Asthma Care Quality Improvement: 

A Resource Guide for State Action

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, Maryland 20850 

www.ahrq.hhs.gov 

Contract No. 290-00-0004 

Thomson Medstat 

The Council of State Governments 

Prepared by: 

Rosanna M. Coffey, Ph.D. 

Karen Ho, M.H.S. 

David M. Adamson, Ph.D. 

Trudi L. Matthews, M.A. 

Jenny Sewell 

AHRQ Publication No. 06-0012-1 

April 2006
ii 

Acknowledgments 

This Resource Guide was prepared for the Agency for Healthcare Research Quality (AHRQ) by 

Thomson Medstat and The Council of State Governments. This effort was motivated and guided 

by the following AHRQ staff: Dwight McNeill, Ph.D., AHRQ Task Leader; Ernest Moy, M.D., 

M.P.H., Director of the National Healthcare Quality Report and Disparities Reports; Denise 

Dougherty, Ph.D., Special Advisor on Child Health; Roxanne Andrews, Senior Health Services 

Researcher, Center for Delivery, Organization, and Markets; and DonnaRae Castillo, 

Publications Editor, Office of Communications and Knowledge Transfer. 

We also acknowledge the special contributions of the following individuals and groups: 

• Several members of AHRQ’s Asthma Steering Committee provided valuable comments and 

recommendations on earlier drafts of this Resource Guide. They are: James Stout (University 

of Washington), Diana Schmidt (National Heart, Lung, and Blood Institute), Stephen Redd 

(Centers for Disease Control and Prevention), David Greenberg (Centers for Medicare & 

Medicaid Services), Ashok Patel (American Thoracic Society member), Maureen George 

(American Thoracic Society member), Kirsten Aired (Oregon Asthma Program), Amy 

Friedman (Allies Against Asthma), Katherine Pruitt (American Lung Association), Asua 

Ofosu (American Thoracic Society), Mary Tyrell (South Carolina State Budget and Control 

Board), Vi Naylor (Georgia Hospital Association), and Carolyn Turner (Florida Agency for 

Health Care Administration). 

• Partners in the Healthcare Cost and Utilization Project (HCUP) from State hospital 

associations and State government agencies contributed data and information on the four 

States featured in this Resource Guide. They are: Maryland—Brian Jacque (Health Services 

Cost Review Commission, Department of Research and Methodology); Michigan—Bob 

Zorn (Michigan Health & Hospital Association); New Jersey—Frances Prestianni (New 

Jersey Department of Health & Senior Services); Vermont—Lauri Scharf (Vermont 

Association of Hospitals and Health Systems). All HCUP Partners make possible the HCUP 

databases and, thus, derived estimates for asthma hospitalizations published in the National 

Healthcare Quality and Disparities Reports and this Resource Guide. 

• A focus group of legislators and program directors, who are members of the Council of State 

Governments, guided our effort to make materials more user friendly to government 

executives. They are: Representative Marilyn Lee (Hawaii), Representative Kathy Miles 

(South Dakota), Representative Jean Hunhoff (South Dakota), and DeeAnne Mansfield 

(Kentucky Legislative Research Commission). 

_____________________________________________________________________________________ 

This document is in the public domain within the United States only and may be used and reproduced 

without permission. AHRQ appreciates citation as to source and the suggested format is below. Foreign 

countries and users who want to distribute content on a global basis in electronic form or print should 

submit specific permission requests for use to: info@ahrq.gov. 

Coffey RM, Ho K, Adamson DM, Matthews TL, Sewell J. Asthma Care Quality Improvement: A 

Resource Guide for State Action. (Prepared by Thomson Medstat and The Council of State Governments 

under Contract No. 290-00-0004). Rockville, MD: Agency for Healthcare Research and Quality, 

Department of Health and Human Services; April 2006. AHRQ Pub. No. 06-0012-1. 
iii

Foreword

Asthma Care Quality Improvement: A Resource Guide for State Action and its accompanying 

Workbook were developed by Thomson Medstat and The Council of State Governments for the 

Agency for Healthcare Research and Quality (AHRQ) as learning tools for all State officials who 

want to improve the quality of health care for people with asthma in their States. Using Statelevel data on asthma care, this Resource Guide is designed to help States assess the quality of 

care in their States and fashion quality improvement strategies suited to State conditions. The 

States mentioned in this Resource Guide gave permission to use their data for illustrative and 

comparative purposes so that others could learn by their examples. 

Many people for whom these learning tools were intended—State elected and appointed leaders 

as well as officials in State health departments, Asthma Prevention and Control Programs, 

Medicaid offices, and elsewhere—provided comments and feedback throughout the development 

and drafting process. From this process, we learned that they intend to use the Resource Guide

and Workbook in many different ways: to assess their current structure and status, to create new 

quality improvement programs, to build on existing programs, to orient new staff, and to share 

with their partners such as the American Lung Association. 

The Resource Guide and its complementary Workbook can serve as tools for those who work on 

quality improvement to use in sharing their expertise, ideas, knowledge, and solutions. The 

various modules are intended for different users. Senior leaders, for example, may want to focus 

on making the case for asthma quality improvement, incorporating a State-led framework into 

their improvement strategy, and taking action; program staff need to provide the measures and 

data necessary to implement the quality improvement plan. The goal is that everyone work as a 

team and, thereby, improve the quality of asthma care in their State. 

If you have any comments or questions on this Resource Guide, please contact AHRQ’s Center 

for Quality Improvement and Patient Safety, 540 Gaither Road, Suite 3000, Rockville, MD 

20850 (phone: 301/427-1734; email: dwight.mcneill@ahrq.hhs.gov). 
iv 

Asthma Steering Committee 

The following experts in asthma care and State health policy were assembled by AHRQ to guide 

the development of this Resource Guide. 

Kirsten Aired 

Oregon Asthma Program 

Michelle Cloutier, MD 

Connecticut Children’s Medical Center 

Amy Friedman 

Allies Against Asthma 

Min Gayles. MPH 

National Committee for Quality Assurance 

Maureen George, PhD, RN 

American Thoracic Society member 

Foster Gesten, MD 

New York State Department of Health 

David Greenberg, MBA 

Centers for Medicare and Medicaid Services 

Jayne Jones, MPH 

Pennsylvania Health Care Cost Containment 

Council 

Carole Lannon, MD, MPH 

North Carolina Center for Children’s 

Healthcare Improvement 

Vi Naylor 

Georgia Hospital Association 

Asua Ofosu 

American Thoracic Society 

Ashok Patel, MD 

American Thoracic Society member 

Katherine Pruitt 

American Lung Association 

Stephen Redd, MD 

Centers for Disease Control and Prevention 

Joyce Reid 

Georgia Hospital Association Research & 

Education Foundation 

Diana Schmidt, MPH 

National Heart, Lung, and Blood Institute 

Sharon Sprenger, MPA, RHIA 

Joint Commission on Accreditation of 

Healthcare Organizations 

James Stout, MD, MPH 

University of Washington 

Carolyn Turner 

Florida Agency for Health Care 

Administration 

Mary Tyrell 

South Carolina State Budget and Control 

Board 

Kevin Weiss, MD, MPH, FACP 

Midwest Center for Health Services and 

Policy Research and Center for Healthcare 

Studies at Feinberg School of Medicine 
v

Executive Summary 

Health care in America is plagued by extensive gaps in quality. Too often care provided to 

patients does not match what the medical community has determined to be the most effective 

care. Abundant research has shown that these gaps in quality are responsible for increased costs, 

wasteful and ineffective care, preventable complications, avoidable hospitalizations, decreased 

quality of life, disability, and premature death. 

The National Healthcare Quality Report (NHQR) and National Healthcare Disparities Report 

(NHDR), published annually by the Agency for Healthcare Research and Quality since 2003, 

provide both extensive research and data on the extent of health care quality gaps as well as 

national benchmarks for quality. This Resource Guide and its accompanying Workbook draw on 

the NHQR and the NHDR to support State-level efforts to improve the quality of asthma care. 

This Resource Guide is designed to help State leaders identify measures of asthma care quality, 

assemble data on asthma care, assess areas of care most in need of improvement, and learn what 

other States have done to improve asthma care. Taken together, the Resource Guide and its 

companion Workbook can help State leaders to develop an asthma quality improvement action 

strategy. 

Why Asthma? 

Asthma is a chronic lung condition that impairs normal breathing. The disease affects a growing 

number of Americans. In 2003, nearly 30 million people had been diagnosed with asthma at 

some point in their lives and nearly 20 million people stated they currently had asthma (Centers 

for Disease Control and Prevention, 2002a). Asthma is also costly: total estimated costs in 2001 

were $14 billion (American Lung Association, 2004). 

For several reasons, asthma presents an opportune target for quality improvement: 

• Increased prevalence, especially among children and adolescents 

• Disparities between socioeconomic groups and between racial/ethnic groups in terms of 

diagnoses and quality of asthma care 

• A range of interventions and treatment that can successfully control the disease and prevent 

attacks 

• High health care costs of uncontrolled asthma and the potential for a positive return on 

investment for purchasers and the health care system as a whole through asthma quality 

improvement. 

Improved quality of asthma care may help to cut costs, reduce disparities, and improve the 

quality of life for millions of people with asthma. 

A State-Led Framework for Improving Asthma Quality of Care 

The Resource Guide introduces a framework for improving health care quality at the State level. 

States have typically viewed their role in quality improvement from a public health perspective 
vi 

or, more narrowly, as a buyer of health insurance for State employees. However, States can play 

a more comprehensive leadership role. Some States are already doing this, at least in part, with 

respect to asthma. 

This approach envisions three central roles for States in quality improvement: 

• Provide leadership, which entails providing a defining vision for change, setting goals, and 

providing an environment that fosters improvement. 

