

to environmental tobacco smoke as a 

minor indoor air quality factor.81(Bates 

no. TIDN0018098)

And a 1990 Tobacco Institute 1990 

plan describes further the use of consultants to assess environmental tobacco 

smoke (ETS) issues:

Develop and maintain a well briefed 

group of academic consultants able 

to review ETS literature for scientific 

media, respond to ETS research 

published in the scientific media, 

and conduct briefings and present 

testimony before Congress as well as 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

federal and state regulatory agencies. 

Encourage publication of at least four 

major analyses of the relationship 

between ETS and health claims 

during 1990. ... Ensure participation 

of scientific consultants in national 

and international symposia on the 

relationship between ETS and health 

claims. ... Continue to encourage 

scientific research and publication of 

articles that objectively assess ETS 

in the context of all indoor air quality 

factors.76(Bates no. TIMN0361605–1606)

Consultants were used in all areas 

where the industry could be attacked, 

not just the smoking or health issue but 

also economic aspects of tobacco and 

tobacco consumption. For example, a 

1992–96 Philip Morris Corporate Affairs 

Plan describes how the industry was going to address the debate about the social 

costs of smoking:

[On the smoking and social cost issue] 

Develop and publish studies:

■ Systematic, external, micro-economic 

models to provide a clear picture of 

smoking’s impact on society.

■ Applicability and measurement of 

social cost and social benefits to fiscal/

tax policy development.

■ The value of free choice in a 

democratic society. ...

Develop studies comparing social fund 

performance vs. PM and industry as 

whole. ...

Sponsor sessions at annual 

conferences on social cost theory 

and its application in public policy 

development.

Co-sponsor symposiums [sic] with 

Centers in Public Policy and Society 

of Government Economists on social 

cost theory for legislators and other 

government officials. ...

[On the Taxation issue] Develop and 

publish studies on:

Bootlegging/cross border activities.

Regressive nature of proposed taxes.

Inflexibility/instability of earmarked 

taxes.

Progressive solutions to 

deficit reductions.84(Bates no. 

2025869601,2025869603)

The tobacco industry also attempted 

to use science as a means to gain credibility, and the science strategy was then 

linked to the industry’s media manipulation and public relations strategies. A 

1998 Philip Morris Worldwide Scientific 

Affairs Strategic Plan Draft asks:

How will we improve our credibility

within the scientific community in 

order to ensure that a balanced view is 

taken on smoking and health science? 

[emphasis in original]101(Bates no. 

2064716946)

Media Manipulation

This concept map cluster reflects tobacco industry tactics to use information 

created by its consultants and scientists, 

as well as its influence with the media 

through advertising expenditures, to convey to the public its perspective on the 

tobacco control debate. For example, a 

1988 Tobacco Institute plan for Minnesota 

discusses how the industry will work with 

the local media to enlist their support:

[The meetings] will feature Tobacco 

Institute resources and resource 
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8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

personnel particularly from the Public 

Affairs Division. The target is local 

media and decision makers (weekly 

newspaper editors, radio station 

managers, advertising company 

employees, service organizations), 

other civic groups, and labor union 

leaders. The specific purpose of these 

meetings would be:

build alliances with, and inform 

attendees of, constitutional, freedom of 

choice, scientific, economic and labor 

issues in targeted legislative districts; ...

activate new print and electronic media 

contacts in areas where media activity 

might not be as well developed as in 

larger markets; and,

concentrate on small weekly 

newspaper editors who are generally 

not consulted on major issues, by 

instituting briefings and continuing 

personal relationships.80(Bates no. 

TIMN0014207)

The 1988 Tobacco Institute Public 

Affairs Division plans and budget stated 

that in response to the increased level of 

interest created in the media by the release of the 1986 U.S. Surgeon General’s 

Report on passive smoking,102 the Institute has had many opportunities to speak 

with the media and

as a result of a more aggressive 

approach, The Institute is seen 

as a reliable source and credible 

newsmaking organization by an 

increasing number of journalists. ... 

Maintaining, and in fact increasing, 

this momentum will remain of 

paramount importance in the 

upcoming year. ... Industry positions 

are generally strong and compelling. 

Allies and expert consultants have 

assisted in gaining a great deal of 

ground. Increasing utilization of both 

resources through carefully planned, 

aggressive media strategies is the 

challenge. ... As we have seen in 

the past, the staging o f preemptive 

media activities works well, and 

allows greater latitude to frame our 

message while weakening that of the 

opposition. [The goals and tactics are 

to] keep the Institute in the driver’s 

seat through speakers’ availability and, 

to the extent possible, knowledge of 

anti-smoking announcements before 

the fact.81(Bates no. TIDN0017995,TIDN0017996,

TIDN0017997,TIDN0017999)

A 1995 Philip Morris media plan for 

Colorado describes some of the details 

of the media strategy to defeat a proposed tax increase initiative:

...the defeat of the 50-cent excise tax 

increase last November and the recent 

opening of the ... smoking lounge 

at the Denver International Airport 

indicate that a significant portion 

of the Colorado media is amenable 

to taking a broader view of tobacco 

industry issues. ... The vast majority 

of Colorado’s print and electronic 

media have been open to meetings, 

educational visits and materials 

which present a balanced and more 

global view of the issues. ... [We] are 

confident we can continue to change 

the media’s views and, ultimately, 

the views of the general public, 

toward a more equitable philosophy 

in regards to those issues affecting 

the industry. ... How will we make 

this happen? ... Initially, we will 

assist in the development of the key 

issues and message points we believe 

will be effective in Colorado. Using 

this “War Book” of comprehensive 
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position statements, we would present 

the broadest range of potentially 

beneficial – or damaging – issues to 

select editors, reporters and editorial 

boards ... [The document describes 

how they would “tailor” the approach 

with individualized media discussion 

in each market.] We will look for 

appropriate opportunities as well 

to present our side of the issue and 

belief with the “Where We Stand” 

packet and other program materials. 

However, this is not advisable now 

until a specific issue appears which 

allows us to engage in opportunities. 

[Hand-written note: “create 

opportunity!”] ... This proactive 

and personal approach will allow us 

to intercept anti-tobacco messages 

earlier in the media pipeline, allowing 

us to respond to negative stories by 

providing balance and perspective 

to both media and public debate. 

[The document also discusses the 

creation of alliances in order to recruit 

spokespeople.]103(Bates no. 2044270550–

0551,2044270553)

Public Relations

This concept map cluster reflects the 

industry’s attempts to portray itself as a 

responsible, socially conscious member 

of the business community and an indispensable member of any community. 

Activities include promotion of its philanthropic endeavors (for example, corporate 

social responsibility programs), framing 

the debate as generally as possible, and 

broadening its scope to extend beyond 

health issues. Examples include efforts 

to link the “right to smoke” to civil rights 

concerns and statements to the effect that 

if the tobacco industry is regulated, other 

industries would be next, creating a dangerous “police state” precedent.

When developing strategies to oppose 

marketing and advertisement restrictions, 

a 1988 Tobacco Institute plan states that 

one of its strategies is to

Demonstrate that the proposed 

restriction of tobacco advertising and 

promotion sets a dangerous precedent 

to other industries and their trade 

groups – the: “Slippery Slope Strategy.” 

... [Some of the tactics included] 

Commission a review article by some 

scientific think tank such as the Franklin 

Institute that would demonstrate that 

(a) tobacco is not unique as an alleged 

health hazard and (b) list the great 

number of citations in the literature of 

other generic products and practices 

that could easily share the same status. 

Seek publication in Science or similar 

professional journal. [emphasis in 

original]81(Bates no. TIDN0018082–8083)

This section of the document also 

addresses the industry as being a responsible corporation:

Increase the level of awareness among 

local, state and federal officials and 

opinion leaders that smoking is 

only one of a constellation of adult 

practices; that social and family factors 

are the primary factors shaping the 

behavior of young people; and that 

member companies act responsibly 

in this regard. ... [One of the goals is 

to] gain professional and legislative 

recognition for the industry’s effort 

to shield youth from cigarette 

advertising.81(Bates no. TIDN0018085–8086)

A 1989 Tobacco Institute document 

provides an example of the industry’s 

attempt to promote the corporate responsibility image:

Focus the attention of elected officials, 

the media and the public on the 
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8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

responsiveness and responsibility 

of the cigarette industry over the 

decades.69(Bates no. TIMN0366866)

In 1994, Philip Morris established 

a task force to investigate the issue of 

youth access. The main concern for Philip Morris was that the “antis” had done a 

good job in co-opting the issue of youth 

smoking and turning it into a political 

strategy at all levels of government (local, state, and federal) and in all three 

branches (legislation, regulation, and 

judicial action), similar to the strategy 

often used by the industry itself:

Anti-smoking groups use the theme 

of “protecting children” to promote 

efforts to restrict smoking by adults. 

These efforts include: Cigarette 

excise taxes, smoking restrictions and 

marketing restrictions. ... Even in the 

debate over addiction, youth smoking 

plays a prominent role in the anti’s call 

for FDA oversight.104(Bates no. 2024687704)

The Philip Morris document discusses 

what the company should do and recommends that Philip Morris work with 

retailers and take an active role in the 

youth access arena. The document outlines the development of the “It’s The 

Law” program.104

Tobacco Industry Tactics: An 

Evaluation Challenge

Measuring a construct as complex and elusive as tobacco industry 

tactics raises a panoply of potential 

Lessons from the Tobacco Industry Documents

The documents search verified that the tobacco industry engages in tactics specifically designed to 

counter public health programs and that some measure of these tactics is available in those documents. 

Because the search of the tobacco industry documents identified specific strategies and actions of the 

tobacco industry to counter ASSIST, the documents search validates the decision to include protobacco 

forces in the ASSIST evaluation design. Moreover, the analysis showed that industry strategies could 

be categorized into eight topic areas, as outlined earlier: 

1. Lobbying and legislative strategies

2. Legal and economic intimidation

3. Creating the illusion of support

4. Usurping the agenda

5. Harassment

6. Undermining science

7. Media manipulation

8. Public relations

This framework could eventually be used to create a summary index similar to the Strength of Tobacco 

Control index (see chapter 2). In addition, such a framework provides a template on which to build a 

broad model of industry counterefforts against public health endeavors. 

While the tobacco industry documents are important for identifying and describing industry tactics, 

they serve as a document collection and not as a complete, searchable database. This means that to use 

them for any index of tobacco industry tactics, either as a whole or on a statewide basis, alternative 

data must be identified.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

measurement challenges and threats. 

These challenges include defining tobacco industry tactics broadly enough 

to encompass tactics that affect public 

health initiatives yet narrowly enough 

for parsimonious measurement, reliably identifying data sources and 

subsequently accessing them. For example, while White and Bero56 (also 

see Monograph 16, chapter 8) identified 

tobacco industry actions employed to 

counter ASSIST, the tobacco industry 

engages in many standard business 

practices, such as marketing (e.g., advertising, price promotions) that counter 

public health. If these standard business practices are not included, is the 

resultant index an adequate one? Alternatively, if all possible tobacco industry 

efforts could be included in a potential 

index, are there reliable and valid data 

sources to parsimoniously measure them 

for each state?

Approaching Limitations and 

Challenges to Measurement

This chapter begins to address some 

of the limitations and challenges to measurement with three separate approaches: 

concept mapping, general literature 

review, and ASSIST-specific document 

searches. This work is primarily focused 

on defining the scope of the problem and 

providing guidance for future research to 

be able to produce appropriate variables 

that can be included in the evaluation of 

tobacco control programs.

The chapter begins by describing an 

empirical effort to define the scope of 

the problem of tobacco industry tactics. 

It discusses a concept mapping project 

that tapped the expertise of individuals in 

the field of tobacco control with respect 

to industry tactics, and presents that experience in a model. This model identifies that indeed tobacco industry tactics 

exist and can potentially be classified 

according to eight domains: attempts to 

undermine science and legitimate messages from scientific quarters, the manipulation of the media, the industry’s public 

relations efforts, the tactics the industry 

uses to gain control of the public agenda, 

lobbying efforts, the use of front groups 

and artificially created grassroots movements, intimidation, and harassment of 

tobacco control professionals. The high 

level of internal consistency in the model 

development lends legitimacy to the 

model for identifying both that tobacco 

industry tactics are an important component of the evaluation of tobacco control 

efforts and that the model can provide a 

framework for evaluating industry tactics.

Summary

Separately, the concept map results, the documents review, and the literature review are not sufficient to develop 

an index variable. They do, however, 

validate the importance of understanding tobacco industry tactics and help to 

define a construct to guide index creation. The consistency among the three 

approaches was very high. A procedure 

involving experts in the field of tobacco 

control led to a concept map of their observations and experiences with tobacco 

industry counterefforts. The review of 

the published literature revealed that 

the tobacco industry implemented these 

same identifiable tactics. The review of 
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8 . E v a l u a t i n g T o b a c c o I n d u s t r y T a c t i c s a s a C o u n t e r f o r c e t o A S S I S T

tobacco industry documents shows that 

the industry had a specific intention to 

disrupt public health programs, including ASSIST, through identifiable tactics. 

The three approaches also show that the 

tactics may be categorized and suggest 

that it may be possible to systematically 

assess these tactics as a first phase to developing a metric.

This chapter has begun the important 

work of identifying tobacco industry 

tactics and laying the groundwork for 

a measurable construct. The tasks that 

remain include defining data sources and 

designing systematic methodologies for 

data collection. Although indexing work 

on the tobacco industry documents105,106

has made this a rich data source, there 

still remains no guarantee that the records 

are or will ever be complete. Because 

these records are not complete, there is 

no way to determine whether information 

is systematically missing and whether 

the documents as a whole are biased in 

some unknown way. Reporting requirements about lobbying and advertising 

vary widely from state to state, making 

reliable state comparisons impossible. 

Tactics that may be very important to the 

tobacco industry may not be assessable 

in real time because their value cannot be 

divorced from their context.

Near-future research efforts might 

focus on more clearly defining those 

constructs that constitute an anti-­public 

health index, and more specifically 

on the operational definitions of those 

constructs. Subsequently, an assessment of factors proposed for that index 

could be examined in the same manner 

that factors for the Strength of Tobacco 

Control index (chapter 2) were assessed 

(on the basis of parsimony, scientific 

support, and feasibility). Our initial assessment suggests that any valid and 

reliable index would be difficult to create 

because the information that it is feasible 

to collect is so limited.

This chapter has begun the effort set 

forth in Stillman et al.1 discussing the 

importance of including protobacco forces in an evaluation design and providing 

guidance toward developing this construct. It is hoped that others will continue this effort and help develop an index 

that can be used within evaluation models. Until an index is created and validated, however, tobacco control evaluations 

should acknowledge and qualitatively 

describe the protobacco forces, so that 

the evaluation report can be interpreted 

within an appropriate context.

Conclusions

1. One of the early objectives of the 

ASSIST evaluation was to define an 

index of protobacco activity as part 

of its analysis. Toward this end, the 

ASSIST evaluation project undertook 

a review of tobacco industry documents and published literature on tobacco industry counterefforts, as well 

as a concept mapping process used to 

identify key tobacco industry tactics.

2. Eight key tobacco industry strategies 

identified include lobbying and legislative strategies, legal and economic 

intimidation, creating the illusion of 

support, usurping the agenda, harassment, undermining science, media 

manipulation, and public relations.

3. A Web-based concept mapping process performed as part of the ASSIST 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

evaluation project engaged tobacco 

control stakeholders to identify tobacco industry tactics. This process 

produced a conceptual map that provided detailed lists and clusters of 

tactics that may be useful in future 

quantitative measurement efforts.

4. A search of tobacco industry documents verified that the tobacco 

industry engages in tactics specifically designed to counter public health 

programs, including ASSIST, and 

specific industry concerns and tactics 

are outlined in those documents.

5. Challenges remain in quantifying the 

impact of tobacco industry counterefforts in public health in a form that 

can be used in the evaluation of tobacco control projects, including data 

sources and collection procedures. 

Possible future areas of study include 

direct and indirect measures of campaign funding, lobbying and advocacy efforts, and legislative measures. 

At a deeper level, a long-term goal 

is the definition of operational constructs for an anti-public health index 

that effectively quantifies the impact 

of tobacco industry counterefforts.

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265

9. Final Outcomes: Analytical Methods and Results

Barry I. Graubard, Anne M. Hartman, Elizabeth A. Gilpin, 

David M. Murray, and Frances A. Stillman

Contributors: James T. Gibson and William Davis

Contents

Introduction.................................................................................................................. 269

Overall Considerations for Analysis.............................................................................. 272

Units of Selection/Observation...........................................................................................272

One-tailed Tests and Statistical Power..............................................................................272

Preliminary Analyses..........................................................................................................272

Model Diagnostics.............................................................................................................273

Adult Smoking Prevalence............................................................................................ 274

Methods.............................................................................................................................274

Data Source................................................................................................................274

Analytic Approach.......................................................................................................274

Results..............................................................................................................................275

Unadjusted Descriptive Results..................................................................................275

Results from Adjusted Two-stage Model.....................................................................276

Per Capita Cigarette Consumption............................................................................... 276

Methods.............................................................................................................................276

Data Sources..............................................................................................................276

Analytic Approach.......................................................................................................276

Primary Results............................................................................................................ 279

Unadjusted Results...........................................................................................................279

Results from Adjusted Analyses.........................................................................................279

Secondary Results (SoTC and IOI Analyses) ................................................................. 279

Methods.............................................................................................................................280

Data Sources..............................................................................................................280

Models........................................................................................................................280

Results..............................................................................................................................283

Did ASSIST Affect SoTC?.............................................................................................283

Did SoTC Affect IOI?....................................................................................................283

Did ASSIST Affect IOI?.................................................................................................283

Did SoTC Affect Prevalence?.......................................................................................284
9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

266

Did IOI Affect Prevalence?..........................................................................................284

Did SoTC Affect per Capita Consumption? .................................................................286

Did IOI Affect per Capita Consumption?.....................................................................287

Summary...................................................................................................................... 289

Conclusions.................................................................................................................. 292

References................................................................................................................... 302

Tables and Figures

Table 9.1. Baseline Characteristics of ASSIST and Non-ASSIST States......................................270

Table 9.2. Covariates at Individual and State Levels ................................................................275

Table 9.3. Results of Regression Analysis of Smoking Prevalence for ASSIST Status, 

by Sex and Age..........................................................................................................................277

Figure 9.1. Bimonthly per Capita Cigarette Consumption in ASSIST and 

Non-ASSIST States....................................................................................................................278

Figure 9.2. Crude Difference in Bimonthly per Capita Consumption of Cigarettes 

between ASSIST and Non-ASSIST States....................................................................................280

Table 9.4. Adjusted Differences in per Capita Cigarette Consumption between 

ASSIST and Non-ASSIST States during the Intervention Period..................................................281

Table 9.5. IOI Analyses of the Impact of SoTC, as a Main Effect, on IOI and the 

Components of IOI.....................................................................................................................282

Figure 9.3. Yearly Initial Outcomes Index Average for ASSIST and Non-ASSIST States...............284

Table 9.6. Results from Regression Analysis of Smoking Prevalence for Strength of 

Tobacco Control.........................................................................................................................285

Table 9.7. Results from Regression Analysis of Smoking Prevalence for Initial 

Outcomes Index.........................................................................................................................285

Table 9.8. Results from Regression Analysis of per Capita Cigarette Consumption 

for Strength of Tobacco Control.................................................................................................286

Table 9.9. Results from Regression Analysis of per Capita Cigarette Consumption 

for Interactions of Strength of Tobacco Control and ASSIST Status...........................................287

Table 9.10. Results from Regression Analysis of Temporal Pattern of per Capita 

Cigarette Consumption for Strength of Tobacco Control............................................................288

Table 9.11. Results from Regression Analysis of per Capita Cigarette Consumption 

for Initial Outcomes Index.........................................................................................................288

Table 9.12. Results from Regression Analysis of per Capita Cigarette Consumption for 

Interactions of Change in Initial Outcomes Index and Its Components with 

ASSIST Status............................................................................................................................289

Figure 9.B.1. Examples of States with Nonlinear Trends in Bimonthly per Capita 

Cigarette Consumption after 1993............................................................................................297

Figure 9.B.2. Random Effects Analysis with 95% Confidence Interval of Difference 

in per Capita Cigarette Consumption (Non-ASSIST Minus ASSIST)............................................298
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

267

Appendices

Appendix 9.A. Issues Related to Selection of Methods for Analyzing Smoking 

Prevalence.................................................................................................................... 293

Appendix 9.B. Approaches to Analyzing per Capita Cigarette Consumption Data ........ 296

Appendix 9.C. Outcome Measures: Per Capita Cigarette Consumption 

(Packs/Month) and Adult (18 Years and Older) Smoking Prevalence (%)..................... 301

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

269

9. Final Outcomes: Analytical Methods and Results

Introduction

T

his chapter presents the methods and results from the formal ASSIST evaluation.1

The aim of ASSIST was “to demonstrate that the application of statewide tobacco 

prevention and control programs and policies would reduce cigarette consumption and 

smoking prevalence.”2(p261) As detailed in Monograph 16, ASSIST sought to change the 

social and environmental influences that affect individuals’ use of tobacco, primarily 

through interventions in four policy areas: (1) smoke-free environments, (2) tobacco advertising and promotion, (3) youth access to tobacco, and (4) tobacco price. Seventeen 

states with staffs with a wide range of abilities and experience in developing and implementing tobacco control programs were chosen for ASSIST funding. These states also 

differed in type and number on baseline (preintervention) factors (such as demographics and economic dependence on tobacco) that have documented relationships with 

smoking prevalence and cigarette consumption rates. It was important to take these factors into consideration when comparing ASSIST and non-ASSIST states because any 

changes observed could potentially be attributable to these differences and not to the 

effects of ASSIST.

This chapter describes the analytical methods and findings for the American 

Stop Smoking Intervention Study (ASSIST). The primary evaluation question was 

whether the ASSIST program reduced adult smoking prevalence and per capita 

cigarette consumption. The evaluation model implied additional secondary research 

questions that were also tested. These analyses investigated the association between 

ASSIST, the Strength of Tobacco Control (SoTC), the Initial Outcomes Index (IOI), 

and the longer term outcomes (adult smoking prevalence and per capita cigarette 

consumption).

Multiple linear regression was used to evaluate the effect of ASSIST on adult 

smoking prevalence, and a mixed effects linear model was used to analyze per capita 

consumption. A detailed discussion of these methods and the rationale for selecting 

them are provided in appendices to this chapter.

ASSIST was shown to reduce the prevalence of adult smoking, at a level that 

would be projected to result in more than 1.2 million fewer smokers if ASSIST had 

been implemented nationwide. However, analyses accounting for between-states 

variation using random effects modeling showed no effect of ASSIST in reducing 

per capita consumption of cigarettes. Among secondary results, adjusted IOI 

was associated with decreased prevalence, while SoTC and the cigarette price 

component of IOI were associated with decreased per capita cigarette consumption.
270

9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

At the start of the intervention, 1992–

93, there were no statistically significant 

differences between the smoking prevalence and per capita cigarette consumption rates in ASSIST and non-ASSIST 

states1 (see table 9.1). By the end of the 

intervention, 1998–99, overall smoking 

prevalence rates in the United States 

had decreased by 2.4%. Subsequent 

statistical analysis, therefore, focused 

on whether the decreases in smoking 

prevalence during the intervention period were greater in ASSIST states than 

in non-ASSIST states to ensure that this 

difference was not simply attributable 

to other factors. Regression analyses 

adjusting for potential confounding factors (see table 9.1 for a list of the factors 

considered) showed that there was a

greater decrease of smoking prevalence 

in ASSIST states than there was in nonASSIST states. Further analyses investigated whether the intervention had a 

greater effect on people of particular 

ages or gender. These analyses found 

that the decreased prevalence effect of 

ASSIST mainly affected women.

Per capita consumption was also examined via a statistical analysis that took 

into account the consumption rates in 

each state during the time when ASSIST 

began, incorporated the state factors associated with cigarette consumption, and 

illustrated each state’s seasonal pattern 

of consumption. This analysis did not 

show any differences in consumption 

rates over time.

Table 9.1. Baseline Characteristics of ASSIST and Non-ASSIST States

Variable

ASSIST states 

(N = 17) SE

Non-ASSIST 

states + DC 

(N = 34) SE pa

Adult smoking prevalence (%) 25.19 0.64 24.41 0.48 .35

Initial Outcomes Index (IOI) 0.20 0.60 –0.10 0.39 .65

Per capita tobacco consumption (packs 

per month)

10.64 0.48 10.54 0.41 .88

Sex 

Female (%) 52.22 0.16 51.76 0.24 .21

Race/ethnicity

Black–non-Hispanic (%) 8.57 1.86 10.28 2.17 .61

Hispanic (%) 5.54 2.10 4.78 1.04 .72

Metropolitan area resident (%) 70.52 4.46 62.76 4.28 .26

Below poverty line (%) 13.85 0.88 14.36 0.78 .69

Economic value of tobacco × 103

(fraction)

5.24 3.58 1.46 1.02 .20

With education above high school (%) 44.63 1.71 45.67 0.96 .57

Mean state population (million) 4.10 0.74 3.54 0.79 .65

Mean age of state population 41.18 0.23 41.03 0.26 .71

Note: Data in tables 9.1, 9.3, 9.6, and 9.7, and in figures 9.2 and 9.3, were published in an earlier analysis in Stillman, F. A., 

A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. M. Murray, and J. T. Gibson. 2003. Evaluation of the American Stop 

Smoking Intervention Study (ASSIST): A report of outcomes. Journal of the National Cancer Institute 95 (22): 1681–91.

aTwo-sided p values are based on t tests.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

The sidebar “Questions to be Addressed in the ASSIST Evaluation” 

describes the conceptual framework 

that guided the evaluation of ASSIST 

and the primary and secondary research 

questions. The next section (“Overall 

Considerations for Analysis”) describes 

the overall considerations that applied to 

both the primary and secondary analyses.

The three sections that follow describe the analytical methods used and 

present the results from the analysis of 

(1) adult smoking prevalence, (2) per 

capita cigarette consumption, and (3) 

the various secondary outcomes for the 

ASSIST evaluation. The last section 

summarizes the main results and places 

them in context with respect to tobacco 

control in the United States during the 

intervention period.

Additional background information 

about the selection of analytic methodologies is contained in two appendices 

to this chapter, “Issues Related to Selection of Methods for Analyzing Smoking 

Prevalence” (appendix 9.A) and “Approaches to Analyzing per Capita Cigarette Consumption Data” (appendix 9.B).

Questions to be Addressed in the ASSIST Evaluation

The primary questions of the ASSIST evaluation are whether the ASSIST program reduced smoking 

prevalence and whether it reduced per capita cigarette consumption.a However, the evaluation conceptual model suggests a number of secondary questions about the relationships between SoTC (see 

chapter 2), IOI (see chapter 4), smoking prevalence, and per capita cigarette consumption. These primary and secondary questions raised in the original evaluation modelb and reported on by Stillman and 

colleaguesc are as follows:

Primary Questions

■ Was ASSIST associated with a decrease in adult smoking prevalence?

■ Was ASSIST associated with a decrease in adult per capita cigarette consumption?

Secondary Questions

■ What was the relationship between ASSIST and SoTC?

■ Was ASSIST or SoTC associated with an increase in IOI?

■ Did states with higher SoTC scores have lower adult smoking prevalence?

■ Did states with higher IOI scores have lower adult smoking prevalence?

■ Did states with higher SoTC scores have lower adult per capita cigarette consumption?

■ Did states with higher IOI scores have lower adult per capita cigarette consumption?

aManley, M., W. Lynn, R. Payne Epps, D. Grande, T. Glynn, and D. Shopland. 1997. The American 

Stop Smoking Intervention Study for Cancer Prevention: An overview. Tobacco Control 6 (Suppl. 2): 

S5–S11.

bStillman, F. A., A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. Chavis, J. Garcia, L-M. Wun, 

W. Lynn, and M. Manley. 1999. The American Stop Smoking Intervention Study: Conceptual framework and evaluation design. Evaluation Review 23 (3): 259–280.

cStillman, F. A., A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. M. Murray, and J. T. Gibson. 2003. 

Evaluation of the American Stop Smoking Intervention Study (ASSIST): A report of outcomes. Journal of the Nation Cancer Institute 95 (22): 1681–1691.
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9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

Overall Considerations for 

Analysis

T

his section outlines, with respect to 

the ASSIST evaluation, several decisions that apply to all of the analyses that 

were conducted for both the primary and 

secondary questions.

Units of Selection/Observation

Although states were not assigned 

randomly to ASSIST or non-ASSIST 

status,3 the state was the unit of selection for ASSIST sites; therefore, the 

state was the unit of analysis for the 

evaluation. In the evaluation models, 

each of the 51 units (50 states plus the 

District of Columbia) is treated as an 

equal unit, regardless of population size. 

With only 51 units of observation, the 

number of variables that can be included 

in a regression analysis is limited. Consequently, the ASSIST evaluation relied 

heavily on developing summary indices 

(see chapters 2 and 4).

One-tailed Tests and Statistical Power

Decisions related to the initial design 

of the ASSIST evaluation included the 

use of one-tailed statistical tests for hypotheses that could be formulated directionally.2 It was assumed that ASSIST 

would lead to lower prevalence and to 

lower per capita cigarette consumption. There was no basis for suspecting 

that this intervention would increase 

prevalence or per capita consumption. 

Tests of means, proportions, and single 

coefficients from regression models 

were, therefore, from one-tailed t tests 

at the .05 level of significance when the 

hypotheses were directional. For ease 

of understanding, two-sided 90% confidence intervals are presented so that 

the reader may focus on the appropriate 

upper or lower confidence limit corresponding to the implied direction of the 

hypothesis. However, when simultaneous inferences about several regression 

coefficients were being made, two-tailed 

F tests at the .05 level of significance 

were used, because the hypotheses involved were multidirectional.

A priori power computations based 

on a one-tailed test for unadjusted smoking prevalence indicated that with the 

17 ASSIST states and 34 non-ASSIST 

states, such an analysis would have a 

power between 63% and 76% to detect 

a difference of 1.5 percentage points 

and between 95% and 99% to detect a 

difference of 2.5 percentage points.2 No 

a priori power computation was performed with respect to differences in per 

capita cigarette consumption.

Preliminary Analyses

A number of preliminary analyses 

were conducted to explore potential 

methods for the evaluation. These analyses used prebaseline and baseline data. 

The analysis approach was selected on 

the basis of these preliminary analyses 

and before any additional analyses were 

conducted. In this way the researchers 

ensured that the method selection was 

not unduly influenced by the results. An 

additional feasibility analysis was performed using interim data through mid1996. This allowed for verification that 

the methods selected were sound without 

adding to the number of statistical tests 

performed on the final data and potential 

Type I error.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Two appendices at the end of this 

chapter document the issues addressed 

in this decision-making process. Appendix 9.A describes issues related to the 

analysis of adult smoking prevalence, 

including the treatment of baseline 

prevalence and the options for adjusting 

for factors or state conditions that might 

differ among the states. Appendix 9.B 

addresses some of the possible ways to 

analyze the per capita consumption data. 

It also addresses the importance of accounting for the state-specific variability in the data; these differences had a 

substantial effect on the analysis results 

(and, in particular, on the size of the confidence intervals) that related ASSIST to 

per capita consumption. The interested 

reader should consult these appendices 

if there are questions concerning why 

the analytic approaches described below 

were used.

Model Diagnostics

Because the unit of analysis was the 

state, and there were a limited number of 

states, an individual state could strongly 

influence the results. Standard regression 

diagnostics were therefore conducted 

for the smoking prevalence analyses in 

which one state at a time was left out 

to identify states that had an unusually 

strong influence on the results.4 Also, 

with respect to the prevalence analyses, 

covariates used in the regression analyses to adjust for state conditions were 

examined for unusually high correlations 

with the exposures (e.g., ASSIST, SoTC, 

and IOI) to determine whether these covariates were unduly inflating variances.4

Separate analyses omitting the District of Columbia are presented because 

some measures developed for the evaluation (SoTC and IOI) require information from local jurisdictions that are not 

present in the District of Columbia.* For 

example, the District of Columbia has an 

inflated IOI score; states that preempted 

local clean indoor air legislation had 

points deducted from the state and local 

clean indoor air rating (see chapter 3), a 

component of the IOI score. However, 

preemption at any level below the unit of 

analysis is not an option in the District of 

Columbia, so no points could be deducted. In addition, the District of Columbia 

IOI score is artificially inflated due to 

strong enforcement of a weak clean indoor air law. These factors, along with 

easy access to less expensive cigarettes 

in Virginia, render the District of Columbia an influential outlier for the smoking 

prevalence-IOI relationship.

