DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Substance Abuse and Mental Health Services Administration 

Office of Applied Studies 

Health Services Utilization by Individuals 

with Substance Abuse and Mental Disorders 

Carol L. Council 

Editor 
ii 

Acknowledgments 

This compendium was prepared by the Office of Applied Studies (OAS), Substance Abuse and Mental 

Health Services Administration (SAMHSA), and by RTI International (a trade name of Research Triangle 

Institute) of Research Triangle Park, North Carolina, under Contract No. 283-99-9018. At SAMHSA, 

Sarah Q. Duffy co-edited the compendium, and Charlene Lewis provided review comments. At RTI, 

Carol L. Council co-edited the compendium. Also at RTI, Joyce Clay-Brooks word processed the 

document; Diane G. Caudill prepared the graphics; Teresa F. Gurley and Pamela Couch Prevatt readied 

its files for the SAMHSA printer and the Web; and Richard S. Straw and K. Scott Chestnut provided 

copyediting and proofreading support. Final compendium production was provided by Beatrice Rouse, 

Coleen Sanderson, and Jane Feldmann at SAMHSA. 

Public Domain Notice 

All material appearing in this compendium is in the public domain and may be reproduced or copied 

without permission from SAMHSA. However, this publication may not be reproduced or distributed for a 

fee without specific, written authorization of the Office of Communications, SAMHSA, U.S. Department 

of Health and Human Services (DHHS). Citation of the source is appreciated. Suggested citation: 

Council, C. L. (Ed.). (2004). Health Services Utilization by Individuals with Substance Abuse and 

Mental Disorders (DHHS Publication No. SMA 04-3949, Analytic Series A-25). Rockville, MD: 

Substance Abuse and Mental Health Services Administration, Office of Applied Studies. 

Disclaimer 

The statements contained in this compendium are solely those of the authors and do not necessarily reflect 

the views, policies, or position of OAS, SAMHSA, or any other part of the DHHS. No official 

endorsement of the authors' views is intended or should be inferred. The authors assume responsibility for 

the accuracy and completeness of the information contained in their separate chapters. 

Obtaining Additional Copies of Publication 

Copies may be obtained, free of charge, from the National Clearinghouse for Alcohol and Drug 

Information (NCADI), a service for SAMHSA: 

National Clearinghouse for Alcohol and Drug Information 

P.O. Box 2345 

Rockville, MD 20847-2345 

(301) 468-2600, 1-800-729-6686, TDD 1-800-487-4889 

Electronic Access to Publication 

http://www.samhsa.gov 

http://www.oas.samhsa.gov

Originating Office 

SAMHSA, OAS 

1 Choke Cherry Road, Room 7-1044 

Rockville, MD 20857 

September 2004 
Table of Contents 

Chapter Page 

iii 

List of Figures ...................................................................................................................vii 

List of Tables....................................................................................................................viii

Highlights........................................................................................................................................ 1

1. Health Services Utilization by Individuals with Substance Use and Mental 

Disorders (Carol L. Council and Jeremy W. Bray)............................................................. 5 

Health Services Research .................................................................................................... 5 

Health Services Research in the Behavioral Health Sciences............................................. 7 

DARP, TOPS, and DATOS ................................................................................. 7 

CODAP and TEDS............................................................................................... 9 

DSRS, SROS, and ADSS..................................................................................... 9 

NTIES ................................................................................................................ 11 

NCS ................................................................................................................... 11 

Overview of Chapters and Key Issues............................................................................... 12 

Access, Financing, and Costs............................................................................. 13 

Treatment Retention........................................................................................... 14 

Data Limitations................................................................................................. 14 

Treatment Choice ............................................................................................... 15 

References ......................................................................................................................... 16 

2. Access to Substance Abuse Treatment and Mental Health Services: A Literature 

Review (Albert Woodward)............................................................................................... 21 

Introduction ....................................................................................................................... 21 

Methods............................................................................................................................. 21 

Definitions of Treatment Access....................................................................................... 21 

Determinants of Mental Health Services Access .............................................................. 23 

Severity of Illness............................................................................................... 23 

Demographics..................................................................................................... 23 

Delivery System ................................................................................................. 24 

Financing............................................................................................................ 24 

Determinants of Substance Abuse Treatment Access....................................................... 26 

Need and Demographics .................................................................................... 26 

Seeking Treatment.............................................................................................. 27 

Financial Barriers ............................................................................................... 28 

Discussion ......................................................................................................................... 29 

References ......................................................................................................................... 31 
Table of Contents (continued) 

Chapter Page 

iv 

3. Substance Use and Mental Disorder Discharges from U.S. Community Hospitals 

in the Early 1990s, Revisited (Sarah Q. Duffy)................................................................. 41 

Introduction ....................................................................................................................... 41 

Data ................................................................................................................................... 42 

Methods............................................................................................................................. 42 

Study Sample...................................................................................................... 42 

Statistical Methods ............................................................................................. 44 

Results ............................................................................................................................... 45 

Discussion ......................................................................................................................... 48 

References ......................................................................................................................... 50 

4. Do Client Characteristics Affect Admission to Inpatient Versus Outpatient 

Alcohol Treatment in Publicly Monitored Programs? (Sarah Q. Duffy, Gary A. 

Zarkin, and Laura J. Dunlap) ........................................................................................... 53 

Introduction ....................................................................................................................... 53 

Background ....................................................................................................................... 54 

Data ................................................................................................................................... 56 

Empirical Framework........................................................................................................ 57 

Baseline Model................................................................................................... 57 

Expected Payer Model........................................................................................ 60 

Results ............................................................................................................................... 60 

Baseline Models ................................................................................................. 65 

Expected Payer Model........................................................................................ 69 

Discussion ......................................................................................................................... 70

References ......................................................................................................................... 74 

5. Client Choice among Standard Outpatient, Intensive Outpatient, Residential, and 

Inpatient Alcohol Treatment in State-Monitored Programs (Sarah Q. Duffy, Laura 

J. Dunlap, and Gary Zarkin)............................................................................................. 79 

Introduction ....................................................................................................................... 79 

Treatment Settings............................................................................................................. 80 

Data ................................................................................................................................... 80 

Empirical Framework........................................................................................................ 81 

Substance Use Disorder Severity ....................................................................... 82 

Socioeconomic and Demographic Characteristics ............................................. 83 

Specification Tests ............................................................................................................ 85 

Results ............................................................................................................................... 86 

Descriptive Statistics.......................................................................................... 86 

Specification Tests ............................................................................................. 89 

Multivariate Results ........................................................................................... 91 

Discussion ....................................................................................................................... 101 

References ....................................................................................................................... 103 
Table of Contents (continued) 

Chapter Page 

v 

6. Length of Stay among Female Clients in Substance Abuse Treatment (Olivia 

Silber Ashley, Lev Sverdlov, and Thomas M. Brady)...................................................... 107 

Introduction ..................................................................................................................... 107 

Empirical Model.............................................................................................................. 108 

Predisposing Characteristics: Age, Race, and Education................................. 108 

Enabling Characteristics: Referral Source and Payment Source...................... 109 

Organizational Characteristics: Substance Abuse Treatment 

Programming for Women..................................................................... 109 

Methods........................................................................................................................... 110 

Sample.............................................................................................................. 111 

Measures........................................................................................................... 111 

Analytic Approach ........................................................................................... 112 

Results ............................................................................................................................. 113 

Retention, by Predisposing Characteristics ...................................................... 117 

Retention, by Enabling Characteristics ............................................................ 120 

Retention, by Organizational Characteristics................................................... 122 

Multivariate Analysis ....................................................................................... 122 

Discussion ....................................................................................................................... 123 

References ....................................................................................................................... 127 

7. A Hybrid Cost Function for Outpatient Nonmethadone Substance Abuse 

Treatment Facilities (Sarah Q. Duffy, Laura J. Dunlap, Moshe Feder, and Gary A. 

Zarkin)............................................................................................................................. 133 

Introduction ..................................................................................................................... 133 

Cost Function Specification ............................................................................................ 134 

Data ................................................................................................................................. 135 

Case Mix Adjustment for Substance Abuse Treatment Facilities.................... 138 

Facility Characteristics ..................................................................................... 141 

Estimation ....................................................................................................................... 143 

Results ............................................................................................................................. 144 

Cost Function Results ...................................................................................... 144 

Discussion ....................................................................................................................... 147 

References ....................................................................................................................... 148 

8. Effects of Reporting Requirements on Estimates from the Treatment Episode Data 

Set (TEDS) (Sarah Q. Duffy).......................................................................................... 155 

Introduction ..................................................................................................................... 155 

Substance Abuse Treatment: A Two-Tiered System ...................................................... 155 

Selection Bias in Studies of Substance Abuse Treatment Facilities ............................... 156 

Bivariate Probit Model.................................................................................................... 158 

Estimation Strategy ......................................................................................................... 159

Data and Model Specifications ....................................................................................... 159 
Table of Contents (continued) 

Chapter Page 

vi 

Seemingly Unrelated Probit Specification ....................................................... 160 

Bivariate Probit ................................................................................................ 161 

Results ............................................................................................................................. 161 

Descriptive Statistics........................................................................................ 161 

Multivariate Analyses....................................................................................... 163 

Bivariate Probit Results Compared with Univariate Probit Results ................ 167 

Policy Simulations............................................................................................ 167 

Discussion ....................................................................................................................... 169 

References ....................................................................................................................... 171 

9. Conclusions and Implications (Carol L. Council and Jeremy W. Bray)......................... 173 

Introduction ..................................................................................................................... 173 

Access, Financing, and Costs of Substance Abuse Treatment and Mental Health 

Services ........................................................................................................................... 173 

Implications for Access for High-Risk Populations......................................... 174 

Implications for Service Delivery .................................................................... 174 

Implications for Treatment Outcomes.............................................................. 175 

Implications for Public- and Private-Sector Care............................................. 175 

Community Hospitalizations of Those with Substance Use and/or Mental 

Disorders ......................................................................................................................... 176 

Implications for the Public Sector.................................................................... 176 

Implications for Community Hospitals and Providers ..................................... 176 

Treatment Retention........................................................................................................ 177 

Implications for Treatment of Specific Populations......................................... 177 

Treatment Choice ............................................................................................................ 177

Implications for States...................................................................................... 178 

Implications for Characterization of Treatment Options ................................. 178 

Data Issues in Behavioral Health Care Research ............................................................ 178 

Implications for Federal and State Data Systems............................................. 179 

Conclusions ..................................................................................................................... 179 

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

About the Authors ....................................................................................................................... 183 
vii 

List of Figures 

Figure Page 

3.1 Age- and Gender-Adjusted Discharge Rates, by Subgroup, 1990 to 1995 ....................... 47 

6.1 Weighted Percentages of Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Facility Type of Care................................................................. 116 

6.2 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Facility Type of Care ................................. 116 
viii 

List of Tables 

Table Page 

3.1 Distribution of Discharges from U.S. Community Hospitals, by Principal 

Diagnosis CCHPR, 1990 and 1995, Substance Use and Mental Disorder Sample........... 43 

3.2 Diagnosis Subgroup Definitions ....................................................................................... 44 

3.3 Substance Use and Mental Disorder Discharges Compared with All Discharges 

from U.S. Community Hospitals, 1990 and 1995 ............................................................. 46

4.1 Analysis Sample Means and Standard Deviations of the Model Variables, by State 

and for All Nine States Pooled.......................................................................................... 61 

4.2 Probability of Seeking Inpatient Alcohol Abuse Treatment: Coefficients and 

Standard Errors from Baseline Logit Models.................................................................... 66 

4.3 Probability of Seeking Inpatient Alcohol Abuse Treatment, by State: Coefficients 

and Standard Errors on the Expected Payer Variables...................................................... 71 

5.1 Variable Means for Iowa and New Jersey......................................................................... 87 

5.2 Likelihood Ratio Test for Pooling..................................................................................... 90 

5.3 Hausman and Small-Hsiao Tests of Irrelevance of Independent Alternatives (IIA) 

Assumption: Iowa and New Jersey ................................................................................... 90 

5.4 Marginal Effects for the Choice of Substance Abuse Treatment Setting: Iowa................ 92 

5.5 Marginal Effects for the Choice of Substance Abuse Treatment Setting: New 

Jersey................................................................................................................................. 93 

5.6 Marginal Effects as a Proportion of the Predicted Probability of Each Outcome: 

Iowa................................................................................................................................... 95 

5.7 Marginal Effects as a Proportion of the Predicted Probability of Each Outcome: 

New Jersey ........................................................................................................................ 96 

6.1 Characteristics of Female Substance Abuse Treatment Clients Aged 18 or Older at 

Admission: 1997-1999 .................................................................................................... 114 

6.2 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Age at Admission and Facility Type of 

Care ................................................................................................................................. 117 

6.3 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Race and Facility Type of Care.................. 118 

6.4 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Education at Admission and Facility 

Type of Care.................................................................................................................... 119
List of Tables (continued) 

Table Page 

ix 

6.5 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Primary Source of Referral and 

Facility Type of Care....................................................................................................... 120

6.6 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Primary Source of Payment for 

Treatment and Facility Type of Care............................................................................... 121 

6.7 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Availability of Child Care Services 

and Facility Type of Care ................................................................................................ 122 

6.8 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment 

Clients Aged 18 or Older at Admission, by Client Composition of Facility and 

Facility Type of Care....................................................................................................... 122

6.9 Weighted Cox's Proportional Hazard Regression Analysis of Length of Stay 

(LOS) among Female Substance Abuse Treatment Clients Aged 18 or Older at 

Admission Discharged from Nonhospital Residential Facilities, Outpatient 

Nonmethadone Facilities, or Combination Facilities...................................................... 124 

7.1 Means and Standard Errors of Analysis Variables, Outpatient Nonmethadone 

Substance Abuse Treatment Facilities, ADSS Phase II Administrator and Cost 

Studies, 1997 (N = 222; Weighted N = 9,166)................................................................ 145 

7.2 Cost Function Results, Outpatient Nonmethadone Substance Abuse Treatment 

Facilities, ADSS Phase II Administrator and Cost Studies, 1997................................... 146 

8.1 Descriptive Statistics, by Facility Funding Source.......................................................... 162 

8.2 Marginal Effects on the Probability of Inpatient Treatment, for Selected Models, 

1996................................................................................................................................. 164 

8.3 Simulation of the Effects of Changes in Client Characteristics on the Probability 

of Inpatient Admission to a Facility That Receives Earmarked Funds: State A ............. 168 

8.4 Simulation of the Effects of Changes in Client Characteristics on the Probability 

of Inpatient Admission to a Facility That Receives Earmarked Funds: State B ............. 169 
x 
1 

Highlights 

This volume is a compendium of health services analyses on a variety of behavioral 

health care issues conducted by the Substance Abuse and Mental Health Services Administration 

(SAMHSA), Office of Applied Studies (OAS). First, a literature review traces recent trends in 

access to treatment for substance use and mental disorders. Then results from analyses based on 

large, representative datasets and economic modeling approaches provide new insights into 

access, treatment choice, retention in treatment, and costs associated with treatment for substance 

use and mental disorders. 

The in-depth review of current research findings on access to treatment for substance use 

and mental disorders (Chapter 2) encompasses the wider area of health care utilization and places 

special emphasis on financial factors impacting access to care. Key highlights from Chapter 2 

include the following. 

Financing and Access to Substance Use and Mental Disorder Treatment 

! There is a growing awareness of the effects of financing and costs on access to treatment. 

Managed care and financing issues appear to be as important as nonfinancial barriers, such 

as the severity of the substance use disorder, in influencing access to care. Homeless people 

with substance use and mental disorders may have the most difficulty in accessing 

treatment, even if they have public health insurance. 

! Managed care for substance abuse treatment not only has shifted treatment from inpatient 

care to outpatient care, but also has shifted the risk to providers, thus constraining provider 

treatment options. 

Substance Use Disorder Severity and Access to Treatment 

! Those with more serious behavioral illnesses may encounter more barriers to access 

because successful treatment for them may be more expensive and because redundant and 

bureaucratic procedures may pose insurmountable obstacles. However, changes in 

government-sponsored systems can promote access for clients with more serious disorders. 

! To better understand the issues of access to services, researchers should study the entire 

universe of persons with substance use and mental disorders, not just those who seek 

treatment. 

Subsequent chapters in this compendium provide insight into the organization, financing, 

management, and delivery of behavioral health services for substance use and mental disorders 

by exploring such issues as utilization, quality, cost, cost-effectiveness, and outcomes. The 

authors examine how the demographic characteristics of an individual affect how, when, where, 

and if a person will seek care, what types of care are chosen or provided, and what happens 

during the delivery of care. Key highlights include: 
2 

Community Hospitalizations of Those with Substance Use and Mental Disorders 

! Discharges from community hospitals of those with substance use and mental disorder 

diagnoses grew substantially between 1990 and 1995, a time of stability in overall hospital 

discharges (Chapter 3). 

! Although the complexity of substance use and mental disorders increased over time, the 

length of stay (LOS) decreased in community hospitals. The decrease was most pronounced 

for those with disorders related to substance use (Chapter 3). 

! Patients with substance use and mental disorder diagnoses were more likely to receive 

uncompensated care or have Medicaid coverage than other community hospital patients 

(Chapter 3). 

! The Federal Government's role in paying for the care of patients with substance use and/or 

mental disorders in community hospitals increased between 1990 and 1995, with Medicare 

and Medicaid paying for the treatment of more than half of discharges with such diagnoses 

(Chapter 3). 

Admissions to Substance Abuse Treatment 

! The criminal justice system was the most common source of treatment referral for adult 

males with alcohol as the primary substance of abuse in most States examined (Chapter 4). 

! Greater disorder severity increased the likelihood of inpatient admission. In addition, daily 

alcohol users were significantly more likely to receive inpatient treatment than clients who 

did not drink in the 30 days prior to admission. Moreover, using cocaine as a secondary 

substance increased the likelihood of inpatient admission in most States (Chapter 4). 

! Referral by an alcohol or drug treatment provider generally increased the likelihood of 

inpatient admission, but in many of the States examined, co-occurring mental disorders did 

not consistently increase the odds of inpatient admission (Chapter 4). 

! Clients who were employed were less likely to have an inpatient admission; clients who 

paid for their own care had a lower likelihood of entering inpatient treatment (Chapter 4). 

! An examination of the choice among five types of treatment (standard outpatient, intensive 

outpatient, long-term residential, short-term residential, and inpatient hospital) in two 

States revealed that those admitted to standard outpatient treatment appeared to have less 

severe alcohol use disorders and were more likely to be employed than those admitted to 

any other treatment setting. Furthermore, analyses that allowed for only two choices, 

inpatient and outpatient, obscured the relationships between client characteristics and 

treatment-setting choice (Chapter 5). 
3 

Retention of Women in Substance Abuse Treatment 

! Substance abuse treatment for women at facilities offering child care services and treatment 

at women-only facilities were associated with a longer LOS (Chapter 6). 

! Lower educational levels (fewer than 8 years) were associated with a shorter LOS among 

female clients (Chapter 6). 

! Among adult female clients at outpatient nonmethadone facilities: 

− LOS was shorter among those aged 55 to 64 than among those aged 18 to 54. 

− The average LOS differed by race. 

− The LOS was longer among those whose primary source of payment was no payment or 

self-payment (Chapter 6). 

! Among adult female clients at nonhospital residential facilities, the LOS was longer among 

women with a criminal justice system referral. 

! Adult female clients in facilities offering combined mental health and substance abuse 

treatment services stayed in treatment longer than in those facilities not offering these 

services. 

! Adult female clients receiving care at facilities offering prenatal care or transportation 

services had shorter LOS. 

Costs of Substance Abuse Treatment 

! Average cost per admission declined as facilities became larger, which suggests that larger 

facilities may be able to provide care at a lower price than smaller facilities (Chapter 7). 

! Facilities with a greater proportion of clients who received Supplemental Security Income 

(SSI) or Social Security Disability Insurance (SSDI) had higher costs (Chapter 7). 

Selection Bias in Analyses of Client Data 

! Analyses of data collected by States only from facilities that receive public funds 

earmarked for substance abuse treatment may be biased. States may want to pursue 

reporting client data by all facilities, regardless of their funding status, to get a more 

accurate understanding of the substance use problems in their State (Chapter 8). 

! Clients admitted to facilities receiving public funds earmarked for treatment of substance 

use disorders generally were younger, less likely to be employed, less likely to be married, 

less likely to have postsecondary education, and less likely to have private insurance pay 

for their treatment than those entering other facilities. However, variations among States 

were found between clients who entered facilities that accepted earmarked funds and those 

who entered facilities that did not accept earmarked funds (Chapter 8). 
4 
5 

Chapter 1. Health Services Utilization by Individuals with 

Substance Use and Mental Disorders 

Carol L. Council, M.S.P.H. 

Jeremy W. Bray, Ph.D. 

Behavioral health care delivery in the United States is undergoing rapid change in both its 

organizational and financial structures. These changes have been precipitated by the complex 

demands of containing costs, maintaining the quality of care, making care available to all who 

need treatment, and focusing resources on those forms of care that offer the best hope of 

successful outcomes. Although these changes are having profound effects on the structure of the 

treatment system and on service delivery in substance abuse treatment and mental health services 

programs, the nature of these changes has not been adequately studied. At the national, State, and 

local levels, policymakers and service providers need new knowledge to understand how these 

changes will affect access to needed care, the quality and effectiveness of care, the utilization of 

services, cost of treatment services, and the outcomes of treatment for people with acute and 

chronic substance use and mental disorders. 

Addressing such issues as the structure, processes, and outcomes of substance abuse 

treatment, mental health services, prevention, and related health services is within the purview of 

health services research. This compendium provides important information on a number of areas 

that facilitate or inhibit the delivery of health services for those persons with substance use or 

mental disorders. It provides important insight into the organization, financing, management, and 

delivery of behavioral health services and explores such issues as accessibility, utilization, 

quality, cost, cost-effectiveness, and outcomes. It examines how the demographic characteristics 

of an individual affect how, when, where, and if a person will seek care; what types of care are 

chosen or provided; and what happens during the delivery of care. Finally, the compendium 

explores potential biases inherent in using many of the large datasets currently available for 

conducting behavioral health services research. 

This chapter includes a brief introduction to health services research, a history of its 

development as a discipline, and how it has been used to study health care systems. In addition, a 

brief review of the history of behavioral health services research is included. This is followed by 

an overview of the remaining chapters and key issues examined in this compendium. 

Health Services Research 

The Institute of Medicine (IOM, 1995) defined health services research as "a 

multidisciplinary field of inquiry, both basic and applied, that examines the use, costs, quality, 

accessibility, delivery, organization, financing, and outcomes of health care services to increase 

knowledge and understanding of the structure, processes, and effects of health services for 

individuals and populations" (pp. 3 and 17). In early 2000, K. N. Lohr and D. M. Steinwachs cochaired an ad hoc committee to develop the following revised definition for the Association for 

Health Services Research (AHSR), now the Academy for Health Services Research and Health 

Policy: 
6 

Health services research is the multidisciplinary field of scientific investigation 

that studies how social factors, financing systems, organizational structures and 

processes, health technologies, and personal behaviors affect access to health 

care, the quality and cost of health care, and ultimately our health and well-being. 

Its research domains are individuals, families, organizations, institutions, 

communities, and populations. (Lohr & Steinwachs, 2002, p. 16) 

Health services research strives to identify the most effective ways to organize, manage, 

finance, and deliver high quality care; reduce medical errors; and improve patient and/or client 

safety.1

 A variety of disciplines study health services research issues. They include medicine, 

biostatistics, economics, epidemiology, management, law, nursing, pharmacy, psychology, and 

sociology. Health services research involves integrating epidemiologic, sociologic, economic, 

and other analytical sciences so that the relationships between need, demand, availability, 

utilization, and outcome of health services may be better understood. Its ultimate research goal is 

maximizing the efficiency and efficacy of health services. 

In general health care, health services research has been used extensively to study health 

care systems and to define best practices. It has provided evidence of both what works and what 

does not. Health care utilization has been studied widely to determine the factors that influence 

treatment-seeking behaviors. The use of information gained from this research has saved billions 

of dollars and improved health care for many Americans. 

Health services research had its beginnings in the 17th century as physicians sought to 

understand variations in hospital mortality rates (McCarthy & White, 2000). Until the 20th

century, studies were limited in size and scope. In 1898, the American Medical Association 

(AMA) Committee for Scientific Research was established to provide grants for fostering 

medical research (AMA, 2004). The National Health Survey conducted in 1935-1936 and other 

studies underscored the disparities in health status and access to medical care associated with 

income (IOM, 1992). Government programs developed during the Great Depression shifted 

responsibility for many social programs to the Federal Government. During the 1940s and 1950s, 

as the number of physicians increased and specialization of medical practice also increased, 

health services research became more important. In the 1960s and 1970s, health services 

research became increasingly institutionalized with the development of a National Institutes of 

Health (NIH) Health Services Research Study Section and the journal Health Services Research

(McCarthy & White, 2000). With the passage of Medicare legislation in 1965 and concurrent 

regional medical programs, there was a need for oversight and better understanding of how 

health services utilization would be affected. In 1966, the Comprehensive Health Planning Act 

was passed by Congress, and Federal funding for health services research began. 

It should be noted that prior to the development of biostatistical methods and consistent 

data collection techniques, health services research could be done on only a small scale. The 

development of the fields of biostatistics and epidemiology, coupled with rapid improvements in 

computer hardware, software technologies, and data collection methodologies during the 1970s, 

 1

 In this compendium, we refer to "patients" in traditional health care settings (e.g., hospitals) and to 

"clients" in behavioral health care/social services settings (e.g., substance abuse treatment). So, in the chapter on 

hospitals (Chapter 3), the word "patient" is used. The other chapters primarily use the word "client." 
7 

enabled more rigorous study of such issues as access, selection of providers, service delivery, 

financing, and outcomes. In 1973, legislation enabling the creation of the National Center for 

Health Statistics (NCHS) was enacted. During the 1970s and 1980s, health economics 

developed, and there was increased awareness that certain patterns of medical practice were 

associated with better health outcomes. 

From 1971 to 1982, the RAND Health Insurance Experiment was undertaken 

(Newhouse, 1999; Newhouse & Insurance Experiment Group, 1994). The conclusion from this 

study was that while the use of co-payments reduced the use of health services, there was no 

apparent change in the health status of most families (Freund, 1994; Normand, 1994). The "sick 

poor" (i.e., persons with low incomes who were ill in particular ways at the start of the 

experiment) were the exception. For them, access to "free" care helped in regard to those 

conditions. These findings had a great impact on health care reform in the United States. 

Over the past three decades, large health care studies have been sponsored by the Federal 

Government and also by foundations such as the Henry J. Kaiser Family Foundation and the 

Robert Wood Johnson Foundation. Many Federal agencies, such as the Centers for Disease 

Control and Prevention (CDC) and the NIH maintain large ongoing studies of the Nation's 

health, and longitudinal datasets are now available to enable the ongoing study of various health 

conditions, as well as special population groups. 

Health Services Research in the Behavioral Health Sciences 

The great societal costs of substance use and mental illnesses, coupled with large 

amounts of Federal, State, and local resources devoted to the amelioration of these problems, 

resulted in the use of health services research methodologies to seek effective models of shortand long-term care for people with acute and chronic substance use and mental disorders. The 

following describes the data collection efforts and evaluations that focused on mental health 

services and substance abuse treatment. 

Federal involvement with mental health research began in 1855 with the creation in 

Washington, DC, of Saint Elizabeth's Hospital, a facility for persons with mental illnesses 

(National Library of Medicine, 2001). A Division of Mental Hygiene was established in the 

Public Health Service (PHS) in 1930 and became in 1949 the National Institute of Mental Health 

(NIMH) (National Library of Medicine, 1998). After passage of legislation in the 1970s that 

provided for community-based treatment, there was increased interest in demonstrating the 

benefits of these large expenditures and a call for research to show the most efficacious 

treatments. Thus began a large-scale research agenda that evolved over the next three decades 

and focused on understanding substance abuse treatment and mental health services, including 

the need for treatment, service utilization, outcomes, and financing. Several national evaluations 

were undertaken, and important national databases were established. They are briefly described 

in the following sections. 

DARP, TOPS, and DATOS 

One of the first behavioral health services research studies was the Drug Abuse Reporting 

Program (DARP). Designed to evaluate the emerging community-based substance abuse 
8 

treatment programs, as well as the use of methadone to treat addiction, DARP began in 1968 and 

was planned as a client reporting system to establish a research database for treatment programs 

funded by the National Institute on Drug Abuse (NIDA) (Sells, 1974). DARP measured 

treatment outcomes on 44,000 clients admitted to 52 treatment programs from 1969 to 1973. 

Methadone maintenance, therapeutic community, outpatient drug-free, and outpatient 

detoxification programs were studied. Findings from DARP demonstrated the effectiveness of 

community-based treatment in reducing substance use and criminal behaviors (Hubbard et al., 

1989). The study also provided useful data on the natural history of heroin use in a treated 

population and evidence that addicted clients treated with methadone had better outcomes if they 

remained in treatment for longer than 90 days (Joe, Simpson, & Hubbard, 1991; Simpson & 

Savage, 1980). 

The Treatment Outcome Prospective Study (TOPS) was designed to expand on DARP 

and to provide longitudinal data on clients to allow the assessment of short- and long-term 

treatment outcomes and to obtain more data on client attributes, program environments, and 

services delivered in treatment (Allison, Hubbard, Craddock, & Rachal, 1982; Ginzburg, 1978). 

Outpatient methadone, short- and long-term residential, and outpatient drug-free programs in 

operation from 1979 to 1981 were studied. 

As in earlier studies, TOPS findings suggested that treatment was cost-effective and costbeneficial in reducing the use of heroin and other illicit drugs (Harwood, Hubbard, Collins, & 

Rachal, 1988). Levels of predatory crime declined during treatment and remained lower than at 

baseline (Harwood et al., 1988; Hubbard et al., 1989). TOPS data showed that clients with 

substance use disorders have a great need for mental health services (Allison et al., 1982). TOPS 

data also indicated that clients with more ancillary services, particularly mental health, 

employment, and general services, had improved outcomes (Joe et al., 1991). 

The Drug Abuse Treatment Outcome Study (DATOS) was a prospective cohort study 

designed to evaluate treatment effectiveness in typical, stable, community-based substance abuse 

treatment organizations operating from 1991 to 1993. DATOS obtained data on approximately 

20,000 clients in four types of treatment programs: outpatient methadone, long-term residential, 

outpatient drug-free, and short-term residential (Tims, Fletcher, & Hubbard, 1991). Adult clients 

were interviewed at admission, during treatment, and at 12 months after the termination of 

treatment (except for clients receiving methadone long term who were interviewed 

approximately 24 months after admission if they were still in treatment during the follow-up 

phase of the study). DATOS used a standard set of instruments to provide diagnostic profiles of 

clients (Fletcher, Tims, & Brown, 1997; Flynn, Craddock, Hubbard, Anderson, & Etheridge, 

1997). 

Findings from DATOS indicated that in each type of treatment, clients lowered their drug 

use from pretreatment baselines at 12 months after treatment. DATOS also was able to document 

the marked decrease in services provided to clients in substance abuse treatment accompanied by 

an increase in unmet service need in the decade since clients entered the TOPS programs. There 

was a shift from more expensive targeted services to the provision of core services. However, 

DATOS found that substance abuse treatment programs appeared to have improved how they 

delivered services of counseling, treatment planning, and use of aftercare (Hubbard, Craddock, 

Flynn, Anderson, & Etheridge, 1997; Simpson, Joe, & Brown, 1997). DATOS was particularly 
9 

important in that it was able to document the change in substance use patterns and examine 

outcomes for community-based cocaine abuse treatment. Moreover, it was the first national 

study conducted after the start of the AIDS epidemic and also after the shift to Substance Abuse 

Prevention and Treatment (SAPT) block grant funding. 

CODAP and TEDS 

The Client Oriented Data Acquisition Process (CODAP) began in 1973 and initially was 

developed to satisfy requirements outlined in the Drug Abuse Office and Treatment Act of 1972. 

At that time, 13 separate Federal agencies were involved in the provision of substance abuse 

treatment, and the resultant duplication of effort and conflicts arising from the situation were best 

resolved by the creation of a single, ongoing management reporting system, agreed upon by all 

Federal agencies (Blanken, 1989). This system was CODAP. 

Designed to monitor drug treatment need and use, CODAP provided current information 

describing clients and the treatment given to them in order to aid in planning, management, and 

evaluation activities (NIDA, 1982). Programs for treatment of alcohol abuse were not included. 

All program recipients of Federal funds for drug abuse treatment and rehabilitation services were 

required to participate in CODAP. Data collection began in 1973. Admission reports for 

approximately a quarter of a million clients were submitted annually by 1,800 to 2,000 

participating treatment programs (Office of Applied Studies [OAS], 2004). CODAP included no 

client, staff, or program surveys, and it depended on treatment units to provide data. Discharge 

data provided parallel information, as well as the reason for discharge. Although CODAP was 

not able to produce client outcome data, data available pertaining to client characteristics, 

geographic distribution, and drug use patterns and trends over time also greatly increased 

national knowledge about the epidemiology of drug use and the provision of treatment services. 

The Alcohol, Drug Abuse, and Mental Health Services (ADMS) block grant program, 

implemented in 1981, transferred Federal funding from individual programs to the States for 

distribution, and required no data reporting (OAS, 2004). Thus, CODAP was no longer a 

required reporting process. 

In 1988, the Comprehensive Alcohol Abuse, Drug Abuse, and Mental Health 

Amendments established a revised SAPT block grant and mandated Federal data collection on 

clients receiving treatment for either alcohol or drug use disorders. Work on the Treatment 

Episode Data Set (TEDS) began in 1989 with the issuance of 3-year development grants to 

States. TEDS includes admissions to facilities that are licensed or certified by State substance 

abuse agencies to provide substance abuse treatment (or are administratively tracked for other 

reasons) (OAS, 2004). 

DSRS, SROS, and ADSS 

In 1990, the Office of National Drug Control Policy (ONDCP) requested that NIDA 

conduct a nationally representative study of the treatment system. The Drug Services Research 

Survey (DSRS) was designed to obtain information on drug abuse treatment providers and client 

characteristics to supplement information from the 1989 National Drug and Alcoholism 

Treatment Unit Survey (NDATUS). 
10 

The DSRS had two phases. In Phase I, a representative probability sample of 1,183 drug 

treatment facilities was drawn from a comprehensive list of organized substance abuse treatment 

facilities (the 1989 NDATUS list). During Phase II, a representative subsample, stratified by 

facility type, of 120 drug treatment facilities was selected. The DSRS provided a picture of 

treatment participation and client characteristics, including demographics, prior treatment 

history, drug use history, and discharge status of clients at each sampled treatment facility 

(Batten et al., 1992, 1993). 

The Services Research Outcomes Study (SROS) was sponsored by the Substance Abuse 

and Mental Health Services Administration (SAMHSA) and was a 5-year postdischarge followup of a broadly representative sample of treatment facilities and clients based on the DSRS 

facility sample. Specifically, 1,799 clients from a sample of 99 drug treatment facilities were 

interviewed. The study compared client behavior in the 5 years before treatment with behavior 5 

years after treatment with respect to drug and alcohol use, criminal involvement, employment, 

physical and mental health, and other behaviors. SROS analyzed treatment results in light of 

client characteristics and the type and cost of treatment services the clients received. It provided 

an examination of multiple treatment episodes before and after treatment in a 1990 population. 

SROS confirmed that both substance use and criminal behavior were reduced following 

inpatient, outpatient, and residential treatment for drug use disorders (OAS, 1998). 

The Alcohol and Drug Services Study (ADSS) built on the work of the 1990 DSRS study 

with a more complex sampling frame, an enhanced sampling design, and improved measures of 

financing and organization. Sponsored by OAS, ADSS was designed to collect information on 

the characteristics of substance abuse treatment facilities and their clients and to study the 

relationships among facility characteristics, treatment services, and clients in treatment. OAS 

was interested in developing better estimates of client length of stay and the costs of treatment 

and to describe the posttreatment status of clients (OAS, 2003b). 

The ADSS sample was selected using a multistaged, stratified design, with selection of 

2,393 facilities in Phase I, a selection of a subset of Phase I responding facilities, and a selection 

of client discharge records in Phase II and client follow-up in Phase III. Facilities in the sampling 

frame were stratified by treatment type: hospital inpatient, nonhospital residential, outpatient 

predominantly alcohol, outpatient predominantly methadone, other outpatient, and combined 

treatment types (OAS, 2003b). 

The ADSS cost study examined treatment costs with validated cost data from a nationally 

representative sample of substance abuse treatment facilities and applied its costing methodology 

to a representative sample of facilities. Its findings supported other studies' findings that 

nonmethadone outpatient treatment is substantially less expensive than residential treatment 

(OAS, 2003a). It also found that the cost per enrolled client day in outpatient methadone 

treatment was only marginally higher than the cost per enrolled client day in nonmethadone 

outpatient treatment. One of the most important conclusions from the ADSS cost study was the 

variability in unit costs within a type of treatment. 
11 

NTIES 

The National Treatment Improvement Evaluation Study (NTIES) was a congressionally 

mandated 5-year longitudinal study of the impact of drug and alcohol treatment on clients in 

treatment units that received public support in 1990-1991 from SAMHSA through its Center for 

Substance Abuse Treatment (CSAT) demonstration grant-funding program. All recipient 

facilities were recruited to provide facility-level information on services, staffing, and costs. Ten 

percent of the facilities were purposively selected for inclusion in a client follow-up study that 

covered the 1993-1994 admission cohort and included a 1-year follow-up study. Client-level data 

were obtained for over 5,000 clients at treatment intake, at treatment exit, and 12 months after 

treatment exit. The study was designed to address two issues: (a) the amount of treatment 

required to achieve successful outcomes; and (b) the extent to which favorable outcomes persist 

following termination from treatment. 

Findings indicated better treatment outcomes for clients in outpatient methadone 

treatment with longer stays in treatment who were still in the program at follow-up (Koenig, 

Denmead, Nguyen, Harrison, & Harwood, 1999). NTIES provided support for expansion of 

methadone treatment, expansion of aftercare services for clients treated with methadone, and 

expansion of ancillary services, such as transportation and day care to support retention in 

treatment. Practice implications suggested the need to develop aftercare plans prior to discharge 

and to provide information and referral services to help discharged clients locate alternative 

treatment services. NTIES underscored the need to study factors that result in increased client 

retention and better treatment. 

NCS 

The National Comorbidity Survey (NCS) was a collaborative epidemiological 

investigation designed to study the prevalence and correlates of disorders defined and described 

in the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) (American 

Psychiatric Association [APA], 1987), as well as the patterns and correlates of service utilization 

for these disorders. The NCS was the first survey to administer a structured psychiatric interview 

to a nationally representative sample. The survey was carried out in the early 1990s with a 

household sample of more than 8,000 respondents aged 15 to 54 years. Baseline NCS 

respondents were reinterviewed in 2001-2002 (NCS-2) to study the patterns and predictors of the 

course of mental and substance use disorders and to evaluate the effects of primary mental 

disorders in predicting the onset and course of secondary substance use disorders (Harvard 

Medical School, Office of Public Affairs, 2003; Kessler et al., 2003). 

The study showed that as many as half of the U.S. population met criteria for a mental 

disorder at some time in their lives and that, in any given year, such mental disorders were highly 

concentrated in a relatively small portion of the population (5 to 8 percent) (Kessler et al., 1997). 

In addition, findings from the NCS indicated that 6 percent of women and 9 percent of men met 

criteria for alcohol dependence and that 23 percent of women and 26 percent of men met criteria 

for tobacco dependence (Kandel, Warner, & Kessler, 1998). 

In summary, changes in the patterns of substance use, the nature of the treatment 

population, and the treatment system have added new challenges to behavioral health services 
12 

research. The evaluations and databases developed over the past three decades provide a good 

foundation to study the nature and impact of these changes. Most were developed to answer 

specific research and practice questions relevant at the time, and each study was costly and 

required long periods of time for planning, pilot testing, data collection, and data analysis. 

Although study methodologies improved greatly over the decades, most of the studies described 

above had similar methodological problems, which are inherent in studying those with substance 

use and mental disorders and treatment services for those populations. For example, treatment 

facilities and modalities change. To ensure that programs will be in operation at the end of data 

collection, it is prudent to select large well-established programs for study. Difficulties in 

following clients after treatment completion or discharge exist because such clients are highly 

mobile and often want anonymity after treatment completion. Some changes in the treatment 

milieu, occasioned by changes in financing structures and the impact of managed care, were 

largely unanticipated. Nonetheless, the evaluations and databases provide a rich source of 

information for behavioral health services researchers. To make optimal use of this information, 

researchers should be aware of the limitations of these datasets, as well as the societal conditions 

and treatment structures in place at the time of the evaluations. These factors should be 

considered in interpreting data on the organization and impact of treatment.

Overview of Chapters and Key Issues 

The large expenditures of State and Federal dollars on substance abuse treatment and 

mental health services have spawned increased demand for accountability and determination of 

best practices. In response, the Federal Government has supported the creation of large databases 

that have been used to gain a better understanding of clients served and services provided. The 

chapters in this compendium use data from a variety of sources, including many of those 

described above, and provide important new knowledge. 

! Chapter 2, "Access to Substance Abuse Treatment and Mental Health Services: A 

Literature Review," examines literature on access to treatment for substance use disorders 

and to services for mental illnesses. 

! Chapter 3, "Substance Use and Mental Disorder Discharges from U.S. Community 

Hospitals in the Early 1990s, Revisited," examines trends in discharges from community 

hospitals nationwide of those with substance use and mental disorders between 1990 and 

1995. 

! Chapter 4, "Do Client Characteristics Affect Admission to Inpatient Versus Outpatient 

Alcohol Treatment in Publicly Monitored Programs?" uses the 1996 Treatment Episode 

Data Set (TEDS) to examine the association between disorder severity and admission to 

publicly monitored inpatient versus outpatient alcohol treatment among adult males. 

! Chapter 5, "Client Choice among Standard Outpatient, Intensive Outpatient, Residential, 

and Inpatient Alcohol Treatment in State-Monitored Programs," examines client 

characteristics that are associated with client choice of treatment setting. 
13 

! Chapter 6, "Length of Stay among Female Clients in Substance Abuse Treatment," 

examines factors associated with retention of female clients in treatment, using ADSS 

Phase II data. 

! Chapter 7, "A Hybrid Cost Function for Outpatient Nonmethadone Substance Abuse 

Treatment Facilities," uses economic modeling of administrative data from ADSS Phase II 

to estimate a cost function for outpatient nonmethadone substance abuse treatment 

facilities. 

! Chapter 8, "Effects of Reporting Requirements on Estimates from the Treatment Episode 

Data Set (TEDS)," examines potential biases that may exist in conducting research using 

TEDS data. 

! Chapter 9, "Conclusions and Implications," summarizes the chief findings of this 

compendium and discusses the implications of these findings with suggestions for future 

research. 

The remaining discussion focuses on key issues from these chapters. These chapters provide new 

knowledge on such issues as access to treatment, treatment financing and costs, treatment 

retention, treatment choice, and some limitations of commonly used data sources. 

Access, Financing, and Costs 

Many of the Federal-sponsored evaluations mentioned earlier focused on issues related to 

populations served, services provided (including specialty services), characteristics of facilities 

providing treatment, and, to the extent possible, financing issues. This compendium further 

explores the financing of and access to substance abuse treatment and mental health services. 

Chapter 2 examines the literature on access to treatment for substance use and mental disorders, 

and it reviews a variety of attributes (with a special emphasis on financial factors) that may affect 

a client's ability to access treatment. In the chapter, the shift to outpatient treatment is discussed, 

as well as its possible ramifications. The importance of managed care and financing on access to 

treatment is underscored, especially among those clients with more serious behavioral health 

disorders. The chapter also includes a discussion of the ability of government-sponsored 

behavioral health care systems to improve access for those clients with more serious disorders. 

Chapter 7 uses economic modeling of administrative data from Phase II of ADSS, 

described earlier, to estimate a cost function for outpatient nonmethadone substance abuse 

treatment facilities with a sample representing 9,166 facilities. In the chapter, the practice of 

using costs estimated from a small number of nonrandomly selected facilities to conduct costbenefit analyses is called into question. Importantly, it is suggested not only that larger facilities 

may be able to provide care at a lower price than smaller facilities, but also that higher costs may 

be appropriate when dealing with high-risk populations, such as clients receiving supplemental 

security income (SSI). 

The importance of considering client characteristics that affect access, as well as 

treatment-seeking behaviors and treatment retention, has been extensively reported upon in the 

health services research literature. Chapter 2 reviews the literature on client demographics and 

treatment-seeking behavior. TEDS data from 1996 are used in Chapter 4 to examine the 
14 

association between disorder severity and admission to publicly monitored inpatient versus 

outpatient alcohol treatment. Subjects studied are adult males with alcohol as a primary 

substance of abuse who were admitted for rehabilitation treatment in a State-monitored program 

in one of nine States. 

In Chapter 4, clients who either paid for treatment out-of-pocket or who received publicly 

funded treatment are examined, as is how the source of payment varied across States. 

Multivariate logistic regression models were used for each State, with results indicating that 

greater disorder severity increased the odds of inpatient admission (an exception was cooccurring mental disorders). Those clients who were employed were less likely to have an 

inpatient admission, while those who were homeless, who had one prior treatment episode, 

and/or who were referred by an alcohol or drug treatment provider were more likely to have an 

inpatient admission. 

Treatment Retention 

As described earlier, several large research studies have indicated that a longer length of 

stay (LOS) is associated with better client outcomes. In Chapter 6, the ADSS Phase II dataset is 

used to derive a nationally representative sample of female clients discharged from substance 

abuse treatment facilities to examine factors associated with treatment retention in one of four 

types of treatment facilities: nonhospital residential treatment only, outpatient methadone 

treatment only, outpatient nonmethadone treatment only, or a combination of types of care. 

Lengths of stay in treatment are compared based on demographic, socioeconomic, and 

organizational characteristics. As reported in earlier evaluations, the total number of services 

provided to clients in treatment was found to be declining. The study described in Chapter 6 

finds that with regard to women's treatment, facilities offering child care services and treatment 

at women-only facilities were associated with longer lengths of stay. 

Data Limitations 

Most databases have important limitations; care must be taken in interpreting findings 

obtained from them and generalizing to the entire population of those with substance use and 

mental disorders. Although biases are evident in earlier evaluations, as well as in Federalmaintained databases, few previous studies have examined the impact of biases on research 

findings. Two chapters in this compendium focus on the potential impact of such biases. Chapter 

3 is a review of the findings from a 1990-1995 study that examined discharge trends from 

community hospitals nationwide of patients with substance use and mental disorders. The 

chapter includes a critique of previous studies that used the National Hospital Discharge Survey 

(NHDS) and focused on community hospitalizations for substance use and mental disorders. 

New estimates are provided in the chapter using the Agency for Healthcare Research and 

Quality's Nationwide Inpatient Sample (NIS). A persuasive argument is made that the NIS is the 

appropriate dataset to use for making such estimates. Contrary to findings from previous 

research, the evidence in Chapter 3 indicates that community hospitalization of patients with 

substance use and mental disorder diagnoses increased between 1990 and 1995. Moreover, even 

though those patients were diagnosed with more complex disorders, they had short hospital 

stays. Because health care utilization studies (e.g., the RAND Health Insurance Experiment) can 
15 

have an important impact on public policy and funding decisions, it is important to select the 

most appropriate dataset for studying and interpreting treatment-seeking behavior. 