• Work in partnership, which involves creating a committed partnership of stakeholders 

dedicated to identifying, proposing, and testing solutions and developing plans for 

improvement. 

• Implement improvement, which means implementing changes, measuring and analyzing 

the results of changes, and applying successful improvements on a broader scale. 

Learning From Current State Quality Improvement Efforts 

Many States have already begun programs or demonstrations to improve the quality of asthma 

care. These actions can inform broader efforts within the State or the efforts of other States. 

This Resource Guide identifies a broad range of current asthma quality improvement activities, 

including public-private coalitions, cross-agency initiatives, data measurement and reporting 

projects, disease management training, and educational outreach programs for minority and rural 

populations. 

Measuring the Quality of Asthma Care 

Assessing State quality of care for asthma requires good data and useful measures. Useful 

quality measures include process measures, which reflect the quality of care delivered, and 

outcome measures, which reflect patient health status. The former can guide health care 

providers on how to change while the latter can gauge whether the changed processes have had 

the intended effect. The NHQR provides a starting point for accessing consensus-based 

measures. The NHQR provides estimates for asthma hospitalizations by State. In addition, this 

Resource Guide incorporates estimates from the Behavioral Risk Factor Surveillance System to 

assess asthma care quality by State. Although a consensus on a few key measures of asthma care 

quality has not yet evolved, an inventory of the many measures available is provided. 

Data are also essential to improve quality. States need performance data on asthma care to assess 

their own performance against national benchmarks and to focus quality improvement efforts by 

identifying potential problem areas. A list of national, State, and local sources for estimates for 

asthma, asthma care, and other related information is also included. 

Moving Ahead: Implications for State Action 

Identifying measures and data sources is only a first step. As part of a systematic initiative to 

improve the quality of asthma care, States will need to bundle these resources into a 

comprehensive, State-specific picture of asthma care that identifies areas for improvement and 

provides a basis for planning among the partners. This picture may require collecting specific 

data on asthma care that focus on a State’s health care systems. Doing so will enable States to 

identify specific quality problems in their own communities, tailor specific solutions, and assess 

the effectiveness of specific interventions 
vii

Contents

Page

Executive Summary.......................................................................................................................v 

Introduction ..................................................................................................................................1

Module 1: Making the Case for Asthma Care Quality Improvement.....................................5 

The Need for Asthma Care Quality Improvement..........................................................................5 

The Quality Improvement Opportunity .........................................................................................15 

Estimating the Costs of Asthma Care and Potential Savings From Quality Improvement ...........18 

Module 2: A Framework for State-Led Quality Improvement..............................................26

Quality Health Care and the Quality Improvement Movement.....................................................26 

A Strategic Role for States.............................................................................................................27 

Developing a Framework for State-Led Quality Improvement.....................................................27 

Information Resources for Quality Improvement..........................................................................35 

Module 3: Learning From Current State Quality Improvement Efforts..............................39

Current State Efforts To Improve the Quality of Asthma Care.....................................................39 

Module 4: Measuring Quality of Care for Asthma..................................................................49

Quality Measurement.....................................................................................................................49 

Multiple Dimensions of Quality for Asthma Care ........................................................................52 

Data Sources for Asthma Quality of Care ....................................................................................55 

Using Benchmarks To Develop State Performance Estimates......................................................59 

Factors That Affect Quality of Asthma Care ................................................................................66 

Module 5: Moving Ahead – Implications for State Action .....................................................74

References ................................................................................................................................77 

Appendixes: 

A. Acronyms Used in This Resource Guide..........................................................................83 

B. Medicaid Spending on Asthma by State ..........................................................................85 

C. National and State Asthma Programs ...............................................................................92 

D. Asthma Measures............................................................................................................104 

E. BRFSS Measures, Data, and Benchmarks......................................................................113 

F. Other Asthma-Related Data Sources ..............................................................................132 

G. Benchmarks From the NHQR.........................................................................................139 

H. Information on Statistical Significance...........................................................................141 
viii 

List of Text Tables and Figures 

Figures

1.1 Children and all ages: Twelve month asthma prevalence 1980-1996, lifetime diagnosis and 

12-month attack prevalence 1997-2003, and current prevalence 2001-2003 ........................8 

1.2 Asthma hospitalizations per 100,000 population, 2001 ......................................................14 

2.1 State-led quality improvement—Stage 1 ............................................................................32 

2.2 State-led quality improvement—Stages 1 and 2 .................................................................33 

2.3 Complete State-led quality improvement framework—Stages 1, 2, and 3 .........................34 

4.1 Six quality measures for asthma: National average, best-in-class average, and State 

 variation, by region, 2003 .....................................................................................................61 

4.2 Percent of adults with asthma with routine checkups, medications, urgent care visits, and 

emergency room visits, 2003: Maryland compared to benchmarks ....................................62 

4.3 Percent of adults with asthma with routine checkups, medications, urgent care visits, and 

emergency room visits, 2003: Michigan compared to benchmarks ....................................63 

4.4 Percent of adults with asthma with routine checkups, medications, urgent care visits, and 

emergency room visits, 2003: New Jersey compared to benchmarks .................................64 

4.5 Percent of adults with asthma with routine checkups, medications, urgent care visits, and 

emergency room visits, 2003: Vermont compared to benchmarks .....................................65 

4.6 Factors that affect disease process and outcome measures .................................................67 

Text tables

1.1 Lifetime asthma prevalence for adults (number of cases per 100 population), by State, 

2000-2003 ............................................................................................................................10 

1.2 Potential for improvement: Percent of asthma hospitalizations that would need to be 

 reduced to achieve best-in-class performance, by State and age group, 2001 ...................11 

1.3 Estimate of indirect, direct and total cost burden of asthma, by State, for 50 States, District 

of Columbia, and Puerto Rico, 2003 .....................................................................................20 

1.4 Medicaid eligible population and estimated asthma prevalence and expenditures for 

medical care for age groups 0-18, 19-64, and 65 and over, by State, 2003 ..........................21 

4.1 Dimensions of asthma care measurement..............................................................................53 

4.2 Six quality measures for asthma: National average, best-in-class average, and poorest 

performing average, 2003......................................................................................................56 

4.3 Asthma hospitalizations by race/ethnicity and community income, United States, 2001.....68 

 
1

Introduction 

Improving the quality of health care in America remains a widely shared national objective. The 

ultimate goal of quality improvement is to close the gap between current practice and best 

practice as defined by the medical community. Closing this gap can contribute to improved 

health care in a number of ways: reduced costs, more efficient care delivery, fewer 

complications, and better quality of life for patients. 

The National Healthcare Quality Report (NHQR) and National Healthcare Disparities Report 

(NHDR) published annually by the Agency for Healthcare Research and Quality (AHRQ) 

provide extensive research and data on the extent of health care quality gaps as well as national 

benchmarks for quality. This Resource Guide draws on the NHQR and the NHDR to support 

State-level efforts to improve the quality of care for asthma. It is the second Resource Guide and 

Workbook published by AHRQ; the first Resource Guide and Workbook addressed diabetes 

quality of care. This Resource Guide combines the data assembled for the NHQR and other 

sources with a variety of background, analysis, and policy information on asthma. 

Why Should States Make Asthma a Priority? 

Asthma is a chronic condition that affects the lungs and is characterized by episodes of 

wheezing, breathlessness, chest tightness, and coughing. During an asthma attack, the airways 

that carry oxygen to the lungs become inflamed and swollen; the muscles surrounding the 

airways tighten; and mucus collects, making it harder to push air in and out of the lungs. These 

episodes are usually the result of exposure to asthma “triggers.” These include infections such as 

colds and bronchitis; irritants such as second-hand tobacco smoke, dust mites, air pollution, and 

cockroach debris; other allergens such as furry pets and mold; and other triggers such as stress, 

exercise, and abrupt changes in the weather. 

The prevalence of asthma among Americans has nearly doubled in the past two decades. In 

2003, nearly 30 million people had been diagnosed with asthma at some point in their lives and 

nearly 20 million people stated they currently had asthma (Centers for Disease Control and 

Prevention [CDC], 2002a). Asthma is also a costly disease: the estimated cost of asthma was 

$14 billion in 2001. The $14 billion is composed of direct costs—estimated at $9.4 billion from 

physician visits, hospital stays, and medications—and indirect costs—estimated at $4.6 billion 

from lost work days, school absenteeism, and lost earnings (American Lung Association [ALA], 

2004). 

For several years, asthma has been a target for quality improvement efforts by States and other 

health care entities because of the following: 

• Increased prevalence of asthma, especially among children and adolescents. 

• Disparities between socioeconomic groups and between racial/ethnic groups in terms of 

diagnoses and quality of asthma care. 

• A range of interventions and treatments that can successfully manage the disease and 

prevent attacks. 
2 

• High health care cost of uncontrolled asthma and the potential for a positive return on 

investment for purchasers and the health care system as a whole through asthma quality 

improvement. 

Data from the NHQR and NHDR demonstrate that there are wide variations in quality of care for 

asthma across States and across different socioeconomic strata and racial and ethnic groups. 

Why and How To Use This Resource Guide 

State leaders can play a central role in leading asthma care quality improvement. This Resource

Guide is designed to equip them with information resources and a model for taking action. 

Purpose of the Resource Guide

The purpose of this Resource Guide and companion Workbook is to assist State policymakers 

and others in planning and implementing a State-level quality improvement initiative for 

improving asthma care. 

Specifically, the Resource Guide: 

• Describes the need for improvement in quality of care for asthma and the potential for returns 

on State investments. 

• Offers a model for how State leaders can lead efforts to improve asthma care quality, along 

with examples of State-level activities underway. 

• Presents examples of current State-led efforts to improve asthma care. 