Additional analyses that exclude 

California are presented. California is an 

influential outlier because it had established a comprehensive, well-funded tobacco control program prior to ASSIST 

and the funding for its tobacco control 

program far exceeded funding in any 

other state during the ASSIST period.

*Although the District of Columbia is obviously not a state, it was treated as a “state” in these analyses 

because it was part of the Centers for Disease Control and Prevention’s Initiatives to Mobilize for the 

Prevention and Control of Tobacco Use (IMPACT) and it was a SmokeLess States grantee during the 

ASSIST period.
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9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

Adult Smoking Prevalence

Methods

Data Source

Smoking prevalence data are from the 

Tobacco Use Supplement to the Current 

Population Survey (TUS-CPS; see chapter 5).5 From the survey data on individuals, the percentage or prevalence of 

current smokers can be computed within 

each state and the District of Columbia. 

The TUS-CPS was conducted at baseline in 1992–93, at an interim point in 

1995–96, and at the end of the program 

in 1998–99. However, only the baseline 

and end of the program data were included in the final analyses.

Analytic Approach

The prevalence data were analyzed 

using a two-stage regression model. At 

the first stage, a single logistic regression model was fit to the TUS-CPS data 

from baseline (1992–93) and from last 

follow-up (1998–99) to model current 

smoking status (1 = current smoker, 0 = 

nonsmoker) at the individual level. This 

logistic regression used the TUS-CPS 

sample weights; it included the variables 

listed in table 9.2 as individual-level 

covariates; and it included interactions 

between sex and age and between sex 

and race/ethnicity. Residuals obtained 

from the logistic regression were averaged within each state for the baseline 

and follow-up period to be used in the 

second stage of the regression analysis. 

These state-level mean residuals represent adjusted prevalences for the states 

that remove state-to-state differences for 

the individual-level covariates.

Multiple linear regression was used 

for the second stage of the analysis to 

adjust for state-level covariates (see table 

9.2). The model for this analysis is described by equation 9.1 where RBs and 

RFs represent the mean residuals for a 

state s from the first-stage logistic regression of the baseline and the follow-up, 

respectively; Es represents the exposure 

variables of interest for a state s; and εs

is the random error. The exposure variable could have been ASSIST status, 

IOI, SoTC, and/or selected interaction(s) 

between these exposures.

RFs = β0 + β1 RBs + β2 Es + εs (9.1)

State-level covariates (see table 9.2) 

were considered for inclusion as independent variables in equation 9.1, using 

an all-possible-subsets procedure based 

on the minimum Mallows Cp statistic.6

This stepwise procedure was applied 

by fixing the baseline mean residual as 

a covariate in the model, omitting the 

exposure variable(s) (e.g., ASSIST), and 

evaluating the possible subsets of statelevel covariates. None of the state conditions was statistically significant when 

this procedure was used. This indicated 

that the first stage of the analysis effectively removed these sources of variation 

at the individual level, so that no further 

variation need be explained by statelevel covariates.

Separate sets of models for smoking 

prevalence were estimated for adult men 

and women and by age group (18–29, 

30–49, 50–64, 65+).
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Results

Unadjusted Descriptive Results

Table 9.1 compares ASSIST states 

with non-ASSIST states at baseline for 

selected demographic and state conditions. Smoking prevalence rates in 

ASSIST states were not significantly 

different from prevalence rates in nonASSIST states (25.19% vs. 24.41%, 

p = .35). There were also no differences 

between ASSIST and non-ASSIST states 

for IOI at baseline (0.20 vs. −0.10, p = 

.65) or for any of the other measures 

evaluated at baseline.1

The mean change in smoking prevalence in the entire United States from 

1992–93 to 1998–99 was −2.4% (SE = 

0.20%). Appendix 9.C presents baseline 

and outcome (unadjusted) prevalence 

data for all states. (See also appendix 

11.A for the ASSIST and non-ASSIST 

crude prevalence rates by state.) Of the 

17 ASSIST states, 12 (71%) equaled 

Table 9.2. Covariates at Individual and State Levels

Source Covariate

Individual-level 

CPS–CORE ■ Age: 18–29, 30–49, 50–64, 65+

Education: <9th grade, 9th–12th (no high school degree), 

high school degree, some college or associate’s degree, 4-year 

college degree or higher 

■

■ Family income: in dollars

Race/ethnicity: black–non-Hispanic, Hispanic, white–nonHispanic, other ■

■ Household size: number of residents 

■ Sex: male, female 

■ Census region: Midwest, West, South, Northeast

■ Employment status: employed, unemployed 

State-level 

Census population data ■ Sex: % female

■ State population: 18 years of age or older

■ Income: % below poverty level

■ Race/ethnicity: % black–non-Hispanic, % Hispanic

CPS–CORE ■ Education: % above high school degree

■ Metropolitan residency: % living in metropolitan area

■ Census region: Midwest, West, South, Northeast

USDA Economic Research Service 

& Department of Commerce’s 

Bureau of Economic Analysis

Economic value of tobacco: fraction of gross state product 

from growing, manufacturing, and processing tobacco

■

Note: CPS indicates Current Population Survey. CORE indicates CPS basic monthly survey questions. USDA 

indicates U.S. Department of Agriculture.
276

9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

or exceeded the national mean change, 

compared with only 15 (44%) of the 34 

non-ASSIST states (including the District of Columbia). Among the ASSIST 

states, Maine and Virginia achieved the 

largest decrease in mean adult smoking prevalence (−5.01%, SE = 1.68%, 

and −4.70%, SE = 1.36%, respectively), 

while Indiana and New York had the 

smallest decreases (−0.78%, SE = 

1.79%, and −0.98%, SE = 0.63%, respectively). Among non-ASSIST states, 

Georgia and Nevada had the greatest decreases in mean adult smoking 

prevalence (−4.43%, SE = 1.57%, and 

−4.31%, SE = 1.52%, respectively), 

whereas Delaware, the District of 

Columbia, and Oklahoma had slight 

increases in prevalence (0.04%, SE = 

1.74%; 0.31%, NA; and 0.78%, SE = 

1.51%, respectively). Taken together, 

ASSIST states achieved a somewhat 

greater decrease in adult smoking 

prevalence than non-ASSIST states (M = 

−3.02% vs. −2.11%, respectively; a difference of −0.91%, p = .015).

Results from Adjusted Two-stage Model

Table 9.3 presents the results of the 

multiple linear regression analysis for 

prevalence (adjusted for baseline prevalence and individual-level demographics) 

between ASSIST and non-ASSIST states 

at the end of the intervention period 

(1998–99). The adjusted difference (regression coefficient) in prevalence was 

−0.63% (90% confidence interval [CI]: 

−1.25, −0.01, p = .049). This relationship 

was slightly weakened when the District of Columbia was removed from the 

analyses (−0.53%; 90% CI: –1.12, 0.07, 

p = .076, data not shown). However, 

without California in the analyses, the 

relationship was similar to the overall 

results (−0.66%; 90% CI: –1.28, –0.04, 

p = .044, data not shown). Table 9.3 also 

shows subgroup analyses for the exposure effects of ASSIST on prevalence, 

by sex and age. ASSIST had a significant effect on prevalence rates among 

females: −0.96% (p = .023). However, 

ASSIST’s effect was not statistically significant among males. No differences in 

effect on prevalence by age were found.

Per Capita Cigarette 

Consumption

Methods

Data Sources

Bimonthly estimates of per capita cigarette consumption for each state were 

the data to be modeled. The sources of 

these data are described elsewhere (see 

chapter 5). Figure 9.1 shows the raw 

bimonthly data for ASSIST and nonASSIST states in the top and bottom 

panels, respectively. The variability with 

respect to the states in the consumption 

data over time, particularly in the nonASSIST states, was substantial, and it is 

not difficult to imagine that it might obscure any signal (e.g., effect of ASSIST) 

to be found in the noise (variability).

Analytic Approach

The analytic model of per capita 

consumption that was implemented is 

a mixed effects linear model,7 given by 

equation 9.2.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Cst = μ + β1 t + β2 t

2 + β3 t2 +

β4 t

4 + β5 sin(t π/3) +

β6 cos(t π/3) + γ Xst +

α1 As + α2 As × t + α3 As ×

t

2 + α4 As × t3+ α5 As × t4 + 

ms+ bs t + εst. (9.2)

The dependent variable is bimonthly 

consumption, denoted by Cst for state s at 

time t. Independent variables include time 

(bimonthly time, t) and powers of time 

(t2, t2, t4), which account for a nonlinear 

trend, and sine and cosine functions of 

time, sin(t π/3), cos(t π/3), which account 

for seasonal periodicity in the consumption data, and ASSIST status, As

.

This model has a state-varying random intercept, ms

, a random regression 

coefficient, bs, for the time variable, and 

an ARIMA(1) autoregressive correlation 

structure for the error terms, εst, across 

time within state.

Forward stepwise regression was used 

to select the time-varying and non–timevarying state-level covariates (table 9.2) 

to be added to an initial model with only 

the terms involving time and without 

interactions with As. The stepwise regression resulted in a final model that included statistically significant state-level 

covariates (Xst), independent variables 

for ASSIST status, interactions between 

ASSIST status and time, powers of time, 

and the sine and cosine of time.

In the modeling approach that was 

used, the interactions between ASSIST 

status and time were over the entire 

period (December 1988–May 1999). 

After adjusting for the state conditions, 

this model permits the trend in cigarette 

consumption for the ASSIST states to 

be different from the trend for the nonASSIST states during the preintervention 

period (December1988–October 1993) 

Table 9.3. Results of Regression Analysis of Smoking Prevalence for ASSIST Status, by Sex 

and Age

ASSIST versus non-ASSIST

Exposure/

Subgroupa ß 90% CI pb R2c

Total –0.63 –1.25, –0.01 .049 .68

Sex

Male 0.09 –0.46, 0.64 .42 .50

Female –0.96 –1.73, –0.19 .023 .65

Age

18–29 –0.60 –1.92, 0.71 .23 .38

30–49 –0.57 –1.50, 0.36 .15 .42

50–64 0.45 –0.46, 1.40 .21 .37

65+ –0.65 –1.56, 0.25 .12 .54

Note: Non-ASSIST states include the District of Columbia. CI indicates confidence interval.

aTests of statistical significance were performed for interactions of sex by ASSIST status (two-sided), p = .18, and age 

group by ASSIST status, p = .30. bOne-sided p values are based on t tests. cR2 was calculated using a standard formula 

for linear regression.
9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

278

Figure 9.1. Bimonthly per Capita Cigarette Consumption 

in ASSIST (top) and Non-ASSIST (bottom) States
279

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

as well as the intervention period (November 1993–May 1999).

It should be mentioned that the timevarying covariates used in the analysis 

were not available at each bimonthly time 

point. Some were available annually, and 

those derived from the TUS-CPS had 

only three distinct values corresponding 

to the three surveys. Therefore, interpolated values from the TUS-CPS for each 

year were generated. The yearly values 

were used for six adjacent bimonthly 

periods. Per capita consumption models 

with IOI or cigarette price as exposures, 

Es, adjusting for state cross-border differentials in price, were also examined.8

Primary Results

Unadjusted Results

The ASSIST and non-ASSIST states 

did not show a significant difference in 

baseline per capita cigarette consumption (table 9.1: 10.64 vs. 10.54 packs/

month, p = .88).

Figure 9.1 shows the state-specific, 

unadjusted, bimonthly per capita consumption rates by ASSIST status. There 

appears to be a common periodic seasonal pattern in these rates, which was the 

motivation behind including the cosine 

and sine time relationships in equation 

9.2. Figure 9.2 shows the difference in the 

aggregated unadjusted mean consumption 

rates (jagged line) between the ASSIST 

and non-ASSIST (ASSIST minus nonASSIST) states over time. ASSIST 

states tended to have higher per capita 

consumption before the intervention period but lower consumption during the 

intervention period.

Results from Adjusted Analyses

Figure 9.2 also shows the difference 

in consumption (smooth line) as calculated by the fitted model given by equation 9.2 together with 90% confidence 

intervals for the difference (dashed 

lines). The adjusted differences between 

the ASSIST and non-ASSIST states in 

consumption for each year as estimated 

by the mixed-effects model (equation 

9.2) are presented in table 9.4. After 

adjusting for the significant state conditions (percentage Hispanic, economic 

value of tobacco, and percentage with 

income below poverty level), ASSIST 

had no statistically significant effect on 

consumption (p = .22).

These results did not differ substantially when the District of Columbia or California was removed from the analyses or 

when the analysis time was extended to 

include data as far back as 1985. Adding terms to the model to account for 

cross-border smuggling did not change 

the overall ASSIST-effect result (e.g., not 

significant), but these terms were significantly related to consumption.

Secondary Results (SoTC and 

IOI Analyses)

T

his section addresses the secondary 

questions related to SoTC and IOI. 

(See “Secondary Questions” section in 

the sidebar on page 271.)
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9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

Methods

Data Sources

The data used in these analyses are 

described in chapter 2 (“Strength of Tobacco Control”) and chapter 4 (“Initial 

Outcomes Index”).

Models

Effect of ASSIST on SoTC. The basic form 

of the model presented in equation 9.1 

was the basis for this analysis. SoTC 

was the dependent variable. Explanatory 

variables included in the model were 

baseline level of funding (this was the 

Figure 9.2. Crude Difference in Bimonthly per Capita Consumption of 

Cigarettes between ASSIST and Non-ASSIST States

Difference in mean per capita cigarette consumption (packs per person per month)

Year

1989

2

1

0

-1

-2

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Notes: The jagged line shows the crude difference in bimonthly mean per capita consumption of cigarettes 

between ASSIST and non-ASSIST states (ASSIST minus non-ASSIST). The smooth solid line shows the 

predicted bimonthly per capita consumption from the mixed-effects model. The dashed lines are the pointwise 

90% confidence intervals for the predicted bimonthly per capita consumption derived from the model.
281

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

only component of SoTC collected at 

baseline), ASSIST status as the exposure 

variable, and covariates selected using 

the Mallows Cp procedure (as described 

previously). Besides SoTC as a composite index, its individual components (see 

chapter 2) were examined in separate 

analyses.

Effect of SoTC or ASSIST on IOI. The 

analysis of IOI as an outcome variable 

was restricted to the baseline and intervention period (September/October 1992 

through April/May 1999); IOI was measured (annually) during this period only. 

The IOI models examine the relationship 

between ASSIST and IOI and between 

SoTC and IOI, using the mixed-effects 

linear model given by equation 9.3:

IOIst = μ + β1 t + β2 t

2 + β3 t3 + 

γ Xst + α1 Es + α2 Es × t +

α3 Es × t2 + α4 Es × t3 + ms + εst (9.3)

The annual IOI measurement, denoted by IOIst for state s at time t, is 

the dependent variable, and independent 

variables include time (annual time, t) 

and powers of time (t2, t3), which account for a nonlinear trend in IOI. The 

model also includes a random intercept, 

ms, which allows for state-varying random intercepts, and an ARIMA(1) autoregressive correlation structure among 

the error terms εst, across time within 

state. Because the dependent variable 

IOI is measured annually, there is no 

need for sine and cosine terms to adjust 

for seasonal periodicity. Time-varying 

and non–time-varying state-level covariates were added to a model that included 

only the terms involving time as independent variables. A forward stepwise 

approach was used to select state-level 

covariates. The final model included the 

significant state-level covariates and independent variables for exposure status, 

Es, indicating either ASSIST status or 

level of SoTC.

Effect of SoTC or IOI on Prevalence. 

Equation 9.1 was used for these analyses, with the exposure variable of interest 

either SoTC or IOI. When the exposure 

variable was SoTC, the final 1999 value 

(only one available) for each state was 

used, and when exposure was IOI, the 

average IOI over the intervention period 

for each state was used. In addition to 

the evaluation of the composite indices, 

separate analyses were conducted using 

each of their components.

Table 9.4. Adjusted Differences in per Capita 

Cigarette Consumption between ASSIST and 

Non-ASSIST States during the Intervention 

Period

Year

ASSIST–

non-ASSIST 

adjusted 

differencea 90% CI

1993 –0.19 –1.28, 0.90

1994 –0.27 –1.40, 0.86

1995 –0.31 –1.49, 0.87

1996 –0.31 –1.54, 0.92

1997 –0.27 –1.55, 1.01

1998 –0.24 –1.58, 1.10

1999 –0.23 –1.61, 1.15

Note: CI indicates confidence interval.

aThe units are in packs per month per person. None 

of the differences was statistically significant; all p

values were greater than .32. The interactions between 

ASSIST status and time and powers of time (Wald test) 

were not statistically significant (p = .22). 
282

9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

Effect of IOI on per Capita Consumption. 

The analysis relating IOI to per capita 

consumption was again restricted to the 

intervention period (see above section 

on “Effect of SoTC or ASSIST on IOI”). 

Because of this restricted time period, a 

new mixed-effects linear model was fit 

to the consumption data and is described 

by equation 9.4.

Cst = μ + β1 t + β2 t

2 + β3 t3 +

β4 sin(t π/3) + β5 cos(t π/3) +

γ Xst + α1 MIOIs + α2 (IOIst − 

MIOIs) + ms+ bs t + εst. (9.4)

The model includes a state-varying 

random intercept, ms, and a random 

regression coefficient for time, bs. This 

model includes the following as independent variables: MIOIs

, the within 

state mean IOI (the average of the annual IOI measurements for a state); and 

IOIst − MIOIs, the difference between 

the annual IOI and the average IOI for 

a state. The regression coefficient for 

IOIst − MIOIs, α2, is the one of interest 

because it describes how change in IOI 

within a state relates to change in per 

capita consumption.9 This model also 

includes an ARIMA (1) autoregressive 

correlation structure for correlation between the error terms, εst, within state. 

The state-level covariates in table 9.2 

were rescreened using forward stepwise regression, starting with a model 

that included only the terms involving 

time as independent variables, without 

the main effects and interactions with 

MIOIs and IOIst. Additional models 

including ASSIST status and interactions 

between ASSIST status and IOI were 

examined to determine how ASSIST 

might have modified the effect of IOI on 

consumption.

Relation of SoTC to per Capita Consumption. The analysis of the effect of SoTC 

on per capita consumption was restricted 

to the intervention period because only 

a single value of SoTC, measured at the 

end of the intervention (1999), was available to represent tobacco control during 

the entire intervention period. The model 

used for the SoTC analysis is given by 

equation 9.5.

Cst = μ + β1 t + β2 t

2 + β3 t3 +

β4 sin(t π/3) + β5 cos(t π/3) +

γ Xst + α1 SoTCs + ms+ bs t + εst (9.5)

Table 9.5. IOI Analyses of the Impact of SoTC, as a Main Effect, on IOI and the Components of IOI

Outcome SoTC ßa 90% CI pb

IOIc 0.27 –0.10, 0.64 .12

Components

Cigarette price 2.95 –1.03, 6.93 .11

Smoke-free workplaces 0.87 –0.12, 1.86 .07

Clean indoor air legislative scorec 0.44 –0.47, 1.35 .21

Note: IOI indicates Initial Outcomes Index. SoTC indicates Strength of Tobacco Control. CI indicates confidence 

interval.

aThe units are standard deviation of IOI (or IOI components) per standard deviation of SoTC. bp tests of significance 

are two-sided. cThe clean indoor air legislation score reflects both the strictness and coverage of clean air ordinances 

within each state. The score includes a preemption penalty and a further adjustment for local ordinance strength (see 

chapter 3).
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

This model is the same as the one 

relating IOI to consumption (equation 

9.4); it included the same state conditions and independent variable for SoTC 

(denoted by SoTCs), which replaced the 

IOI terms.

Results

Did ASSIST Affect SoTC?

As in chapter 2, where the mean 

SoTC scores did not differ by ASSIST 

status, ASSIST status was not significantly related to SoTC in the regression 

analysis that adjusted for baseline funding, metropolitan residency, age, and 

education above high school (ASSIST 

regression coefficient, β = .062, 90% CI: 

–0.445, 0.569, p = .42). Excluding the 

District of Columbia or California from 

the analyses did not change the results. 

ASSIST was also not related to the 

separate components of SoTC (capacity, 

resources, effort), with or without the 

District of Columbia or California in the 

analyses. (See appendix 11.A for statelevel crude SoTC estimates.)

Did SoTC Affect IOI?

Using a mixed model analysis based 

on equation 9.3 with SoTC as only a 

main effect, SoTC was not found to be 

related to IOI (β = .27, standard deviation of IOI per standard deviation of 

SoTC, 90% CI: −0.10, 0.64, p = .12), 

after adjusting for state conditions (education above high school, metropolitan 

residency, and southern census region) 

(table 9.5). Also, SoTC was not related 

to the components of IOI (clean indoor 

air legislation, cigarette price, smokefree workplaces), and omitting the 

District of Columbia or California had 

no effect.

Did ASSIST Affect IOI?

The overall mean change in IOI in the 

United States from 1993 to 1999 was 

4.05. (Appendix 11.A shows baseline and 

final time points for unadjusted IOI by 

state.) Of the 17 ASSIST states, 12 (71%) 

achieved this level of increase in IOI, 

compared with 10 (29%) of the 34 nonASSIST states (see chapter 4, table 4.5).

Figure 9.3 shows that for each year, 

starting in 1992–93 through 1994, the 

unadjusted (for state conditions) mean 

IOI in the ASSIST states was higher than 

it was in the non-ASSIST states. Thereafter, both groups increased their IOI 

scores each year. In addition, although 

the ASSIST states gained a 1.1-point 

lead in 1994 and maintained this lead 

in IOI over time, they did not accrue a 

greater lead by the end of the project in 

1999. Nevertheless, when adjusted for 

state conditions (education above high 

school education, metropolitan residency, and southern census region), ASSIST 

status was not significantly related to 

change in IOI over the intervention period (p = .13). This adjusted analysis 

further confirmed that ASSIST did not 

differentially increase IOI during most 

of the intervention period. Excluding the 

District of Columbia or California from 

the analyses did not change these results. 

Similar analyses of the separate components of IOI (clean indoor air legislation, 

cigarette price, smoke-free workplaces) 

did not reveal significant relationships 

between the components and ASSIST, 

with or without the District of Columbia 

or California in the analyses.
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9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

Did SoTC Affect Prevalence?

SoTC had an inverse association 

(unadjusted) with smoking prevalence 

in 1998–99 (r = –.42). However, this 

relationship was not maintained after 

adjusting for baseline prevalence and individual-level factors (β = −.19, change 

in prevalence [%] per standard deviation 

of SoTC, 90% CI: –0.43, 0.06, p = .11) 

(table 9.6). In addition, the SoTC components (i.e., resources, capacity, efforts) 

were not statistically significantly associated with smoking prevalence after 

adjustment for individual-level factors. 

Results remained the same without the 

District of Columbia or California in 

the analyses, and the analyses within 

gender or age group also showed no association between SoTC and smoking 

prevalence. Because of these statistically 

nonsignificant results, the interaction 

between SoTC and ASSIST was not 

explored.

Did IOI Affect Prevalence?

Unadjusted IOI had a moderately 

strong negative correlation with smoking prevalence in 1998–99 (r = −.52; 

see chapter 4). Analyses that adjusted 

for baseline prevalence and individuallevel factors found that IOI was inversely 

associated with prevalence, although 

the association was not statistically 

significant (β = −.11, prevalence [%] 

per standard deviation of IOI, 90% 

CI: –0.22, 0.01, p = .063; table 9.7). 

When the District of Columbia was 

removed from the analysis, IOI was statistically significantly inversely associated with smoking prevalence (β = −.15, 

90% CI: –0.26, –0.04, p = .015). Without California, the results were similar 

Figure 9.3. Yearly Initial Outcomes Index Average for ASSIST and Non-ASSIST States

1992–93

-1

0

1

2

3

4

IOI average

1993 1994 1995 1996 1997 1998 1998–99

5

Year

Non-ASSIST ASSIST
285

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

to the overall analysis (β = −.11, 90% 

CI: –0.22, 0.01, p = .07). Again, because 

the adjusted main effects did not reach 

statistical significance, interactions with 

ASSIST were not explored.

With all states and the District of Columbia in the model, none of the IOI

component variables (i.e., smoke-free 

workplace, cigarette price, or state and 

local ratings for clean indoor air legislation), when analyzed separately, was 

statistically significantly associated with 

smoking prevalence, when adjusted for 

baseline smoking prevalence. However, 

Table 9.6. Results from Regression Analysis of Smoking Prevalence for Strength of Tobacco 

Control (SoTC) 

Exposure/Subgroup Prevalence/SoTCa 90% CI pb R2c

Total –0.19 –0.43, 0.06 .11 .67

Sex

Male –0.17 –0.45, 0.11 .16 .51

Female –0.23 –0.54, 0.08 .12 .63

Age

18–29 –0.22 –0.73, 0.29 .24 .38

30–49 –0.15 –0.52, 0.22 .25 .41

50–64 –0.31 –0.66, 0.05 .08 .39

65+ –0.05 –0.40, 0.31 .41 .52

Note: CI indicates confidence interval.

aThe units are percent prevalence per standard deviation unit in SoTC. bOne-sided p values are based on t tests. cR2 was 

calculated using a standard formula for linear regression.

Table 9.7. Results from Regression Analysis of Smoking Prevalence for Initial Outcomes 

Index (IOI)

Exposure/Subgroup

Prevalence/ 

IOI ßa 90% CI pb R2c

Total –0.11 –0.22, 0.01 .063 .67

Sex

Male –0.02 –0.16, 0.11 .40 .50

Female –0.20 –0.35, –0.06 .012 .65

Age

18–29 –0.13 –0.37, 0.11 .18 .39

30–49 –0.04 –0.22, 0.14 .36 .41

50–64 –0.17 –0.35, 0.01 .06 .39

65+ –0.11 –0.29, 0.08 .17 .53

Note: CI indicates confidence interval.

aThe units are percent prevalence per standard deviation unit in IOI. bOne-sided p values are based on t tests. cR2 was 

calculated using a standard formula for linear regression.
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9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

when the District of Columbia was 

removed from the model, all of the 

IOI components individually were statistically significantly associated with 

lower smoking prevalence. This finding 

emphasizes the difference between the 

District of Columbia and states in their 

mechanisms for implementation of tobacco control policy (see subsection in 

this chapter on “Model Diagnostics” in 

the section on “Overall Considerations 

for Analysis,” page 273). With the District of Columbia removed, a higher percentage of smoke-free work-site policies 

was statistically significantly associated 

with lower smoking prevalence (β = 

−.046, CI: –0.08, –0.01, p = .022); higher cigarette price was statistically significantly associated with lower smoking 

prevalence (β = −.013, CI: –0.025, 

–0.001, p = .047); and higher clean indoor air local and state legislation ratings 

were statistically significantly associated 

with lower smoking prevalence 

(β = −.053, 90% CI: –0.105, –0.002, 

p = .049). Table 9.7 also indicates that 

states with higher IOI scores have 

significantly lower prevalence rates 

among females (p = 0.012) but not 

among males. No age effect was found.

Did SoTC Affect per Capita Consumption?

After adjustment for state-level factors, the SoTC index was found to be 

statistically significantly inversely associated with per capita cigarette consumption (β = −.39, packs per person per 

month per standard deviation of SoTC, 

90% CI: –0.01, –0.77, p = .046). States 

with higher SoTC scores had lower per 

capita consumption. In all states combined, per capita consumption decreased 

by 0.61 per person per month (90% CI: 

0.02, 1.20), with a change from the 25th

percentile to the 75th percentile of SoTC 

over all states. In separate analyses, the 

capacity component of the SoTC index 

was found to be inversely and statistically significantly associated with per 

capita consumption (β = −.64, 90% CI: 

−0.28, –1.00, p = .003). States with higher levels of capacity had lower per capita 

consumption, regardless of their ASSIST 

Table 9.8. Results from Regression Analysis of per Capita Cigarette Consumption for Strength of 

Tobacco Control (SoTC)

Exposure

Per capita 

cigarette 

consumption/ 

SoTC ßa 90% CI pb

Adjusted effect size 

75th–25th percentile 

difference in exposure 90% CI

Total SoTC –0.39 –0.776, –0.003 .046 –0.61 –1.215, –0.005

Components

Resources –0.11 –0.53, 0.31 .32 –0.039 –0.180, 0.102

Capacity –0.64 –1.00, –0.27 .003 –1.270 –2.009, –0.531

Effort –0.04 –0.39, 0.31 .43 –0.077 –0.749, 0.595

Note: CI indicates confidence interval.

aThe units are packs per person per month per standard deviation of SoTC or of components of SoTC. bp tests of 

significance are one-sided.
287

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

status. Results were unchanged without 

the District of Columbia or California 

(see table 9.8).

Analyses were also conducted to 

determine whether the effect of SoTC 

(and its individual components) on per 

capita consumption differed between the 

ASSIST and non-ASSIST states. This 

analysis was based on a mixed-effects 

model that tested interactions between 

SoTC (or its components) and ASSIST 

status. Table 9.9 shows no statistical difference between the effect of SoTC or 

its components by ASSIST status. These 

results remained unchanged without the 

District of Columbia or California.

Finally, analyses were conducted to 

determine if SoTC affected the temporal 

pattern of bimonthly per capita consumption over the intervention period. 

Table 9.10 shows no significant interaction between SoTC and time.

Did IOI Affect per Capita Consumption?

After adjusting for state-level factors 

(i.e., percentage Hispanic, economic 

value of tobacco, and percentage with 

incomes below poverty level), states 

with larger changes in IOI score over 

time were associated with lower per 

capita cigarette consumption than states 

with smaller changes in IOI (β = −.32, 

packs per person per month per standard deviation of IOI, 90% CI: −0.39, 

–0.25, p < .001). For a state, per capita 

consumption was estimated to decrease 

by 0.57 packs per person per month as 

the IOI values increased from the 25th 

to the 75th percentile over the intervention period (90% CI: 0.45, 0.69). When 

analyzed separately, the only component 

of the IOI score with a statistically significant association with consumption 

was cigarette price, which was statistically significantly inversely associated 

with consumption (β = −.023, 90% CI: 

−0.020, –0.026, p < .001). States with 

higher cigarette prices and larger changes in cigarette price during the ASSIST 

period had lower per capita consumption. Results remained unchanged 

without the District of Columbia or California in the analyses (see table 9.11).

The interaction of change in IOI with 

ASSIST status was also examined. The 

mixed effects model for this analysis was 

similar to equation 9.4 but included an 

Table 9.9. Results from Regression Analysis of per Capita Cigarette Consumption for 

Interactions of Strength of Tobacco Control (SoTC) and ASSIST Status

Exposure Interaction with ASSIST ßa 90% CI pb

Total SoTC –0.71 –1.73, 0.31 .13

Components

Resources –0.66 –1.65, 0.33 .14

Capacity –0.17 –1.03, 0.69 .36

Effort 0.10 –0.76, 0.96 .42

Note: CI indicates confidence interval.

aThe units are packs per person per month per standard deviation of SoTC or of components of SoTC. bp tests of 

significance are one-sided.
9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

288

Table 9.11. Results from Regression Analysis of per Capita Cigarette Consumption for Initial 

Outcomes Index (IOI)

Exposure

Per capita 

cigarette 

consumption/ 

IOI ßa 90% CI pb

Adjusted effect size 

75th–25th percentile 

difference in exposure 90% CI

Change in IOIc –0.32 –0.39, 

–0.25

<.001 –0.57 –0.69, –0.45

Components

Change in cigarette 

price

–0.023 –0.026, 

–0.020

<.001 –0.420 –0.494, 

–0.346

Change in smokefree workplaces

0.015 –0.007, 

0.037

.14 0.190 –0.096, 

0.476

Change in clean 

indoor air legislative 

scorec

–0.017 –0.047, 

0.013

.17 –0.004 –0.011, 

0.003

Note: CI indicates confidence interval.

aThe units are packs per person per month per standard deviation of IOI or of components of IOI. bp tests of 

significance are one-sided. cThe clean indoor air legislation score reflects both the strictness and coverage of clean air 

ordinances within each state. The score includes a preemption penalty and a further adjustment for local ordinance 

strength (see chapter 3).