Chapter 8 examines biases that may be introduced into datasets, such as TEDS, as a result 

of State-level reporting requirements. Each State sets requirements on the types of providers or 

facilities that must report into the TEDS system, with some States only requiring that facilities 

receiving earmarked funds for substance abuse treatment report on their clients, while other 

States require that all facilities report regardless of their funding sources. This chapter examines 

whether or not selection bias occurs in the collection and reporting of data to TEDS for adult 

males with alcohol as a primary substance of abuse who were admitted for rehabilitation 

treatment. TEDS data are used from two States that collect information from both types of 

facilities. Findings indicate that biases do exist. Clients admitted to facilities receiving Federal 

earmarked funds had a different client demographic profile. Moreover, large variations in these 

profiles existed between States. Thus, it may be important for States to require reporting by all 

facilities, regardless of their funding status, if they want an accurate understanding of their 

substance use disorder. 

Treatment Choice 

Earlier research has underscored the shift of treatment services provision to outpatient 

settings, as well as the impact of managed care on provider treatment options. Chapter 5 

examines client characteristics associated with choice of treatment setting in State-monitored 

facilities, including standard outpatient, intensive outpatient, short-term residential, long-term 

residential, and inpatient hospital rehabilitation. The chapter discusses the appropriateness of 

combining types of inpatient and outpatient treatment into two broad categories for analysis 

purposes. As in Chapter 8, this study uses 1996 TEDS data on adult males with alcohol as the 

primary substance of abuse in two States that collect information on all clients admitted to 

treatment, regardless of the use of earmarked funds. Multivariate multinomial logistic models 

were estimated for each State, and covariates included disorder severity and socioeconomic 

measures. Those admitted to the standard outpatient setting appeared to have less severe alcohol 

use disorders and were far more likely to be employed at admission than those admitted to other 

settings. These findings suggest that client treatment-setting choice should not be studied as a 

dichotomous choice between two types of treatment, but rather as a choice among multiple 

settings. 

The chapters in this compendium add to the body of knowledge concerning the provision 

of treatment services in behavioral health care. However, it is important to note that although 

much of the research obtained from the general health services continuum has relevance for 

treating substance use and mental disorders, important differences should be considered when 

making policy inferences. In Chapter 9, several of the issues raised in the compendium are 

discussed, their implications for policymakers are presented, and areas for further research are 

suggested. 
16 

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http://165.112.78.61/pdf/monographs/download106.html]
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21 

Chapter 2. Access to Substance Abuse Treatment and 

Mental Health Services: A Literature Review 

Albert Woodward, Ph.D. 

Introduction 

This chapter reviews the increasing literature on access to substance abuse treatment and 

mental health services. The review is focused on the factors critical in shaping access to these 

services. This knowledge is necessary to help make informed resource allocation decisions that 

will enhance access for individuals most in need of treatment. 

The following sections outline the methods used in the literature review and discuss the 

determinants of access to substance abuse treatment and mental health services. The chapter 

concludes with a discussion of the findings. 

Methods 

To develop a body of literature for this review, three online searches of the health care 

literature covering the years from the mid-1990s to the present were conducted. Articles were 

obtained from the HealthSTAR, PsycINFO, Sociological Abstracts, and MEDLINE databases 

using the keywords "access" and "substance abuse treatment." A second search identified 

literature from the PsycINFO and MEDLINE databases using the keywords "access" and "mental 

health treatment and services." A third search identified literature from the MEDLINE and 

PubMed databases using the keywords "access" and "health care," as well as variants of the 

terms "substance abuse" and "mental health." After a review of the identified articles, a few were 

eliminated from consideration because they were not directly related to mental health services or 

substance abuse treatment access. Also, articles that did not present original research were 

eliminated. The remaining articles were considered for the literature review. Most of the articles 

came from "field" journals (i.e., journals specializing in mental health or substance abuse). In 

addition to these online journal article searches, several books, reports, and other documents 

were added to the review if relevant to the discussion. 

Definitions of Treatment Access 

Several definitions of treatment access can be applied to both mental health services and 

substance abuse treatment. Myers (1965) proposed that there had to be four essential elements 

for "good" medical care, one of which is accessibility. She defined accessibility in terms of three 

components: personal accessibility, comprehensive services, and quantitative adequacy. Personal 

accessibility means that there must be defined points of entry into the health care system. A 

comprehensive range of services is needed because complex problems may require input from a 

variety of specialties. Quantitative adequacy refers to the supply of a comprehensive range of 

personal health services sufficient to meet the need. 
22 

A widely used definition of access was developed by Aday, Fleming, and Andersen 

(1984): 

...those dimensions which describe the potential and actual entry of a given 

population group to the health care delivery system. The probability of an 

individual's entry into the health care system is influenced by the structure of the 

delivery system itself (the availability and organization of health care resources) 

and the nature of the wants, resources and needs that potential consumers may 

bring to the care-seeking process. (p. 13) 

Aday et al. (1984) focused on personal characteristics, health behavior, and attributes of the 

health system. They viewed health services use as a result of a predisposition to use services, 

factors that facilitate or impede the use and the need for care. Donabedian (1973) developed a 

similar concept of access, but with a focus on the health system—access "comprises those 

characteristics of the resource that facilitate or obstruct use by potential clients" (p. 419). 

Need, as a key component of treatment access, can be measured in terms of selfperceived health status, number of chronic conditions, or functional limitations. Clinical 

definitions of treatment need reflect circumstances under which a client seeks or is required to 

obtain treatment (Jeffers, Bognanno, & Bartlett, 1971). The decision to seek treatment typically 

is initiated by the patient. Patient choice is affected by need (e.g., incidence of illness), culturaldemographic characteristics, the role of the health care provider (especially in managed care) 

and/or family as an "agent" for the patient, and external and economic factors. The provider, 

acting as the patient's agent, determines the patient's demand for treatment. 

Myers (1965), Aday and Andersen (1975), and Donabedian (1973) wrote before the 

advent and explosive growth of managed care and related changes in the health care market. As a 

result, their discussions of access are somewhat incomplete because they do not account for 

changes resulting from the growth of managed care or the competition among providers and 

payers (Gold, 1998; Miller, 1998). Prior to these changes, organization and financing were seen 

as independent, static variables among a list of system variables that influenced access. At that 

time, such system variables were of secondary importance to personal variables, including an 

individual's predisposing characteristics or their need for care (Booth, Staton, & Leukefeld, 

2001). However, since the institution of managed care, attitudes have changed, and many 

researchers now believe that system variables may be even more important than many 

nonfinancial barriers to access (Berk & Schur, 1997, 1998; Sondik & Hunter, 1998). 

The literature regarding access to mental health services and substance abuse treatment 

generally is consistent with the general health care literature in terms of the determinants of 

access (Woodward, Dwinell, & Arons, 1992). However, a growing number of researchers 

suggest that managed care has hindered access to both mental health services and substance 

abuse treatment (Mechanic, 1996) and to other health care services for vulnerable populations or 

patients with chronic conditions (Miller, 1998). Moreover, access often is measured by health 

care utilization data obtained in surveys. These surveys, however, usually do not include persons 

with mental disorders, who face barriers to access and participation in these surveys. Therefore, 

it is difficult to evaluate the access of those with mental disorders (Gold, 1998). 
23 

Determinants of Mental Health Services Access 

The mental health services literature has examined a wide range of personal and 

environmental attributes that influence access to mental health services. These include 

demographics, health status and functional limitations, severity of condition, socioeconomic 

status and employment, patient view of mental illness, acculturation, ethnicity, community 

support, church participation, provider sensitivity, structural and operating aspects of providers, 

and a variety of economic and financial barriers (Woodward et al., 1992). Both financial and 

nonfinancial determinants or barriers to access to substance abuse treatment and mental health 

services are discussed in this chapter. 

Severity of Illness 

Research has found that patients with more serious mental illness experience difficulty in 

obtaining treatment for this illness (Gonzalez & Rosenheck, 2002; Wang, Demler, & Kessler, 

2002). Perhaps those with more serious mental illness encounter more barriers to access than the 

general population because successful treatment may be more expensive due to the severity of 

the disorder. For example, veterans with co-occurring mental and substance use disorders incur 

higher overall treatment costs in Department of Veterans Affairs (VA) facilities, largely because 

of the severity of their conditions (Hoff & Rosenheck, 1998). Homeless persons and others with 

the human immunodeficiency virus (HIV) who have mental and substance use disorders have 

better outcomes if they are able to receive extensive services, especially substance abuse 

treatment (Burnam et al., 2001; Gonzales & Rosenheck, 2002). Substance abuse treatment clients 

who do not complete treatment appear to have more health problems at both the beginning and 

termination of treatment. Specifically, clients in outpatient nonmethadone treatment who do not 

complete treatment have a significantly greater number of diagnoses per client at both intake and 

discharge than those completing treatment (Woodward, Raskin, & Blacklow, 2004). 

Demographics 

Children and adolescents face significant obstacles in accessing mental health services 

and substance abuse treatment. One estimate suggests that approximately half of the children 

experiencing depression are not receiving care (Glied & Neufeld, 2001). Parental perceptions of 

children's mental illness and resulting parenting difficulties can act as a barrier to mental health 

services (Owens et al., 2002). Moreover, parental illness, including mental and substance use 

disorders, can further impede a child's access to treatment (Cornelius, Pringle, Jernigan, Kirisci, 

& Clark, 2001). 

Race and ethnicity are attributes or predisposing factors that also can affect access to 

mental health services or substance abuse treatment (Snowden, 2001; Wang et al., 2002). Racial 

and ethnic differences in perceptions about mental illness, treatment system biases, and reliance 

on voluntary support networks act in ways that hamper treatment access (Dana, 2002; Kales et 

al., 2000; Snowden, 2001). As a result, African Americans and Hispanics are likely to receive 

fewer mental health services or less substance abuse treatment than needed (Wells, Klap, Koike, 

& Sherbourne, 2001). African Americans use proportionately fewer outpatient mental health 

services than white patients (Kales et al., 2000), regardless of access to private health insurance 

(Thomas & Snowden, 2001). 
24 

Delivery System 

The mental health services system also can act as a barrier to access, even more so than 

can patient attributes or environmental issues. The delivery system for such care has been 

characterized by the President's New Freedom Commission on Mental Health (2002) as 

... incapable of efficiently delivering and financing effective treatments—such as 

medications, psychotherapies, and other services—that have taken decades to 

develop. Responsibility for these services is scattered among agencies, programs, 

and levels of government. There are so many programs operating under such 

different rules that it is often impossible for families and consumers to find the 

care that they urgently need. The efforts of countless skilled and caring 

professionals are frustrated by the system's fragmentation. ("Letter to the 

President," October 29, 2002). 

This message is not new. Many researchers have called for comprehensive systems of integrated 

care for people with mental illness, especially for those who are homeless (Dennis, Steadman, & 

Cocozza, 2000). 

Treatment access is determined largely at the local level, where most mental health 

services are offered. Local market area studies of mental health services and substance abuse 

treatment (Condelli, Bonito, Ennett, & Fairbank, 1996; Goldsmith, Wagenfeld, Manderscheid, & 

Stiles, 1996) have indicated that specialty services are concentrated in more urbanized areas, 

providing urban populations with better access. Capacity or the availability of supply is crucial to 

understand access (and meet treatment need); both are influenced by the composition of 

treatment ownership, organization, and services and specialty mix (Schlesinger & Dorwart, 

1992). 

Local treatment can be restrictive and bureaucratic, making it difficult for persons with 

mental illness to obtain care. For example, one study found that persons with mental illness who 

are homeless in New York City received less Medicaid, food stamps, and other relief services 

than other persons who are homeless (Nuttbrock, Rosenblum, Magura, & McQuistion, 2002). 

Further, some rural areas have insufficient services to meet the needs of their population (Fox, 

Blank, Rovnyak, & Barnett, 2001; Hartley, Britain, & Sulzbacher, 2002). 

Not all the changes in government programs have lessened access. The restructuring of 

California's public mental health system, for example, promoted access to treatment by patients 

with more serious mental illness (Snowden, Scheffler, & Zhang, 2002). Homeless persons with 

mental illnesses who receive coordinated and intensive mental health and support services have 

been discharged from treatment to community support services without loss of mental health 

status and social functioning (Rosenheck & Dennis, 2001). 

Financing 

Once persons with mental illnesses decide to seek treatment, they are confronted not only 

with the challenges of the health care system, but also with the challenge of paying for that care. 

Financial access to treatment is a function of ability to pay (either out-of-pocket or through 

private coverage or through public funding sources). In contrast, funding for most other health 
25 

care is heavily dependent on private health insurance financing (Frank, McGuire, Regier, 

Manderscheid, & Woodward, 1994). The literature on health care demand has focused 

principally on the relationship between health care demand and the demand for health insurance, 

as well as on the relationships between the type of health insurance package and health care 

utilization (Feldstein, 1973). In general, this literature includes mental illness but excludes 

substance use as determinants of health care utilization or health insurance choice (Frank & 

Manning, 1992; Keeler, Wells, Manning, Rumpel, & Hanley, 1986; Wells, Manning, Duan, 

Ware, & Newhouse, 1982). 

Health insurance affects demand and access in two ways: Insured individuals may choose 

to demand more treatment services (moral hazard, in the conventional sense), or they may select 

specific coverage in anticipation of using services for themselves or dependents (adverse 

selection) (Larsen, Horgan, Marsden, & Tompkins, 1996; Steinberg, 1992). These two factors 

contribute to increased utilization over some optimal social welfare norm, which may be a "good 

thing" for those who avoid treatment (Steinberg, 1992, p. 275). Manning and Frank (1992) 

expressed the same idea: "As long as the incremental risk-pooling gains from reduced cost 

sharing more than offset the incremental increases in costs from demand response, we should 

expand mental health coverage" (p. 214). 

Most persons seeking mental health services rely on public financing, which substitutes 

for health insurance and funds most mental health care (McKusick et al., 1998). This funding, 

however, is often inadequate to meet the needs of those with mental illness (Wang et al., 2002). 

Most mental health care is available through publicly funded programs that are part of the group 

of "safety net providers." These providers, who have been adversely affected by the changes in 

public financing, treat patients who might otherwise not have access to medical care (Baxter & 

Mechanic, 1997). Although the growth of Medicaid managed care made payments available to 

safety net providers, many States provide only limited mental health coverage and no methadone 

maintenance under Medicaid (McCarty, Frank, & Denmead, 1999). 

Some public financing and private health insurance have moved from coverage of more 

costly inpatient hospital treatment to lower cost, but equally effective, residential care (Fenton, 

Hoch, Herrell, Mosher, & Dixon, 2002). Persons with mental illness often come to rely on more 

than one program for care. Changes in the financing of one type of program can affect other 

programs and access. A study of veterans with mental illnesses who used the VA mental health 

systems and non-VA State hospitals is illustrative (Desai & Rosenheck, 2000). In the eight States 

analyzed in the study, the use of State hospitals by veterans was correlated with VA funding: 

A 50% increase in VA per capita mental health spending was associated with a 

30% decrease in veterans' use of state hospitals (elasticity of -0.6). Conversely, a 

50% increase in state hospital per capita funding was associated with only an 

11% increase in veterans' use of state hospitals (elasticity of 0.06). (p. 61) 

Per capita funding of State hospitals and VA mental health systems directly affects access, as 

measured by utilization. The VA system has recently improved access and quality of care in 

comparison with that of privately insured populations (Leslie & Rosenheck, 2000). 
26 

Managed care appears to have constrained access to mental health services over the past 

decade. It has shifted financial risk onto providers and constrained provider treatment options 

through close oversight, financial incentives, and controls. However, nationally representative 

data are not available, and results must be interpreted with caution (Rosenbaum, Mauery, 

Teitelbaum, & Vandivort-Warren, 2002). For example, Cuffel and Regier (2001) observed that 

increased spending on behavioral health care leads to greater access. Although some studies have 

found that access is reduced as a consequence of managed care (e.g., Bloom et al., 2002; Leslie, 

Rosenheck, & Horwitz, 2001), other studies found no impact on health care utilization (e.g., 

Alegria et al., 2001-2002). Most of these studies have examined private-sector mental health care 

organizations. Referrals of patients to psychiatrists are constrained by the limits imposed by 

managed care plans (Grembowski et al., 2002). As mental health managed care becomes more 

concentrated among fewer firms, providers will have less opportunity to change their delivery 

systems to promote access (O'Brien, 2000). Korper and Raskin (2002) argued that the delivery 

system and managed care adversely have affected the treatment of older patients with substance 

use and mental disorders: 

Reduced time for doctor-patient interactions makes it difficult to identify patient 

problems with substances and drug interactions. The health care system has 

experienced reduced hospital lengths of stay, increased reliance on primary care 

physicians, dwindling outpatient resources, and reduced nursing home beds. Older 

adults...have fewer options as to where they can live and receive care. (p. 10) 

In response to managed care, mental health care advocates have supported State and 

Federal legislation to make mental health benefits comparable with those of general medical care 

("parity"). The effects of parity on access to mental health services are ambiguous. Managed care 

controls utilization by circumventing the benefit-design improvements that parity attempts to 

achieve (Frank & McGuire, 1998). In one large employer group, access for subgroups subject to 

a parity mandate was no different from that for subgroups not subject to parity—treatment 

prevalence rose for both types of subgroups (Zuvekas, Regier, Rae, Rupp, & Narrow, 2002). 

Parity can lead to improved mental health coverage and, therefore, access for a slightly higher 

number of people with mental illnesses. However, it also can have negative consequences, 

including the loss of all health insurance coverage for some people with mental illnesses (Sturm, 

2000a). States with parity legislation have not experienced large increases in mental health 

services utilization, perhaps as a result of reductions in private health insurance coverage for 

mental health services (Pacula & Sturm, 2000). 

Determinants of Substance Abuse Treatment Access 

Need and Demographics 

The need for substance abuse treatment has been estimated at the national and State 

levels based on responses to questions in the annual National Survey on Drug Use and Health 

(NSDUH), formerly the National Household Survey on Drug Abuse (NHSDA). This nationally 

representative survey assesses dependence and abuse of substances and treatment received 

(Office of Applied Studies [OAS], 2002). Findings based on this survey generally have been 

consistent with studies using other surveys and frequently agree with anecdotal treatment 

perceptions. Age at first use of alcohol or illicit drugs is a very important factor in understanding 
27 

an individual's need for treatment—the earlier the use of marijuana, for example, the greater is 

the likelihood for substance abuse treatment at a later age (Gfroerer, Wu, & Penne, 2002). Men 

are more likely to need treatment than women. The likelihood of seeking treatment increases 

with age up to the mid-30s and then declines; problems of substance use and need for treatment 

by race and ethnicity are similar to other illness conditions in the U.S. population (Flewelling, 

Ennett, Rachal, & Theisen, 1993; Gerstein, Foote, & Ghadialy, 1997; OAS, 1998). Family 

structure, living arrangements, and residential stability influence substance use and treatment 

need (Bachman, Wadsworth, O'Malley, Johnston, & Schulenberg, 1997; Johnson, Hoffman, & 

Gerstein, 1996). 

The influence of predisposing factors—such as level of educational attainment, income, 

and employment status—on treatment need is still being evaluated. Because these factors often 

are interrelated, researchers have found it difficult to explore the separate effects of these 

variables. Thus, studies have somewhat contradictory findings. One study found no consistent 

associations among these predisposing variables and heavy or frequent use of substances, which 

is an indicator of treatment need (Flewelling et al., 1993). However, other studies have found a 

correlation between lower income and need for treatment among those over 25 years of age, but 

this correlation could indicate a relationship between different career and education paths and 

different levels of treatment need (Bachman et al., 1997; Gfroerer et al., 2002). The nature of the 

relationships observed between race/ethnicity and need for treatment could be confounded by the 

relationship between race and socioeconomic status (Flewelling et al., 1993). 

Access to substance abuse treatment can be affected by such demographic factors as 

race/ethnicity and urbanization of residence, among others (OAS, 1998). For example, African 

Americans and Hispanics are less likely to have access to substance abuse treatment than are 

whites (Wells et al., 2001). Rural residency is a greater barrier to treatment than urban status. 

Rural at-risk drinkers had more difficulty obtaining care and were sick more often than their 

urban counterparts (Booth, Kirchner, Fortney, Ross, & Rost, 2000). Homeless persons with 

substance use disorders may have the most difficulty accessing treatment, even if they have 

public health insurance (Kushel, Vittinghoff, & Hass, 2001; Wenzel et al., 2001). Persons 

enrolled in a health maintenance organization (HMO) are more likely to initiate treatment after 

assessment if they are employed (with pressure from employers or colleagues to enter treatment) 

and have more serious substance use disorders (Mertens & Weisner, 2002). Persons who inject 

drugs or have HIV face particular barriers to care. The literature on these groups covers a wide 

variety of determinants of access to care. However, these studies are lacking, as these groups are 

difficult to study in a representative manner. Even so, there is agreement that these groups 

receive suboptimal care, which may be indicative of access constraints and an inability to 

comply with a prescribed treatment regimen (Burnam et al., 2001; Chitwood, Comerford, & 

McCoy, 2002; Knowlton et al., 2001; Weissman et al., 1995). 

Seeking Treatment 

Understanding the demand for substance abuse treatment is more complex than assessing 

the need for treatment. Demand depends on multiple factors—the person's behavior consequent 

to substance use, the seriousness of the substance use disorder, the price for treatment, patient 

income and education, and other market and personal characteristics. It is not uncommon for 

those with a health problem to delay seeking treatment. Those with substance use disorders also 
28 

are likely to deny that they need treatment (McCoy, Metsch, Chitwood, & Miles, 2001). Persons 

with substance use disorders often have an altered perception of their use that may contribute to 

their avoidance of treatment (Grossman, 1993). 

When substance use disorders reach a point where an individual no longer can cope, then 

individuals will seek or be coerced into treatment. The time between the recognition of the need 

for treatment and actually seeking treatment may be as long as a decade or more (Kessler et al., 

2001). The reasons for seeking treatment are "illuminating, although their logic proves to be 

unintelligible in some cases, and they may be evasive or deceptive in others" (Institute of 

Medicine [IOM], 1990, p. 109). Typically, the individual's reluctance to seek treatment has to be 

overcome. In many cases, the individual may have to be coerced into treatment by court order, 

family, or employer. The physical consequences of substance use, and subsequent attention to 

the disorder by health care professionals, motivate some people to seek treatment (Weisner & 

Matzger, 2002). 

Financial Barriers 

Multiple factors affect treatment access (Kessler et al., 2001), including financial barriers. 

As previously noted, a substantive body of literature has examined the relationship between 

demand for mental health services and health insurance coverage. More research remains to be 

done regarding the impact of insurance on substance abuse treatment access. 

Many of those who seek substance abuse treatment have low incomes, which may 

hamper their ability to pay out-of-pocket, as well as their ability to acquire adequate health 

insurance coverage (Larsen et al., 1996; Sturm & Sherbourne, 2001). As a result, they often are 

forced to rely on subsidized treatment provided by publicly funded programs. 

Persons with lower income are not the only group who face difficulties obtaining care. 

Older persons frequently have undiagnosed substance use disorders and, as a consequence, do 

not receive necessary treatment (Korper & Council, 2002). Older patients with diagnosed 

substance use disorders also face difficulty in obtaining needed outpatient mental health care, 

perhaps because of limits in Medicare benefits coverage (Brennan, Kagay, Geppert, & Moos, 

2001). 

The effect of managed care on access to substance abuse treatment is comparable with 

that for access to mental health services. Managed care, in general, shows evidence of systemic 

reductions in access to inpatient care for both substance use and mental disorders while 

increasing the reliance on outpatient treatment (Steenrod, Brisson, McCarty, & Hodgkin, 2001). 

Most substance abuse managed care also is "carved out" of the general health insurance plan or 

State Medicaid plan (Sosin & D'Aunno, 2001). As is the case with mandated mental health 

benefits, mandated substance abuse benefits may not increase utilization because managed care 

constrains that utilization (Sturm, 2000b). 

States have introduced changes to welfare programs and Medicaid plans as a result of 

Federal legislative changes. Most of these changes have not improved access for persons with 

substance use disorders. For example, under welfare reform, welfare recipients with substance 

use disorders in the State of Washington face difficulty in obtaining treatment and vocational 
29 

counseling in their efforts to become self-sufficient (Wickizer, Campbell, Krupski, & Stark, 

2000). Although treatment access may be constrained by changes to State programs, two 

separate studies found that substance abuse treatment access improved as a result of programs in 

Massachusetts and Oregon (Beinecke, Shepard, Tetreault, Hodgkin, & Marckres, 2001; Deck, 

McFarland, Titus, Laws, & Gabriel, 2000). 

The structure and organization of treatment providers can affect access to substance 

abuse treatment. For-profit treatment programs are more likely to provide treatment to clients 

with health insurance coverage or the ability to pay—clients who generally are not treated in 

publicly financed treatment programs (Wheeler & Nahra, 2000). Thus, substance abuse treatment 

is a "two-tiered" public and private system. Centralized intake assessments prior to treatment 

initiation serve to place publicly financed clients into treatment programs, thereby promoting 

treatment access (Guydish, Woods, Davis, Bostrom, & Frazier, 2001). However, one study found 

lower rates of treatment placement for women after centralized intake assessment (Arfken, 

Borisova, Klein, di Menza, & Schuster, 2002). Women with special needs (e.g., those who are 

pregnant) and men and women who injected drugs were given higher priority for treatment. 

Treatment access can be improved for women by providing the range of social support services 

they need, especially services for mothers (Marsh, D'Aunno, & Smith, 2000; Nakashian, 2002). 

Publicly funded treatment facilities may not have sufficient capacity to provide services 

to all individuals who request treatment. Changes that increase staff burden, reduce or eliminate 

certain services, or lessen methadone availability are likely to erode patient access to substance 

abuse treatment programs (Friedmann, Alexander, & D'Aunno, 1999). Too often, individuals 

with substance use disorders end up going through short-term detoxification multiple times 

before beginning more long-term treatment solutions or relying on emergency departments for 

palliative treatment (McCarty, Capsi, Panas, Krakow, & Mulligan, 2000; McGeary & French, 

2000; Wingerson, Russo, Ries, Dagadakis, & Roy-Byrne, 2001). Methadone maintenance 

programs may offer access to treatment for those addicted to heroin but may have insufficient 

funding to provide appropriate dosage or sufficient long-term treatment (Brands, Blake, & 

Marsh, 2002; Joseph, Stancliff, & Langrod, 2000; Sees et al., 2000; Weinrich & Stuart, 2000). 

The Medicaid program could itself be a barrier to treatment for these patients in the 25 States 

that do not cover methadone maintenance medication (McCarty et al., 1999). 

Discussion 

This literature review has covered a wide variety of the attributes of access to treatment 

for substance use and mental disorders, with an emphasis on financial impediments. The 

determinants of treatment access were divided into mental health and substance use topics 

because much of the literature discusses them separately. Nonetheless, the determinants are 

similar for both mental health services and substance abuse treatment. The literature shows a 

growing awareness of the impact of financing and costs as critical determinants of treatment 

access, reflecting the growth of managed care in the past decade. This growth has affected 

treatment of both disorders. 

Treatment access, of course, is only the first step to successful outcomes. Persons with 

mental or substance use disorders cannot be treated if they cannot gain access to treatment, nor 

can they be treated successfully if treatment is not effective. Although treatment effectiveness is 
30 

beyond the subject of this chapter, effectiveness also depends, to some extent, on access to care. 

The IOM (1990) report sums up treatment effectiveness: 

No single treatment "works" for a majority of the people who seek treatment. 

Each of the treatment modalities for which there is a baseline of adequate studies 

can fairly be said to work for many of the people who seek that treatment; and 

enough of them do find the right treatment, and stay with it long enough, to make 

the current aggregate of treatment programs worthwhile. (p. 191) 

The IOM report points out that access to appropriate treatment frequently is constrained by the 

lack of capacity in treatment programs, the restrictive costs of treatment, the lack of adequate 

intake assessment, and the lack of information or transportation. The critical first part of 

treatment effectiveness is initial assessment and assignment to the appropriate treatment, which 

often is missing. This is true for both mental health services and substance abuse treatment. 

Despite a large number of studies on the topic, the reasons that people with mental or 

substance use disorders seek treatment are not fully known. Booth et al. (2001) argued that 

This broader definition of access can generally only be studied from community 

samples, where substance using individuals are identified and followed 

prospectively to see how access influences their use of treatment or other services. 

We know that relatively few individuals with "substance use disorders" use 

treatment services, and it is critical to identify the effect size for access, as a 

potentially modifiable policy-related factor, in increasing treatment-seeking. 

Additional information is needed to understand more about broad inequities in 

access, particularly for posited and actual vulnerable and generally powerless 

populations such as minorities and adolescents. (p. 676) 

The authors suggested that the focus of new research should be on persons with substance use 

disorders in the community, as distinct from those getting substance abuse treatment, if the 

determinants of "treatment-seeking" are to be understood. 
31 

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40 
41 

Chapter 3. Substance Use and Mental Disorder Discharges 

from U.S. Community Hospitals in the Early 1990s, 

Revisited 

Sarah Q. Duffy, Ph.D. 

Introduction 

Managed care and behavioral health care carve-outs proliferated during the early 1990s, 

and research suggests these arrangements reduce inpatient mental health services and substance 

abuse treatment (Callahan, Shepard, Beinecke, Larson, & Cavanaugh, 1995; Etheridge, Hubbard, 

Anderson, Craddock, & Flynn, 1997; Goldman, McCulloch, & Sturm, 1998; Iglehart, 1996; Ma 

& McGuire, 1998; Mechanic, 1997b). Based on these findings, one might expect to have seen a 

coinciding decline in admissions to community hospitals of patients with substance use and/or 

mental disorders (SU/MD). Such short-term, general, non-Federal hospitals have long been 

involved in SU/MD treatment and have accounted for a large share of inpatient stays for those 

with SU/MD, including approximately 54 percent of all such stays in 1985 and 69 percent of 

those of Medicare beneficiaries in 1995 (Cano, Hennessy, Warren, & Lubitz, 1997; Kiesler & 

Simpkins, 1993; Mechanic, 1997a). 

However, much of the research on managed care has relied on methods, such as simple 

pre- and post-comparisons of aggregate claims from privately insured populations, that may fail 

to capture the experience of many with SU/MD (Callahan et al., 1995; Goldman et al., 1998; Ma 

& McGuire, 1998). Other reports suggest that these patients may receive inadequate substitutes 

for inpatient mental health services, that a treatment gap exists, and that a growing percentage of 

the U.S. population lacks insurance (Bae, 1997; Dana, Conner, & Allen, 1996; Hirschfeld et al., 

1997; Mechanic, Schlesinger, & McAlpine, 1995; Robertson, 1997; Rouse, 1998; U.S. Bureau of 

the Census, 1997). If the result is inadequate or fragmented community-based specialty treatment 

for those with SU/MD, they may be more likely to be admitted to local community hospitals for 

stabilization and detoxification (Olfson, 1993; Olfson & Walkup, 1997; Walkup, 1997; Wolfe & 

Sorensen, 1989). We examine these concerns by analyzing trends in discharges of those with 

SU/MD from community hospitals nationwide during the first half of the 1990s. 

Two studies, one by Maynard and Cox (1998) and the other by Mechanic, McAlpine, and 

Olfson (1998), examined trends in community hospitalizations of those with SU/MD in the early 

1990s using the National Center for Health Statistics (NCHS) National Hospital Discharge 

Survey (NHDS). However, their reports provided vastly different trend estimates. According to 

Maynard and Cox (1998), SU/MD discharges increased only 0.5 percent between 1990 and 

1994. Mechanic et al. (1998), on the other hand, reported that SU/MD discharges increased by 

35 percent between 1988 and 1994. Furthermore, Maynard and Cox (1998) reported that there 

was no change in the number of discharges with a co-occurring disorder—one substance use and 

one mental disorder—during the time period (a trend that Mechanic et al. did not examine). 

In this study, we reexamine trends during this time period both by explaining how these 

different estimates could have been generated by the NHDS data and by providing new estimates 
42 

using a dataset more appropriate for examining community hospitalizations of those with 

SU/MD. The findings presented here will contribute to our understanding of the impact of the 

changes in the health care system in the early 1990s on those with SU/MD diagnoses. 

Data 

In this study, we use data covering 1990-1995 from the Agency for Healthcare Research 

and Quality's Nationwide Inpatient Sample (NIS), which contains discharge abstract records that 

hospitals report to State data organizations (Agency for Health Care Policy and Research, 1995). 

With more than 6 million records per year, it approximates a 20 percent sample of U.S. 

community hospitals and includes information necessary to compute national estimates and 

standard errors using methods for complex database designs, such as those available in 

SUDAAN software (Shah, Barnwell, & Bieler, 1996). 

For a number of reasons, NIS data are more appropriate for studying community 

hospitalizations of individuals with SU/MD during the early 1990s than are the NHDS data. 

First, unlike the NIS, the NHDS is a sample of all short-term hospitals, including short-term 

psychiatric hospitals. According to one estimate, 13 percent of the discharges with mental 

disorder diagnoses in the NHDS were from psychiatric hospitals. Failure to account for them in 

the NHDS data caused at least one team of researchers to vastly overestimate the number of 

individuals with mental disorders receiving care in swing beds in general hospitals (Kiesler & 

Simpkins, 1993). The NIS also is a much larger sample than the NHDS and allows analysis of 

patients by more refined diagnosis categories, which is useful because those with SU/MD 

diagnoses are a diverse group. Finally, the NIS has been consistently coded across years, which 

makes it easier to use. Inconsistencies in coding across years in the NHDS were likely 

responsible for the results reported by Maynard and Cox (1998). These inconsistencies will be 

described in the next section. 

Methods 

Study Sample 

We study discharges coded with the Clinical Classifications for Health Policy Research 

(CCHPR) principal diagnosis (DCCHPR1) categories, a variable available on the NIS. CCHPRs 

reclassify codes from the International Classification of Diseases, 9th Revision, Clinical 

Modification (ICD-9-CM) into broader reporting categories (Elixhauser, 1996). Clients with a 

DCCHPR1 listed in Table 3.1 were included in the analysis. This definition of SU/MD is 

consistent with much of the existing research on such hospitalizations (Cano et al., 1997; Kiesler 

& Simpkins, 1993; Maynard & Cox, 1998; Mechanic et al., 1998).1 The sample sizes varied 

between 250,000 and 340,000 per year. 

 1

 For example, Maynard and Cox's (1998) selection criteria differed from the one reported here only in that 

they exclude mental retardation cases, which accounted for less than 1 percent of the sample used here. Mechanic et 

al. (1998) selected on a different, but related, variable. Using their selection criteria on 1995 NIS data yields a 

weighted estimate of SU/MD discharges that is 0.43 percent lower than the estimate reported here and a very similar 

distribution of discharges by type of diagnosis. 
43 

Table 3.1 Distribution of Discharges from U.S. Community Hospitals, by Principal Diagnosis CCHPR, 1990 

and 1995, Substance Use and Mental Disorder Sample

1990 1995 

Principal Diagnosis CCHPR (DCCHPR1) 1 National

Estimate 

% of

SU/MD 

Sample

2

National

Estimate 

% of

SU/MD 

Sample

2

% 

Change 

65 Mental Retardation 887 0.06 593 0.03 -33.14 

66 Alcohol-Related Mental Disorders (acute alcohol 

intoxication; other and unspecified alcohol 

dependence; nondependent alcohol abuse; other 

alcohol-related mental disorders)

302,821 21.80 280,651 16.17 -7.32 

67 Substance-Related Mental Disorders (opioid 

dependence; cocaine dependence; other, combined, 

and unspecified drug dependence; cocaine abuse; 

other, mixed, or unspecified drug abuse; other 

substance-related mental disorders)

111,517 8.03 240,792 13.88 115.92 

68 Senility and Organic Mental Disorders (senile 

dementia, uncomplicated; arteriosclerotic dementia; 

transient organic psychotic conditions; specific 

nonpsychotic mental disorders due to organic brain

damage; presenile dementia, uncomplicated; senile 

dementia with delirium; other senility and organic 

mental disorders) 

94,290 6.79 129,795 7.48 37.66 

69 Affective Disorders (major depressive disorder, 

single episode; major depressive disorder, recurrent

episode; neurotic depression; bipolar affective 

disorder; manic-depressive psychosis; other 

affective disorders)

409,126 29.45 557,445 32.13 36.25 

70 Schizophrenia and Related Disorders (paranoid 

schizophrenia; schizo-affective type; other 

schizophrenia)

186,913 13.45 238,188 13.73 27.43 

71 Other Psychoses 53,299 3.84 56,457 3.25 5.93 

72 Anxiety, Somatoform, Dissociative, and

Personality Disorders (anxiety states; personality

disorders; other anxiety, somatoform, dissociative, 

and personality disorders) 

64,294 4.63 67,627 3.90 5.18 

73 Preadult Disorders 17,606 1.27 18,086 1.04 2.73 

74 Other Mental Conditions (adjustment reaction; 

depressive disorder, not elsewhere classified) 

147,965 10.65 145,145 8.36 -1.91 

75 Personal History of Mental Disorder, etc. 463 0.03 407 0.02 -12.10 

CCHPR = Clinical Classifications for Health Policy Research; DCCHPR1 = CCHPR principal diagnosis; SU/MD = substance use/mental

disorders. 

1 See Elixhauser (1996) or http://www.ahrq.gov for more information.

2 Difference in distribution of DCCHPRs over time significant at better than 1 percent level. 

Source: SAMHSA, Office of Applied Studies' analysis of Agency for Healthcare Research and Quality's National Inpatient Sample, 1990-1995. 
44 

We present trends for all discharges with SU/MD and for five, mutually exclusive 

subgroups based on principal and all secondary diagnoses. To create these subgroups, we

grouped all secondary diagnoses with the CCHPR software program, which is freely available 

for downloading at http://www.ahrq.gov/data/hcup/ccs.htm (Elixhauser, 1996). These subgroups, 

which appear in Table 3.2, reflect differences in complexity and in the ability and willingness of 

community-based providers to treat patients (Etheridge et al., 1997; Mechanic, 1997b). For 

example, those with both a substance use and another mental disorder diagnosis are a distinct 

subgroup here because, during the early 1990s, the substance abuse treatment and mental health 

services systems often were separate. Changes in the health care system during that time may 

have made it especially difficult for patients with both types of disorders to navigate two separate 

systems. 

Table 3.2 Diagnosis Subgroup Definitions 

Diagnosis Subgroup Includes records with... 

Substance Use Only Only substance use diagnoses (DCCHPR codes 66 or 67). 

Substance Use and Mental Disorder At least two diagnoses—at least one mental disorder (DCCHPR 65, 68-

75) and at least one substance use, either one of which may be principal. 

Substance Use and Medical At least two diagnoses—a substance use principal and one non-SU/MD 

(DCCHPR not in 65-75). 

Mental Disorder Only Only mental disorder diagnoses. 

Mental Disorder and Medical At least two diagnoses—a mental disorder principal and at least one nonSU/MD. 

DCCHPR = Clinical Classifications for Health Policy Research diagnosis code; SU/MD = substance use/mental disorders. 

These methods are similar to those used by Maynard and Cox (1998), except that those 

authors appear to have missed a change in the way diagnoses were coded in the 1994 NHDS that 

required a modification to the CCHPRS formatting program.2 Failure to account for this change 

likely resulted in their undercounting both the number of discharges with SU/MD and the 

number of those with a co-occurring disorder in the Nation's short-term hospitals reported by the 

NHDS in that year. This can be verified by referencing several NCHS publications (e.g., Gillum, 

Graves, & Kozak, 1996; Graves & Gillum, 1997) that report counts of discharges by disease 

category. According to these publications, the number of discharges with SU/MD diagnoses 

increased from approximately 1,538,000 to 2,112,000, or 37 percent, between 1990 and 1994. 

Statistical Methods 

We present weighted means, percentages, and age- and gender-adjusted discharge rates 

per 10,000 population. We discuss in the text differences that are significant at or better than the 

5 percent level. For comparisons among groups of diagnoses in the same year, we computed t 

tests for continuous variables and chi-square (x

2

) tests for categorical variables using SUDAAN 

 2

 Community hospital discharge abstract data are coded in the ICD-9-CM system. Under this system, each 

code can be between three and five characters in length. The data are usually right justified and filled with blanks. 

So, for example, the code 300.3 (obsessive-compulsive disorder) would appear on the tape as a 3003 with a blank 

space after it. For some reason, the 1994 NHDS data filled with dashes instead of blanks. So, 3003 appeared as 

"3003-" and, therefore, was not identified by the CCHPR formatting program. 
45 

(Shah et al., 1996), along the lines of the example provided with the NIS documentation (Duffy 

& Sommers, 1999). To examine trends in discharge rates, we computed the Estimated Annual 

Percentage Change (EAPC) (Ries et al., 1997). The EAPC is 100(em-1), where m is the 

coefficient on a regression of the natural logarithm of the standardized discharge rates on 

calendar year. A negative EAPC indicates that the standardized rate has declined, while a 

positive EAPC indicates that it has increased. We used the standard error (SE) from the 

regression to compute t statistics. To determine whether differences over time were significant 

for other variables, we computed test statistics based on the differences in value between 1990 

and 1995 using a method that accounted for hospitals that appear in the sample both years. 

Although we focus our discussion on differences between 1990 and 1995, statistics computed 

using data from all 6 years confirm the trends we report. 

Results 

Discharges of those with SU/MD grew substantially between 1990 and 1995, and Table 

3.3 shows that this growth, from 1.39 million to 1.74 million (t test, p <0.0001, df = 1,302), 

contrasts with the stability of total discharges.3

 Figure 3.1, which displays age- and genderadjusted discharge rates, reveals that those with both a substance use and mental disorder 

diagnosis accounted for most of the increase. Discharges of individuals with both diagnoses 

increased from 9.4 to 17.22 per 10,000 population (EAPC t value = 14.774, p = 0.0001, df = 5). 

Discharges with both a mental disorder and medical diagnosis increased as well, but at a lower 

rate, from 19.3 to 22.5 per 10,000 population (EAPC t value = 4.222, p = 0.0135, df = 5). A 

small decline in the rate of discharges with mental disorders alone, from 14.0 to 11.8 per 10,000 

population (EAPC t value = -6.288, p = 0.0033, df = 5), only partially offsets these increases. 

As the number and discharge rate of those with SU/MD grew, their average length of stay 

(ALOS) declined by 25 percent (t = 7.17, p < 0.001, df = 1,302) compared with a 13 percent 

decline for all discharges (t = 9.984, p < 0.0001, df =1,337). Although ALOS declined for all 

subgroups, and the decline was most pronounced for the three substance-related subgroups, the 

ALOS ranking remained the same over time. Discharges with mental disorder and medical 

diagnoses had the longest ALOS throughout the period, which declined 20 percent (t = 6.28, p < 

0.001, df = 1,277) while those with substance use diagnoses alone had the shortest, which 

declined 36 percent (t = 4.47, p < 0.0001, df = 927). 

The age distribution of SU/MD discharges also changed substantially during this time (P2

= 242.23, p < 0.0001, df = 6). Table 3.3 displays information on those aged 35 to 45 years, who 

increased the most among the groups analyzed (< 12, 12 to 17, 18 to 25, 26 to 34, 35 to 45, 46 to 

64, 65 or older). They comprised the largest share of discharges with SU/MD diagnoses in 1995 

at 36 percent, replacing the 26 to 34 year olds, who had the largest share of discharges in 1990. 

The growth of those aged 35 to 45 years occurred among all SU/MD subgroups, but was most 

noticeable within the substance-related subgroups. 

Although the age distribution changed between 1990 and 1995, Table 3.3 also shows that 

the gender distribution did not. However, there were differences in these distributions between 

those with SU/MD and all discharges (P2

 = 183.79, p < 0.0001, df = 1), as well as across 

 3

 As expected, these counts are somewhat lower than those estimated from the NHDS. 
46 

Table 3.3 Substance Use and Mental Disorder Discharges Compared with All Discharges from U.S. Community Hospitals, 199

Discharge 

Statusb,c Expected Primar

Sample Year 

Count 

in 

1,000s 

Rate1 per 

10,000 

Population 

Length 

of Stay 

(days)a

% a,b 

Male 

% a,b,c

Age 35-

45 

% 

Died 

% 

AMA 

% 

Medicare 

% 

Medicaid 

P

1990 35,215 1,420d 6.1e All Discharges 42.2 9.6 2.8 0.8 

1995 34,802 1,328 d 5.3e 41.5 10.7 2.6 0.9 36.5 18.0 

1990 1,389e 56.1 d 12.7e SU/MD 50.0 22.1 0.3 6.3 

1995 1,735e 66.3 d 9.5e 51.3 27.5 0.2 6.3 29.9 30.0 

1990 144.3 5.8 8.6e Substance Use 73.4 26.7 0.01 15.2 

Only 1995 163.4 6.7 5.5e 73.7 36.2 0.01 19.3 6.7 38.5 

1990 233.6e 9.4 d 11.5e Substance Use 58.1 25.8 0.03 8.6 

and Mental Disorder 1995 446.2e 17.2 d 8.3e 59.1 34.1 0.03 6.6 21.5 34.4 

1990 185.2 7.5 10.0e Substance Use 71.6 28.2 0.32 10.2 

and Medical 1995 223.8 8.5 6.1e 70.6 36.1 0.21 12.1 18.0 36.7 

1990 346.8 14.0 d 13.1e Mental Disorder 43.1 22.4 0.05 4.52 

Only 1995 306.5 11.8 d 10.6e 43.7 24.6 0.02 3.62 23.0 31.4 

1990 479.2e 19.3d 15.2e Mental Disorder 35.7 16.3 0.63 2.37 

and Medical 1995 595.3e 22.5d 12.1e 35.9 18.4 0.41 1.78 49.2 21.0 

AMA = against medical advice; SU/MD = all substance use and mental disorder discharges; UCC = uncompensated care. Note: The following symbols represent significant differences at or better than the 5 percent level: 

a Distributions across subgroups in 1995 (P2). b Distribution between all discharges and SU/MD discharges in 1995. c Distribution over time (except for discharge status for substance use disorder only). d Rates over time. e Within groups across years. 

1 Age- and gender-adjusted discharge rate. 

Source: SAMHSA, Office of Applied Studies' analysis of Agency for Healthcare Research and Quality's National Inpatient Sample, 1990-1995. 
47 

Figure 3.1 Age- and Gender-Adjusted Discharge Rates, by Subgroup, 1990 to 1995 

Source: SAMHSA, Office of Applied Studies' analysis of Agency for Healthcare Research and Quality's National Inpatient Sample, 1990-1995. 

diagnosis groups (P2 = 866.18, p < 0.0001, df = 4). Slightly over 51 percent of those with SU/MD 

diagnoses were male compared with fewer than 42 percent of all discharges. Among the 

subgroups, most likely to be male were those with substance use diagnoses, varying from 59.1 

percent for those with co-occurring mental and substance use disorders to 73.7 percent for those 

with only substance use diagnoses. 