• Presents the multiple dimensions within which health care quality for asthma can be 

measured, examines metrics for assessing State performance, and provides data from the 

NHQR and other data sources on asthma to help inform State decisionmaking. 

Audiences for This Resource Guide

Quality health care is delivered by providers in clinical settings. Thus, quality improvement 

ultimately needs to influence what happens in a doctor’s office, hospital, or clinic. Even so, 

State leaders and policymakers can have an enormous impact on health care: 

• They can articulate a vision that inspires action and change. 

• They can involve strategic partners and champions who can reach the front lines of health 

care. 

• They can assemble information that focuses the attention of health care providers at the local 

level, just as the NHQR does at the national and State levels. 

• They can enable health care improvement strategies to be tailored more skillfully for State 

and local health care markets. 

As purchasers and regulators, States can supply incentives for providers to make the changes 

necessary to improve the quality of health care. Thus, the main audiences for this Resource 

Guide include: 
3

• State elected leaders — Governors and legislators (and their staffs) who provide leadership 

on health policy. 

• State executive branch officials — Executive office appointees and career staff charged 

with taking action on important health issues, such as State health department and State 

Medicaid officials. 

• Nongovernmental State and local health care leaders — Members of professional 

societies, provider associations, quality improvement organizations, voluntary health 

organizations, health plans, hospital associations, business coalitions, community 

organizations, consumer groups, and others who want to stimulate action on health care 

quality improvement at the State level. 

Organization of This Resource Guide 

This Resource Guide is divided into five modules. To assist readers in finding the information 

they need, the beginning of each module previews the contents and highlights key ideas. Each 

module ends with a summary and synthesis to demonstrate how to use the module and how to 

move to the next step. Also, a resource list for further reading and a discussion of associated 

appendixes are included where applicable. 

State leaders in different parts of State government have different roles in quality improvement. 

This Resource Guide is addressed to State leaders, who have key contributions to make to the 

quality improvement process. Users can skip to the sections that are most relevant and 

appropriate for them. 

The modules are organized as follows: 

• Module 1: Making the Case for Asthma Care Quality Improvement describes both the 

need and opportunity for quality improvement in asthma care. The module answers the 

following questions: What is asthma? What are current trends in the prevalence of asthma 

and the cost burden for people with asthma? What opportunities exist for improving care and 

outcomes for people with asthma and reducing the cost of asthma care? 

• Module 2: A Framework for State-Led Quality Improvement presents an operational 

approach for leaders to use in their quality improvement efforts. Synthesized from existing 

models of health care quality improvement, the framework outlines a leadership role for 

States in setting goals for improvement, convening partners, designing interventions, and 

assessing their impact through careful measurement and data analysis. 

• Module 3: Learning From Curreng State Quality Improvement Efforts examines current 

State efforts to improve the quality of care for asthma. This module summarizes various 

approaches to asthma quality improvement as they relate to championing quality, creating 

partnerships, planning for change, implementing the vision, evaluating effectiveness, and 

spreading success. It also highlights State activities underway at each stage of quality 

improvement. 
4 

• Module 4: Measuring Quality of Care for Asthma examines measures and data issues that 

affect asthma care quality and improvement. This module describes current measurement 

issues and current metrics for assessing asthma care quality and examines a variety of data 

sources that State leaders can use to assess the quality of care in their States. It provides 

specific benchmarks of process and outcome measures from the NHQR and the Behavioral 

Risk Factor Surveillance System (BRFSS) on asthma care. An analysis using BRFSS data 

from four States—Maryland, Michigan, New Jersey, and Vermont—presents concrete 

examples of how one can draw conclusions from the data that can spur local action. Finally, 

the module shows how to derive estimates from available data to fill data gaps for particular 

States. These include examples for estimating the direct and indirect costs of asthma, 

Medicaid spending for each State, and cost effectiveness of an asthma intervention for 

Medicaid primary care case management programs. 

• Module 5: Moving Ahead—Implications for State Action describes how State leaders can 

initiate a public policy-focused quality improvement effort for asthma care. This module 

describes specific steps that States can take in each of the three basic areas of activity: lead, 

partner, and improve. 

Supplementary information on data sources and other resources for State leaders as they address 

asthma care quality improvement are provided in the appendixes. 

A complementary Workbook mirrors the five modules presented in this Resource Guide and 

provides a set of exercises and more detailed instructions on how State leaders can find and 

develop their own State data for asthma care quality improvement. Overall, this Resource Guide 

and its companion Workbook are designed to be a complete manual for State leaders at all levels 

interested in improving the quality of care for asthma in their States.
5

Module 1: Making the Case for Asthma Care Quality 

Improvement 

Asthma is a serious chronic respiratory illness that affects a growing number of Americans. 

According to the National Health Interview Survey (NHIS), conducted by the National Center 

for Health Statistics (NCHS), 20.3 million Americans had asthma in 2001, a substantial increase 

over the prior two decades (CDC, 2002a). It is also costly disease that can seriously impair 

normal functioning, and it erodes the quality of life for those who have it, as well as their 

caregivers (CDC, 2002a). 

The Need for Asthma Care Quality Improvement 

Many factors suggest that efforts to improve the quality of asthma care are warranted: 

• Increased prevalence of asthma, especially among children and adolescents. 

• The high health care cost of uncontrolled asthma. 

• The disparities among various socioeconomic, racial, and ethnic groups in how carefully 

they are diagnosed and treated. 

• Variation in interventions and treatment that can successfully manage the disease and 

prevent attacks. 

These points are discussed in more detail in the following sections. 

Increased Prevalence 

Cases of asthma have increased dramatically in recent decades. The growth of asthma cases in 

the United States has been labeled an “epidemic” (RAND, 2002). Information gathered by the 

CDC from 1980 to 1996 shows that the number of Americans with self-reported asthma more 

than doubled during that time, from almost 7 million to over 14 million (CDC, 2002b).1

 

1

 Changes in survey design over time make it impossible to compare current data with data collected before 1996. 

Key Ideas in Module 1: 

• The number of Americans diagnosed with asthma has grown dramatically in recent years, 

especially among children and adolescents. 

• The cost burden of uncontrolled asthma can be substantial. 

• Asthma disproportionately affects African Americans, children, and low-income individuals. 

• Quality of care received by people with asthma can vary widely across States and population 

groups. 

• Interventions and treatment can successfully control the disease and prevent attacks. 

• There is potential for return on investment for purchasers and the health care system as a whole 

through asthma quality improvement. 
6 

Especially troubling are the rates of increase among children: over that 16-year period, asthma 

prevalence among children under age 5 increased 115 percent. For children between 5 and 14, 

prevalence increased 81 percent (CDC, 2002b). Figure 1.1 shows the rising trend for children 0-

17 and the same for all ages, until 1996. The CDC surveillance survey questions changed in 1997 

and began to track asthma attacks in the past 12 months. This modification should reflect more 

closely changes in the quality of care and self-management practices of people with chronic 

asthma, especially when compared with the number of people who say they currently have 

asthma, a statistic which has been collected since 2001. Table 1.1 shows the increase in lifetime 

asthma prevalence by State between 2000 and 2003. Even in that short period, asthma prevalence 

increased fairly steadily for nearly all States. 

What Is Asthma and How Is It Treated? 

Asthma is a chronic inflammatory disorder of the airways. Such inflammation can cause recurring 

episodes of wheezing, breathlessness, chest tightness, and cough, particularly at night and in the early 

morning. During an asthma attack, the airways that carry oxygen to the lungs become inflamed and 

swollen, the muscles surrounding the airways tighten, and mucus collects, making it harder to push air 

out of the lungs. Although asthma triggers are not the cause of asthma itself, they may exacerbate an 

asthma attack. The most common triggers of asthma attacks are respiratory infections, especially 

colds. Other triggers include various irritants such as second-hand tobacco smoke, dust mites, air 

pollution, cockroaches, furry pets, mold, stress, exercise, and changes in the weather. 

Treatment. The goal of asthma treatment is to reduce underlying inflammation and decrease the 

daily symptom burden by preventing asthma attacks from recurring. High quality asthma care 

minimizes the need for emergency care or hospitalization. There are several components of high 

quality asthma care recommended by the Clinical Guidelines of the National Asthma Education and 

Prevention Program of the National Heart, Lung, and Blood Institute (NHLBI): 

• Component 1: Measures of Assessment and Monitoring. Initial assessment and diagnosis 

of asthma is extremely important to determine appropriate treatment based on the patient’s 

level of asthma severity. 

• Component 2: Control Factors Contributing to Asthma Severity. Controlling asthma 

triggers and reducing exposure to environmental allergens and irritants help limit asthma 

severity. Thus, treatment and prevention of co-occurring respiratory and other conditions 

(such as rhinitis, sinusitis, chronic obstructive pulmonary disease, and gastroesophageal reflux 

disease) should be considered. 

• Component 3: Pharmacologic Therapy. Medications should be prescribed according to the 

severity of the patient’s asthma, and medication use should be monitored. Two classes of 

drugs are involved: long-term drugs (inhaled corticosteroids [ICS]) to control the 

inflammatory process of persistent asthma and quick-relief medications (beta-agonists) to treat 

symptoms and attacks. The objective is to maintain control with ICS and to avoid attacks and 

the need for emergency treatment. 

• Component 4: Education for a Partnership in Asthma Care. Patients and their families 

play an important role in their asthma care. They need to understand how to monitor their 

symptoms, what to do during an asthma attack, and how to use their medications 

appropriately. People with asthma must learn to “manage” their condition so as to avoid 

triggers and anticipate problems. 

Source: National Heart, Lung, and Blood Institute, 1997.
7

How Is Asthma Diagnosed and Severity Assessed? 