Table 9.10. Results from Regression Analysis of Temporal Pattern of per Capita Cigarette 

Consumption for Strength of Tobacco Control (SoTC)

Model terms ß for interaction SE pa,b

Interactions with total SoTC

SoTC × time –0.0128 0.0161 .19

SoTC × time2 0.0001 0.0009

SoTC × time3 0.0000 0.0000c

Components

Resources × time –0.0051 0.0166 .44

Resources × time2 –0.0002 0.0009

Resources × time3 0.0000 0.0000c

Capacity × time –0.0243 0.0165 .10

Capacity × time2 0.0008 0.0009

Capacity × time3 –0.0000 0.0000c

Effort × time 0.0026 0.0151 .67

Effort × time2 –0.0004 0.0008

Effort × time3 0.0000 0.0000c

ap for simultaneous inference for all interactions with time. bp tests of significance are two-sided. cNumbers shown to 

four significant figures and were less than 0.0001.
289

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

interaction between ASSIST status and 

the within-state change in IOI and an 

interaction between ASSIST status and 

the mean state IOI; the first interaction 

was the one of interest (table 9.12). 

The association of the change in IOI 

and consumption did not vary between 

the ASSIST and non-ASSIST states 

(p = .49), but there was a small effect in 

change in price between ASSIST and 

non-ASSIST states (β = .006, 90% CI: 

−0.001, 0.013).

Summary

Our analyses demonstrate that ASSIST states had statistically significant 

lower adult smoking prevalence than 

non-ASSIST states at the end of the 

intervention period. Our data also suggest that much of the decrease in adult 

smoking prevalence may be associated 

with decreases in smoking prevalence 

among women. However, this finding 

needs to be interpreted with caution 

because this was a subset analysis and 

because the statistical test of interaction 

between sex and ASSIST status for a difference in the association of ASSIST on 

smoking prevalence was not statistically 

significant. Nevertheless, this finding 

is still of interest to the general health 

community, because women were one of 

several priority populations for interventions in the ASSIST project.10

Although the per capita cigarette consumption rates were lower in ASSIST 

states than in non-ASSIST states at the 

end of the intervention period, these 

differences were not statistically significant. However, changes in per capita 

consumption and smoking prevalence 

Table 9.12. Results from Regression Analysis of per Capita Cigarette Consumption for 

Interactions of Change in Initial Outcomes Index (IOI) and Its Components with ASSIST Status

Interaction with ASSIST Adjusted effect size

Exposure ßa 90% CI pb

75th–25th 

percentile 

difference 90% CI

Change in IOIc –0.001 –0.103, 0.101 .49 –0.0022 –0.1870, 0.1826

Components

Change in cigarette 

price

0.006 –0.001, 0.013 .05 0.110 0.001, 0.219

Change in smokefree workplaces–0.010 –0.032, 0.012 .23 –0.120 –0.389, 0.149

Change in clean 

indoor air legislative 

scorec

–0.066 –0.143, 0.011 .08 –0.015 –0.032, 0.002

Note: CI indicates confidence interval.

aThe units are packs per person per month per standard deviation of IOI or of components of IOI. bp tests of 

significance are one-sided. cThe clean indoor air legislation score reflects both the strictness and coverage of clean air 

ordinances within each state. The score includes a preemption penalty and a further adjustment for local ordinance 

strength (see chapter 3).
290

9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

do not always occur together, as was 

the case for the prevalence analysis, 

which showed a statistically significant 

decrease over the intervention period. In 

California, per capita cigarette consumption continued to decline significantly 

during a period of unchanged smoking prevalence.11 Also, the extensive 

between-states and within-state variability in per capita consumption data 

appears to have overshadowed any small 

difference in per capita consumption 

rates between ASSIST and non-ASSIST 

states (figure 9.2). A post hoc power 

analysis indicated that there was only 

an 11% power to detect the largest difference in per capita consumption rate 

observed between ASSIST and nonASSIST states during the intervention 

period (table 9.4).

IOI was only marginally related to 

adult smoking prevalence (p = .063) 

when the District of Columbia was included in the analysis. However, when 

the District of Columbia was removed, 

IOI was significantly and negatively 

related to smoking prevalence. IOI was 

significantly and negatively related to 

per capita cigarette consumption. In addition, IOI was higher in ASSIST states 

and changed more in ASSIST states over 

the intervention period; however, a multivariate analysis that was adjusted for 

state conditions did not show a significant main effect or interaction between 

IOI and ASSIST status. That is, there 

was not a statistically significant differential effect of ASSIST on increasing 

IOI during most of the intervention period. However, there was a suggestion of 

an ASSIST interaction on the association 

between change in price and per capita 

consumption.

SoTC was significantly negatively related to prevalence but not when adjusted 

for baseline prevalence and individuallevel covariates. SoTC was significantly 

negatively related to per capita consumption after adjustment for state-level covariates. However, SoTC was not related 

to IOI over the intervention period. Finally, ASSIST was not related to SoTC, 

after adjustment for important state-level 

covariates. The SoTC measure was available only at one point in time (1999); if 

it had been measured consistently before 

and during the intervention period, the 

results might have been different. For 

example, if SoTC had been measured at 

The Importance of State Variations

With the state as the fundamental unit of 

measure, these per capita consumption results 

underscored the importance of accounting for 

variations between states within the analysis. 

An earlier study by Manley and colleaguesa

showed that ASSIST states had lower per 

capita consumption than non-ASSIST states 

at the midpoint of the intervention; however, 

this conclusion was incorrect because the 

analysis was flawed, having failed to properly account for between-state variability in 

per capita consumption of cigarettes. Further details on this analysis are provided in 

appendix 9.B.

Source: Davis, W. W., B. I. Graubard, 

A. M. Hartman, and F. A. Stillman. 2003. 

Descriptive methods for evaluation of statebased intervention programs. Evaluation 

Review 27 (5): 506–34.

aManley, M. W., J. P. Pierce, E. A. Gilpin, 

B. Rosbrook, C. Berry, and L-P. Wan. 1997. 

Impact of the American Stop Smoking Intervention Study on cigarette consumption. 

Tobacco Control 6 (Suppl. 2): S12–S16.
291

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

baseline, one would have expected to see 

increases over time.

Since the analysis treated all states 

and the District of Columbia equally, 

it is not surprising that removing the 

District of Columbia from the analysis 

increased the impact of policy on smoking prevalence. IOI and SoTC were 

constructed to capture state effects, and 

since the District of Columbia is not a 

state and does not have the same infrastructure and governmental jurisdictions 

as a state, the District of Columbia data 

were artificially increased and of questionable accuracy for those analyses.

It must be remembered that ASSIST 

was not a randomized trial, and the 

evaluation was restricted to 51 units of 

observation, assessed during a period of 

tremendous nationwide changes affecting tobacco control. Many non-ASSIST 

states instituted tobacco control programs. Some of these programs were 

initiated pre-ASSIST, and others were 

initiated during the intervention period. 

In addition to initiation of state-level 

tobacco control programs, litigation 

between the states’ attorneys general 

and the tobacco industry resulted in 

considerable attention to tobacco issues, 

including negative publicity for the tobacco industry from the release of their 

previously confidential internal documents. During this same time, the U.S. 

Food and Drug Administration attempted 

but failed in its bid to regulate tobacco.

Finally, the tobacco industry appears 

to have actively allocated effort and 

resources to counter tobacco control efforts generally and ASSIST specifically 

at the state level. For example, Slater 

et al. found more pervasive Marlboro 

promotional offers and advertisements 

in states with comprehensive tobacco 

control programs.12 White and Bero13,14

identified multiple tactics and coordinated efforts that the tobacco industry used 

to attack ASSIST. Had the evaluation 

successfully measured and accounted for 

these efforts in the analyses described 

in this chapter, we might have seen a 

greater ASSIST effect on the primary 

outcomes of smoking prevalence and per 

capita cigarette consumption.

Despite these challenges, the ASSIST 

evaluation did successfully answer some 

of the primary and secondary questions 

addressed in the conceptual framework. 

ASSIST status was associated with decreased smoking prevalence, and higher 

IOI was associated with lower prevalence. 

In addition, SoTC and IOI were associated with lower per capita consumption.

The small but statistically significant 

differences in the reduction of adult 

smoking prevalence in ASSIST states, 

when applied on a population basis, 

could be expected to have a large impact on the public.15,16 Indeed, if all 

50 states and the District of Columbia 

had implemented ASSIST among the 

baseline population 18 years of age or 

older (i.e., 192,322,966),17 the decrease 

in adult smoking prevalence would represent approximately 1,213,000 (95% 

CI: −235,200, 2,661,300) fewer smokers 

nationally.

The methods used to evaluate ASSIST 

were necessarily very complex. However, many of the challenges encountered 

in the ASSIST evaluation will also be 

present in the evaluation of state-level 
292

9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

tobacco control programs. The units of 

observation (counties or jurisdictions 

where interventions are administered 

and applied) will likely be limited so 

that summary measures or indices (e.g., 

SoTC, IOI) will be required. The effects 

observed over time may be attributable 

to influences from national programs 

(e.g., American Legacy Foundation’s 

TRUTH campaign) or spillover from 

other states’ programs, rather than interventions within the state. It will be 

appropriate to account for secular trends 

occurring even before the interventions 

took place and to account for demographic and other differences among the 

observational units.

Conclusions

1. Change in prevalence across all states 

was analyzed using multiple linear 

regression that adjusted for potential confounding factors. Per capita 

cigarette consumption was examined 

using mixed effects linear modeling 

incorporating initial consumption 

rates and state factors associated with 

cigarette consumption.

2. Primary analysis results showed that 

ASSIST states exhibited statistically greater decreases in smoking 

prevalence for women. ASSIST states 

also exhibited lower per capita cigarette consumption over the course 

of the intervention period; however, 

ASSIST status was not significantly 

related to per capita cigarette consumption when these results were 

adjusted for state conditions.

3. Secondary results were as follows:

■ ASSIST Status. The ASSIST 

status of a state was not 

significantly related to the Strength 

of Tobacco Control index or to 

the Initial Outcomes Index after 

adjusting for state conditions.

■ Strength of Tobacco Control.

Strength of tobacco control was 

significantly related to decreased 

per capita cigarette consumption. 

However, strength of tobacco 

control and its components (i.e., 

resources, capacity, or efforts) 

were not statistically significantly 

associated with smoking 

prevalence after adjustment for 

individual-level factors. Strength of 

tobacco control was also not found 

to be significantly related to the 

Initial Outcomes Index.

■ Initial Outcomes Index. The Initial 

Outcomes Index was significantly 

associated with reduced smoking 

prevalence among women as well 

as with overall smoking prevalence 

when the District of Columbia 

was removed from the analysis. 

Initial Outcomes Index and its 

cigarette price component were also 

significantly related to decreased per 

capita cigarette consumption. While 

the Initial Outcomes Index was not 

significantly related to ASSIST 

status, there was a relationship 

between its cigarette price 

component and ASSIST status.

4. Combining these results shows that 

if ASSIST had been implemented 

nationwide, projections indicate that 

there would have been over 1.2 million fewer smokers in the United 

States.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

293

Appendix 9.A. Issues Related to Selection of Methods for Analyzing 

Smoking Prevalence

This appendix presents background material about how the methods used to analyze 

smoking prevalence were selected. The authors made decisions about how to account 

for baseline prevalence in the analytic model and how to adjust for state factors, such 

as inequalities in demographics, that affect adult smoking prevalence. The details of the 

models used are presented in the main body of the chapter.

Baseline Prevalence

One of the first issues to be addressed in formulating the model is how to treat the 

baseline and final prevalence estimates. The simplest approach would be to ignore the 

baseline data and assume that the mean prevalence for the ASSIST and non-ASSIST 

states was similar at baseline. This assumption may or may not be valid. It might also 

be more appropriate to use an analysis of covariance approach to adjust for the baseline 

value, because states with high initial prevalence rates could exhibit a different degree 

of change than states with low initial prevalence rates. Another choice would be to use 

the change score (difference) in smoking prevalence from baseline to the end of the 

program as the dependent variable in the analyses.

This issue is not unique to the ASSIST evaluation, and a discussion of the advantages of the baseline as covariate over the other approaches is presented in Bonate.18

Because the correlation between baseline and outcome is less than 1, the correlation 

between baseline and change score is negative. Thus, an observed difference between 

groups at baseline is predictive not only of a difference in raw outcomes but also of a 

difference in change scores in the opposite direction. If the intervention group is at an 

unfair disadvantage compared with the control group when its effects are measured in 

raw outcomes (due to an imbalance at baseline), change scores will maintain that unfair advantage. In contrast, the baseline-as-covariate method produces a result that is 

uncorrelated with the baseline; it essentially subtracts a fraction of the baseline from 

the outcome. Further, the variance of parameters of interest is generally lower with the 

baseline as covariate approach than would be obtained with the other approaches.

Adjusting for Differences in Demographics and Other Factors

It is possible that ASSIST states might collectively show a different demographic 

profile or differ from non-ASSIST states in underlying characteristics related to smoking status at the individual level. The following approaches were considered for adjusting the analyses for differences between the ASSIST and non-ASSIST states in 

state- and individual-level covariates. These methods were empirically tested using prebaseline and baseline data so that the selection of the approaches that were used for the 

evaluation of ASSIST was not influenced by the results of the evaluation.
9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

294

Propensity Scores

One way to account for these differences is to calculate a propensity score that 

predicts ASSIST status. This propensity score is subsequently used as a covariate in 

the model.19 Accordingly, with ASSIST status as the dependent variable, state-level 

data (mean values of various demographic characteristics from the CPS or from other 

sources; see table 9.1 in the main body of this chapter) were subjected to a logistic regression analysis. The resultant probability of being an ASSIST state from this model 

for each state would become the value of a covariate for the analysis. However, with 

only 51 units of observation, none of the independent state-level variables were strongly related to ASSIST status.

Matching

Another approach to account for baseline differences among states is to match each 

of the 17 ASSIST states to a comparable non-ASSIST state. This was done in a previous community intervention study,20 and matching was suggested21 and investigated as 

a possibly superior approach22 to regression techniques for the ASSIST evaluation.

Using matching would reduce the number of observations to 34. Using power analysis methods developed by Martin et al.,23 the resulting statistical power to detect a 1.5% 

difference in prevalence between ASSIST and non-ASSIST was less than 60%. In addition to the overall loss of statistical power, even difficulties in matching states with respect to enough important variables (e.g., preintervention prevalence, cigarette tax rate, 

demographics) to consider the two states comparable were insurmountable.21 Thus, 

matching was abandoned in favor of regression approaches for the ASSIST evaluation.

Another approach related to matching that was considered was to use demographic 

methods of direct standardization of the smoking prevalence rates for the states using 

the U.S. distribution of the sociodemographic covariates as a reference population for 

the standardization. This approach was not used because of limitations on the number 

of variables that could be used for standardization and because of difficulties in adjusting race and ethnicity categories that were not represented in all states.

Two-stage Regression Model

The prevalence of adult smoking was compared between the ASSIST and nonASSIST states using a two-stage regression analysis that was similar to the approach 

used in the analysis of the Community Intervention Trial for Smoking Cessation 

(COMMIT).24 The first stage of the regression analysis was used to adjust for differences in individual-level demographic factors (table 9.2) that exist among states and 

are associated with smoking. At this stage of the regression analysis, current smoking 

at the individual level was predicted using a logistic regression model that was fit to the 

combined TUS-CPS data from the baseline (1992–93) and follow-up (1998–99) periods. The logistic regression was weighted by TUS-CPS sample weights and included 
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

295

individual-level variables and the interactions between sex and age and between sex 

and race/ethnicity. Residuals obtained from the logistic regression averaged (using the 

TUS-CPS sample weights) within each state for each of the baseline and follow-up periods were used in the second stage of the regression analysis.

Multiple linear regression was used for the second stage of the regression analysis 

to adjust for state-level factors (table 9.1) and baseline smoking prevalence (using the 

adjusted baseline state-level prevalences from the first-stage regression) and to evaluate 

the relationship between an exposure (e.g., ASSIST, SOTC, or IOI) and adult smoking 

prevalence. With only 51 units of observation (50 states and the District of Columbia) 

available for the second stage regression analysis, it was important that the model at 

this stage be as parsimonious as possible. The task was to select only a few predictors 

that together explained the most variance in the dependent variable. State-level factors 

were selected for inclusion as independent variables in the regression analysis using an 

all-possible-subsets procedure with a minimum Mallows Cp statistic criteria.6

Before the two-stage regression analysis was implemented, it was tested and confirmed by a preliminary analysis of prebaseline national smoking prevalence data from 

1985 and 1989 Current Population surveys, which served as a test baseline sample, 

and 1992–93 baseline TUS-CPS data, which served as a test follow-up sample. Further 

evaluation of the two-stage regression analysis was performed with the 1992–93 TUSCPS data as the baseline and the 1995–96 TUS-CPS data as the follow-up. These preliminary analyses were useful for demonstrating that the two-stage regression approach 

could be successfully implemented in the ASSIST evaluation.

How State-level Covariates Were Selected for the Second Stage

With only 51 units of observation used in the second stage of the regression analysis, 

it was important that the regression model be as parsimonious as possible. The task was 

to select only a few predictors that together explained the most variance in the dependent variable. The procedure chosen to select the most predictive subset of covariates 

was an all-possible-subsets regression analysis judged by the minimum Mallows Cp 

statistic,6 which worked well in the preliminary analyses.

Model Diagnostics

It was important to determine whether any one individual state unduly influenced the 

results. The method used computed the DFITS statistic, which allowed for identification of states that altered the prediction of the models, and the DFBETAS statistic to 

identify states that most influenced the estimates of the regression coefficients.4 Collinearity diagnostics were also performed to identify state-level covariates with unusually 

high correlations, which might lead to numerical instability in the estimation of the regression coefficients or inflate the variances associated with the regression coefficients.4

The collinearity diagnostics computed the eigenvalues for the design matrix times its 

transpose to obtain a condition index used to check for numerical instability.
9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

296

Appendix 9.B. Approaches to Analyzing per Capita Cigarette 

Consumption Data

This appendix describes some of the approaches that were considered for the analysis of the per capita cigarette consumption data and provides more detail regarding the 

development of the approach used in the final ASSIST evaluation.

Slopes Approach

Linear regression was used to compute a slope for the bimonthly data points in each 

state before the start of ASSIST. A second slope was computed for data points during 

the intervention period. The intervention slope was then modeled as a function of the 

preintervention slope along with the set of covariates (selected as described in appendix 

9.A for prevalence) and a variable for ASSIST status (1 = ASSIST, 0 = non-ASSIST). 

Prior to 1993 and from then until 1996, the linear model provided a reasonable fit to the 

data. However, as figure 9.B.1 illustrates, in some states trends in per capita cigarette 

consumption over a longer period were decidedly nonlinear, which implied that another 

approach was required.

Accounting for State-to-State Variability in Consumption Trends

Because ASSIST and the evaluation design specified state as the unit of observation, 

it was important that the variability between states be accounted for using appropriate 

random-effects models. The impact of conducting a fixed versus random effects type of 

analysis is discussed below.

Manley et al.25 analyzed per capita cigarette consumption from December 1984 

through January 1996 and concluded that there appeared to be an ASSIST effect. In that 

analysis, bimonthly per capita consumption values were weighted by state population 

and were then averaged for the ASSIST and non-ASSIST states. Attention centered on 

the difference in the mean per capita consumption levels for ASSIST and non-ASSIST 

states over time. A regression-based smoothing technique was then used to compute 

confidence intervals for the smoothed trend in the difference, and since the lower 95% 

confidence interval of the smoothed trend in the difference was above zero after 1994, it 

was concluded that ASSIST had reduced per capita cigarette consumption.

Revisiting this analysis, it was noted that between-states variability was not considered because state was not explicitly the unit of analysis.26 Also, the variance of the 

intervention effect can differ greatly depending on whether the states’ effects are considered as fixed or random effects. When a random effects model was used to assess the 

difference and the state variability was taken into account with each state having the 

same weight, the 95% confidence interval included zero for the entire period considered. However, a weighted analysis produced similar results (figure 9.B.2).
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

297

Figure 9.B.1. Examples of States with Nonlinear Trends in Bimonthly 

per Capita Cigarette Consumption after 1993

Per capita consumption Per capita consumption

Raw bimonthly

Seasonally adjusted

SABL trend

Raw bimonthly

Seasonally adjusted

SABL trend

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1983

17

16

15

14

13

12

11

10

9

8

18

16

15

14

13

12

11

10

9

8

7

17

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Per Capita Cigarette Consumption in Oklahoma

Per Capita Cigarette Consumption in Colorado
9 . F i n a l O u t c o m e s : A n a l y t i c a l M e t h o d s a n d R e s u l t s

298

These analyses illustrate the importance of accounting for the state-to-state variability in the analysis of the intervention effect.

Mixed Effects Time-dependent Models

Mixed random and fixed effects linear modeling was used to analyze the bimonthly 

per capita consumption data and the annual IOI data. An alternative modeling approach 

that was considered was the generalized estimation equation method. However, because 

there were only 51 states or units of analysis, there was concern that the robust variance 

estimation and the resulting significance tests that used these robust variances would be 

inaccurate.27 Therefore, the likelihood-based approach of mixed effects linear modeling 

was chosen as the method of analysis.

Figure 9.B.2. Random Effects Analysis with 95% Confidence Interval of Difference in per 

Capita Cigarette Consumption (Non-ASSIST Minus ASSIST)

Smoothed difference

Upper bootstrap limit

Lower bootstrap limit

Actual difference

ASSIST state funding

Year

83

3

2

1

0

-1

-2

84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99

Packs per capita

Source: Davis, W. W., B. I. Graubard, A. M. Hartman, and F. A. Stillman. 2003. Descriptive methods for 

evaluation of state-based intervention programs. Evaluation Review 27: 522. Used with permission.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

299

There were several steps required in deriving the final analytical models for the per 

capita cigarette consumption data. These were as follows:

■ The trend over time in the consumption data for all of the states was modeled by 

a fourth-order polynomial. A spectral analysis was conducted on the detrended 

bimonthly consumption data, combining the data from all 50 states and the District of 

Columbia to determine if there was a periodicity in the data. The analysis was carried 

out using the SAS time series procedure PROC SPECTRA.28 A strong annual period 

component was estimated from the periodogram, which warranted inclusion of a sine 

and a cosine term in the mixed effects models to account for this periodicity.

■ Upon examination of the likelihood ratio test, it was determined that a random 

intercept and a random slope for time were necessary to properly model the state-tostate variability in the consumption patterns.

■ An unstructured correlation (the most general approach, requiring no assumptions) 

matrix was used to estimate the correlation between the random effects.

■ After inclusion of the periodicity terms, trend, and random effects, residual within 

state correlation remained. This was significantly modeled by an ARIMA(1) variance 

structure.

■ Regarding selection of state-level covariates, the Mallows Cp procedure could not be 

easily automated for these types of models. Instead, a forward-stepping procedure 

was used. Without the exposure variable(s) of interest, and after including the terms 

for time, including periodicity, and the random effects for the intercept and slope, 

the state condition most significantly related to per capita consumption was selected 

at each step, and the final model included all those that were significant at the twosided p < .05 level.

■ Wald F tests were used to determine the level of statistical significance of the fixed 

effect regression coefficients in the models. The denominator degrees of freedom 

depended on the number of state-level time-dependent covariates included in the 

model, where the degrees of freedom for the variance were reduced by 1 from 50 

degrees (1 less than the number of units of analysis) for each of the covariates added 

to the model. There was also a reduction of 1 degree of freedom for each non–timedependent variable.

■ Finally, the model to evaluate the exposure variable of interest included all of the 

features indicated above together with the exposure variable of interest. For instance, 

a dummy variable indicating ASSIST status (1 = ASSIST, 0 = non-ASSIST) and 

appropriate interaction terms with the polynomial time terms were evaluated in the 

final model for this exposure. Again, Wald F tests were used to determine statistical 

significance of the set of exposure variables.

Two versions of the final model were evaluated. In the first version, the interactions 

between ASSIST status and time and powers of time began only after the implementation of ASSIST (at the end of 1993). After adjusting for the state conditions and other 

considerations, this model assumes that the trend in cigarette consumption for ASSIST 
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300

states is the same as the trend for non-ASSIST states during the period before ASSIST 

was implemented (1988–93).29 Figure 9.2 in the main body of this chapter suggests that 

the trends for ASSIST and non-ASSIST states did differ prior to the intervention period, 

because the difference prior to 1993 diminished (was not constant). Also, results for 

this model produced predicted differences during the intervention period that were not 

consistent with the observed data. Thus, another version of the model was adopted. In 

this version, the interactions of ASSIST and time (and powers of time) were considered 

over the entire time interval, which allowed the trend for consumption to be different between the ASSIST and non-ASSIST states in both the pre- and postintervention periods.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

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Appendix 9.C. Outcome Measures: Per Capita Cigarette 

Consumption (Packs/Month) and Adult (18 Years and Older) 

Smoking Prevalence (%)

(Shading indicates ASSIST states.)

State

Per capita cigarette 

consumption

Change in per 

capita cigarette 

consumption: 

1992–93 to 1998–99

Adult smoking prevalence Change in adult 

smoking prevalence 

1992–93 1995–96 1998–99 1992–93 1995–96 1998–99 1992–93 to 1998–99

AK 10.4 11.4 8.2 –2.2 27.61 25.31 26.69 −0.92

AL 11.3 11.0 10.6 –0.7 25.74 23.22 22.29 −3.45

AR 11.9 12.7 11.3 –0.6 28.73 26.57 25.99 −2.74

AZ 8.9 7.7 6.9 –2.0 22.37 22.80 19.86 −2.51

CA 7.2 6.4 5.2 –2.0 18.94 17.92 16.59 −2.35

CO 9.1 9.0 8.7 –0.4 24.16 22.55 20.20 −3.96

CT 8.7 8.3 7.9 –0.8 22.31 19.81 20.56 −1.75

DC 7.0 5.9 5.0 –2.0 23.23 22.25 23.54 0.31

DE 13.0 13.6 14.7 1.7 23.30 24.83 23.34 0.04

FL 9.9 10.0 9.4 –0.5 23.89 22.24 20.66 −0.23

GA 11.5 11.2 10.2 –1.3 24.32 22.79 19.89 −4.43

HI 6.3 5.9 3.5 –2.8 22.18 21.75 18.34 −3.84

IA 10.2 10.3 10.0 –0.2 23.50 23.40 22.37 −1.13

ID 9.4 9.1 8.2 –1.2 23.61 22.32 21.84 −1.77

IL 10.0 9.0 7.9 –2.1 24.47 23.67 22.92 −1.55

IN 13.6 14.8 14.3 0.7 27.81 28.92 27.03 −0.78

KS 9.9 9.9 9.3 –0.6 24.22 25.87 22.45 −1.77

KY 18.0 19.4 17.7 –0.3 31.98 29.69 29.81 −2.17

LA 12.1 12.3 11.2 –0.9 25.37 25.83 22.74 −2.63

MA 9.0 8.2 6.5 –2.5 21.40 20.68 19.34 −2.06

MD 8.9 8.3 7.5 –1.4 23.59 21.07 19.46 −4.13

ME 11.7 10.9 9.1 –2.6 28.52 25.71 23.51 −5.01

MI 11.6 8.9 9.2 –2.4 27.20 25.67 23.36 −3.84

MN 9.0 9.3 8.8 –0.2 25.11 22.85 21.19 −3.92

MO 13.2 13.8 12.7 –0.5 26.24 25.97 23.49 −2.75

MS 11.6 12.2 11.7 0.1 25.46 23.23 21.53 −3.93

MT 9.3 10.1 9.1 –0.2 23.53 23.94 23.29 −0.24

NC 13.9 13.9 12.7 –1.2 26.93 26.21 22.98 −3.95

ND 9.4 8.9 8.2 –1.2 22.18 23.73 20.47 −1.71

NE 9.8 9.6 8.8 –1.0 21.97 21.47 21.18 −0.79

NH 16.6 17.6 18.2 1.6 24.69 23.67 22.04 −2.65

NJ 8.8 8.7 6.8 –2.0 20.38 20.50 19.84 −0.54

NM 7.9 7.6 6.8 –1.1 23.99 23.95 20.93 −3.06

NV 11.0 10.2 10.9 –0.1 28.37 28.09 24.06 −4.31

NY 8.5 7.3 6.8 –1.7 21.52 20.86 20.54 −0.98

OH 11.8 12.1 11.4 –0.4 25.96 26.07 24.01 −1.95

OK 9.4 12.4 12.5 3.1 26.74 25.52 27.52 0.78

OR 10.5 10.4 8.6 –1.9 22.93 22.52 21.16 −1.77

PA 10.2 10.3 9.8 –0.4 23.34 24.08 22.88 −0.46

RI 10.1 9.6 9.4 –0.7 23.20 23.77 19.87 −3.33

SC 12.6 11.0 11.7 –0.9 25.71 25.14 22.73 −2.98

SD 10.1 10.6 9.2 –0.9 25.51 22.73 24.08 −1.43

TN 12.5 13.2 12.2 –0.3 28.53 27.21 25.99 −2.54

TX 9.0 8.6 7.6 –1.4 23.45 23.32 20.89 −2.56

UT 7.4 6.7 5.2 –2.2 16.90 15.04 13.73 −3.17

VA 11.1 11.7 10.8 –0.3 25.47 24.95 20.77 −4.70

VT 12.8 11.9 9.9 –2.9 26.26 24.89 22.34 −3.92

WA 8.4 6.5 5.9 –2.5 24.14 23.28 20.13 −4.01

WI 10.5 10.2 9.3 –1.2 26.15 25.04 24.54 −1.61

WV 11.9 12.2 12.0 0.1 30.25 27.83 26.38 −3.87

WY 12.4 12.7 12.2 –0.2 24.82 25.06 23.70 −1.12
302

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effects from large data sets using 

propensity scores. Annals of Internal 

Medicine 127 (8 pt. 2): 757–63.

20. Freedman, L. S., S. B. Green, and 

D. P. Byar. 1990. Assessing the 

gain in efficiency due to matching 

in a community intervention study. 

Statistics in Medicine 9 (8): 943–52.

21. Kessler, L. G., M. Carlyn, R. Windsor, 

and L. Biesiadecki. 1996. Evaluation of 

the American Stop Smoking Intervention 

Study. In Health Survey Research 

Methods Conference Proceedings

(PHS publication no. 96-1013), ed. 

R.Warnecke, 215–20. Hyattsville, MD: 

U.S. Department of Health and Human 

Services, Public Health Service, Centers 

for Disease Control and Prevention, 

National Center for Health Statistics.

22. Muller, K. E., L. M. LaVange, 

S. L. Ramey, and C. T. Ramey. 1992. 

Power calculations for general linear 

multivariate models including repeated 

measures applications. Journal of the 

American Statistical Association 87 

(420): 1209–26.

23. Martin, D. C., P. Diehr, E. B. Perrin, 

and T. D. Koepsell. 1993. The effect of 

matching on the power of randomized 

community intervention studies. 

Statistics in Medicine 12 (3–4): 329–38.

24. Gail, M. H., S. D. Mark, R. J. Carroll, 

S. B. Green, and D. Pee. 1996. 

On design considerations and 

randomization-based inference for 

community intervention trials. Statistics 

in Medicine 15 (11): 1069–92.

25. Manley, M. W., J. P. Pierce, 

E. A. Gilpin, B. Rosbrook, C. Berry, 

and L. M. Wun. 1997. Impact of the 

American Stop Smoking Intervention 

Study on cigarette consumption. 

Tobacco Control 6 Suppl. no. 2: 

S12–S16.

26. Davis, W. W., B. I. Graubard, 

A. M. Hartman, and F. A. Stillman. 

2003. Descriptive methods for 

evaluation of state-based intervention 

programs. Evaluation Review 27 (5): 

506–34.

27. Murray, D. M., P. J. Hannan, 

R. D. Wolfinger, W. L. Baker, and 

J. H. Dwyer. 1998. Analysis of data 

from group-randomized trials with 

repeat observations on the same 

groups. Statistics in Medicine 17 (14): 

1581–1600.

28. SAS Institute, Inc. 1997. SAS/STAT 

software: Changes and enhancements 

through release 6.12. Cary, NC: SAS 

Institute, Inc. http://support.sas.com/

rnd/app/doc.html.

29. Murray, D. M., P. J. Hannan, 

D. R. Jacobs, P. J. McGovern, 

L. Schmid, W. L. Baker, and C. Gray. 

1994. Assessing intervention effects in 

the Minnesota Heart Health Program. 

American Journal of Epidemiology 139 

(1): 91–103.