We examined two rough indicators of outcomes based on patient disposition at discharge: 

the in-hospital mortality rate and the percentage who leave against medical advice (AMA). The 

distribution of SU/MD patients at discharge was significantly different from that of all 

discharges (1995: P2 = 827.46, p < 0.0001, df = 3), and across SU/MD diagnosis groups (P2 = 

818.52, p < 0.0001, df = 12). Table 3.3 reveals that the in-hospital mortality rate for SU/MD 

discharges was substantially lower than that for all discharges. It varied among subgroups, from 

a low of 0.01 percent for those with substance use diagnoses alone in 1995 to a high of 0.41 

percent for those with both mental disorder and medical diagnoses. However, discharges with 

SU/MD diagnoses, especially those with substance-related disorders, were much more likely to 

leave AMA than were other discharges, varying from 1.78 percent of the mental disorder and 

medical subgroup to 19.3 percent of the substance use only subgroup. 

"Expected primary payer" is defined as the payer who is expected, at the time of the 

admission, to pay the hospital bill. It would be most informative to analyze this variable over 

time and examine separately those insured under managed care arrangements. Unfortunately, 

neither of these is possible due to data limitations, so the following categories are examined for 

1995 only: Medicare, Medicaid, private (Blue Cross, PPO, commercial, HMO, prepaid health 

plan), uncompensated care (UCC: self-pay, no charge), and other coverage (Title V, workers' 

0

5

10

15

20

25

1990 1991 1992 1993 1994 1995

Year

Substance Use

Disorder Only 

Substance Use

Disorder & Mental

Disorder

Substance Use

Disorder & Medical

Mental Disorder

Only

Mental Disorder &

Medical

Discharge Rate Per 10,000 Population

Diagnosis

0

5

10

15

20

25

1990 1991 1992 1993 1994 1995

Year

Substance Use

Disorder Only 

Substance Use

Disorder & Mental

Disorder

Substance Use

Disorder & Medical

Mental Disorder

Only

Mental Disorder &

Medical

Discharge Rate Per 10,000 Population

Diagnosis
48 

compensation, CHAMPUS/CHAMPVA, other government).4 Table 3.3 reveals that those with 

SU/MD diagnoses were more likely than all discharges to receive uncompensated care or have 

Medicaid coverage and were less likely to be covered by Medicare or private insurance (P2 = 

202.87, p < 0.0001, df = 4). 

Substantial differences existed among SU/MD subgroups (P2 = 1,010.01, p < 0.0001, df = 

16). Almost 50 percent of discharges with both a mental disorder and medical diagnosis had 

Medicare coverage, while those diagnosed with mental disorders alone most frequently had 

private coverage, and those with substance-related disorders alone most frequently had Medicaid 

coverage. Only 4.1 percent of those with both mental disorder and medical diagnoses had no 

coverage compared with 22 percent of those in the substance use only subgroup. 

Referring to Table 3.1, one can see that the vast majority of the SU/MD discharges had 

principal mental disorder diagnoses, with smaller but almost equal percentages with alcoholrelated and substance-related diagnoses in 1995. However, there were changes in the distribution 

over time (P2

 = 86.65, p < 0.0001, df = 10). Although in 1995 the top two DCCHPRs remained 

Affective Disorders and Alcohol-Related Mental Disorders, the third most prevalent in 1995, 

Substance-Related Mental Disorders, had been fifth in 1990. 

Discussion 

Community hospitals remained important in caring for individuals with SU/MD 

diagnoses in 1995, and such patients were a growing part of community hospitals' inpatient 

business. While total discharges remained stable during the first half of the 1990s, we found, as 

did Mechanic et al. (1998), that discharges of those with SU/MD diagnoses increased 

substantially. Affective disorders, among mental disorders, and alcohol-related disorders, among 

substance use disorders, remained among the most frequent diagnoses, although abuse of other 

substances increased. This increase appears to have become permanent, as, according to more 

recent NIS data, the number of discharges from community hospitals of those with SU/MD has 

continued to be above 1.7 million through the year 2001 (the most recent year for which data are 

available), when they topped 1.9 million. 

Contrary to previously published reports (Maynard & Cox, 1998), the percentage of 

discharges with at least one substance use and one mental disorder increased substantially during 

the 1990s. Although this growth may reflect more accurate diagnosis and coding, the negligible 

offsetting reduction in the single diagnosis categories argues against that explanation. At the 

same time, although a smaller percentage of patients with SU/MD diagnoses died in the hospital 

compared with all patients, a much larger percentage left the hospital against medical advice 

(AMA). 

As with all discharges in 1995, Medicare and Medicaid paid for more than 50 percent of 

discharges for those with SU/MD diagnoses. Unfortunately, due to data limitations, we cannot 

compare this figure with earlier years of the NIS data. As a point of comparison, we can turn to 

estimates based on 1985 NHDS data, which, although they suffer from the shortcomings 

 4

 PPO = preferred provider organization; HMO = health maintenance organization; CHAMPUS = Civilian 

Hospital and Medical Care for the Uniformed Services; CHAMPVA = Civilian Health and Medical Program for the 

Department of Veterans Affairs. 
49 

described earlier, were the only nationwide hospital discharge abstract data publicly available 

before 1988. According to these 1985 data, commercial insurance (then consisting mostly of feefor-service plans) paid for 44 percent of inpatients with SU/MD, followed by Medicare at 20 

percent and Medicaid at 16 percent (Kiesler & Simpkins, 1993). This comparison suggests that 

the Federal Government's role in paying for these patients may have increased substantially since 

the mid-1980s. 

Sharp declines in length of stay suggest that hospitals may provide short-term lifesaving 

services, such as detoxification and stabilization, but not treatment for their chronic underlying 

disorders (Jayaram, Tien, Sullivan, & Gwon, 1996; National Institute of Mental Health, 1998, 

2003). Discharged patients subsequently may receive outpatient treatment, which may be 

entirely appropriate (Kiesler & Simpkins, 1993). However, the extent to which this is occurring 

is unclear given some evidence of hospitals' infrequent follow-up of patients referred to 

outpatient aftercare and the reluctance of many outpatient mental health providers to treat those 

with co-occurring mental and substance use disorders or those with medical complications, who 

showed the greatest increases in hospitalizations reported here (Etheridge et al., 1997; Mechanic, 

1997b; Olfson, 1993; Olfson & Walkup, 1997; Walkup, 1997). Shorter stays may mean patients 

are being discharged or leaving AMA in sicker condition and may need to be rehospitalized 

(Olfson & Walkup, 1997). One limitation of the NIS (as well as the NHDS) is that it does not 

allow linkages across individuals, so we cannot determine whether patients are being 

rehospitalized. 

Although these results cannot prove causality because they are based on a series of crosssectional observations rather than following specific individuals through time, they do not 

diminish concern that changes during the early 1990s adversely affected those with SU/MD and 

may continue to affect them today. They suggest that further study into the causes of the 

increases in community hospital discharges of those with SU/MD and a more thorough study of 

effects on outcomes are warranted. Clearly, in 1995, U.S. community hospitals remained 

important in caring for those with SU/MD. 
50 

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53 

Chapter 4. Do Client Characteristics Affect Admission to 

Inpatient Versus Outpatient Alcohol Treatment in Publicly 

Monitored Programs? 

Sarah Q. Duffy, Ph.D. 

Gary A. Zarkin, Ph.D. 

Laura J. Dunlap, M.A. 

Introduction 

Alcohol use disorders cost the United States some 100,000 lives and $184.6 billion 

annually, and 14 million people meet the diagnostic criteria for alcohol abuse and alcoholism 

(National Institute on Alcohol Abuse and Alcoholism [NIAAA], 2000; Subcommittee on Health 

Services Research, 1997). The Nation spends approximately $6.1 billion per year on treatment 

for those with alcohol use disorders, 63 percent of which is funded by Federal, State, and local 

governments (Mark et al., 1999). Of the more than 1.5 million admissions annually to substance 

abuse treatment facilities in the United States, almost 50 percent list alcohol as the primary 

substance of abuse (Office of Applied Studies [OAS], 1999). 

Substance abuse treatment policy is largely a State responsibility, especially since the 

establishment of the Federal Substance Abuse Prevention and Treatment (SAPT) block grant 

program in 1981 (Jacobsen & McGuire, 1996). States undertake treatment facility credentialing 

and licensing, and by 1997, through either their own funding or Federal funding that they 

managed, States and local governments managed more than 47 percent of all substance abuse 

funding and 74 percent of all public funding (Coffey et al., 2001). 

Descriptive evidence suggests that substantial variations in treatment systems may exist 

across States. For example, in 1989, per capita alcohol treatment funding varied from $5.85 in 

Mississippi to $51.76 in Alaska (Dayhoff, Pope, & Huber, 1994). In 1998, the proportion of 

clients admitted to inpatient treatment varied from 3 percent of all substance abuse treatment 

clients in Vermont to 30 percent in North Dakota (OAS, 2000). 

Still, one aspect of the publicly funded treatment system shared by many States is 

insufficient publicly funded treatment capacity (e.g., see New Jersey Substance Abuse Prevention 

and Treatment Advisory Task Force, 2001). One way in which many States attempt to improve 

care and make the best use of their limited resources is by implementing guidelines to help place 

clients receiving publicly funded treatment in different levels of care, including whether they are 

treated as inpatients or outpatients (Gastfriend & McLellan, 1997; Mattson, 2003). According to 

these guidelines, clients with more severe substance use, emotional, and behavioral disorders are 

candidates for inpatient care. The purpose of this study, using the administrative data that States 

use to monitor their treatment systems, is to estimate the effect of disorder severity on the odds of 

inpatient admission and to explore how that effect varies across States. 
54 

We extend the analysis of treatment admission to the publicly monitored treatment 

systems in several States. In doing so, we include either those who pay for treatment out-ofpocket or those who receive publicly funded treatment to explore the extent to which results for 

this augmented population are consistent with the findings reported in earlier research on 

privately insured individuals (Goodman, Holder, Nishiura, & Hankin, 1992; Goodman, Nishiura, 

& Hankin, 1998). Further, we examine whether variables, such as age of first intoxication, 

employment, and housing status, which research has found are associated with referral to 

inpatient treatment (Gregoire, 2000) but that are unavailable in insurance claims data, are 

correlated with inpatient admission. Finally, we examine the extent to which the estimated 

relationships vary across States. Our findings suggest that, although there are differences across 

States in client characteristics and in the effect of these characteristics on admission, clients with 

more severe substance use disorders generally are more likely to receive inpatient treatment. 

These results suggest that admission decisions in the State-monitored substance abuse treatment 

system conform, at least to some extent, to available placement criteria. Given the considerable 

barriers that can exist in implementing these criteria (Gastfriend, Lu, & Sharon, 2000; Kosanke, 

Magura, Staines, Foote, & DeLuca, 2002), our results suggest that States' attempts to manage 

their substance abuse treatment resources effectively are meeting with some success. 

Background 

Alcohol rehabilitation treatment is aimed at changing drinking behavior and often 

consists of psychotherapy and sometimes pharmacotherapy. It may take place in a number of 

settings, including outpatient and residential specialty substance abuse treatment facilities 

(including some in hospitals) or the offices of private practitioners. In our work, we examine data 

on clients in the specialty substance abuse treatment system that is monitored by State substance 

abuse treatment agencies. We exclude data on those who obtain care from private practitioners 

and those involved only in self-help groups, such as Alcoholics Anonymous, because data on 

admissions to such programs are not systematically collected. 

On a per-episode basis, outpatient substance abuse rehabilitation treatment costs less than 

inpatient rehabilitation treatment. Costs for residential programs average between $4,000 and 

$6,800, depending on their length, or more than twice the $1,800 cost of the average outpatient 

program (U.S. General Accounting Office [GAO], 1998). Inpatient treatment also may be more 

disruptive and costly for clients than outpatient treatment. For example, employed clients who 

enter inpatient treatment must miss work, either losing pay or using sick leave. If both types of 

treatment were equally effective for all clients, providing treatment only in outpatient settings 

would be most efficient. However, if the two types of treatment are not equally effective, 

providing solely outpatient treatment may not be cost-effective. 

That these treatment options may not be equally effective for all clients has been 

recognized in guidelines, such as the American Society of Addiction Medicine Patient Placement 

Criteria (ASAM-PPC) (ASAM, 1996). The ASAM-PPCs consider indicators across several 

psychosocial dimensions to determine optimal client placement and suggest that clients with 

emotional or behavioral disorders and complications, high risk for relapse, or a poor recovery 

environment may benefit from inpatient treatment (ASAM, 1996; McKay et al., 1997). Although 

not universally accepted, the ASAM-PPCs are the most widely distributed, implemented, 
55 

discussed, and reviewed criteria available (Gartner & Mee-Lee, 1995; Mattson, 2003). Several 

States, such as Iowa, Colorado, Massachusetts, and New Jersey, either use the ASAM-PPCs or 

other similar criteria as guidelines for client placement. State modifications generally include 

adding treatment settings, such as halfway houses and longer-term residential treatment, not 

recognized in the original ASAM-PPCs (Gartner & Mee-Lee, 1995). 

The results of several empirical studies conducted in the 1970s and 1980s, however, 

suggested that inpatient treatment may not have been worth the extra cost (Annis, 1985-1986; 

Miller & Hester, 1986). Miller and Hester (1986), for example, reviewed several controlled 

studies and concluded that few differences in outcomes arose between more intensive and less 

intensive programs, except in some cases where the less intensive programs produced superior 

outcomes. Such findings, coupled with the growth of managed behavioral health care, led to a 

decline in the number of inpatient admissions for substance abuse treatment throughout the early 

1990s (Subcommittee on Health Services Research, 1997). 

However, none of the controlled studies that Miller and Hester (1986) reviewed included 

individuals with a co-occurring mental disorder, an important clinical indicator for inpatient 

treatment (Pettinati, Meyers, Jensen, Kaplan, & Evans, 1993). Although one small randomized 

study suggested that inpatient treatment is not more effective for those who are appropriately 

matched to it (McKay et al., 1997), other observational studies and more recent reviews of the 

earlier controlled studies suggest that inpatient programs benefit those with more severe 

disorders (Finney, Hahn, & Moos, 1996; Finney & Moos, 1996; Gastfriend et al., 2000; Harrison 

& Asche, 1999; Hartmann, Sullivan, & Wolk, 1993; Mattson, 2003; Pettinatti et al., 1999; 

Simpson, Joe, Fletcher, Hubbard, & Anglin, 1999). The authors of these studies concluded that 

inpatient substance abuse treatment should remain an option. 

Two studies by Goodman et al. (1992, 1998) used private insurance claims data to 

examine factors affecting the choice between inpatient and outpatient substance abuse treatment. 

The first study examined data on 879 individuals with employer-sponsored, fee-for-service health 

insurance with comprehensive alcoholism coverage. The authors found that admission to shortterm inpatient treatment was more likely for those with a diagnosis of alcohol dependence (vs. 

abuse) and a co-occurring substance use and mental disorder. The second study analyzed the 

relative contributions of client- and employer-level factors to treatment choice by examining 

claims submitted on behalf of 9,878 individuals who received their health insurance through 10 

large self-insured firms from 1989 to 1991. The authors found that clients were more likely to 

receive inpatient treatment if they had a diagnosis of dependence (vs. abuse) or a psychosis, used 

drugs other than opiates, were younger or male, and received hourly wages as opposed to 

salaries. However, a large part of the observed variation occurred at the employer level. The 

authors concluded that treatment choice was driven mainly by firm or health insurance 

administrator policy (they could not distinguish between the two) and that treatment expenditures 

at some firms could be reduced by shifting treatment to outpatient settings. Although these 

results are suggestive, they are not generalizable to those who receive treatment in the publicly 

monitored system, many of whom not only have no employer-sponsored health insurance, but 

also often are unemployed and/or homeless. 
56 

A study by Gregoire (2000) provides clues about this population. The study examined 

referrals to inpatient versus outpatient substance abuse treatment in a study of 3,093 individuals 

diagnosed as drug dependent who sought admission to publicly funded treatment in Wichita, 

Kansas. Although the study revealed that referrals generally were consistent with clinical criteria, 

the two variables that were the strongest predictors of referrals to inpatient treatment were 

housing and employment status. Those who were homeless and unemployed were more likely to 

be referred to inpatient treatment than those with stable housing and those who were employed. 

Although these results are suggestive, the study has some limitations. First, it examined only 

drug-dependent clients in a single city. Second, it concerned referrals, not admissions. Because 

research suggests that substantial numbers of clients fail to attend treatment as referred (e.g., see 

Donovan, Rosengren, Downey, Cox, & Sloan, 2001), and many clients self-refer into treatment, 

it is of interest to see whether these same client characteristics affect treatment choice in a 

broader admissions sample. 

Data 

We used data from the 1996 Treatment Episode Data Set (TEDS), maintained by the 

Office of Applied Studies (OAS) of the Substance Abuse and Mental Health Services 

Administration (SAMSHA) (OAS, 1999). TEDS contains admissions data routinely collected by 

treatment providers at client admission and sent to State agencies, which use them to monitor 

their substance abuse treatment systems. These State data systems, which were enacted to satisfy 

the mandate to collect client data in the Comprehensive Alcohol Abuse, Drug Abuse, and Mental 

Health Amendments (1988), were designed with input from each State's treatment providers and 

with input and funding from SAMHSA. The data are submitted at regular intervals by the States 

in a common format to SAMHSA. The data include disorder severity information important in 

determining clients' treatment needs, as well as socioeconomic measures. Although these data 

have been used by the States and the Federal Government to generate descriptive reports, they 

have seldom been used for health services research (McCarty, McGuire, Harwood, & Field, 

1998). 

Our analysis focuses on adult males with alcohol as their primary substance of abuse. We 

did not include women because a variable that might be relevant to their treatment setting choice, 

whether or not they are pregnant, is not well reported, and another variable, whether or not they 

have dependent children, is not collected. We examine only alcohol clients because a relevant 

measure of the disorder severity for most other drugs, route of administration, also is not 

consistently reported. 

Unfortunately, States vary in their ability to report all variables or collect data from all 

substance abuse treatment facilities in the TEDS universe (those receiving public substance 

abuse treatment funding). Therefore, we focus on nine States (Colorado, Iowa, Maine, 

Massachusetts, Nevada, New Jersey, New York, North Dakota, and Rhode Island) that provided 

data covering 90 percent or more of their estimated substance abuse treatment clients in 

programs receiving public funds in 1996 and that collected variables hypothesized to affect 

substance abuse treatment admission. 
57 

Although these data are fairly consistent across States, our review of information from 

each State, such as the instruction manuals that States give to providers, data collection forms, 

and the crosswalk between the State data systems and SAMHSA's common format, reveals that 

there are some differences. One important difference is the universe of reporting facilities. In 

some States, such as New York, all alcohol treatment facilities are required to report admissions 

data, regardless of whether they receive public funding or not. In North Dakota, in contrast, only 

the programs at the State's eight regional services centers and the State hospital report these data. 

In other States, such as New Jersey, only facilities receiving public funds are required to report 

these data, but many more do so voluntarily. 

Another important difference among States is the definition of an admission. Although 

SAMHSA requests that States report only the initial admission to a treatment episode as an 

admission (OAS, 1999), the nature of the help-seeking behavior of those with substance use 

disorders can make it difficult for States to comply. Fewer than 53 percent of substance abuse 

treatment clients nationwide complete their planned treatment, and it is common for those with 

substance use disorders to make more than one attempt at treatment (OAS, 2004). The question 

then becomes, when is an admission a new admission, as opposed to a continuation of the same 

treatment episode? States do not provide uniform instructions to providers. Iowa, for example, 

instructs providers to report an admission as an initial admission only if 2 months or more have 

passed since the individual's last discharge; in Nevada, the relevant time period is 30 days. And 

although SAMHSA requests that States report changes of service (e.g., from detoxification to 

rehabilitation) within an episode as a transfer rather than a new admission, four of the nine States 

we include in our analysis do not (Maine, Massachusetts, New York, and Rhode Island). As 

discussed below, these differences among States are one reason we chose to estimate the model 

separately for each State. 

Empirical Framework 

Rather than rely on a standard model of health care demand, such as the Health Capital 

Model (Grossman, 1972; Muurinen, 1982) or the Behavioral Healthcare Model (Andersen & 

Newman, 1973), which have each been used in studies of the demand for behavioral health care 

(Haas-Wilson, Cheadle, & Scheffler, 1989; Pottick, Hansell, Gutterman, & White, 1995), we 

combine elements of both approaches with unique characteristics of the substance abuse 

treatment system and its clients to inform our empirical specification. This exploratory approach 

is consistent with recent calls to integrate both behavioral and economic variables in empirical 

behavioral health services research (Brito & Strain, 1996; Green & Kagel, 1996; Montoya, 

Atkinson, & Trevino, 2000). 

Baseline Model 

We model desired alcohol treatment intensity as an underlying, unobserved, continuous 

dependent variable y* for which we have a discrete realization, yi, that equals 1 for inpatient 

admission and 0 for outpatient admission. The individual's observed treatment setting is a 

function of his demand for alcohol treatment, which is a function of his disorder severity and 

other characteristics, and the availability of treatment options, which is a function of State 

treatment policy. Given this, and the differences in the data systems described above, we estimate 
58 

the model separately for each State. Importantly, we rejected the null hypothesis of a single 

pooled model based on the results of a chi-square test. 

The probability that client i in State j is admitted to inpatient treatment is 

1 1

( 1) ( )

n n

ij j jk ik j i

k

Pry f X R = == = α+ β + β ∑ ∑ l l

l

, (1) 

where yij equals 1 if individual i in State j is admitted to inpatient care and equals 0 otherwise; f(C) 

is the logistic function; αj is the intercept for State j; Xik is a vector of k demand variables and 

client characteristics; RiR is a vector of R referral source indicators; and the βs are parameters to be 

estimated. 

Virtually all health care demand models indicate that problem severity affects the 

intensity of care demanded. We use several variables to measure the severity of the client's 

substance use disorder (hypothesized effect in parentheses). Frequency of use (+): We include 

four dichotomous variables that reflect how this information is coded on each client's record at 

admission: daily use, use three to six times in the past week, use one to two times in the past 

week, and use one to three times in the past month. No use in the past month is the reference 

category. Although other studies have used International Classification of Diseases (ICD) or 

Diagnostic and Statistical Manual of Mental Disorders (DSM) code information to measure 

severity of substance use disorders, we believe the frequency of use variable, in combination with 

other variables in our model, is more appropriate for our purpose, especially given that it is better 

reported. Only 18 of the more than 50 States and jurisdictions that report to TEDS collect ICD or 

DSM data (OAS, 1999). Furthermore, of those 18 States, only 3 obtain valid values on 99 

percent or more of their admissions. Frequency of use, in contrast, is collected in 47 States and 

jurisdictions, some 32 of which obtain it on 99 percent or more of their admissions. The fact that 

frequency of use is much better reported may mean that it is easier for treatment personnel to 

collect, and, given that they had input into the data elements that would be collected by States, 

perhaps more useful to them in their treatment planning decisions than DSM or ICD criteria. 

We include several other variables to measure client severity as well. Intoxication before 

age 15 (+): Research suggests that individuals who first use alcohol before the age of 15 are 

more likely to become alcohol dependent (Grant & Dawson, 1997). We include a dichotomous 

variable indicating first alcohol intoxication before age 15. Secondary substance (+): Having a 

second substance of abuse can indicate a more severe disorder. We include dichotomous 

variables indicating marijuana/hashish, cocaine, and other secondary substance use, with no 

secondary substance use as the reference category. Number of prior treatment episodes (?): This 

variable is used in many treatment studies as an indicator of disorder severity (e.g., Etheridge, 

Craddock, Hubbard, & Rounds-Bryant, 1999; McLellan et al., 1999). We enter this as a 

categorical variable to allow the estimated relationship to be something other than linear. We 

include indicator variables for one prior treatment and two or more prior treatments, using no 

prior treatment as the reference category. Co-occurring mental disorder (+), Homeless (+):

Based on the ASAM-PPCs, the literature on treatment effectiveness, and the results of prior 

research on referrals (Gregoire, 2000) and treatment matching (Kosanke et al., 2002), we expect 
59 

that those with a co-occurring mental disorder and those who are homeless may have a higher 

likelihood of admission to inpatient treatment. To capture this, we enter two indicator variables, 

one for co-occurring mental disorder and the other for homelessness. 

The research mentioned earlier and other economic research (Becker & Murphy, 1988) 

conceptualize the behavior of those with substance use disorders as consistent with choice 

theory, suggesting that socioeconomic and other client characteristics should be included in 

models that predict their behavior. We include the following variables and note that potentially 

offsetting effects render the predicted direction of many of the effects uncertain. Employment 

status (?): Employed individuals have higher time cost associated with inpatient treatment and 

may be less likely to engage in inpatient treatment, other things equal (Kosanke et al., 2002). 

Furthermore, the fact that they are employed suggests that they may have a less severe disorder, 

and treatment providers may believe that those who are unemployed are more likely to benefit 

from inpatient treatment (Gregoire, 2000). On the other hand, employed individuals may be 

better able to pay for more intensive treatment. To examine which of these hypotheses the data 

support, we include a dichotomous variable indicating whether the client was employed at 

admission, either full- or part-time, with those who are unemployed or not members of the labor 

force comprising the reference category. Education level (?): According to Muurinen (1982), the 

relationship between years of education and the demand for medical care should be negative 

because the rate of depreciation of the health stock should be lower for better-educated 

individuals. At the same time, education may proxy higher income, which may suggest a more 

intensive treatment choice. We included two dichotomous variables to measure education: less 

than high school graduate and high school graduate. Some postsecondary education is the 

reference category. Age (?): Human capital theory suggests that age has a positive effect on 

treatment intensity because the rate of depreciation of the health stock is a positive function of 

age (Muurinen, 1982). On the other hand, one version of the model by Suranovic, Goldfarb, and 

Leonard (1999) suggests that those who are older are more motivated to quit their substance use, 

perhaps making it less likely that they would need intensive treatment. Because it is unclear 

which effect may dominate, we enter age and age-squared to allow for a nonlinear relationship. 

Race/ethnicity (?): According to the 4th edition of the Diagnostic and Statistical Manual of 

Mental Disorders (DSM-IV), people of different races and ethnicities tend to have different 

cultural attitudes about and physiological responses to alcohol (American Psychiatric Association 

[APA], 1994). However, this variable also may capture placement in a treatment setting that was 

not clinically indicated because of the lack of culturally competent alternatives in the individual's 

area (Gartner & Mee-Lee, 1995). We include three indicator variables to capture the client's 

race/ethnicity (Hispanic, non-Hispanic white, and non-Hispanic black) with "other" as the 

reference category. This was the only way to code this information uniformly across the States 

included in our sample. Marital status (-): Although imperfect, marital status may proxy for the 

ASAM-PPCs' emotional and behavioral disorders criteria. Currently married clients may have 

less severe emotional and behavioral disorders than those who are single, divorced, separated, or 

widowed, all of whom comprise the reference cell. 

Another variable that may affect client placement is season of admission (?): The time of 

year the client is admitted also may affect the odds of inpatient treatment. For example, those 

who are seasonally employed, such as teachers and college professors, may be more likely to 
60 

accept assignment to inpatient treatment in the summer. We include indicator variables for 

summer, fall, and winter, with spring as the reference category. 

Finally, we include indicator variables for referral source (?). Although the process by 

which clients obtain referrals, and the interplay between the various referral sources and the 

treatment system is admittedly complex (e.g., see Kosanke et al., 2002), we believe it is 

important to include referral source as a control variable. For example, although the criminal 

justice system is a frequent source of referral into substance abuse treatment, and referral through 

that system may affect client placement, we make no a priori judgment about whether criminal 

justice referrals are more or less likely to be admitted to inpatient alcohol treatment. The effect 

likely depends on the referral practices of criminal justice systems, and the availability of 

different types of treatment, which vary across States. For example, in some States, such as New 

Jersey, the State substance abuse treatment agency is actively involved in assessing prerelease 

inmate and parolee needs and referring clients to treatment (New Jersey Substance Abuse 

Prevention and Treatment Advisory Task Force, 2001). In others, the court may mandate both 

treatment and the modality, as the Massachusetts court does for second-time drunk driving 

offenders (Bureau of Substance Abuse Services, 2001). We include indicator variables for selfreferral, referral by an alcohol or drug treatment provider, other medical provider referrals, and 

community (employer, school, etc.) referral. Referral by the criminal justice system serves as the 

reference cell. 

Expected Payer Model 

In addition to the baseline model, we present coefficients from a model including 

expected pay source for this admission and estimate it for each of the seven States that collected 

these data. Economic theory suggests that individuals who pay out of pocket for their own 

treatment may demand less costly treatment than those who do not. However, we only observe 

the expected payer for this particular admission and do not know, for example, whether the 

client's insurance covered both inpatient and outpatient treatment. In some cases, expected payer 

and treatment setting may be jointly determined if, for example, an indigent client is placed in an 

inpatient treatment program because no publicly funded outpatient slots are available (Gartner & 

Mee-Lee, 1995). Therefore, the direction of the expected payer effects is unclear a priori, and the 

results of the analysis should be considered tentative. We include the following categories: selfpay, Medicare, Medicaid, private, other (e.g., worker's comp), and other government funding/no 

charge (the reference category). The reference category includes clients whose treatment is 

funded by State agency funds, including those received through the Federal SAPT block grant 

program. 

Results 

Table 4.1 displays the means and standard deviations for the combined sample and by 

State. It shows that States varied substantially in the proportion of adult males in treatment for 

alcohol use disorders admitted to inpatient care, ranging from about 13 percent in Iowa to 32 

percent in New York. Statistically significant differences exist across States for all variables, 

except for no secondary substance use and high school education. 
61 

Table 4.1 Analysis Sample Means and Standard Deviations of the Model Variables, by State and for All Nine States Pooled 

All States Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New Yo

N 113,948 4,654 17,591 3,762 10,797 1,615 10,426 62,093 Inpatient Treatment 0.255* 0.300 0.126 0.150 0.162 0.305 0.212 0.320  (0.436) (0.458) (0.332) (0.357) (0.368) (0.461) (0.409) (0.467Frequency of Use† 

No use in the past month 0.324* 0.358 0.389 0.532 0.055 0.363 0.218 0.353  (0.468) (0.480) (0.488) (0.499) (0.228) (0.481) (0.413) (0.4781 to 3 times in the past month 0.145* 0.169 0.226 0.112 0.110 0.172 0.121 0.130  (0.352) (0.375) (0.418) (0.315) (0.313) (0.377) (0.326) (0.3361 to 2 times in the past week 0.125* 0.130 0.125 0.042 0.239 0.107 0.186 0.099  (0.331) (0.337) (0.331) (0.200) (0.427) (0.309) (0.389) (0.2983 to 6 times in the past week 0.118* 0.137 0.109 0.157 0.193 0.102 0.138 0.100 

(0.322) (0.344) (0.312) (0.364) (0.395) (0.302) (0.345) (0.300

Daily 0.288* 0.206 0.151 0.157 0.402 0.257 0.337 0.318 

 (0.453) (0.404) (0.358) (0.364) (0.490) (0.437) (0.473) (0.466Age of First Intoxication Less Than 15 Years 

0.387* 0.394 0.345 0.443 0.415 0.359 0.339 0.396 

 (0.487) (0.489) (0.475) (0.497) (0.493) (0.480) (0.473) (0.489Secondary Drug† 

None 0.484 0.597 0.592 0.624 0.510 0.661 0.587 0.409 

(0.500) (0.490) (0.491) (0.484) (0.500) (0.473) (0.492) (0.492

Marijuana/hashish 0.229* 0.249 0.302 0.308 0.259 0.160 0.171 0.206  (0.420) (0.432) (0.459) (0.462) (0.438) (0.367) (0.376) (0.404) Cocaine/crack 0.229* 0.102 0.036 0.036 0.182 0.070 0.184 0.328  (0.420) (0.302) (0.187) (0.186) (0.386) (0.255) (0.388) (0.470) Other 0.058* 0.052 0.070 0.031 0.049 0.108 0.058 0.057 

 (0.235) (0.222) (0.255) (0.174) (0.215) (0.311) (0.234) (0.233) 
62 

Table 4.1 Analysis Sample Means and Standard Deviations of the Model Variables, by State and for All Nine States Pooled (Co

All States Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New Yo

Prior Treatment Episodes† 

None 0.325* 0.307 0.415 0.312 0.287 0.547 0.502 0.270  (0.468) (0.461) (0.493) (0.463) (0.453) (0.498) (0.500) (0.4441 episode 0.249* 0.218 0.287 0.285 0.241 0.289 0.251 0.239  (0.432) (0.413) (0.452) (0.452) (0.427) (0.453) (0.434) (0.4262 episodes or more 0.426* 0.475 0.299 0.403 0.472 0.164 0.246 0.491 

 (0.495) (0.499) (0.458) (0.491) (0.499) (0.370) (0.431) (0.500Demographics 

Age 35.770* 34.165 34.293 35.401 35.948 36.692 36.152 36.237  (10.359) (10.218) (10.675) (10.308) (10.764) (9.923) (10.461) (10.153Employed 0.447* 0.526 0.651 0.455 0.476 0.490 0.562 0.357  (0.497) (0.499) (0.477) (0.498) (0.499) (0.500) (0.496) (0.479Homeless 0.124* 0.109 0.014 0.065 0.057 0.209 0.045 0.185  (0.330) (0.312) (0.116) (0.247) (0.231) (0.407) (0.208) (0.388Mental disorders 0.174* 0.170 0.158 0.201 0.265 0.051 0.085 0.179  (0.379) (0.376) (0.365) (0.401) (0.441) (0.221) (0.279) (0.383Married 0.246* 0.244 0.338 0.206 0.191 0.302 0.253 0.231 

 (0.431) (0.429) (0.473) (0.404) (0.393) (0.459) (0.435) (0.421Education Level† 

No high school 0.318* 0.309 0.204 0.302 0.303 0.291 0.265 0.362  (0.466) (0.462) (0.403) (0.459) (0.460) (0.454) (0.442) (0.481High school 0.466 0.448 0.565 0.511 0.478 0.473 0.502 0.430  (0.499) (0.497) (0.496) (0.500) (0.500) (0.499) (0.500) (0.495Post high school 0.216* 0.243 0.230 0.188 0.219 0.236 0.233 0.208 

 (0.412) (0.429) (0.421) (0.390) (0.413) (0.425) (0.423) (0.406
63 

Table 4.1 Analysis Sample Means and Standard Deviations of the Model Variables, by State and for All Nine States Pooled (Co

All States Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New Yo

Race/Ethnicity† 

Non-Hispanic white 0.674* 0.602 0.904 0.957 0.778 0.638 0.687 0.573  (0.469) (0.490) (0.295) (0.203) (0.416) (0.481) (0.464) (0.495) Non-Hispanic black 0.191* 0.063 0.044 0.012 0.093 0.071 0.196 0.279  (0.393) (0.242) (0.205) (0.109) (0.291) (0.256) (0.397) (0.448) Hispanic 0.106* 0.293 0.034 0.007 0.099 0.064 0.103 0.126  (0.309) (0.455) (0.182) (0.081) (0.298) (0.246) (0.304) (0.331) Other 0.027* 0.043 0.018 0.024 0.029 0.225 0.014 0.023 

 (0.164) (0.202) (0.133) (0.154) (0.167) (0.418) (0.118) (0.149) Primary Source of Referral† 

Individual 0.176* 0.177 0.176 0.173 0.201 0.146 0.193 0.168  (0.381) (0.382) (0.381) (0.378) (0.400) (0.353) (0.394) (0.374) Alcohol/drug treatment provider 0.239* 0.150 0.072 0.150 0.177 0.063 0.099 0.343  (0.426) (0.358) (0.259) (0.357) (0.381) (0.243) (0.299) (0.475) Other health care provider 0.078* 0.052 0.068 0.073 0.083 0.050 0.101 0.080  (0.269) (0.222) (0.251) (0.260) (0.276) (0.218) (0.301) (0.272) School, employer, community 0.086* 0.082 0.046 0.052 0.069 0.110 0.075 0.101  (0.280) (0.275) (0.210) (0.222) (0.253) (0.313) (0.263) (0.301) Criminal justice 0.422* 0.538 0.637 0.552 0.471 0.631 0.532 0.308 

 (0.494) (0.499) (0.481) (0.497) (0.499) (0.483) (0.499) (0.462) Season Entering Treatment† 

Spring 0.265* 0.259 0.322 0.278 0.257 0.221 0.259 0.251  (0.441) (0.438) (0.467) (0.448) (0.437) (0.415) (0.438) (0.433) Summer 0.238* 0.257 0.190 0.227 0.249 0.285 0.255 0.246  (0.426) (0.437) (0.392) (0.419) (0.432) (0.452) (0.436) (0.431) Fall 0.239* 0.239 0.188 0.234 0.244 0.323 0.230 0.253  (0.427) (0.427) (0.391) (0.423) (0.430) (0.468) (0.421) (0.435) Winter 0.257* 0.244 0.300 0.261 0.250 0.171 0.256 0.250  (0.437) (0.430) (0.458) (0.439) (0.433) (0.377) (0.437) (0.433) 
64 

Table 4.1 Analysis Sample Means and Standard Deviations of the Model Variables, by State and for All Nine States Pooled (Co

All States Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New Yo

Expected Source of Payment† 

N 40,824 4,654 17,591 3,762 NA 1,615 10,414 NA Self-pay 0.200* 0.402 0.065 0.175 NA 0.515 0.322 NA  (0.400) (0.490) (0.247) (0.326) NA (0.500) (0.467) NA Private 0.141* 0.028 0.159 0.100 NA 0.057 0.185 NA  (0.348) (0.165) (0.366) (0.301) NA (0.233) (0.389) NA Medicare 0.018* 0.007 0.016 0.012 NA 0.001 0.030 NA  (0.132) (0.083) (0.127) (0.108) NA (0.024) (0.172) NA Medicaid 0.050* 0.005 0.053 0.125 NA 0.005 0.033 NA  (0.219) (0.069) (0.223) (0.331) NA (0.070) (0.179) NA Other 0.044* 0.111 0.003 0.183 NA 0.032 0.042 NA  (0.206) (0.314) (0.059) (0.387) NA (0.175) (0.201) NA Other government/no charge 0.541* 0.437 0.703 0.405 NA 0.396 0.387 NA  (0.498) (0.496) (0.457) (0.491) NA (0.489) (0.487) NA Unknown 0.005* 0.010 0.000 0.000 NA 0.001 0.000 NA 

 (0.074) (0.101) 0.000 0.000 NA (0.025) 0.000 NA * Statistically significant differences in means among States at the 0.01 level. †Statistically significant differences in distributions among States at the 0.01 level using Pearson's chi square. NA = not available (States do not collect the variable). Note: Standard deviations are included in parentheses. Source: SAMSHA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
65 

Almost 30 percent of clients in our sample reported using alcohol daily at admission. The 

percentage ranged from 15 percent in Iowa to 40 percent in Massachusetts. Another third of 

clients reported no use in the past month, ranging from 6 percent in Massachusetts to 53 percent 

in Maine. The percentage of clients who reported having been first intoxicated before age 15 was 

somewhat less variable across States, averaging about 39 percent and ranging from 34 percent in 

New Jersey to 52 percent in North Dakota. Almost half of the clients did not report any 

secondary substance use. Marijuana was the most common secondary substance of abuse in most 

States, ranging from 16 percent of clients in Nevada to 36 percent in North Dakota. However, in 

New Jersey and New York, cocaine (including crack) was the most commonly reported 

secondary substance. In all States but Nevada and New Jersey, most clients had at least one prior 

treatment episode. 

Only 17 percent of clients across States indicated a mental disorder; however, this 

percentage ranged from 5 percent of clients in Nevada to 39 percent in North Dakota. 

Homelessness also varied across States, ranging from 1.4 percent in Iowa to almost 21 percent in 

Nevada. 

The average client in the dataset was almost 36 years old, and the majority of clients were 

non-Hispanic white (67 percent overall). Although age did not vary substantially across States, 

race/ethnicity did: The percentage of non-Hispanic white clients ranged from 57 percent in New 

York to 96 percent in Maine. Almost 70 percent of clients in our sample had at least a high 

school education, and almost 45 percent were employed. Fewer than 25 percent were currently 

married. 

The criminal justice system was the most common route of treatment referral for clients 

in all States except New York, ranging from 31 percent in New York to almost 64 percent in 

Iowa. The most common route of referral in New York was an alcohol or drug treatment provider 

(34 percent). In all States but New York and North Dakota, self-referral was the second most 

common referral route. 

Baseline Models 

Table 4.2 shows the coefficients from our baseline logit models. Clients with more severe 

alcohol disorders, as measured by TEDS data, generally were more likely to receive inpatient 

alcohol treatment. However, there were differences across the States in the estimated magnitudes. 

Daily alcohol users were significantly more likely to receive inpatient treatment than clients who 

did not use in the 30 days prior to admission (omitted category). In Colorado, clients who 

reported daily alcohol use in the past 30 days were twice as likely to enter inpatient treatment as 

clients who reported no use in the past 30 days (based on eβ

, which gives the effect of a one-unit 

change in the independent variable on the odds of inpatient treatment, where β is the estimated 

coefficient). In the other States exhibiting this relationship, the increases in the odds due to daily 

use were much larger (e.g., 4.5 in New Jersey, 7.2 in New York, 11.1 in Iowa). The exception is 

Massachusetts, where the frequency of use variables were not statistically significant. Reporting 

cocaine as a secondary substance of abuse increased the odds of inpatient admission in all States 

except North Dakota (which, as shown in Table 4.1, also has the lowest proportion of clients, 2 

percent) and Massachusetts. Effects ranged from 1.44 in Iowa to 4 in Rhode Island. On the other 
66 

Table 4.2 Probability of Seeking Inpatient Alcohol Abuse Treatment: Coefficients and Standard Errors from Baseline Logit Mod

Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New York 

N 4,654 17,591 3,762 10,797 1,615 10,426 62,093 Frequency of Use 

1 to 3 times in the past month -0.358*** 0.403*** -0.311 0.109 0.537** -0.605*** 0.005  (0.127) (0.090) (0.245) (0.149) (0.236) (0.169) (0.043) 1 to 2 times in the past week 0.152 0.926*** -1.017* 0.239* 0.701** -0.249* 0.670***  (0.127) (0.097) (0.572) (0.136) (0.271) (0.133) (0.044) 3 to 6 times in the past week 0.334*** 1.703*** -0.375* 0.018 1.183*** 0.686*** 1.258***  (0.119) (0.088) (0.202) (0.142) (0.248) (0.112) (0.038) Daily 0.767*** 2.403*** 0.368** -0.043 2.095*** 1.507*** 1.980***  (0.108) (0.078) (0.151) (0.140) (0.194) (0.093) (0.028) 

Age of First Intoxication Less Than 15 Years 

0.136* 0.107* 0.307** 0.167** 0.032 0.050 0.064*** 

 (0.081) (0.057) (0.135) (0.066) (0.154) (0.063) (0.023) Secondary Drug 

Marijuana/hashish 0.253*** -0.278*** 0.350** -0.136* -0.112 -0.113 0.273***  (0.094) (0.065) (0.148) (0.075) (0.209) (0.091) (0.034) Cocaine/crack 0.651*** 0.369*** 0.991*** 0.162* 0.855*** 0.452*** 0.636***  (0.123) (0.118) (0.278) (0.089) (0.291) (0.079) (0.029) Other 0.511*** 0.170* 0.059 -0.166 0.289 0.616*** 0.489*** 

 (0.162) (0.096) (0.299) (0.163) (0.227) (0.113) (0.047) Prior Treatment Episodes 

1 episode 0.094 0.230*** 0.032 1.803*** 0.545*** 0.175** 0.733***  (0.114) (0.068) (0.215) (0.092) (0.169) (0.076) (0.037) 2 episodes or more 0.236** 0.428*** 0.336* 1.028*** 0.306 0.001 0.829*** 

 (-0.101) (0.066) (0.189) (0.096) (0.208) (0.076) (0.034) 
67 

Table 4.2 Probability of Seeking Inpatient Alcohol Abuse Treatment: Coefficients and Standard Errors from Baseline Logit Mod

Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New York 

Demographics 

Age 0.153*** 0.037*** 0.037 0.091*** 0.058 -0.027* -0.040***  (0.022) (0.013) (0.033) (0.017) (0.046) (0.016) (0.006) Age squared -0.002*** -0.001*** 0.000 -0.001*** -0.001 0.000 0.000***  (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) No high school education -0.020 0.221*** -0.554*** -0.547*** 0.047 0.239*** -0.079**  (0.112) (0.082) (0.181) (0.087) (0.214) (0.088) (0.031) High school education 0.149 0.112* -0.527*** -0.256*** 0.128 0.086 0.005  (0.097) (0.067) (0.165) (0.074) (0.181) (0.078) (0.030) Employed -1.608*** -0.995*** -1.870*** -0.043 -2.085*** -0.923*** -0.856***  (0.082) (0.056) (0.212) (0.065) (0.166) (0.066) (0.029) Non-Hispanic white 0.145 -0.830*** -0.609* 0.433** 0.205 -0.266 0.024  (0.189) (0.153) (0.322) (0.194) (-0.192) (0.270) (0.077) Non-Hispanic black -0.091 -0.673*** 0.074 -0.473** -0.003 -0.500* -0.043  (0.239) (0.188) (0.635) (0.232) (-0.323) (0.277) (0.079) Hispanic -0.032 -0.811*** -3.073*** 0.291 -2.334*** -0.167 -0.347***  (0.196) (0.215) (1.158) (0.220) (0.589) (0.286) (0.083) Homeless 0.508*** 1.748*** 3.346*** 1.188*** 0.354* 1.699*** 0.256***  (0.124) (0.163) (0.254) (0.143) (0.187) (0.138) (0.027) Mental disorders -0.497*** 0.141** -0.044 -0.415*** -0.058 1.172*** -0.156***  (0.107) (0.065) (0.142) (0.082) (0.334) (0.090) (0.028) Married -0.106 -0.456*** -0.130 0.093 -0.182 -0.080 -0.025 

 (0.096) (0.061) (0.182) (0.076) (0.174) (0.076) (0.029) 
68 

Table 4.2 Probability of Seeking Inpatient Alcohol Abuse Treatment: Coefficients and Standard Errors from Baseline Logit Mod

Colorado Iowa Maine Massachusetts Nevada 

New 

Jersey New York 

 Primary Source of Referral 

Individual -0.264** 0.206*** 1.567*** -2.641*** 0.525** 1.190*** 0.170***  (0.110) (0.070) (0.203) (0.136) (0.216) (0.083) (0.038) Alcohol/drug treatment provider 1.274*** 1.475*** 2.606*** -1.491*** 1.415*** 1.561*** 1.846***  (0.119) (0.080) (0.199) (0.091) (0.307) (0.099) (0.032) Other health care provider -0.284* 0.447*** 1.857*** -4.360*** 0.764** 1.085*** 0.319***  (0.169) (0.089) (0.246) (0.453) (0.311) (0.099) (0.047) School, employer, community -1.477*** -0.464*** 0.824** -3.844*** -0.944*** 0.316** 0.083*  (0.196) (0.147) (0.337) (0.344) (0.273) (0.126) (0.045) 

 Season Entering Treatment 

Summer 0.090 0.206*** 0.034 -0.257*** -1.486*** 0.088 0.036  (0.105) (0.075) (0.177) (0.084) (0.209) (0.082) (0.031) Fall -0.056 0.067 -0.130 -0.216*** -1.846*** 0.007 -0.032  (0.109) (0.078) (0.180) (0.083) (0.208) (0.089) (0.031) Winter 0.037 0.247*** -0.167 -0.150* -0.247 0.043 0.000  (0.107) (0.068) (0.176) (0.082) (0.222) (0.082) (0.031) Intercept -3.642*** -2.918*** -3.171*** -3.972*** -1.370 -1.909*** -2.187***  (0.460) (0.301) (0.779) (0.399) (0.924) (0.421) (0.148) Pseudo R2 0.2446 0.2528 0.4650 0.2451 0.3913 0.3290 0.3340 ne = not estimable (see text). * Significant at 0.10 level. ** Significant at 0.05 level. *** Significant at 0.01 level. Note: Standard errors in parentheses. Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
69 

hand, marijuana/hashish as a secondary substance significantly increased the odds of inpatient 

admission in three of the nine States (Colorado, Maine, and New York) and decreased it in two 

others (Iowa and North Dakota). Reporting other drugs as a secondary substance increased the 

odds of inpatient admission in four of the nine States (Colorado, New Jersey, New York, and 

Rhode Island) and was insignificant in the other States. Another severity measure, age of first 

intoxication younger than 15, significantly increased the odds of inpatient admission in three of 

nine States (Maine, Massachusetts, and New York) and was insignificant in all others. Having 

one prior treatment episode increased the odds of admission to inpatient treatment compared with 

having no prior treatment in the six States for which it was significant (Iowa, Massachusetts, 

Nevada, New Jersey, New York, and Rhode Island). Effects ranged from 1.2 in New Jersey to 

6.06 in Massachusetts. Having two or more prior treatment episodes also increased the odds of 

inpatient treatment for clients in Colorado, Iowa, Massachusetts, New York, and Rhode Island. 