Diagnosing asthma and assessing asthma severity are important first steps to quality asthma care. 

Diagnosing asthma can be difficult and, as a result, it may at times be mislabeled as other problems. 

Below are steps recommended by NHLBI Clinical Guidelines to diagnose asthma and classify its 

severity. 

Methods for diagnosing asthma. The first step in providing quality asthma care is to make a correct 

diagnosis. Clinical judgment is required because signs and symptoms vary widely from patient to 

patient as well as within each patient over time. To establish the diagnosis of asthma, the clinician 

must determine that: 1) episodic symptoms of airflow obstruction are present; 2) airflow obstruction is 

at least partially reversible; and 3) alternative diagnoses are excluded. No one test or set of tests is 

appropriate for every patient. Usually, a detailed medical history, a physical exam focusing on the 

upper respiratory tract, chest, and skin; and spirometry to demonstrate reversibility of airflow 

obstruction will enable a clinician to see a pattern of symptoms and history of recurrent episodes and 

rule out other conditions. Additional tests may be done to evaluate alternative diagnoses, identify 

triggers, assess severity, and investigate potential complications. 

Classifying asthma severity. At the initial visit, the physician should assign the patient to a severity 

grade to help guide medication decisions. The severity classifications are based on the frequency of 

the patient's symptoms and his or her lung function measurements. The characteristics noted in the 

chart below are general and may overlap because asthma is highly variable. In addition, the patient's 

severity classification may change over time. The severity of the patient's asthma should be rechecked 

at every visit. Severity is currently divided into four levels, as shown in the following table: 

Classification 

of asthma 

severity 

Days with 

symptoms 

Nights with 

symptoms 

FEV1* or PEF* 

percentage 

predicted normal 

PEF variability 

between morning 

and night test 

Severe 

persistent Continual Frequent <60% >30% 

Moderate 

persistent Daily >1 night per week 60%-80% >30% 

Mild persistent >2 days per week 

but <1 time per day 

>2 nights 

per month >80% 20%-30% 

Mild 

intermittent <2 days per week <2 nights 

per month >80 % <20% 

*For adults and children over 5 years who can use a spirometer or peak flow meter, the percentage predicted 

values for forced expiratory volume in 1 second (FEV1) and percentage of personal best for peak expiratory 

flow (PEF) (NHLBI, 2003). 

Barriers to diagnosis and severity assessment. Improving asthma care quality requires 

understanding how asthma is diagnosed and assessed. Asthma care depends on initial assessments and 

monitoring to determine appropriate care. Patients or their caregivers must be able to give detailed 

descriptions of frequency and severity of symptoms which are sometimes difficult to recognize. Also, 

diagnosing asthma in children is difficult because diagnosis may be unclear until recurrence of signs 

and symptoms is established (NHLBI, 2003). Thus, some patients who actually have asthma may be 

assessed as having other conditions and may remain untreated until diagnosed accurately. Access to 

quality lung function testing is often unavailable. These barriers must be addressed to improve asthma 

care quality. 
8 

Figure 1.1. Children (top) and all ages (bottom): Twelve-month asthma prevalence 1980-1996, 

lifetime diagnosis and 12-month attack prevalence 1997-2003, and current prevalence 2001-2003 

Source: Centers for Disease Control and Prevention, National Center for Health Statistics, National Health 

Interview Survey. 

Note: Twelve-month asthma prevalence for all ages was collected from 1982 to 1996, a shorter period than for 

children only. 

Asthma prevalence among children rose markedly between 1980 and 1996, while 

their asthma attacks remained relatively constant since measured in 1997

0

20

40

60

80

100

120

140

1980 1985 1990 1995 2000

Prevalence per 1,000 children

12-month prevalence (“During the 

past 12 months did anyone in the 

family have asthma?”)

Lifetime diagnosis (“Has a 

doctor or other health 

professional ever told you that 

you have asthma?”)

12-month attack prevalence

(First question above, plus: 

“During last 12 months have 

you had an episode of asthma 

or asthma attack?”)

Current prevalence (Above 

plus: “Do you still have 

asthma?”)

Asthma prevalence among children rose markedly between 1980 and 1996, while 

their asthma attacks remained relatively constant since measured in 1997

0

20

40

60

80

100

120

140

1980 1985 1990 1995 2000

Prevalence per 1,000 children

12-month prevalence (“During the 

past 12 months did anyone in the 

family have asthma?”)

Lifetime diagnosis (“Has a 

doctor or other health 

professional ever told you that 

you have asthma?”)

12-month attack prevalence

(First question above, plus: 

“During last 12 months have 

you had an episode of asthma 

or asthma attack?”)

Current prevalence (Above 

plus: “Do you still have 

asthma?”)

0

20

40

60

80

100

120

140

1982 1986 1990 1994 1998 2002

Prevalence per 1,000 people

All Ages: Asthma prevalence rose steadily between 1980 and 1996, while asthma 

attacks remained stable since measured in 1997

12-month prevalence (“During the 

past 12 months did anyone in the 

family have asthma?”)

12-month attack prevalence

(First question above, plus: 

“During last 12 months have you 

had an episode of asthma or 

asthma attack?”)

Current prevalence (Above 

question plus: “Do you still 

have asthma?”)

Lifetime diagnosis (“Has a 

doctor or other health 

professional ever told you that 

you have asthma?”)

0

20

40

60

80

100

120

140

1982 1986 1990 1994 1998 2002

Prevalence per 1,000 people

All Ages: Asthma prevalence rose steadily between 1980 and 1996, while asthma 

attacks remained stable since measured in 1997

12-month prevalence (“During the 

past 12 months did anyone in the 

family have asthma?”)

12-month attack prevalence

(First question above, plus: 

“During last 12 months have you 

had an episode of asthma or 

asthma attack?”)

Current prevalence (Above 

question plus: “Do you still 

have asthma?”)

Lifetime diagnosis (“Has a 

doctor or other health 

professional ever told you that 

you have asthma?”)
9

What is causing this upsurge in asthma cases? Because doctors are still unsure why some people 

develop asthma while others do not, further research is needed to identify the exact causes of 

asthma. Such research is underway at the Environmental Protection Agency, National Institutes 

of Health, Centers for Disease Control and Prevention, and elsewhere. Risk factors—including 

genetic predisposition and early exposure to irritants—may contribute, but are certainly not the 

only reasons for the increase. Even without pinpointing the cause, however, efforts to improve 

the quality of care for asthma can help control the severity of the condition. 

High Cost 

Uncontrolled asthma is costly to treat. In the most recent economic analysis of asthma 

commissioned by the American Lung Association, the estimated annual cost of asthma in 2004 

was $16.1 billion. This analysis evaluated both direct costs including physician visits, hospital 

stays, and medications, as well as indirect costs such as lost work days, school absenteeism, and 

lost earnings ($11.5 billion direct and $4.6 billion indirect, respectively). Included in the 2004 

estimate (ALA, 2005) were: 

• 484,000 hospitalizations. 

• 1.2 million hospital outpatient department visits. 

• 1.9 million emergency room visits. 

• 12.7 million doctor office visits. 

• $1.5 million in lost school days. 

• $1.4 million in loss of work. 

The most expensive direct cost was hospitalizations ($3.6 billion) and the most expensive 

indirect cost was lost school days (almost $1.5 billion [ALA, 2005]). Although the per-person 

cost of asthma is not the highest among chronic diseases, asthma and chronic obstructive 

pulmonary disease together represent the fifth most costly disease for the population at $45 

billion annually, or nearly 3 percent of all health care spending (Medical Expenditure Panel 

Survey [MEPS], 2002). 

Much of this economic burden falls on people with asthma and their caregivers – one study 

found that the average family in the United States spends between 5.5 percent and 14.5 percent 

of its total income on treating an asthmatic child (HHS, 2003). In addition, payers also pick up a 

significant amount of the cost. A study published in February 2002 found that the cost to 

employers of treating someone with asthma was twice that of treating someone without 

asthma—$5,385 vs. $2,121 (HHS, 2003). Another study (Brodsky, 2002) found that families 

spend 21⁄2 times more on children with asthma than on children without asthma—$618.42 vs. 

$248.67 (in 1996 dollars, inflated to 2003 dollars). 

As a payer through State Medicaid and State employee health care programs, States have a 

financial stake in encouraging providers to provide high quality care to plan participants with 

asthma. Prevention of even a small number of hospitalizations through better management of the 

disease could affect expenditures significantly. 

Children are more likely to be hospitalized for asthma than adults (189 per 100,000 children vs. 

113 per 100,000 adults ages 18-64 (see Table 1.2). According to another study, asthma 
10 

admissions accounted for 7.4 percent of all hospital admissions for children and adolescents in 

2000 (Owens et al., 2003). 