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305

10. Cost-effectiveness of ASSIST

Richard M. Peck and Mary Ellen Wewers

Contributors: Anne M. Hartman and Barry I. Graubard

Contents

Introduction.................................................................................................................. 307

A Cost-effectiveness Analysis: The Basic Approach ..................................................... 308

Present Discounted Value..................................................................................................308

The Role of Sensitivity Analysis in Cost-effectiveness.......................................................310

Cost-effectiveness: ASSIST .......................................................................................... 310

Data Sources.....................................................................................................................310

Population Data..........................................................................................................310

Expenditure Data........................................................................................................310

Discounted Expenditure Data.............................................................................................312

Cost per Quit and Noninitiation of Smoking.......................................................................315

Interpreting Changes in Prevalence............................................................................316

Life-years Gained by Smoking Cessation ...........................................................................317

ASSIST Cost per Life-year Gained.......................................................................................320

Discussion .........................................................................................................................321

Summary...................................................................................................................... 322

Conclusions.................................................................................................................. 323

References................................................................................................................... 323

Tables

Table 10.1. ASSIST Total State Expenditures..............................................................................311

Table 10.2. ASSIST per Capita Expenditures (Adult Population, 18 Years Old and Older) ..........312

Table 10.3. Life-years Gained Attributable to ASSIST: Women ...................................................313

Table 10.4. Cost-effectiveness with Various Recidivism Rates.................................................313

Table 10.5. Cost-effectiveness with Various Changes in Women’s Prevalence Rates................314

Table 10.6. Fraction of Lifetime Smoker Quits Attributable to ASSIST .......................................314

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

307

10. Cost-effectiveness of ASSIST

Introduction

T

his chapter documents the cost-effectiveness of ASSIST. The analysis demonstrated 

that the conservatively estimated cost of each life-year gained from ASSIST compares favorably with the cost per life-year gained from many other population-level 

public health interventions.

The decision to implement any publicly funded public health intervention must address two basic questions. First, does the intervention have beneficial effects? Second, 

are the effects large enough to justify the expense of the intervention—in other words, 

is the intervention cost-effective? Examining cost-effectiveness enables an assessment 

of the value per dollar spent on one program compared with amounts spent on other 

programs and, therefore, provides essential information for decisions about how to 

spend scarce resources.

In this chapter, two effects of ASSIST are examined in light of the total expenditures 

of the project: how many people quit smoking and how many life-years were gained. 

The analyses show that ASSIST was cost-effective and yielded an improvement in 

public health, per dollar spent, that is highly competitive with a broad range of public 

health interventions. The estimated cost per quit is $1,255, and the dollar amount spent 

per life-year gained is $2,093. In the context of other public health interventions, the 

cost per life-year gained attributable to ASSIST is less than the cost per life-year gained 

attributable to other types of preventive interventions.

The data on the cost-effectiveness of the American Stop Smoking Intervention 

Study (ASSIST) were analyzed in terms of cost per life-year gained and cost per quit. 

This chapter reports the findings of those analyses and examines the methodology 

used to estimate this cost-effectiveness, as well as factors and assumptions affecting 

the results.

The analyses yielded a cost-effectiveness best estimate of $1,255 per quit and 

$2,093.02 per life-year gained, based on a conservative assumption of a 0.63 

decrease in tobacco prevalence rates attributable to ASSIST. These estimates 

compare favorably with alternatives such as intensive physician-based interventions 

and the implementation of formal smoking cessation guidelines, as well as other 

accepted public health-related interventions such as mammography. Moreover, 

effects not considered in the analysis, such as the long-term effects of policy 

interventions and the development of a tobacco control infrastructure, have the 

potential to reduce these costs further. Thus, the ASSIST intervention represents a 

cost-effective approach to the improvement of public health.
308

1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

The population reached by ASSIST 

was large—in 1999, about 73.3 million 

adults throughout 17 states. Of these 

73.3 million adults, about 20 million 

were smokers. Thus, even a small reduction of smoking prevalence in this 

population would represent a large effect 

in terms of the number of people who 

either quit smoking or did not initiate 

smoking. For example, for a population 

of this size, a decrease of 0.1 percentage points in the average prevalence 

of smoking would translate into about 

70,000 individuals. Therefore, on a per 

person basis, the cost of preventing 

tobacco use is likely to be low. A costeffectiveness analysis is a method for 

determining that cost.1

The principal application of costeffectiveness calculations is to compare 

competing interventions; therefore, an 

important consideration in selecting the 

outcome measure is its comparability 

with data from other relevant interventions. Several other types of calculations 

provide benchmarks by which the cost 

of a program can be measured. Numerous studies have estimated the dollar 

cost per life-year gained for a variety of 

interventions, thereby providing useful 

benchmarks.2

A Cost-effectiveness Analysis: 

The Basic Approach

T

he basic approach to a costeffectiveness analysis is as follows.

■ An intervention is undertaken: for 

example, a medical procedure, an 

educational program, the placement of 

a guardrail or additional lighting on a 

busy highway, and so forth.

■ Corresponding to the particular 

intervention, an outcome (or a set of 

outcomes) is identified: typically a 

measure that can be quantified, such 

as a death rate, lives gained, lifeyears gained, or some other effect 

determined by the nature and purpose 

of the intervention.

■ The measure is assessed prior to the 

intervention to establish its baseline 

level of occurrence in the absence of 

exposure to the intervention.

■ The intervention is implemented.

■ After the intervention has occurred, 

the outcome measure is re-assessed, 

and change in the measure is 

determined from the baseline level.

The total cost (fixed, variable, and 

marginal costs combined) of the intervention is then divided by the change 

in the outcome measure to compute the 

dollar cost per unit change in outcome. 

Expressed as an equation, the basic costeffectiveness measure is a ratio:

cost-effectiveness = total cost/

change in outcome due to 

intervention. (10.1)

The lower the resulting number, the 

more cost-effective, or economical, is 

the program.

Present Discounted Value

Rarely are the costs and outcomes 

so clearly defined and simultaneously 

related as in the guardrail example (see 

sidebar). More typically, expenditures 

and outcomes occur at different points 

in time, and the costs are not constant. 
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Indeed, often the full effect of an intervention is not manifest until many 

years after the intervention actually 

takes place. In the case of ASSIST, expenditures were incurred beginning in 

1991 (through 1999), but the principal 

outcome, the decrease in smoking prevalence, was not measured until 1999. 

Moreover, the reason the decrease in 

smoking prevalence is important is that 

it is linked to a subsequent decrease in 

morbidity and mortality, but these effects 

are not realized for many years.

The procedure for taking into account 

the scattered timing of outcomes and 

expenditures is called discounting. The 

basic premise underlying discounting is 

time preference: a dollar now is worth 

more than receiving a dollar a year from 

now. Accordingly, saving a life now is 

viewed as being more valuable than saving a life in the distant future. Similarly, 

society prefers to have resources available now as opposed to later, because 

those resources yield benefits in the interim. Accordingly, an expenditure E that 

takes place S years prior to the outcome 

has a value in the year for which the 

analysis is being conducted. This value 

is called a present discounted value

(PDV) and is expressed by the equation

PDV of E = E(1+r)S. (10.2)

In equation 10.2, r is called the discount rate. For example, if r is 3%,* then 

$100 spent 10 years ago has a present 

value of about $134. A past expenditure 

has a present discounted value that is 

typically larger than the original amount 

of the expenditure. An expenditure T

years in the future, on the other hand, 

has a present discounted value given by 

the formula

PDV of E = E(1 + r)-T = 

E/(1 + r)T. (10.3)

The present discounted value of a 

future expenditure E is typically smaller 

than the actual amount E that will accrue 

in the future. Thus, for r equals 3% (r = 

0.03), $100 that will be spent 10 years 

from now has a present discounted value 

of $74.71.

Briefly, discounting enters into the 

ASSIST expenditure calculations as 

follows. Some ASSIST expenditures 

An Example of Cost-effectiveness

The following simple example illustrates 

the basic approach to analyzing costeffectiveness.

Suppose there is a stretch of road where 

a large number of fatal auto accidents occur; the number of deaths per year is 10. A 

guardrail, which will last about 5 years and 

costs $2.5 million, is installed. It lasts only 5 

years because it is struck by cars and quickly 

becomes ineffective and must be replaced. 

After the guardrail is installed, the total 

number of fatalities decreases to 8 per year. 

Hence, the annual cost per year is $500,000, 

and the number of lives gained is 2. Thus, the 

cost per life gained is $250,000. The policy 

issue is, of course, whether there is another 

intervention that would give a lower cost per 

life gained. If installation of additional traffic 

lights costs $1 million per year but gains 10 

lives, the cost per life gained of this intervention would be $100,000. Traffic lights would 

be a more cost-effective policy option. If 

funds are limited, cost-effectiveness suggests 

that traffic lights should probably take precedence over the installation of a guardrail.

*A standard assumption in cost-effectiveness estimates is that r = 3%.
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1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

occurred years before the outcomes were 

measured. The present value of these 

expenditures, as measured in 1999, is 

higher than the actual amount spent in 

earlier years. While the actual amount 

spent over the years 1991–98 was $124.3 

million, after adjusting for inflation and 

making present discounted value adjustments, the amount comes to $150.2 

million. In addition, the outcome of lifeyears gained from smoking cessation induced by ASSIST will be realized many 

years in the future. Here, discounting 

yields a smaller value for the life-years 

gained due to ASSIST. The full effects of 

discounting are presented in tables 10.1 

and 10. 2 (for expenditures) and table 

10.3 (for discounted life-years saved).

The Role of Sensitivity Analysis in 

Cost-effectiveness

A standard procedure in a costeffectiveness analysis is to examine how 

sensitive results and conclusions are to 

various key assumptions in the analysis. 

One needs to undertake this exercise 

because virtually all cost-effectiveness 

studies involve some uncertainty about 

assumptions. This analysis is done by 

changing assumptions and parameters of 

the study and then examining how conclusions and results are altered. A sensitivity analysis was performed as part 

of evaluating the cost-effectiveness of 

the ASSIST interventions, and details of 

this analysis are presented in tables 10.4, 

10.5, and 10.6.

It is standard in a well-constructed 

cost-effectiveness study to include a discussion of alternative assumptions; essentially, one wants to test how sensitive 

the ranking of various alternatives is to 

“tweaking” or modifying these assumptions. Further discussions of the role of 

sensitivity analyses in cost-effectiveness 

studies can be found in Methods for the 

Economic Evaluation of Health Care 

Programs.1

Cost-effectiveness: ASSIST

Data Sources

Population Data

The Tobacco Use Supplement of the 

Current Population Survey (TUS-CPS),3

developed by National Cancer Institute 

staff, was used to ascertain smoking 

prevalences for each state and the District of Columbia. Prevalence estimates 

from September 1998, January 1999, and 

May 1999 were combined and served as 

the outcome measure of smoking prevalence for each ASSIST state. The methodology is described in chapters 4 and 9 

and has been published elsewhere.4,5

Expenditure Data

Staff from the National Cancer Institute’s Contracts Office calculated total 

annual expenditures for each of the 17 

ASSIST states. These expenditures were 

derived from quarterly financial reports 

submitted to the Contracts Office by 

each designated state ASSIST budget 

officer during the years 1991–98. Direct 

expenditure categories included (1) total 

labor, including fringe benefits; (2) nonexpendable equipment; (3) materials/

supplies; (4) local travel; (5) national 

travel; (6) advertising; (7) intervention; 

(8) mobilization; (9) other costs (e.g., 

printing, conference expenditures); and 
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(10) cost-sharing. Indirect expenditures, 

as estimated by each ASSIST state, were 

added to the direct expenditures to yield 

total expenditures.

The raw expenditures are listed in the 

second column of table 10.1. The total 

actual (or nominal) expenditure by the 

17 ASSIST states from 1991 to 1998 

was about $124.3 million. The amounts 

spent by each state varied, from about

$4.7 million (West Virginia) to $12.4 

million (New York). The average spent 

per state was $7.3 million. Although 

the more-populated states, such as New 

York and Michigan, had the highest total 

expenditures, their per capita amounts 

were less than in the less-populated 

states. The average per capita expenditure for the entire ASSIST project for 

the period 1991 to 1998 was $2.45. (See 

Table 10.1. ASSIST Total State Expenditures

Total raw expenditures

State 1991–98 dollars Constant 1999 dollars Discounted 1999 dollars

Colorado 8,260,979 8,984,179 10,055,436

Indiana 5,763,030 6,186,421 6,818,329

Maine 5,210,617 5,647,276 6,295,698

Massachusetts 7,486,699 8,028,884 8,837,345

Michigan 9,900,100 10,751,896 12,011,014

Minnesota 7,577,788 8,205,738 9,138,988

Missouri 6,882,817 7,484,172 8,378,485

New Jersey 7,597,419 8,226,867 9,163,094

New Mexico 5,330,994 5,805,699 6,512,573

New York 12,422,191 13,438,427 14,951,936

North Carolina 9,518,407 10,347,318 11,582,019

Rhode Island 5,120,477 5,547,537 6,185,110

South Carolina 5,846,040 6,302,111 6,985,959

Virginia 7,195,520 7,802,116 8,704,722

Washington 8,265,514 8,983,315 10,053,423

West Virginia 4,714,941 5,107,610 5,693,737

Wisconsin 7,241,516 7,885,140 8,841,855

Total 124,335,049 134,734,705 150,209,722

All ASSIST states

Mean 7,313,826 7,925,571 8,835,866

SD 2,004,863 2,176,815 2,432,420

Coefficient of variation 0.27411962 0.27465721 0.27528936

Maximum/minimum

Maximum (New York) 12,422,191 13,438,427 14,951,936

Minimum (West Virginia) 4,714,941 5,107,610 5,693,737
312

1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

table 10.2.) At the state level, per capita 

expenditures ranged from about $0.90 

(New York) to $6.83 (Rhode Island).

Discounted Expenditure Data

The raw expenditure data were adjusted in the following ways: The first year 

of ASSIST expenditures is 1991, and 

the last year assessed in this analysis is 

1998. (Although the project was funded 

through October 1, 1999, the prevalence 

data do not extend that far.) Though annual inflation rates were low during that 

time, over 8 years the consumer price 

index rose 22.3%; thus, it is necessary to 

adjust for inflation in the computations. 

Accordingly, total ASSIST expenditure, 

measured in inflation-adjusted constant 

1999 dollars, was $134.7 million (table 

10.1). Per capita inflation-adjusted expenditures (i.e., constant dollars measured in terms of 1999 prices and not 

Table 10.2. ASSIST per Capita Expenditures (Adult Population, 18 Years Old and Older)

Per capita expenditures

State 1991–98 dollars Constant 1999 dollars Discounted 1999 dollars

Colorado 2.76 3.00 3.36

Indiana 1.31 1.40 1.54

Maine 5.41 5.87 6.54

Massachusetts 1.59 1.71 1.88

Michigan 1.36 1.47 1.64

Minnesota 2.16 2.34 2.61

Missouri 1.69 1.84 2.06

New Jersey 1.24 1.34 1.49

New Mexico 4.28 4.67 5.23

New York 0.90 0.98 1.09

North Carolina 1.67 1.81 2.03

Rhode Island 6.83 7.40 8.25

South Carolina 2.00 2.15 2.38

Virginia 1.38 1.50 1.67

Washington 1.94 2.10 2.35

West Virginia 3.36 3.64 4.06

Wisconsin 1.86 2.02 2.27

All ASSIST states

Mean 2.45 2.66 2.97

SD 1.63 1.77 1.98

Coefficient of variation 0.66 0.67 0.67

Maximum/minimum

Maximum (Rhode Island) 6.83 7.40 8.25

Minimum (New York) 0.90 0.98 1.09
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

313

Table 10.3. Life-years Gained Attributable to ASSIST: Women

Age of 

quitting 

in 1999

Expected 

life-years 

gained

Expected 

life of 

smoker

Years to 

expected 

death

Discounted 

life-years 

gained 

(1999)

Total 

number 

of quits

Total 

life-years 

gained 

(population)

Discounted 

life-years 

gained 

(population)a

18.5 4.30 75.7 57.20 0.725 6,559 28,202 4,757

22 4.30 75.7 53.70 0.806 15,503 66,661 12,489

27 3.93 76.3 49.27 0.846 16,343 64,281 13,830

32 3.57 76.7 44.73 0.884 18,264 65,141 16,144

37 3.20 77.4 40.40 0.908 20,812 66,597 18,898

42 2.83 78.0 35.97 0.923 20,555 58,240 18,980

47 2.47 78.7 31.73 0.918 18,074 44,583 16,586

52 2.10 79.6 27.60 0.889 15,548 32,651 13,826

57 1.73 80.7 23.67 0.830 12,234 21,206 10,159

62 1.37 82.0 20.03 0.734 9,913 13,548 7,277

67 1.00 83.6 16.60 0.599 9,096 9,096 5,446

72 0.63 85.4 13.37 0.420 8,721 5,523 3,664

77 0.27 87.5 10.53 0.194 7,635 2,036 1,478

Total life-years gained 477,765 143,534

Note: The assumed recidivism rate is 0.50, and all quitters are assumed to be lifetime smokers.

aDiscounted life-years gained (population) = Discounted life-years gained (per capita) × total number of quits.

Table 10.4. Cost-effectiveness with Various Recidivism Rates

Recidivism 

rate Life-years gained Discounted life-years gained

Dollars per 

discounted life-year gained

0.0 449,563 143,534 1,046.51

0.1 404,607 129,181 1,162.79

0.2 359,651 114,827 1,308.14

0.3 314,694 100,474 1,495.01

0.4 269,738 86,120 1,744.18

0.5 224,782 71,767 2,093.02

0.6 179,825 57,414 2,616.28

0.7 134,869 43,060 3,488.37

0.8 89,913 28,707 5,232.55

0.9 44,956 14,353 10,465.10

Notes: Discount rate = 0.03. Decrease in prevalence ratio, women = 0.0096. Fraction of permanent quitters who are 

lifetime smokers = 0.50. Total ASSIST expenditure = $150,209,722 (discounted constant value expenditures).
314

1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

discounted) for individuals 18 years old 

and older across all 8 years averaged 

$2.66 (table 10.2).

The second adjustment converts raw 

expenditures into present values for 

the year of the analysis. The effect of 

ASSIST in terms of reduced smoking 

prevalence was measured for 1999 compared with 1991, and ASSIST

expenditures began in 1991. The 1991 

dollar value is different from its 1999 

value, just as an expenditure to be made 

in the distant future has a different value 

in any earlier year. These present values 

are determined by adjusting for the opportunity cost of foregone interest, that 

is, by computing the present discounted 

value. Let W represent the year 

Table 10.5. Cost-effectiveness with Various Changes in Women’s Prevalence Rates

Change in women’s 

prevalence

Expected life-years 

gained

Discounted expected 

life-years gained Dollars per life gained

0.002 46,829 14,951 10,046.49

0.004 93,659 29,903 5,023.25

0.006 140,488 44,854 3,348.83

0.008 187,318 59,806 2,511.62

0.010 234,147 74,757 2,009.30

0.012 280,977 89,709 1,674.42

0.0096 224,782 71,767 2,093.02

0.0014 32,781 10,466 14,352.14

Notes: Recidivism rate = 0.5%. Permanent quit rate = 0.5%. Change in male prevalence rate = 0.000.

Table 10.6. Fraction of Lifetime Smoker Quits Attributable to ASSIST

Fraction of quitters 

attributable to 

ASSIST

Life-years 

gained

Discounted 

expected life-years 

gained

Dollars per lifeyear gainedDollars per 

permanent quit

0.1 44,956 14,353 10,465.10 7,900.16

0.2 89,913 28,707 5,232.55 3,950.08

0.3 134,869 43,060 3,488.37 2,633.39

0.4 179,825 57,414 2,616.28 1,975.04

0.5 224,782 71,767 2,093.02 1,580.03

0.6 269,738 86,120 1,744.18 1,316.69

0.7 314,694 100,474 1,495.01 1,128.59

0.8 359,651 114,827 1,308.00 987.52

0.9 404,607 129,181 1,162.79 877.80

1.0 449,563 143,534 1,046.51 790.02
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

in which an expenditure was made. The 

expenditure made in year W is converted 

to its 1999 value as follows: One dollar spent in year W has a 1999 value of 

(1 + r),1999–W so that Z dollars spent in 

W have a 1999 value of Z(1 + r).1999–W

For example, for W = 1991, 1999 minus 1991 equals 8 years, so that if r is 

0.03 (i.e., a 3% rate of interest), then 

the value of that 1991 dollar is $1.27 

in 1999. After adjusting for inflation 

and after expressing all expenditures in 

values for the year of analysis, the total 

amount spent by the ASSIST states, 

expressed as a 1999 discounted value, 

is $150.2 million. The per capita expenditure, expressed as a 1999 discounted 

value, is $2.97 per adult. Detailed values 

are given in tables 10.1 and 10.2.

Cost per Quit and Noninitiation of 

Smoking

An important effect of ASSIST is the 

decrease in smoking prevalence in the 

ASSIST states. As reported in chapter 9, 

the decrease in the prevalence rate attributable to ASSIST is 0.63 percentage 

points.6 There are significant gender 

differences, however, in the effects of 

ASSIST: For men, the prevalence rate 

declined for the total sample by 0.09 percentage points; for women, the ASSISTattributable decline was 0.96 percentage 

points. A complete discussion of the assumptions underlying these estimates is 

provided in chapter 9; sensitivity analysis is provided in tables 10.4, 10.5, and 

10.6. These figures are used to determine 

the decrease in the number of smokers 

(those who quit and those who did not 

initiate smoking); the 1999 adult population (18 years old and older) of ASSIST 

states is multiplied by the ASSISTinduced decrease in smoking prevalence:

decrease in smokers = 

(population)(decrease in prevalence)

100 (10.4)

or

ASSIST-attributable decrease in smokers = 

population × 0.0063. (10.5)

The total ASSIST-attributable decrease in smokers was about 478,860 

for all 17 states combined. The total raw 

expenditure was $124,335,049. The total 

1999 discounted value of the program, 

over all states and all years through 

1998, was $150,209,722. The following 

ratio expresses the cost per quit:

cost per quit = 

total discounted expenditure

decrease in the number of smokers (10.6)

With no inflation or present value adjustments, the cost per quit is $259.65; 

the 1999 present discounted value is 

$313.68.

These initial estimates reflect relatively optimistic assumptions about the 

efficacy of ASSIST. They assume that 

there is no recidivism (or relapses) and 

that all smoking cessation (quits) generated by ASSIST occur among individuals who, but for ASSIST, would have 

remained lifelong smokers. If we assume 

that instead fully half of the quitters take 

up smoking again within 3 years, and 

that half of those who quit permanently 

would have done so on their own within 

3 years (these assumptions follow from 

Gilpin et al.),7 even in the absence of 

ASSIST, the net permanent reduction 

in smokers due to ASSIST is closer to 
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1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

119,735, which in turn raises the cost per 

quit to $1,255.

In summary, ASSIST, in the short 

run, reduced the number of adult smokers by about 478,860 in ASSIST states. 

Given that the 1999 discounted value of 

ASSIST is $150.2 million, the cost of 

this reduction per individual is $313. If 

the effects of ASSIST are assumed to 

persist, so that the total number of discounted quits increases over time and 

eventually approaches the discounted 

number of 877,730 in the ASSIST states, 

the cost per quit is as low as $171. Under more pessimistic assumptions about 

the long-run effect of ASSIST, the cost 

per quit is, of course, higher. Thus, the 

estimated cost per quit associated with 

ASSIST lies in a range between about 

$150 and $1,500. However, as previously noted, the conservative best estimate of the cost per quit is $1,255. By 

comparison, the cost per quit associated 

with brief, unsolicited advice from a 

physician (a 5-minute talk about the dangers of smoking and simple strategies for 

quitting) during a regular consultation is 

$500 for individuals who abstain from 

smoking for at least 1 year.8 The cost per 

quit of the Agency for Health Care Policy and Research guidelines on smoking 

cessation is $4,119.9

Interpreting Changes in Prevalence

For any age cohort, the smoking 

prevalence is the number of smokers in 

Examining ASSIST’s Long-term Impact on Cost per Quit

To the extent that ASSIST activities resulted in permanent policy changes (youth access laws, clean indoor air acts, and higher excise taxes), an assumption may be warranted that the decrease in prevalence 

persists indefinitely into the future. Thus, the percentage of 18-year-olds who smoke is permanently 

reduced by 0.63%. An estimate of how such effects might alter the cost per quit was calculated as follows. The 1999 state population estimates for 18-year-olds were obtained for the 17 ASSIST states. It 

was assumed that 0.63% fewer of the individuals in this group would smoke. Thus, for the year 1999, 

approximately 8,496 fewer 18-year-olds smoked as a result of ASSIST. Assuming that the population 

of 18-year-olds would increase over time at the standard rate of 0.85% per year,a in each subsequent 

year there are 8,496 (1 + 0.0085)t fewer 18-year-old smokers. Since these noninitiations/quits occur in 

the future, these future nonsmokers attributable to ASSIST are discounted at the rate of 3%.

Discounting these growing cohorts of future nonsmokers (attributable to ASSIST) at 3% implies a net 

discount rate of 2.13% (i.e., [1.03/1.0085] – 1). Accordingly, the total discounted number of fewer 

18-year-old smokers is 398,871. Here the standard formula for a perpetuity, 8496/i, is applied, where i

is the net discount rate. When this figure is added to the short-run effect of ASSIST (478,860 fewer 

smokers in 1999), the cost per additional nonsmoker generated decreases to $171. If, on one hand, a 

smaller population growth rate is assumed, say an annual rate of 0.425%, the cost per quit is somewhat 

higher, $185. On the other hand, if we assume that in the long run only 25% of the short-run decrease 

in smokers persists, the cost per permanent quit rises to $290 if the population growth rate is 0.85% 

and to $333 if the population growth rate is only 0.425%. One way to interpret these last calculations is 

as a measure of the cost per quit if the effects of ASSIST decay over time. Further sensitivity analysis 

on the cost per quit is reported in the next section.

aU.S. Directorate of Intelligence. 1999. CIA world fact book. Washington, DC: U.S. Central Intelligence Agency. http://www.photius.com/wfb1999/rankings/population_growth_0.html
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

that age cohort divided by the total number of people in that cohort; for example, 

adult smoking prevalence is calculated 

by dividing the number of adult smokers by the total number of adults in the 

population. The number of smokers can 

decrease in three ways: Individuals quit, 

move out of the state, or die. The number 

of smokers can increase in three ways: 

nonsmokers begin to smoke (initiation), 

ex-smokers begin to smoke again (reinitiation), or smokers move into the state. 

Leaving aside sampling and measurement differences, the prevalence can 

decrease if the number of nonsmokers 

increases while the number of smokers 

remains constant.

Between 1991 and 1998, smoking 

prevalence decreased from an average 

of 25.19% in ASSIST states to 22.17%. 

Statistical analyses (see chapter 9) indicate that about 0.63 of this 3.02 percentage point decrease, that is, about 21% 

of the decrease, can be attributed to 

ASSIST. Given the 1999 adult population of the ASSIST states, this decline 

means that about 478,860 people who 

would otherwise be smoking are not 

smoking because of ASSIST.

Most of the change in prevalence attributable to ASSIST is because of lower 

rates of initiation and smoking cessation. Most adult smokers began smoking 

when they were teenagers; more than 

90% of adult smokers were smoking by 

the time they were 20. The average age 

of adult smokers in the ASSIST population was 41.48; the median age was 

35.9. To calculate the most conservative 

cost-effectiveness estimate, one would 

assume that the entire decrease in prevalence is attributable to smoking cessation. Thus, to the extent that ASSIST 

reduces smoking prevalence by discouraging initiation, the gain in expected 

life-years is biased downward by this 

assumption, because increases in life expectancy are larger if an individual never 

initiates smoking. However, the differences in lifetime mortality and morbidity 

for individuals who quit in their early 

twenties do not differ much from those 

of lifelong nonsmokers. The degree of 

bias introduced for very young adults by 

the assumption that all of the reduction 

in prevalence arises from quits is probably small.

Life-years Gained by Smoking 

Cessation

Smoking is related to a number of diseases: heart disease, stroke, lung cancer, 

other cancers, and various pulmonary 

diseases (chronic obstructive pulmonary 

disease, emphysema, and pneumonia). 

The 1990 report of the surgeon general, 

The Health Benefits of Smoking Cessation,10 provides an overview of the health 

benefits of smoking cessation; this section relies extensively on that report. 

Suppose a person has been smoking 

for years but then quits.* What happens 

to that person’s mortality and morbidity risks compared with a person who 

never smokes in his or her lifetime 

(a never-smoker) and an individual who 

continues to smoke? Such risks decrease 

but do not revert completely to the level 

*It has become standard to measure effectiveness in terms of quality-adjusted life-years (QALY) gained. A 

limitation of this analysis is that it does not include measures of QALYs gained.
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1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

of a never-smoker. Risks do over time 

decline relative to an individual who 

continues to smoke. Whereas for some 

smoking-related diseases (heart attacks, 

congestive heart failure, and stroke) 

the risks decrease over time to that of a 

never-smoker, for other diseases (lung 

and other cancers) the risk decreases but 

stays above the level of a never-smoker. 

While the rate of decrease in lung capacity is halted, the damage to lung capacity 

is only slightly reversed with cessation. 

Decreases in lung capacity are linked to 

the incidence of various pulmonary diseases. Taken together, mortality and morbidity risks significantly decrease with 

cessation but never fall completely to the 

level of a person who has never smoked.

What is the gain in life expectancy? 

Only a few estimates are available. In 

the present analyses, the estimates were 

used from the Framingham study data 

that were used by D’Agostino and colleagues11 based on the Framingham data 

as reported in the 1990 surgeon general’s 

report (The Health Benefits of Smoking 

Cessation)10 to compute the expected 

life-years gained by the decrease in 

prevalence attributable to ASSIST. A 

man who is a moderate smoker and who 

quits smoking between the ages of 35 

and 39 gains 5.2 years of expected life. 

His life expectancy is 69, whereas the 

life expectancy of a never-smoker is 77. 

A long-term male smoker who ceases 

to smoke between the ages of 65 and 69 

gains about 1.3 years in life expectancy. 

In the same Framingham study, women 

who quit between 35 and 39 years of 

age gained 3.2 years, and women who 

quit between 65 and 69 gained 1.0.11 In 

general, the younger the smoker is when 

he or she quits smoking, the greater is 

the gain in expected life-years. While the 

absolute number of expected life-years 

is greater if an individual quits while 

young, the percentage gain in expected 

life-years from the point in time of quitting is about the same for both younger 

and older individuals.

For men, the ASSIST-attributable 

change in prevalence percentage is 0.09 

(p = .042); for women, the ASSISTattributable drop is –0.96 (p = .023; see 

chapter 9 for details). Since the drop 

attributable to ASSIST is statistically 

significant only for women, expected 

life-years gained were computed solely 

for women. In particular, the estimates 

reported by D’Agostino and colleagues11

were used in order to linearly extrapolate and interpolate expected life-years 

gained for different age categories for 

women. Assuming that a woman who 

quits smoking at age 37 gains 3.2 years 

and one who stops at age 67 gains 1.0, 

the linear extrapolation-interpolation 

equation has the following form:

expected life years gained for female 

smokers when quitting at AGE

= (2.2/30)(37 – AGE) + 3.2. (10.7)

This approach predicts that a woman 

who never smokes lives on average 

4.3 years longer than a woman who is 

a lifetime smoker. (For equation 10.7, 

never smoked is equivalent to setting 

AGE = 21.) These estimates are roughly 

consistent with the findings of Peto and 

colleagues.12

Life-years gained are, for the most 

part, in the distant future. The standard 

procedure is to discount future life-years 

gained to generate a value for the year of 
319

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

analysis. For the ASSIST analysis, the 

assumption is that the 1999 death rates 

from smoking-related diseases persist 

into the future. However, such a projection is uncertain because there may 

be major breakthroughs in the prevention, early detection, and cures of some 

smoking-related diseases, which could 

lead to lower mortality and morbidity, or 

rates could increase if exacerbating circumstances occurred, such as increased 

air pollution. Because of such uncertainty, for the purposes of decision making about the allocation of resources, a 

life-year gained 40 years in the future is 

not treated as equivalent to a life-year in 

the year of the assessment.*

Discounting of future life-years 

gained is done in much the same way 

that a future monetary payment is discounted. The value of a life-year gained 

T years from the present (or a specified 

year) is 1/(1 + r)T. For example, if r = 

0.03 and T equals 30, then a life-year 

gained 30 years from now has a discounted value of 0.41. For decisionmaking purposes, if there were two 

interventions, A and B, with the same 

cost, but A would yield benefits in the 

present whereas B would yield benefits 

30 years from now, B would have to save 

at least 2.44 life-years for every single 

life gained by A in order to be more beneficial than A. Discounting reveals the 

tradeoffs between interventions in terms 

of time and benefits.