Effects ranged from 1.3 in Colorado to 2.8 in Massachusetts. Having two or more prior treatment 

episodes was not significant in the other four States. 

The presence of a co-occurring mental disorder increased the odds of inpatient admission 

in only three States (Iowa, New Jersey, and North Dakota). Contrary to expectations based on 

clinical criteria and previous studies of insured individuals, it significantly decreased the odds in 

Colorado, Massachusetts, and New York and it was insignificant in the other three States. 

Homelessness, on the other hand, significantly increased the odds of inpatient admission in eight 

of the nine States examined, and the effect was generally large. 

Several socioeconomic variables were significantly associated with the probability of 

inpatient treatment, and, again, the results varied across States. Age was significant in four of the 

nine States, in all cases in a nonlinear form. Employed clients were significantly less likely to be 

admitted to inpatient treatment in seven of the nine States (Colorado, Iowa, Maine, Nevada, New 

Jersey, New York, and Rhode Island). The education variables were significant in six of the nine 

States but revealed inconsistent effects. Race/ethnicity variables were significant in five of the 

nine States, and in four States (Iowa, Maine, Nevada, and New York), Hispanic clients were less 

likely to have been admitted to inpatient treatment, other things equal. Marital status was 

significant in only one State, and season of admission was significant in three States but with no 

discernible pattern. 

In all States except Massachusetts and North Dakota, individuals referred by an alcohol or 

drug treatment provider had greater odds of entering inpatient treatment than those referred by 

the criminal justice system. In Massachusetts, being referred by an alcohol or drug treatment 

provider decreased the odds of entering inpatient treatment. In North Dakota, all those referred 

by an alcohol or drug treatment provider entered outpatient treatment, which is why we do not 

report parameter estimates for that cell. In six States (Iowa, Maine, Nevada, New Jersey, New 

York, Rhode Island), individuals who self-referred into treatment had higher odds of being 

admitted to inpatient treatment than those referred by the criminal justice system. 

Expected Payer Model 

Descriptive statistics on the expected payer analysis file variables are displayed at the end 

of Table 4.1. In most States, the primary expected payer was other government funding/no 
70 

charge. The proportion of clients in this category, which includes those whose care is funded by 

the SAPT block grant, ranged from 39 percent in New Jersey to 70 percent in Iowa. The second 

most frequent expected payer in most States was the individual, ranging from 6.5 percent in Iowa 

to over 51 percent in Nevada. Taken together, these two categories of expected payer account for 

74 percent of the clients in the sample, revealing that this is a very different population than that 

studied in the research mentioned earlier (Goodman et al., 1992, 1998). 

Table 4.3 displays the coefficients and standard errors from our expected payer model. 

Coefficients and standard errors on the additional variables, which did not change much from 

those in the baseline model, are available from the lead author upon request. Massachusetts and 

New York are excluded from Table 4.3 because they did not collect data on expected payer. For 

Nevada, we do not report estimated parameters for Medicaid and Medicare as expected payer 

because all of those with Medicaid or Medicare entered outpatient treatment. Similarly, we do 

not report parameter estimates for other insurance in Rhode Island because all clients with that 

payer were admitted to outpatient treatment, or unknown insurance in North Dakota, as all clients 

with that payer were admitted into inpatient treatment. In Nevada and Rhode Island, the number 

of clients with these payers was fewer than 10, rendering any inference inconclusive at best. In 

North Dakota, however, the payer for almost 17 percent of the clients was unknown at the time 

of admission, providing better evidence of an association. 

As Table 4.3 reveals, in five of the seven States, individuals who were expected to pay for 

care themselves had significantly lower odds of entering inpatient treatment than those in the 

omitted category (i.e., other government funding/no charge). Individuals with private health 

insurance, Medicare, or Medicaid also in many cases had lower odds of entering inpatient 

treatment facilities than individuals with other government funding/no charge. A notable 

exception was North Dakota, in which individuals with private health insurance, Medicare, or 

Medicaid had much higher odds of entering inpatient treatment and in which all of those with 

unknown insurance entered outpatient treatment. 

Discussion 

This study examined the effects of the severity of the alcohol use disorder, as measured 

by routinely collected administrative data, on the odds of admission to alcohol treatment 

programs in nine States. In contrast to previous studies, which used claims data from clients with 

employer-sponsored health insurance, our data include clients who were unemployed, uninsured, 

and homeless. Our results reveal that having a more severe disorder generally increased the odds 

of inpatient treatment, but the magnitude, and sometimes the direction, of the estimated effects 

vary across the nine States considered here. 

Individuals with more severe substance use disorders (as measured by more frequent 

alcohol use, use of cocaine as a secondary substance, and a prior treatment episode), as well as 

those who were homeless, generally had higher odds of admission to inpatient treatment. Those 

who were employed had consistently lower odds of inpatient admission. Thus, Gregoire's (2000) 

finding extends to admission, at least in these nine States. Whether employed persons really have 

less severe disorders, or choose outpatient treatment due to time constraints or some other reason, 
71 

Table 4.3 Probability of Seeking Inpatient Alcohol Abuse Treatment, by State: Coefficients and Standard Errors on the Ex

 Variables 

Colorado Iowa Maine Nevada New Jersey North Dako

N 4,654 17,591 3,762 1,615 10,414 1,028 Expected Source of Payment 

Self-pay -1.239*** 0.182* -1.369*** -1.076*** -1.263*** -0.273  (0.101) (0.101) (0.384) (0.175) (0.098) (1.488) Private -0.708** -0.153* -1.501*** 0.242 -0.115 2.401** (0.300) (0.081) (0.370) (0.333) (0.092) (0.849) Medicare -0.007 -0.004 -1.433** ne -0.354** 2.760**  (0.417) (0.156) (0.645) (0.155) (1.095) Medicaid -0.964* -0.344*** -0.556*** ne -0.973*** 2.573** (0.575) (0.109) (0.170) (0.159) (0.928) Other 2.054*** -0.531 -1.155*** -1.899*** -0.021 4.323**

(0.140) (0.476) (0.198) (0.501) (0.128) (1.274) 

Unknown -0.775* na na ne na ne 

 (0.397) Pseudo R2 0.3509 0.2542 0.4850 0.4217 0.3496 0.8108 

na = not applicable (i.e., the State had no clients in that category). ne = not estimable (see text). * Significant at 0.10 level. ** Significant at 0.05 level. *** Significant at 0.01 level. Notes: Standard errors in parentheses. Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
72 

is a question for future research. Taken together, our findings suggest that client placement in 

these State substance abuse treatment systems appear, at least to some extent, to conform with 

the current thinking on client placement. 

However, co-occurring mental disorders did not play a consistent role across even most 

States, and it was negatively related to the likelihood of inpatient treatment in several. This is 

surprising given its importance as a clinical indicator for inpatient treatment and its significance 

in earlier studies (Goodman et al., 1992, 1998). We cannot tell whether we obtain our results 

because individuals with co-occurring mental disorders are less likely to choose inpatient 

treatment settings, are less likely to be referred to them, or are refused admission to them. 

However, of note is that two of the three States for which co-occurring mental disorders 

increased the odds of inpatient admission, New Jersey and North Dakota, had special programs 

for those with both mental and substance use disorders (New Jersey Substance Abuse Prevention 

and Treatment Advisory Task Force, 2001; North Dakota Department of Human Services, 2003). 

Iowa, the third State for which co-occurring mental disorders significantly increased the odds of 

inpatient admission, had two characteristics that might have worked together to promote 

appropriate placement. The first was that it promulgated its own set of client placement 

guidelines, which, similar to the ASAM-PPCs, consider emotional and behavioral factors in 

client placement (Chemical Dependency Treatment Programs of Iowa, Iowa Substance Abuse 

Program Directors Association, & Iowa Department of Public Health, 1991). The second is that it 

was one of the first public systems in the country to contract with a behavioral managed care 

company (Division of Criminal and Juvenile Justice Planning, 1998). Taken together, these 

results suggest that States may need to take steps in lieu of or in addition to promulgating 

guidelines to promote admission of clients with co-occurring mental disorders to inpatient 

treatment. Both Colorado and Massachusetts had guidelines at the time, but in both States we 

find that co-occurring mental disorders decreased the odds of inpatient admission (Gartner & 

Mee-Lee, 1995; O'Keefe & Fisher, 2001). Further research is needed to determine the effect of 

specific State policies on client placement. 

Another finding meriting further examination is that, in the four States for which it was 

significant, Hispanic ethnicity reduced the odds of inpatient admission. Again, we cannot discern 

whether this is because Hispanic individuals chose not to enter inpatient treatment, were not 

screened as carefully, or because culturally competent inpatient treatment was unavailable. 

Although the latter might be understandable in Maine, which reported having only 26 Hispanics 

in treatment in 1996, it would be less understandable in New York and Nevada, which have 

substantial Hispanic populations. 

Finally, of mention is the finding that, in seven of our nine States, referral by an alcohol 

or drug treatment provider is strongly and positively associated with inpatient treatment. One 

possible explanation for this finding may be that providers of inpatient detoxification treatment 

believe that clients should be stepped down gradually to lower levels of care, so they refer their 

clients to inpatient treatment programs, as found in a small study by Kosanke et al. (2002). 

Because we cannot link records to create an episode for a given client, we are unable to test this 

hypothesis empirically. However, it is a plausible explanation for our empirical finding. 
73 

In other chapters in this compendium, we extend the research on setting choice. Chapter 5 

investigates the choice among types of inpatient treatment (hospital, long-term residential, and 

short-term residential) and outpatient treatment (standard outpatient and intensive outpatient). 

Chapter 8 investigates the effect of reporting requirements, one of the ways in which State data 

systems diverge, on estimates of the effects of client characteristics on treatment setting choice. 
74 

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79 

Chapter 5. Client Choice among Standard Outpatient, 

Intensive Outpatient, Residential, and Inpatient Alcohol 

Treatment in State-Monitored Programs 

Sarah Q. Duffy, Ph.D. 

Laura J. Dunlap, M.A. 

Gary A. Zarkin, Ph.D. 

Introduction 

Treatment for those with substance use disorders has evolved over the years from a 

largely inpatient to a largely outpatient activity. In the 1970s and early 1980s, treatment 

providers believed that inpatient was the only acceptable treatment setting because individuals 

needed to be removed from their environments to overcome their disorders (Washton, 1997). 

However, several studies conducted in the mid-1980s concluded that outcomes were the same for 

both treatment settings, and, because outpatient treatment is less costly, it was more costeffective (Annis, 1985-1986; Miller & Hester, 1986). Those findings, coupled with the growth of 

managed behavioral health care and the burden on the treatment system caused by the influx of 

cocaine-addicted clients in the mid- to late 1980s, led treatment to shift from predominantly 

inpatient to predominantly outpatient settings (Washton, 1997). By October 1, 1998, 89 percent 

of the almost 1 million individuals in treatment for substance use disorders were in some form of 

outpatient treatment (Office of Applied Studies [OAS], 2000). By that time, however, there also 

was a growing recognition that although many clients may not need inpatient treatment, some 

needed more structure than is provided in the standard outpatient (SOP) settings (Gottheil, 1997). 

This increased structure could be provided by intensive outpatient (IOP) treatment. By 1998, 

approximately 20 percent of clients in treatment nationwide were in IOP treatment (OAS, 2000). 

In this chapter, we extend the research on treatment-setting choice by examining the 

association between characteristics of alcohol-abusing clients and admission to State-monitored 

inpatient hospital (IPH), short- or long-term residential (STR or LTR), IOP, or SOP 

rehabilitation treatment settings. Earlier studies of the choice between inpatient and outpatient 

treatment provided evidence that both alcohol disorder severity factors, such as frequency of use, 

and socioeconomic variables, such as homelessness and employment status, were associated with 

admission or referral to inpatient versus outpatient treatment (Goodman, Holder, Nishiura, & 

Hankin, 1992; Goodman, Nishiura, & Hankin, 1998; Gregoire, 2000), but Chapter 4 in this 

compendium indicates that those relationships varied across States. A study of care 

authorizations made by a managed behavioral health care organization among inpatient/acute, 

residential, partial hospitalization, intensive outpatient, and standard outpatient treatment found 

that age and severity variables were positively associated with referrals to a more intensive 

treatment (Marques et al., 2001). In this chapter, we study the choice of admission to one of five 

types of treatment settings among those in the State-monitored treatment sector of two States to 

examine whether it is appropriate to combine types of inpatient and outpatient treatment into two 

broad choices for analyses of this population. Then we explore whether the same general 

findings regarding the associations between client severity and socioeconomic variables and 
80 

treatment-setting choice are revealed when the determinants of treatment-setting choice are 

examined in a five-choice model. 

To do so, we used data from the 1996 Treatment Episode Data Set (TEDS) for adult 

males from Iowa and New Jersey with alcohol as the primary substance of abuse. Our findings 

suggest that it is preferable to examine treatment-setting choice as a five-setting choice rather 

than collapsing it into an inpatient versus outpatient choice. We find that the key distinctions 

among clients occur between SOP and all other clients. Those who enter SOP treatment have less 

severe alcohol disorders and are more likely to be employed than are those who enter any other 

type of setting. 

Treatment Settings 

As defined by the American Society of Addiction Medicine's Patient Placement Criteria 

(ASAM-PPCs), IOP (Level II) falls between traditional SOP (Level I) and residential/inpatient 

services (Level III) in terms of treatment intensity. For adults, IOP treatment generally involves a 

structured day or evening treatment program of 9 hours of skilled treatment services provided 

each week.1

 Such services may include individual and group counseling, family therapy, 

educational groups, occupational and recreational therapy, psychotherapy, or other therapies 

(ASAM, 1996). SOP treatment encompasses similar nonresidential services as IOP that are 

provided in regularly scheduled sessions. However, SOP treatment generally consists of fewer 

than 9 contact hours per week. Both SOP and IOP treatment are geared toward individuals who 

do not suffer from severe medical problems and who prefer a modality that fits well with their 

daily work schedule. However, IOP treatment may be more appropriate for individuals whose 

emotional conditions distract from recovery, so they need monitoring in excess of that provided 

under SOP treatment. These individuals may also be resistant to treatment and lack a supportive 

recovery environment, both of which signal the need for a more structured program. 

Residential treatment serves clients who need a safe and stable living environment to 

develop sufficient recovery skills (ASAM, 1996). It provides organized services by designated 

treatment personnel who provide a planned regimen of care in a 24-hour setting. Residential 

treatment can be classified according to intensity (ASAM, 1996) or length of stay. In our data, 

short-term residential (STR) treatment is defined as 30 days or fewer with long-term residential 

(LTR) treatment lasting for more than 30 days. Inpatient hospital (IPH) treatment is usually more 

intensive than residential treatment but with shorter lengths of stay. 

Data 

As in Chapter 4, we used data from the 1996 Treatment Episode Data Set (TEDS), 

maintained by the Office of Applied Studies (OAS) of the Substance Abuse and Mental Health 

Services Administration (SAMSHA) (OAS, 1999). TEDS contains admissions data collected by 

treatment providers and sent to State agencies, which use them to monitor their substance abuse 

treatment systems. The data are submitted at regular intervals by the States in a common format 

to SAMHSA. The data include admissions information—such as the primary and secondary 

substances of abuse, frequency of use, age at first use, source of referral, number of prior 

 1

 In the TEDS data, States are to identify as IOP treatment the client receives that lasts 2 or more hours per 

day on 3 or more days per week (OAS, 1999). 
81 

treatment episodes, and planned type of service—as well as mental health and socioeconomic 

measures. Our analysis focused on adult males with alcohol as their primary substance of abuse. 

We did not include women because a variable that might be relevant to their treatment setting 

choice, whether or not they are pregnant, is not well reported. 

Unfortunately, all States do not report all variables or collect data from all substance 

abuse treatment facilities in the TEDS universe (i.e., those receiving public substance abuse 

treatment funding). Therefore, we focused on two States, Iowa and New Jersey, that 

differentiated IOP admissions from SOP admissions, provided data covering 90 percent or more 

of their estimated substance abuse treatment clients in programs receiving public funds in 1996, 

and collected data from some private facilities on a voluntary basis. These States also collected 

variables hypothesized to affect alcohol treatment choice. 

Empirical Framework 

Our empirical specification was motivated by random utility theory, which is commonly 

used to model choice behavior when the alternatives are naturally discontinuous (Ben-Akiva & 

Lerman, 1985). Random utility theory assumes that the consumer is rational and that he or she 

can compare alternatives based on his or her tastes and preferences. The individual's 

characteristics, which are associated with his or her tastes and preferences, as well as other 

individual-specific variables that may affect choice, enter an objective function, the utility 

function, that the individual is assumed to maximize. The individual does that by selecting the 

alternative that provides him or her with the highest utility. Thus, the coefficients, which can be 

estimated using maximum likelihood methods, are based on revealed preference exhibited by a 

sample of individuals. 

We assumed that the individual chooses from among five alternative treatment settings: 

IPH, STR, LTR, IOP, and SOP. Formally, the individual maximizes the following utility 

function, Ui: 

 Ui = Ui(IPH, STR, LTR, IOP, SOP), (1) 

where 

IPH = inpatient hospital, 

STR = short-term residential, 

LTR = long-term residential, 

IOP = intensive outpatient, and 

SOP = standard outpatient. 

The level of utility that individual i obtains from setting j is a function of the individual's 

characteristics (Xi): 

Uij = Uj(Xi), j = (IPH, STR, LTR, IOP, SOP), i = 1,..., N, (2) 

where N is the number of individuals in the sample. 

Random utility theory assumes that there is a deterministic portion of each of these 

utilities, which is known with certainty to the individual making the choice, and a random 
82 

component, which is due to measurement error or some other process that clouds the analyst's 

ability to fully model the individual's utility. Assuming a linear functional form, the deterministic 

portion of the utility function for the five setting choices is 

:ij = $j Xi, j = (IPH, STR, LTR, IOP, SOP), i = 1,..., N, (3) 

where :ij is the deterministic component of the individual's utility from each choice, Xi is as 

defined above, and the $j's are choice-specific coefficients on individual characteristics. To 

obtain the full random utility model for each choice, we added on an error term, ,ij, assumed to 

be distributed jointly according to the extreme value distribution, to yield (4): 

Uij = :ij + ,ij. (4)

The probability that individual i will choose treatment setting j is the probability that 

setting j will bring the individual the greatest utility: 

Prob (Uij > Uik), for all k ... j. (5) 

Given the distributional assumption noted above, and removing an indeterminacy, we have 

'

' 1

1

( ) ,

1

j i

j i

X

i J

X

k

e Pr Y j

e

β

− β

=

= =

+∑

 (6) 

where J is the total number of choices (in our case, five treatment settings) (Greene, 2000). We 

estimated the model using standard multinomial logit (MNL) techniques. We chose this 

approach over an ordinal approach based on an underlying latent variable, such as the one used 

by Marques et al. (2001), because although these treatment settings may vary with regard to the 

intensity of services, we do not believe that intensity is the only factor clients use in deciding 

among settings. Furthermore, as has been noted elsewhere, it is not always true that inpatient 

treatment settings provide the most intensive treatment (Samarasinghe, 1996). For these reasons, 

we chose the multinomial model, which does not impose an ordering a priori. 

We included several individual-specific characteristics in the model, based on prior 

research on treatment-setting choice (Goodman et al., 1992, 1998; Gregiore, 2000; also see 

Chapter 4). The variables are similar to those used in Chapter 4, but they have been collapsed as 

necessary to allow estimation of the MNL model.2

Substance Use Disorder Severity 

Previous studies of treatment-setting choice revealed that disorder severity affects the 

intensity of care received, as would be predicted by models of health services utilization and 

demand. This suggests that individuals with more severe alcohol disorders may receive greater 

 2

 Specifically, small cell sizes led to unreasonably large point estimates and standard errors in the MNL 

model. To increase cell sizes, we combined categories for a number of independent variables. 
83 

utility from choosing a more intensive treatment setting. We used several variables to measure 

the individual's alcohol use disorder severity. 

Frequency of Use: We hypothesized that the greater the frequency of alcohol use, the 

more severe the individual's disorder, and the greater the likelihood that the individual will 

demand a more intensive treatment setting. We included two dichotomous variables that measure 

frequency of use in the month prior to admission: used 3 or more times in the week before 

admission; and used at least once in the month prior, but less than 3 times in the week prior to 

admission. No use in the past month was the reference category. 

Intoxication Before Age 15: Research suggests that individuals who first use alcohol 

before the age of 15 are more likely to become alcohol dependent (Grant & Dawson, 1997), 

suggesting that they may demand more intense treatment. We included a dichotomous variable 

indicating first alcohol intoxication before age 15. 

Secondary Substance: Having a secondary substance of abuse can indicate a more severe 

alcohol use disorder. We included a dichotomous variable that equaled 1 if the client used any 

secondary substance, 0 otherwise. 

Prior Treatment: Prior treatment is used in many treatment studies as another indicator of 

disorder severity (e.g., Etheridge, Craddock, Hubbard, & Rounds-Bryant, 1999; French & 

Zarkin, 1992; Hubbard et al., 1989; McLellan et al., 1999; Simpson, Joe, Rowan-Szal, & 

Greener, 1995). We entered this as a dichotomous variable that equaled 1 if the individual had at 

least one prior treatment, 0 otherwise. 

Co-Occurring Mental Disorders, Homelessness: Based on the ASAM-PPCs and the 

results of prior research on treatment choice (e.g., Goodman et al., 1992, 1998; also see Chapter 

4), we expected that those with a co-occurring mental disorder and those who are homeless may 

have a higher likelihood of entering more intensive treatment setting. To capture this, we entered 

two indicator variables, one for co-occurring mental disorder and the other for homelessness. 

Socioeconomic and Demographic Characteristics 

Several authors have conceptualized addictive behavior as consistent with choice theory 

and suggested that socioeconomic variables should be included in models examining choice 

behavior among those with substance use disorders (Becker & Murphy, 1988; Brito & Strain, 

1996; Green & Kagel, 1996; Montoya, Atkinson, & Trevino, 2000). We included several 

variables to measure these characteristics. 

Employment Status: Employed individuals have higher time cost associated with inpatient 

treatment, other things equal, and therefore may gain greater utility from some form of outpatient 

treatment. Furthermore, the fact that they are employed suggests that they may have a less severe 

disorder in ways we cannot otherwise measure. Our research on the inpatient/outpatient choice 

found that employed individuals are less likely to enter inpatient treatment settings (see Chapter 

4). To examine whether a similar relationship is uncovered when we consider a more finely 

divided set of treatment-setting options, we included a dichotomous variable that equaled 1 if the 

client was employed at admission, either full- or part-time, and 0 for those who were 

unemployed or not members of the labor force. 
84 

Expected Payer: Economic models suggest that individuals who pay out of pocket for 

their own treatment may demand less costly treatment than those who do not. However, we only 

observed the expected payer for this particular admission and did not know, for example, 

whether the client's insurance covered both inpatient and outpatient treatment. In some cases, a 

payer may limit the individual's choice set if, for example, an indigent client chooses an inpatient 

treatment program because no publicly funded outpatient slots are available. Therefore, the 

direction of the expected payer effects is unclear a priori, and the variables should be thought of 

as control variables. The estimated coefficients may not accurately reflect any direct causal effect 

of pay source on treatment choice. We included the following three categories: (a) self-pay, (b) 

private health insurance, and (c) Medicare, Medicaid, and other insurance. Other government 

funding and no charge formed the reference category. The reference category consisted of clients 

whose treatment was funded by State agency funds, including those received through the Federal 

Government's Substance Abuse Prevention and Treatment (SAPT) block grant program. 

Education Level: Based on Muurinen (1982), better-educated individuals may be less 

likely to demand care or may demand less intensive care because, all else equal, the rate of 

depreciation of the health stock should be lower for better-educated individuals. At the same 

time, better-educated individuals may have higher income, which may mean they can afford a 

more intensive treatment choice. We included a dichotomous variable to measure education, 

which equaled 1 if the client had at least a high school education, 0 otherwise. 

Age: Human capital theory suggests that age has a positive effect on an individual's 

choice of treatment intensity because the rate of depreciation of health stock is a positive 

function of age (Muurinen, 1982). On the other hand, Suranovic, Goldfarb, and Leonard (1999) 

suggested that those who are older are more motivated to quit using substances, perhaps making 

it less likely that they would need intensive treatment. Therefore, we entered age without an a 

priori prediction on the sign of its impact. 

Race/Ethnicity: Research has shown that people of different races and ethnicities have 

different cultural attitudes about seeking treatment and the types of treatment sought (Arroyo, 

Westerberg, & Tonigan, 1998; Lundgren, Amodeo, Ferguson, & Davis, 2001; Sheikh & 

Furnham, 2000). We included three indicator variables to capture the client's race/ethnicity: 

Hispanic, non-Hispanic black, and other race. Non-Hispanic white was the reference category. 

Marital Status: Although imperfect, marital status may proxy for the ASAM-PPCs' 

emotional and behavioral disorders criteria. Currently married clients may have less severe 

emotional and behavioral disorders than those who are single, divorced, separated, or widowed. 

Marital status also may proxy a more stable living environment, more family responsibilities, or 

more family support, all of which may make it less likely that a married individual would enter 

the inpatient or residential settings. The variable equaled 1 if the individual was currently 

married, 0 otherwise. 

Referral Source: An individual's referral source may influence his choice of treatment 

setting and the utility he derives from that choice. The referral may help the individual choose a 

treatment setting by providing him with information about the best treatment choice for his 

needs; or the referral source may limit the individual's choice set, causing him to choose from 

among the remaining options. 
85 

Clients who self-refer into alcohol treatment may do so more because of life problems 

associated with their use of alcohol than because of their alcohol use (Majella Jordan & Oei, 

1989). These individuals may be more likely to enter a program based on convenience than on 

their clinical needs. Likewise, clients referred by third parties, such as their schools, employers, 

or physicians, also may choose a program based more on convenience than on clinical factors 

because they may be entering treatment to appease their referrer. On the other hand, clients 

referred by an alcohol or drug treatment provider, who may have knowledge of clinical 

placement criteria, may be more likely than others to be referred to the setting most consistent 

with their needs. 

Finally, the criminal justice system has became a frequent source of referral into alcohol 

treatment. The likelihood that clients referred in this way will choose treatment consistent with 

their needs depends, in part, on the placement criteria used by the referring criminal justice 

system (CJS). Clients with a criminal justice referral may face a limited choice set. For example, 

they may face a simple choice between the treatment setting dictated by the criminal justice 

agency or incarceration. However, in our data a CJS referral also may occur for clients with other 

CJS involvement, such as being on parole, on work or home furlough, or for a civil commitment. 

Therefore, having a CJS referral does not necessarily mean that treatment referred was required 

in lieu of prison. The client may still face a choice among multiple treatment settings. We 

included three dichotomous variables that indicated whether the client was referred by an alcohol 

or drug abuse treatment provider; another medical care provider; his employer or school; or was 

self-referred. CJS referral was the reference category. 

Season of Admission: The time of year the client is admitted also may affect the 

likelihood that an individual chooses inpatient treatment. For example, those who are seasonally 

employed, such as teachers and college professors, may be more likely to choose inpatient 

treatment in the summer. We included indicator variables for summer, fall, and winter, with 

spring as the reference category. 

Specification Tests 

Because most of the literature on treatment-setting choice examined a simple 

dichotomous choice between inpatient and outpatient treatment, we first tested whether 

combining the treatment settings into those two broad categories was supported by the data. To 

do so, we ran a series of likelihood ratio chi-square tests to determine whether the coefficient 

vectors for each possible pair of treatment-setting choices were significantly different. The 

results appear below. 

We then tested whether the multinomial logit (MNL) model was an appropriate way to 

estimate our model. MNL is a popular way to estimate polychotomous choice models, such as 

the one we estimate here, because of its relative ease of estimation and interpretation. However, 

use of the MNL also imposes the rather restrictive "irrelevance of independent alternatives" (IIA) 

assumption. Behaviorally, IIA implies that the ratio of the utility levels between two choices, say 

IPH and SOP, remain constant regardless of how many other choices there are. We implemented 

two tests that are frequently used to test this assumption—the Hausman and Small-Hsiao tests. 

The results appear below. 
86 

Results 

Descriptive Statistics 

Table 5.1 displays the mean values and proportions by State of the variables included in 

our analysis. It shows that there are several statistically significant differences in the variable 

means and proportions between the two States. To determine statistical significance, we 

conducted simple t tests of the means for the continuous variables and chi-square tests for 

differences in proportions for the dichotomous and categorical variables. Unless otherwise 

indicated, differences discussed in the text are significant at or better than the 5 percent level. As 

Table 5.1 shows, we found that statistically significant differences existed between the Iowa and 

New Jersey clients for all variables except secondary substance use and using drugs prior to 15 

years of age. 

In both States, the majority of clients entered SOP, although the proportion was 

somewhat higher in Iowa (71 percent) than New Jersey (59 percent). Clients next most 

frequently entered IOP, 16 percent in Iowa compared with almost 20 percent in New Jersey. STR 

was the next most frequent setting, accounting for 8.7 percent of the admissions in Iowa and 

almost 10 percent in New Jersey. LTR was the fourth most frequent treatment setting for Iowa 

clients, while it was the fifth most frequent for New Jersey clients. IPH, on the other hand, was 

the fifth most frequent for Iowa clients, and the fourth most frequent for New Jersey clients. 

Turning to the explanatory variables, Iowa and New Jersey clients differed on measures 

of disorder severity, although not always in the same direction. For example, in New Jersey 

almost 48 percent of the clients had used alcohol more than 3 times in the week prior to 

admission compared with only 26 percent of the clients in Iowa. Almost 39 percent of the clients 

in Iowa had no drug use in the week prior to admission compared with fewer than 22 percent of 

the clients in New Jersey. However, in Iowa almost 59 percent of the clients had at least one 

prior treatment episode compared with fewer than 50 percent in New Jersey. Clients in both 

States were similarly likely to have a secondary drug of abuse (approximately 41 percent) and to 

have become intoxicated prior to age 15 (approximately 34 percent). Fewer than 9 percent of 

clients in New Jersey indicated a mental disorder compared with almost 16 percent of clients in 

Iowa. Homelessness also varied between the two States, with fewer than 2 percent of clients in 

Iowa being homeless at treatment admission compared with almost 5 percent of clients in New 

Jersey. 

Differences between the two States also existed among the socioeconomic and 

demographic variables. Almost 66 percent of clients in Iowa were employed at treatment 

admission compared with only 56 percent of clients in New Jersey. About 30 percent of clients 

in New Jersey paid for their own treatment (i.e., self-paid) compared with fewer than 7 percent in 

Iowa. Other government/no pay was the overwhelming payment form for clients in Iowa, 

accounting for 70 percent, compared with only about 39 percent of clients in New Jersey. More 

consistent across States was the proportion of clients whose expected payer was either private 

health insurance (16 percent in Iowa and 18.4 percent in New Jersey) and or another payer (7.2 

percent in Iowa and 10.5 percent in New Jersey). 
87 

Table 5.1 Variable Means for Iowa and New Jersey 

Variable Iowa New Jersey 

Number of Observations 17,495 10,151 

Treatment Setting (dependent variable)*** 

Inpatient hospital (IPH) 0.015 0.065 

 (0.123) (0.247) 

Short-term residential (STR) 0.087 0.099 

 (0.282) (0.299) 

Long-term residential (LTR) 0.025 0.049 

 (0.156) (0.216) 

Intensive outpatient (IOP) 0.160 0.199 

 (0.366) (0.399) 

Standard outpatient (SOP) 0.713 0.588 

 (0.452) (0.492) 

Alcohol Use Prior to Admission*** 

Used at least 3 times in the week before admission 0.261 0.475 

 (0.439) (0.499) 

Used at least 1 time in the month before admission 0.353 0.308 

 (0.478) (0.462) 

No use in the past month 0.386 0.218 

 (0.487) (0.413) 

Had a secondary substance of abuse 0.407 0.415 

 (0.491) (0.493) 

Intoxicated prior to 15 years of age 0.343 0.338 

 (0.475) (0.473) 

Had at least one prior treatment episode*** 0.585 0.498 

 (0.493) (0.500) 

Existing mental disorder*** 0.159 0.087 

 (0.365) (0.282) 

Homeless at treatment admission*** 0.014 0.046 

 (0.117) (0.210) 

Employed at treatment admission*** 0.655 0.560 

 (0.475) (0.496) 

Expected Form of Payment*** 

Self-pay 0.066 0.321 

 (0.248) (0.467) 

Private health insurance 0.160 0.184 

 (0.367) (0.388) 

Other pay (e.g., Medicaid, Medicare, worker's compensation) 0.072 0.105 

 (0.259) (0.307) 

Other government pay, no charge 0.702 0.389 

 (0.457) (0.488) 

High school graduate*** 0.796 0.733 

 (0.403) (0.442) 
88 

Table 5.1 Variable Means for Iowa and New Jersey (continued) 

Variable Iowa New Jersey 

Age of respondent*** 34.292 36.131 

 (10.674) (10.425) 

Race/Ethnicity*** 

Non-Hispanic white 0.905 0.6824 

 (0.294) (0.466) 

Non-Hispanic black 0.043 0.199 

 (0.204) (0.399) 

Hispanic 0.034 0.105 

 (0.181) (0.306) 

Other race 0.018 0.014 

 (0.132) (0.119) 

Married*** 0.338 0.250 

 (0.473) (0.433) 

Referral Source to Treatment*** 

Self-referred 0.177 0.194 

 (0.382) (0.395) 

Alcohol/drug treatment provider 0.073 0.099 

 (0.260) (0.299) 

Other (employer, school, physician) 0.114 0.175 

 (0.318) (0.380) 

Criminal justice referral 0.636 0.532 

 (0.481) (0.499) 

Season of Admission*** 

Spring 0.323 0.258 

 (0.468) (0.438) 

Summer 0.189 0.258 

 (0.392) (0.438) 

Fall 0.188 0.231 

 (0.390) (0.421) 

Winter 0.300 0.253 

 (0.458) (0.435) 

*** Statistically significant at the 0.01 level. 

** Statistically significant at the 0.05 level. 

Note: Standard deviations are in parentheses; t tests and chi-square tests were used where appropriate. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
89 

About 91 percent of clients in Iowa were non-Hispanic white compared with only 68 

percent of clients in New Jersey. Fewer than 5 percent of clients in Iowa were non-Hispanic 

black compared with almost 20 percent of clients in New Jersey. A minority of clients in both 

States were currently married, about 34 percent of clients in Iowa compared with 25 percent in 

New Jersey, but most clients in both States had at least a high school education (about 80 percent 

in Iowa and 73 percent in New Jersey). 

The criminal justice system (CJS) was the most common route of treatment referral for 

clients in both States although a significantly greater proportion of clients in Iowa were CJSreferred compared with New Jersey (64 percent in Iowa vs. 53 percent in New Jersey). The 

second most common route for both States was self-referral, which was the route to treatment for 

almost 18 percent of clients in Iowa and more than 19 percent of those in New Jersey. A 

significantly greater proportion of clients in New Jersey were referred by the next most common 

referral category, referral by the client's employer, school, or health care provider other than an 

alcohol or drug treatment provider, compared with Iowa (almost 18 vs. 11 percent), while the 

least common route in both States was an alcohol or drug treatment provider (7.3 percent in Iowa 

and 9.9 percent in New Jersey). 

Finally, season of admission appeared to differ somewhat across the States. Although 

New Jersey had a fairly even distribution of clients admitted across the four seasons, about 25 

percent in each season, more than 60 percent of clients in Iowa were admitted in winter and 

spring compared with fewer than 40 percent in fall and summer. 

Specification Tests 

Pooling 

The results of the likelihood ratio tests appear in Table 5.2. These tests reveal that the null 

hypothesis of no difference in the coefficients for each pair can be rejected at better than the 1 

percent level. Thus, we ran our MNL model with all five treatment settings as distinct outcomes. 

IIA Tests 

Unfortunately, the two tests that are commonly used to test for IIA, the Small-Hsiao and 

Hausman tests, frequently arrive at different conclusions. Table 5.3 shows that was the case 

when we tested our data. Therefore, the results of the IIA tests were inconclusive. We explored 

using a multinomial probit model, which also can be used for multinomial choice estimation and 

does not suffer from IIA, but which can be difficult to estimate with more than three choices 

unless other restrictive assumptions are imposed. We attempted to run the model for both States 

using assumptions (standard deviation = 1; correlation = 0.5) that are far from those implied by 

the MNL. Although we could not get the New Jersey model to converge, we did successfully 

estimate the Iowa model. Because the results from that model were similar qualitatively to the 

MNL Iowa model, and because we did not have unequivocal information that the IIA was 

violated by our data, we believe the MNL results are reasonable and report them here. 
90 

Table 5.2 Likelihood Ratio Test for Pooling 

Categories Tested Chi-Square 

Degrees of 

Freedom p > Chi-Square

IPH STR 361.700 23 0.000 

IPH LTR 560.325 23 0.000 

IPH IOP 471.707 23 0.000 

IPH SOP 890.473 23 0.000 

STR LTR 425.733 23 0.000 

STR IOP 1,096.722 23 0.000 

STR SOP 2,514.803 23 0.000 

LTR IOP 1,074.206 23 0.000 

LTR SOP 1,308.670 23 0.000 

IOP SOP 1,009.398 23 0.000 

IOP = intensive outpatient. 

IPH = inpatient hospital. 

LTR = long-term residential. 

SOP = standard outpatient. 

STR = short-term residential. 

Null hypothesis: All coefficients expect intercepts associated with given pair of outcomes are 0 

(i.e., categories can be collapsed). 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 

Table 5.3 Hausman and Small-Hsiao Tests of Irrelevance of Independent Alternatives (IIA) 

Assumption: Iowa and New Jersey 

Hausman Results Small-Hsiao Results 

Category Omitted 

Chi-Square 

Value p Value 

Chi-Square 

Value p Value 

Iowa 

Inpatient hospital (IPH) -5.136 ----- 78.490 0.000 

Short-term residential (STR) 25.112 1.000 76.239 0.000 

Long-term residential (LTR) -32.088 ----- 75.345 0.000 

Intensive outpatient (IOP) -26.465 ----- 79.938 0.000 

New Jersey 

Inpatient hospital (IPH) -55.732 ----- 74.294 0.000 

Short-term residential (STR) -51.492 ----- 76.996 0.000 

Long-term residential (LTR) 8.772 1.000 67.746 0.000 

Intensive outpatient (IOP) -72.283 ----- 103.954 0.000 

Null hypothesis: Odds (Outcome-J vs. Outcome-K) are independent of other alternatives. 

Note: If chi-square < 0, the estimated model does not meet asymptotic assumptions of the test. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
91 

Multivariate Results 

Tables 5.4 through 5.7 present the results from our MNL estimation for Iowa and New 

Jersey. Estimated coefficients from MNL models can be difficult to interpret because the sign of 

the coefficient may not be equivalent to the change in the probability due to a change in the 

independent variable. Therefore, in Tables 5.4 and 5.5, we present the estimated marginal effects 

(me) of each explanatory variable (xi) on each alternative j. The marginal effects were estimated 

at the means of the independent variables, and their standard errors were computed using the 

Delta Method. To further aid in interpretation, we used those marginal effects to compute the 

percentage change in the predicted probability of each outcome as a function of each 

independent variable. Those results, which we discuss in the text, appear in Tables 5.6 and 5.7. 

We present the results separately for the two States. 

Iowa 

Severity Variables. Table 5.6 reveals that several of the variables measuring the alcohol 

use disorder severity had significant effects on treatment-setting admission among clients in 

Iowa. As might be expected based on previous research, clients with greater severity prior to 

treatment entry (as measured by frequency of alcohol use) were more likely to enter what are 

usually thought of as more intensive treatment settings. The main distinction, however, appears 

to be between SOP and all other settings. Admission to SOP treatment was generally associated 

with lower levels of severity. For example, having used alcohol 3 or more times in the week 

before entering treatment decreased the probability of entering SOP compared with not having 

used in the month before treatment. The marginal effect of -0.343 (see Table 5.4) translates into 

approximately a 44 percent decline in the probability of entering SOP.3

 The marginal effect of -

0.102 of use in the past month translates into a 13.2 percent lower probability of entering SOP 

compared with not having used in the past month. Intoxication prior to age 15 reduced the 

probability of SOP admission by about 3 percent compared with having begun using later. Those 

with at least one prior treatment had a 4.8 percent lower probability of entering SOP than did 

those with no prior treatment. Being homeless at admission led to a 36 percent lower probability 

of entering the SOP setting. 

In contrast, most severity variables generally were positively associated with entry into 

settings more likely to provide more intensive treatment. For example, those who drank more 

than 3 times in the week before treatment had a 55.2 percent higher probability of entering IOP 

treatment than those who had not drunk in the month before treatment, and those who drank at 

least once in the month before treatment had a 28.6 percent higher probability of entering IOP 

treatment. Likewise, those who became intoxicated prior to age 15 had an 8.2 percent higher 

probability of entering IOP treatment than those who started drinking later. 

 3

 The percentage change in the probability is equal to the marginal effect divided by the predicted 

probability. In this case, that is -0.343 / 0.772 = -0.444. 
92 

Table 5.4 Marginal Effects for the Choice of Substance Abuse Treatment Setting: Iowa 

Variable 

Inpatient 

Hospital 

(IPH) 

Short-Term 

Residential 

(STR) 

Long-Term 

Residential 

(LTR) 

Intensive 

Outpatient 

(IOP) 

Standard 

Outpatient 

(SOP) 

Number of Observations 270 1,518 437 2,791 12,479 

Alcohol Use Prior to Admission 

Used at least 3 times in the week 

before admission 

0.023***

(0.004) 

0.224***

(0.011) 

0.007***

(0.002) 

0.089***

(0.009) 

-0.343***

(0.012) 

Used at least 1 time in the month 

before admission 

0.006***

(0.002) 

0.052***

(0.006) 

-0.003** 

(0.001) 

0.046***

(0.008) 

-0.102***

(0.009) 

Intoxicated prior to 15 years of age -4.86E-4 0.009** 0.002 0.013** -0.023*** 

 (0.001) (0.003) (0.001) (0.007) (0.007) 

Had a secondary substance of abuse -1.38E-4 -0.007** 0.001 -1.48E-4 0.006 

 (0.001) (0.003) (0.001) (0.006) (0.007) 

Had at least one prior treatment 

episode 

-0.001 

(0.001) 

0.015***

(0.003) 

0.011***

(0.001) 

0.012 

(0.006) 

-0.037***

(0.007) 

Existing mental disorder 0.004*** 0.004 0.003** -0.011 2.43E-4 

 (0.001) (0.004) (0.001) (0.008) (0.009) 

Homeless at admission 0.012** 0.158*** 0.043*** 0.066 -0.279*** 

 (0.005) (0.029) (0.010) (0.038) (0.047) 

Employed at admission -0.003*** -0.060*** -0.015*** -0.028*** 0.105*** 

 (0.001) (0.004) (0.002) (0.007) (0.008) 

Expected Form of Payment 

Self-pay 0.013*** 0.009 -0.004*** 0.164*** -0.183*** 

 (0.003) (0.006) (0.001) (0.016) (0.016) 

Private health insurance 0.010*** -0.013*** -0.007*** 0.180*** -0.169*** 

 (0.002) (0.004) (0.001) (0.011) (0.012) 

Other pay (e.g., Medicaid, 

Medicare, worker's 

compensation) 

0.012***

(0.003) 

-0.012***

(0.004) 

-0.007***

(0.001) 

0.014 

(0.013) 

-0.007 

(0.014) 

High school graduate -0.002*** -0.003 -0.002 -0.001 0.009 

 (0.001) (0.004) (0.001) (0.008) (0.009) 

Age of respondent 1.10E-4 -2.43E-4 3.17E-5 1.43E-6 1.01E-4 

 (3.00E-5) (1.50E-4) (5.00E-5) (3.00E-4) (3.40E-4) 

Race/Ethnicity 

Non-Hispanic black -0.002 0.032*** -3.12E-4 0.068*** -0.098*** 

 (0.001) (0.009) (0.002) (0.017) (0.019) 

Hispanic 0.004 -4.80E-4 -0.001 0.012 -0.015 

 (0.003) (0.009) (0.003) (0.017) (0.019) 

Other race 0.004 0.068*** 0.003 -0.005 -0.069** 

Married -0.003*** -0.016*** -0.006*** -0.016*** 0.041*** 

 (0.001) (0.003) (0.001) (0.006) (0.007) 
93 

Table 5.4 Marginal Effects for the Choice of Substance Abuse Treatment Setting: Iowa (continued) 

Variable 

Inpatient 

Hospital 

(IPH) 

Short-Term 

Residential 

(STR) 

Long-Term 

Residential 

(LTR) 

Intensive 

Outpatient 

(IOP) 

Standard 

Outpatient 

(SOP) 

Referral Source to Treatment 

Self-referred 0.002** 0.006 0.011*** -0.021*** 0.002 

 (0.001) (0.004) (0.003) (0.008) (0.009) 

Alcohol/drug treatment provider 0.002 

(0.002) 

0.089*** 

(0.010) 

0.067***

(0.009) 

-0.071*** 

(0.010) 

-0.087***

(0.016) 

Other (employer, school, 

physician) 

0.003***

(0.001) 

0.005 

(0.005) 

0.006** 

(0.002) 

-0.024*** 

(0.009) 

0.009 

(0.011) 

Season of Admission 

Summer -0.001 0.023*** -0.002 0.023** -0.043*** 

 (0.001) (0.005) (0.001) (0.009) (0.010) 

Fall -0.002*** 0.015*** -0.002 0.009 -0.021** 

 (0.001) (0.005) (0.001) (0.009) (0.010) 

Winter 0.002*** 0.007 0.002 0.010 -0.022** 

 (0.001) (0.004) (0.001) (0.008) (0.009) 

*** Statistically significant at the 0.01 level. 

** Statistically significant at the 0.05 level. 

LR chi-square: 5028.39. Prob > chi-square: 0.0000. Pseudo R-square: 0.1592. 