Table 1.1. Lifetime asthma prevalence for adults (number of cases per 100 population), by State, 

2000-2003 

State 2000 2001 2002 2003

Nationwide 10.5 11.2 11.8 11.7

Alabama 9.1 9.7 11.0 11.6

Alaska 11.3 11.5 11.6 13.3

Arizona 11.1 12.4 13.9 12.5

Arkansas 9.9 10.6 12.1 11.3

California 11.5 12.4 12.7 13.4

Colorado 9.5 12.1 12.1 12.4

Connecticut 10.8 12.3 13.2 12.2

Delaware 10.4 12.0 11.8 11.7

District of Columbia 11.0 12.0 14.2 12.7

Florida 9.1 9.9 10.5 10.1

Georgia 9.6 11.0 11.7 11.8

Guam -- 7.5 12.0 10.3

Hawaii 11.4 12.2 13.4 11.6

Idaho 10.8 11.7 11.8 11.7

Illinois 10.5 11.3 10.7 11.1

Indiana 11.2 11.3 11.3 12.0

Iowa 8.5 9.7 9.0 10.3

Kansas 10.9 11.7 11.2 11.5

Kentucky 10.7 10.9 12.8 12.6

Louisiana 8.0 9.1 10.4 10.2

Maine 12.5 12.6 13.6 13.4

Maryland 10.6 11.1 12.7 12.3

Massachusetts 11.9 13.1 12.9 14.4

Michigan 10.3 12.4 12.8 13.6

Minnesota 9.5 10.1 11.3 10.5

Mississippi 9.8 9.2 10.6 10.9

Missouri 10.6 12.0 12.5 11.9

Montana 11.4 11.8 14.5 11.1

Nebraska 8.7 8.4 10.6 10.3

Nevada 13.4 13.3 12.4 11.4

New Hampshire 12.0 12.5 13.9 12.9

New Jersey 8.7 9.4 11.8 10.9

New Mexico 10.0 10.8 11.7 10.5

New York 10.7 11.1 11.5 11.7

North Carolina 10.1 10.1 10.9 11.3

North Dakota 9.2 9.1 10.3 10.1

Ohio 10.9 9.8 10.3 10.8

Oklahoma 9.2 10.1 11.2 11.8

Oregon 12.1 13.0 14.0 14.7

Pennsylvania 9.3 10.7 11.5 11.9

Puerto Rico 15.9 19.6 19.6 20.6

Rhode Island 11.7 12.1 12.8 14.4

South Carolina 10.4 10.8 10.0 10.1

South Dakota 8.0 7.7 8.6 10.7

Tennessee 10.4 9.3 12.2 11.8

Texas 10.5 9.6 11.6 11.3

Utah 10.3 10.7 12.3 11.3

Vermont 9.7 12.1 12.7 12.2

Virginia 10.5 11.4 12.1 12.1

Virgin Islands -- 9.2 9.4 9.2

Washington 11.9 12.0 14.3 13.8

West Virginia 11.7 12.5 12.8 11.8

Wisconsin 10.6 10.9 11.7 11.0

Wyoming 11.8 11.6 11.1 11.2

Source: Centers for Disease Control and Prevention, Behavioral Risk

Factor Surveillance System, Prevalence Data, 2000-2003. 

http://apps.nccd.cdc.gov/brfss/index.asp
11

Table 1.2. Potential for improvement: Percent of asthma hospitalizations that would need to be 

reduced to achieve best-in-class performance, by State and age group, 2001 

State Adjusted rate

Percent to be 

reduced to 

achieve 

best-in-class Adjusted rate

Percent to be 

reduced to 

achieve 

best-in-class Adjusted rate

Percent to be 

reduced to 

achieve 

best-in-class

Total U.S. 188.601 -- 112.842 -- 170.640 --

Best in class1 72.300 -- 60.236 -- 118.238 --

Arizona 114.738 37.0% 83.521 27.9% 133.953 11.7%

California 149.063 51.5 84.342 28.6 156.833 24.6

Colorado 159.413 54.6 72.479 16.9 128.170 7.7

Connecticut 176.096 58.9 98.236 38.7 127.568 7.3

Florida 242.276 70.2 113.580 47.0 157.601 25.0

Georgia 176.636 59.1 104.199 42.2 170.351 30.6

Hawaii 125.625 42.4 108.158 44.3 215.131 45.0

Illinois 187.391 61.4 150.377 59.9 212.426 44.3

Iowa 106.256 32.0 87.880 31.5 119.272 0.9

Kansas 159.981 54.8 97.570 38.3 131.663 10.2

Kentucky 279.351 74.1 135.524 55.6 173.842 32.0

Maine 106.210 31.9 81.981 26.5 124.889 5.3

Maryland 215.772 66.5 106.566 43.5 158.142 25.2

Massachusetts 169.959 57.5 112.798 46.6 164.245 28.0

Michigan 221.439 67.3 121.201 50.3 155.207 23.8

Minnesota 129.228 44.1 89.547 32.7 151.976 22.2

Missouri 220.948 67.3 104.117 42.1 119.085 0.7

Nebraska 88.752 18.5 70.099 14.1 139.944 15.5

New Jersey 266.117 72.8 126.858 52.5 165.702 28.6

New York 315.306 77.1 162.367 62.9 229.554 48.5

North Carolina 188.597 61.7 111.983 46.2 179.210 34.0

Oregon 66.304 -9.0 61.118 1.4 117.304 -0.8

Pennsylvania 268.755 73.1 136.292 55.8 196.169 39.7

Rhode Island 195.887 63.1 107.551 44.0 159.814 26.0

South Carolina 274.802 73.7 123.468 51.2 183.610 35.6

Tennessee 199.400 63.7 109.064 44.8 168.632 29.9

Texas 192.289 62.4 96.236 37.4 179.766 34.2

Utah 72.123 -0.2 53.298 -13.0 118.301 0.1

Vermont 81.211 11.0 61.625 2.3 123.848 4.5

Virginia 223.643 67.7 109.548 45.0 181.404 34.8

Washington 134.869 46.4 70.923 15.1 123.240 4.1

West Virginia 215.682 66.5 122.903 51.0 187.267 36.9

Wisconsin 120.575 40.0 89.716 32.9 132.113 10.5

Bold estimates are for States in, or within the range of, the best-in-class average (lowest rate of hospitalizations for asthma). Those 

within the range have a p-value greater than 0.05, meaning that the State rate is statistically no different from the average of the 

 three (10 percent) best performing States.

1

Best in class rate is calculated from the weighted average of the lowest 10 percent of States' hospitalization rates. 

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (AHRQ, 2004b).

Hospital admissions for pediatric 

asthma per 100,000 population 

under age 18 

Hospital admissions for adult 

asthma per 100,000 population 

ages 18-64 

Hospital admissions for adult 

asthma per 100,000 population 

ages 65+
12 

Racial, Ethnic, and Income Disparities 

Asthma does not affect all groups equally. Asthma is more prevalent among minorities and low 

income persons, and asthma attack rates and mortality are higher among Blacks compared with 

Whites (AHRQ, 2003a). In addition, Black children in the United States are almost 31⁄2 times as 

likely to be admitted to a hospital for asthma as White children (AHRQ, 2004a, Table 76a). 

Black adults age 18 to 64 are three times as likely to be hospitalized as White adults for asthma 

(AHRQ, 2004a, Table 77a). 

A 2002 National Health Interview Survey (CDC, 2004) showed that: 

• Current asthma prevalence is 80 percent higher for Puerto Ricans compared with nonHispanic Whites. Non-Hispanic Blacks and American Indians had 30 percent higher 

current asthma prevalence compared to non-Hispanic Whites. 

• In 2002, Puerto Ricans also had the highest rate of asthma attacks in the previous year, 

100 percent higher than non-Hispanic Whites. Blacks had an asthma attack rate about 30 

percent higher than non-Hispanic Whites. American Indians had about a 10 percent 

higher rate than non-Hispanic Whites. 

• Blacks had an asthma hospitalization rate 225 percent higher than Whites. 

Blacks were most likely to die from asthma and had an asthma death rate over 200 percent 

higher than Whites. Blacks also had a 160 percent higher asthma death rate than Hispanics. 

There are also significant racial/ethnic disparities among children in asthma status and selfmanagement practices. A study by Lieu et al. (2002) showed that Black and Hispanic children 

have more severe asthma based on number of symptom days, missed school days, and health 

status scores than White children with similar insurance and socioeconomic status. Black and 

Hispanic children were also less likely than White children to be using daily inhaled antiinflammatory medications (28 percent and 22 percent, respectively, compared with 33 percent). 

Income also plays a role. Children in poor families are more likely than other children to have 

been diagnosed with asthma (16 vs. 11 percent). And, although not all single-parent families are 

low income, children in single-mother families are more likely to have asthma (17 percent) than 

children from two-parent families (11 percent) or than children from single-father families (10 

percent) (CDC, 2002c). 

Another study looking into indoor and outdoor allergies among children with asthma found that 

Puerto Rican and Black children were at greater risk for multiple allergies. The study found that 

Puerto Rican children with asthma are up to three times more likely to be allergic to indoor and 

outdoor allergens than White children with asthma. The study also found that Black children 

with asthma are two to three times more likely to have allergic reactions to outdoor allergens 

(Celedón et al., 2004). 

Intervention and Treatment Variation 

Clinical guidelines for care—including developing an asthma management plan with physicians, 

eliminating or decreasing exposure to triggers, and proper use of medications—offer people with 
13

asthma a way of minimizing its effects on daily living, avoiding hospitalizations, and reducing 

trips to the emergency room. Data gathered in national surveys, however, show that many 

people do not have control of their asthma: 

• The 2004 NHQR reported that, according to national estimates from the National 

Committee for Quality Assurance Health Plan Employer Data and Information Set 

(HEDIS®), nearly a third of children and adults suffering from persistent asthma are not 

receiving inhaled corticosteroids to control their asthma (AHRQ, 2004b). 

• The Medical Expenditure Panel Survey determined that only one-third of respondents 

with asthma in 2002 used a peak flow meter recommended at that time to self-monitor 

the severity of their asthma (MEPS, 2002). 

• Despite the fact that most asthma deaths are preventable if care is received in time, 4,487 

deaths were attributed to asthma in 2000 (CDC, 2002a). 

There is also considerable variation from State to State in the care received by people with 

asthma. The following chart and table show two of the asthma measures that are available 

nationwide—hospitalizations for asthma and use of inhaled corticosteroids—with data for States 

grouped by region to allow for regional comparisons.2

 Comparisons can also be made across all 

States to the national average and the best-in-class average (the 10 percent of States with the best 

value). The percentage of people receiving specific, recommended services and the percentage 

difference between the lowest and the highest performing State vary by service.3 

The use of the most expensive service—inpatient care—varies three to five times across the 

States and shows variation within each region, especially for children (Figure 1.2 and Table 1.2). 