Life-years gained from smoking cessation differ significantly by gender; 

accordingly, gender differences in the 

effect of ASSIST have been analyzed. 

The estimated effect of ASSIST on the 

smoking prevalence of women is a statistically significant absolute decrease 

of 0.96 percentage points (p = .023). 

The estimated effect of ASSIST on male 

prevalence is a 0.09 increase in percentage prevalence and is not statistically 

significant (p = .42); the change in male 

prevalence attributable to ASSIST is assumed to be zero. These two estimates 

are used in calculating discounted lifeyears gained.

In contrast to gender, the effect of 

ASSIST does not seem to vary by age; 

that is, the effect of ASSIST is the same 

for all adult age cohorts, once gender is 

taken into account. Table 10.3 presents 

the calculations for life-years gained for 

women and by age of quit. The life-years 

gained by quits occur in the future and 

are discounted back (table 10.3, column 

5) to the age of expected death in the 

absence of smoking cessation (table 

10.3, column 4). Suppose, in the absence 

of cessation, a lifetime smoker can be 

expected to live T additional years. An 

individual who quits gains G expected 

life-years, so that his or her total expected life remaining is T + G. Hence, at the 

original expected time of death T, the discounted value of this gain of G years is

(1/ )[1 ] . 0= − = ∫ − −

G

rG rt D r e e dt (10.8)

D is a value generated at time T in 

the future. To determine the value of 

discounted life-years (DLY), D in turn is 

discounted as follows:

DLY = D(e–rT). (10.9)

*A more fundamental reason for discounting future lives saved, of course, is the underlying positive rate of 

time preference.
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1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

Equation 10.9 yields the discounted 

number of life-years gained for the age 

of cessation. For example, a 37-year-old 

man who quits smoking will gain on 

average 5.1 years of life. If he had not 

quit smoking, his life expectancy would 

have been 71 years, but with cessation 

his life expectancy is 76.1 years; hence, 

G = 5.1 and T = 34.0. Using equation 

10.9, the discounted life-years that are 

gained for the age of cessation are 1.705 

years. There are important gender differences in life-years gained. A 37-year-old 

woman who quits gains only 0.908 discounted life-years: The gain in expected 

life-years is smaller for women, and because women live longer, the future gain 

is discounted more for women than for 

men—that is, T is larger for women.

The total for life-years gained by 

ASSIST is calculated by taking the 

discounted number of life-years gained 

for each individual for each age cohort 

and multiplying this figure by the total 

number of individuals in the age cohort 

who have quit (attributable to ASSIST). 

For example, the total number of women 

between 35 and 39 in the ASSIST states 

is 4.3 million. The estimated decrease in 

the smoking prevalence rate of women 

attributable to ASSIST is 0.96 percentage 

points. Hence, the total number of quits 

attributable to ASSIST for the 35- to 39-

year-old age cohort of women is 0.0096 

times 4.3 million: about 41,000. If only 

50% of these quits are permanent, then 

the total number of permanent quits is 

about 21,000. Multiplying this 21,000 by 

0.9 years yields the total number of discounted life-years gained for the 35- to 

39-year-old age cohort of women: about 

19,000. These calculations are performed 

for each age cohort in the adult female 

population, and then the figures are 

summed to yield a total.

Applying this approach to the 

ASSIST population, assuming a 0.96 

decrease in prevalence rate for women 

and assuming a permanent quit rate of 

50%, the total gain in 1999 life-years 

is about 450,000. The total gain in discounted life-years is about 150,000. The 

average gain in discounted life-years for 

women is roughly 0.75.

ASSIST Cost per Life-year Gained

Cost per life-year gained depends on 

a variety of assumptions. The ASSIST 

analysis assumes that the long-run effect 

of ASSIST is a permanent decrease of 

about 95,068 smokers; this figure yields 

a total discounted number of life-years 

gained of about 71,767. In computing 

discounted life-years gained, the discount 

rate is assumed to be 3%, a standard assumption in cost-­effectiveness estimates. 

(A smaller discount rate would yield a 

lower cost per life-year estimate.) Tables 

10.4, 10.5, and 10.6 present the cost per 

life-year gained under a variety of different assumptions about the effectiveness 

of ASSIST and the reinitiation rate. One 

issue is the rate of relapse. Cromwell and 

colleagues assumed a short-run relapse 

rate of 45% and concluded from longterm follow-up data that over a 5-year 

period an additional 30% of quitters fail 

to abstain from tobacco use.9 Gilpin and 

colleagues found similar estimates for 

recidivism.7 Hence, a conservative approach is to assume that in the long run, 

only 50% of all ASSIST quitters are permanent quitters. Under this assumption, 

the cost per life-year is $790.
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

By assuming that each quit represents someone who otherwise would 

have been a lifetime smoker, the costeffectiveness of ASSIST may be overstated. People who smoke differ in their 

propensity to quit; some are very close 

to quitting and will do so in the near 

future. A program such as ASSIST can 

hasten this decision. Others are likely 

to never quit, so that a program like 

ASSIST has no effect on their consumption of tobacco products. Although there 

is little guidance in the cessation literature about how individual differences in 

quitting might be incorporated into this 

analysis, such differences must be accounted for. Therefore, it is assumed that 

half of the permanent quits attributable 

to ASSIST represent individuals who 

would have quit anyway within the near 

future (assume 3 years). Under this assumption, the number of permanent quits 

that, in the long run, are attributable to 

ASSIST is 95,068. The corresponding 

number of discounted life years saved is 

71,767, and the cost per life-year saved 

is $2,093. The cost per permanent quit 

is $1,580.03 if just the drop in female 

prevalence of 0.96 is used; the change 

in male prevalence attributed to ASSIST 

is zero. Costs per permanent quit are 

slightly lower, $1,255, if the overall population drop in prevalence attributable to 

ASSIST is 0.63.

Discussion

The actual cost-effectiveness of 

ASSIST may be lower than the best (and 

very conservative) estimate of $2.09 

per life-year gained we have calculated. 

First, this estimate is based on the assumption that there is a one-time effect 

of ASSIST on smoking prevalence and 

that this effect does not persist beyond 

1999. In fact, the programs, excise tax 

increases, and policies brought about 

by ASSIST are likely to have an enduring effect on lowering rates of smoking 

initiation by teenagers for a long time 

and will result in a continuing stream 

of individuals who would have become 

smokers but do not because of ASSIST. 

In addition, the programs, tax increases 

and policies instituted by ASSIST are 

likely to continue motivating and helping 

smokers to quit. Factoring in the lifeyears gained by dissuading people from 

smoking and from becoming smokers 

would further reduce the cost per lifeyear gained attributable to ASSIST.

The second reason for suspecting that 

both of the dollar estimates might be 

too high is that ASSIST helped establish 

tobacco control infrastructures in the 

states. Part of the ASSIST legacy is the 

experienced cadre of tobacco control 

practitioners who have been well trained 

in program design, advocacy, and media 

relations. Presumably these individuals 

will continue to train others, who will 

continue to conduct effective programs 

that in turn will lead to lower smoking 

prevalence, yielding additional lifeyears gained.

The estimates of cost per quit may be 

too low for at least two reasons. The first 

consideration is recidivism. To a large 

extent, decreases in prevalence are attributable to quits or prevented relapses. It 

is well documented that typically smokers do not permanently quit on the first 

try and that the overall recidivism rate 

is also quite high.7 These factors were 

taken into account by assuming that 
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1 0 . C o s t - e f f e c t i v e n e s s o f A S S I S T

only a certain fraction of the estimated 

quits will be permanent. In the baseline 

calculation, a recidivism rate of 50% 

is assumed, along with the assumption 

that half of all quitters would be, but for 

ASSIST, lifetime smokers. Table 10.4 

presents cost-effectiveness ratios for 

various assumptions about recidivism. 

If the recidivism rate were 70%, for example, the cost per discounted life-year 

gained would be $3,488. In the most 

optimistic calculations, in which all quits 

are permanent, the cost per quit becomes 

$395, and the cost per life-year gained 

becomes $451.

Another consideration is timing. In 

the period prior to ASSIST (between 

1968 and 1990), roughly 2.5% of all 

smokers quit permanently each year. At 

least some of the ASSIST-attributable 

decrease in prevalence occurred because 

ASSIST may have induced smokers 

who would have quit smoking anyway 

to have quit sooner. Therefore, some of 

the quits may not be fully attributable to 

ASSIST. If they could be accounted for, 

those quits would increase the cost per 

quit estimate.

Unfortunately, data are not available 

for a systematic investigation of these 

considerations. Little is known about the 

quit distribution age: For example, for a 

smoking cohort of age 40, the percentage who will permanently quit at age 40 

+ t, t = 1, 2, 3 . . . , and the fraction who 

will never quit is unknown. If these data 

were available, the next step would be 

to determine how ASSIST changed the 

shape of this distribution (i.e., induced 

people to quit earlier than they would 

have otherwise) and how ASSIST raised 

the cessation levels of smokers who 

would otherwise never have quit. Some 

rather mechanical steps can take this 

consideration into account. For example, 

if half the individuals who quit because 

of ASSIST would have quit within the 

next 5 years anyway, then the cost per 

quit of the ASSIST intervention doubles. 

This assumption was made in the best 

estimates of cost-effectiveness. Table 

10.6 presents calculations in which the 

net effect of ASSIST is varied.

Summary

T

he purpose of computing the cost per 

life-year gained is to enable comparisons of the cost-effectiveness of ASSIST 

with other public health interventions 

for tobacco use and other public health 

issues. (The standard reference for a 

compendium of cost-effectiveness calculations is a 1995 article by Tengs and 

colleagues.2 See also the “league tables” 

provided by the Harvard Center for Risk 

Analysis.13) While some interventions 

are more cost-effective than ASSIST, 

many are less cost-effective, including 

mammograms, exercise electrocardiograms, and other widely promoted interventions. Mammograms cost $2,700 per 

life-year gained, and electrocardiograms 

for 40-year-old men cost $108,000 per 

life-year gained. Among smoking interventions, a brief personal warning from 

a physician is very cost-effective (not 

so much because of a large effect, but 

because this advice is very cheap, about 

$10 per patient) as is smoking cessation 

advice for pregnant women. Smoking 

cessation advice for pregnant women reduces the number of low birth weight babies. Low birth weight babies often have 

medical problems that are expensive to 
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M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

treat, so cessation advice has a net negative cost, and the cost-effectiveness ratio 

is less than zero.

ASSIST was, however, more costeffective than an intensive physician 

antismoking intervention in Maryland 

with a cost per life-year gained of 

$2,587. According to Cromwell and colleagues, nationwide implementation of 

the Agency for Health Care Policy and 

Research guidelines on smoking cessation has a cost per life-year gained of 

$2,820 (1999 discounted dollars).9 At a 

cost per life-year gained of about $2,093, 

ASSIST appears to have been a relatively economical public health intervention.

Conclusions

1. The cost-effectiveness of ASSIST 

was analyzed relative to its cost per 

quit and cost per life-year gained. 

This analysis was based on population data from the Tobacco Use Supplement of the Current Population 

Survey and National Cancer Institute 

cost estimates for ASSIST, as well as 

quantitative assumptions regarding 

changes in smoking behavior over the 

term of the period studied.

2. The cost per quit of ASSIST interventions was estimated as $1,255 and 

cost per life-year gained was estimated as $2,093.02. These figures were 

based on best-estimate assumptions 

for factors such as the effectiveness 

of ASSIST interventions, long-term 

quit rates, and recidivism rates. Under 

the most optimistic assumptions, the 

cost per quit was been computed to be 

as low as $171, and cost per life-year 

gained as low as $395.

3. On a per capita basis, the overall cost 

of ASSIST interventions averaged 

$2.45 per person, with costs for individual states ranging from a low of 

$0.90 per person to a high of $6.83 

per person.

4. From a cost standpoint, relative to 

improved health and lives saved, 

ASSIST compared favorably with 

other accepted public health interventions such as mammograms and electrocardiograms, as well as other less 

expensive tobacco control interventions such as physician counseling.

References

1. Drummond, M. F., B. J. O’Brien, 

G. L. Stoddart, and G. W. Torrance. 

1997. Methods for the economic 

evaluation of health care programs. 2nd 

ed. New York: Oxford Univ. Press.

2. Tengs, T. O., M. E. Adams, 

J. S. Pliskin, D. G. Safran, 

J. E. Siegel, M. C. Weinstein, and 

J. D. Graham. 1995. Five-hundred 

life-saving interventions and their costeffectiveness. Risk Analysis 15 (3): 

369–90.

3. National Cancer Institute. 2005. What is 

the TUS-CPS? http://riskfactor.cancer.

gov/studies/tus-cps.

4. Hansen, R. H. 1985. Current 

Population Survey: Design and 

methodology (Technical paper no. 40). 

Washington, DC: U.S. Department 

of Commerce, Bureau of the Census. 

http://www.bls.census.gov/cps.

5. U.S. Department of Labor and U.S. 

Department of Commerce. 2000. 

Current Population Survey: Design 

and methodology (Technical paper no. 

63). Washington, DC: U.S. Department 
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of Commerce, Bureau of the Census. 

http://www.bls.census.gov/cps.

6. Stillman, F. A., A. M. Hartman, 

B. I. Graubard, E. A. Gilpin, 

D. M. Murray, and J. T. Gibson. 2003. 

Evaluation of the American Stop 

Smoking Intervention Study (ASSIST): 

A report of outcomes. Journal of the 

National Cancer Institute 95 (22): 

1681–91.

7. Gilpin, E. A., J. P. Pierce, and 

A. J. Farkas. 1997. Duration of smoking 

abstinence and success in quitting. 

Journal of the National Cancer Institute

89 (8): 572–76.

8. Law, M., and J. L. Tang. 1995. 

An analysis of the effectiveness of 

interventions intended to help people 

stop smoking. Archives of Internal 

Medicine 155 (18): 1933–41.

9. Cromwell, J., W. J. Bartosch, 

M. C. Fiore, V. Hasselblad, and 

T. Baker. 1997. Cost-effectiveness of 

the clinical practice recommendations 

in the AHCPR guideline for smoking 

cessation. Agency for Health Care 

Policy and Research. JAMA: The 

Journal of the American Medical 

Association 278 (21): 1759–66.

10. U.S. Department of Health and Human 

Services. 1990. The health benefits 

of smoking cessation: A report of the 

surgeon general (CDC publication no. 

90-8416). Atlanta: U.S. Department of 

Heath and Human Services, Centers for 

Disease Control and Prevention, Public 

Health Service, Office on Smoking and 

Health.

11. D’Agostino, R. B., W. B. Kannel, 

A. J. Belanger, and P. A. Sytkowski. 

1989. Trends in CHD and risk factors 

at age 55–64 in the Framingham Study. 

International Journal of Epidemiology

18 (3 Suppl. 1): 80.

12. Peto, R., A. D. Lopez, J. Boreham, 

M. Thun, and C. Heath Jr. 1992. 

Mortality from tobacco in developed 

countries: Indirect estimation from 

national vital statistics. Lancet 339 

(8804): 1268–78.

13. Harvard Center for Risk Analysis. n.d. 

The cost-effectiveness analysis registry. 

http://www.tufts-nemc.org/cearegistry/

index.html.
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325

11. The ASSIST Evaluation: Contributions to 

Evaluation of Complex Public Health Initiatives

Frances A. Stillman and Carol L. Schmitt

Contents

Introduction.................................................................................................................. 328

Challenges to Evaluating ASSIST................................................................................. 328

Response to Evaluation Challenges............................................................................. 329

Key Findings of the ASSIST Evaluation ......................................................................... 331

What Do These Results Mean? ..................................................................................... 332

The Legacy of the ASSIST Evaluation............................................................................ 334

The Future of Tobacco Control ...................................................................................... 336

Using the ASSIST Evaluation Approach for Other Public Health Initiatives................... 338

Summary...................................................................................................................... 339

Conclusions.................................................................................................................. 340

References................................................................................................................... 344

Appendix

Appendix 11.A. Crude Prevalence, Initial Outcomes Index, and 

Strength of Tobacco Control Index, by State................................................................. 342

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

327

11. The ASSIST Evaluation: Contributions to Evaluation of Complex 

Public Health Initiatives

The evaluation of the American Stop Smoking Intervention Study (ASSIST) 

successfully documented public health outcomes attributable to ASSIST, an 8-

year publicly funded partnership between the National Cancer Institute (NCI) and 

the American Cancer Society, implemented through cooperative agreements with 

17 state health departments. Because ASSIST was designed to change tobacco use 

by changing the social and physical environments that promoted such use, ASSIST’s 

efforts focused on building state capacity to deliver comprehensive tobacco use 

prevention and control, promoting public and private policy change, using welldesigned media campaigns, and advocating for media coverage of tobacco control 

issues. The inherent complexity of this large-scale public health approach to tobacco 

use prevention and control raised significant challenges for the evaluation of the 

program. These challenges were met with the novel approach described in this 

monograph.

The ASSIST evaluation documented the association between “upstream” changes 

attributable to ASSIST and subsequent reductions in tobacco use prevalence and 

cigarette consumption. In the process, this evaluation broke new ground in several 

key areas:

■ It successfully documented the effectiveness of a large-scale demonstration 

project in a rigorous and statistically valid manner, even though the project 

interventions were uniquely adapted to each ASSIST state and were also widely 

adopted outside the bounds of the ASSIST states.

■ The evaluation team developed a methodology that included empirically 

validated, aggregate measures of tobacco control inputs and intermediate policy 

outcomes, which were found to be related to two long-term outcomes: tobacco use 

prevalence rates and cigarette consumption rates.

■ The evaluation represented an early systems-based approach to evaluation, 

including the construction of logic models, the use of a network of stakeholders 

for model validation, and the development of a prototype knowledge base in the 

specific area of newspaper coverage of tobacco issues.

■ The evaluation validated the importance of upstream interventions in future 

tobacco control and other public health efforts.

The ASSIST evaluation leaves an important legacy of methods and measures that 

will guide the field for years to come, and the evaluation itself serves as a roadmap 

for future assessments of population-level public health efforts.
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Introduction

Politics is essential for effective 

public health, and thus is the 

inescapable context of public 

health interventions. To disregard 

sociopolitical determinants of health is 

to relegate public health to prevention 

and promotion of individual risk 

behaviors.1(p49)

—John B. McKinlay and 

Lisa D. Marceau,“Upstream 

Healthy Public Policy: Lessons 

from the Battle of Tobacco”

T

he ASSIST evaluation measured the 

success of a project that was based 

on a bold hypothesis—that interventions 

aimed at the individual alone would 

not result in substantive changes in tobacco use and health outcomes. Rather, 

ASSIST incorporated decades of tobacco 

control research that strongly suggested 

that the highest possible level of tobacco 

use prevention and control could be 

achieved through interventions that altered a social environment that supported 

tobacco use. The ASSIST project was 

an ambitious, publicly funded effort that 

used upstream tobacco control interventions—efforts that would yield changes 

in the social environment of tobacco use 

and subsequently affect smoking prevalence and cigarette consumption rates. 

Its evaluation was an equally ambitious 

endeavor that measured the impact of 

those interventions on the public health 

outcomes of tobacco use prevalence and 

cigarette consumption. This evaluation 

broke new ground in the assessment of 

complex public health initiatives while 

documenting the success of ASSIST.

Challenges to Evaluating 

ASSIST

T

he ASSIST evaluation was one of the 

first large-scale studies of upstream 

interventions for tobacco control, focusing on policy advocacy, media, and 

capacity building. ASSIST’s goal was to 

change policies, regulations, and social 

norms so that nonsmoking became the 

norm. The program accomplished its 

goals by providing states with a modest 

level of funding to develop the unified 

network of national, state, and local organizations needed to deliver tobacco 

control (i.e., capacity), thus using the 

state’s policy environment to change tobacco use. The ASSIST evaluation team 

faced challenges as a result of the nature 

of the intervention itself, thus precluding 

the original evaluation plan of comparing ASSIST states with non-ASSIST 

states. In addition, operational and practical challenges needed to be overcome: 

the evaluation effort did not begin until 

several years after the project was underway, the budget for the evaluation was 

limited, and significant effort was expended to assemble the final evaluation 

team. Therefore, the evaluation had to be 

focused and parsimonious. As a result, 

only those evaluation factors that were 

judged essential and that could be feasibly measured were included.

ASSIST was never envisioned as a 

randomized trial, and states were not 

randomly selected for the evaluation. 

Each state adapted the protocol to its 

unique political, social, and cultural context. ASSIST did not collect information 
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from the individual states on how each 

implemented its programs. Therefore, the 

evaluation team could not evaluate overall implementation of the interventions. 

In addition, data collected were available 

only from ASSIST states and were not 

available from non-ASSIST states. Finally, ASSIST targeted a much larger and 

more diverse population than any previous community-based tobacco control 

intervention, and no attempt was made to 

prevent ASSIST-like interventions from 

being adopted in non-ASSIST states. 

ASSIST states had a combined population of 91 million people, more than onethird the population of the United States, 

including more than 10 million African 

Americans and 7 million people of Hispanic and other racial/ethnic minority 

groups.2 The combination of these features made it difficult to assess ASSIST 

with standard outcome3 or process evaluation methods.4

Response to Evaluation 

Challenges

I

n response to these challenges, the 

central issue of the ASSIST evaluation 

(and a broader issue for the evaluation 

of upstream interventions in general) 

became how to measure and document 

the causal relationships among broad, 

population-based measures and public 

health outcomes. What had been originally envisioned as a simple evaluation 

of a demonstration project became 

a complex evaluation effort that engaged a diverse group of scientists and 

practitioners and involved extensive data 

sources.

The evaluation was guided by an 

a priori conceptual model that represented the sequential process of change 

hypothesized to occur in response to 

ASSIST.5 This model was based on 

ecological theory (see sidebar, “ASSIST 

and Systems Methods in Tobacco Control”) and included those factors identified as essential and measurable, while 

acknowledging that many important 

political, economic, and social factors 

could not be consistently or accurately 

measured across all states.

The ASSIST evaluation model included components that had never been 

used before, such as Strength of Tobacco 

Control (SoTC) and the Initial Outcomes 

Index (IOI). These measures had to be 

defined, measured, and quantified before 

they could be incorporated into the statistical models used in the evaluation. As 

the chapters in this monograph describe, 

the extant literature and the expertise of 

researchers and practitioners were used 

to define these components. In many 

cases, new measures and data-collection 

systems were created to collect this information at the state level. For example, 

the Tobacco Use Supplement for the 

Current Population Survey (TUS-CPS) 

was developed with the assistance of the 

United States Census Bureau to measure 

state factors (see chapter 5). Methods 

were devised to measure newspaper 

coverage of tobacco-related issues (see 

chapter 7), to measure legislative action (see chapter 3), and to measure the 

“dose” of tobacco use control and prevention at the state level (see chapter 2).

The ASSIST evaluation team had 

to meet an additional challenge before 
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these new components could be used 

in the evaluation. The fact that only 

50 states and the District of Columbia 

could be included as “observations” 

created statistical limits on how many 

factors could be included in the analysis. 

For example, this meant that instead of 

being able to include all of the individual 

measures of intermediate outcomes, such 

as policy measures and state excise taxes, in the statistical models, one measure 

or number that represented all of these 

outcomes had to be created for each 

state. The process that resulted in the 

IOI is documented in chapter 4, and the 

process that resulted in the SoTC score 

is documented in chapter 2. In addition, 

although many state-level factors could 

have affected the implementation and 

effects of a comprehensive tobacco 

control program, only a few could be included in the analysis. State-level factors 

considered for inclusion in the analysis 

and those selected for the analysis are 

discussed in chapter 5, along with the 

data sources for these factors.

ASSIST and Systems Methods in Tobacco Control

Ecological theories guided the design of many current public health programs during the 1990s,a and 

ecological theory was the basis for the ASSIST conceptual framework (see Monograph 16, chapter 

11). However, before the ASSIST evaluation, there was little guidance for evaluating these programs.b

The ASSIST evaluation was among the first to define the models, linkages, and appropriate measurement strategies for an ecologically based intervention by identifying constructs and relationships hypothesized to account for any reductions in smoking prevalence and cigarette consumption that might 

be attributable to a state tobacco control program.c

During the later stages of the ASSIST evaluation, the ecological perspective was widened to include 

a focus on systems methods applied to public health areas such as tobacco control. Within NCI, for 

example, the Initiative for the Study and Implementation of Systems (ISIS) is currently researching 

methodological areas such as systems modeling, network analysis, knowledge management, and largescale organizational change within a context of complex, interrelated systems of behavior—exactly the 

kind of environment that is now seen within tobacco control, with multiple stakeholders, countervailing forces from the tobacco industry, complex models of behavior, and other factors.d This systems 

view of the world represents a potentially important way of understanding and managing the kinds of 

upstream public health interventions that could evolve in the future.

Although the ASSIST evaluation concluded at about the same time that efforts such as ISIS were beginning, ASSIST provided some of the first evidence that systems methods could feasibly be used to 

evaluate complex public health programs. The systems methods used in ASSIST were the following:

■ Concept mapping—a participatory, multi-stakeholder approach to decision making within groups—

was used as part of the validation of SoTC criteria and was used to identify potential measures of 

tobacco industry tactics. Concept mapping applies a mixed-methods analysis to produce visual maps 

of participant ideas and their relationships, to aid planning and evaluation activities within groups.e

■ Tobacco control professionals and researchers were involved at the national, state, and local levels 

to participate in the validation of the SoTC measure.
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Key Findings of the ASSIST 

Evaluation

T

he ASSIST evaluation effort documented the following key findings 

that associated ASSIST intervention factors with public health outcomes:

■ ASSIST states had a greater decrease 

in adult smoking prevalence than nonASSIST states.

■ States that experienced greater 

improvement in tobacco control 

policies had larger decreases in per 

capita cigarette consumption than 

states that had experienced less 

improvement in tobacco control 

policies.

■ States with higher policy scores also 

had lower smoking prevalence and 

lower cigarette consumption.*

*This finding was significant only when the District of Columbia was not included as a “state” in the 

analysis. Chapter 9 discusses the challenges associated with equating District-level tobacco control 

programs and outcomes with state-level ones.

■ The development of SoTC involved the creation and validation of a logic model across multiple 

interrelated factors, a precursor to many of the systems dynamics methods used for simulation of 

complex behavior.

■ A database of newspaper coverage of tobacco issues, described earlier in this volume as a demonstration project, parallels the development of knowledge bases for decision support within a systems 

environment.

All of these factors suggest that the future of public health lies in programs that can be implemented 

upstream and subsequently measured in complex environments—an approach with the potential to 

yield great advances in public health. This environment was the context within which the ASSIST 

evaluation was designed and implemented. In the words of McKinlay and Marceau, “The perspectives 

and methods developed during the infectious and chronic disease eras have limited utility in the face 

of newly emerging challenges to public health.”f(p25) Efforts such as the ASSIST evaluation were designed to address this new reality.

aSallis, J. F., and N. Owen. 1997. Ecological models. In Health behavior and health education: 

Theory, research, and practice, eds. K. Glanz, F. Lewis, and B. Rimer, 2nd ed., 403–24. San Francisco: 

Jossey-Bass.

bGreen, L. W., L. Richard, and L. Potvin. 1996. Ecological foundations of health promotion. American 

Journal of Health Promotion 10 (4): 270–81.

cStillman, F., A. Hartman, B. Graubard, E. Gilpin, D. Chavis, J. Garcia, L-M. Wun, W. Lynn, and 

M. Manley. 1999. The American Stop Smoking Intervention Study: Conceptual framework and evaluation. Evaluation Review 23 (3): 259–80.

dBest, A. L., R. V. Tenkasi, W. Trochim, F. Lau, B. Holmes, T. Huerta, G. Moor, S. Leischow, and 

P. Clark. 2005. Systemic transformational changes in tobacco control: An overview of the Initiative 

for the Study and Implementation of Systems (ISIS). In Innovations in health care: A reality check,

ed. A. Casebeer, A. Harrison, and A. E. Mark. New York: Palgrave Macmillan.

eTrochim, W., and R. Linton. 1986. Conceptualization for evaluation and planning. Evaluation and 

Program Planning 9:289–308.

f

McKinlay, J. B., and L. D. Marceau. 2000. To boldly go... American Journal of Public Health 90 (1): 

25–33.
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■ States with greater “capacity,” 

or ability to implement tobacco 

control activities—such as states 

with highly functioning tobacco 

control infrastructures in the health 

department, staff experience in 

tobacco use control and prevention, 

and strong interagency and statewide 

relationships—had lower per capita 

cigarette consumption.

■ The cost-effectiveness of ASSIST—

the cost per life-year gained—

compares favorably with other 

accepted preventive public health 

interventions.

Appendix 11.A summarizes several major findings of the ASSIST 

evaluation. It includes ASSIST versus 

non-ASSIST state means and standard 

errors for smoking prevalence rates and 

IOI scores at baseline and at the end of 

ASSIST (1999), along with SoTC scores 

for 1999. The appendix also includes 

the same information for each of the 

50 states and the District of Columbia.

What Do These Results Mean?

T

he results from the ASSIST evaluation provide evidence that investment 

in state tobacco control programs that 

focus on strong tobacco control regulations and policies is an effective strategy 

for reducing tobacco use. The small but 

statistically significant differences in the 

reduction of adult smoking prevalence in 

ASSIST states, when applied on a population basis, could be expected to have 

a large impact on the public’s health. If 

all 50 states and the District of Columbia 

had implemented ASSIST, there would 

have been approximately 1,213,000 

fewer smokers nationally.

States with stronger tobacco control 

policies also had larger decreases in 

per capita cigarette consumption. This 

suggests that policy-focused tobacco 

interventions can have a strong and sustained effect on the number of cigarettes 

smoked. More recent analyses support 

the effectiveness of policy interventions to decrease tobacco use. Smokefree workplace policies have been 

associated with reduced daily cigarette 

consumption,6–8 higher quit rates,8–10

lower smoking prevalence rates,6,7,9,10

and longer sustained cessation.10 Although policy efforts take time, they 

can bring about major changes in social 

norms, including smoking behavior.

The ASSIST evaluation went beyond 

simply reporting aggregate serial trend 

data; it is the first such study to link 

these outcomes to tobacco control program components. This linkage was accomplished by systematically assessing 

states’ capacities to implement tobacco 

control programs and determining how 

these capacities were related to smoking prevalence and cigarette consumption. The ASSIST evaluation was the 

first time that state capacity for tobacco 

control was measured and subsequently 

associated with decreased tobacco use. 

States with stronger infrastructures or 

capacities (ability to implement tobacco 

control activities) had lower per capita 

cigarette consumption, serving as evidence that when tobacco control programs are strong and well supported, a 

decrease in the amount of smoking can 

be achieved.
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

333

Measuring Capacity

The ASSIST evaluation was the first time that the components of the tobacco control infrastructure 

at the state level had been defined and its capacity (its ability to perform or produce) successfully 

measured and subsequently linked to outcomes within a conceptual model.a Including capacity in the 

evaluation model was essential because one of the major legacies of ASSIST was the creation of “an 

evolving infrastructure for implementing comprehensive tobacco prevention and control initiatives” 

(see Monograph 16, chapter 11, p. 480). This infrastructure provides the capacity to conduct modern 

tobacco use prevention and control efforts. 

Both before and since the ASSIST evaluation, a large body of literature has accumulated regarding 

developing capacity for public health efforts.b,c,d,e,f,g,h,i However, at the time of the ASSIST evaluation, 

there were few published papers on measuring capacity, and those studies were mainly at the community level.j

 Since the ASSIST evaluation, the literature on measuring capacity has grown. Currently researchers have documented measures for capacity components including leadership,k coalitions,l,m and 

interagency relationships.n,o,p Evaluating changes in capacity will be especially important as tobacco 

use prevention and control programs face funding shortages, which in some states have resulted in the 

elimination of many program activitiesq and in measurable threats to capacity, including weakened 

interagency relationships.r Maintaining capacity for tobacco control—including a sufficient number 

of skilled staff and strong interagency relationships—has been identified as key to continuing gains in 

tobacco use prevention and control goals.s

aStillman, F. A., A. M. Hartman, B. I. Graubard, E. A. Gilpin, D. M. Murray, and J. T. Gibson. 2003. 