Note: Standard errors are in parentheses. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 

Table 5.5 Marginal Effects for the Choice of Substance Abuse Treatment Setting: New Jersey 

Variable 

Inpatient 

Hospital 

(IPH) 

Short-Term 

Residential 

(STR) 

Long-Term 

Residential 

(LTR) 

Intensive 

Outpatient 

(IOP) 

Standard 

Outpatient 

(SOP) 

Number of Observations 663 1,006 498 2,018 5,966 

Alcohol Use Prior to Admission 

Used at least 3 times in the week 

before admission 

0.061***

(0.009) 

0.047***

(0.007) 

0.004***

(0.001) 

0.170***

(0.014) 

-0.281***

(0.015) 

Used at least 1 time in the month 

before admission 

0.026***

(0.008) 

-0.043***

(0.007) 

-0.003 

(0.001) 

0.082***

(0.017) 

-0.062***

(0.018) 

Intoxicated prior to 15 years of age 2.35E-4 0.002 0.001 0.024** -0.028** 

 (0.002) (0.005) (0.001) (0.010) (0.012) 

Had a secondary substance of abuse 6.42E-5 0.031*** 0.002** 0.075*** -0.108*** 

 (0.002) (0.005) (0.001) (0.011) (0.012) 

Had at least one prior treatment 

episode 

0.009***

(0.002) 

-0.004 

(0.005) 

0.005***

(0.001) 

0.034***

(0.010) 

-0.044***

(0.011) 

Existing mental disorder 0.084*** 0.025*** -0.004*** -0.078*** -0.028 

 (0.011) (0.009) (0.001) (0.015) (0.022) 

Homeless at admission 0.016** 2.04E-4 0.083*** -0.132*** 0.032 

 (0.007) (0.011) (0.018) (0.022) (0.035) 

Employed at admission -0.016*** -0.051*** -0.011*** -0.079*** 0.157*** 

 (0.003) (0.006) (0.002) (0.011) (0.013) 
94 

Table 5.5 Marginal Effects for the Choice of Substance Abuse Treatment Setting: New Jersey (continued) 

Variable 

Inpatient 

Hospital 

(IPH) 

Short-Term 

Residential 

(STR) 

Long-Term 

Residential 

(LTR) 

Intensive 

Outpatient 

(IOP) 

Standard 

Outpatient 

(SOP) 

Expected Form of Payment 

Self-pay 0.003 -0.093*** -0.006*** -0.088*** 0.184*** 

 (0.003) (0.006) (0.001) (0.012) (0.013) 

Private health insurance 0.034*** -0.017*** -0.007*** 0.142*** -0.153*** 

 (0.006) (0.005) (0.001) (0.017) (0.019) 

Other pay (e.g., Medicaid, 

Medicare, worker's 

compensation) 

0.038***

(0.007) 

-0.033***

(0.004) 

-0.007***

(0.001) 

0.079***

(0.018) 

-0.078***

(0.020) 

High school graduate 0.002 -0.014*** -0.001 0.018 -0.006 

 (0.002) (0.005) (0.001) (0.011) (0.013) 

Age of respondent 4.66E-5 -0.001*** -5.25E-6 -2.91E-5 0.001 

 (8.00E-5) (2.40E-4) (4.00E-5) (0.001) (0.001) 

Race/Ethnicity 

Non-Hispanic black -0.007*** -0.018*** 0.001 0.029** -0.005 

 (0.002) (0.005) (0.001) (0.013) (0.014) 

Hispanic -0.010*** 0.020** 1.98E-4 -0.051*** 0.041** 

 (0.002) (0.009) (0.001) (0.016) (0.018) 

Other race -0.001 0.014 0.005 -0.104*** 0.086** 

 (0.007) (0.024) (0.006) (0.033) (0.042) 

Married -0.003 0.006 -0.004*** -1.30E-4 0.001 

 (0.002) (0.006) (0.001) (0.012) (0.013) 

Referral Source to Treatment 

Self-referred 0.045*** 0.058*** 0.009*** 0.056*** -0.167*** 

 (0.007) (0.010) (0.002) (0.014) (0.017) 

Alcohol/drug treatment provider -0.003 

(0.003) 

0.163***

(0.020) 

0.047***

(0.009) 

0.039** 

(0.019) 

-0.245***

(0.024) 

Other (employer, school, 

physician) 

0.019***

(0.005) 

0.063***

(0.010) 

-0.001 

(0.001) 

0.080***

(0.015) 

-0.161***

(0.017) 

Season of Admission 

Summer -0.003 0.016** 0.001 -0.009 -0.005 

 (0.002) (0.007) (0.001) (0.013) (0.015) 

Fall -0.009*** 0.022*** 0.001 -0.005 -0.009 

 (0.002) (0.008) (0.001) (0.014) (0.016) 

Winter -0.006*** 0.020*** 0.001 0.019 -0.035** 

 (0.002) (0.007) (0.001) (0.013) (0.015) 

*** Statistically significant at the 0.01 level. 

** Statistically significant at the 0.05 level. 

LR chi-square: 7,326.61. Prob > chi-square: 0.0000. Pseudo R-square: 0.3036. 

Note: Standard errors are in parentheses. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
95 

Table 5.6 Marginal Effects as a Proportion of the Predicted Probability of Each Outcome: Iowa 

Variable 

Inpatient 

Hospital 

(IPH) 

Short-Term 

Residential 

(STR) 

Long-Term 

Residential 

(LTR) 

Intensive 

Outpatient 

(IOP) 

Standard 

Outpatient 

(SOP) 

Number of Observations 0.004 0.053 0.009 0.162 0.772 

Alcohol Use Prior to Admission 

Used at least 3 times in the week 

before admission 5.628*** 4.234*** 0.760*** 0.552*** -0.444*** 

Used at least 1 time in the month 

before admission 1.456*** 0.989*** -0.285** 0.286*** -0.132*** 

Intoxicated prior to 15 years of age -0.122 0.161** 0.198 0.082** -0.030*** 

Had a secondary substance of abuse -0.035 -0.133** 0.096 -0.001 0.008 

Had at least one prior treatment episode -0.152 0.283*** 1.277*** 0.071 -0.048*** 

Existing mental disorder 1.011*** 0.072 0.314** -0.067 0.000 

Homeless at admission 3.104** 2.977*** 4.729*** 0.406 -0.361*** 

Employed at admission -0.644*** -1.123*** -1.672*** -0.172*** 0.136*** 

Expected Form of Payment 

Self-pay 3.267*** 0.172 -0.406*** 1.013*** -0.237*** 

Private health insurance 2.456*** -0.249*** -0.779*** 1.110*** -0.219*** 

Other pay (e.g., Medicaid, Medicare, 

worker's compensation) 

2.922*** -0.230*** -0.732*** 0.088 -0.009 

High school graduate -0.589*** -0.065 -0.205 -0.007 0.011 

Age of respondent 0.027 -0.005 0.004 0.000 0.000 

Race/Ethnicity 

Non-Hispanic black -0.405 0.606*** -0.035 0.419*** -0.127*** 

Hispanic 0.901 -0.009 -0.063 0.075 -0.019 

Other race 0.897 1.275*** 0.280 -0.030 -0.089** 

Married -0.627*** -0.309*** -0.644*** -0.102*** 0.053*** 

Referral Source to Treatment 

Self-referred 0.608** 0.105 1.220*** -0.128*** 0.002 

Alcohol/drug treatment provider 0.411 1.678*** 7.478*** -0.438*** -0.113*** 

Other (employer, school, physician) 0.819*** 0.100 0.665** -0.147*** 0.012 

Season of Admission 

Summer -0.271 0.434*** -0.182 0.141** -0.056*** 

Fall -0.551*** 0.290*** -0.177 0.057 -0.027** 

Winter 0.624*** 0.135 0.256 0.062 -0.028** 

*** Statistically significant at the 0.01 level. 

** Statistically significant at the 0.05 level. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
96 

Table 5.7 Marginal Effects as a Proportion of the Predicted Probability of Each Outcome: New Jersey 

Variable 

Inpatient 

Hospital 

(IPH) 

Short-Term 

Residential 

(STR) 

Long-Term 

Residential 

(LTR) 

Intensive 

Outpatient 

(IOP) 

Standard 

Outpatient 

(SOP) 

Number of Observations 0.016 0.061 0.006 0.219 0.699 

Alcohol Use Prior to Admission 

Used at least 3 times in the week 

before admission 3.820*** 0.770*** 0.724*** 0.777*** -0.402*** 

Used at least 1 time in the month 

before admission 1.617*** -0.706*** -0.472 0.375*** -0.089*** 

Intoxicated prior to 15 years of age 0.015 0.040 0.166 0.111** -0.040** 

Had a secondary substance of abuse 0.004 0.505*** 0.346** 0.343*** -0.154*** 

Had at least one prior treatment episode 0.592*** -0.068 0.973*** 0.154*** -0.064*** 

Existing mental disorder 5.307*** 0.418*** -0.811*** -0.354*** -0.039 

Homeless at admission 0.998** 0.003 15.062*** -0.601*** 0.046 

Employed at admission -1.028*** -0.835*** -1.977*** -0.361*** 0.225*** 

Expected Form of Payment 

Self-pay 0.162 -1.535*** -1.035*** -0.400*** 0.263*** 

Private health insurance 2.167*** -0.279*** -1.288*** 0.650*** -0.218*** 

Other pay (e.g., Medicaid, Medicare, 

worker's compensation) 

2.374*** -0.539*** -1.225*** 0.363*** -0.111*** 

High school graduate 0.157 -0.230*** -0.106 0.081 -0.008 

Age of respondent 0.003 -0.014*** -0.001 0.000 0.001 

Race/Ethnicity 

Non-Hispanic black -0.437*** -0.290*** 0.106 0.132** -0.007 

Hispanic -0.623*** 0.324** 0.036 -0.234*** 0.059** 

Other race -0.062 0.238 0.950 -0.476*** 0.122** 

Married -0.185 0.097 -0.702*** -0.001 0.001 

Referral Source to Treatment 

Self-referred 2.836*** 0.958*** 1.551*** 0.255*** -0.240*** 

Alcohol/drug treatment provider -0.210 2.681*** 8.454*** 0.180** -0.351*** 

Other (employer, school, physician) 1.216*** 1.045*** -0.208 0.365*** -0.231*** 

Season of Admission 

Summer -0.211 0.271** 0.258 -0.043 -0.007 

Fall -0.547*** 0.367*** 0.123 -0.023 -0.013 

Winter -0.367*** 0.328*** 0.266 0.087 -0.049** 

*** Statistically significant at the 0.01 level. 

** Statistically significant at the 0.05 level. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
97 

The proportionate effect of a change in severity measures on the probability of entry into 

LTR treatment was not always as great as it was for IOP. Although using alcohol 3 or more 

times in the week before admission increased the probability of entry into LTR by 76 percent 

compared with 55 percent for IOP, use in the past month actually decreased the probability of 

entering LTR treatment by about 29 percent. Intoxication prior to age 15 did not significantly 

affect the probability of entry into LTR, although it had for IOP. Other measures of severity, 

however, did have a greater proportional effect on the probability of entering into LTR than they 

did on admission to IOP. A prior treatment episode, for example, increased the probability of 

entering LTR by 128 percent, being homeless increased it by 473 percent, and having an existing 

mental disorder increased it by 31 percent. None of these three variables affected the probability 

of entry into IOP. 

Severity measures had a more mixed effect on the probability of admission to STR 

treatment. On the one hand, the frequency of use variables had large impacts on the probability 

of admission. Use 3 or more times in the week before admission increased the probability of 

admission to STR by 423 percent, which is an enormous impact compared with its effect on 

LTR. Use at least one time in the past month increased the probability of admission to STR about 

99 percent compared with a decrease of 28.5 percent for LTR. Unlike LTR, having been 

intoxicated prior to age 15 increased the probability of STR admission by 16.1 percent. Having 

had prior treatment also increased the probability of admission to STR, although by a smaller 

28.3 percent compared with 128 percent for LTR. Those who were homeless had an increased 

probability of admission to STR of about 298 percent compared with those who were not 

homeless, which was somewhat smaller than the effect on the probability of admission to LTR. 

On the other hand, those with a secondary substance of abuse had a 13.3 percent lower 

probability of entering STR than did those without a secondary substance of abuse. Secondary 

substance use was not significantly associated with the probability of entering LTR. A client 

having a mental disorder had no higher or lower probability of entering STR than did someone 

without a mental disorder, in contrast to LTR where having a mental disorder did increase the 

probability of admission. 

As for the IPH setting, frequency of use again affected the probability of admission, and 

the effects were greater than they were in STR. Use at least 3 times in the week prior to treatment 

increased the probability of IPH by about 563 percent compared with no use, which was 

somewhat larger than its effect on the probability of entry into STR. Use at least once in the past 

month increased the probability of IPH by about 146 percent compared with no use. Both of 

these magnitudes were larger than those for STR and LTR. However, although both intoxication 

before age 15 and having had a prior treatment episode increased the probability of STR 

admission, neither affected the probability of IPH admission. Homeless clients had a 310 percent 

higher probability of IPH treatment compared with those who were not homeless, which was 

slightly larger than the effect of homelessness on the probability than it was for STR, but smaller 

than that for LTR. Those having a mental disorder at admission, on the other hand, had a 101 

percent greater probability of IPH treatment than did those without one, which was larger than its 

impact on the probability of admission to STR or LTR. 

Socioeconomic and Demographic Variables. A distinction between SOP and all other 

treatment settings appears again with respect to employment. Clients who were employed at 

admission had a significantly higher probability of entering SOP treatment than did unemployed 
98 

clients, and a significantly lower probability of entering any of the more intensive settings. 

Employment had the largest proportional negative impact on entry to LTR and the smallest on 

entry to IOP. 

There also were differences among the effects of expected source of payment on the 

probability of admission to each setting, but they were not as might be expected. As mentioned 

earlier, the estimated coefficients may not accurately measure the effect of expected payer on the 

probability of entry because the two may be codetermined. These variables should be thought of 

as control variables. We found that self-payment was associated with a 23.7 percent lower 

probability of entering SOP, the least costly option, than was the reference group, charity care or 

other government assistance (excluding Medicaid or Medicare). Self-payment also was 

associated with a more than 100 percent higher probability of entering IOP, but a 40.6 percent 

lower probability of entering LTR. We found similar results for private health insurance and 

other sources of payments (e.g., Medicaid, Medicare, worker's compensation). Compared with 

the reference cell, private insurance coverage was associated with a lower probability of entering 

SOP and a higher probability of entering IOP or IPH. However, in contrast to self-payment, 

private insurance coverage was associated with a lower probability of entering either residential 

setting compared with the reference cell. Other payment source was likewise positively 

associated with admission to IPH and a lower probability of admission to either type of 

residential setting. However, its association with entry into either type of outpatient setting was 

no different statistically than the reference cell. 

We found some evidence that the SOP setting in Iowa was further distinguished from the 

other settings with regard to the race/ethnicity variables. We found that non-Hispanic black 

clients in Iowa had a 41.9 percent higher probability of entering IOP and a 60.6 percent higher 

probability of entering STR than non-Hispanic white clients, but non-Hispanic black clients had 

a 12.7 percent lower probability of entering SOP compared with non-Hispanic white clients. A 

similar pattern emerged for those of other race when compared with non-Hispanic whites. Those 

of other race had an 8.9 percent lower probability of entering SOP, but a 127.5 percent higher 

probability of entering STR than did non-Hispanic whites. However, the probability that 

Hispanics would enter any of the treatment settings was not different from that of whites. 

A distinction between SOP and all other treatment settings existed in Iowa for marital 

status as well. Married clients had a 5.3 percent greater probability of entering SOP than did 

those not currently married and a lower probability of entering any other treatment setting. The 

proportional effect on these probabilities was greatest for LTR and smallest for IOP. 

Referral Source and Season of Admission. Referral source also was associated with 

treatment-setting choice in Iowa, although this time the pattern was somewhat different. Rather 

than having the demarcation at SOP as with most of the other variables, it appeared between IOP 

treatment and the inpatient treatments. Compared with those referred by the CJS, those who were 

referred by any other source had a lower probability of entering the IOP setting. However, those 

with any other referral source had a higher probability of entering one of the inpatient settings, 

especially LTR. 
99 

Finally, we found that season of admission also affected choice in Iowa with clients 

entering treatment in summer, fall, or winter months being less likely to enter SOP than clients 

entering treatment during the spring. 

New Jersey 

Severity Variables. As in Iowa, the main distinction with respect to most severity 

variables appears to have been between clients admitted to SOP compared with those admitted to 

all other treatment settings (Table 5.7). Again, those with higher severity levels had lower 

probabilities of entering SOP. Those who drank alcohol 3 or more times in the week before 

admission were 40.2 percent less likely to enter SOP than were those who did not drink in the 

month prior to treatment. Those who drank at least once in the past month were 8.9 percent less 

likely to enter SOP. Secondary substance use, intoxication prior to age 15, and having at least 

one prior treatment episode also decreased the probability of entering SOP by 15.4, 4.0, and 6.4 

percent, respectively, compared with their reference categories. However, those who were 

homeless at admission or had a mental disorder were no more or less likely to enter an SOP 

setting in New Jersey than were those who did not. 

On the other hand, those who drank alcohol 3 or more times in the week before treatment 

had a 77.7 percent higher probability of entering IOP treatment than those who had not drunk at 

all. Those who drank at least once in the month before treatment had a 37.5 percent higher 

probability of entering IOP than those who did not drink in that time period. Those with 

secondary drug use, who first became intoxicated before age 15, or who had at least one prior 

treatment episode also had higher probability of admission to IOP treatment than those who did 

not. Those who were homeless or who had an existing mental disorder were less likely to enter 

IOP treatment than those who were not homeless or did not have a mental disorder. 

Those who were homeless did, however, have a significantly higher probability of 

entering LTR than those who were not. Those who drank alcohol at least 3 times in the month 

before admission, had a secondary substance of abuse, or had at least one prior treatment episode 

also had a greater probability of admission to LTR. However, those who drank at least once in 

the month before treatment or had become intoxicated with alcohol before age 15 had the same 

probability of entering LTR as those who did not, and those who had an existing mental disorder 

had a significantly lower probability of entering LTR than those who did not. 

In contrast, clients with an existing mental disorder did have a 41.8 percent higher 

probability of admission to STR than those who did not have a mental disorder. Additionally, 

those who drank at least 3 times in the week prior to admission had a 77 percent higher 

probability of entering STR than those who did not drink in the month prior to admission, and 

those with secondary substance use had a 50.5 percent higher probability of doing so. However, 

those who drank at least once in the past month had a 71 percent lower probability of entering 

STR than those who did not, and neither intoxication prior to age 15 nor homelessness had any 

effect on the probability. 

Regarding entry to IPH, almost all of the severity variables that were significant were 

positively associated with the probability of admission. Those who drank alcohol at least 3 times 

in the week prior to treatment were 382 percent more likely to enter IPH than were those who 
100 

had not drunk during that time, while those who drank at least once in the month prior to 

admission had an almost 162 percent higher probability. Those with at least one prior treatment 

episode had a 59.2 percent higher probability of admission to IPH, those who were homeless a 

99.8 percent higher probability, and those with an existing mental disorder almost a 530.7 

percent higher probability. The exceptions were secondary drug use and intoxication before age 

15, which did not significantly affect the probability of entry into the IPH setting. 

Socioeconomic and Demographic Variables. As was the case in Iowa, employment at 

admission was significantly negatively associated with entry into all settings except SOP. Those 

who were employed had a 36.1 percent lower probability of entering IOP, a 197.7 percent lower 

probability of entering LTR, an 83.5 percent lower probability of entering STR, and a 102.8 

percent lower probability of entering IPH than those who were not employed. In contrast, those 

who were employed at admission had a 22.5 percent higher probability of entering SOP 

treatment compared with those who were not employed. 

The results for expected payer were again not straightforward, but again care should be 

used in their interpretation. In contrast to Iowa, self-payment was positively associated with 

entry to SOP and negatively associated with the presumably more expensive IOP, LTR, and STR 

than was the reference category, government funding or charity care. However, self-payment 

was not associated with entry into IPH. The associations revealed for the other payment sources, 

on the other hand, were more similar to those in New Jersey. Private insurance or other pay 

source (e.g., Medicaid, Medicare) was associated with a lower probability of entering SOP and 

higher probabilities of entering the presumably more expensive IOP and IPH than the reference 

category, but a lower probability of entering either type of residential care. 

Race and ethnicity variables were significantly associated with entry into the various 

modes of treatment. Although Hispanics and those in the other race category had a higher 

probability of entering SOP and a lower probability of entering IOP than did non-Hispanic 

whites (the reference category), neither group was any more or less likely to enter LTR. 

Hispanics had a higher probability of entering STR, but a lower probability of entering IPH than 

did non-Hispanic whites. Non-Hispanic blacks were more likely to enter IOP, less likely to enter 

both STR and IPH, but equally likely as non-Hispanic whites to enter the other two treatment 

settings. 

In contrast to Iowa, marital status did not strongly affect the probability of entry into most 

settings. The only significant effect of marital status was on the probability of entering LTR. 

Clients who were married had a 7.02 percent lower probability of entering LTR than those who 

were unmarried. 

Referral Source and Season of Admission. Referral source was strongly associated with 

the probability of entering different treatment settings in New Jersey, although the pattern was 

somewhat different than it was in Iowa. In New Jersey, those referred by any of the other sources 

(e.g., self-referral, alcohol/drug treatment provider) had a significantly lower probability of 

entering SOP than did those who were referred by the CJS. Clients referred by these other 

sources also had significantly higher probabilities of entering both IOP and STR than did the CJS 

clients. In addition, those who self-referred had significantly higher probabilities of entering both 
101 

LTR and IPH than those referred by the CJS, while those referred by an employer, school, or 

physician had significantly higher probabilities of being admitted to the STR and IPH settings. 

As for season of admission, clients who were admitted to treatment in the fall and winter 

had a significantly higher probability of entering STR, and a lower probability of entering IPH, 

than did those who were admitted to treatment in the spring. Those admitted in the winter had a 

4.9 percent lower probability of entering SOP treatment than those entering in the spring. 

Discussion 

As in our earlier work on choice of alcohol treatment setting, we found that, in general, 

those with more severe alcohol use disorders were admitted to treatment settings generally 

considered more intensive, but that socioeconomic and demographic variables also affected 

treatment-setting admission. However, when extending the analysis to a polychotomous choice 

among several different treatment settings, rather than a dichotomous choice between inpatient 

and outpatient, the major distinction appeared between SOP and all other treatment settings, 

including IOP. In both States, clients with more severe alcohol use disorders, as measured by 

such variables as frequency of use, intoxication prior to age 15, and having had a prior treatment 

episode, generally had a reduced probability of being admitted to SOP, but an increased 

probability of being admitted to any of the more intensive settings. Furthermore, the results do 

not suggest an inherent ordering among the more intensive settings. The proportionate effects of 

the severity variables were not always monotonically related to the continuum of intensity as it is 

often described, for example, by the ASAM criteria. These findings, along with the results of the 

chi-square test, suggest that treatment settings can be better studied as a polychotomous, rather 

than dichotomous, choice, and that a multinomial approach is more appropriate than an ordinal 

approach. 

Our analysis revealed other key similarities between the Iowa and New Jersey client 

samples regarding treatment-setting choice. For example, in both States, clients who were 

employed were significantly more likely to enter SOP and less likely to enter any of the other 

treatment settings than were unemployed clients. Also similar in both States was the pattern 

associated with private health insurance coverage. Private health insurance was associated with a 

higher probability of entering IOP or IPH, but lower probability of entering SOP or either form 

of residential care than government assistance or charity care. Furthermore, clients referred by an 

alcohol or drug treatment provider in either State were more likely to enter STR and LTR and 

less likely to enter SOP treatment compared with clients referred through the CJS. 

Our analysis also revealed some interesting differences between the two States. For 

example, in New Jersey clients who self-referred, were referred by an alcohol or drug treatment 

provider, or were referred by another source were more likely to enter IOP treatment compared 

with clients referred by the CJS. However, in Iowa we found the opposite to be true in that selfreferred clients, clients referred by an alcohol or drug treatment provider, and clients referred by 

another source were less likely to enter IOP. Another interesting difference was the pattern 

revealed for self-payment. In Iowa, self-payment was positively associated with entering IOP 

and negatively associated with entering SOP compared with the reference cell. In New Jersey, 

we again found the opposite, with self-payment being positively associated with entering SOP 

and negatively associated with IOP compared with the reference cell. Further research using data 
102 

that is sufficient to identify the possibly joint determination of payment source and treatment 

setting is needed to fully understand the effects of expected source of payment on the probability 

of entry into the various settings. 

Although the results differed to some extent across the two States considered here, in 

both States, there is evidence that client placement is associated with the severity of the client's 

disorder, suggesting that clients are gaining admission to the facilities that may best meet their 

needs. Furthermore, use of IOP suggests that they are attempting to use their limited resources in 

the most cost-effective way. This finding suggests that States have been able to adapt to changes 

in the treatment practices in ways that both improve services and contain costs. 
103 

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106 
107 

Chapter 6. Length of Stay among Female Clients in 

Substance Abuse Treatment 

Olivia Silber Ashley, Dr.P.H. 

Lev Sverdlov, M.D., Ph.D. 

Thomas M. Brady, Ph.D. 

Introduction 

Research suggests that increased length of stay (LOS) in substance abuse treatment is 

associated with improved postdischarge outcomes (Comfort, Loverro, & Kaltenbach, 2000; 

Gottheil, McLellan, & Druley, 1992; Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997; 

Luchansky, He, Krupski, & Stark, 2000; McKay, Alterman, McLellan, & Snider, 1994; Pettinati 

et al., 1996; Simpson, Joe, & Broome, 2002; Simpson, Joe, Fletcher, Hubbard, & Anglin, 1999; 

Simpson, Joe, & Rowan-Szal, 1997b). Favorable outcomes measured in these studies after 

exposure to substance abuse treatment included abstinence from drugs, gains in employment, and 

decreases in criminal behaviors. Among pregnant women receiving residential substance abuse 

treatment, longer stay in treatment prior to delivery has resulted in reduced likelihood of adverse 

birth outcomes (Clark, 2001). In addition, clients with shorter stays are at increased risk of poor 

outcomes, such as readmission to treatment (Moos, Brennan, & Mertens, 1994a; Moos, Mertens, 

& Brennan, 1994b). 

Although research indicates that LOS in treatment is a strong predictor of posttreatment 

success, studies of LOS in treatment have been problematic. For example, definitions of LOS 

vary widely, and study samples are typically small and nonrepresentative (Ashley, Marsden, & 

Brady, 2003; Bartholomew, Rowan-Szal, Chatham, & Simpson, 1994; Egelko, Galanter, 

Dermatis, & DeMaio, 1998; McComish et al., 1999; Roberts & Nishimoto, 1996; Stark, 1992; 

Wobie, Eyler, Conlon, Clarke, & Behnke, 1997). In addition, many studies examining the 

predictors of retention in substance abuse treatment programs have focused on retention in 

therapeutic communities (Condelli, 1994; De Leon, Melnick, & Kressel, 1997) or have been 

limited to homeless or other public treatment populations (De Leon et al., 1997; Kingree, 1995; 

McCusker, Bigelow, Luippold, Zorn, & Lewis, 1995; Westreich, Heitner, Cooper, Galanter, & 

Guedj, 1997). Studies examining the predictors of retention in substance abuse treatment 

programs generally have used samples with few or no women (e.g., De Leon et al., 1997; 

Westreich et al., 1997) because the treatment population has been historically male. 

Although a few studies have shown that women stay longer in treatment than men (e.g., 

Broome, Flynn, & Simpson, 1999), most studies have found that, in contrast, men stay in 

treatment longer than women (Arfken, Klein, di Menza, & Schuster, 2001; Baekeland & 

Lundwall, 1975; Mammo & Weinbaum, 1993; McCaul, Svikis, & Moore, 2001; Sayre et al., 

2002; Simpson, Joe, Broome, et al., 1997a; Stark, 1992). Furthermore, research indicates that 

different factors influence treatment retention for women than men, including income, referral 

source, age, and payer type (Green, Polen, Dickinson, Lynch, & Bennett, 2002; Kingree, 1995; 

Mertens & Weisner, 2000). Hser, Polinsky, Maglione, and Anglin (1999) reported that matching 

clients to treatment based on gender-specific needs resulted in longer stays in treatment. Unique 
108 

substance abuse treatment needs for women include child care services, women-only treatment, 

and prenatal care services. 

More comprehensive data on LOS of women in substance abuse treatment facilities by 

individual and treatment-related characteristics are needed to help clinicians identify women at 

risk for shorter LOS and to develop strategies to improve retention. Information about the impact 

of child care and other gender-specific factors for women on treatment retention is particularly 

needed to address the unique substance abuse treatment needs of women, including the welfare 

of their children. 

Empirical Model 

Anderson and colleagues described a model of access and utilization of health care 

services that incorporated domains of "individual determinants" of health care utilization (Aday 

et al., 1999). Predisposing characteristics occur before onset and include demographic 

characteristics (e.g., age, race/ethnicity, education). Enabling characteristics include such factors 

as referral source for treatment and insurance status. The model suggests that organizational 

characteristics (e.g., provision of child care services and women-only facilities) also influence 

access and utilization. The distinction among predisposing, enabling, and organizational 

characteristics is used in our study to examine factors accounting for variation in retention 

among women.

1

 

Predisposing Characteristics: Age, Race, and Education 

Age has been shown to be an important determinant of LOS in treatment, as some reports 

show that older adults remain in treatment longer (Wickizer et al., 1994). For example, analyses 

of women in residential drug treatment programs in the Drug Abuse Treatment Outcome Study 

(DATOS) found that age had a significant positive effect on retention (Grella, Joshi, & Hser, 

2000). In addition, race/ethnicity has been found to be associated with LOS (McCaul et al., 

2001). For example, in a study based in a health maintenance organization (HMO) outpatient 

alcohol and drug treatment program (Mertens & Weisner, 2000), characteristics predictive of 

retention among women included belonging to racial/ethnic groups other than African American. 

Education also has been related to LOS. In general, higher educational attainment is associated 

with longer LOS (Kelly, Blacksin, & Mason, 2001; Knight, Logan, & Simpson, 2001). 

Other predisposing characteristics affecting LOS have been identified, including marital 

status, presenting substance abuse problem at admission, severity of substance use, age at first 

use, and mental disorder symptom severity (Fishman, Reynolds, & Riedel, 1999; Joe, Simpson, 

& Broome, 1999; Lang & Belenko, 2000; Maglione, Chao, & Anglin, 2000; Mertens & Weisner, 

2000; Rowan-Szal, Joe, & Simpson, 2000; Veach, Remley, Kippers, & Sorg, 2000; Williams & 

Roberts, 1991). Although these characteristics are not major variables of interest in the present 

study, our analyses controlled for many of these potentially confounding variables. 

 1

 The model also describes need characteristics, which refer to the level or severity of the illness. 

Identifying severity of substance abuse was not possible in the present study because of data limitations. 
109 

Enabling Characteristics: Referral Source and Payment Source 

Prior research has associated LOS in treatment with referral source (Wickizer et al., 

1994). Women often are referred to substance abuse treatment through child protective services 

or the criminal justice system. One federally sponsored project supporting 35 residential 

treatment programs for substance-abusing pregnant or postpartum women found that child 

protective services or the criminal justice system referred 37 percent of female clients (Clark, 

2001). Involvement with the criminal justice system has been associated with longer stays in 

substance abuse treatment, even if the criminal justice system was not the primary source of 

referral for treatment (Green et al., 2002; Grella, Anglin, Wugalter, Rawson, & Hasson, 1994; 

Nishimoto & Roberts, 2001). 

Payment source may be a factor in predicting LOS in substance abuse treatment. Many 

insurance plans (including Medicare, Medicaid, and private plans) limit outpatient visits and 

residential stays for substance abuse treatment (Sharfstein, Stoline, & Goldman, 1993; Zarkin & 

Dunlap, 1999). In a study of outpatient admissions for alcohol treatment from New Jersey, type 

of insurance coverage was associated with the likelihood of dropping out of treatment (Mammo 

& Weinbaum, 1993). 

Organizational Characteristics: Substance Abuse Treatment Programming for Women 

Because the tendency of women to leave treatment earlier than men might be attributed to 

the inability of treatment programs to meet the complex medical, social, and emotional needs of 

women, research suggests that treatment programming factors need to be included in studies of 

retention (Roberts & Nishimoto, 1996). Studies have begun to examine the efficacy of substance 

abuse treatment programming for women and the "active ingredients" in programs that might be 

associated with improved outcomes among women, including retention (Copeland, Hall, Didcott, 

& Biggs, 1993; Stark, 1992). 

One programmatic characteristic of substance abuse treatment intended to increase access 

and LOS for women is the provision of ancillary social services, such as child care. Limited 

access to child care services is one of the most important and frequently cited barriers to 

treatment among women who seek help (Allen, 1995; Coletti, 1998; Copeland, 1997; Grella, 

1997; Wechsberg, Craddock, & Hubbard, 1998). 

Studies also have found that programmatic changes to enable women to bring their 

children into residential treatment were associated with increased LOS (Hughes et al., 1995; 

Stevens, Arbiter, & Glider, 1989; Wobie et al., 1997). One study suggested that the earlier a 

mother's infant resides with her in the treatment setting, the longer her stay in treatment will be 

(Wobie et al., 1997). However, these studies were conducted with small samples of women in 

residential treatment. 

Another characteristic of substance abuse programming associated with increased LOS 

for women is the provision of women-only treatment (Grella, 1999; Roberts & Nishimoto, 1996), 

which may foster longer stays in treatment by creating an environment more focused on women's 

issues. Women-only facilities are more likely than mixed-gender facilities to retain particularly 

vulnerable women, such as lesbian women, women with a history of physical or sexual abuse, or 
110 

women who have worked in the sex trade (Copeland & Hall, 1992; Fullilove, Lown, & Fullilove, 

1992; Grella, 1997; Pottieger, Inciardi, & Tressell, 1996). 

Although these studies highlight factors important to retention for women, more 

nationally representative data are needed to examine client and organizational characteristics 

among subgroups of women in multiple types of treatment facilities. The current study increases 

our understanding of women and substance abuse treatment by examining LOS and experiences 

specific to females with substance use disorders. It also analyzes data from a large, nationally 

representative sample of clients in treatment facilities, making the findings more generalizable 

than those from previous studies, which have been limited to small convenience samples based at 

single treatment sites. In addition, no prior studies have analyzed a national sample of substance 

abuse treatment facilities to examine the role of organizational characteristics, such as on-site 

child care and women-only facilities, in enhancing the LOS of females with substance use 

disorders. 

Methods 

This chapter uses data from the Alcohol and Drug Services Study (ADSS), a nationally 

representative study designed to collect data about substance abuse treatment facilities, clients in 

treatment, and posttreatment status. The ADSS data, collected from 1996 to 1999, provide an 

important research opportunity to explore the experiences of women in substance abuse 

treatment. ADSS examined the characteristics of public and private substance abuse treatment 

facilities with active programs in all 50 States and the District of Columbia. More than 18,000 

facilities were identified from the Substance Abuse and Mental Health Services Administration 

(SAMHSA) National Facility Register (NFR) and other supplementary independent sources or 

organizations. For additional information about the methods for ADSS, see Office of Applied 

Studies (OAS, 2003). 

The sampling scheme varied by type of care in order to produce the best estimates of all 

treatment programs. To ensure reasonably precise and stable estimates of reported variables, 

hospital inpatient, residential, outpatient methadone, and combined facilities were oversampled 

relative to their proportional allocation. 

For the Phase I survey of treatment programs, a screening telephone interview was 

administered between October 1996 and April 1997. The screening garnered responses from 

2,395 treatment facilities representing 12,387 treatment facilities nationwide. The Phase I 

questionnaire administered by phone with facility directors collected information concerning 

each facility's most recent 12-month data on admissions, discharges, and treatment services 

offered. 

At Phase II, the sample of 2,395 Phase I facility respondents was further reduced by 

eliminating hospital inpatient facilities, facilities in which 100 percent of clients were treated for 

alcohol use disorders, and facilities outside primary sampling units (PSUs). Of 1,052 remaining 

Phase I facilities, a target Phase II facility sample was set of 306 facilities, and 280 cooperated 

with all steps of the Phase II study protocol.2

 Substance abuse treatment discharges during the 

 2

 This total includes 234 facilities in the original Phase II facility sample and 46 facilities added to replace 

nonresponding facilities. 
111 

most recent 6 months were listed for these 280 facilities, and client discharge records were 

randomly selected from the client lists. To be included on the list, clients must have spent at least 

1 day in treatment or have had at least one outpatient visit. Client-level discharge data were 

abstracted from facility records for 5,005 clients. A client record abstract form was used during 

site visits conducted with facility directors or administrators between August 1997 and April 

1999. Facilities participating in Phase II data collection were assured that the information they 

shared was confidential.3

Upon completion of Phase II data collection, no imputation was performed to fill in 

missing values for key variables. Full sample weights were created. The sum of the full sample 

weights for each of the client records in the ADSS Phase II client abstract file provides the 

estimate of 2,229,060 total discharged clients in the population in a 12-month period. For the 

purpose of the present study, the Phase I and Phase II files were merged. 

Sample 

Of the 5,005 clients included in ADSS Phase II, 1,334 records were for female substance 

abuse treatment clients; 95 records were excluded because they indicated that the client was 

younger than age 18. The analyses used data from the remaining 1,239 records of adult female 

substance abuse treatment clients aged 18 or older (representing 504,177 clients nationwide). 

The final sample included females discharged from nonhospital residential treatment, outpatient 

methadone treatment, outpatient nonmethadone treatment, and combinations of these types of 

care.4

 

Measures 

Measures of LOS and client characteristics were based on information abstracted from 

client treatment records during Phase II of ADSS. LOS was indicated by the number of days 

between the client's date of admission and the date of discharge from a substance abuse treatment 

facility. Exact age at admission was recorded. For descriptive analyses, clients were grouped into 

six age categories: 18 to 24 years, 25 to 34 years, 35 to 44 years, 45 to 54 years, 55 to 64 years, 

and 65 years or older. For multivariate analyses, age was treated as a continuous variable, and 

clients were grouped into three racial groups (white, unknown/not mentioned, and all other 

races),5

 three educational groups (less than high school graduate, high school graduate/general 

equivalence diploma [GED] or higher, and unknown/not mentioned), two referral groups 

(criminal justice system vs. all other referral sources), and five payment groups (no payment or 

client self-payment, private health insurance, criminal justice system, Medicaid or Medicare, and 

other funding). Organizational characteristics were measured using data from Phase I of ADSS. 

A dichotomous variable was created to distinguish client records at facilities offering child care 

services from those at facilities not offering such services, as well as facilities serving women 

only. 

 3

 For information on confidentiality procedures in ADSS, see the full methodology report (OAS, 2003). 

4

 All 196 client records from outpatient methadone facilities were excluded from analyses of women-only 

versus mixed-gender facilities because just two outpatient methadone facilities in the ADSS sample treated women 

only. 5

 Ethnicity was measured in a separate field on the client abstract form. Because 27 percent of the ADSS 

data regarding ethnicity was missing, this variable was not included in the analyses. 
112 

Analytic Approach 

Based on previous research, we hypothesized that predisposing characteristics of older 

age, white race, and higher educational attainment, as well as the enabling characteristic of 

criminal justice referral to treatment, would be associated with longer stay in treatment. We 

hypothesized that private health insurance, Medicaid, or Medicare payment for treatment would 

be associated with decreased LOS. We further hypothesized that organizational characteristics of 

receiving treatment at facilities offering child care services or at women-only facilities would be 

associated with longer stay in treatment. 

One of the major issues confronting studies of treatment retention is how to address 

differences in LOS among types of care (Roberts & Nishimoto, 1996; Smith, North, & Fox, 

1995; Strantz &Welch, 1995). Because many prior studies examining retention have analyzed 

clients separately by type of care received, descriptive analyses in the present study were 

stratified by type of care. Multivariate analyses were conducted on the full sample of adult 

female clients to preserve the large sample. Accordingly, three dummy variables were included 

in the model indicating treatment at residential facilities, outpatient nonmethadone facilities, or 

facilities offering a combination of types of care. Inasmuch as outpatient methadone facilities 

involve much longer stays in treatment than other types of care, and because ADSS collected 

only a subset of client information from records at outpatient methadone facilities, client records 

from outpatient methadone treatment facilities were excluded from the multivariate analysis. In 

addition, because LOS is associated with marital status, having children, presenting substance 

use disorder at admission, and receiving treatment at facilities offering transportation services 

(Broome et al., 1999; Fishman & Reynolds, 1999; Friedmann, Lemon, & Stein, 2001; Mammo & 

Weinbaum, 1993; Mertens & Weisner, 2000; Rowan-Szal et al., 2000; Veach et al., 2000; 

Williams & Roberts, 1991), the multivariate analysis controlled for these client and 

organizational characteristics.6

 Although LOS has not been linked in the research literature with 

receiving treatment at facilities offering prenatal care or combined substance abuse treatment and 

mental health services, the multivariate analysis controlled for these organizational 

characteristics to avoid possible confounding. 

Statistical procedures. Descriptive analyses used F tests to compare the distributions of 

mean LOS between (1) female clients with differing predisposing and enabling characteristics, 

(2) female clients at facilities offering child care services and female clients at facilities not 

offering child care services, and (3) clients at women-only facilities and female clients at mixedgender facilities. Statistically significant differences in LOS were examined using two-tailed 

Student's t tests. Descriptive analyses of LOS were conducted using sample weights developed 

by Westat and using WesVar (Westat, Inc., 2000) and jackknife variance estimation. 

Correlations between organizational characteristics were calculated to test for 

multicollinearity of variables prior to modeling and to test assumptions about relationships 

between these characteristics. Although many organizational characteristics were significantly 

correlated, Pearson product-moment correlation coefficients were relatively low (range = -0.09 

to 0.40), indicating that multicollinearity was not an issue in the analysis. Therefore, all 

 6

 Co-occurring mental disorders also have been linked to LOS among women (Broome et al., 1999; Haller, 

Miles, & Dawson, 2002; Joe et al., 1999; Lang & Belenko, 2000; Mertens & Weisner, 2000), but missing data 

prevented inclusion of this control variable. 
113 

organizational characteristics were included in one model. Survival analysis, specifically Cox's 

proportional hazard regression (Hosmer & Lemeshow, 1999; Parmar & Machin, 1995), was used 

to model LOS. Survival analysis considers situations in which (1) a dependent variable 

represents a time to a terminal event, and (2) the duration of the study is limited in time. In this 

analysis, the terminal event was discharge from treatment. An observation is considered censored 

by the end of the study period if the terminal event had not yet occurred or if the observation was 

followed up to a certain time point, after which there was no further information on the client. 

For the purpose of this analysis, the time of study was determined as 3,000 days, and 

observations with a longer stay in treatment than 3,000 days were considered to be censored. 

Hazard ratios (HRs) and 95 percent confidence intervals (CIs) were calculated. HR is a 

comparative measure of the association between exposure (measured in terms of one or more 

independent variables) and an outcome (e.g., discharge from treatment) over time for a defined 

population. In Cox's hazard regression analysis, it is assumed that (1) a hazard function is 

constant over time, and (2) all deviations are random. Under the assumption that a hazard 

function is constant, it is possible to say that a higher HR suggests that clients in the "exposed" 

group stay in treatment a shorter time than the comparison group. Furthermore, an HR of less 

than one indicates a variable that may be protective of leaving treatment early. 

The comprehensive model analyzed LOS as a function of predisposing, enabling, and 

organizational characteristics of interest, controlling for facility type of care; marital status at 

admission; having children at admission; type of presenting substance use disorder at admission; 

and treatment at facilities offering prenatal care, transportation, or combined substance abuse 

treatment and mental health services. Survival analyses were conducted using SUDAAN (Shah, 

Barnwell, Hunt, & LaVange, 1994) with Taylor series variance estimation. All estimates are 

weighted. 

Results 

The characteristics of the sample used in the analyses are presented in Table 6.1. Of the 

1,239 adult female substance abuse treatment clients in the ADSS Phase II sample, 12 percent 

were aged 18 to 24 years, 41 percent were aged 25 to 34 years, and 35 percent were aged 35 to 

44 years. Almost 56 percent were white, 31 percent were black, 6 percent were from other racial 

groups, and 8 percent were of unknown race. At admission, a majority of the sample had only a 

high school education or less. At admission, most women in the sample were not married, 63 

percent had a child/children, 55 percent were unemployed, 28 percent worked full or part time, 

and 23 percent lived with a spouse/partner. Alcohol was the most common substance of choice 

specified at admission (33 percent). 
114 

Table 6.1 Characteristics of Female Substance Abuse Treatment Clients Aged 18 or Older at Admission: 

1997-1999 

Characteristic n % Characteristic n % 

Age at Admission (years) Race 

18 to 24 146 11.8 White 687 55.5 

25 to 34 506 40.8 Black 381 30.8 

35 to 44 437 35.3 American Indian or Alaskan Native 16 1.3 

45 to 54 116 9.4 Asian or Pacific Islander 14 1.1 

55 or older 34 2.7 Other 38 3.1 

 Unknown/not mentioned 103 8.3 

Education at 

Admission 

 Primary Source of Referral to 

Treatment 

 

Less than 8 years 20 1.6 Self-referred/voluntary 367 29.6 

8-11 years 301 24.3 Criminal justice system 336 27.1 

Less than high school graduate, not 

otherwise specified 

40 3.2 Welfare office or other social service 

agencies 

139 11.2 

High school graduate/GED 358 28.9 Other treatment facility 128 10.3 

Some college 210 17.0 Health care or mental health 

providers 

123 9.9 

College graduate 41 3.3 Family or friend 66 5.4 

Postgraduate 13 1.1 Other 18 1.4 

Unknown/not mentioned 60 4.8 Unknown/not mentioned 62 5.0 

Methadone clients (data not collected) 196 15.8 

Primary Source of Payment for 

Treatment 

 Marital Status at 

Admission 

 

Medicaid 323 26.1 Separated/divorced 319 25.8 

Client self-payment 293 23.6 Never married 295 23.8 

Private health insurance 147 11.9 Married/common law 218 17.6 

No payment 67 5.4 Single 143 11.5 

Criminal justice system 51 4.1 Other 30 2.5 

Medicare 19 1.5 Unknown/not mentioned 38 3.1 

Other 

Unknown/not mentioned 

192 

147 

15.5 

11.9 

Methadone clients (data not 

collected) 196 15.8 

Have Child/Children at Admission Employment at Admission 

Yes 780 63.0 Unemployed 683 55.1 

No 178 14.4 Full-time (35 hr/wk or more) 194 15.7 

Unknown/not mentioned 85 6.9 Part-time (less than 35 hr/wk) 100 8.1 

Methadone clients (data not collected) 196 15.8 Employed, not otherwise specified 51 4.1 

 Keeping house, not otherwise 

employed 74 6.0 

 Disabled 51 4.1 

 Other 22 1.8 

 Unknown/not mentioned 64 5.2 
115 

Table 6.1 Characteristics of Female Substance Abuse Treatment Clients Aged 18 or Older at Admission: 

1997-1999 (continued) 

Characteristic n % Characteristic n % 

Living Arrangement at 

Admission 

 Substance of Choice Specified at 

Admission 

 

With spouse/partner 280 22.6 Alcohol 411 33.2 

With parent(s) 139 11.2 Crack (if unable to separate, 

combined with cocaine) 

175 14.1 

With other family 120 9.7 Cocaine 111 9.0 

Alone 120 9.7 Marijuana, hashish, THC 61 4.9 

With no other adult(s)/children only 90 7.3 Heroin 48 3.9 

No stable arrangement (include 

homeless, shelters) 

82 6.6 Amphetamines (speed, 

methamphetamine, meth, 

stimulants, crank) 

37 3.0 

With friends 52 4.2 Other or multiple 44 3.6 

Correctional facility 9 0.7 No substance of choice 24 1.9 

Other institution/closed facility 25 2.0 Substance(s) used not specified in 

record 

132 10.7 

Unknown/not mentioned 126 10.2 Methadone clients (data not 

collected) 

196 15.8 

Methadone clients (data not collected) 196 15.8 

Presenting Substance Use Disorder 

at Admission 

 

Alcohol and drug use 469 37.9 

Drug use only (excluding alcohol) 260 21.0 

Alcohol use only 259 20.9 

Other 6 0.5 

Unknown/not mentioned 49 4.0 

Methadone clients (data not collected) 196 15.8 

GED = general equivalence diploma; HMO = health maintenance organization; PPO = preferred provider organization; THC = 

tetrahydrocannabinol. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase II client data (1997-1999). 