For every 100,000 State adult residents age 18 to 64, from 53 to 162 people will be admitted to 

the hospital with asthma. For every 100,000 State child residents, from 66 to 315 children will 

be admitted (HCUP, 2001). Little of the variation in hospitalizations is likely to be due to 

differences in asthma prevalence across States (see Table 1.1). Asthma prevalence rates only 

ranged from 10.1 to 14.7 percent across the States represented in the HCUP data. Thus, the top 

State in terms of prevalence has 45 percent more residents with asthma than the bottom State. 

Contrast that with the top State in terms of pediatric hospitalizations, which has 375 percent 

more children admitted to the hospital during a year than the State with the lowest hospitalization 

rate for children. 

 

2

 U.S. Census regions are: Northeast=CT, ME, MA, NH, RI, VT, NJ, NY, PA); Midwest=IN, IL, MI, OH, WI, IA, 

KS, MN, MO, NE, ND, SD; South=DE, DC, FL, GA, MD, NC, SC, VA, WV, AL, KY, MS, TN, AR, LA, OK, TX; 

West=AZ, CO, ID, NM, MT, UT, NV, WY, AK, CA, HI, OR, WA. 

3

 For example, BRFSS data for 2003 show that receipt of flu shots among adults with asthma varied by State from 

32 percent to 56 percent, a difference of 24 percentage points, while the proportion of adults who had an emergency 

room visit for asthma ranged from 13 percent to 27 percent, a difference of 12 percentage points. Regional and State 

variation is discussed further in Module 4: Measuring Quality of Care for Asthma. 
14 

Figure 1.2. Asthma hospitalizations per 100,000 population, 2001 

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, 2001. 

Use of inhaled corticosteroids by people with persistent asthma—measured from health plan 

claims across regions—varied from 2001 to 2003 as shown below: 

Use of inhaled corticosteroid medications by people with asthma, by U.S. Census region

 2003 2001 

Region 

No. of 

plans 

Mean 

%

Standard 

error 

No. of 

plans

Mean 

%

Standard 

error 

 

National average 408 69.7 16.6 417 65.0 14.6 

Best-in-class average 190 72.1 

Northeast 89 73.1 17.6 91 66.9 23.8 

South 101 71.2 24.9 128 63.9 20.1 

Midwest 109 70.6 32.2 119 68.4 17.9 

West 103 66.3 33.4 85 63.1 40.3 

None reported 6 43.0 208.9 4 36.8 210.5 

 

Note: All means are weighted by the eligible populations of the plans. 

Source: National Committee for Quality Assurance, HEDIS data from The State of Healthcare Quality, 2004. 
15

This variation suggests possible bias in terms of which plans report fully or which are regional 

versus national plans. Because of the large difference between reporting and non-reporting plans, 

full reporting might change the above regional estimates significantly. These regional HEDIS 

averages compare with the national average of 69.7 percent from BRFSS and the best-in-class 

State average of 72.1 percent for use of this important type of medication. 

Regardless of data sources (State-run surveys, claims for payment, or hospital discharges) and 

regardless of differences in asthma prevalence, there is considerable variation in asthma care. 

These figures illustrate this variation across States and regions for asthma measures. This 

variation suggests room for improvement for many States. The States with the best rates on the 

asthma measures—the best-in-class States—provide examples of quality performance that is 

achievable. However, even the best results may leave room for improvement. 

Implications for State Policy 

Disparities in the prevalence and management of asthma and in quality of care have important 

implications for States and the public sector more generally. Care for low income individuals 

who are hospitalized is often financed by public sources such as Medicaid and uncompensated 

care funds. Ensuring effective care can help people with asthma remain healthy and productive, 

prevent attacks, and reduce health care costs. 

These differences are important for two reasons as States undertake asthma quality improvement 

initiatives. First, the racial, ethnic, and socioeconomic makeup of a given State influences the 

prevalence of asthma in the State. Second, improvement in quality of care may require targeted 

efforts to minority and low income groups in order to be successful. 

The Quality Improvement Opportunity 

Despite this gloomy picture of asthma’s care quality and cost burdens, significant opportunities 

for improvement exist. There is potential for high returns on investment made by purchasers and 

the health care system as a whole through asthma care quality improvement. 

Availability of Asthma Management Guidelines 

Great strides in the care and treatment of people with asthma have occurred over the last 15 

years. Although there is no cure for asthma, the disease can be managed and the severity and 

frequency of asthma attacks can be controlled through appropriate monitoring, effective use of 

medications, and eliminating or decreasing exposure to triggers. 

In 1997, Guidelines for the Diagnosis and Management of Asthma was published by the National 

Asthma Education and Prevention Program (NAEPP), coordinated by NHLBI. These Guidelines

(updated in 2002) represent a science-based strategy for the diagnosis and management of 

asthma and ask patients, families, and providers to work together to control the condition. In 

addition the NAEPP has published Key Clinical Activities for Quality Asthma Care: 

Recommendations of the National Asthma Education and Prevention Program, which identifies 

four components of care and recommends a core set of 10 key clinical activities for ensuring 

quality asthma care, as follows: 
16 

Components of asthma care Key associated clinical activities 

1. Establish asthma diagnosis. 

2. Classify severity of asthma. 

3. Schedule routine followup care. Assessment and monitoring 

4. Assess for referral to specialty care. 

Control of factors contributing to asthma 5. Recommend measures to control asthma triggers. 

severity 6. Treat or prevent comorbid conditions. 

7. Prescribe medications according to severity. Pharmacotherapy 8. Monitor use of beta-2-agonist drugs. 

9. Develop a written asthma management plan. Education for partnership in care 10. Provide routine education on patient self-management. 

Source: National Heart, Lung and Blood Institute, 2003. 

More information on steps associated with these key clinical activities and updates to the 

Guidelines is available at: http://www.nhlbi.nih.gov/health/prof/lung/asthma/asthmacare.pdf. 

By applying these guidelines, health care professionals can provide the best care available for 

their patients. In the future, guidelines could change. And, to provide the best treatment possible 

for their patients, clinicians must keep abreast of changes in the best practices. 

Much remains to be done in improving the scientific basis for clinical practice across all of 

medical care, and asthma is no exception. An AHRQ-supported Evidence-based Practice Center 

conducted a systematic review of interventions for the management of asthma in 2001. The 

report (BCBS Technology Evaluation Center, 2001) examined five types of asthma interventions 

and concluded the following: 

• Chronic use of inhaled corticosteroids (ICS) for children with mild-to-moderate asthma 

improves their long-term outcomes; however, studies had insufficient follow-up time or 

patient numbers to assess the cumulative effects of using ICS. 

• Evidence is insufficient for showing that early initiation of ICS prevents asthma 

progression. 

• Limited evidence suggests that ICS dosage may be reduced without diminishing asthma 

control. 

• Limited evidence also suggests that there is no benefit to using antibiotics routinely in 

addition to ICS. 

• There is insufficient evidence to determine whether the use of a written asthma action 

plan, including a peak-flowmeter-based vs. a symptom-based plan, improves outcomes. 

These inconclusive findings illustrate the early stage of research on asthma care quality. 

Nevertheless, the expert judgment of clinical specialists, assembled by the NAEPP, establishes 

the best practice today for helping patients and providers achieve optimal asthma care. 
17

Potential for Positive Return on Investment 

State government officials want programs that improve the health of their residents; but at the 

same time, they must weigh the cost of those programs against all of the competing demands of 

society. Therefore, for State officials to wear the mantel of quality improvement, such programs 

must result in enough savings to offset their expense, at the very least. 

Research suggests that investing in asthma prevention and control initiatives can improve health 

outcomes and reduce health care costs. Just as clinical research on effective asthma care is new 

and emerging, so is research on the return on investment for asthma quality improvement. A 

systematic review of return on investment for asthma suggests positive potential financial 

savings (Goetzel et al., 2005).4 In that review, $2.72 was saved for every dollar spent on asthma 

disease management programs, on average, across six studies that provided sufficient data to 

calculate per-participant cost savings relative to program costs. The average program cost was 

$269 and the average cost saving was $729 per participant. Thus, while it is early to draw 

definitive conclusions, the results are quite promising. 

One of the reviewed studies evaluated an asthma intervention, the Virginia Health Outcomes 

Partnership (VHOP), targeted to reduce emergency visits by low-income asthma patients in a 

Medicaid primary care case management program (Rossiter et al., 2000). About 20 percent of 

Medicaid asthma-related claims in Virginia were for emergency department visits (Rossiter, 

2005). The VHOP invited physicians in one community to participate in training to improve their 

management of patients with asthma, including patient education, medication use, and need for 

emergency care. The VHOP also provided feedback reports to participating physicians on their 

patients’ use of services. One-third of about 200 physicians invited actually participated. These 

physicians reduced their patients’ use of emergency services by 41 percent from the same quarter 

a year earlier, compared to only an 18-percent reduction for a comparison group that was not 

invited to participate. All of the 200 physicians invited to participate (counting those not trained) 

reduced their patients’ use of emergency services by 6 percent more than the non-intervention 

group. At the same time, physicians in the participating community dispensed more asthma 

medications. The increased drug costs were more than offset by lower emergency care costs. The 

projected direct savings to Medicaid was $3 to $4 for every dollar spent on training for 

participating physicians. 

More recent studies also support the conclusion that disease management programs for asthma 

can save money. Patients of physicians who participated in another asthma education program 

were less likely to be admitted to an emergency room or a hospital to treat their asthma than 

patients whose physicians did not participate (Brown et al., 2004). An asthma disease 

management program implemented by Colorado Medicaid from 2002 to 2003 showed that the 

program saved $203,000 in health care expenditures beyond the cost of the program, compared 

to the pre-program costs of treating asthma (National Jewish Medical and Research Center, 

2004). Not only did emergency room visits decline, but missed work days also declined. 