Evaluation of the American Stop Smoking Intervention Study (ASSIST): A report of outcomes. Journal of the National Cancer Institute 95 (22): 1681–91.

bJensen, M., and W. Meckling. 1976. Theory of the firm: Managerial behaviour, agency costs, and 

ownership structure. Journal of Financial Economics 3:305–60. 

cRoper, W. L., E. L. Baker, W. W. Dyal, and R. M. Nicola. 1992. Strengthening the public health system. Public Health Reports 107:609–15. 

dMeissner, H. I., L. Bergner, and K. M. Marconi. 1992. Developing cancer control capacity in state and 

local public health agencies. Public Health Reports 107:15–23.

eSchwartz, R., C. Smith, and M. A. Speers. 1992. Capacity-building resource needs of state health 

agencies to implement community-based cardiovascular disease prevention programs. Journal of Public Health Policy 14:480–94. 

f

King, L., and M. Wise. 2000. Building capacity for public health. New South Wales Public Health 

Bulletin 11:1–2. 

gBeaglehole, R., and M. R. Dal Poz. 2003. Public health workforce: Challenges and policy issues. Human Resources for Health 1:4. 

hPotter, C., and R. Brough. 2004. Systemic capacity building: A hierarchy of needs. Health Policy and 

Planning 19:336–45. 

i

Gonzalez-Block, M. A. 2004. Health policy and systems research agendas in developing countries. 

Health Research Policy and Systems 2:6.

j

Goodman, R. M., M. A. Speers, K. McLeroy, S. Fawcett, M. Kegler, E. Parker, S. R. Smith, T. D. 

Sterling, and N. Wallerstein. 1998. Identifying and defining the dimensions of community capacity to 

provide a basis for measurement. Health Education and Behavior 25:258–78.

kLempa, M., R. M. Goodman, J. Rice, and A. B. Becker. Forthcoming. Development of scales measuring the capacity of community-based initiatives. Health Education and Behavior.
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A subsequent cost-effectiveness 

analysis (see chapter 10) confirmed 

that the ASSIST interventions were 

economically competitive with other 

population-level interventions as well as 

with intensive individual interventions. 

The Legacy of the ASSIST 

Evaluation

ASSIST was a quasi- or natural experiment, not a randomized experiment 

and, as such, presented the evaluation 

challenges described earlier in this chapter: states were not randomly selected 

for the evaluation; implementation standardization was not a core feature of 

ASSIST; ASSIST targeted a much larger 

and more diverse population than any 

previous community-based intervention; 

and other tobacco control initiatives, 

such as SmokeLess States and the Initiatives to Mobilize for the Prevention 

and Control of Tobacco Use (IMPACT), 

were fielded during the ASSIST period. 

In addition, the number of “observations” (states) to be evaluated placed statistical restrictions on how many factors 

could be included in the analyses.

ASSIST also epitomized Rogers’s 

“diffusion of innovation.” “Diffusion 

is the process by which an innovation 

is communicated through certain channels over time among the members of a 

social system.”11(p115) Diffusion studies 

describe trends of successive or sequential adoption of programs or policies. 

ASSIST practices were diffused formally 

and informally into all 50 states and the 

District of Columbia. The ASSIST coordinating center held 17 national training 

workshops—information exchanges and 

conferences where strategy, technology, 

l

Berkowitz, B. 2001. Studying the outcomes of community-based coalitions. American Journal of 

Community Psychology 29:213–27.

mGranner, M. L., and P. A. Sharpe. 2004. Evaluating community coalition characteristics and functioning: A summary of measurement tools. Health Education Research 19:514–32.

nProvan, K. G., and H. B. Milward. 2001. Do networks really work? A framework for evaluating public sector organizational networks. Administration Review 61:400–09.

oProvan, K. G., M A. Veazie, N. I. Teufel-Shone, and C. Huddleston. 2004. Network analysis as a tool 

for assessing and building community capacity for provision of chronic disease services. Health Promotion Practice 5 (2): 174–81.

pKrauss, M., N. Mueller, and D. Luke. 2004. Interorganizational relationships within state tobacco 

control networks: A social network analysis. Preventing Chronic Disease 1 (4): A08.

qHamilton, W. L., C. N. Rodger, X. Chen, T. K. Njobe, R. Kling, and G. Norton. 2003. Independent 

evaluation of the Massachusetts Tobacco Control Program. Eighth Annual Report. January 1994-June 

2001. http://www.mass.gov/dph/mtcp/reports/2002/abt8th.htm.

r

Center for Tobacco Policy Research. 2004. Florida’s challenge to maintain its tobacco control program: A rapid response report about Florida’s tobacco control program. http://ctpr.slu.edu/documents/

FLRRR.pdf.

s

Center for Tobacco Policy Research. 2004. Turning the tide: North Carolina’s tobacco prevention and 

control efforts. http://ctpr.slu.edu/documents/NCRRR.pdf.
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resources, and technical assistance were 

shared. Although some of these events 

were limited only to ASSIST states, nonASSIST states were included in others, 

including the first national tobacco control conference meeting in 1997. In this 

way diffusion occurred both between 

ASSIST states and consequently into 

non-ASSIST states.

Stillman et al.12 provide an early 

indication that these new and effective 

techniques diffused into non-ASSIST 

states, resulting in changes subsequently 

associated with decreases in tobacco 

use. ASSIST states had greater increases 

in IOI scores, a measure of tobacco 

control policy implementation, than 

non-ASSIST states only in the first years 

of the program, 1993 through 1994. 

Thereafter, IOI scores increased for both 

groups of states at a similar rate (see 

chapter 9, figure 9.3).

The ASSIST evaluation legacy includes the successful development of 

several new measures, including the 

TUS-CPS, and methods and measures to track legislative change (see 

chapter 3), state tobacco dependence 

on tobacco growing and manufacturing 

(see chapter 6), and exposure to tobacco 

control programs (SoTC, chapter 2). The 

legacy also includes substantial progress 

on other measures. These include methods and measures to track changes in 

media coverage of tobacco control topics 

(chapter 7) and tobacco industry actions 

that counter public health programs 

(chapter 8).

A number of the components created 

for the ASSIST evaluation have been institutionalized at the national level. NCI 

also continues to sponsor the TUS-CPS, 

a key source of national- and state-level 

data on smoking and other tobacco use.13

NCI also continues to support the update of two legislative indices developed 

under the ASSIST evaluation—the extensiveness of state tobacco control laws 

related to youth access to tobacco and 

clean indoor air.14 The SmokeLess States 

initiative adopted the ASSIST evaluation 

conceptual framework, including the media coverage of tobacco control issues, 

policy scores, and the Strength of Tobacco Control. The Robert Wood Johnson 

Foundation (RWJF) will continue to support administration of the SoTC survey 

in 2006 and 2008.

In addition to new data collection 

methods and systems, the ASSIST evaluation informed and influenced changes 

in how states evaluate their tobacco 

control programs. These changes include 

a move toward evaluating programs 

in context,15,16 linking state and local 

data,17 and accounting for factors such 

as the state political environment18 and 

tobacco industry activities.19–25

Likewise, the sequential process of 

change depicted in the ASSIST evaluation conceptual model endures as the 

basis for current tobacco use prevention and control programs. This process 

and its components and measures are 

represented in the evidence-based logic 

models of the three goal areas for the 

Office on Smoking and Health at the 

Centers for Disease Control and Prevention26,27 and are the basis for the current 

SmokeLess States evaluation.28 Research 

has continued to support the causal relationships between components of the 

conceptual model. This research includes 
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analyses of media coverage29–35 and 

policy outcomes.36,37

Research on tobacco industry efforts 

to counter public health initiatives has 

grown since the ASSIST evaluation, and 

initiatives such as the digital tobacco 

industry library at the University of 

California San Francisco38 have made 

searching and obtaining these documents 

significantly more reliable and productive. Although an analysis of Tobacco 

Institute budgets was funded by NCI for 

the ASSIST evaluation,39 these budgets 

were not comprehensive and could not 

be used to consistently measure these 

efforts across all states. In addition, the 

budgets were available only until 1997. 

Despite these significant advances in 

document accessibility, the successful 

documentation of tobacco industry efforts to thwart ASSIST40 (see Monograph 16, chapter 8), and the progress 

made toward a measure of protobacco 

efforts (see chapter 8, this monograph), 

publicly available data (at both the federal and state level) for such a score remain difficult to standardize at the state 

level. Developing this score remains a 

challenge for future research.

The ASSIST evaluation legacy includes the tools to measure the milestones 

and upstream markers of success that allow researchers and practitioners to document the presence, outcomes, and benefits 

of tobacco control initiatives. Building 

on the ASSIST evaluation legacy and 

addressing the remaining challenges in 

tobacco control will require a long-term 

commitment. The following section describes some potential approaches to ensure that this commitment is met.

The Future of Tobacco Control

T

he ASSIST evaluation provided evidence that the “blueprint” for tobacco 

control detailed in NCI Smoking and 

Tobacco Control Monograph 141 and 

implemented for ASSIST was feasible 

and effective and that the sequential processes of change described were valid. 

Much still needs to be changed—­policies 

that do not protect people from secondhand smoke; low taxes on tobacco products that keep them affordable; barriers to 

effective cessation aids that help smokers 

quit; and tobacco product advertising 

that promotes these products as attractive 

and normative, and minimizes their risk. 

Despite continued progress toward these 

changes, in early 2005 adult smoking 

prevalence was 20.9%, and reaching the 

U.S. Surgeon General’s Healthy People 

201042 goal of reducing adult smoking 

prevalence in the United States to 12% 

by 2010 appears unlikely.43,44

Renewed progress toward these 

goals requires a better understanding 

of the magnitude of interventions, the 

relative contributions of their program 

components, and their impact on at-risk 

populations.45 This better understanding will come from new surveillance 

and methodological strategies that can 

delineate the biological, behavioral, and 

social influences underlying tobacco use, 

with emphasis on groups that exhibit 

health-related disparities. These new 

surveillance measures should include 

the environmental factors that lead to 

tobacco experimentation and subsequent 

addiction, and expanded data on attitudes and beliefs about “smoke-free” 

accommodations and workplaces. As 
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tobacco control initiatives increasingly 

move to the state and local levels, we 

need to expand capabilities to monitor 

the dissemination, quality, and outcomes 

of those efforts. The TUS-CPS tobacco 

use questionnaire should be modified to 

incorporate new measures of tobaccouse behaviors, and new supplements 

should be commissioned to cover new 

and emerging areas of importance.

Data are currently being collected by 

multiple entities: individual states collect data,15,17,46–50 research groups such 

as the Center for Tobacco Policy Research51 and ImpacTeen52 collect data, 

and the federal government collects data. 

These data are used for individual state 

evaluations, evaluations of initiatives 

(e.g., SmokeLess States), and monitoring 

of individual factors (e.g., prevalence). 

However, national leadership is needed 

to engage stakeholders and build a 

comprehensive surveillance plan at the 

federal level. A comprehensive tobacco 

control surveillance network could begin 

to integrate available data, help create 

a more comprehensive data system that 

could track state tobacco control program development, help in the observation of patterns, and provide the essential 

data needed to test effectiveness at the 

societal level. These data could be used 

to assess all of the different tobacco control programs in the United States and 

would be useful for ranking states and 

reporting on their progress.

In addition, new public access databases need to be established to make 

these critically important state-level 

factors available to federal and other 

researchers. These data would greatly expand the current capacity of states to evaluate their own tobacco control programs. 

Such data would also help researchers 

develop more comprehensive models to 

document the relationships between the 

factors that promote or impede tobacco 

use, and ultimately establish an association between these factors and reduction 

in the tobacco-related cancer burden.

Key Questions for Tobacco Control

Tobacco control efforts must continue to address different types of questions, and to do so will require 

different but overlapping data as well as different methods. For example, program administrators at the 

state, local, and national levels want to know the optimal mix of interventions and funding levels to 

reduce the burden of tobacco use. This is extremely important because funding for tobacco control is 

currently decreasing and program administrators must make important decisions concerning the health 

of their populations. The key issues are how effective interventions are in terms of magnitude of change, 

relative contribution of program components, and relative impact for different target populations:

■ Do specific components matter in the real context?

■ How do we translate clinical trial results into other, less controlled, settings?

■ What should a practitioner do in his or her state?

■ Has the program been implemented effectively?

■ Does the cost effectiveness of this program compare favorably with other interventions?

■ Are trends moving in the anticipated directions?

It is also important to understand the research resources needed to provide support to answer these important questions.
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Finally, sufficient funding for tobacco 

use prevention and control is necessary for continued progress in reducing 

tobacco use. Recent reductions in funding represent one of the greatest threats 

to the capacity that ASSIST built. Only 

four states (Colorado, Delaware, Maine, 

and Mississippi) funded their programs 

in fiscal year 2005 at even the minimal 

levels recommended by the Centers 

for Disease Control and Prevention. 

Preliminary evidence suggests that as a 

result fewer mass media campaigns have 

been fielded and that decreases in youth 

smoking have stalled.53

Using the ASSIST Evaluation 

Approach for Other Public 

Health Initiatives

I

n 1991, the ASSIST focus on changing the social environment of smoking 

to address what is an individual behavior—smoking—was a revolutionary 

concept.54 Today, this approach is being 

applied to our nation’s most pressing 

health risk behaviors. For example, 

environmental factors that promote cardiovascular disease have been identified, 

as have potential environmental interventions to modify them.55–57 However, 

cardiovascular disease and obesity share 

behavioral determinants with environmental influences—physical activity 

and food intake—and the majority of 

the research in these areas has focused 

on obesity. While some research on 

obesity addresses genetic and individual 

influences, much of the current research 

focuses on the interactive environmental 

determinants of food intake and physical 

activity.58–81 The comprehensive tobacco 

use prevention and control model has 

been identified as an appropriate guide to 

obesity prevention,82 and tobacco control 

researchers will recognize many current 

themes in the obesity research; policy 

change can be used to address both environmental determinants of overeating 

and underexercising,69,75,81,83 comprehensive models need to be articulated so 

that interventions can be implemented,84

and states need capacity to deliver these 

interventions and monitor their effects.83

The ASSIST evaluation strategy is extremely relevant to the analysis of these 

so-called “new public health” programs. 

These large-scale, nonrandomized studies are better assessed by a real-world 

perspective that moves beyond the study 

of risk factors and interventions directed 

solely at changing the behavior of individuals. Evaluations of large-scale 

studies historically focus on analytical 

techniques at the expense of study design. For example, community intervention trials in cardiovascular disease and 

evaluations of state interventions suggest 

that similar to tobacco use control and 

prevention interventions, realistic effects 

are modest changes, over a long time 

frame, that are difficult to distinguish 

from secular trends. These programs 

would also benefit from continuous data 

collection that includes upstream factors 

such as infrastructure, policies, and program components.

If public health interventions continue 

to address complex social phenomena, 

they will, like ASSIST, require more 

sophisticated evaluation designs: A traditional before-and-after comparison group 

design with the intervention represented 
339

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

by a single variable is inadequate in this 

context and would surely mask important effects. Studies of social phenomena 

must go beyond the “black box approach,” in which the focus is only on 

the outcome and the inner processes are 

hidden. Most studies relate exposures to 

outcomes without actually describing the 

process used to achieve their stated goals.

This need mirrors a growing systems 

view of the world, where outcomes cannot just be described by cause-and-effect 

observation from simple logic models. 

As our ability to model increasingly 

complex phenomena grows, we are finding that the interrelationship and feedback between factors have an increasing 

role in outcomes—from general cases 

of how behavior leads to unintended 

outcomes, to specifics such as how the 

countervailing efforts of the tobacco 

industry affect interventions like the 

ASSIST project. This trend points to the 

need for evolving, dynamic models of 

behavior as well as an evaluation methodology that links these dynamic factors 

to measurable outcomes.

Tobacco use, like many of our current 

public health challenges, is a complex 

societal problem that involves individual 

behavioral factors, economic factors, 

political factors, and sociocultural factors, as well as vested interests of U.S. 

and transnational corporations. Successful public health interventions must 

continue to focus on development of appropriate public health policies, as well 

as educating governments to take appropriate steps to protect their populations. 

This is clearly stated by McKinlay and 

Marceau85(p29) in their article “To Boldly 

Go...”: “The success of public health in 

the 21st century, especially interventions 

at the level of social policy, will depend 

in large part on the role of the state.”

Summary

T

he ASSIST evaluation effort broke 

new ground on several fronts. It provided (1) a rigorous, validated assessment of a large-scale, upstream tobacco 

control initiative with interventions that 

led to successful outcomes in tobacco 

consumption; and (2) evidence that specific factors affect tobacco prevalence. 

It serves as a model that can be used 

to guide future evaluation efforts in 

evidence-based public health, which by 

nature does not always lend itself to the 

randomized controlled trial model used 

in other areas, such as medicine.

Perhaps most important, this evaluation helped establish the broader 

legitimacy of upstream public health 

interventions, using tobacco control as a 

proof of concept. Tobacco use remains 

the country’s leading cause of preventable death, despite substantial reductions in individual use of tobacco in the 

decades since the release of the first 

Surgeon General’s Report on smoking 

and health in 1964. In the eyes of many 

public health professionals, our best 

hope for significant further reductions in 

the disease burden of tobacco is encompassed in the quote from McKinlay and 

Marceau1 that begins this chapter and 

points us beyond the traditional public 

health focus on individual behavior, toward upstream measures that take place 

at a societal level. The ASSIST project 

and its subsequent evaluation served as 

important milestones in validating the 
340

1 1 . T h e A S S I S T E v a l u a t i o n : C o n t r i b u t i o n s

hypothesis that the key to a smoke-free 

society is an environment where smoking is viewed as non-normative.

Upstream measures are seen as a 

fairly recent trend in tobacco control (the 

early discussions leading to the ASSIST 

project took place in the late 1980s), yet 

in many ways these efforts also connect 

with the original activism of early public health efforts to confront death and 

disease. From the yellow fever epidemic 

of the early nineteenth century, which 

was managed in part by policy measures ranging from ship inspections to 

citywide quarantines,86 to John Snow’s 

intervention against contaminated water 

sources in nineteenth-century London,1

to social activist Jacob Riis’s call in the 

early twentieth century to pass laws affecting New York City tenement life,87

there is a direct link to modern-day, 

upstream interventions such as cleanair laws and restrictions on tobacco 

advertising. Moreover, most of these 

efforts were opposed by the same kinds 

of powerful social forces discussed in 

the preceding section. This means, in 

a very real sense, that history is teaching us once again that a move toward 

upstream public health efforts requires 

a new approach to implementation and 

evaluation, from a purely scientific examination of causes and effects, to a living, breathing process that evolves as a 

system.

Both ASSIST and its evaluation represent a growing trend within public 

health to move beyond its historical base 

of disease control and prevention into 

advocacy for policy and infrastructure 

changes that could drive more permanent and far-reaching changes in health 

outcomes. Moreover, these projects 

represent a milestone within the broader 

area of evidence-based public health by 

not only funding promising interventions 

but also developing unique measures 

that correlate a composite dose level of 

intervention strength with outcomes in 

prevalence and consumption. In using a 

participatory, expert-based approach to 

develop and validate indirect criteria that 

were ultimately correlated to outcomes, 

the ASSIST evaluation effort made an 

important contribution to the measurement of tobacco control efforts and to 

the advancement of tobacco control as a 

discipline.

Looking to the future, the ASSIST 

evaluation represents a starting point 

for further research into the refinement 

and evolution of its own criteria, based 

on factors such as the interplay between 

tobacco control constructs (resources, 

capacity, and efforts) and countervailing 

forces such as the efforts of the tobacco 

industry. As such, it represents an important step toward a growing systems 

view of the world, which takes a more 

ecological approach to the dynamics of 

how public behavior and public health 

are changed. This, in turn, holds the 

promise that future public health efforts 

will result in a better quality of life for 

all people.

Conclusions

1. ASSIST was an ambitious, 8-year 

project designed to reduce tobacco 

use prevalence and consumption at 

the state level by changing the social, 

political, and media environment surrounding tobacco use. Its evaluation 
341

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

required the assessment of a complex 

network of interventions, many of 

which became widely adopted in other 

states over the course of the project.

2. Originally designed as a state-bystate comparison of a demonstration 

project, the ASSIST evaluation methodology evolved to assess a broad 

range of upstream, population-level 

tobacco control practices and their 

outcomes. This effort eventually produced metrics such as the Strength 

of Tobacco Control index for statelevel tobacco control effectiveness 

and the Initial Outcomes Index for 

preliminary outcomes in policy and 

legislative issues, as well as promising efforts in areas such as tracking 

media coverage.

3. The ASSIST evaluation serves as a 

promising model for other complex, 

population-level public health initiatives that do not fit other evaluation 

models such as randomized controlled trials.

4. The ASSIST evaluation established 

the effectiveness of the ASSIST interventions, including lower adult 

smoking prevalence in ASSIST 

states, greater decreases in per capita 

cigarette consumption in states with 

stronger tobacco control, and significant correlations between specific 

evaluation components and reductions in tobacco use.

5. Future directions in upstream tobacco control include improved data 

sources and evaluation metrics, stronger assessment of tobacco industry 

counterefforts, and a growing need to 

address tobacco control efforts from 

a systems perspective.
1 1 . T h e A S S I S T E v a l u a t i o n : C o n t r i b u t i o n s

342

Prevalence (%) IOI (%) SoTC (%)

States Baseline SE Final SE

Final–

baseline SE Baseline SE Final SE

Final–

baseline SE

Only at 

final SE

ASSIST

Colorado 24.16 0.96 20.20 0.74 –3.96 –0.90 2.75 3.65 –0.40Indiana 27.81 1.34 27.03 1.19 –0.78 –3.14 1.42 4.56 –1.08Maine 28.52 1.08 23.51 1.29 –5.01 1.87 6.96 5.09 –1.25Massachusetts 21.40 0.64 19.34 0.81 –2.06 1.66 8.63 6.97 0.46Michigan 27.20 0.59 23.36 0.69 –3.84 0.76 6.64 5.88 0.90Minnesota 25.11 1.31 21.19 0.94 –3.92 3.83 5.96 2.13 1.74Missouri 26.24 1.47 23.49 1.10 –2.75 –1.01 3.38 4.39 –0.79New Jersey 20.38 0.51 19.84 0.75 –0.54 1.92 7.93 6.01 1.12New Mexico 23.99 1.00 20.93 0.94 –3.06 0.17 2.70 2.52 –0.53New York 21.52 0.42 20.54 0.47 –0.98 2.18 8.03 5.85 0.69North Carolina 26.93 0.71 22.98 0.68 –3.95 –4.26 0.41 4.67 –0.15Rhode Island 23.20 0.63 19.87 0.97 –3.33 1.61 6.88 5.28 1.09South Carolina 25.71 1.00 22.73 1.20 –2.98 –1.91 0.47 2.38 –0.48Virginia 25.47 1.17 20.77 0.69 –4.70 –2.37 1.44 3.81 0.07Washington 24.14 0.97 20.13 0.92 –4.01 4.40 8.45 4.05 0.23West Virginia 30.25 1.33 26.38 1.37 –3.87 –2.48 1.77 4.24 –0.53Wisconsin 26.15 1.34 24.54 1.19 –1.61 1.20 5.74 4.54 –0.04Overall average 25.19 0.64 22.17 0.57 –3.02 0.33 0.21 0.60 4.68 0.73 4.47 0.32 0.06 0.20

Non–ASSIST 

Alabama 25.74 0.74 22.29 1.13 –3.45 –2.62 0.89 3.51 –0.18Alaska 27.61 1.40 26.69 1.69 –0.92 4.57 10.55 5.99 0.30Arizona 22.37 0.87 19.86 0.78 –2.51 1.06 5.25 4.20 4.04Arkansas 28.73 1.19 25.99 1.19 –2.74 –2.20 1.99 4.19 0.08California 18.94 0.34 16.59 0.43 –2.35 4.25 6.74 2.48 3.73Connecticut 22.31 0.83 20.56 1.37 –1.75 0.44 4.22 3.78 0.37

Appendix 11.A. Crude Prevalence, Initial Outcomes Index (IOI), and Strength of Tobacco Control Index (SoTC), by State
M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

343

Prevalence (%) IOI (%) SoTC (%)

States Baseline SE Final SE

Final–

baseline SE Baseline SE Final SE

Final–

baseline SE

Only at 

final SE

Delaware 23.30 1.38 23.34 1.06 0.04 –1.31 2.07 3.38 –1.07District of Columbia 23.23 0.85 23.54 1.14 0.31 3.10 6.85 3.76 –0.87Florida 23.89 0.57 20.66 0.51 –3.23 1.23 3.53 2.30 1.70Georgia 24.32 1.18 19.89 0.77 –4.43 –1.87 1.73 3.61 0.34Hawaii 22.18 1.18 18.34 1.03 –3.84 3.10 9.04 5.93 0.96Idaho 23.61 1.10 21.84 1.52 –1.77 1.02 3.78 2.76 0.13Illinois 24.47 0.56 22.92 0.49 –1.55 –1.13 4.61 5.75 –0.71Iowa 23.50 0.93 22.37 0.98 –1.13 –1.24 2.17 3.42 0.41Kansas 24.22 1.26 22.45 1.07 –1.77 0.93 4.90 3.95 0.47Kentucky 31.98 1.07 29.81 1.22 –2.17 –4.81 –1.09 3.72 –0.19Louisiana 25.37 1.32 22.74 1.10 –2.63 –1.99 2.64 4.63 –2.30Maryland 23.59 1.08 19.46 1.09 –4.13 0.65 8.24 7.59 0.97Mississippi 25.46 0.96 21.53 1.23 –3.93 –2.37 0.76 3.13 1.28Montana 23.53 1.12 23.29 0.99 –0.24 –0.40 2.88 3.28 –1.60Nebraska 21.97 0.99 21.18 0.91 –0.79 –0.33 3.61 3.94 –0.31Nevada 28.37 1.03 24.06 1.12 –4.31 –1.12 1.27 2.39 –1.42New Hampshire 24.69 1.21 22.04 1.08 –2.65 1.39 5.42 4.03 –0.45North Dakota 22.18 1.53 20.47 1.21 –1.71 1.10 5.04 3.94 –0.93Ohio 25.96 0.48 24.01 0.77 –1.95 –1.96 2.40 4.36 –1.05Oklahoma 26.74 1.34 27.52 0.70 0.78 –2.13 1.46 3.58 0.84Oregon 22.93 0.95 21.16 0.96 –1.77 1.68 4.96 3.28 0.90Pennsylvania 23.34 0.54 22.88 0.50 –0.46 –1.87 1.79 3.66 –0.68South Dakota 25.51 1.12 24.08 1.24 –1.43 –1.40 0.75 2.14 –1.20Tennessee 28.53 1.13 25.99 0.98 –2.54 –3.08 0.58 3.66 –1.28Texas 23.45 0.60 20.89 0.52 –2.56 1.54 3.75 2.22 –0.61Utah 16.90 0.95 13.73 1.11 –3.17 3.68 7.77 4.09 –0.29Vermont 26.26 1.45 22.34 0.97 –3.92 1.09 6.37 5.28 –1.50Wyoming 24.82 1.02 23.70 1.08 –1.12 –2.51 0.33 2.84 –0.92Overall average 24.41 0.48 22.30 0.52 –2.11 0.33 –0.10 0.39 3.74 0.47 3.85 0.20 –0.03 0.23
344

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62. Cervero, R., and M. Duncan. 2003. 

Walking, bicycling, and urban 

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63. Cunningham, G. O., and Y. L. Michael. 

2004. Concepts guiding the study of 

the impact of the built environment 

on physical activity for older adults: 

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64. Dehghan, M., N. Khtar-Danesh, and 

A. T. Merchant. 2005. Childhood 

obesity, prevalence and prevention. 

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65. Ells, L. J., K. Campbell, J. Lidstone, 

S. Kelly, R. Lang, and C. Summerbell. 

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Best Practice and Research Clinical 

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66. Estabrooks, P. A., R. E. Lee, and 

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physical activity participation: Does 

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Annals of Behavioral Medicine 25 (2): 

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67. Frank, L. D., M. A. Andresen, 

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68. French, S. A., M. Story, and 

R. W. Jeffery. 2001. Environmental 

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69. Hayne, C. L., P. A. Moran, and 

M. M. Ford. 2004. Regulating 

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70. Katz, D. L., M. O’Connell, M. C. Yeh, 

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health strategies for preventing and 

controlling overweight and obesity in 

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72. Lee, R. E., and C. Cubbin. 2002. 

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75. Orleans, C. T., M. K. Kraft, J. F. Marx, 

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77. Pohanka, M., and S. Fitzgerald. 2004. 

Urban sprawl and you: How sprawl 

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78. Popkin, B. M., K. Duffey, and 

P. Gordon-Larsen. 2005. Environmental 

influences on food choice, physical 

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79. Poston, W. S., and J. P. Foreyt. 1999. 

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80. Salmon, J., N. Owen, D. Crawford, 

A. Bauman, and J. F. Sallis. 2003. 

Physical activity and sedentary 

behavior: A population-based study of 

barriers, enjoyment, and preference. 

Health Psychology 22 (2): 178–88.

81. Schilling, J., and L. S. Linton. 2005. 

The public health roots of zoning: 

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genealogy. American Journal of 

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82. Mercer, S. L., L. W. Green, 

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Possible lessons from the tobacco 

experience for obesity control. 

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83. Yee, S. L., P. Williams-Piehota, 

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85. McKinlay, J. B., and L. D. Marceau. 