An estimated 14 percent of adult female substance abuse treatment clients received 

treatment at nonhospital residential facilities, 7 percent received treatment at outpatient 

methadone facilities, 49 percent received treatment at outpatient nonmethadone facilities, and 29 

percent received treatment at combination facilities (Figure 6.1). The average LOS among adult 

female clients across types of care was 162 days. Among this population, the average LOS was 

34 days at nonhospital residential facilities, 531 days at outpatient methadone facilities, 154 days 

at outpatient nonmethadone facilities, and 147 days at combination facilities (Figure 6.2). 
116 

Figure 6.1 Weighted Percentages of Female Substance Abuse Treatment Clients Aged 18 or Older at 

Admission, by Facility Type of Care 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase 

II data (1997-1999). 

Figure 6.2 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Facility Type of Care 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase 

II data (1997-1999). 

Outpatient 

Methadone

7.2%

Outpatient 

Nonmethadone

49.1%

Nonhospital

Residential

14.3%

Combination 

Facilities

29.4%

Outpatient 

Methadone

7.2%

Outpatient 

Nonmethadone

49.1%

Nonhospital

Residential

14.3%

Combination 

Facilities

29.4%

34.0

531.4

153.8 146.7

0

100

200

300

400

500

600

Nonhospital

Residential

Outpatient

Methadone

Outpatient

Nonmethadone

Combination

Facilities

Facility Type of Care

LOS in Days
117 

Retention, by Predisposing Characteristics 

Age at admission. Among adult female clients at outpatient nonmethadone facilities, the 

average LOS differed by age group (F = 10.76, df = 64, p < 0.001) (Table 6.2). Student's t test 

showed that among adult female clients at outpatient nonmethadone facilities, LOS was shorter 

among those aged 55 to 64 (mean = 50 days) than among those aged 18 to 54 (mean = 139 to 

177 days). Analyses of age groups of adult female clients at nonhospital residential or outpatient 

methadone facilities could not be conducted because of small sample size in one or more age 

groups, weights that were zero for all records, or variables that were zero or a linear combination 

of other variables. 

Table 6.2 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Age at Admission and Facility Type of Care 

Facility Type of Care/Age at Admission Weighted n Average LOS F p 

Total 504,177 161.7 

Nonhospital Residential 

18 to 24 years 9,360 32.6 

25 to 44 years 35,315 37.0 

35 to 44 years 22,981 28.2 

45 to 54 years 3,042 39.3 

55 to 64 years * * 

65 years or older * * 

Subtotal 72,325 34.0 ---- 

Outpatient Methadone 

18 to 24 years 2,257 733.0 

25 to 44 years 14,713 702.6 

35 to 44 years 14,439 395.5 

45 to 54 years 4,759 296.9 

55 to 64 years * * 

65 years or older 0 n/a 

Subtotal 36,269 531.4 ---- 

Outpatient Nonmethadone 

18 to 24 years 36,397 162.0 

25 to 44 years 100,032 164.3 

35 to 44 years 69,666 139.0 

45 to 54 years 30,167 177.0 

55 to 64 years 11,135 49.5 

65 years or older * * 

Subtotal 247,607 153.8 10.8 <0.001 

Combination Facilities 

18 to 24 years 16,782 52.6 

25 to 44 years 47,022 284.0 

35 to 44 years 54,567 120.3 

45 to 54 years 25,526 31.7 

55 to 64 years * * 

65 years or older 0 n/a 

Subtotal 147,976 146.7 2.03 0.10 

Note: Of the total 1,239 female client records among clients aged 18 or older, 1,238 (99.9 percent) contained information about

LOS and age at admission. 

Degrees of freedom: 64. 

* Low precision; no estimate reported. 

--- F value cannot be calculated due to small sample size in one or more age groups, weights that are zero for all records, or 

variables that are zero or a linear combination of other variables. 

n/a = LOS cannot be calculated due to zero sample size in this age group. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase II 

data (1997-1999). 
118 

Race. Among adult female clients at outpatient methadone facilities, the average LOS 

differed by race (F = 4.85, df = 64, p = 0.004) (Table 6.3). However, small sample sizes for 

American Indian or Alaskan Native adult female clients at outpatient methadone facilities 

prevented further comparisons between racial groups. The average LOS was similar among 

racial groups of adult female clients at nonhospital residential (F = 2.06, df = 64, p = 0.11) or 

outpatient nonmethadone facilities (F = 0.71, df = 64, p = 0.59). Analyses of LOS by race among 

adult female clients at combination facilities could not be conducted. 

Table 6.3 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Race and Facility Type of Care 

Facility Type of Care/Race Weighted n Average LOS F p 

Total 456,276 159.7 

Nonhospital Residential 

White 39,110 38.1 

Black 22,877 32.9 

American Indian or Alaskan Native 0 n/a 

Asian or Pacific Islander * * 

Other 2,909 35.1 

Subtotal 65,887 35.7 2.06 0.11 

Outpatient Methadone 

White 17,772 396.4 

Black 9,042 941.7 

American Indian or Alaskan Native * * 

Asian or Pacific Islander 0 n/a 

Other 4,356 552.5 

Subtotal 31,981 563.0 4.85 0.004 

Outpatient Nonmethadone 

White 160,620 157.7 

Black 54,995 145.1 

American Indian or Alaskan Native 3,581 135.1 

Asian or Pacific Islander 1,360 114.7 

Other 2,646 112.7 

Subtotal 223,201 153.5 0.71 0.59 

Combination Facilities 

White 93,533 68.1 

Black 43,537 328.5 

American Indian or Alaskan Native * * 

Asian or Pacific Islander * * 

Other 0 n/a 

Subtotal 143,998 146.1 ---- 

Note: Of the total 1,239 female client records among clients aged 18 or older, 1,135 (91.6 percent) contained information about

LOS and race. 

Degrees of freedom: 64. 

* Low precision; no estimate reported. 

--- F value cannot be calculated due to small sample size in one or more racial groups, weights that are zero for all records, or 

variables that are zero or a linear combination of other variables. 

n/a = LOS cannot be calculated due to zero sample size in this racial group. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase II 

data (1997-1999). 
119 

Education at admission. Among adult female clients at outpatient nonmethadone (F = 

3.73, df = 56, p = 0.003) or combination facilities (F = 2.67, df = 56, p = 0.02), the average LOS 

differed by education at admission (Table 6.4). Among adult female clients at outpatient 

nonmethadone facilities, the LOS was shorter among those with fewer than 8 years of education 

(mean = 68 days) than among those with higher levels of education (mean = 28 to 173 days). The 

LOS also was shorter among those with postgraduate education (mean = 85 days) than among 

those with between 8 and 11 years of education (mean = 154 days) or high school graduates 

(mean = 173 days). Among adult female clients at combination facilities, the LOS was shorter 

among college graduates than among those with 8 to 11 years of education or high school 

graduates (college graduates: 17 days vs. 8 to 11 years: 56 days, t = 5.03, df = 56, p < 0.001; 

college graduates: 17 days vs. high school graduates: 79 days, t = 4.49, df = 56, p < 0.001). 

Table 6.4 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Education at Admission and Facility Type of Care 

Facility Type of Care/Education at Admission Weighted n Average LOS F p 

Total 430,548 110.7 

Nonhospital Residential 

Less than 8 years * * 

8-11 years 23,659 35.4 

Less than high school graduate not otherwise 

specified 0 n/a 

High school graduate/GED 29,610 35.7 

Some college 15,234 28.3 

College graduate 0 n/a 

Postgraduate 0 n/a 

Other 

Subtotal 68,751 34.1 0.12 0.95 

Outpatient Nonmethadone 

Less than 8 years 2,819 67.5 

8-11 years 74,947 154.1 

Less than high school graduate not otherwise 

specified 8,250 132.2 

High school graduate/GED 89,535 173.0 

Some college 44,701 139.4 

College graduate 12,626 127.8 

Postgraduate 2,218 84.8 

Other 0 n/a 

Subtotal 235,096 154.6 3.73 0.003 

Combination Facilities 

Less than 8 years * * 

8-11 years 35,168 56.1 

Less than high school graduate not otherwise 

specified 5,421 35.2 

High school graduate/GED 37,108 79.4 

Some college 33,948 106.3 

College graduate 4,455 16.7 

Postgraduate * * 

Other 0 n/a 

Subtotal 126,701 70.6 2.67 0.02 

Note: Of the total 1,239 female client records among clients aged 18 or older, 196 female methadone client records were 

excluded; of the remaining 1,043 records, 982 (94.2 percent) contained information about LOS and education at admission. 

Degrees of freedom: 56. 

* Low precision; no estimate reported. 

GED = general equivalence diploma; n/a = LOS cannot be calculated due to zero sample size in this education category. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase II 

data (1997-1999). 
120 

Retention, by Enabling Characteristics 

Primary source of referral. Among adult female clients at nonhospital residential 

facilities, the average LOS differed by primary source of referral to treatment (F = 2.53, df = 64, 

p = 0.047) (Table 6.5). Among adult female clients at nonhospital residential facilities, the LOS 

was longer among women with a criminal justice system referral (mean = 75 days) than among 

those referred by other sources (mean = 23 to 31 days). The LOS was similar among adult 

female clients with different referral sources at outpatient nonmethadone (F = 0.74, df = 64, p = 

0.57) or combination facilities (F = 0.91, df = 64, p = 0.46). Analyses of the LOS by referral 

source among adult female clients at outpatient methadone facilities could not be conducted. 

Table 6.5 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Primary Source of Referral and Facility Type of Care 

Facility Type of Care/Primary Source of Referral Weighted n Average LOS F p 

Total 474,299 163.7 

Nonhospital Residential 

Other treatment facility 13,759 22.7 

Criminal justice system 9,742 74.5 

Self-referred/voluntary, welfare office or other social service 

agencies, family, friend, or employer 36,971 30.9 

Health care or mental health providers 9,022 24.2 

Other 0 n/a 

Subtotal 69,495 34.5 2.53 0.047 

Outpatient Methadone 

Other treatment facility 4,259 628.6 

Criminal justice system * * 

Self-referred/voluntary, welfare office or other social service 

agencies, family, friend, or employer 27,790 535.8 

Health care or mental health providers * * 

Other * * 

Subtotal 33,505 539.4 --- 

Outpatient Nonmethadone 

Other treatment facility 13,381 194.6 

Criminal justice system 106,740 148.8 

Self-referred/voluntary, welfare office or other social service 

agencies, family, friend, or employer 89,521 150.6 

Health care or mental health providers 23,913 193.9 

Other * * 

Subtotal 236,632 157.2 0.74 0.57 

Combination Facilities 

Other treatment facility 23,562 71.0 

Criminal justice system 24,309 99.6 

Self-referred/voluntary, welfare office or other social service 

agencies, family, friend, or employer 79,243 195.1 

Health care or mental health providers 6,746 39.7 

Other * * 

Subtotal 134,666 148.1 0.91 0.46 

Note: Of the total 1,239 female client records among clients aged 18 or older, 1,176 (94.9 percent) contained information about

LOS and referral source. 

Degrees of freedom: 64. 

* Low precision; no estimate reported. 

--- F value cannot be calculated due to small sample size in one or more categories, weights that are zero for all records, or 

variables that are zero or a linear combination of other variables. 

n/a = LOS cannot be calculated due to zero sample size in this category. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase II 

data (1997-1999). 
121 

Primary source of payment. Among adult female clients at outpatient nonmethadone (F = 

7.68, df = 64, p < 0.001) or combination facilities (F = 3.35, df = 64, p = 0.01), the LOS differed 

by primary source of payment for treatment (Table 6.6). Among adult female clients at outpatient 

nonmethadone facilities, the LOS was longer among those whose primary source of payment 

was no payment or client self-payment (mean = 139 days) or Medicare or Medicaid (mean = 227 

days) than among those whose payment source was private health insurance (mean = 90 days). 

Among adult female clients at combination facilities, the LOS was longer among those whose 

payment source was Medicare or Medicaid (mean = 159 days) than among those whose payment 

source was private health insurance (mean = 27 days). 

Table 6.6 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 or 

Older at Admission, by Primary Source of Payment for Treatment and Facility Type of Care 

Facility Type of Care/Primary Source of Payment Weighted n Average LOS F p 

Total 415,513 169.0 

Nonhospital Residential 

No payment or client self-payment 18,797 34.9 

Private health insurance, fee-for-service, or 

HMO/PPO/managed care * * 

Criminal justice system 0 n/a 

Medicare or Medicaid 11,184 23.1 

Other 32,738 37.1 

Subtotal 65,070 33.1 1.62 0.19 

Outpatient Methadone 

No payment or client self-payment 9,912 921.2 

Private health insurance, fee-for-service, or 

HMO/PPO/managed care 2,339 315.6 

Criminal justice system 0 n/a 

Medicare or Medicaid 12,631 411.2 

Other 5,943 415.7 

Subtotal 30,826 568.8 0.57 0.64 

Outpatient Nonmethadone 

No payment or client self-payment 82,417 138.6 

Private health insurance, fee-for-service, or 

HMO/PPO/managed care 35,212 90.2 

Criminal justice system 26,647 118.8 

Medicare or Medicaid 41,234 227.0 

Other 18,924 267.3 

Subtotal 204,435 157.4 7.68 < 0.001 

Combination Facilities 

No payment or client self-payment 29,657 94.8 

Private health insurance, fee-for-service, or 

HMO/PPO/managed care 34,619 27.0 

Criminal justice system * * 

Medicare or Medicaid 26,712 159.0 

Other 19,819 510.3 

Subtotal 115,183 159.3 3.35 0.01 

Note: Of the total 1,239 female client records among clients aged 18 or older, 1,091 (88.1 percent) contained information about

LOS and primary source of payment for treatment. 

Degrees of freedom: 64. 

* Low precision; no estimate reported. 

HMO = health maintenance organization; PPO = preferred provider organization; n/a = LOS cannot be calculated due to zero 

sample size in this category. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase II 

data (1997-1999). 
122 

Retention, by Organizational Characteristics 

Facilities offering child care services. Among adult female clients at nonhospital 

residential facilities, the LOS was longer among those at facilities offering child care services 

than among those at facilities without such services (child care: mean = 97 days vs. no child care: 

mean = 33 days, t = 4.77, df = 64, p < 0.01) (Table 6.7). 

Table 6.7 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 

or Older at Admission, by Availability of Child Care Services and Facility Type of Care 

Weighted n Average LOS 

Facility Type of Care Child Care 

No Child 

Care 

Child 

Care 

No Child 

Care t p df 

Nonhospital Residential 1,719 70,606 96.7 32.5 4.77 <0.01 64 

Outpatient Methadone 3,731 32,538 386.8 548.0 -1.06 0.29 64 

Outpatient Nonmethadone 76,348 171,259 168.4 147.3 0.55 0.58 64 

Combination 41,069 106,906 353.0 67.5 1.29 0.20 64 

Note: Of the 1,239 female client records among clients aged 18 or older, 1,238 (99.9 percent) contained information 

about LOS and availability of child care services and facility type. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and 

Phase II data (1997-1999). 

Women-only facilities. Among adult female clients at nonhospital residential facilities, 

the LOS was longer among those at women-only facilities than among females at mixed-gender 

facilities (women-only: mean = 83 days vs. mixed-gender: mean = 22 days, t = 3.90, df = 64, p < 

0.01) (Table 6.8). Among adult female clients at combination facilities, the LOS was shorter 

among those at women-only facilities than among those at mixed-gender facilities (women-only: 

mean = 52 days vs. mixed-gender: mean = 148 days, t = -2.01, df = 64, p = 0.048). 

Table 6.8 Weighted Length of Stay (LOS) among Female Substance Abuse Treatment Clients Aged 18 

or Older at Admission, by Client Composition of Facility and Facility Type of Care 

Weighted n Average LOS 

Facility Type of Care

WomenOnly MixedGender WomenOnly MixedGender t p df 

Nonhospital Residential 13,956 58,369 83.1 22.3 3.90 <0.01 64 

Outpatient Nonmethadone 2,484 245,123 295.5 152.4 0.90 0.37 64 

Combination 1,454 146,522 51.9 147.7 -2.01 0.048 64 

Note: Of the 1,239 female client records among clients aged 18 or older, 1,238 (99.9 percent) contained information 

about LOS and facility clientele composition and facility type. Of these, 153 client records from outpatient 

methadone facilities were excluded. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) 

and Phase II data (1997-1999). 

Multivariate Analysis 

A multivariate analysis showed that several predisposing, enabling, and organizational 

characteristics were associated with LOS among adult female clients (Table 6.9). Adult females 

who did not complete high school (HR = 1.32; 95 percent CI = 1.07, 1.63; p = 0.01) or whose 

primary source of referral was not the criminal justice system (HR = 1.32; 95 percent CI = 1.02, 

1.70; p = 0.04) were more likely to leave treatment earlier than high school graduates or those 

referred by the criminal justice system. Adult female clients whose primary source of payment 
123 

was other than private health insurance stayed in treatment longer than those whose primary 

source of payment was private health insurance. Adult females receiving treatment at facilities 

offering child care services (HR = 0.51; 95 percent CI = 0.36, 0.73; p = 0.0003) or at womenonly facilities (HR = 0.34; 95 percent CI = 0.13, 0.89; p = 0.03) stayed in treatment longer than 

those at facilities without child care services or at mixed-gender facilities. These multivariate 

analysis results are similar to descriptive analysis findings, although multivariate analyses were 

conducted using a reduced sample (n = 962) because of missing data for one or more covariates. 

Age at admission and race were not associated with LOS among adult female clients in 

multivariate analyses. Some control variables also were significantly associated with LOS among 

women discharged from nonhospital residential, outpatient nonmethadone, or combination 

facilities. Women at nonhospital residential or combination facilities were more likely to leave 

treatment earlier than those at outpatient nonmethadone facilities. Adult female clients at 

facilities offering combined substance abuse treatment and mental health services stayed in 

treatment longer than those at facilities not offering these services. Contrary to expectations, 

receiving treatment at facilities offering prenatal care or transportation services was associated 

with leaving treatment earlier among women. 

Discussion 

This study examined the association between predisposing characteristics (age, race, and 

education), enabling characteristics (referral source and payment source), and organizational 

characteristics (availability of child care services and women-only facilities) and LOS among 

adult female substance abuse treatment clients. Consistent with findings from another study 

(Knight et al., 2001), adult females who did not complete high school were more likely to leave 

treatment earlier than high school graduates. Adult females referred by sources other than the 

criminal justice system were more likely to leave treatment earlier than those referred by the 

criminal justice system, which is similar to findings by Green et al. (2002). Adult female clients 

whose primary source of payment was private health insurance left treatment earlier than those 

with other payment sources. As noted in other studies (Hughes et al., 1995; Stevens et al., 1989; 

Wobie et al., 1997), women at facilities offering child care services stayed in treatment longer 

than women at facilities without such services. Adult females receiving treatment at women-only 

facilities were retained in treatment longer than those at mixed-gender facilities, which is 

consistent with the findings of other studies (Grella, 1999; Roberts & Nishimoto, 1996). In an 

unanticipated finding, age at admission and race were not associated with LOS among adult 

female clients, despite previous research that has linked these two client characteristics to LOS 

(Grella et al., 2000; McCaul et al., 2001; Mertens & Weisner, 2000; Strantz & Welch, 1995; 

Wickizer et al., 1994). 

The results of the multivariate analysis support the positive effect of on-site child care 

and women-only treatment on LOS of women in substance abuse treatment. However, the results 

of the descriptive analysis suggest that this finding may only be relevant to women in 

nonhospital residential treatment. It is important to note that certain factors make it difficult to 

estimate the association between child care and LOS. For example, child care may be bundled 

with other services, such as relapse prevention groups and positive contingency awards for 

abstinence (Carroll, Chang, Behr, Clinton, & Kosten, 1995), or it may be associated with other 
124 

Table 6.9 Weighted Cox's Proportional Hazard Regression Analysis of Length of Stay (LOS) among Female 

Substance Abuse Treatment Clients Aged 18 or Older at Admission Discharged from Nonhospital 

Residential Facilities, Outpatient Nonmethadone Facilities, or Combination Facilities 

Independent Variables HR (95% CI) df Adj df p 

Age at Admission 1.00 (0.99, 1.01) 1 1.00 0.64 

Race (compared with white) 2 1.68 

 All other races 0.97 (0.71, 1.33) 0.86 

 Unknown/not mentioned 1.26 (0.89, 1.77) 0.19 

Education at Admission (compared with high school graduate/GED or 

more) 2 1.81 

 Less than high school graduate 1.32 (1.07, 1.63) 0.01 

 Unknown/not mentioned 0.95 (0.65, 1.39) 0.78 

Primary Source of Referral for Treatment (compared with criminal 

justice system) 1 1.00 

 Other sources 1.32 (1.02, 1.70) 0.04 

Primary Source of Payment for Treatment (compared with private health 

insurance, fee-for-service or HMO/PPO/managed care) 4 3.78 

 No payment or client self-payment 0.67 (0.46, 0.97) 0.03 

 Medicare/Medicaid 0.54 (0.38, 0.78) 0.001 

 Criminal justice system 0.51 (0.31, 0.83) 0.007 

 Other funding 0.74 (0.47, 1.17) 0.20 

Child Care Services (compared with services not offered) 1 1.00 

 Offered 0.51 (0.36, 0.73) 0.0003 

Facility Clientele Composition (compared with mixed-gender) 1 1.00 

 Women-only 0.34 (0.13, 0.89) 0.03 

Marital Status at Admission (compared with never married, widowed, 

separated/divorced, or single) 1 1.00 

 Married/common law 1.11 (0.87, 1.42) 0.40 

Have Child/Children at Admission (compared with no child/children) 2 1.94 

 Unknown/not mentioned 1.15 (0.74, 1.78) 0.52 

 Have child/children 0.83 (0.63, 1.09) 0.18 

Presenting Substance Use Disorder at Admission (compared with alcohol 

abuse only) 3 2.03 

 Drug abuse only 0.83 (0.65, 1.04) 0.10 

 Alcohol and drug abuse 0.77 (0.60, 1.00) 0.05 

 Substance not specified 0.51 (0.26, 0.98) 0.04 

Facility Type of Care (compared with outpatient nonmethadone) 2 2.00 

Nonhospital residential 4.39 (2.62, 7.35) <0.0001 

Combination 2.65 (1.84, 3.82) <0.0001 

Prenatal Care Services (compared with services not offered) 1 1.00 

 Offered 1.48 (1.09, 2.02) 0.01 

Transportation Services (compared with services not offered) 1 1.00 

 Offered 1.61 (1.11, 2.34) 0.01 

Combined Substance Abuse Treatment and Mental Health Services 

(compared with services not offered) 1 1.00 

 Offered 0.61 (0.47, 0.80) 0.0005 

Number of observations used in the analysis: 962. 

Weighted count: 417,592. 

GED = general equivalence diploma; HMO = health maintenance organization; HR = hazard ratio; PPO = preferred provider 

organization. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study (ADSS), Phase I data (1996-1997) and Phase II 

data (1997-1999). 
125 

confounders, such as the availability of social workers, physicians, and nurse practitioners to 

clients. In addition, facilities offering child care services or women-only facilities may be more 

likely than other facilities to offer parenting and women's issues groups or to serve clients with 

different characteristics, and these factors may result in longer LOS. Furthermore, although 

receiving treatment in women-only facilities was associated with LOS in the multivariate 

analysis and among women in nonhospital residential facilities in the descriptive analysis, among 

women in combination facilities, LOS was shorter in women-only facilities compared with 

mixed-gender facilities. The cross-sectional design of this study does not allow for conclusions 

about the direct causal effects on LOS of such factors as availability of child care or women-only 

facilities because of "confounding by indication" (Walker, 1996); that is, the clients were not 

randomly assigned to facilities and therefore may have made their choice of facilities based on 

their different attitudes toward treatment or child care, different personality traits, or other 

factors. 

The extent to which data were missing data was a major limitation of this study. The 

ADSS Phase II data were abstracted retroactively from client records, and limited data were 

collected about methadone clients. Missing data imposed limitations on conducting descriptive, 

inferential, and multivariate analyses. The survival analysis procedure eliminated all records in 

which any variable in the model included a missing value. The number of observations 

eliminated due to missing values progressively increased as the number of variables included in 

the model increased. It is possible that multicollinearity among some covariates may have 

affected our results. Although we tested for multicollinearity among organizational 

characteristics, we did not evaluate all covariates. These limitations may have reduced the 

statistical significance of some variables as correlates of LOS. In addition, many relevant 

variables could not be included in our multivariate analysis because of missing data, and not 

controlling for these factors may have caused some covariates to appear statistically significant 

that should not have. 

In descriptive and inferential analyses, the lack of a statistically significant difference 

between two means may be misleading if extreme values in a small number of records skewed 

the means, resulting in larger standard errors. In addition, the potentially nonlinear relationship 

between age and LOS suggested by some descriptive analyses may explain why age was not 

correlated with LOS in the multivariate analysis. 

This study explored the association between a set of single factors on retention. The 

multivariate analysis did not explicitly examine interactions between variables. However, 

addressing single separate factors was a necessary first step in exploring the relationships 

between study variables and LOS using a nationally representative sample of adult female 

substance abuse treatment clients. 

The finding that adult female clients whose primary source of payment was private health 

insurance remained in treatment for shorter LOS than those with other payment sources suggests 

that insurance coverage for substance abuse treatment, which likely includes caps on coverage, 

has important implications for LOS. It is possible that women with insurance coverage do not 

leave treatment early of their own accord, but rather due to funding restrictions. However, we did 

not adjust for severity or "need" in our analyses because of data limitations. Also, it is possible 

that those who have private coverage are less severely impaired (possibly because they have 
126 

some attachment to the labor force) and did not require as long a stay as the average client. This 

issue requires further examination of the role that private health insurance plays in women's 

reasons for leaving treatment and the effects that leaving treatment has on outcomes. 

Providing on-site child care appears to be beneficial to women in substance abuse 

treatment. This finding may have implications for treatment providers, as well as for child 

welfare policy and the courts making custody and permanency decisions. Child welfare agencies 

and juvenile dependency courts are challenged to address clients' substance use disorders in 

order to achieve positive outcomes for children and families. Prior research has shown that 

unless the parent with a substance use disorder has been engaged in a treatment program or is 

otherwise moving into recovery, the child's prognosis for long-term emotional, social, and 

physical well-being is poor. A mother seeking substance abuse treatment may need to utilize the 

child care services at a substance abuse treatment facility. This may make the difference as to 

whether she seeks treatment, stays in treatment, and completes treatment. Child care has 

specifically been identified as a service that is needed to support child welfare clients' 

participation in substance abuse treatment (Semidei, Radel, & Nolan, 2001). Furthermore, the 

Adoption and Safe Families Act (ASFA) of 1997 has shortened the timeline for parents with 

substance use disorders whose children have been removed for abuse or neglect to seek treatment 

and stabilize their lives before termination of parental rights occurs. Therefore, offering child 

care services at treatment facilities, because of its association with increased LOS and improved 

outcomes, may be essential if families are to be afforded real opportunities for recovery within 

ASFA timelines. 

Future research using targeted data collection approaches and large samples may be able 

to shed more light on the role of gender in LOS. These studies could help to identify a profile of 

women with children who would benefit most from treatment in the facilities providing on-site 

child care, as well as factors influencing the LOS of these women in treatment. More studies are 

needed that focus on facility characteristics. Full consideration will need to be given to the 

specifics of the therapeutic programs and their objectives that foster longer stays in treatment. In 

addition, more detailed service utilization measures are needed, such as the number of individual 

therapy visits, group therapy visits, and family counseling visits. Overall, the findings of the 

current study suggest that addressing women's specialized substance abuse treatment needs 

through offering of child care services or providing women-only treatment may lead to longer 

LOS and better substance abuse treatment outcomes. 
127 

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(1994). Completion rates of clients discharged from drug and alcohol treatment programs in 

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25-35. 
133 

Chapter 7. A Hybrid Cost Function for Outpatient 

Nonmethadone Substance Abuse Treatment Facilities 

Sarah Q. Duffy, Ph.D. 

Laura J. Dunlap, M.A. 

Moshe Feder, D.Sc. 

Gary A. Zarkin, Ph.D. 

Introduction 

A substantial literature provides estimates of the average cost of an episode of substance 

abuse treatment (e.g., see Anderson, Bowland, Cartwright, & Bassin, 1998; McGeary, French, 

Sacks, McKendrick, & De Leon, 2000), and various methods of estimating these costs have been 

used in benefit-cost analyses (see Cartwright, 2000, for a review). However, little work has been 

done to examine the cost structure of the substance abuse treatment industry. Extant work has 

focused on the association between cost and a measure of size based on point-prevalence client 

counts of residential treatment programs, a small and declining segment of the industry 

(Harwood, Kallinis, & Liu, 2001), or client counts and revenue in a convenience sample of 

outpatient substance abuse treatment facilities (Mark et al., 2000). Neither study employed 

economic methods commonly used to study cost structures in other health care industries. This 

chapter expands our understanding of the substance abuse treatment industry by using methods 

informed by economic theory to examine the cost structure of a national sample of substance 

abuse rehabilitation facilities that offer outpatient nonmethadone treatment. In 1999, such 

facilities accounted for approximately 82 percent of all substance abuse treatment facilities 

nationwide (Office of Applied Studies [OAS], 2001). 

This chapter offers empirical evidence on the extent to which economies of scale exist in 

the outpatient nonmethadone substance abuse treatment industry, holding constant for client 

characteristics. If there are scale economies, and other studies find outcomes are no better in 

small programs than in large ones, State substance abuse treatment agencies and other payers 

may be able to encourage, through their licensing and payment policies, the formation of larger, 

more efficient programs, as long as the desired level of client access can be maintained. In 

addition, this research provides national estimates of costs for outpatient substance abuse 

treatment facilities, and how they vary with different mixes of clients and other characteristics, 

which could inform benefit-cost analyses. Most existing benefit-cost and cost-effectiveness 

analyses rely on a simple average cost obtained from data on a small number of purposively 

sampled facilities (Cartwright, 2000) or from revenue data from a national survey of substance 

abuse treatment facilities (Harwood, Hubbard, Collins, & Rachal, 1995). Incorporating findings 

based on nationally representative treatment cost data would improve the generalizability of 

findings from these studies. Finally, the research presented here includes measures of the 

characteristics of each facility's clients as a way to examine whether client characteristics are an 

important factor in facility costs. Such information may be important to policy makers who may 

wish to compare on the basis of costs programs that serve different types of clients. 
134 

Cost Function Specification 

Economists often estimate cost functions to determine whether or not economies of scale 

can be realized in a particular industry. An industry is said to exhibit economies of scale if the 

average cost of producing a unit of output declines as more output is produced. If scale 

economies exist, the industry would be more efficient if it consisted of a few large firms rather 

than many small firms, other things being equal and given other constraints. In the case of 

substance abuse treatment, such a constraint might be ensuring adequate access. Economists also 

use cost functions to estimate the effect on costs of firm characteristics, such as the effect of 

teaching programs on hospital costs. In the substance abuse treatment industry, policy makers 

may wish to examine, for example, whether for-profit substance abuse treatment facilities have 

higher costs than nonprofit facilities, or the effect on costs of offering a variety of special 

programs. The purpose of this study is to explore these issues by specifying and estimating a cost 

function for a nationally representative sample of outpatient nonmethadone substance abuse 

treatment facilities. 

Although, to our knowledge, no one has estimated an economic cost function for 

substance abuse treatment facilities, economists have estimated cost functions for other health 

care facilities, such as hospitals, nursing homes, and physician practices.1

 These studies usually 

have used a functional form that is less restrictive than the standard textbook economic cost 

function. The standard textbook cost function, which is derived from the economic theory of the 

firm, models cost as a function of the volume of output and input prices only (e.g., see 

Silberberg, 1978, pp. 173-213). For an industry with N outputs (Y) and K input prices (w), the 

cost function would be 

C = C (Y1, Y2, Y3, ... YN; w 1, w2, w 3, ..., wK), (1) 

where C is the cost of production. To obtain unbiased and efficient parameter estimates from a 

textbook cost function requires, among other things, that all firms in an industry use identical 

inputs and produce identical outputs, and that data on the quantity of all outputs produced and 

the prices of all inputs used by each firm are entered into the model. Economists use textbook 

cost functions to determine the economic properties of an industry's cost function, such as 

economies of scale and scope and the degree of input substitutability.2

 When using data on 

industries that conform to the required assumptions, coefficient restrictions implied by the 

economic theory of the firm can be imposed on the estimation to improve statistical efficiency. 

Economists estimating cost functions for health care facilities rarely use the textbook cost 

function for a variety of reasons, many of which apply to the outpatient drug-free substance 

 1

 Examples include the following: Anderson and Lave (1986); Bilodeau, Crémieux, and Ouellette (2000); 

Carey (1997); Cowing and Holtman (1983); Custer and Wilke (1991); Dor, Duffy, and Wong (1997); Duffy, 

Ruseski, and Cavanaugh (2000); Escarce and Pauly (1998); Evans (1971); Gaskin and Hadley (1997); Grannemann, 

Brown, and Pauly (1986); Hadley (1983); Hadley and Swartz (1989); Hornbrook and Monheit (1985); Lave and 

Lave (1970); Li and Rosenman (2001); McKay (1988); Rogowski and Newhouse (1992); Salkever, Steinwachs, and 

Rupp (1986); Sloan, Feldman, and Steinwald (1983); Sloan and Steinwald (1980); Thorpe (1988); Troyer (2002); 

Vitaliano (1987); Welch (1987); and Zuckerman, Hadley, and Iezzoni (1994). 2

 Economies of scope are reductions in per unit costs that can be achieved when more than one product is 

produced. Input substitutability refers to the ease with which one input can be substituted for another in the 

production process. 
135 

abuse treatment industry. First, and perhaps most important, health care firms within an industry 

generally are not identical in terms of their inputs or outputs. Second, complete data outputs or 

input prices are rarely available. Finally, the focus of these studies often is the effect on costs of 

facility characteristics beyond output and input prices, such as whether for-profit providers have 

higher costs than nonprofit providers, or rural providers have lower costs than urban providers. 

Instead, health economists often estimate what has been called a "hybrid" cost function 

(Rosko & Broyles, 1988). A hybrid cost function models costs as a function of measurable 

outputs and input prices, as well as other facility characteristics that may affect costs. We have 

C = C(Y, w, X, F, A), (2) 

where, as before, Y is a vector of outputs and w is a vector of input prices. To these standard cost 

function variables we add a vector of measures of the case mix or severity of the facility's clients 

(X), a vector of facility characteristics (F), such as the number of services it offers, and a vector 

of characteristics describing the facility's location (A). In this chapter, we estimate a model 

similar to equation (2). The one difference is that because we have only one output, Y will be a 

scalar rather than a vector. 

To estimate a cost function, the model must be written as a specific functional form that 

allows for the possibility for economies of scale. A linear function does not allow for economies 

of scale because it constrains the relationship between cost and the quantity of output to be the 

same over the entire output range, rather than allowing it to decline, as would be the case under 

economies of scale, or increase, as in the case of diseconomies of scale. We follow Vitaliano 

(1987) who used the following logarithmic estimating equation derived from a theoretical model 

proposed by Nerlove: 

0

1 11 1

ln α α ln β ln δ μ λ ε,

K LM R

y k k l l mm r r

k lm r

C Y wX F A = == ==+ + + + + + ∑ ∑∑ ∑ (3) 

where C, Y, w, X, F, and A are as defined before; α, β, δ, μ, and λ are coefficients to be estimated; 

and ε is a random, normally, and identically distributed error term. Like Vitaliano (1987), we 

used Ramsey's Regression Specification Error Test (RESET) to test the suitability of this 

functional form, as well as other popular functional forms, including the quadradic and translog 

specifications. Our testing revealed that the logarithmic model was the only functional form for 

which the null hypothesis of no specification error could not be rejected at conventional levels of 

significance. 

Data 

We used data from the Alcohol and Drug Services Study (ADSS) on 222 outpatient 

nonmethadone treatment facilities. ADSS was conducted under the auspices of the Office of 

Applied Studies (OAS) at the Substance Abuse and Mental Health Services Administration 

(SAMHSA) (OAS, 2000a). Among the objectives of ADSS was the collection of detailed 

information on the characteristics of a random, nationally representative sample of substance 

abuse treatment facilities and of clients discharged from those facilities. Such data allow 

development of better estimates of the costs of treatment than had been available using 
136 

previously existing datasets. The study consisted of three phases, two of which we draw from for 

this analysis. 

Phase I of ADSS was a telephone interview with a nationally representative, stratified 

probability proportionate to size (pps) sample of 2,395 substance abuse treatment facilities, 

representing a 91.4 percent response rate from the 2,621 eligible facilities. Facility administrators 

were asked about the characteristics of the facility and its clients. Phase I missing data were 

imputed according to standard statistical procedures (OAS, 2000b). Final facility weights 

included a nonresponse adjustment, and methods used to impute missing values included logical 

imputation, imputation from external sources, regression, and random within-class hot-deck 

procedures. 

Phase II facilities were selected from among Phase I facilities located in 1 of 62 primary 

sampling units (PSUs) again according to a stratified pps design. Excluded from Phase II were 

hospital inpatient facilities and facilities that treated only those with alcohol use disorders. These 

facilities were considered out-of-scope. Phase II data were initially collected during a site visit to 

280 facilities. These 280 facilities included 234 of the 294 eligible facilities that agreed to be 

surveyed, as well as 46 shadow3

 facilities for the 60 that chose not to participate. Thus, the 

overall response rate for cost study facilities was approximately 86 percent. The site visit 

consisted of an in-person interview with the facility director or administrator to collect data on 

the facility's characteristics, including expense and revenue information; compilation of a 

sampling frame and selection of a representative sample of client records; and collection of 

client-level data from the sample of client records at each facility (OAS, 2000a). In addition to 

the variables that were imputed on the final Phase II facility file, we used the same methods to 

impute additional missing variables that we needed for our analysis (Krenzke & Mohadjer, 

2002). The variables and the number of observations we imputed are noted below. 

Phase II administrator data were used as the basis for the ADSS cost study, the main 

source of data for this chapter. Data on facilities' expenses, revenues, and client volumes were 

entered into a data audit spreadsheet developed by Capital Consulting Corporation (CCC) to 

check for accuracy (OAS, 2003). CCC had developed this audit instrument after intensive study 

of some 400 substance abuse treatment facilities using their Cost Allocation Methodology (CCC, 

1998; CCC, The Lewin Group, & Caliber Associates, 1998). In that study, CCC sent 

professional accountants to facilities to collect cost data. From this information, CCC developed 

data reliability and validation procedures to test the accuracy of other provider-supplied data. 

Those procedures are at the heart of the data audit spreadsheet used in the ADSS cost study. The 

facilities' original responses were entered into the audit instrument, and key financial ratios were 

examined. Facilities with anomalous results on their Phase II cost, client volume, or personnel 

variables were contacted and given the opportunity to change their responses. Some 96 percent 

of the facilities required a callback for at least one variable. Of those, most were able to either 

verify the originally reported data or provide new information, much of it documented with 

information from the facility's financial information system. Some facilities either could not be 

contacted or could not provide more information on their expenses. For those facilities, which 

amounted to about 26 percent of the outpatient nonmethadone facilities studied here, data were 

 3

 A shadow unit is a unit that was not part of the original sample, but was identified as a possible 

replacement if the originally sampled unit declined to participate in the study. 
137 

imputed. In most cases, almost 22 percent of all outpatient nonmethadone facilities, missing 

expense data were imputed using the facility's own volume information and the data audit 

instrument, or the facility's Phase I response to the expense question. In 4.5 percent of the 

facilities, expenses were imputed using information from other facilities (OAS, 2003). The cost 

study data file includes final Phase II facility weights, which were adjusted for facility 

nonresponse using a raking procedure (OAS, 2000a). In addition to using the ADSS data, we 

used some supplementary data, as described below. 

The variables included in the analysis are described in the following paragraphs. 

Costs: Our measure of costs, the dependent variable, was the natural logarithm of the 

total substance abuse treatment costs in the facility, as reported by the administrator in the Phase 

II administrator interview and verified or edited as described above. 

Output: We estimated a single output model, where we considered a unit of output as a 

treatment episode for a given client within a given type of care within a given facility. However, 

due to the characteristics of the substance abuse treatment industry and its clients, and the 

measures available in the data, we estimated the models using two different measures to 

determine whether they exhibited different relationships with costs. The number of annual 

admissions was the first measure of output we considered. Many substance abuse treatment 

facilities have to keep track of admissions because they are required to report them to their State 

substance abuse treatment agencies (OAS, 2000c). However, the number of admissions may 

overstate the annual output of a treatment center because a large number of clients who are 

admitted do not finish treatment. In the ADSS Phase I sample, for example, administrators 

reported that, on average, more than 40 percent of the clients in outpatient nonmethadone 

facilities failed to complete their planned treatment. In our study, we entered the facility's 

completion rate as an explanatory variable to partially control for that issue. We also considered 

using discharges, an output measure commonly used in hospital studies. However, that measure 

also has its limitations. As mentioned earlier, a large number of clients leave programs without 

finishing their treatment plan. Their discharge often is not documented until some time period 

elapses, usually 30 days, during which they have received no treatment. Furthermore, some 

facilities do not formally discharge anyone, due to the relapsing nature of the substance use 

disorder. In these facilities, clients are not discharged and are allowed to return for more care if 

needed. Therefore, discharge numbers may underestimate treatment output. In this study, we 

estimated two models—one with admissions as the measure of output and one with discharges. 

Because the results were virtually identical, we report only the results of the admissions 

regressions here. Given that many facilities must report these routinely to State substance abuse 

treatment authorities, we believe they may be more accurately reported than are discharges. 

Input Prices: The inputs for which we had prices were various categories of labor and 

office space. To measure wages, the input price of labor, we used the Metropolitan and Balance 

of State Area Occupational Employment and Wage Estimates for selected occupations, collected 

by the Bureau of Labor Statistics (2003b) as part of their Occupational Employment Statistics 

Survey (OESS). The three occupations we included were (a) Substance Abuse Counselors, (b) 

Senior Administrative (includes five job categories: administrative services manager, general 

managers and other top executives, all other managers, and financial managers), and (c) 

Administrative Clerical Workers (includes seven job categories: file clerks, general office clerks, 
138 

payroll and timekeeping clerks, and secretaries [excluding legal and medical]). Although the 

latter two occupation categories are not specific to substance abuse treatment facilities, we 

believe the average wages across administrative workers in all industries should serve as a good 

proxy for administrative wages in substance abuse treatment facilities. Even though the ADSS 

cost study collected facility-specific wage information, we used the OESS estimates of the 

market wage rates to overcome two possible problems in estimation. The first was the problem 

of missing observations. Not all facilities employ all types of workers, so we would not have 

wage information for all possible types of employees for all facilities, which is required for 

estimating a cost function. The second potential problem was endogeneity. If we were to use a 

weighted average of each facility's wages as its wage variable, to overcome the missing values 

problem, we would no longer just be measuring the effect of exogenous factors, such as location 

in a high cost or highly unionized area, on the facility's cost function. Rather, we would be 

measuring both the prevailing wage rate and the facility's choice of inputs, which is endogenous. 

For example, a facility's higher costs may be attributed to its being in a higher wage area, when, 

in reality, its costs are higher because it has an inefficient staffing mix. To account for this 

possibility, economists often use local area wage rates (e.g., see Custer & Wilke, 1991; Salkever 

et al., 1986; Zuckerman et al., 1994), as was our plan here. Because the OESS does not collect 

data on all occupations for each location each year, for some variables we used data from later 

years, deflated using the average Consumer Price Index (CPI) representing changes in the prices 

of all goods and services purchased for consumption by urban households (Bureau of Labor 

Statistics, 2003a). 

We also included a variable measuring the cost of office space in the facility's area, again 

turning to an external source, this time because the variable was not collected in ADSS. 

However, it too is potentially endogenous. Unfortunately, to our knowledge, no data exist on 

commercial real estate rental rates throughout the Nation. So, instead, we used the four-bedroom 

Section 8 Fair Market Rent as defined by the U.S. Department of Housing and Urban 

Development's Office of Policy Development and Research (1995) as a proxy for commercial 

rents. This is the variable that is used in SAMHSA's Substance Abuse Prevention and Treatment 

(SAPT) block grant formula to measure the rental component of the cost of doing business in 

each State (U.S. Department of Health and Human Services [DHHS], 1996). 