 

4

 This review found 12 studies. However, only 6 provided sufficient data for a return on investment calculation, and 

some of those studies were limited by small numbers of cases, incomplete patient care costs, and study designs that 

did not control for rising health care costs and other shifting external factors. 
18 

These interventions can deliver substantial cost savings if they reduce the number of repeat 

hospitalizations and emergency visits. A study using 1997 data found that each hospitalization 

increased annual expenditures for asthma significantly—from $305 for someone not 

hospitalized, to $1,690 for someone hospitalized once, to $5,987 for someone hospitalized twice 

(Atherly et al., 2003). 

Thus, not only can health care professionals improve asthma care to help their patients achieve 

better control of asthma symptoms and improve their lives, they can also reduce the use of 

expensive health care services and, thereby, cut the cost of asthma care. These consequences of 

quality improvement would benefit not only consumers of health care, but also the two other 

groups that bear the cost—third-party payers (public and private) who incur the cost of asthma 

care and employers who incur the cost of health insurance and lost productivity for their workers 

with asthma. 

Estimating the Costs of Asthma Care and Potential Savings From 

Quality Improvement 

To bring the potential of quality improvement home, State officials will want to know what the 

potential cost savings are in their State. For example, what could be saved in Medicaid costs? 

Medicaid recipients are an important focus since they include people with low incomes and 

children who have higher prevalence and hospitalization rates for asthma (CDC, 2002a; CDC, 

2002c). 

This section estimates the cost of asthma care from three perspectives: (1) the cost of asthma care 

statewide, (2) the cost for Medicaid, and (3) the cost of excess hospitalizations for asthma. Next, 

this section guides State analysts through the steps they could take to estimate the potential 

savings in the State while implementing a Medicaid disease management program in asthma like 

the one in Virginia. (Those savings were not calculated here because the number of physicians 

participating in Medicaid in each State was not available.) 

A caveat about estimating costs. Data on the cost of asthma care are not available uniformly 

across States. Some States may have tallied the costs for their Medicaid recipients, but probably 

few States have estimated the costs of asthma for their entire population. The numbers in this 

section simply apply various national averages from published research to State data to estimate 

what the cost might be in each State. Where possible the national averages are age or race 

specific. To assume that the cost for every State by age and racial subgroup will equal the 

national subgroup is unrealistic.5

 Therefore, AHRQ urges State analysts to use local data to 

develop better estimates of the cost of asthma for their State. The numbers presented are intended 

to help State and local officials think about the scale of problem and of the impact that they 

might be able to make with quality improvement initiatives for asthma. 

 

5 Several other factors are not accounted for in these estimates: First, changes in the typical services used between 1994 and 2003 

are excluded, despite that fact that medication costs have risen (Sullivan et al., 1996), and inpatient stays have declined (Mannino 

et al., 1998). Second, differences in use of services by age are not always included, despite the fact that from 1985 to 1994 the 

estimated real direct cost of asthma care actually declined per affected child, but increased per adult (Weiss et al., 2000). Third, 

differences in the age distribution across racial/ethnicity groups is not factored into the State-level estimates. Finally, the asthma 

cost calculated here is not net of health care cost without chronic disease because it was not available; subtracting the cost of 

those without chronic illness from those with asthma would indicate how much a State spends for asthma care alone. Thus, the 

State-level estimates in this section could overestimate or under estimate of today’s true cost of asthma to States and their 

residents. 
19

Cost of Asthma Care Statewide 

A statewide view of asthma costs is provided to encourage States to stimulate quality 

improvement on a statewide basis, not only in Medicaid. Three sources were combined to 

calculate the direct cost of medical care on a statewide basis: Weiss et al. (2000) for national 

expenditure data, the U.S. Census for State population estimates, and the Behavioral Risk Factor 

Surveillance System for State-level asthma prevalence. Direct costs include medical 

expenditures for hospital care, physician services, and medications. The Weiss study, which 

provides expense per person with asthma, is for the year 1994 and was updated to 2003 here, 

using the medical care component of the Consumer Price Index. The total cost for asthma care in 

the State was calculated by multiplying the per-person cost by the number of people with asthma 

in the State. 

Table 1.3 shows the calculated estimates by State. Across all the States, spending on asthma care 

totaled to over $13 billion, according to these estimates. This sum is higher than the most 

recently published estimate of $9.4 billion in 2001 dollars (ALA, 2004); when inflated to 2003 

dollars, the amount totals $10.2 billion. The higher summed State estimate points out the 

imprecision of the method here, noted above. Thus, State analysts should attempt to develop 

these estimates with their own data. 

Expenditures on asthma in the top four States in asthma costs—California, Texas, New York, 

and Florida—together were estimated at over $7 billion. Improving asthma care and reducing 

avoidable admissions and emergency care might save health care systems in States substantial 

dollars. 

Cost of Asthma Care for Medicaid

Three components were used to estimate the cost of asthma care for Medicaid: 

• National asthma prevalence separately by age and by race/ethnicity. 

• State Medicaid populations separately by age and by race/ethnicity. 

• Estimated national expenditures per person with asthma. 

Data sources for each of these components are listed below:

Components needed to estimate 

Medicaid costs of asthma 

Source of information 

National asthma prevalence separately 

by age and by race/ethnicity 

CDC Asthma Data on Demand Web site available at: 

http://209.217.72.34/asthma/ReportFolders/DirPageInfo.

asp?CS_referer=&CS_ChosenLang=en 

State Medicaid populations separately 

by age and by race/ethnicity 

Centers for Medicare & Medicaid Services (CMS) Web 

site available at: 

http://www.cms.hhs.gov/medicaid/msis/tables2002.asp 

Estimated national expenditures per 

person with asthma 

Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of 

illness for asthma in the United States, 1985-1994. J 

Allergy Clin Immunol. September 2000; 106(3):493-99. 

Note: See Appendix Figure B.1 for more information on the flow of data, assumptions, and calculations made to 

derive Medicaid spending for asthma by State and Appendix Tables B.1-B.6 for subgroups eligible for Medicaid in 

each State by age and race/ethnicity. 
20 

Table 1.3. Estimate of indirect, direct and total cost burden of asthma, by State, for 50 States, 

District of Columbia, and Puerto Rico, 2003 

State

Population 

estimate1

Percent of 

population 

with asthma2

Asthma 

prevalence

Indirect asthma 

costs for State3

Direct asthma costs for 

State3

Total asthma costs for 

State

Nationwide 290,788,976 7.6 22,099,962 $9,967,966,940 $13,383,958,093 $23,351,925,033