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86. Arnebeck, R. 1999. Destroying angel: 

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351

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Index

A

“accommodation” campaigns, 234, 239

advertising, tobacco. See tobacco advertising

advocacy

media (See media advocacy)

in public health, 189–190, 340

African Americans

in ASSIST population, 329

smoking prevalence rates, 147–148

age

and life expectancy gains after smoking 

cessation, 318–320

and smoking prevalence rates, 146, 276, 277t,

285t

and timing of quit, 322

Agency for Health Care Policy and Research, 

smoking cessation guidelines, costeffectiveness of, 316, 323

agenda usurping, by tobacco industry, 227, 228t,

243–244

concept map rating, 221t

Alabama, tobacco manufacturing in, 171

Alaska

clean indoor air legislative score, 123

IOI values, 130

real cigarette prices, 120

American Cancer Society (ACS)

and ASSIST, 8

and SmokeLess States, 10

SoTC data collection, 40

work for tobacco control, 152

American Economics Group, economic impact 

study (1996) by, 171–172

American Heart Association

and SmokeLess States, 10

SoTC data collection, 40

work for tobacco control, 152

American Lung Association

SLATI database, 104

and SmokeLess States, 10

SoTC data collection, 40

work for tobacco control, 152

American Medical Association, and SmokeLess 

States, 9

American Nonsmokers’ Rights Foundation 

(ANRF), local ordinance data from, 90, 91, 

97–98, 99, 105

American Public Health Association, on state 

preemption laws, 91

American Stop Smoking Intervention Study for 

Cancer Prevention. See ASSIST (American 

Stop Smoking Intervention Study for Cancer 

Prevention)

Anchorage, Alaska, 238

ANRF. See American Nonsmokers’ Rights 

Foundation (ANRF)

anti-public health index, 256

Arizona, tobacco control efforts in, 153, 155

Arkansas, smoke-free workplace reports in, 126

ASSIST (American Stop Smoking Intervention 

Study for Cancer Prevention)

about, 3n, 4, 26, 269, 328

aims of, 4, 14, 26, 269, 328

COMMIT compared to, 7–8, 12t, 13

components, diffusion to non-ASSIST states, 

10, 13, 22, 334–335

end of, 11

evaluation of (See ASSIST evaluation)

expenditure data on, 310–312, 311t, 312t,

315–316

funding of, 8–9

history of, 8–9

media interventions in, 188, 189, 190, 198, 203

policy areas addressed by, 90, 119–120, 269

population reached by, 308

scientific framework of, 7–8

SmokeLess States compared to, 10

societal change as objective of, 14, 328

states involved in (See ASSIST states)

tobacco industry counterefforts aimed at, 22, 

215–216, 291

industry documents on, 236, 239–240, 242, 

244, 246–250

ASSIST: Shaping the Future of Tobacco 

Prevention and Control (Tobacco Control 

Monograph 16), 9, 11

Note: Page numbers with t indicate tables; page numbers with f indicate figures; and page numbers with 

n indicate footnotes.
352

I n d e x

ASSIST Coalition Assessment, 16–17

ASSIST Coordinating Center, 10

and ASSIST Newspaper Tracking System, 

189–193, 196

ASSIST evaluation, 3–26, 328

of capacity, 14, 331–332, 333

and complex program evaluation, 11, 338–339

conceptual framework, 15–21, 18f, 26, 329

assumptions of, 16, 272

evolution of, 16–17, 23–24, 24f

key constructs, 17–20, 19t, 23–24, 24f, 25t

as legacy, 335

variables in, 19t, 23, 25t

contributions/lessons of, 26, 328–341

ASSIST Newspaper Tracking System, 

201–202

conclusions, 340–341

for media intervention evaluation, 190, 202

for public health intervention evaluation, 

338–339

for state tobacco control intervention 

evaluation, 15–16, 291–292, 335

summary, 339–340

for tobacco control intervention evaluation, 

55, 137, 155–156, 334–336

of cost-effectiveness (See cost-effectiveness of 

ASSIST)

ecological approach to, 4–5, 14, 16, 330

of economic dependence on tobacco (See

economic dependence on tobacco)

Expert Panel, 17, 97, 99

of final outcomes (See final outcomes)

funding levels, 15

historical context, 5–13

indices in, 53–54, 272, 330 (See also specific 

indices)

of initial outcomes (See initial outcomes; Initial 

Outcomes Index (IOI))

key findings of, 330–332

legacy of, 334–336

of legislative changes (See legislative changes, 

measurement of)

of media interventions (See Newspaper 

Tracking System, ASSIST)

methodological approach, 4–5, 13–24

analytic challenges, 21–23, 26

capacity measurement in, 14

data collection timeline, 17, 18f

ecological/systems model in, 4–5, 14, 16, 

330–331, 340

evolution of, 16–17

indices, 53–54, 272, 330

key constructs, 17–20

key principles, 14–16

model diagnostics, 273

one-tailed statistical tests, 272

outcome measures, 5, 14–16, 19t, 20–21, 25t

preliminary analyses, 272–273

research questions, 21, 21t, 271

societal change in, 14

units of selection, 272

of societal change, 14, 338–339

of state-level conditions affecting tobacco 

control (See economic dependence on 

tobacco; state conditions)

of strength of intervention (See Strength of 

Tobacco Control (SoTC) index)

summary and conclusions, 24–26

timeline, 18f

of tobacco industry counterefforts and tactics 

(See tobacco industry counterefforts and tactics)

ASSIST Newspaper Tracking System. See

Newspaper Tracking System, ASSIST

ASSIST states

compared to non-ASSIST states (See ASSIST 

states versus non-ASSIST states)

expenditure data, 310–311, 311t, 315

funding for tobacco control, 153–155, 154t

selection of, 23

tobacco-growing/manufacturing, 150

ASSIST states versus non-ASSIST states

cigarette prices, real, 121t

clean indoor air ratings (legislative scores), 92, 

103, 103t, 124t, 130

demographic factors, baseline, 270t

final outcomes, 269–270, 292

funding for tobacco control, 154t, 155

initial outcomes, 136

IOI scores, 130, 131t, 283, 284f, 342t–343t

after diffusion of ASSIST practices, 335

newspaper coverage of tobacco policy, 199t,

199–200

per capita cigarette consumption, 278f, 279, 

280f, 281t, 289–290, 292, 301t

baseline, 270, 270t

and IOI changes, 287–289, 289t, 290

and SoTC scores, 287, 287t
353

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

smoke-free workplace reports, 127t

smoking prevalence, adult, 270, 275–276, 289, 

292, 301t

and age, 276, 277t

baseline, 23, 270, 270t

sex differences, 276, 277t, 289

by state, 342t–343t

smoking prevalence reduction, 331, 332

projection to U.S. population, 291, 292, 332

SoTC scores, 44, 45t, 283, 290, 342t–343t

and per capita cigarette consumption, 287, 

287t

state conditions baseline, 270t

tobacco as share of GSP, 177t

tobacco industry counterefforts in, 291

attitudes. See public opinion; views and attitudes

B

“balanced scorecard” performance measures, 

42–43

Behavioral Risk Factor Surveillance System 

(BRFSS), 16, 21

benchmark systems

for cost-effectiveness evaluation, 308

for rating state tobacco control laws, 91–92

Bero, L., work on tobacco industry counterefforts 

to ASSIST, 215–216, 236, 255, 291

Beverly Hills, California, 238

Bialous, Stella Aguinaga

on tobacco industry counterefforts and tactics, 

213

Bonate, P. L., 293

Boston, Massachusetts, 239

Boucher, Rick, 248

Boyle, R. G.

on political ideology and tobacco control, 

152–153

on state spending on tobacco control, 153

on tobacco-growing states, 148

BRFSS. See Behavioral Risk Factor Surveillance 

System (BRFSS)

Briant, Tom, 249

Brownson, Ross C.

on policy and legislative changes, 87

Brown & Williamson, tobacco control 

counterefforts by, 238, 246

C

California

analytic treatment of, 273, 276

clean indoor air legislative score, 123

and IMPACT, 9

per capita cigarette consumption, 290

smoking prevalence trends, 51

in youth, 146–147

tobacco control in, 136

cigarette/tobacco tax, 10, 153

funding of, 51, 153

local smoke-free policies, 152

timing of outcomes, 20

Campaign for Tobacco-Free Kids, 104, 136

cancer control research phases, 7f

capacity

definitions and components of, 14, 333

for tobacco control, ASSIST evaluation and 

findings, 14, 331–332, 333

capacity (SoTC component), 20, 35

definitions, 34, 35, 36, 39

scores, 45t, 47f

correlations, 48, 48t

domain-level, 49–51, 50t, 53

variables measuring, 35–36, 56t, 58t–59t

Capilouto, E. I.

on political ideology and tobacco control, 

152–153

on state spending on tobacco control, 153

on tobacco-growing states, 148

cardiovascular disease prevention, 338

case studies

of media interventions, 200, 201

of tobacco industry counterefforts and tactics, 

230–233

CDC. See Centers for Disease Control and 

Prevention (CDC)

Census of Agriculture, 166

Census of Manufacturers, 170

Center for Indoor Air Research, 250

Center for Tobacco Policy Research, 337

Centers for Disease Control and Prevention 

(CDC)

best practices, 14

BRFSS, 16

IMPACT, 9–10, 41, 136–137

NTCP, 11

Office on Smoking and Health, 335
354

I n d e x

in SoTC data collection, 39, 41

STATE database, 104

Syndemics initiative, 7

Chaloupka, Frank J.

on economic impact of tobacco, 163

on political activism and tobacco control, 153

on tobacco control spending, 153

on tobacco-growing/producing states, 148, 166

Chase Econometrics study (1985), 172

Chriqui, Jamie

on policy and legislative changes, 87

on political ideology and tobacco control, 153

cigarette(s)

exports, 168, 169f

imports, 167

sales data, 115

cigarette consumption

per capita (See per capita cigarette 

consumption)

U.S., decline in, 167, 169f

cigarette excise tax, 10

and cigarette prices, 120

and Master Settlement Agreement payments, 171

newspaper coverage of, 197f, 198

proliferation of, 153

in tobacco-growing/producing states, 148, 166

cigarette prices, real, 120–122

as ASSIST policy priority, 119–120

definition and determinants of, 120

and economic impact of tobacco 

manufacturing, 176

and final outcomes, 130, 133t

as initial outcome measure, 118, 120

and per capita cigarette consumption, 120, 122, 

123f, 130, 133t, 137–138, 287, 288t, 290, 292

and smoking prevalence, adult, 120, 122f,

285–286

by state, 120, 121t

cigarette production, 170. See also tobacco 

manufacturing

U.S., decline in, 167, 169f

cigarette smoking. See entries under smoking

Clark, Pamela I.

on Strength of Tobacco Control (SoTC) index, 31

clean indoor air laws

in California, 152

local ordinances, 90, 91, 152

rating system, 90, 92, 97–99

state+local results, 102–103, 103t, 124t

newspaper coverage of, 196, 197f, 198

rating methods, 93–99

limitations of, 96–97

local laws, 97–99

state laws, 93–96

rating results (See clean indoor air ratings)

state laws (See state clean indoor air laws)

in tobacco-growing/producing states, 148, 166

workplace (See entries under smoke-free 

workplace)

clean indoor air ratings (legislative scores), 92, 

100, 102–103, 103t, 122–123, 124t

changes in, 100–102, 101t, 102f

ASSIST versus non-ASSIST states, 92, 103, 

103t, 124t, 130

methods, 93–99

limitations of, 96–97

local laws, 97–99

state laws, 93–96

and per capita cigarette consumption, 123, 

126f, 133t

and smoking prevalence, adult, 123, 125f, 133t,

285–286

and SoTC scores, 44, 48t

by state, 122–123, 124t

state+local results, 102–103, 103t, 124t

cluster analysis, in concept mapping, 219, 220, 

223f, 224f

coalition assessment measure, 16–17

Coalition on Smoking OR Health, and SoTC data 

collection, 40

coalitions

protobacco, tobacco industry documents on, 

245–246

tobacco control (See tobacco control coalitions)

Cochrane Collaboration, 15

Cohen, Bernard, 189

Colorado

per capita cigarette consumption, 297f

tobacco control funding, 338

tobacco industry media plan for, 252–253

Columbus, Christopher, 165

COMMIT (Community Intervention Trial for 

Smoking Cessation), 6

ASSIST compared to, 7–8, 12t, 13

evaluation methodology, 13

community

interventions based on, 6

religious, and smoking behavior, 151–152

and view of tobacco use, 144
355

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

Community Intervention Trial for Smoking 

Cessation. See COMMIT

competing factors, 22–23, 291–292

complexity, as evaluation challenge, 11, 338–339

concept mapping, 330

methods of, 217

reliability of, 220

in SoTC index validation, 85, 85f, 330

of tobacco industry counterefforts and tactics 

(See tobacco industry counterefforts and 

tactics, concept mapping)

conceptual framework, of ASSIST evaluation, 

15–21, 18f, 26, 329, 335

assumptions of, 16, 272

evolution of, 16–17, 23–24, 24f

key constructs, 17–20, 19t, 23–24, 24f, 25t

variables in, 19t, 23, 25t

Connecticut

economic importance of tobacco in, 168

smoke-free workplace law in, 153

tobacco industry counterefforts in, 241

core sector (tobacco industry), 171, 172, 174

cost(s), total, calculation of, 308

cost-effectiveness analysis, 308–310

of ASSIST (See cost-effectiveness of ASSIST, 

analysis of)

example of, 309

sensitivity analysis in, 310

cost-effectiveness of ASSIST

analysis of, 307–323

data sources, 310–312, 323

discounting in, 309–310

flaws in, 321–322

outcome measures, 308

sensitivity analysis in, 310, 313t, 314t, 315

compared to other programs, 316, 322–323

estimates of, 321

key findings on, 332

and life-years gained after smoking cessation, 

317–321

and recidivism rates, 313t, 315, 320–322

and smoking prevalence rate changes, 314t,

315, 316–317

and tobacco control establishment, 321

cost per life-year gained, 307, 321

cost per person, of tobacco use prevention, 308, 

316, 323

cost per quit, 307

ASSIST, 315–316, 321, 323

compared to other programs, 316, 322–323

and cost-effectiveness, 314t, 315

long-term impact of, 316, 321

and timing issues, 322

covariates

in ASSIST evaluation, 145–146

for smoking prevalence, 23

CPS. See Current Population Survey (CPS)

Cromwell, J., on cost per life-year gained, 320, 323

Cullen, Joseph, cancer control phases of, 7f

culture, and smoking behavior, 148, 151, 152

Current Population Survey (CPS), 114, 149

Tobacco Use Supplement to (TUS) (See

Tobacco Use Supplement to the Current 

Population Survey (TUS-CPS))

D

Dagli, E., 216

D’Agostino, R. B., 318

databases, for tobacco control evaluation, need 

for, 337

data collection

for ASSIST evaluation, timeline of, 17, 18f

for ASSIST Newspaper Tracking System, 191

demographic data, 149

for future tobacco control research efforts and 

evaluations, 336–337

standardized, for outcomes, 118

data sources

for ASSIST evaluation, 21–22

on cost-effectiveness of ASSIST, 310–312, 323

on local tobacco control laws, 90, 91, 97–98, 

99, 105

on per capita cigarette consumption, 115

on smoking prevalence, 114–115, 149–150

on state tobacco control laws, 90, 91, 104–105

on tobacco manufacturing, 174–175

Davis, William

on final outcomes, 265

Daynard, R. A., 216

Delaware

adult smoking prevalence changes, 276

smoke-free workplace law, 153

tobacco control funding, 338

demographic factors, 22, 146t, 146–148, 155. See 

also age; sex differences; state conditions

baseline, in ASSIST states versus non-ASSIST 

states, 270t

data sources, 149–150
356

I n d e x

diffusion, 334

of ASSIST practices to non-ASSIST states, 10, 

13, 22, 334–335

discounting

in ASSIST cost evaluation, 309–310, 314–316

and future life-years gained, 318–319

of ASSIST expenditure data, 312, 314–315

in cost-effectiveness analysis, 309–310

discount rate, calculation of, 309

disease risk, smoking-related, after smoking 

cessation, 317–318

disempowerment, by protobacco groups, 227

District of Columbia

analytic treatment of, 272–273

and smoking prevalence analysis, 276, 285–

286, 291

document review, of tobacco industry 

counterefforts, 236–254, 255–257

lessons from, 254

methods of, 236–237

results of, 237–254

“dose measure” performance measures, 42–43

E

ecological model, 4–5, 14, 16, 330, 340

economic dependence on tobacco, 22, 148–149, 

155, 165–182

as ASSIST evaluation measure, 146, 150, 166, 

174–175, 182

data sources, 174–175

tobacco growing and manufacturing 

(combined), 175

conclusions, 182

data results, 150, 175–176, 181

tobacco as share of GDP, 176, 178f

tobacco as share of GSP, 175–181, 176f,

177t, 178f–181f

impact on tobacco control policies, 148, 155, 

165–166, 171, 182

cigarette excise taxes, 148

clean indoor air laws (smoking restrictions), 

148

studies of, 148–149, 171–174

non–tobacco-industry-sponsored, 148–149, 

172–174

tobacco-industry-sponsored, 148–149, 

171–174

summary, 181–182

tobacco growing and, 166–170, 173–174

tobacco industry claims about, 148–149, 

171–174

tobacco manufacturing and, 170–171

and views on tobacco use, 148

economic impact studies, 148–149, 171–174

non–tobacco-industry-sponsored, 148–149, 

172–174

tobacco-industry-sponsored, 148–149, 171–174

economic intimidation, by tobacco industry, 227, 

228t, 240–242

concept map rating of, 221t

economic sectors, impact of tobacco in, 171–172

editorials, on tobacco control policy

case study, 200, 201

trend analysis, 198

educational attainment, and smoking prevalence, 

146, 148

education-based interventions, 5

initial outcomes of, 117–118

efforts (SoTC component), 20, 35

definitions, 34, 35, 39

scores, 45t, 47f, 52t

correlations, 44, 48t

domain-level, 51–53, 52t

variables measuring, 35–36, 56t, 60t

elite press, 190

employment

impact of tobacco control on, 148–149, 172–

173, 182

in tobacco growing, 175

in tobacco manufacturing, 150, 170, 171

empowerment, of protobacco groups, 227

Environmental Protection Agency, report on 

health consequences of involuntary smoking 

(1993), 90

environmental tobacco smoke (ETS), health risks 

of, 90, 117–118, 250–251

evaluation methodology

in ASSIST evaluation (See ASSIST evaluation, 

methodological approach)

systems methods in, 330–331, 339

Evans, W. Douglas

on ASSIST Newspaper Tracking System, 185

evidence-based approaches, 13

excise tax. See cigarette excise tax

expenditure data, on ASSIST, 310–312, 311t, 312t

and cost-effectiveness analysis, 315–316

discounted, 312, 314–315
357

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

expenditure-induced sector, 172

expert panel

in ASSIST evaluation, 17, 97, 99

concept map interpretation by, 219, 220, 225f

F

family, and smoking behavior, 144, 151, 152

FDA. See Food and Drug Administration (FDA)

feasibility

of ASSIST evaluation analytic methods, 

272–273

of SoTC index variables, 35, 36, 56

federal law, on youth access to tobacco, 90–91

Federal Trade Commission, cigarette sales data 

reported to, 115

final outcomes, 14, 20, 116, 269–292. See also

per capita cigarette consumption; smoking 

prevalence

analytic considerations, 272–273

in ASSIST versus non-ASSIST states, 269–

270, 292

attribution link with initial and intermediate 

outcomes, 113, 116–118, 118–119

conclusions, 292

correlations with initial outcomes, 130, 133t

IOI, 130–133, 136, 290, 292

correlations with SoTC, 290–291, 292

funding and, 49–51

results, 275–276, 279–291, 292

summary, 289–292

Finland, 6

Florida

economic importance of tobacco in, 168, 171

tobacco control funding in, 51

Food and Drug Administration (FDA), tobacco 

regulation attempts by, 91, 241–242, 291

Fox, Brion J.

on tobacco industry counterefforts and tactics, 

213

Freedom of Information Act (FOIA) requests, by 

tobacco industry, 227–229, 249–250

front groups, tobacco industry funding of, 245–247

Frosh, Marcy

on policy and legislative changes, 87

Fulton, G. A., on economic impact of tobacco, 

148–149, 172–173

funding

for ASSIST, 8–9

for ASSIST evaluation, 15

for state tobacco control programs, 49–51, 50t

in ASSIST versus non-ASSIST states, 154t,

155

and capacity, 14, 333

changes in, 51f, 153–155, 154t

future of, 337–338

G

Gale, H. F., Jr., 170

GDP. See gross domestic product (GDP)

genetics, 151

geography, 146, 148

Georgia

adult smoking prevalence changes, 276

cigarette excise tax, 148, 166

economic importance of tobacco in, 168, 171

tobacco as share of GSP, 176, 180f, 181

Gibson, James T.

on final outcomes, 265

Gilpin, Elizabeth A.

on final outcomes, 265

on Initial Outcomes Index (IOI), 111

on smoking recidivism, 320

on state conditions affecting tobacco control, 141

Glantz, Stanton A., on tobacco industry policyrelated activity, 234–235

Global Tobacco Research Network, 7

grassroots campaigns, led by tobacco industry, 

245–247

Graubard, Barry I.

on cost-effectiveness of ASSIST, 305

on final outcomes, 265

Greenwald, Peter

cancer control phases, 7f

on lobbying through ASSIST, 248

Grimes, D. R., 173

gross domestic product (GDP), tobacco as share 

of, 176, 178f

gross state domestic product (GSP), tobacco as 

share of, 175–176, 176f, 177t
358

I n d e x

H

harassment, by tobacco industry, 227–229, 228t,

247–250

concept map rating of, 222t

Hartman, Anne M.

on cost-effectiveness of ASSIST, 305

on final outcomes, 265

on Initial Outcomes Index (IOI), 111

on state conditions affecting tobacco control, 141

Hatch, Orrin, 249

Hawaii, real cigarette prices in, 120

health promotion, and media, 189–190

health risks

of ETS, 90, 117–118, 250–251

smoking-related, after smoking cessation, 

317–318

Healthy People 2010, 336

Hispanics

in ASSIST population, 329

and per capita cigarette consumption rates, 150

smoking prevalence rates, 147

homes, smoke-free, 117–118

hospitality industry, 243

I

Ierulli, Kathryn

on economic impact of tobacco, 163

Illinois, tobacco manufacturing in, 171

IMPACT (Initiatives to Mobilize for the 

Prevention and Control of Tobacco), 9–10, 

136–137, 137

in SoTC data collection, 41

ImpacTeen, 105, 337

income, impact of tobacco control on, 173–174

index (indices)

anti-public health, 256

in ASSIST evaluation, rationale for, 53–54, 

272, 330

of clean indoor air law strength (See clean 

indoor air ratings (legislative scores))

of initial outcomes (See Initial Outcomes Index 

(IOI))

of strength of tobacco control policies (See

Strength of Tobacco Control (SoTC) index)

of tobacco industry tactics, 215–216, 223, 233, 

234–235, 254–255

challenges of, 254–257

Indiana

adult smoking prevalence changes in, 276

economic importance of tobacco in, 168

individual(s)

conditions affecting tobacco control 

interventions, 143

interventions based on, 5–6

political participation by, 166

smoking behavior of, factors affecting, 144f,

144–148, 151–152

adjusting smoking prevalence data for, 

293–295

individual-level covariates, 143, 274, 275t

adjustment for, 293–295

inflation, adjustment for, 312, 315

initial outcomes, 14, 20, 116–119, 135. See also

intermediate outcomes

for ASSIST evaluation, 119–126, 135–136 (See 

also cigarette prices, real; clean indoor air 

ratings (legislative scores); Initial Outcomes 

Index (IOI); smoke-free workplace reports 

(measure))

in ASSIST versus non-ASSIST states, 136

correlations among, 129, 129t

correlations with final outcomes, 130, 133t

lessons for tobacco control program 

evaluation, 137

limitations of, 136

attribution link with final outcomes, 113, 

118–119

attribution link with intervention, 116–118

definition of, 135

examples of, 117

need for, 113–114, 135

selection criteria, 118–119, 135–136

summary, 135–137

youth access law enforcement as, 117, 137

Initial Outcomes Index (IOI), 14, 17, 113, 114, 

129–138, 131t, 145

in ASSIST versus non-ASSIST states, 130, 

131t, 283, 284f

diffusion of ASSIST practices and, 335

and per capita cigarette consumption, 287–

289, 289t, 290

changes in

in ASSIST versus non-ASSIST states, 283, 

284f, 287–289, 289t, 290, 335
359

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

and per capita cigarette consumption, 130, 

133, 133t, 134f, 287–289, 289t, 290

and smoking prevalence, adult, 130, 133, 

133t, 134f

component measures (See cigarette prices, real; 

clean indoor air ratings (legislative scores); 

smoke-free workplace reports (measure))

construction of, 129–130

correlations among initial outcomes, 129, 

129t

data collection timing, 17, 18f

correlations with final outcomes, 130–133, 136, 

279–289, 291, 292

correlations with per capita cigarette 

consumption, 130, 133t, 287–289, 288t

analytical method, 282

at baseline, 130, 132f

and changes in IOI, 130, 133, 133t, 134f,

287–289, 289t, 290

trends in states grouped by IOI, 133, 135f

correlations with smoking prevalence, adult, 

130, 133t, 284–286, 285t, 290

analytical method, 281

at baseline, 130, 132f

and changes in IOI, 130, 133, 133t, 134f

correlation with SoTC, 281, 282t, 283, 290

for District of Columbia, 273

newspaper coverage measurement and, 199

rationale for, 114, 129, 136, 330

by state, 130, 131t, 342t–343t

initiative campaigns, 234

Initiative for the Study and Implementation of 

Systems (ISIS), 7, 330

Initiatives to Mobilize for the Prevention and 

Control of Tobacco. See IMPACT (Initiatives 

to Mobilize for the Prevention and Control of 

Tobacco)

intermediate outcomes, 14, 20, 116–118. See also

initial outcomes

IOI. See Initial Outcomes Index (IOI)

Iowa, clean indoor air legislative score for, 123

J

Jefferson, Thomas, 4

jurisdiction, and local ordinances, 99

K

Kentucky

cigarette excise tax, 148, 166

clean indoor air legislative score, 123

economic importance of tobacco in, 168

manufacturing, 170–171

tobacco as share of GSP, 176, 179f, 181

IOI values, 130

real cigarette prices, 120

smoke-free workplace reports, 126

L

laws, tobacco control. See tobacco control laws

lawsuits

Master Settlement Agreement (1998), 11, 145, 

171, 236

against tobacco industry, 11

by tobacco industry, 234, 242

legal intimidation, by tobacco industry, 227–229, 

228t, 240–242

concept map rating of, 221t

legislative changes, measurement of, 89–106

conclusions, 105–106

importance of, 91–92, 104

rating methods, 93–99

limitations of, 96–97

local laws, 97–99

state laws, 93–96

rating results, 100–103, 105, 106

clean indoor air laws, 100–103, 101t, 102f,

103t

state+local, 100–103

youth access laws, 100, 100t, 101f

summary, 104–105

tool for, 89, 91–99, 105, 106

legislative scores. See clean indoor air ratings 

(legislative scores)

legislative strategies. See also lobbying

of tobacco industry, 226t, 226–227, 234, 

238–240

concept map rating of, 221t

Liang, Lan

on economic impact of tobacco, 163

life-years gained after smoking cessation

and age of quit, 319–320

attributable to ASSIST, 313t, 315, 320–321, 323

and cost-effectiveness of ASSIST, 317–321
360

I n d e x

cost per, 307, 321

discounting for, 318–319

and sex, 319–320

limited number of observations, 23, 330

Linton, R., 217

literature review, of tobacco industry 

counterefforts, 224–233, 255

case studies, 230–233

limitations of, 233

methods for, 225–226

results of, 226–230

lobbying

through ASSIST, prohibition of, 10, 248–249

by tobacco industry, 171, 226t, 226–227, 234, 

238–240

concept map rating of, 221t

local tobacco control ordinances

changes in, measurement of, 90, 91–92

clean indoor air, 90, 91, 152

rating system, 90, 92, 97–99

state+local results, 102–103, 103t, 124t

data sources, 90, 91, 97–98, 99, 105

state preemption of (See state preemption laws)

tobacco industry counterefforts to, 152, 232t, 238

on youth access to tobacco, 90, 91

logic model, in SoTC, 331

Lorillard, 240

lung capacity, and smoking cessation, 318

M

Maine

adult smoking prevalence changes in, 276

tobacco control efforts in, 153, 338

tobacco industry counterefforts in, 247

Manley, M. W., 290, 298

Marceau, Lisa D., 328, 331, 339

Marcus, Stephen E.

on Strength of Tobacco Control (SoTC) index, 31

Martin, D. C., 294

Maryland

clean indoor air legislative score, 93, 97, 123

economic importance of tobacco in, 168

efforts scores (SoTC), 153

tobacco industry counterefforts in, 241

Massachusetts

economic importance of tobacco in, 168

tobacco control efforts in, 10, 51, 153

tobacco industry counterefforts in, 239, 242

Master Settlement Agreement (1998), 11, 145, 

171, 236

MAV. See media advocacy variable (MAV)

McConnell, M., 249

McKinlay, John B., 328, 331, 339

media advocacy, 188, 189–190

scores (SoTC variable), 52t

uses of, 53

media advocacy variable (MAV), 195

media coverage. See also newspaper coverage; 

Newspaper Tracking System, ASSIST

influence of, 189, 190

as initial outcome, 117

media interventions

ASSIST, 188, 189, 190, 198, 203

evaluation of (See Newspaper Tracking 

System, ASSIST)

awareness from, as initial outcome, 117, 119

evaluation of, 189–190, 200

media manipulation, by tobacco industry, 229–

230, 231t, 251–253

concept map rating of, 222t

MEDLINE database, 225

methodology(ies)

of ASSIST evaluation (See ASSIST evaluation, 

methodological approach)

evaluation, systems methods in, 330–331, 339

of future tobacco control efforts, 336–337

Michigan

economic impact of tobacco in, 172–173

efforts scores (SoTC), 153

Minnesota

real cigarette prices, 120

tobacco control funding, 51

tobacco industry counterefforts in, 244, 246, 

250, 251–252

tobacco settlement, 236

Minnesota Coalition of Responsible Retailers, 244

Minnesota Grocers Association, 250

Minnesota Wholesale Marketers Association, 

Inc., 250

“The Minutemen Project,” 246

Mississippi

clean indoor air legislative score, 123

lawsuit against tobacco industry, 11

tobacco control funding, 51, 338

Missouri, economic importance of tobacco in, 168

monitoring, of tobacco control laws

data sources, 104–105
361

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

importance of, 91–92, 104

measures, for future efforts, 336–337

Montana, smoke-free workplace law in, 153

Mormons, 151–152

multidimensional scaling (MDS) analysis, in 

concept mapping, 218–219

Murray, David M.

on final outcomes, 265

N

Nagaraja, Jyothi

on Strength of Tobacco Control (SoTC) index, 31

National Cancer Institute (NCI)

ASSIST investment and implementation, 8, 9, 

336

ASSIST Newspaper Tracking System, 189, 196

cancer control research phases, 7f

COMMIT, 6, 7–8, 12t

and evidence-based approaches, 13

ISIS, 7, 330

SCLD, 90, 91, 104

and SoTC questionnaire, 39

and state legislative indices, 91–92, 93–99, 335

tobacco control monographs (See tobacco 

control monographs (NCI))

TUS-CPS, 15, 16, 22, 114, 329, 335

National Research Council, report on 

environmental tobacco smoke, 90

National Tobacco Control Program (NTCP), 11

NCI. See National Cancer Institute (NCI)

Nevada, adult smoking prevalence changes in, 276

New Jersey, tobacco control efforts in, 153, 238

“new public health,” 16, 338

news media. See under media

newspaper coverage. See also media coverage

influence of, 190

of tobacco control policy, 195–204

by article type, 196–199, 197f

in ASSIST versus non-ASSIST states, 199t,

199–200

editorial points of view in, 198, 200, 201

editorial policies and, 202

North Carolina case study, 200, 201

by topic, 196–199, 197f

tracking/evaluation system (See Newspaper 

Tracking System, ASSIST)

by volume (rate variable), 195, 199t, 199–200

Newspaper Tracking System, ASSIST, 187–204

article coding, 192–193

coded variables/topics, 193, 194t, 205–206

article collection, 191

key words, 191, 192t

search strategies, 191, 207–210

article relevance, determining, 191–192

article tracking, 190–195

case study, 200, 201

conclusions, 204

contributions to program evaluation, 190, 202

frequency studies, 193–199, 197t, 197f

future directions, 203–204

indices developed from, 193–195

limitations of, 202

steps in developing, 190

summary, 201–204

as systems method, 331

trend analyses, 195–200

by article type, 196–199, 197f

ASSIST versus non-ASSIST states, 199t,

199–200

by editorial point of view, 198

by policy topic, 196–199, 197f

New York

adult smoking prevalence changes, 276

smoke-free workplace law, 153

New York Times, 191

Nicolas, P., 173

non-ASSIST states

ASSIST services to, 10

compared to ASSIST states (See ASSIST states 

versus non-ASSIST states)

diffusion of ASSIST-like practices to, 10, 13, 

22, 334–335

tobacco control efforts in, 22, 136–137

North Carolina

cigarette excise tax, 148, 166

economic importance of tobacco in, 168, 170

tobacco as share of GSP, 176, 178f, 181

newspaper editorials on youth access issues in, 

200, 201

tobacco industry lobbying against ASSIST in, 

239–240

North Karelia demonstration project, 6

NTCP. See National Tobacco Control Program 

(NTCP)
362

I n d e x

O

obesity prevention, 338

observations, limited number of, 23, 330

Occupational Safety and Health Administration 

(OSHA) regulations, tobacco industry 

opposition to, 241–242

Ohio, economic importance of tobacco in, 168

Ohsfeldt, R. L.

on political ideology and tobacco control, 

152–153

on state spending and tobacco control, 153

on tobacco-growing/producing states, 148, 166

Oklahoma

adult smoking prevalence changes, 276

clean indoor air legislative score, 123

per capita cigarette consumption, 297f

one-tailed tests, 272

Oregon

smoke-free workplace reports, 126

tobacco tax initiative, 153

outcome measures, for ASSIST evaluation, 5, 

14–16, 19t, 20–21, 25t

final (See final outcomes)

initial (See initial outcomes; Initial Outcomes 

Index (IOI))

multiple, rationale for, 14–15, 20

outcomes

final (See final outcomes)

initial (See initial outcomes)

interim, 14

intermediate, 20, 116

link with intervention, 116–118

multiple, relationships among, 14, 20–21

timing of, 16, 20, 119

P

parsimony, of SoTC index variables, 35, 56

PDV. See present discounted value (PDV)

Peck, Richard M.