Case Mix Adjustment for Substance Abuse Treatment Facilities 

Adjusting for case mix in analyses of substance abuse treatment facilities has become 

more common in the past few years as governments and other payers demand accountability 

from service providers. One way to determine whether a provider is accountable is to compare 

that provider's performance with that of other providers. For the comparison to be fair and 

meaningful, however, it must account for differences across providers in the mix of clients they 

treat. Several recent studies of substance abuse treatment providers have used the same basic 

method of case mix adjustment to study outcomes of substance abuse treatment (Koenig, Fields, 

Dall, Ameen, & Harwood, 2000; Moos, Moos, & Andrassy, 1999; Phibbs, Swindle, & Recine, 

1997; Phillips et al., 1995), access to substance abuse treatment (Deck, McFarland, Titus, Laws, 

& Gabriel, 2000), and participation in self-help groups among those in treatment (Ouimette et al., 

2001). Although the results from these studies are informative and useful in outcome studies, 

they unfortunately provide little guidance on how to measure case mix complexity as it relates to 

the cost of treatment at a given facility. 
139 

The study conducted by Moos et al. (1999) is illustrative. These researchers analyzed 

outcomes of four treatment approaches (therapeutic community, psychosocial rehabilitation, 12-

step, and undifferentiated) in 88 residential programs. The outcomes they investigated were 

posttreatment abstinence, substance use disorders, distress, mental disorder symptoms, arrests, 

and employment. They presented results from separate regressions run with each outcome as the 

dependent variable and the client as the unit of analysis. Each regression included variables 

describing clients and the programs in which they had been treated. Client characteristics 

included age, marital status, prior mental disorder episode, mental disorder diagnosis, and the 

value of the outcome variable at intake. With the exception of the outcome variable measured at 

intake, no variable was significant in all regressions. Only one, prior mental disorder episode, 

had the same effect on all the outcomes for which it was significant. 

This lack of consistency in results holds across the other studies cited as well. No variable 

had a similar effect on all outcomes in all of the studies in which it was included. In state-of-theart studies of substance abuse treatment, case mix adjustment methods vary by the purpose of the 

analysis and the outcome and population being studied. To our knowledge, no one has attempted 

to adjust for the costliness of a provider's client population when comparing substance abuse 

treatment providers on the basis of costs. 

This is in contrast to cost studies of the hospital industry in which case mix adjustment, 

based on data routinely collected by hospitals from patients' discharge abstracts, has been used 

for more than 20 years. Information on the clients' medical diagnoses, procedures, length of stay, 

age, and gender is abstracted from each patient's record, then standardized and combined with 

information on the amount the patient was charged for the stay to create an electronic discharge 

abstract record. Electronic databases containing these records then are forwarded to some 40 

States across the country that require hospitals to submit these data, many of which make it 

available for researchers. Using such data, researchers create a case mix index that describes the 

costliness of treating each hospital's patients. The case mix index usually takes the following 

form: 

CMIh = hospital h's case mix index; 

weightj = a resource use weight, such as the total charge or the length of stay, assigned 

to homogeneous patient group j; 

grouphj = proportion of hospital h's patients in group j; and 

groupj = average proportion of patients in group j. 

A hospital with a CMI greater than 1 has a case mix that is more costly to treat than the average 

hospital's; a hospital with values less than 1 has a case mix that is less costly to treat than the 

average hospital's. 

When examining hospital costs, researchers often use groupings and weights created by 

the U.S. Government as part of Medicare's hospital inpatient prospective payment system. The 

groups, called diagnosis related groups (DRGs), are groupings of patients that, according to 

analysis of discharge abstract data, cost a similar amount to treat. The weights are the average 

costs incurred in treating cases in that DRG, relative to the average costs incurred in treating all 

DRGs. Each hospital discharge can be classified into a DRG based on the principal diagnosis, up 
140 

to eight additional diagnoses, and up to six procedures performed during the stay, as well as age, 

gender, and discharge status of the patient. There are more than 500 DRGs, and their weights are 

reviewed each year and updated as necessary (Centers for Medicare & Medicaid Services 

[CMS], 2001). 

Unfortunately, the data required to create a grouping system that could be used to 

construct weights for substance abuse treatment client groups cannot easily be obtained. 

Although States routinely require substance abuse treatment providers to submit data on 

individual clients, these are usually admissions data, which do not include any information on the 

costs of the client's treatment or the client's disposition at discharge. Sometimes these data 

elements are collected in special studies, as was done in ADSS. However, an examination of the 

ADSS Phase II client discharge abstract data reveals that the data are missing from a large 

number of clients. Total charge was missing or invalid for some 35 percent of the outpatient 

nonmethadone clients in ADSS, it was zero for another 12 percent, and it was on a sliding scale 

or otherwise reduced for another 17 percent. Without complete charge or cost information for 

each client, it is difficult to create groups of clients based on resource use. Although length of 

stay could conceivably be used, as it was in the early work on DRGs (Fetter, Shin, Freeman, 

Averill, & Thompson, 1980), the problems noted above about how discharges are documented 

and handled make using length of stay to measure resource use questionable. 

For these reasons, we did not control for case mix among substance abuse treatment 

facilities using a single case mix index. Instead, we used a method that was employed in hospital 

studies before case mix indices were widely adopted. We included several summary measures of 

characteristics that we believe may make clients more costly to treat. 

To determine which summary measures to use, we reviewed the substance abuse 

treatment literature relating treatment costs to client characteristics. Unfortunately, there appears 

to be a dearth of literature on this subject as well, and most of what is available is based on 

analyses of claims data, usually for specific time periods rather than by treatment episode at a 

given facility (Ettner, Frank, McGuire, Newhouse, & Notman, 1998; Goodman, Holder, 

Nishiura, & Hankin, 1992; Goodman, Nishiura, & Hankin, 1998; Goodman, Nishiura, Hankin, 

Holder, & Tilford, 1996; Holder & Blose, 1991; Huskamp, 1999; Salomé, French, Scott, Foss, & 

Dennis, 2003; Westermeyer, Eames, & Nugent, 1998). 

Thus, we chose the following summary measures of client severity based on data 

availability and characteristics that we expected to affect the cost of treatment. Some clients have 

more complex substance use disorders and therefore require more resources to treat. These may 

include those who, at admission, used both drugs and alcohol, injected drugs (imputed for nine 

facilities), or who received supplemental security income (SSI) or social security disability 

insurance (SSDI) (imputed for two facilities). We included in our models measures representing 

the percentage of each facility's admissions with each of these characteristics. This information 

comes from the discharge abstracts, when available, or from the Phase I ADSS administrator 

survey (OAS, 2000a). 

Other clients may be more costly to treat for other reasons. Those who are referred by the 

criminal justice system, for example, may be less compliant and ready for treatment and 

therefore require more resources to treat compared with those referred in some other way, so we 
141 

included the percentage of clients who were referred by the criminal justice system in the model 

(imputed for one facility). Minority clients may be more costly to treat if they require more 

services due to their social and economic disadvantages (D'Aunno & Vaughn, 1995). To capture 

this, we entered four variables from the Phase I administrator survey: the administrator's estimate 

of the percentage of each facility's clients who were Hispanic, non-Hispanic black, other race, or 

unknown race on the point-prevalence date (October 1, 1996), with non-Hispanic white as the 

reference category (imputed for one facility). Ten facilities reported that the race of all of their 

clients was "unknown," an allowable response to the survey. It is unclear if these facilities truly 

did not know the race of all their clients, or chose not to respond to the question. If they truly did 

not know or ask, it could be that race/ethnicity plays no part in their treatment decisions and 

therefore resources required to treat these clients. Because this was unclear, we ran the model 

both including and excluding these 10 facilities and found that the results did not differ 

significantly. We report the results including all of the facilities and include "unknown" race as a 

category. 

We ran versions of our model with other client mix variables we thought might affect 

facility costs, but they were not significant, and removing them from the model did not affect any 

of the coefficients on the remaining variables. The variables we considered but excluded were 

the percentage of clients who were homeless, unemployed, or had co-occurring substance use 

and mental disorders at admission, and the percentage who completed treatment. 

Facility Characteristics 

We included several facility characteristics in the model. We entered dummy variables to 

measure the extent of urbanization of the facility's location. These dummy variables indicated 

whether the facility was in a small- or medium-sized metropolitan area or a nonmetropolitan 

area, with large metropolitan area comprising the reference cell. Metropolitan area classifications 

were based on the Beale urbanicity codes, which were assigned based on the facility's zip code 

(Butler & Beale, 1994). Nonmetropolitan facilities are located in nonmetropolitan counties. 

Small and medium-sized metropolitan area facilities are located in metropolitan areas with fewer 

than 1 million people. Large metropolitan area facilities are located in metropolitan areas with 

more than 1 million people. We expected facilities in large urban areas to have higher costs than 

other facilities for input prices we did not measure (e.g., security). In addition, we entered a 

dummy variable indicating whether or not the facility was owned by a private for-profit entity. 

Government-owned and nonprofit facilities comprised the reference group. According to 

economic theory, in a competitive market, the profit motive forces firms to minimize the costs of 

doing business. Private nonprofit and government-owned facilities face no profit incentive, so 

they may be less likely to be cost minimizers.4

 Previous research suggested that such differences 

may exist in the substance abuse treatment industry (Wheeler, Fadel, & D'Aunno, 1992). 

However, it also should be noted that, if our client characteristic variables did not adequately 

account for case mix, we also might have expected a profit/nonprofit differential—private forprofit facilities may admit less severely ill clients compared with private nonprofit and 

government-owned facilities, which may be more severely ill clients' last resort. 

 4

 We also ran the model including a dummy variable for private, nonprofit ownership to test the hypothesis 

that government-owned facilities, because they must function under larger bureaucracies, may have higher costs. 

The results did not indicate a difference between private nonprofit and government-owned facilities, so we collapsed 

the two categories to conserve degrees of freedom. 
142 

Another difference among facilities is the number of services and the number of special 

programs for specific populations they offer. A substantial number of those with substance use 

disorders also have other problems, such as homelessness, unemployment, or physical or mental 

illnesses that must be addressed for treatment to be successful (D'Aunno & Vaughn, 1995). 

Because, again, we could find no direction in the literature as to which services were most likely 

to affect costs, we included a variable that measured the number of the following services offered 

by the facility, based on the administrator's response to the Phase I ADSS survey: comprehensive 

assessment/diagnosis, transportation, individual therapy, relapse prevention, family counseling, 

employment counseling, academic education, HIV/AIDS education/counseling/support, 

combined substance abuse treatment and mental health services, tuberculosis (TB) screening, 

prenatal care, smoking cessation, acupuncture, aftercare, outcome follow-up, urine screens, 

alcohol and other drug tests, medical detox, mental health services, and medical treatment. 

Finally, the diversity of a facility's clients may affect its cost due to the importance of 

offering culturally competent care. For example, facilities that serve clients who speak many 

different languages may need to hire a greater variety of counselors or counselors with special 

skills. To measure this, we computed a race/ethnicity index similar to the Herfindahl index 

commonly used in the industrial organization literature to measure market structure. The 

race/ethnicity index for facility t, Rt, is 

2

1

,

N

t tj

i

R S

=

= ∑

where Stj is the share of the i

th racial/ethnic group in facility t. We included the five groups 

identified earlier: non-Hispanic white, non-Hispanic black, Hispanic, other, and unknown. Rt is 

bounded by 0 and 1 and is inversely related to diversity. The greater Rt is, the less diverse is the 

facility; facilities with only one racial/ethnic group have Rt = 1. Rt declines as the number of 

racial/ethnic groups at the facility increases. It increases with rising inequality among any given 

number of racial/ethnic groups. We hypothesized that Rt is negatively related to costs. 

Several other facility characteristics also may affect costs. One such characteristic is 

whether or not the facility is part of a larger organization. Although facilities were encouraged to 

report total costs, including dollar values of items supplied by a parent company, some facilities 

may have had trouble doing so. Furthermore, being part of a larger entity may allow the facility 

to obtain inputs at lower prices, or take part in other efficiencies, which may lower its costs. 

Therefore, we hypothesized that facilities that were part of a larger organization may have had 

lower costs. Because many substance abuse treatment facilities are nonprofit, they may received 

in-kind donations of goods or services, such as space, furniture, or volunteer help. Facilities that 

received in-kind donations should have had lower expenses, so we included a dummy variable 

from the ADSS Phase II survey that indicated that the facility received such donations (imputed 

for one facility). 

We suspected other facility characteristics might have affected cost, such as the number 

of special programs offered by the facility (intensive outpatient treatment and special programs 

for women, pregnant women, adolescents, DWI/DUI clients, AIDS/HIV positive, and cooccurring disorder clients), the age of the facility, and a variable indicating that the facility was a 

multimodality facility. However, we excluded these variables from the final model because the 
143 

estimated parameters were not significantly different from zero, and excluding these variables 

did not affect the other results. 

Estimation 

We estimated the cost function parameters using design-weighted least squares regression 

techniques. Because the ADSS data were collected according to a complex survey design, 

including stratification, unequal probabilities of inclusion and clustering, as well as nonresponse 

adjustment, we used methods appropriate for the design to compute the point estimates, standard 

errors, and statistical tests. ADSS was designed to be used with a jackknife replicate variance 

estimation method. However, comparing the jackknife variance estimates with those computed 

using the Taylor linearization method revealed great differences between these estimates (with 

the jackknife standard errors between 22 and 242 percent greater than the Taylor linearization 

ones). Bell and McCaffrey (2002) and McCaffrey, Bell, and Botts (2001) demonstrated that, 

under certain conditions, the jackknife method overestimates the variance, although the Taylor 

linearization method underestimates it. These authors offered methods for estimating an unbiased 

variance using data from unstratified designs, both weighted and unweighted. However, the 

extension of these methods to stratified designs is not straightforward. Further, the assumptions 

made regarding the population model may be hard to justify in our case. Therefore, we 

developed, using simulations, a new method to estimate standard errors from stratified data. 

We started with the observation that the Taylor linearization and the jackknife methods 

normally yield closer estimates when the number of clusters in the sample is large and the 

number of estimated parameters is not very large. However, when this is not the case, the biases 

of these methods may be significant. This appears to be the case with our use of the ADSS cost 

study data because the estimates obtained by the jackknife method were much larger than those 

obtained by the Taylor series method, and our model contains a relatively large number of 

parameters. We estimated the standard error using the Taylor linearization method, then adjusted 

the result upward by a factor. This method was motivated by the known result that the full 

maximum likelihood estimate of the variance in a linear regression model with independently 

and identically distributed (IID) data is negatively biased on the order of p/n, where p is the 

number of covariates and n is the sample size. This bias arises from overfitting, as was also 

suggested by Bell and McCaffrey (2002) and validated by our simulations. We showed that, in 

the case of complex data, the bias of the variance estimated using Taylor linearization is still 

approximately p/n where now n, the effective sample size, is a function of the number of strata, 

clusters, and units within a cluster, and the intracluster correlations. We used a simulation 

approach to determine this effective sample size. Our simulations used a sample design similar to 

that of the ADSS sample, with a similar mix of continuous and dichotomous covariates. These 

simulations showed that the Taylor linearization bias was approximately -p/46. Thus, we used an 

effective sample size of 46 for correction of the bias of the Taylor linearization variance 

estimation. Accordingly, we multiplied the Taylor series standard error estimates by the square 

root of [n/(n-p)], or 1.356. 
144 

Results 

Table 7.1 displays the weighted mean estimates of the variables included in our analysis 

for our sample of outpatient nonmethadone substance abuse treatment facilities. After 

incorporating the final facility weights, the total population size represented was 9,166 facilities 

(N = 222, unweighted sample). 

The mean of the log of total costs was approximately 12.13 in 1997, which translates into 

$185,350. The mean of the log of annual admissions was approximately 4.94, or 140 admissions, 

yielding a cost per admission of $1,324. Of the three different mean hourly wages included as 

input prices, the senior administrative wage was the highest ($24.29), followed by the substance 

abuse counselor wage ($12.55), and the clerical administrative wage ($9.97). The average of our 

proxy for office space costs was approximately $916 per month. 

The average facility treated a client population that was somewhat racially diverse. Most 

clients in the facilities were non-Hispanic white, with the mean across facilities of 63 percent of 

clients. The second largest racial/ethnic group in most facilities was non-Hispanic black, with the 

mean across facilities of 22 percent of clients. Hispanics accounted for, on average, about 9 

percent of a facility's client population. Clients of other races comprised about 2 percent of total 

clients, and those with an unknown race or ethnicity, approximately 4 percent. The derived 

race/ethnicity index had a weighted mean estimate of 0.7 across facilities, indicating at least 

some diversity in the average facility. 

Almost half of the clients in the facilities were referred by the criminal justice system, 

with the mean across facilities of 42 percent. A majority of clients in the facilities received 

treatment for both alcohol and drug use disorders, with the mean across facilities of 54 percent. 

The average proportion of those who inject drugs within a facility was low (10 percent), and few 

clients reported receiving SSI or SSDI (mean across facilities of 8 percent of clients). 

Most of the facilities in the sample were private nonprofit (59 percent). An estimated 27 

percent were private for-profit, and 14 percent were government-owned. On average, each 

facility offered approximately 10 services. Finally, most facilities were located in a large-sized 

metropolitan area (55 percent), with 35 percent located in a small to medium-sized metropolitan 

area and 11 percent in a nonmetropolitan area. 

Cost Function Results 

As mentioned earlier, estimating standard errors using these data was not straightforward. 

In Table 7.2, we present the coefficient estimates, along with three estimates of the standard 

errors. As Table 7.2 shows, the standard errors estimated using the Taylor series method are 

much smaller than those estimated using the Jackknife method. Because we believe there is bias 

in both estimatesTaylor standard errors are biased downward and Jackknife estimates are 

biased upwardwe adjusted the Taylor standard errors to create an adjusted standard error that 

falls between the two. We present this adjusted standard error as well and its p value. These are 

the results we discuss below. 
145 

Table 7.1 Means and Standard Errors of Analysis Variables, Outpatient Nonmethadone Substance Abuse 

Treatment Facilities, ADSS Phase II Administrator and Cost Studies, 1997 (N = 222; Weighted N = 

9,166) 

Variable Mean Standard Error 

Log of Costs ($185,350 total costs) 12.13 0.09 

Log of Admissions ($1,324 per admission, 140 admissions) 4.94 0.12 

Log of Substance Abuse Counselor Wage ($12.55 per hour) 2.53 0.03 

Log of Clerical Administrative Wage ($9.97 per hour) 2.30 0.02 

Log of Senior Administrative Wage ($24.29 per hour) 3.19 0.03 

Log of Office Space Cost ($916 per month) 6.82 0.04 

% of Clients Who Are White 0.63 0.04 

% of Clients Who Are Black 0.22 0.03 

% of Clients Who Are Hispanic 0.09 0.02 

% of Clients Who Are Another Race 0.02 0.01 

% of Clients Whose Race Is Unknown 0.04 0.02 

Herfindahl Index 0.70 0.03 

% of Clients Referred by Criminal Justice System 0.42 0.05 

% of Clients with Drug and Alcohol Use Disorders 0.54 0.05 

% of Clients Who Injected Drugs 0.10 0.02 

% of Clients Who Received SSI or SSDI 0.08 0.02 

Private For-Profit Facility 0.27 0.07 

Private Nonprofit Facility 0.59 0.07 

Government Facility 0.14 0.04 

Sum of Special Services 10.38 0.33 

Part of a Larger Organization 0.57 0.07 

Facility Receives In-Kind Goods or Services 0.45 0.08 

Facility Located in a Large Metro Area 0.55 0.08 

Facility Located in a Medium or Small Metro Area 0.35 0.08 

Facility Located in a Nonmetro Area 0.11 0.06 

SSI = supplemental security income. 

SSDI = social security disability income. 

Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study, Phase II administrator data, 1997 to 1999, and 

ADSS cost study, 1997. 

Our results suggest that there are substantial economies of scale in the outpatient 

nonmethadone substance abuse treatment industry. We found that a 10 percent increase in the 

total number of admissions was associated with only a 6.7 percent increase in total costs (p < 

0.0001). Because the estimate also was statistically different from 1 at the 0.01 percent level, the 

results suggest that the outpatient nonmethadone substance abuse treatment industry experiences 

economies of scale. Larger facilities were less costly on a per admission basis than smaller ones. 

With one exception, the facility characteristics were insignificant. The only client mix 

variable that was significant was the percentage of clients with SSI or SSDI. Facilities with a 

larger percentage of clients on SSI or SSDI had higher costs (p = 0.0418). 
146 

Table 7.2 Cost Function Results, Outpatient Nonmethadone Substance Abuse Treatment Facilities, ADSS Phase II Administra

1997 

Variable Coefficient

Taylor Series 

Standard 

Error

Jackknife 

Replicate 

Standard Error

Adjusted 

Standard 

Error

Ad

S

Intercept 6.71 1.41 2.25 1.91 Log of Admissions 0.67 0.04 0.06 0.05 Log of Substance Abuse Counselor Wage -0.20 0.42 0.67 0.57 Log of Clerical Administrative Wage -1.09 1.06 1.84 1.44 Log of Senior Administrative Wage 0.92 0.58 0.91 0.79 Log of Office Space Cost 0.23 0.41 0.61 0.56 % of Clients Who Are Black 0.17 0.19 0.26 0.26 % of Clients Who Are Hispanic 0.00 0.24 0.39 0.33 % of Clients Who Are Another Race -1.66 0.84 2.88 1.14 % of Clients Whose Race Is Unknown 0.23 0.29 0.54 0.39 Herfindahl Index 0.50 0.29 0.50 0.39 % of Clients Referred by Criminal Justice System -0.12 0.20 0.31 0.27 % of Clients with Drug and Alcohol Use Disorders -0.12 0.15 0.24 0.20 % of Clients Who Injected Drugs 0.28 0.32 0.39 0.43 % of Clients Who Received SSI or SSDI 0.65 0.23 0.35 0.31 Private For-Profit Facility -0.15 0.14 0.20 0.19 Sum of Special Services 0.04 0.02 0.03 0.03 Part of a Larger Organization -0.09 0.10 0.15 0.14 Facility Receives In-Kind Goods or Services 0.23 0.11 0.15 0.15 Facility Located in a Medium or Small Metro Area -0.21 0.12 0.18 0.16 Facility Located in a Nonmetro Area -0.40 0.21 0.43 0.28 SSI = supplemental security income. SSDI = social security disability income. Source: SAMHSA, Office of Applied Studies, Alcohol and Drug Services Study, Phase II administrator data, 1997 to 1999, and ADSS cost study, 19
147 

Discussion 

Our results reveal that economies of scale exist throughout the output range in outpatient 

nonmethadone substance abuse treatment facilities. This suggests that larger facilities may be 

able to provide care at a lower price than smaller facilities. If other studies conclude that larger 

facilities provide care that is at least as good as smaller facilities, and client access can be 

maintained, then State governments and other payers may wish to consider promoting the 

formation of larger programs through their payment and licensing policies. For example, they 

could consider setting payments at a rate at which smaller facilities cannot survive.5

 Other things 

being equal, such policies may free resources that can be used to treat more individuals with 

these disorders. 

With one exception, we find that the mix of clients, at least to the extent we can measure 

it, does not appear to explain variations in the cost of running a substance abuse treatment 

facility. That one exception was clients who were on SSDI or SSI. These clients appear to be 

more costly to treat. No other facility characteristics were significant. 

The presence of economies of scale calls into question the practice of using costs 

estimated from a small number of nonrandomly selected facilities in benefit-cost and costeffectiveness analyses of the substance abuse treatment industry. If the facilities included in 

those studies have costs that are sufficiently higher than the average facility because they are 

much smaller than the average facility, a promising addition to treatment may fail to be 

implemented because the additional benefits do not appear to justify the costs. However, the 

costs may be justified at a more efficient facility. The opposite may hold for results from studies 

that are conducted at larger facilities. Treatments that are cost-effective in larger facilities may 

not be in smaller ones. 

Some limitations must be noted. First, further research is needed to overcome at least one 

limitation of this study. Although we attempted to control for case mix using the data and 

methods currently available, these methods are not as advanced as those used to examine other 

health care facilities, and it is likely that they are not entirely sufficient. Better controls for case 

mix that are based on detailed analyses of data on the costs associated with treating clients with 

different kinds of disorders and treatment needs have to be developed to promote a fairer and 

more accurate comparison of treatment facility performance. Second, there are some limitations 

to the data used in this study. Especially noteworthy is that a relatively high percentage of the 

expense data could not be used as originally submitted and had to be revised or imputed using 

the methods described earlier. Also as described earlier, we used proxies for some of the input 

prices because exact measures were unavailable. Improved data collection would lead to more 

accurate results. 

 5

 At least one State, New Jersey, has implemented what it calls an "economies of scale" adjustment. 

According to a recent report, New Jersey pays 10 percent less per slot to facilities that exceed 40 residential, 150 

methadone, or 75 outpatient slots (New Jersey Substance Abuse Prevention and Treatment Advisory Task Force, 

2001). However, this type of adjustment does not promote larger facilities. If anything, it promotes the establishment 

of smaller facilities, which New Jersey may have intended for other reasons. 
148 

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155 

Chapter 8. Effects of Reporting Requirements on Estimates 

from the Treatment Episode Data Set (TEDS) 

Sarah Q. Duffy, Ph.D. 

Introduction 

The Treatment Episode Data Set (TEDS) is a compilation of admissions data that State 

substance abuse treatment agencies collect from substance abuse treatment providers. States use 

these data to monitor their treatment systems. The Substance Abuse and Mental Health Services 

Administration (SAMHSA), which administers the Federal Government's Substance Abuse 

Prevention and Treatment (SAPT) block grant program, requests that States send a minimum 

dataset consisting of commonly defined variables on all clients admitted to treatment facilities 

that accept public funds earmarked for substance abuse treatment, including funds from the 

SAPT block grant. 

Each State, however, sets its own requirements on the types of providers that must report. 

Some States' requirements exactly match what SAMHSA requests. Other States provide 

information only on admissions that are paid with public funds. Still other States require data 

from all licensed providers, whether for-profit or nonprofit, and whether or not they accept 

public funds earmarked for substance abuse treatment. Other States' requirements fall between 

those extremes. In some States, providers that are not required to report do so voluntarily. States 

typically send all of the records they collect to SAMHSA, which compiles them into TEDS. 

Because admission to facilities that do or do not accept earmarked funds is unlikely to be 

random, results from analyses of admissions data solely from such facilities may be subject to 

selection bias. This chapter presents the results of an investigation into whether or not estimates 

from TEDS data appear to be subject to selection bias. If selection bias occurs, it may be 

important for States to collect data on all clients from all facilities, not just those that receive 

earmarked funds, to fully understand the publicly funded treatment system. If such selection bias 

does not occur, SAMHSA and the States can have greater confidence in analytic results using 

TEDS data as they are currently collected. 

Substance Abuse Treatment: A Two-Tiered System 

The substance abuse treatment system has been characterized as having two tiers 

(Institute of Medicine [IOM], 1990; Pauly, 1991; Wheeler & Nahra, 2000). One tier serves 

clients who are wealthy or insured and consists of privately run and, in many cases, for-profit 

firms. Providers in this tier tend to be more likely to provide care in inpatient settings, not to 

accept earmarked funds, and to have excess capacity. Providers in the other tier serve those who 

have no insurance coverage for these disorders, as well as those who cannot pay the full cost of 

their care, if they can pay anything at all. This tier consists of publicly or privately owned, 

largely nonprofit providers that accept earmarked funds and that tend to have excess demand for 

care. 
156 

Studies that compare or describe substance abuse treatment clients by the characteristics 

of the facilities to which they have been admitted, however, typically have focused on 

differences in facility ownership status, whether the provider is for-profit, private nonprofit, or 

government-owned, rather than on funding sources (Hays, Farabee, & Patel, 1999; Wheeler, 

Fadel, & D'Aunno, 1992; Wheeler & Nahra, 2000). Dayhoff, Pope, and Huber (1993) noted that 

although ownership and financing are related, they are not identical. Some private, for-profit 

substance abuse treatment facilities accept earmarked funds, and some government-run facilities 

accept private insurance. Still, findings from these studies provide evidence that there are real 

differences in clients based on facility ownership status, which may translate to differences based 

on program funding sources. Most recently, Wheeler and Nahra (2000) reported differences in 

variables, such as primary substance of abuse, expected source of payment, and whether or not 

clients pay a reduced fee. 

Based on these findings of differences in clients by facility ownership status, there is 

reason to believe that clients in facilities receiving earmarked funding may differ in important 

ways from clients in facilities that do not. This raises two questions about the applicability of the 

inferences drawn from data from only facilities that receive earmarked funding. First, if a State 

only requires facilities that receive earmarked funding to report data, can the State use those data 

to get a good picture of the clients in treatment in that State? Second, is there a selection effect 

that biases coefficients estimated using data only on facilities that receive earmarked funding, so 

the results do not even apply to that population? To examine these issues, we first present a 

simple model of how selection bias may occur in analyses using data from TEDS. Then we 

analyze TEDS data from two States that collect sufficient data from both types of facilities to 

explore the effects on inferences and predictions based on data from only those who are admitted 

to facilities that receive earmarked funds. To focus the discussion on the estimation issues, we do 

not identify by name the two States whose data we use in the analysis. 

Selection Bias in Studies of Substance Abuse Treatment Facilities 

Selection bias can appear in coefficients estimated using data from nonrandomly selected 

samples, such as the samples from the States that require reporting only by facilities that receive 

earmarked funds. In the substance abuse treatment field, such effects have been found, for 

example, in estimates of the effectiveness of Alcoholics Anonymous (AA), either as a standalone treatment (Humphreys, Phibbs, & Moos, 1996) or aftercare (Fortney, Booth, Zhang, 

Humphrey, & Weisman, 1998). As an example of how this might occur in admissions data, 

consider the following model along the lines of one described by O'Higgins (1994). 

Suppose we are interested in estimating the effect of various client characteristics on the 

probability of admission to an inpatient substance abuse treatment setting, such as a hospital or 

long- or short-term residential facility, to explore whether treatment resources are being used 

according to current thinking on appropriate treatment setting. Suppose further that we begin 

with a single equation (univariate) probit model. Assume that the variables in the model include 

a dummy variable (y) indicating whether or not the admission was to a facility that accepted 

earmarked funds. This would lead to the following model: 

 e* = X$ + (y + g, (1) 
157 

where 

e* = a latent variable that represents the individual's demand for treatment intensity 

(if it is positive, the individual chooses inpatient treatment; otherwise, the 

individual chooses outpatient treatment); 

y = a dummy variable set equal to 1 when the client is admitted to a treatment 

facility that accepts earmarked funds and 0 otherwise; 

X = a vector of individual characteristics; 

$, ( = parameters to be estimated; and 

g = a normally distributed error term with variance normalized (without loss of 

generality) to 1. 

The probability that an individual is admitted to inpatient substance abuse treatment, then, is 

β γ 1 2

2 ε ( 1) ( β γ ) (2π) exp ε

2

X y

Pre X y d ,

+ −

−∞

    = =Φ + = −      ∫ (2) 

where e = an observed dummy variable that equals 1 if the client was admitted to an inpatient 

facility (e* > 0) and 0 otherwise. This leads to the probit likelihood function, 

( )( )1 βγ βγ , e e Xy Xy   − ΠΦ + Φ− −         (3) 

where M is the normal distribution function. 

But suppose that admission to facilities across funding sources is not random. Suppose 

that individuals admitted to facilities that receive earmarked funds differ from individuals 

admitted to other facilities on some uncollected variable that affects both the facility and 

treatment choices. An example might be treatment readiness. Suppose that clients who enter 

facilities that accept earmarked funds are, on average, more "ready" for treatment than those who 

enter programs that do not. Then those in a facility that accepts earmarked funds might have 

been less likely to be admitted to inpatient treatment, according to the American Society of 

Addiction Medicine's Patient Placement Criteria (ASAM, 1991), even if they had not chosen a 

facility that accepted earmarked funds. A bias exists, in this example, because the parameter ((), 

which is the estimated effect of having sought treatment in a facility that accepts earmarked 

funds on the probability of being admitted to inpatient treatment, will include the unobserved 

factors influencing the choice of treatment based both on funding type and treatment intensity. 

The more general problem, of which the above example is one possible cause, is that g

may be correlated with the variables on the right-hand side of the equation. To illustrate, assume 

that y and g in equation (2) are correlated. Assume that the choice of entering a facility that 

accepts earmarked funds may itself be thought of as a latent variable. We can model this as 
158 

y* = Z" + <, (4) 

where 

y* = an underlying continuous variable measuring some characteristic of the client's 

perception of treatment facilities, such as quality, convenience, location, or 

types of clients, that is related to program financing; 

Z = a vector of client characteristics determining y*; and 

< = an error term that is distributed normally with a variance normalized to 1. 

Let y be a dummy variable that equals 1 if y* > 0 and 0 otherwise. The probability that the 

individual would choose a facility that accepts public funding would be 

 Pr(y = 1) = M(Z"). (5) 

If g and < are correlated, meaning their correlation, D, does not equal 0, then g and y are 

correlated [E(g | y) ... 0]. The estimate of ( in equation (3) will be biased. To obtain unbiased 

estimates, alternative methods would have to be employed. 

Bivariate Probit Model 

If the correlated error terms are individually normally distributed, then the model can be 

estimated consistently using a bivariate probit technique. The joint probability that the individual 

chooses a facility that receives public funding and is admitted to inpatient treatment is 

α βγ

( 1, 1) (ε, ν;ρ) ε ν

Z X

Pre y dd

∞ ∞

−−−

= == φ ∫ ∫ (6) 

=Φ +γ (X Z β , α;ρ) , 

where φ (A) and M(A) are the standardized bivariate normal density and distribution functions, 

respectively. The bivariate probit estimates the effect of client characteristics on the joint 

determination of admission to facilities that accept earmarked funds and inpatient treatment. 

The conditional probability that the individual is admitted to inpatient treatment, given 

that he or she is admitted to a facility that accepts earmarked funds, then, is 

( 1)

( 1, 1) ( 1| 1) == = = = = Pr yPr e y Pr e y (7) 
159 

( )

( )

2

α β

ε ρν (ν) ε ν

1 ρ

α

Z X

d d

Z

∞ ∞

− − −γ

  +

φ φ  

  −   = Φ

∫ ∫

( )

( )

2

β ρν ν ν

1 ρ . α

Zα

X d

Z

∞

−

  +γ− φ Φ  −   = Φ

∫

The model is identified (i.e., the estimates it provides will be unique) as long as Z, the vector of 

explanatory variables in the earmarked funding equation, contains at least one independent 

variable not in X, the vector in the inpatient/outpatient (IP/OP) equation (O'Higgins, 1994). 

Estimation Strategy 

We first conducted preliminary analyses to provide evidence as to whether or not 

selectivity may be a problem in the TEDS data. There are two econometric issues. The first is the 

stability of coefficient estimates between the models run on clients from facilities with different 

funding sources. To address this issue, we ran separate IP/OP probits on the samples from 

facilities that received earmarked funds and those in facilities that did not. We used standard 

likelihood ratio (LR) tests to determine whether the differences in the coefficients were 

significant at conventional levels (Greene, 2000).1

 The second econometric issue is the 

correlation between the unobservable factors affecting the IP/OP decision and the unobservable 

factors affecting the facility choice. To address this second issue, we estimated a variant of the 

bivariate probit model called the "seemingly unrelated probit" (Stata Corporation, 2001). In this 

model, both the facility and the IP/OP decisions depend on the same set of independent 

variables, and the correlation between the two error terms is estimated as an auxiliary parameter. 

By modeling the correlation between the two decisions, we provide evidence on the significance 

and direction of the correlation between the two decisions. The advantage of the seemingly 

unrelated probit over the bivariate probit described above is that the seemingly unrelated probit is 

identified by distributional assumptions alone. We can investigate whether or not the correlation, 

D, is significantly different from zero, without making a priori identifying restrictions, as would 

be preferable when using the standard bivariate probit (Powell, Czart Ciecierski, Chaloupka, & 

Wechsler, 2002). We report the results of these preliminary investigations and of our final 

models in the following section. 

Data and Model Specifications 

We used data from the 1996 TEDS, which is maintained by SAMHSA's Office of 

Applied Studies (OAS, 1998; also see Chapter 4 in the present compendium). We used data on 

 1

 We conducted the test by first estimating the equation separately on the sample of clients from facilities 

with public funding and on the sample of clients from facilities with no public funding to obtain the LRs. Then, 

summing these LRs, we got the unrestricted LR. Next, we estimated the equation on the pooled sample of clients 

from all facilities in our dataset to obtain the restricted LR. Finally, by subtracting the restricted and unrestricted 

LRs and multiplying by 2, we derived the LR statistic. 
160 

adult males with alcohol as a primary substance of abuse from two States that collect data both 

from both types of facilities. We refer to these States as "State A" and "State B." 

We used two methods to determine which facilities received earmarked funding. First, we 

identified facilities in the TEDS file that reported to another OAS data file, the Uniform Facility 

Data Set (UFDS). UFDS data come from a survey of facility administrators that collects 

information on a variety of facility characteristics, including information on ownership and 

funding sources, such as whether they accept earmarked funds (OAS, 1997). Using this method, 

we were able to identify funding status for 88 percent of the facilities and 93 percent of the 

admissions in State A and 74 percent of both the facilities and admissions in State B. Second, for 

those facilities that did not report funding information to UFDS, we used expected payer 

information from the TEDS files to identify which facilities received earmarked funds and which 

did not. Unfortunately, this latter method is not exact, as the field in which earmarked funding is 

reported, "other government funds," also includes funding from other government sources, such 

as the Department of Veterans Affairs (VA) and CHAMPUS.2

 However, we expect the 

proportions of those payment sources to be small enough not to affect the findings materially. 

Seemingly Unrelated Probit Specification 

As mentioned earlier, the seemingly unrelated probit model does not require exclusion 

restrictions to provide meaningful estimates, particularly of D. Based on the model developed in 

earlier work (see Chapter 4), we include several variables in both the IP/OP and earmarked 

funding equations measuring client characteristics at the time of admission to explain these 

decisions. Measures of client severity include a set of dummy variables indicating (a) frequency 

of use (daily use, use three to six times in the past week, use one to two times in the past week, 

and use one to three times in the past month, with no use in the past month as the reference 

category), (b) intoxication before age 15, (c) secondary substance use (marijuana/hashish, 

cocaine, and other, with no secondary substance use as the reference category), (d) one or two 

prior treatments (with no prior treatment as the reference category), (e) co-occurring mental 

disorders, and (f) homelessness. 

We also include socioeconomic characteristics in the form of dummy variables indicating 

(a) part-time or full-time employment (not employed as the reference cell), (b) the client's 

race/ethnicity (Hispanic, non-Hispanic white, and non-Hispanic black, with "other" as the 

reference category), (c) marital status, and (d) education status (no high school, which equals 1 if 

the client did not complete high school and 0 otherwise, and high school, which equals 1 if the 

client completed high school, with some college as the reference category). We include season of 

admission variables (summer, fall, winter, with spring as the reference cell) to capture 

differences among seasons, due to such things as the weather or the client's obligations, in a 

client's probability of entering inpatient treatment. We include indicator variables for referral 

source (self, alcohol or drug abuse services provider, other health care provider, other sector 

provider, with criminal justice system as the reference cell) and expected payer (self-pay, 

Medicare, Medicaid, private, other [e.g., worker's compensation], and other government 

 2

 CHAMPUS stands for the Civilian Health and Medical Program for the Uniform Services. It provides 

health care in private facilities for dependents of military personnel on active duty or retired for reasons other than 

disability. 
161 

funding/no charge as the reference category) to control for any differences that might be caused 

by these factors. Government funding and no charge are combined in the reference category 

because no charge is a relatively small category, especially in State A, and because we believe 

clients who are not charged likely are more similar to those who receive government funding for 

their care than those who have insurance coverage that pays for care. 

Bivariate Probit 

Although it also is technically acceptable to rely on functional form to identify the 

bivariate probit (Greene, 2000), we follow Powell et al. (2002) in choosing to impose exclusion 

restrictions to increase our confidence in the model. Our bivariate probit model is identical to our 

seemingly unrelated probit model except that, in addition to including a dummy variable in the 

IP/OP equation identifying whether or not the admission was to a facility that received 

earmarked funding, we exclude the education variables from the IP/OP equation. This 

specification passed standard overidentification and instrument validity tests applied to a twostage least squares version of the model (Davidson & MacKinnon, 1993), following Powell et al. 

(2002). However, although the education variables were highly significant in the earmarked 

funding equation for State B, they were not significant in the earmarked funding equation for 

State A. Because the excluded variables should be correlated with the dependent variable in the 

earmarked funding equation to ensure consistent estimates, and given that we had no other good 

candidates for exclusion, we present the bivariate probit results only for State B. 

Results 

Descriptive Statistics 

Table 8.1 shows that, in these two States, there were several differences between clients 

who entered facilities that accepted earmarked funds compared with those who entered other 

facilities, and that, for some variables, the differences varied by State. For example, in State A, 

clients admitted to facilities that accepted earmarked funds were significantly less likely to be 

admitted to inpatient treatment, while in State B they were significantly more likely to be 

admitted to inpatient treatment. In State B, clients admitted to facilities that accepted earmarked 

funds were more likely to use alcohol daily at admission than were those in other facilities, while 

in State A, the opposite was true. In State B, clients in programs that accepted earmarked funds 

were more likely to have first used alcohol prior to age 15, while in State A the difference 

between clients admitted to different types of facilities was insignificant. However, in both 

States, clients admitted to facilities that received earmarked funds were more likely to have a 

secondary substance of abuse, less likely to have co-occurring mental disorders, and more likely 

to have had two or more prior treatment episodes than were clients admitted to facilities that did 

not accept earmarked funds. 

Differences across facility funding source in demographic and socioeconomic variables 

were more similar between the two States. In both States, clients admitted to facilities that 

received earmarked funds were younger, less likely to be employed, less likely to be married, 

less likely to have any postsecondary education, and less likely to have had private insurance pay 

for their substance abuse treatment. The only major difference between the two States in 

socioeconomic variables was in the race/ethnicity variable. In State B, clients in facilities that 
162 

Table 8.1 Descriptive Statistics, by Facility Funding Source 

State A State B 

Variable 

Earmarked Funds 

(n = 15,317)

No Earmarked Funds 

(n = 2,274)

Earmarked Funds 

(n = 7,560)

No Earmarked Funds

(n = 2,854)

Percent Inpatient* 0.12 0.19 0.23 0.18 

Referral Source** 

Self 0.16 0.27 0.19 0.19 

Alcohol/drug treatment provider 0.07 0.11 0.21 0.06 

Other health care provider 0.06 0.12 0.10 0.11 

Other sector 0.04 0.12 0.08 0.06 

Criminal justice 0.67 0.38 0.52 0.58 

Frequency of Use** 

None 0.41 0.26 0.23 0.19 

1-3 times in past month 0.24 0.14 0.11 0.14 

1-2 times in past week 0.12 0.14 0.17 0.24 

3-6 times in past week 0.10 0.17 0.14 0.14 

Daily 0.13 0.30 0.35 0.29 

Used Alcohol Prior to Age 15*** 0.34 0.36 0.35 0.31 

Secondary Substance** 

None 0.58 0.66 0.56 0.65 

Marijuana 0.31 0.23 0.18 0.15 

Cocaine/crack 0.03 0.05 0.20 0.15 

Other 0.07 0.06 0.06 0.05 

Prior Treatment Episode** 

None 0.41 0.44 0.50 0.52 

One 0.29 0.30 0.25 0.26 

Two or more 0.30 0.26 0.26 0.22 

Mental Disorder* 0.15 0.21 0.07 0.13 

Homeless 0.01 0.02 0.05 0.05 

Age* 33.84 37.32 35.87 36.89 

Employed* 0.64 0.70 0.53 0.64 

Married* 0.33 0.40 0.25 0.27 

Education** 

No high school 0.21 0.17 0.28 0.23 

High school 0.57 0.55 0.51 0.49 

Postsecondary 0.22 0.28 0.21 0.29 

Race/Ethnicity** 

Non-Hispanic white 0.90 0.91 0.67 0.74 

Non-Hispanic black 0.04 0.05 0.20 0.17 

Hispanic 0.04 0.03 0.12 0.07 

Other 0.12 0.14 0.01 0.02 

Season of Admission  

Winter 0.30 0.29 0.26 0.25 

Spring 0.32 0.28 0.26 0.27 

Summer 0.19 0.21 0.25 0.26 

Fall 0.19 0.22 0.23 0.22 

Payment Source**  

Self 0.06 0.09 0.29 0.41 

Private insurance 0.11 0.49 0.17 0.23 

Medicare 0.01 0.06 0.03 0.04 

Medicaid 0.05 0.05 0.03 0.06 

Other government and no charge 0.76 0.30 0.44 0.24 

Other 0.00 0.02 0.05 0.03 

*Differences by funding source significant at better than the 5 percent level for both States. 

**Differences in distributions significant at better than the 5 percent level for both States. 

***Difference significant at better than the 5 percent level for State B only. 

 Difference significant at better than the 5 percent level for State A only. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
163 

received earmarked funds were less likely to be white than those in other facilities, while in State 

A the proportions were approximately the same. 

Multivariate Analyses 

Table 8.2 displays, for each State, the marginal effects3

 and standard errors for three 

equations: Model 1, univariate probit IP/OP model estimated on observations from programs that 

received earmarked funds; Model 2, a univariate probit IP/OP model estimated on observations 

from programs that did not receive earmarked funds; and Model 3, a seemingly unrelated (SUR) 

probit of the IP/OP and earmarked funding equations. In addition, Table 8.2 displays results from 

the bivariate probit model (Model 4) for State B. 

Comparing Models 1 and 2 in Table 8.2 shows that, for both States, there are many 

differences in magnitude and significance levels of the effects of client characteristics on the 

probability of inpatient admission across facility funding type. Consider, for example, the 

referral source variables. In State A, self-referred clients in the earmarked funding sample were 

more likely to be admitted to inpatient treatment than those in the reference cell (those referred 

by the criminal justice system). In contrast, self-referred individuals in the non-earmarked 

funding sample were no more or less likely to be admitted to inpatient treatment than those 

referred by the criminal justice system. In State B, although self-referred clients were 

significantly more likely to be admitted to inpatient treatment than those referred by the criminal 

justice system, the marginal effect in the earmarked funding sample was more than twice as large 

as that in the other sample. In both States, those referred by alcohol or drug abuse treatment 

providers were more likely to be admitted to inpatient treatment in both samples, but the 

magnitude of the marginal effect differed. In State A, the effect was larger among those admitted 

to facilities that did not receive earmarked funds, while in State B, the effect was larger among 

those who were admitted to facilities that did not receive earmarked funds. 

As another example, consider the effect of having a co-occurring mental disorder. In 

State A, having a co-occurring mental disorder had a very small, only marginally significant 

effect on inpatient admission in facilities that received earmarked funds. It had a much larger and 

more strongly significant effect among those in facilities that received no earmarked funds. In 

State B, having a mental disorder was positively associated with inpatient admission in both 

types of facilities, but the effect was almost 3 times as large in facilities that receive earmarked 

funds compared with those that did not. The likelihood ratio test provided support for what is 

apparent from casual observation: The relationship between the covariates and the IP/OP 

decision is influenced by the facility choice decision.4

 Therefore, a single model run on the 

pooled sample would not be appropriate. 

Table 8.2 also displays the seemingly unrelated results (SUR) for both States. As 

suspected, the D's in both equations were statistically significantly different from zero. However, 

they were of opposite signs. The negative estimated value of D in State A suggests that the 

 3

 Marginal effects are the derivative of the estimated equation as a function of the independent variable of 

interest. They represent the change in the probability of inpatient admission due to a small change in the independent 

variable. In a standard linear ordinary least squares regression, the derivative is simply the estimated coefficient. In 

nonlinear models, such as the bivariate probit, the derivative is more complicated. See Greene (2000, p. 852). 4

 The statistic was 174.02 for State A and 355.65 for State B, significant at better than the p = 0.001 level. 
164 

Table 8.2 Marginal Effects on the Probability of Inpatient Treatment, for Selected Models, 1996 

State A State B 

Model 1 

Univariate Probit 

IP/OP Model 

Obs. from Facilities 

w/Earmarked Funds 

(standard error)

Model 2 

Univariate Probit 

IP/OP Model Obs. 

from Facilities w/o 

Earmarked Funds 

(standard error)

Model 3 

SUR Probit IP/OP and 

Earmarked Funds, Full 

Sample (standard 

error)

Model 1 

Univariate Probit 

IP/OP Model Obs. 

from Facilities 

w/Earmarked Funds 

(standard error)

Model 2 

Univariate Probit 

IP/OP Model Obs. 

from Facilities w/o 

Earmarked Funds 

(standard error)

Mo

SUR Pro

and Ear

Funds, Fu

(standa

Number of Observations 15,317 2,274 17,591 7,560 2,854 10,Referral Status 

Self-referred 0.017** * 0.011 -0.016*** 0.121*** 0.056*** 0. (0.006) (0.021) (0.005) (0.012) (0.011) (0.Alcohol/drug abuse 

treatment provider 

0.107*** 

(0.007) 

0.151*** 

(0.025) 

0.116*** 

(0.007) 

0.213*** 

(0.014) 

0.026*** 

(0.009) 

0.

(0.

Other health care provider 0.033*** 0.055** 0.037*** 0.121*** 0.039*** 0. (0.008) (0.025) (0.007) (0.015) (0.009) (0.School, employer, and 

other community 

-0.035*** (0.013) 

-0.035 (0.029) 

-0.035*** (0.006) 

0.037** 

(0.017) 

0.010 

(0.009) 

0.0

(0.

Drug Use in the Month Prior to Admission 

1-3 times in the past month 0.018*** 0.210*** 0.033*** -0.077*** 0.035** -0. (0.006) (0.027) (0.006) (0.022) (0.014) (0.1-2 times in the past week 0.064*** 0.163*** 0.071*** -0.035** 0.035*** -0. (0.007) (0.030) (0.007) (0.018) (0.013) (0.3-6 times in the past week 0.121*** 0.230*** 0.128*** 0.086*** 0.044*** 0.0 (0.007) (0.028) (0.002) (0.015) (0.013) (0.Daily use 0.179*** 0.298*** 0.187*** 0.159*** 0.083*** 0.

 (0.007) (0.025) (0.006) (0.013) (0.017) (0.Used Alcohol Prior to Age 15 

0.016*** 

(0.004) 

-0.026 (0.016) 

0.010** 

(0.004) 

0.005 

(0.009) 

-0.003 (0.004) 

0.

(0.

Secondary Drug of Abuse 

Marijuana -0.020*** -0.011 -0.020*** -0.018 0.002 -0. (0.005) (0.020) (0.005) (0.013) (0.006) (0.Cocaine 0.028*** 0.043 0.040*** 0.061*** 0.008 0.0 (0.010) (0.031) (0.009) (0.011) (0.006) (0.Other substance 0.006 0.070** 0.012 0.076*** 0.019** 0.0

 (0.008) (0.031) (0.008) (0.016) (0.009) (0.Prior Number of Treatment Episodes 

One prior treatment 

episode 

0.018*** 

(0.005) 

0.005 

(0.019) 

0.017*** 

(0.005) 

0.021* 

(0.011) 

0.014** 

(0.006) 

0.0

(0.

2 or more prior treatment 

episodes 

0.030*** 

(0.005) 

0.047** 

(0.019) 

0.032*** 

(0.005) 

0.000 

(0.011) 

0.009* 

(0.006) 

0.0

(0.
165 

Table 8.2 Marginal Effects on the Probability of Inpatient Treatment, for Selected Models, 1996 (Continued) 

State A State B 

Model 1 

Univariate Probit 

IP/OP Model 

Obs. from Facilities 

w/Earmarked Funds 

(standard error)

Model 2 

Univariate Probit 

IP/OP Model Obs. 

from Facilities w/o 

Earmarked Funds 

(standard error)

Model 3 

SUR Probit IP/OP and 

Earmarked Funds, Full 

Sample (standard 

error)

Model 1 

Univariate Probit 

IP/OP Model Obs. 

from Facilities 

w/Earmarked Funds 

(standard error)

Model 2 

Univariate Probit 

IP/OP Model Obs. 

from Facilities w/o 

Earmarked Funds 

(standard error)

Mo

SUR Pro

and Ear

Funds, Fu

(standa

Age 

Age of respondent 0.003** 0.007** 0.003*** -0.003 0.000 -0.0 (0.001) (0.003) (0.001) (0.002) (0.001) (0.0Age-squared 0.000*** 0.000** 0.000*** 0.000 0.000 0.0

 (0.000) (0.000) (0.000) (0.000) (0.000) (0.0Education/Employment 

Non-high school graduate 0.016** 0.040* 0.018*** 0.028** -0.002 0.0 (0.007) (0.024) (0.006) (0.013) (0.006) (0.0High school graduate 0.006 0.013 0.009 0.002 0.001 0.0 (0.005) (0.018) (0.005) (0.012) (0.005) (0.0Employed prior to 

admission 

-0.075*** (0.004) 

-0.067*** (0.018) 

-0.076*** (0.005) 

-0.116*** (0.010) 

-0.017*** (0.006) 

-0.1(0.0

Race/Other Demographics 

Non-Hispanic white -0.054*** -0.150*** -0.064*** -0.038 0.003 -0.0 (0.012) (0.054) (0.013) (0.039) (0.021) (0.0Non-Hispanic black -0.034** -0.185*** -0.048*** -0.083** 0.002 -0.0 (0.015) (0.063) (0.015) (0.040) (0.021) (0.0Hispanic -0.057*** -0.122* -0.062*** -0.035 0.003 -0.0

 (0.017) (0.071) (0.017) (0.041) (0.023) (0.0Homeless at Time of Admission 

0.120*** 

(0.015) 

0.285*** 

(0.049) 

0.143*** 

(0.014) 

0.182*** 

(0.020) 

0.101*** 

(0.023) 

0.2

(0.0

Existing Mental Disorder 0.009* 

(0.005) 

0.061*** 

(0.018) 

-0.015*** (0.005) 

0.147*** 

(0.015) 

0.048*** 

(0.011) 

0.1

(0.0

Married -0.031*** -0.051*** -0.033** -0.009 -0.010* 0.0 (0.005) (0.016) (0.004) (0.011) (0.005) (0.0Time of Admission 

Summer 0.017*** -0.002 0.015*** 0.028** -0.009* 0.0 (0.006) (0.021) (0.006) (0.012) (0.006) (0.0Fall 0.011* -0.038* 0.006 0.039*** -0.030*** 0.0 (0.006) (0.022) (0.006) (0.013) (0.009) (0.0Winter 0.019*** 0.007 0.019*** 0.028** -0.012** 0.0

 (0.005) (0.020) (0.005) (0.012) (0.006) (0.0
166 

Table 8.2 Marginal Effects on the Probability of Inpatient Treatment, for Selected Models, 1996 (Continued) 

State A State B 

Model 1 

Univariate Probit 

IP/OP Model 

Obs. from Facilities 

w/Earmarked Funds 

(standard error)

Model 2 

Univariate Probit 

IP/OP Model Obs. 

from Facilities w/o 

Earmarked Funds 

(standard error)

Model 3 

SUR Probit IP/OP and 

Earmarked Funds, Full 

Sample (standard 

error)

Model 1 

Univariate Probit 

IP/OP Model Obs. 

from Facilities 

w/Earmarked Funds, 

(standard error)

Model 2 

Univariate Probit 

IP/OP Model Obs. 

from Facilities w/o 

Earmarked Funds 

(standard error)

Mo

SUR Pro

and Ear

Funds, Fu

(standa

Expected Form of Payment 

Self-pay 0.026*** -0.153*** 0.014 -0.137*** -0.040*** -0 (0.008) (0.032) (0.008) (0.013) (0.009) (0BCBS or other health 

insurance co-pay 

0.014** 

(0.007) 

-0.154*** (0.021) 

-0.011 (0.006) 

-0.038*** (0.014) 

0.022*** 

(0.008) 

-0(0

Medicare 0.011 -0.061* 0.001 -0.121*** 0.035*** -0 (0.016) (0.033) (0.013) (0.027) (0.012) (0Medicaid -0.028*** -0.057 -0.027*** -0.254*** 0.017** -0 (0.009) (0.035) (0.008) (0.033) (0.009) (0Other payment or 

worker's compensation 

0.007 

(0.047) 

-0.170** (0.070) 

-0.027 (0.028) 

0.005 

(0.018) 

-0.009 (0.012) 

-0(0

Publicly Funded Treatment 

 

Constant -0.223*** -0.298*** -0.234*** -0.178*** -0.134*** -0 (0.024) (0.093) (0.024) (0.061) (0.039) (0D -0.082*** 0

(0.023) (0

***Significant at the 0.01 level. ** Significant at the 0.05 level. *Significant at the 0.10 level. BCBS = Blue Cross/Blue Shield. IP/OP = inpatient/outpatient. Obs. = observation. SUR = seemingly unrelated. Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 
167 

unobserved characteristics that led to entry into a facility that received earmarked funds made it 

less likely that an individual would be admitted to inpatient treatment. In State B, the positive 

estimated value of D suggests that the unobserved characteristics that led to entry into a facility 

that received earmarked funds made it more likely that the individual would be admitted to 

inpatient treatment. The results from these preliminary analyses suggest that a bivariate probit 

analysis jointly estimating the facility and treatment location choices would be appropriate. 

Bivariate Probit Results Compared with Univariate Probit Results 

As mentioned earlier, we could specify the bivariate probit model only for State B 

because we could not satisfy the exclusion restriction in the State A equations. Table 8.2 reveals 

that in State B the bivariate probit estimate of D suggests that the unobservables in the IP/OP 

equation were positively associated with those in the facility choice equation, as did the SUR 

probit equation, although the magnitude of the effect (0.92) was much larger. 

For most variables, the bivariate probit (Model 4) estimates were similar in sign and level 

of significance to the univariate probit model estimates run on the sample of clients admitted to 

facilities with earmarked funds (Model 1), which is not surprising given the relative sizes of the 

earmarked-funding and no earmarked-funding samples. The magnitude of some of the marginal 

effects, however, did differ substantially across specifications. For example, although both 

specifications revealed that referral status was associated with inpatient treatment, those 

associations estimated using the bivariate probit model were much larger, ranging from 1.33 

times for the other health care provider category to 2.05 times for the school, employer, and 

other community category than those estimated using the SUR probit. The marginal effect of 

homelessness at the time of admission, again positive and significant in both equations, was only 

70 percent as large in the bivariate probit equation as in the univariate probit equation. Similarly, 

the presence of a co-occurring mental disorder was positive in both equations, but only 70 

percent as large in the bivariate probit equation as it was in the univariate probit equation. 

For other variables, substantive differences existed in the level of significance of the 

estimated coefficient. For example, having had two or more prior treatments increased the 

probability of inpatient admission in the bivariate probit model, but was insignificant in the 

univariate probit model. Conversely, non-Hispanic blacks were significantly less likely than 

those in the reference cell, other race, to be admitted to inpatient treatment according to the 

univariate probit, but were no more or less likely in the bivariate probit model. Finally, the 

effects of season of admission were significant in the univariate probit equation, but insignificant 

in the bivariate probit equation. The bivariate probit model also revealed that individuals 

admitted to facilities that received earmarked funding were less likely to be admitted to inpatient 

treatment, holding other factors constant. 

Policy Simulations 

Aside from providing substantively different marginal effects estimates, potentially 

leading to invalid inferences, these different specifications can affect the resulting predictions 

about the effect of changes in client characteristics on the probability of admission to inpatient 

treatment in facilities in the earmarked funding sector. To illustrate, we simulated the effects on 
168 

the number of inpatient admissions to facilities that received earmarked funds of the following 

changes in client characteristics: 

• 10 percent increase in the proportion of clients with cocaine as a secondary drug, 

• 10 percent increase in the number of clients who were unemployed, 

• 10 percent decrease in the number of clients who were homeless, 

• 10 percent increase in the number of clients with a co-occurring mental disorder, and 

• 10 percent increase in the number of clients who paid for their own care. 

Table 8.3 displays the simulation results for Models 1 and 3 for State A, and Table 8.4 displays 

those from Models 1, 3, and 4 for State B. 

Table 8.3 Simulation of the Effects of Changes in Client Characteristics on the Probability of Inpatient 

Admission to a Facility That Receives Earmarked Funds: State A 

Change 

Model 1 Univariate 

Probit, Earmarked 

Funding Sample 

Model 3 SUR Probit, 

Full Sample 

10 percent increase in secondary cocaine No change 0.22% 

10 percent increase in unemployment 2.15% 1.5% 

10 percent decrease in homelessness -1.00% -3.23% 

10 percent increase in co-occurring mental disorders 0.57% No change 

10 percent increase in the number who paid for their own care 0.29% No change 

SUR = seemingly unrelated. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 

In State A, Models 1 and 3 yielded several dissimilar predictions about the magnitudes of 

the effects of the changes. For example, Model 1, the univariate probit model based on only the 

sample of clients entering programs that received earmarked funds, suggested that a 10 percent 

decrease in the number of homeless clients would lead to a 1 percent decrease in the number of 

individuals entering inpatient treatment in the sector that received earmarked funds. The SUR 

probit (Model 3), however, which was estimated on all clients in the sample and allowed for a 

correlation among the IP/OP and facility choice equations, revealed that there would be a 3.2 

percent decrease in the number of clients admitted to inpatient treatment in facilities that 

accepted earmarked funds. Similarly, according to the univariate probit model, a 10 percent 

increase in the number of clients that had cocaine as a secondary substance would result in no 

change in the number admitted to inpatient treatment. The SUR probit suggested that there 

would be a small increase of 0.22 percent. Increases in the number with a co-occurring mental 

disorder or those who would pay for their own care would appear to increase the number of 

clients admitted to inpatient treatment in facilities that received earmarked funds according to the 

univariate probit, but not according to the SUR probit model. 

There were differences in the findings among the three State B models as well, as Table 

8.4 reveals. For example, while all three models revealed that an increase in unemployment 
169 

would increase the number of clients admitted to inpatient treatment in facilities that received 

earmarked funds, the increase estimated by the bivariate probit model, 2.52 percent, was 

substantially larger than the increase estimated by the univariate probit model and almost twice 

that estimated by the SUR probit model. Likewise, the effect of a 10 percent increase in the 

number of clients who paid for their own care was substantially larger in the bivariate probit 

model than in the other two models. A 10 percent increase in the number of clients with a cooccurring mental disorder, in contrast, had a much smaller estimated effect in the bivariate probit 

than in the other two models. 

Table 8.4 Simulation of the Effects of Changes in Client Characteristics on the Probability of Inpatient 

Admission to a Facility That Receives Earmarked Funds: State B 

Change 

Model 1 

Univariate Probit, 

Earmarked 

Funding Sample 

Model 3 SUR 

Probit, Full 

Sample 

Model 4 

Bivariate Probit, 

Full Sample 

10 percent increase in secondary cocaine 0.59% 1.09% 0.42% 

10 percent increase in unemployment 1.51% 1.33% 2.52% 

10 percent decrease in homelessness -0.59% -1.33% -0.58% 

10 percent increase in co-occurring mental disorders 0.75% 1.25% 0.31% 

10 percent increase in the number who paid for their own 

care 

-2.76% -1.88% -3.31% 

SUR = seemingly unrelated. 

Source: SAMHSA, Office of Applied Studies, Treatment Episode Data Set, 1996. 

Discussion 

This analysis suggests that the IP/OP choice and the choice of facility are not 

independent, at least in the two States studied here, and that selectivity bias is a problem that 

should be dealt with in analyses of admissions data. If this finding extends to other States, it 

suggests that estimates based on data from only those facilities that receive earmarked funds may 

not always lead to an accurate understanding of the substance abuse treatment problem that the 

State faces. Use of these data may result in a misunderstanding of the influences of various 

characteristics on treatment setting choice, as well inaccuracy in predictions for clients the States 

are trying to monitor—those in facilities that receive earmarked funding. Reliance on the 

univariate probit model in State B, for example, would lead one to believe that having two or 

more prior treatments was not associated with the probability of inpatient treatment in the sector 

that accepts earmarked funds, while the bivariate probit model suggested that it would increase 

that probability. Also in State B, the effect of unemployment on the probability of inpatient 

treatment was much smaller in the univariate probit model than in the bivariate probit model. 

These findings provide an example of the value that States may derive from collecting 

data on all of the facilities that clients can attend, not just those that receive earmarked funding. 

Some States collect these data already. New York, for example, requires reporting of client data 
170 

to the State as a condition of licensure, whether or not the facility accepts earmarked funds.5

Second, when conducting analyses, States may want to consider allowing for complicated 

relationships among the many factors that lead clients into substance abuse treatment. These 

steps would improve each State's understanding of their treatment systems and their ability to 

monitor their treatment systems effectively. 

 5

 We did not include New York in this analysis because their data do not contain "expected source of 

payment," which is an important control variable in the model. 
171 

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Office of Applied Studies. (1998). National admissions to substance abuse treatment services: 

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Drug and Alcohol Services Information System Series S-5). Rockville, MD: Substance Abuse 

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173 

Chapter 9. Conclusions and Implications 

Carol L. Council, M.S.P.H. 

Jeremy W. Bray, Ph.D. 

Introduction 

Over the past two decades, the behavioral health care delivery system in the United States 

has experienced rapid change in terms of clients served, as well as organizational and financial 

structures. New patterns of substance use, the changing demographics of affected populations, 

the availability of pharmacotherapies, and the growing presence of managed care have 

challenged the system and its study. These changes are the result of competing demands to 

contain costs, maintain the quality of care, make care available to all who need treatment, and 

focus resources on those forms of care that offer the best hope of successful outcomes. Building 

upon such disciplines as epidemiology, biostatistics, and health economics, as well as the 

impressive body of health services research findings, the field of behavioral health research has 

been called on increasingly to analyze these changes and their effects. 

The chapters in this compendium extend our knowledge by providing information on 

factors that may facilitate or inhibit the delivery of behavioral health services for those persons 

with substance use or mental disorders. Valuable new information is provided on accessibility, 

utilization, quality, cost, cost-effectiveness, and outcomes. The findings describe the relationship 

between demographic characteristics and how, when, where, and if a person will seek care, as 

well as what types of care are chosen and what happens during the delivery of care. Finally, the 

findings presented in this compendium also reveal potential biases that may occur when using 

many of the large datasets currently available for conducting research in the behavioral health 

sciences. 

This chapter explores the implications of these findings in key areas for policymakers, the 

treatment community, and researchers and suggests directions for future research. 

Access, Financing, and Costs of Substance Abuse Treatment and Mental Health Services 

Several of the chapters in this compendium explored the issue of access to care and 

underscored the role of managed care in bringing financing and cost issues to the forefront as a 

potential barrier to client access to needed services. Dr. Woodward in his Chapter 2 literature 

review, identified access as a critical first step to getting clients needed treatment. Although it is 

generally accepted that people with substance use and mental disorders may be affected severely 

by access problems, individuals with more serious behavioral illnesses may encounter additional 

barriers to access because successful treatment may be more expensive, because redundant and 

bureaucratic procedures may pose insurmountable obstacles, because needed services may not be 

available, and because the private-payer managed care system may benefit financially from 

placing barriers to treatment on such people. On the positive side, reports indicate that a few 

State-sponsored programs have been able to increase access for high-risk populations. 

In Chapter 4, Dr. Duffy and colleagues examined the factors influencing admission to 

treatment and reported that in most States examined, the criminal justice system was the most 
174 

common source of treatment referral for adult males, with alcohol as the primary substance of 

abuse. They also showed that referral by an alcohol or drug treatment provider generally 

increased the likelihood of inpatient admission. Clients who were employed were less likely to 

have an inpatient admission, and clients who were expected to pay for their own care had a lower 

likelihood of entering inpatient treatment. In many of the States examined, co-occurring mental 

disorders did not consistently increase the odds of inpatient admission as would be expected 

given the accepted clinical placement criteria at the time. 

In Chapter 7, Dr. Duffy and colleagues estimated cost functions for substance abuse 

treatment programs as a function of size. Findings indicated that the average cost per admission 

declined as facilities became larger, which suggests that larger facilities may be able to provide 

care at a lower price than smaller facilities. These economies may suggest policies that 

encourage the use of large facilities. 

Implications for Access for High-Risk Populations 

The research presented suggests that the effects of benefit and financing factors on access 

to care may be different for behavioral health care clients than for medical care clients. 

Policymakers may wish to consider this when weighing the adoption of financing and delivery 

mechanisms used to finance care. Rather than preventing unnecessary use of resources (i.e., 

moral hazard), aggressive cost sharing and case management policies may deter medically 

necessary and potentially cost-beneficial substance abuse treatment and mental health services. 

In fact, such policies may force providers to avoid providing care to expensive high-risk 

populations, such as homeless persons or those with co-occurring disorders, by creating 

administrative barriers that effectively exclude those with the greatest need. 

Variability in client level of need and service provision across programs affects client 

outcomes. Reports of effective programs providing limited treatment services should be 

reviewed by policymakers with care to be certain that clients served in those programs have 

levels of need similar to those in other programs. Although it may be tempting to provide fewer 

costlier services, treatment research suggests that optimal length of stay should be determined on 

a case-by-case basis, using all available clinical information rather than dictated by a universal 

policy. Although a shorter length of stay may contain short-term costs, it may not contain longterm costs; thus, clients may end up costing both society and the health care system much more 

in the long run. At a minimum, treatment-funding systems may be required to provide costlier 

services to populations on SSI or SSDI because these populations, in general, manifest more 

severe problems related to their substance use. As discussed in Chapter 7, facilities with a greater 

proportion of clients who received supplemental security income (SSI) or social security 

disability insurance (SSDI) had higher costs. 

Implications for Service Delivery 

Several of the large national evaluations discussed earlier in this volume reported shifts 

from inpatient to outpatient care and suggested that the quantity and intensity of services to 

clients are decreasing. Further, there is some suggestion that programs are being eliminated or 

are merging into organized networks of behavioral care that are able to provide a wider 

continuum of services. These shifts may be very beneficial in terms of providing access to a 
175 

larger pool of clients in need of treatment. Indeed, Dr. Duffy and colleagues in Chapter 7 

reported that the average cost per admission declined as facilities became larger, suggesting that 

larger facilities may be able to provide care at a lower price than smaller facilities. On the other 

hand, pressure to reduce costs may result in substantial reductions in the quantity and quality of 

treatment services, especially in those systems that manage resources through a defined level of 

service benefit. 

Although such approaches may help to extend the reach of treatment services to more 

individuals, in-depth study is needed to explore whether high-risk clients with more severe 

substance use disorder-related problems are getting the intensity of care they need. Clearly, 

better information about both severity and actual service parameters provided to all treatment 

clients is necessary to understand the impact of these changes on treatment access, retention, and 

outcomes. In addition, research is needed to determine whether client outcomes vary with facility 

size (particularly outcomes for high-risk populations). Research also needs to determine whether 

higher costs are unavoidable with certain populations, or whether alternative treatment 

technologies or administrative arrangements could reduce costs without negatively affecting 

outcomes. 

Implications for Treatment Outcomes 

At the provider level, if provider organizations grow larger and more centralized to 

capture economies of scale, what impact will this have on treatment access in rural areas? 

Further, what impact will streamlining of providers' treatment regimes have on their ability and 

willingness to try new approaches for treating clients and/or participate in research? Just as a 

host of research in the late 1980s and early 1990s examined the effect of hospital mergers and 

acquisitions on medical outcomes (Finklestein, et al., in press; Hoerger, 1991), behavioral health 

services researchers might now explore the effects of treatment program scale and scope on 

substance use and mental disorder outcomes, such as treatment retention, relapse rates, and social 

functioning. 

Implications for Public- and Private-Sector Care 

The impact of changes in private-sector financing strategies on the public sector needs to 

be better understood. When the Federal Government made the decision to shift funding of 

behavioral health care to the States, it was implied that States would provide services for the 

neediest and most fragile portions of their populations. During the past decade, private health 

insurance coverage for behavioral health problems declined at the same time that an increasing 

number of Americans had no health insurance at all. This has placed an added burden on public 

behavioral health care systems. Thus, a larger and more diverse array of clients may present to 

public treatment programs. Moreover, as Dr. Woodward discussed in Chapter 2, treatment 

facilities that depend on public funding may not have sufficient capacity to provide services to 

all individuals demanding treatment services, let alone those who have not yet sought service. 

More information is needed regarding the level of funding required to provide services to 

uninsured persons and ways of making such funding cost-effective. Clearly, in order to study this 

issue, we need detailed information on the types of services provided to specific types of clients 

in both the public and private sectors, and we need information on clients who have exhausted 

their private-payer benefit for substance abuse treatment and mental health services. 
176 

Community Hospitalizations of Those with Substance Use and/or Mental Disorders 

Dr. Duffy in Chapter 3 discussed in detail the impact of access and financing with regard 

to community hospitalizations of those with behavioral health problems. As Dr. Duffy reported, 

managed care and behavioral health care carve-outs usually result in reductions in inpatient 

mental health services and inpatient substance abuse treatment. She reported that, contrary to 

expectations, the discharge rate for individuals with substance use and mental disorders in 

community hospitals increased over the period from 1990 to 1995, with increases being greater 

for patients with co-occurring disorders and those aged 35 to 45. Even though the patients who 

were admitted had more complex disorders, their lengths of stay decreased. Patients with 

substance use and mental disorder diagnoses were more likely to receive uncompensated care or 

to have Medicaid coverage than other community hospital patients. 

Implications for the Public Sector 

The Federal Government's role in paying for the care of substance use and mental 

disorder patients in community hospitals increased in the early 1990s, with Medicare and 

Medicaid paying for the treatment of more than half of discharges with such diagnoses. If the 

Federal Government is paying for a large portion of this care, it may be in a position to develop 

incentives to ensure that high-risk patients receive adequate treatment support when returning to 

the community. At a minimum, research is needed to clarify the impact of system changes in 

substance abuse treatment and mental health services on access to care for the most fragile 

populations and perhaps to explore the possible need for less expensive residential care options 

for those patients requiring longer periods of care. 

Implications for Community Hospitals and Providers 

Many inpatient treatment facilities, such as mental health hospitals and residential 

substance abuse treatment facilities, closed during the 1990s. The increased number of substance 

use and mental disorder admissions to community hospitals during the early 1990s suggests that 

patients with more severe disorders formerly served by other types of inpatient treatment 

facilities may now be presenting to community hospitals. In turn, community hospitals with 

resources depleted by reductions in standard lengths of stay for traditional patients may have 

provided lifesaving services, such as detoxification and stabilization to substance use and mental 

disorder patients and discharged them back to the community as soon as possible. Community 

hospitals may not be in a position to provide longer-term residential treatment to such patients. 

To enhance the likelihood that they will not be readmitted, community hospitals might consider 

developing strong links with providers of community-based substance abuse treatment and 

mental health services. This might ensure that high-risk patients transitioning back to the 

community are engaged in appropriate aftercare programs. 

The use of community hospitals as a substitute for State-supported detoxification services 

and patient stabilization may be financially beneficial if realistic procedures are in place for 

shifting the management of care at discharge to community-based providers. At a minimum, to 

determine the level of case management support that such patients need when they return to the 

community, providers need information on the level of functioning of discharged community 

hospital patients, as well as the specific services the patients received while in the community 
177 

hospital. In addition, it would be beneficial to understand whether the patients served were those 

who were unable to access needed care earlier in a community setting. 

Treatment Retention 

In Chapter 6, Dr. Ashley and colleagues examined the role of women-only treatment 

programs in retaining women in substance abuse treatment. They found that substance abuse 

treatment for women at facilities offering child care services and treatment at women-only 

facilities were associated with longer stays. However, lower educational levels among female 

clients were associated with shorter stays. 

Implications for Treatment of Specific Populations 

Based on these findings, policymakers may want to develop financing systems to 

encourage the development of gender-appropriate treatment programs. Tailoring treatment 

programs to meet special needs of certain population groups not only makes treatment more 

attractive to those who need it, but it also helps to address many of the logistical and pragmatic 

barriers to treatment faced by special populations. 

It is understandable that behavioral health sciences research has focused largely on the 

neediest and most fragile portions of the substance use and mental disorder population. Given the 

prevalence of substance use and mental disorders across all population groups, it may be 

appropriate to focus on the impact of shifts in financing and access on access for other 

population groups as well. In Chapters 4 and 7, Dr. Duffy and colleagues reported that employed 

persons are less apt to receive inpatient treatment. Furthermore, lengths of stay in treatment are 

shorter for employed women (Chapter 6), suggesting that other segments of the population may 

be affected by managed care practices. Although it may be hypothesized that employed persons 

in treatment may have less severe disorders, more information about these groups is needed, 

including what factors influence treatment-seeking behaviors, their level of functioning, and their 

support environment. More information is needed about the stigma of having a behavioral health 

disorder and the extent to which it prevents people from seeking needed treatment or from 

completing their treatment plan. 

Treatment Choice 

In Chapter 4, Dr. Duffy and colleagues used the 1996 Treatment Episode Data Set 

(TEDS) to explore factors related to treatment admission for adult males, with alcohol as the 

primary substance of abuse. They found that greater substance use disorder severity increased the 

likelihood of inpatient admission, but in many of the States examined, co-occurring mental 

disorders did not consistently increase the odds of an admission. Also notable was the fact that 

referral by an alcohol or drug treatment provider generally increased the odds of inpatient 

admission. This may reflect changes to the treatment system in which treatment providers serve 

as gatekeepers to higher levels of care. 

Further, Dr. Duffy and colleagues in Chapter 5 examined treatment choice in two States 

from among five types of treatment (standard outpatient, intensive outpatient, long-term 

residential, short-term residential, and inpatient hospital) instead of the standard two (inpatient 

and outpatient). They reported that those admitted to standard outpatient treatment appeared to 
178 

have less severe alcohol disorders and were more likely to be employed than those admitted to 

any other treatment setting. Furthermore, they reported that analyses that allowed for only two 

choices, inpatient and outpatient, obscured the relationships between client characteristics and 

treatment-setting choice. 

Implications for States 

In general, it appears that the States are following best practice recommendations with 

regard to client placement. In many of the States examined, however, co-occurring mental 

disorders did not consistently increase the odds of inpatient admission as might have been 

expected given current clinical guidelines regarding placement criteria. This may not be a 

function of States' not adhering to best practices, but rather may be the result of client choice. 

We do not understand fully the differences between States' substance use and mental 

disorder treatment programs, nor how those differences might affect the organization of 

treatment at the State level (i.e., where the responsibility for substance abuse treatment and 

mental health services falls in State government), and the effect that the differences have on 

access to care for the sickest clients as well as on treatment choice. Many State substance abuse 

treatment programs are part of State mental health departments, others are part of State health 

departments, and still others are individual, stand-alone State agencies. In addition, some States 

have developed mechanisms for sharing data on client populations served by several State 

agencies. Closer examination of the effect that administrative and organizational factors may 

have on access to care may reveal those factors that may contribute to better access for high-risk 

populations. Similarly, differences exist in the extent to which State funding resources are 

utilized to support substance abuse treatment and mental health services. In order to understand 

access to care, more information is needed about State and local funding levels for substance 

abuse treatment and mental health services and their impact on access and treatment. 

Implications for Characterization of Treatment Options 

To understand treatment choices, researchers need to expand their concept of treatment 

modality beyond outpatient and inpatient in order to recognize better the full range of treatment 

modalities now available. More client-level service data that include the full range of treatment 

options utilized are needed by policymakers so they may account for more complex treatment 

choices when developing cost-sharing and financing policies. 

Data Issues in Behavioral Health Care Research 

As reported throughout this compendium, much of the research reported upon in the 

literature relies on analyses of small, selected, nonrepresentative samples, often with very low 

response rates and many missing observations. At best, this leads to difficulty in comparing 

studies and synthesizing findings reported in the literature. At worst, it can lead to the generation 

of misleading, even false, information. Large-scale data collection efforts, including those at the 

Federal and State levels (some of which have been used in the chapters of this compendium), 

have made strides in improving the data available to researchers. However, these data pose 

challenges as well. 
179 

Most State-level substance abuse treatment authorities provide treatment episode data to 

the Federal Government. However, variations exist at the State level with regard to which 

facilities must report treatment episode data. In Chapter 8, Dr. Duffy looked at selection biases in 

data reporting systems between States that require the reporting of substance abuse treatment and 

mental health services for all clients and those that require the reporting of such data only from 

facilities receiving public funding earmarked for substance abuse treatment. She found variations 

across States between clients who entered facilities that accepted earmarked funds and those who 

entered facilities that did not accept earmarked funds. Clients admitted to facilities receiving 

earmarked funds generally were younger, less likely to be employed, less likely to be married, 

less likely to have postsecondary education, and less likely to have private insurance pay for their 

treatment than those entering other facilities. 

Dr. Duffy concluded that analyses of data collected by States only from facilities that 

receive public funds earmarked for substance abuse treatment may be biased and cautioned that 

selection effects may bias estimates of the impact that client demographics have on the 

probability of inpatient admission to publicly funded treatment facilities. 

The lack of client-level data on the level of need and care provided during an episode of 

treatment was mentioned by several authors. The costs per unit of service and per episode of care 

are frequently unavailable. The absence of this information hampers cost-benefit analysis, as 

well as outcomes research. 

Implications for Federal and State Data Systems 

Because not all treatment facilities report data, policymakers may have an incomplete 

understanding of the potential effects of policy changes. States may want to consider the required 

reporting of client data by all facilities, regardless of their funding status. If this is not politically 

feasible, the Federal Government may choose to use synthetic estimation to adjust for State 

reporting differences in federally maintained databases. 

Because performance-based programming is a priority at both the national and State 

levels, many States will need more detailed cost and service information. The use of uniform 

client assessment procedures, as well as the development of management information systems 

that will provide client-level treatment episode data, will enhance performance monitoring. 

Conclusions 

Recent changes in the organizational and financial structures of the behavioral health care 

delivery system in the United States have had a large effect on the structure of the behavioral 

health treatment system and on behavioral health service delivery. These changes have not been 

adequately studied. This compendium has presented new research that helps to fill this gap. It 

gives policymakers and service providers at the Federal, State, and local levels a better 

understanding of how these changes are affecting access to needed care, the quality and 

effectiveness of care, the utilization of services, cost of treatment services, and the outcomes of 

treatment for people with acute and chronic substance use and mental disorders. 

Several overarching conclusions emerge from the findings presented. Much can be 

learned by studying the findings of general health services research. Both the behavioral health 
180 

and the general health systems have been greatly influenced by managed care, and not all the 

influences have been negative. Managed care entities often use the results of research to guide 

clinical treatment. In the behavioral health area, research indicating that inpatient care did not 

provide better outcomes for most clients than outpatient care was used to modify clinical 

guidelines and resulted in the shift to decreased use of inpatient care. 

On the other hand, important differences exist between the behavioral health care system 

and the general health system. The behavioral health care system has in large part not suffered 

from overutilization of services. In fact, State systems have been criticized because they have not 

been able to close the treatment gap by engaging more of the population in need. This 

compendium underscores the need for better information on the impact that changes in the 

behavioral health care system have had on access to both fragile high-risk populations and 

persons needing services in the general population. 

Fragile populations (such as individuals who are homeless and displaced youths) may 

need more intense and longer-term support. As reported for the RAND Insurance Experiment 

(Newhouse & Insurance Experiment Group, 1994), one such fragile population is affected 

negatively by managed care approaches to treatment. Populations with substance use and mental 

disorders frequently experience economic, social, and health problems that may detract from 

their ability to access and remain in treatment. Providing treatment services to fragile populations 

presents a difficult challenge to behavioral health care treatment providers because they often 

require more intensive, lengthy treatment. 

As in the health services field in general, nationally based, representative datasets are 

available for additional study. The existence of large national datasets provide policymakers and 

researchers with an important base from which to study both the populations manifesting 

substance use disorders and the systems providing services to these populations. Data collection 

and management systems, including Web-based systems, have been developed that enable the 

collection of much richer data on a much broader population of clients and providers. Another 

key implication of this compendium is that to support effective policy, more information is 

needed on service need, types of services available, the cost of an entire episode of treatment, 

and State and local differences in treatment policies. In this age of Web-based reporting, 

increased opportunities exist to provide this information. In addition, improved data analysis 

techniques permit the synthesis of information from varied sources. 

As an additional step to improving the quality of data collected, clear definitions are 

needed for assessing clients, and for defining the types of services they receive. Two remaining 

problems affect the analyses of available data. First, the datasets themselves have biases that 

should be fully understood before drawing conclusions from them. Second, the information in 

these systems could be substantially improved. In the past, the field has had to depend on small 

well-funded studies of service providers that may or may not have represented typical service 

provision. Although many of these studies yielded compelling data on outcomes, these findings 

were very difficult to use as the basis for policy because they were narrow and potentially 

nonrepresentative. Improvements in the quality and quantity of service and client information 

have been made, but more is needed. In particular, guidance is needed on potential approaches to 

developing more detailed service and cost information. 
181 

Managed care for substance abuse treatment and mental health services has shifted 

treatment from inpatient to outpatient settings and has shifted the financial risk to providers, thus 

constraining provider treatment options. Although the shift from inpatient to outpatient settings 

may be appropriate for many clients, the most severely troubled clients may be in danger of 

being routed to less effective and less cost-effective care. In addition, the shifting of risk to 

providers may deter the use of more expensive and intensive therapies that are more costeffective from a societal standpoint but pose a greater financial risk to providers. Similarly, 

providers may be unwilling to adopt best practices because of the financial costs of so doing. In a 

truly capitated treatment system with a stable population of plan enrollees, long-term outcomes 

have greater value. In managed care plans, such as Medicaid, where clients are constantly 

switching health care insurance plans, the plans have more incentive to manage short-term costs 

because long-term costs may be borne by another managed care entity. Research is needed to 

explore the sharing of risk between the public and private sectors. Health economics research is 

needed to develop a system that enables the sharing of financial risk among all payers while 

providing financial support for the adoption of best practices. 

This compendium provides additional examples of how health services research in the 

behavioral health care sector can inform policy and have clear implications for researchers, 

policymakers, providers, and clients. Although more work is needed in critical areas, such as the 

role of client characteristics in the treatment system, determining the most cost-effective care 

modalities, and identifying the minimal set of data reporting programs and characteristics 

necessary, this compendium clearly shows that health services research can and does play a 

critical role in the formation of effective policy. It is hoped that the research presented here will 

help to inspire both new and more effective policies and also new research that will continue to 

improve the lives of individuals with substance use or mental disorders. 
182 

References 

Finkelstein, E. A., Bray, J. W., Larson, M. J., Miller, K., Tompkins, C., Keme, A., & 

Manderscheid, R. (in press). Prevalence of and payments for mental health and substance abuse 

conditions in public and private sector health plans. In R. W. Manderscheid & M. J. Henderson 

(Eds.), Mental health, United States, 2002. Rockville, MD: U.S. Department of Health and 

Human Services, Center for Mental Health Services. 

Hoerger, T. J. (1991). "Profit" variability in for-profit and not-for-profit hospitals. Journal of 

Health Economics, 10, 259-289. 

Newhouse, J. P., & Insurance Experiment Group. (1994). Free for all? Lessons from the RAND 

Health Insurance Experiment. Cambridge, MA: Harvard University Press. 
183 

About the Authors 

Olivia Silber Ashley, Dr.P.H., a public health researcher at RTI International (a trade 

name of Research Triangle Institute), has 15 years of professional experience in prevention and 

etiology research, program management, and direct services related to a variety of issues, 

including substance abuse, parenting, and family influences on behavior. She is currently editing 

a monograph on women in substance abuse treatment for the Office of Applied Studies (OAS), 

Substance Abuse and Mental Health Services Administration (SAMHSA), and she has published 

peer-reviewed research on this topic. Prior to joining RTI, she worked as a program manager for 

the North Carolina Division of Maternal and Child Health, as a child protective services social 

worker, and as a substance abuse counselor.

Thomas M. Brady, Ph.D., is with SAMHSA's OAS. Dr. Brady's research interests include 

women's health, co-occurring mental disorders, and substance abuse treatment utilization. He 

completed his doctoral training at the University of Illinois, School of Public Health, in Chicago 

where his research was supported by the National Institute of Mental Health and the University 

of Illinois, Department of Psychiatry. He also has received degrees from Emory University in 

Atlanta, Georgia, and the University of Wisconsin at Madison. He is a native of Philadelphia, 

Pennsylvania. 

Jeremy Bray, Ph.D., is a health economist at RTI. His research focuses on two primary 

areas of interest: the economics of substance use and mental disorders, and the cost-effectiveness 

of substance abuse treatment and prevention. In his work on the economics of substance use and 

mental disorders, he has examined the labor market effects of substance use, abuse, and 

dependence. Currently, his research focuses on the impact of alcohol use and abuse on 

educational attainment. In his research on the cost-effectiveness of substance abuse treatment and 

prevention, he has estimated the costs and effectiveness of enhanced employee assistance 

program (EAP) services designed to increase employee utilization of the EAP. In addition, he 

serves as the cross-site economic analysis task leader on the Center for Substance Abuse 

Prevention's Workplace Managed Care initiative and on the Robert Wood Johnson's Cutting 

BackTM project to determine the cost-effectiveness of screening and brief intervention for risky 

drinking. At RTI, he directs SAMHSA's "Medicare, Medicaid, and Managed Care Analysis" 

project that is examining the prevalence and costs of publicly funded mental health services and 

substance abuse treatment, and he leads the analysis of SAMHSA's "Integrated Database" project 

through a subcontract with The MEDSTAT Group. His research findings have been published in 

peer-reviewed journals, including the Journal of Health Economics, Labour Economics, Public 

Health Reports, Health Economics, and Employee Assistance Quarterly. 

Carol L. Council, M.S.P.H., is a senior research associate at RTI. Prior to assuming her 

position at RTI, she was director of research and planning for substance abuse treatment to the 

State of North Carolina. Earlier, she worked as a research associate in the Department of 

Epidemiology at the University of North Carolina's School of Public Health as well as their 

Highway Safety Research Center. She has 30 years' experience in designing and conducting 

research and evaluation on a variety of subjects, including community mental disorder studies, 

community hypertension and diabetes studies, evaluations of the effectiveness of family nurse 
184 

practitioners, highway safety studies with particular emphasis on drinking and driving research, 

and the development of performance outcomes measures for publicly funded substance abuse 

treatment programs. Ms. Council was codirector of North Carolina's Practice and Research 

Consortium, which focuses on enhancing the collaboration between policymakers, researchers, 

and practitioners. She served as Secretary of the International Council on Alcohol Drugs and 

Traffic Safety. She has directed grants and contracts funded by a variety of sources, including the 

National Highway Traffic Safety Administration, the Center for Substance Abuse Treatment, and 

the Center for Substance Abuse Prevention. 

Laura J. Dunlap, M.A., a research economist at RTI, has participated in several studies 

involving the estimation of the costs of drug treatment programs. She has evaluated the effect of 

the treatment process on the costs of providing treatment services and the effect of treatment 

services on economic outcomes. Recently, she has been involved in estimating the costs and 

cost-effectiveness of human immunodeficiency virus (HIV) health interventions aimed at lowincome, drug-using women. Her primary research interests include studying factors that affect the 

demand for substance abuse treatment and influence treatment-seeking decisions, examining 

program and client characteristics that affect the costs and cost-effectiveness of treatment 

programs, and health service delivery for special populations. 

Sarah Q. Duffy, Ph.D., is with the SAMHSA's OAS, where she conducts economic and 

health services research. Prior to SAMHSA, she held research positions at the Maryland Health 

Services Cost Review Commission and the Agency for Health Research and Quality, and she 

taught health economics at Eastern Michigan University. She has a Ph.D. in economics from the 

University of North Carolina, Chapel Hill, and a B.A. in economics from Smith College. She has 

published in economics and health services research. 

Moshe Feder, D.Sc., is a senior research statistician at RTI. His interests include survey 

sampling, time series analysis of cross-sectional survey data, longitudinal analysis of panel 

survey data, and other data analyses. He holds a doctorate in mathematics from the Israel Institute 

of Technology and a master's degree in statistics from Carleton University, Ottawa, Canada. Prior 

to coming to RTI in 2000, he worked on statistics research projects and taught statistics and 

statistical computing at the University of Southampton, United Kingdom, and at Statistics 

Canada. 

Lev S. Sverdlov, M.D., Ph.D., Doct. of Med. Sci., is a senior health researcher for 

Synectics for Management Decisions, Inc. In this capacity, he is involved in substance abuse 

research for the National Analytical Center sponsored by SAMHSA's OAS. Prior to joining 

Synectics in 2001, he held a number of academic and research positions and also worked as an 

independent contractor with government agencies, including SAMHSA, the National Institute on 

Drug Abuse, and Travis Air Force Base in California. He has conducted and managed a number 

of research projects and managed clinical and training programs. He has developed a curriculum 

for training special education teachers in child and adolescent mental health; participated in the 

development of a curriculum for training primary care providers in substance abuse treatment 

and prevention sponsored by SAMHSA; and designed, developed, and implemented researchbased cost-effective prevention/disease management programs for a military managed care 

organization. Prior to coming to the United States in 1991 as a distinguished visiting professor at 
185 

Florida International University, he was a director of the Department of Outpatient Psychiatry 

and Prevention of Mental Disorders of the Bekhterev Research Psychoneurological Institute, St. 

Petersburg, Russia. He authored over 100 books, chapters, and articles on a broad spectrum of 

issues related to substance use and mental disorders, including clinical and outcomes research, 

clinical psychopharmacotherapy, clinical psychology, anthropology, epidemiology, health 

economics, and research methodology. 

Albert Woodward, Ph.D., is with SAMHSA's OAS, where he works on health economic 

and financing analysis. He served on the President's Task Force on Health Care Reform. Prior to 

his Federal service, he worked for several organizations in the health care area, including the 

Appalachian Regional Commission, Blue Cross-Blue Shield, Coopers & Lybrand, and Arthur 

Young. He has a Ph.D. in economics (with distinction in mathematical economics) from 

American University, an M.B.A. from the Wharton School, and a B.A. from Haverford College. 

He has published on access to health care, health insurance, and the economics and financing of 

substance use and mental disorders. 

Gary A. Zarkin, Ph.D., is director of RTI's Center for Interdisciplinary Substance Abuse 

Research and the Behavioral Health Economics Program. He has led or participated in several 

projects involving the economic analysis of drug treatment, prevention, and studies of workplace 

substance abuse. He has published several papers on cost, cost-effectiveness, and the benefit-cost 

of substance abuse interventions. He recently led the development of a new method to estimate 

the cost of services for methadone treatment. He was previously an assistant professor of 

economics at Duke University and a research associate professor in Duke University's Institute of 

Statistics and Decision Sciences. He has published extensively in economic, substance abuse, 

and health service journals, including the American Journal of Public Health, Evaluation and 

Program Planning, Health Services Research, Inquiry, Journal of Health Economics, Journal of 

Labor Economics, and Journal of Substance Abuse Treatment. 
186