on cost-effectiveness of ASSIST, 305

Pennsylvania, economic importance of tobacco in, 

168, 171

per capita cigarette consumption, 114, 270, 331, 

332

analytic methods, 272–273, 276–279, 292, 

297–300

mixed effects time-dependent models, 

298–300

slopes approach, 297–298

state-to-state variability, 298

ASSIST versus non-ASSIST states, 278f, 279, 

280f, 281t, 289–290, 292, 301t

and IOI changes, 287–289, 289t, 290

and SoTC score, 287, 287t

baseline, 270t

changes in, 5, 301t

and adult smoking prevalence changes, 

115–116, 289–290

and initial outcomes, 130, 133t

and IOI scores, 130, 133, 133t, 134f, 290

as outcome measure, 14–15, 20–21

and real cigarette prices, 130, 133t, 137–138

timing of, 16, 20

correlations with initial outcomes, 130, 133t

clean indoor air legislative scores, 123, 126f

IOI, 130, 132f, 133, 133t, 135f, 287–289, 

288t, 290, 292

real cigarette prices, 120, 122, 123f, 287, 

288t, 290, 292

smoke-free workplace reports, 126, 128f

correlations with state conditions, 150

demographic factors, 150, 277t

correlation with SoTC, 286t, 286–287, 288t,

290, 292

analytic method, 282–283

in ASSIST versus non-ASSIST states, 287, 

287t

data collection, 17, 18f

data sources, 115

key findings on, 331–332

by state, 301t

state variations in, 290, 298

trends in, by baseline IOI group, 133, 135f

per capita expenditure, for ASSIST project, 

311–312, 312t

discounted value for, 315

Philip Morris USA, 171

accommodation/preemption laws and, 239

documents on tobacco control counterefforts, 

237, 244, 247, 249, 251

Colorado media plan, 252–253

youth access policies, 254

physicians, smoking cessation advice from, costeffectiveness of, 316, 322–323

policy

media coverage influence on, 90, 189

tobacco control (See tobacco control policies)
363

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

policy-level interventions, 8–11, 90

media interventions for promotion of, 188

monitoring of, 91–92

political contributions, by tobacco industry, 226, 

227, 234, 239

political ideology, and tobacco control, 152–153, 

155

political participation, and tobacco control, 166

politics, and public health interventions, 328

population-level interventions, 6–7, 11–12

population surveys, 118

poverty, and per capital cigarette consumption, 150

preemption laws. See state preemption laws

preemption reduction, in legislative scores, 95, 123

clean air laws, 99, 101t, 101–102, 102f, 123

youth access laws, 100, 100t, 101f

pregnant women, smoking cessation advice for, 

cost-effectiveness of, 322–323

present discounted value (PDV), 308–310

and ASSIST cost-effectiveness analysis, 

314–316

calculation of, 309

prohealth point of view, in newspaper editorials, 

198, 201

protobacco point of view, in newspaper editorials, 

198, 201

public awareness, as initial outcome, 117–118, 119

public education interventions, 5

initial outcomes of, 117–118

public health interventions

advocacy in, 189–190, 340

complex, evaluation and, 11, 338–339

media and, 189–190

“new,” 16, 338

politics and, 328

randomized clinical trials and, 15

undermining of, by tobacco industry, 227, 

247–248, 250–251

public opinion. See also views and attitudes

media coverage and, 189

tobacco industry tactics shaping, 229–230

on tobacco use and policies

ASSIST aims for, 14, 328, 339

factors affecting, 144–145, 148

historic changes in, 8, 11, 144

in tobacco-growing/producing states, 148

public relations activities, of tobacco industry, 

229–230, 231t, 253–254

concept map rating of, 222t

Q

quality-adjusted life-years (QALY), 317

questionnaire (agency worksheet), for SoTC, 39, 

61f–64f

administration of, 41–42

quit. See also smoking cessation

cost per (See cost per quit)

distribution age, 322

life-years gained after (See life-years gained 

after smoking cessation)

R

race/ethnicity. See also African Americans; 

Hispanics

in ASSIST population, 329

and smoking prevalence, 146, 147–148

randomized clinical trials, 15

rate index (ASSIST Newspaper Tracking System), 

195, 199

raw frequency index (ASSIST Newspaper 

Tracking System), 193–195

recidivism rates (smoking), and cost-effectiveness 

analysis, 313t, 315, 320–322

referendum campaigns, 234

relative frequency index (ASSIST Newspaper 

Tracking System), 193–195

of article type by year, 196, 197f

of policy topic by year, 196–199, 197f

religious communities, 151–152

REMI model, 172

research questions, 21, 21t, 271

resources (SoTC component), 20, 35

definitions, 34, 35, 36

funding, 49–51, 50t

in ASSIST versus non-ASSIST states, 154t,

155

and capacity, 14, 333

changes in, 49, 51f, 153–155, 154t

scores, 45t, 46f

correlations, 44, 48t

domain-level, 49–51, 50t, 53

variables measuring, 35–36, 56t, 57t

retailers’ associations, 244

Rhode Island

efforts scores (SoTC), 153

smoke-free workplace law, 153

tobacco industry counterefforts in, 247
364

I n d e x

Riis, Jacob, 340

risk assessment, after smoking cessation, 317–318

R.J. Reynolds, actions against ASSIST, 240, 

248–249

Robert Wood Johnson Foundation

ImpacTeen project, 105

SmokeLess States, 9, 10, 41, 136, 137, 202

and SoTC survey, 39, 41, 335

tobacco control programs funded by, 136–137

Rockville, Maryland, 98–99

S

Saffer, H.

political activism and tobacco control, 153

tobacco-growing/producing states, 148, 166

sales tax, 120

Saloojee, Y., 216

sample size, trends in, 11–12

Satcher, David, on data monitoring and analysis, 

91, 104

Schmitt, Carol L.

on ASSIST evaluation contributions, 325

on ASSIST evaluation overview, 1

on Strength of Tobacco Control (SoTC) index, 31

on tobacco industry counterefforts and tactics, 

213

science, undermining by tobacco industry, 229, 

230t, 250–251

concept map rating of, 222t

scientific support, of SoTC index variables, 35, 

36, 56

SCLD. See State Cancer Legislative Database 

(SCLD)

SCLD Updates Index, 104

search strategies

for ASSIST Newspaper Tracking System, 191, 

192t, 207–210

for tobacco industry counterefforts evaluation, 

225–226

secondhand smoke. See environmental tobacco 

smoke (ETS)

secular trends, 22, 291–292

sensitivity analysis, in cost-effectiveness analysis, 

310, 313t, 314t, 315

Seventh-Day Adventists, 151–152

sex differences. See also women

in smoking cessation, 147

life expectancy gains after, 318–320

in smoking prevalence, 146, 147

and ASSIST cost-effectiveness, 315

ASSIST versus non-ASSIST states, 276, 

277t, 289

and IOI score, 285t

similarity matrix, in concept mapping, 218

Slater, Sandy J.

on state conditions affecting tobacco control, 141

on tobacco industry counterefforts, 291

SLATI. See State Legislated Actions on Tobacco 

Issues (SLATI)

smoke-free homes, 117–118

smoke-free workplace reports (measure)

changes in, 130, 133t

correlations with final outcomes, 130, 133t

correlation with adult smoking prevalence, 126, 

128f, 285–286

correlation with per capita cigarette 

consumption, 126, 128f

findings, 332

as initial outcome, 123–125

by state, 125–126, 127t

smoke-free workplaces

causes of, 116–117

state government, rating of, 94, 96

state laws on, factors promoting, 152, 153

SmokeLess States National Tobacco Policy 

Initiative, 9, 136, 137

and ASSIST, 10, 335

evaluation, 202, 335

in SoTC data collection, 41

smokers’ rights campaigns, 234, 241

smoking behavior

factors affecting, 144f, 144–148, 151–152

smoking restrictions and, 123, 144

of youth, 144–148

smoking cessation

and cost-effectiveness of ASSIST, 317

factors affecting, 144, 145, 147, 151

interventions, 5–6, 117

life expectancy gains after, 318–320 (See also

life-years gained after smoking cessation)

recidivism rates, and cost-effectiveness of 

ASSIST, 313t, 315, 320–322

smoking-related disease risk after, 317–318

smoking initiation

among African Americans, 147–148

factors affecting, 144, 145, 151

rate, and cost-effectiveness of ASSIST, 317
365

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

smoking noninitiation, and ASSIST costeffectiveness, 315, 316

smoking prevalence, 114

adjusted two-stage results, 276, 277t

by age, 276, 277t

analytic methods, 272–273, 274, 292, 293–296

basic calculation, 293, 316–317

covariates, 23, 274, 275t, 293–295

model diagnostics, 295

for SoTC and IOI relationships, 281

ASSIST versus non-ASSIST states, 270, 

275–276, 289, 292, 301t, 342t–343t

baseline, 23, 270, 270t

final results, 270, 289

and sex, 276, 277t, 289, 292

baseline data, 23, 270, 270t, 275, 293

changes in, 5, 275–276, 301t (See also smoking 

prevalence reduction)

and ASSIST cost-effectiveness, 314t, 315

and initial outcomes, 130, 133t

and IOI scores, 130, 133, 133t, 134f, 284–

286, 285t

as outcome measure, 14–15, 20–21

and per capita cigarette consumption 

changes, 115–116, 289–290

correlations with initial outcomes, 130, 133t

cigarette prices, 120, 122f, 285–286

clean indoor air legislative scores, 123, 125f,

285–286

IOI scores, 130, 132f, 133t, 284–286, 285t,

290, 292

smoke-free workplace reports, 126, 128f,

285–286

correlation with SoTC, 44, 48t, 284, 285t,

290–291, 292

data sources, 114–115, 274, 310

demographic factors, 146–148

adjusting for, 293–295

key findings on, 331, 332

reduction in (See smoking prevalence 

reduction)

religious factors in, 151–152

results, 270, 275–276, 292

sex differences, 276, 277t, 315

by state, 301t, 342t–343t

state-level conditions associated with, 146–148, 

150, 151–152

in 2005, 336

unadjusted descriptive results, 275–276

views on tobacco use and, 144

smoking prevalence reduction. See also smoking 

prevalence, changes in

in ASSIST cost-effectiveness analysis, 314t,

315, 316–317

in ASSIST states, projected to U.S. population, 

291, 292, 332

calculation of, 317

in California, 10, 146–147

factors affecting, 20, 144–145

historical, 5

in Massachusetts, 10

national goal for, 336

as outcome measure, 14–15, 20–21

overall U.S., 11, 12, 270, 275

timing of, 16, 20

in women, 276, 277t, 289

in youth, 146–147

smoking-related disease risk, after smoking 

cessation, 317–318

smoking restrictions

in homes, 117–118

and smoking behavior, 123, 144

in workplaces (See smoke-free workplaces)

Snow, John, 340

socioeconomic status

and per capita cigarette consumption, 150

and smoking prevalence, 146, 147, 148, 155

societal changes

evaluation of, 14, 338–339

as intervention objective, 14, 116, 338

in tobacco use

as ASSIST objective, 14, 328, 339

factors affecting, 144–145

in 1990s, 8, 11

SoTC index. See Strength of Tobacco Control 

(SoTC) index

South Carolina

cigarette excise tax, 148, 166

economic importance of tobacco in, 168

tobacco as share of GSP, 176, 180f

Southeast, economic impact of tobacco use 

decline in, 173

Spearman Management, Inc., 239

spillover effect, 153

stakeholders, proliferation of, 12

Stanford Three Community Study, 6

STATE. See State Tobacco Activities Tracking 

and Evaluation System (STATE)

state(s). See also individual states

ASSIST (See ASSIST states)
366

I n d e x

compared to non-ASSIST (See ASSIST 

states versus non-ASSIST states)

bias in selecting, 23

cigarette prices, real, 120, 121t

clean indoor air legislative scores, 122–123, 124t

conditions in, affecting tobacco control (See

state conditions)

economic dependence on tobacco (See

economic dependence on tobacco)

IOI scores, 130, 131t, 275–276, 342t–343t

lawsuits against tobacco industry, 11

non-ASSIST (See non-ASSIST states)

per capita cigarette consumption, 301t

smoke-free workplace reports, 125–126, 127t

smoking prevalence, adult, 275–276, 301t,

342t–343t

changes in, 301t

SoTC scores, 44, 45t, 46f, 342t–343t

tobacco as share of GSP, 175–176, 177t

tobacco control coalitions, 16–17, 40

tobacco control funding, 49–51, 50t, 51f,

153–155, 154t

tobacco control laws (See state tobacco control 

laws)

tobacco control policies (See state tobacco 

control policies)

tobacco-growing/manufacturing (See tobaccogrowing/manufacturing states)

tobacco industry behavior in, studies of, 231, 232t

as unit of analysis, 272

variations among, as analytical consideration, 

290, 298

State Cancer Legislative Database (SCLD), 90, 

91, 104

state clean indoor air laws

as ASSIST policy priority, 119

rating results, 100–103, 101t, 102f, 106

ASSIST versus non-ASSIST states, 92, 103, 

103t, 124t, 130

changes in, with and without preemption, 91, 

100–102, 101t, 102f

state+local, 102–103, 103t

rating system, 89, 90, 92, 93–97, 105–106

preemption reduction, 95, 123

state+local, 99

target criteria, 93–95, 95t, 105–106

smoke-free workplace, 94, 96, 152, 153

state conditions (affecting tobacco control), 22, 

143–156, 330. See also economic dependence 

on tobacco

as analytic covariates, 274, 275t

ASSIST evaluation measures, 145–149, 146t

data sources and collection, 146t, 149–150

conclusions, 156

demographic, 146t, 146–148, 149–150, 155

family, 151, 152

local tobacco control initiatives, 152

political ideology, 152–153, 155

religious communities, 151–153

summary, 155–156

wealth/funding for tobacco control, 49–51, 50t,

51f, 153–155, 154t

state government worksites, smoke-free, 94, 96

state health departments

and ASSIST, 8, 10

as SoTC respondents, 40, 41

State Legislated Actions on Tobacco Issues 

(SLATI), 104

state+local clean indoor air rating, 92

calculation of, 99

results (See clean indoor air ratings (legislative 

scores))

state preemption laws

and clean indoor air laws, 91, 100–102, 101t, 102f

definition of, 91

rating (legislative score) reduction based on, 

95, 123

clean air laws, 99, 101t, 101–102, 102f, 123

youth access laws, 100, 100t, 101f

as tobacco industry tactic, 152, 239

trend toward, 91

and youth access laws, 91, 100, 100t, 101f

State Tobacco Activities Tracking and Evaluation 

System (STATE), 104

state tobacco control laws

changes in, measurement of (See legislative 

changes, measurement of)

clean indoor air (See state clean indoor air laws)

data sources, 90, 91, 104–105

economic importance of tobacco and, 165–166

history of, 90–91

monitoring, importance of, 91–92

preemption provisions in (See state preemption 

laws)

rating of (See legislative changes, measurement 

of)

smoke-free workplace, 152, 153

on youth access to tobacco (See state youth 

access laws)
367

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

state tobacco control policies. See also state 

tobacco control laws; individual states

activities associated with, measurement of (See

efforts (SoTC component))

ASSIST and, 8–9, 10

economic arguments as barriers to, 148, 155, 

165–166, 171, 182

evaluation of

ASSIST evaluation’s contribution to, 15–16, 

291–292, 335

future needs for, 336–337

SoTC as tool for, 49–53

funding, 49–51, 50t

in ASSIST versus non-ASSIST states, 154t,

155

and capacity, 14, 333

changes in, 49, 51f, 153–155, 154t

future of, 336–337

future needs for, 336–337

of individual states, 10

SoTC as evaluation tool for, 49–53

initial outcomes of, measure of (See Initial 

Outcomes Index (IOI))

national initiatives promoting, 8–10

newspaper coverage trends, 196–199

proliferation of, 22, 291

state-level conditions affecting (See state 

conditions)

strength of, measure of (See Strength of 

Tobacco Control (SoTC) index)

tracked by ASSIST Newspaper Tracking 

System, 193, 194t

trend analysis, 197f

state youth access laws. See also youth access 

laws

history of, 90–91

rating results, 100, 100t, 101f

rating system, 89, 90, 92, 93–97

target criteria, 93–95, 94t, 105

statistical tests, one-tailed, 272

Stillman, Frances A.

on ASSIST evaluation contributions, 325

on ASSIST evaluation overview, 1

on ASSIST Newspaper Tracking System, 185, 

200

on diffusion of ASSIST practices to nonASSIST states, 335

on final outcomes, 265

on Initial Outcomes Index (IOI), 111

on legislative changes rating system, 87, 91–92

on newspaper coverage comparison methods, 

200

on Strength of Tobacco Control (SoTC) index, 31

on tobacco industry counterefforts and tactics, 

213, 216, 217, 256

Strategies to Control Tobacco Use in the United 

States: A Blueprint for Public Health Action 

in the 1990’s (first NCI tobacco control 

monograph), 5, 8, 336

Strauss, Warren

on Strength of Tobacco Control (SoTC) index, 31

Strength of Tobacco Control (SoTC) index, 17, 

20, 33–55, 145, 215, 223

capacity (construct), 20, 35

definitions of, 34, 35, 36, 39

domain-level scores, 49–51, 50t, 53

key findings, 331–332

scores, 45t, 47f

scores correlations, 44, 48t

variables measuring, 35–36, 56t, 58t–59t

component constructs, 20, 33–39, 37f, 38t

combination of, 43

scores, 45t, 46f–47f

conceptual/heuristic model, 34, 35, 36

limitations of, 48

map, 37f

validation of, 40, 43–44, 77–82, 82f–84f

conclusions, 55

continuation after ASSIST, 335

data analysis methods, 40, 42–43, 77–78, 

280–283

correlation with ASSIST status, 280–281

correlation with per capita cigarette 

consumption, 282–283

correlation with smoking prevalence, adult, 

281

data collection, 40–42

instruments, 39

questionnaire (worksheet), 39, 41–42, 

61f–64f

respondents, 40–42, 48–49

telephone interview (survey 2), 39, 41, 

65f–76f

timeline, 17, 18f

development of, 34–39

domains/indicators, 36, 38t, 39, 57t–60t

scores by, 49–53

efforts (construct), 20, 35

definitions of, 34, 35, 39

domain-level scores, 51–53, 52t

scores, 45t, 47f, 52t
368

I n d e x

scores correlations, 44, 48t

variables measuring, 35–36, 56t, 60t

individual state program evaluation using, 

49–53

lessons for tobacco control program evaluation, 

55

limitations, 48–49

rationale for, 53–54, 330

resources (construct), 20, 35

definitions of, 34, 35, 36

domain-level scores, 49–51, 50t, 53

funding, 49–51, 50t

funding changes, 49, 51f, 153–155, 154t

scores, 45t, 46f

scores correlations, 44, 48t

variables measuring, 35–36, 56t, 57t

results (scores), 44–49, 45t, 46f

ASSIST status and, 44, 45t, 283, 342t–343t

of component constructs, 45t, 46f–47f

correlations with/effect on IOI, 281, 282t,

283, 290

correlations with final outcomes, 290–291, 292

correlation with legislative scores, 44, 48t

correlation with per capita cigarette 

consumption, 286t, 286–287, 288t, 290, 

292, 332

in ASSIST versus non-ASSIST states, 

287, 287t

correlation with smoking prevalence, adult, 

44, 48t, 284, 285t, 290–291, 292

domain-level, 49–53

by state, 45t, 46f, 342t–343t

summary, 53–54

systems methods in, 330–331

tracking trends with, 54

validation of, 40, 43–44, 77–82, 82f–84f,

330–331

concept-mapping, 85, 85f, 330

correlation analysis, 77, 78–80, 79t, 80t

factor analysis, 43, 44, 77–78, 80–81, 82f

principal components analysis, 43, 77–78, 

80, 81t

structural equations modeling, 43, 44, 78, 

80–82, 83f, 84f

variables measuring, 57t–60t

selection and validation, 35–36, 56t

Sullivan, Louis, 249

supplier sector, impact of tobacco in, 171–172

support, creating illusion of, by tobacco industry, 

227, 228t, 245–247

concept map rating of, 221t

Supreme Court, and youth access regulations, 91

Surgeon General’s reports

on environmental tobacco smoke (1986), 90, 252

on health benefits of smoking cessation (1990), 

317, 318

Healthy People 2010, 336

on smoking and health, first, 5

on women and smoking (2001), 147

surveillance. See monitoring

Sweda, E. L., Jr., 216

Synar amendment, 90–91

Syndemics initiative, 7

system-level interventions, 7

systems evaluation methods, 330–331, 339

systems view of the world, 330, 340. See also

ecological model

T

tax, excise. See cigarette excise tax

tax revenues, tobacco-generated, 172

telephone interview (survey 2), for SoTC, 39, 

65f–76f

administration of, 41

Tennessee

cigarette excise tax, 148, 166

economic importance of tobacco in, 168, 171

tobacco as share of GSP, 176, 181f

timing

of data collection for ASSIST evaluation, 17, 18f

of outcomes of tobacco control interventions, 

16, 20, 119

of quits attributable to ASSIST, 322

tobacco

economic dependence of state on (See

economic dependence on tobacco)

varieties, 168

warehousing, 174

tobacco advertising

as countereffort to tobacco control, 145, 291

expenditures, 9, 23, 170

newspaper articles on, 197t, 198

restrictions, 119, 145, 239

tobacco consumption. See also per capita cigarette 

consumption; tobacco use

changes in

economic impact forecasts, 171–174

as outcome measure, 20–21

U.S. cigarette consumption, 167, 169f
369

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

tobacco control

future of, 336–338

media advocacy for, 188

tobacco control coalitions, 40, 117

and capacity, 14, 333

measure of, 16–17

tobacco control community, harassment of, 227–

229, 228t, 247–250

concept map rating of, 222t

tobacco control evaluation

ASSIST evaluation’s contributions to, 334–336

initial outcomes methodology, 137

SoTC model, 55

state-level conditions, 155–156

for complex programs, 11, 338–339

future challenges for, 336–338

of program components, 17

trends over time in, 6f

tobacco control interventions

ASSIST-like, diffusion to non-ASSIST states, 

10, 13, 22, 334–335

community impacts of, 144

community-level, 6

complex, evaluation of, 11, 338–339

components of, evaluation of, 17

cost per person, 308, 316, 323

current trends, 11–13

education-based, 5

environmental factors affecting (See state 

conditions)

establishment of, and cost-effectiveness of 

ASSIST, 321

evaluation of (See tobacco control evaluation)

evolutionary phases of, 5–8, 6f

historical trends in, 4–13, 6f

individual-level, 5–6

key questions for, 337

in 1990s, 8–11

outcomes/outcome measures of (See outcome 

measures; outcomes)

policy-level, 8–11, 90 (See also tobacco control 

laws; tobacco control policies)

population-level, 6–7, 11–12

science-based model for, 7–8

state-based (See state tobacco control laws; 

state tobacco control policies)

system-level, 7

and tobacco industry, 11 (See also tobacco 

industry counterefforts and tactics)

tobacco control laws. See also tobacco control 

policies

aimed at tobacco industry, 11, 145

changes in, measurement of (See legislative 

changes, measurement of)

clean indoor air (See clean indoor air laws)

data sources, 90, 91, 97–98, 99, 104–105

local (See local tobacco control ordinances)

preemptive (See state preemption laws)

rating results, 100–103

rating system, 89, 91–99, 105, 106

smoke-free workplace, 152, 153

spillover among states, 153

state (See state tobacco control laws)

tobacco advertising restrictions, 119, 145, 239

on youth access to tobacco (See youth access 

laws)

tobacco control monographs (NCI)

1, Strategies to Control Tobacco Use in the 

United States: A Blueprint for Public Health 

Action in the 1990’s (the “blueprint”), 5, 8, 

336

16, ASSIST: Shaping the Future of Tobacco 

Prevention and Control, 9, 11

tobacco control policies. See also tobacco control 

laws

ASSIST policy areas, 90, 119–120, 269

as intervention focus, 339, 340

media interventions promoting, 188

monitoring of, 91–92, 104

newspaper coverage of (See newspaper 

coverage, of tobacco control policy)

in 1990s, 8–11

state (See state tobacco control policies)

in tobacco-growing/producing states, 148, 

165–166

tobacco industry efforts and tactics counter 

to (See tobacco industry counterefforts and 

tactics)

tobacco growing, 165–170

decline in, 167, 167f

economic impacts of, 173–174

factors contributing to, 167–170, 169f

in economic dependence measure, 150, 174, 175

employment in, 175

as share of GDP, 176, 178f

as share of GSP, 176f, 178f–181f

states most dependent on, 150, 168
370

I n d e x

tobacco growing and manufacturing (combined 

measure), 175

as share of GDP, 176, 178f

as share of GSP, 175–181, 177t

selected states, 176, 176f, 178f–181f

tobacco-growing/manufacturing states, 150, 168, 

170–171. See also individual states

economic impact forecasts, 173–174

tobacco as share of GSP, 176, 176f, 178f–181f,

181

tobacco control barriers in, 148, 155, 165–166, 

171, 182

views on tobacco use in, 148

tobacco industry. See also tobacco growing; 

tobacco manufacturing

advertising by (See tobacco advertising)

cigarette sales data, 115

definition of, 215n

documents on tobacco control counterefforts, 

236–254, 255–257

economic dependence of state on (See

economic dependence on tobacco)

economic impact claims of, 148–149, 171–172

on economic impact of tobacco, 148–149

studies, 171–174

economic impacts, 176

history of, 165

lawsuits against, 11

and local ordinances, 152, 232t, 238

marketing expenditures, 9, 23, 170

Master Settlement Agreement (1998) and, 11, 

145, 171, 236

and youth access policies, 227, 244, 254

tobacco industry counterefforts and tactics, 11, 

22–23, 215–257

advertising as, 145, 170, 291

agenda usurping as, 227, 228t, 243–244

concept map rating, 221t

aimed at ASSIST, 22, 215–216, 291

industry documents on, 236, 239–240, 242, 

244, 246–250

aimed at specific public health initiatives, case 

studies, 231, 232t

combination of, 231–233

concept mapping, 215, 216, 217–224, 255, 256

background for, 216–217

data validation for, 223–224

expert panel interpretation of, 219, 220, 225f

lessons from, 220, 223–224

participants in, 217

procedures for, 217–219

results of, 219–220, 221t–222t, 223f, 224f

conclusions, 256–257

creating illusion of support as, 227, 228t,

245–247

concept map rating, 221t

definition of, 216

direct versus indirect, 227, 231

document review, 236–254, 255–257

lessons from, 254

methods, 236–237

results, 237–254

economic intimidation as, 227, 228t, 240–242

concept map rating, 221t

evaluation challenges, 254–257

harassment as, 227–229, 228t, 247–250

concept map rating, 222t

index/measurement of, 215–216, 223, 233–235, 

254–257

legal intimidation as, 227–229, 228t, 240–242

concept map rating, 221t

legislative strategies as, 226t, 226–227, 234, 

238–240

concept map rating, 221t

literature review, 224–233, 255

case studies, 230–233

limitations, 233

methods, 225–226

results, 226–230

lobbying as, 171, 226t, 226–227, 234, 238–240

concept map rating, 221t

local-level, 152, 232t, 238

Master Settlement Agreement (1989) and, 145, 

236

media manipulation as, 229–230, 231t, 251–253

concept map rating, 222t

public relations as, 229–230, 231t, 253–254

concept map rating, 222t

quantification of, 234–235

research on, since ASSIST evaluation, 335–336

state-level, case studies of, 231, 232t

state preemption laws as, 152, 239

summary, 255–256

undermining science as, 229, 230t, 250–251

concept map rating, 222t

Tobacco Institute

budgets, 336

cigarette sales data, 115

documents on tobacco control counterefforts, 

237–246, 250–253
371

M o n o g r a p h 1 7 . E v a l u a t i n g A S S I S T

tobacco leaf

exports, 169f, 170

imports, 168–170, 169f

production (See tobacco growing)

tobacco manufacturing, 165–166, 170–171. See 

also economic dependence on tobacco

cigarette production decline, 167, 169f

in economic dependence measure, 150, 174–175

as share of GDP, 176, 178f

as share of GSP, 176f, 178f–181f

states most dependent on, 150, 170–171

tobacco product(s)

laws prohibiting sales to minors (See youth 

access laws)

prices (See also cigarette prices)

as ASSIST policy priority, 119–120

tobacco tax. See cigarette excise tax

tobacco use. See also entries under smoking

factors affecting, 144f, 144–145

prevalence of (See smoking prevalence)

reduction, economic impact studies of, 171–174

views on

changing, as ASSIST objective, 14, 328, 339

factors affecting, 144–145

historical changes in, 8, 11, 144

in tobacco-growing/producing states, 148

tobacco use prevention and control. See entries 

under tobacco control

Tobacco Use Supplement to the Current 

Population Survey (TUS-CPS)

adult smoking prevalence data, 15, 16, 21, 

114–115, 122, 150, 274, 310

in cost-effectiveness analysis, 310, 323

development of, 329, 335

future directions, 336–337

workplace smoking restrictions data, 125, 149

Trochim, William M. K.

on Strength of Tobacco Control (SoTC) index, 31

on tobacco industry counterefforts and tactics, 

213, 217, 218, 220

TUS-CPS. See Tobacco Use Supplement to the 

Current Population Survey (TUS-CPS)

U

U. S. Department of Health and Human Services, 

on state preemption laws, 91

Ulasevich, Alec

on ASSIST Newspaper Tracking System, 185

University of California

at San Francisco, digital tobacco industry 

library of, 335–336

Social Science Data Collection Web site, 150

University of Illinois at Chicago, ImpacTeen 

project, 105

upstream interventions, 13, 339. See also tobacco 

control policies

U.S. Bureau of Economic Analysis, economic 

impact data from, 174, 175

U.S. Bureau of Labor Statistics, ES-202 reports, 

174, 175

U.S. Census Bureau

CPS, 114, 149

demographic data, 149

economic data, 175

population estimate data, 98, 115

TUS-CPS, 15, 16, 21, 114

U.S. Congress, tobacco industry and, 11, 248–249

USDA, economic impact study by, 173–174

Utah, smoking prevalence in, 152

V

variability

of initial outcomes, 118

state-to-state, as analytical consideration, 290, 

298

Vermont, smoke-free workplace law in, 153

views and attitudes

on social issues, media coverage and, 189

on tobacco control policies, in newspaper 

editorials, 198, 200, 201

on tobacco use

changes in, as ASSIST objective, 14, 328, 339

factors affecting, 144–145, 148

historical changes in, 8, 11, 144

in tobacco-growing/producing states, 148

Virginia

cigarette excise tax, 148, 166

economic importance of tobacco in, 168, 170

tobacco as share of GSP, 176, 179f, 181

smoking prevalence changes, 276

Viswanath, K.

on ASSIST Newspaper Tracking System, 185

voluntary organizations, as SoTC respondents, 

40, 41
372

I n d e x

W

Wakefield, M., on tobacco control program 

spending, 153

Wallack, L. K., 189

Wallop, Malcolm, on lobbying through ASSIST, 

248, 249

Walls, Tina, 239

Warner, K. E., on economic impact of tobacco, 

148–149, 172–173

The Washington Post, 191, 199–200

Washington State, smoke-free workplaces in, 126, 

153

Waxman, Henry, 11

wealth of state, and tobacco control success, 

153–155

West Virginia

cigarette excise tax, 148

economic importance of tobacco in, 168, 171

Wewers, Mary Ellen

on cost-effectiveness of ASSIST, 305

White, J., on work on tobacco industry 

counterefforts to ASSIST, 215–216, 236, 255, 

291

Wisconsin, economic importance of tobacco in, 168

women. See also sex differences

life expectancy gains after smoking cessation, 

318–320

life-years gained attributable to ASSIST, 313t,

315

pregnant, cost-effectiveness of smoking 

cessation advice for, 322–323

smoking prevalence

and ASSIST cost-effectiveness analysis, 

314t, 315

in ASSIST versus non-ASSIST states, 276, 

277t, 289

Women and Smoking: A Report of the Surgeon 

General (2001), 147

workplace smoking restrictions. See also smokefree workplaces

causes of, 116–117

as initial outcome, 20, 123–125

Y

yellow fever epidemic, 340

youth

factors affecting smoking behavior, 144, 145, 

146–147, 151

smoking prevalence rates, and costeffectiveness of ASSIST, 317

youth access laws

as ASSIST policy priority, 119

enforcement of, 117, 137

local, 90, 91

newspaper coverage of, 196, 197f, 198, 199

North Carolina case study, 200, 201

state, 90–91

rating results, 100, 100t, 101f

rating system, 89, 90, 92–97, 94t, 105

target criteria, 93–95, 94t, 105

tobacco industry and, 227, 244, 254