Alabama 4,503,726 7.5 337,779 152,352,043 204,562,613 356,914,656

Alaska 648,280 9.1 58,993 26,608,419 35,727,041 62,335,461

Arizona 5,579,222 8.3 463,075 208,865,540 280,443,109 489,308,649

Arkansas 2,727,774 7.3 199,128 89,814,469 120,593,606 210,408,075

California 35,462,712 8.4 2,978,868 1,343,588,536 1,804,032,133 3,147,620,669

Colorado 4,547,633 8.3 377,454 170,246,644 228,589,638 398,836,282

Connecticut 3,486,960 8.3 289,418 130,538,950 175,274,241 305,813,192

Delaware 818,166 7.5 61,362 27,676,919 37,161,713 64,838,633

District of Columbia 557,620 7.8 43,494 19,617,696 26,340,619 45,958,315

Florida 16,999,181 6.1 1,036,950 467,705,946 627,987,314 1,095,693,261

Georgia 8,676,460 7.0 607,352 273,940,136 367,818,566 641,758,702

Hawaii 1,248,755 5.6 69,930 31,541,353 42,350,477 73,891,830

Idaho 1,367,034 7.9 107,996 48,710,374 65,403,267 114,113,642

Illinois 12,649,087 7.4 936,032 422,188,071 566,870,605 989,058,676

Indiana 6,199,571 8.1 502,165 226,496,615 304,116,298 530,612,912

Iowa 2,941,976 6.2 182,403 82,270,829 110,464,785 192,735,614

Kansas 2,724,786 7.5 204,359 92,174,061 123,761,824 215,935,885

Kentucky 4,118,189 9.8 403,583 182,031,861 244,413,611 426,445,472

Louisiana 4,493,665 6.2 278,607 125,663,005 168,727,325 294,390,330

Maine 1,309,205 9.9 129,611 58,459,878 78,493,896 136,953,775

Maryland 5,512,310 7.8 429,960 193,929,240 260,388,185 454,317,424

Massachusetts 6,420,357 9.9 635,615 286,687,944 384,935,008 671,622,952

Michigan 10,082,364 9.3 937,660 422,922,100 567,856,183 990,778,283

Minnesota 5,064,172 6.8 344,364 155,321,801 208,550,098 363,871,899

Mississippi 2,882,594 6.9 198,899 89,711,399 120,455,215 210,166,614

Missouri 5,719,204 8.0 457,536 206,367,182 277,088,571 483,455,753

Montana 918,157 7.9 72,534 32,715,917 43,927,560 76,643,477

Nebraska 1,737,475 7.1 123,361 55,640,621 74,708,489 130,349,110

Nevada 2,242,207 6.6 147,986 66,747,453 89,621,597 156,369,050

New Hampshire 1,288,705 8.5 109,540 49,406,888 66,338,474 115,745,362

New Jersey 8,642,412 7.1 613,611 276,763,219 371,609,110 648,372,329

New Mexico 1,878,562 6.7 125,864 56,769,543 76,224,287 132,993,830

New York 19,212,425 7.6 1,460,144 658,583,485 884,277,990 1,542,861,475

North Carolina 8,421,190 7.1 597,904 269,678,841 362,096,938 631,775,779

North Dakota 633,400 7.0 44,338 19,998,212 26,851,536 46,849,748

Ohio 11,437,680 7.1 812,075 366,278,434 491,800,910 858,079,345

Oklahoma 3,506,469 7.6 266,492 120,198,391 161,390,005 281,588,396

Oregon 3,564,330 9.3 331,483 149,511,952 200,749,232 350,261,184

Pennsylvania 12,370,761 8.3 1,026,773 463,115,767 621,824,095 1,084,939,863

Puerto Rico 3,877,881 10.8 418,811 188,900,580 253,636,219 442,536,800

Rhode Island 1,076,084 9.6 103,304 46,594,265 62,561,974 109,156,239

South Carolina 4,148,744 6.1 253,073 114,146,219 153,263,772 267,409,991

South Dakota 764,905 7.3 55,838 25,185,201 33,816,091 59,001,291

Tennessee 5,845,208 7.9 461,771 208,277,387 279,653,397 487,930,784

Texas 22,103,374 6.9 1,525,133 687,895,901 923,635,679 1,611,531,579

Utah 2,352,119 7.4 174,057 78,506,582 105,410,542 183,917,124

Vermont 619,343 8.4 52,025 23,465,271 31,506,746 54,972,018

Virginia 7,365,284 7.6 559,762 252,474,865 338,997,213 591,472,078

Washington 6,131,298 9.1 557,948 251,656,919 337,898,960 589,555,879

West Virginia 1,811,440 8.1 146,727 66,179,584 88,859,120 155,038,704

Wisconsin 5,509,026 7.5 413,177 186,359,332 250,224,093 436,583,424

Wyoming 506,529 7.5 37,990 17,134,863 23,006,927 40,141,790

2 Prevalence based on most recent estimates (BRFSS 2003). Accessed at: 

http://apps.nccd.cdc.gov/brfss/list.asp?cat=AS&yr=2003&qkey=4416&state=All.

3 Calculations based on Weiss, Sullivan, Lyttle, 2000 inflated to 2003 dollars (Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of 

illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9). 

1

 U.S. Census annual estimates of the population for the United States and States, and for Puerto Rico: April 1, 2000 to July1, 2004.

Table 1.4 shows the estimated expenditures likely to occur by State Medicaid agency, based on 

the above calculations. Nationally, Medicaid programs spent, according to these estimates, over 

$4 billion dollars on asthma alone. The States with the highest expenditures (California, Texas, 

New York, and Florida) spent well over $1.5 billion for asthma care for their Medicaid enrollees. 
21

Table 1.4. Medicaid eligible population and estimated asthma prevalence and expenditures for 

medical care for age groups 0-18, 19-64, and 65 and over, by State, 2003 

 State 

Medicaid 

population age 

0-18 with 

asthma1

Estimated 

Medicaid expense 

for age 0-18 with 

asthma2

Medicaid 

population age 

19-64 with 

asthma1

Estimated 

Medicaid expense 

for age 19-64 with 

asthma2

Mediciad 

population age 

65 and over with 

asthma1

Estimated 

Medicaid expense 

for age 65 and 

over with asthma2

Total estimated 

Medicaid spending on 

asthma3

Total US 2,234,609 $2,361,199,292 1,362,264 $1,439,435,844 330,403 $349,120,091 $4,149,755,227

Alabama 6,770 7,153,840 2,432 2,569,294 425 449,198 10,172,332

Alaska 36,646 38,721,787 20,454 21,612,820 7,178 7,584,236 67,918,843

Arizona 29,239 30,895,502 13,995 14,787,299 3,795 4,009,739 49,692,540

Arkansas 46,246 48,866,133 31,357 33,133,013 3,477 3,674,444 85,673,591

California 320,813 338,987,094 327,683 346,246,056 49,363 52,159,867 737,393,017

Colorado 21,458 22,673,459 9,616 10,160,615 2,903 3,067,629 35,901,703

Connecticut 20,987 22,175,798 12,416 13,119,625 3,739 3,950,473 39,245,896

Delaware 6,024 6,365,303 4,516 4,771,723 673 711,304 11,848,330

District of Columbia 8,326 8,798,059 6,051 6,393,598 1,183 1,250,330 16,441,987

Florida 124,172 131,206,825 61,203 64,669,899 21,072 22,265,776 218,142,500

Georgia 77,782 82,188,576 28,742 30,369,852 8,051 8,506,619 121,065,046

Hawaii 7,996 8,448,678 5,696 6,018,859 1,169 1,234,834 15,702,371

Idaho 11,211 11,846,082 3,641 3,847,192 763 806,151 16,499,424

Illinois 95,675 101,095,472 44,129 46,628,592 19,012 20,089,142 167,813,207

Indiana 46,051 48,659,576 18,304 19,340,896 4,709 4,975,618 72,976,090

Iowa 16,242 17,162,593 8,771 9,268,254 2,498 2,639,687 29,070,534

Kansas 15,492 16,369,355 6,265 6,620,423 1,992 2,105,036 25,094,814

Kentucky 34,653 36,616,374 18,673 19,730,455 5,619 5,937,547 62,284,376

Louisiana 53,485 56,514,412 17,978 18,995,927 6,289 6,645,521 82,155,860

Maine 9,515 10,053,810 10,917 11,535,116 4,567 4,825,849 26,414,775

Maryland 37,376 39,492,972 17,100 18,068,435 4,039 4,268,041 61,829,449

Massachusetts 42,517 44,925,818 38,807 41,005,737 8,584 9,070,433 95,001,988

Michigan 74,311 78,520,937 36,446 38,510,767 7,824 8,267,160 125,298,864

Minnesota 29,002 30,644,983 17,234 18,209,808 5,474 5,784,603 54,639,394

Mississippi 34,851 36,825,469 14,218 15,023,446 5,662 5,982,630 57,831,544

Missouri 50,411 53,267,187 28,191 29,787,505 6,010 6,350,802 89,405,495

Montana 4,868 5,143,722 2,641 2,790,925 659 696,779 8,631,426

Nebraska 13,689 14,464,978 5,309 5,609,862 1,428 1,509,038 21,583,877

Nevada 9,448 9,983,373 4,968 5,249,548 1,243 1,313,277 16,546,198

New Hampshire 5,932 6,267,543 2,316 2,447,648 757 800,321 9,515,512

New Jersey 43,510 45,974,871 22,976 24,277,665 8,422 8,898,728 79,151,263

New Mexico 32,828 34,687,649 12,083 12,767,988 2,389 2,524,475 49,980,112

New York 142,336 150,399,653 127,326 134,538,891 29,291 30,949,882 315,888,426

North Carolina 63,422 67,015,066 32,716 34,569,721 10,715 11,321,520 112,906,306

North Dakota 2,895 3,058,675 1,909 2,017,466 602 636,017 5,712,158

Ohio 82,286 86,947,760 44,574 47,099,279 8,753 9,249,179 143,296,217

Oklahoma 37,958 40,107,933 11,710 12,373,163 3,856 4,074,956 56,556,052

Oregon 23,126 24,436,452 22,051 23,299,919 2,782 2,939,377 50,675,748

Pennsylvania 73,672 77,845,832 43,962 46,452,154 12,680 13,398,318 137,696,303

Rhode Island 8,493 8,973,914 5,638 5,957,185 1,417 1,496,885 16,427,985

South Carolina 42,357 44,756,632 22,214 23,472,637 4,707 4,974,170 73,203,439

South Dakota 6,067 6,411,155 2,136 2,257,081 724 765,117 9,433,353

Tennessee 61,894 65,400,305 56,363 59,555,557 9,461 9,997,471 134,953,333

Texas 170,381 180,033,486 57,056 60,288,734 23,049 24,354,282 264,676,503

Utah 12,241 12,934,335 5,336 5,637,826 768 811,129 19,383,289

Vermont 35,196 37,190,046 14,819 15,658,657 6,080 6,423,957 59,272,660

Virginia 5,916 6,251,655 4,596 4,856,628 1,262 1,333,935 12,442,218

Washington 54,297 57,372,417 26,970 28,498,087 4,747 5,015,916 90,886,421

West Virginia 22,962 24,262,314 13,079 13,819,820 2,781 2,939,010 41,021,144

Wisconsin 32,133 33,953,655 19,773 20,893,145 6,816 7,201,873 62,048,673

Wyoming 3,659 3,866,659 1,500 1,584,872 318 335,859 5,787,390

Note: Projections to 2003 Medicaid eligibles based on A Profile of Medicaid 2000 Chartbook available at 

http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp and A Profile of 

Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf for 2003 projections.

2 Calculations of prevalence rates based on national prevalence rates for 0-17 years, weighted average with US Census population estimates for 2002 of 18-

44 years and 45-64 years, and 65+ years. Centers for Disease Control and Prevention, National Center for Health Statistics, Asthma on Demand. National 

Health Interview Survey, 1999-2003. Table Asthma Prevalence by Age, Gender, Race/Ethnicity, and Geographic Region/Division 1999-2003. 

http://209.217.72.34/asthma/ReportFolders/DirPageInfo.asp?CS_referer=&CS_ChosenLang=en

3 Calculations of direct cost per person based on Weiss et al 1994 direct cost estimates inflated by medical care component of CPI to 2003 dollars. Indirect 

cost person based on Weiss et al 1994 direct cost estimates inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. 

Trends in the cost of illness for asthma in the United States, 1985-1994.

J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, 65+, while NHIS prevalence rates are 

for age groups 0-17, 18-64, and 65+. 
22 

Improving asthma care by reducing emergency room visits and avoidable hospitalizations (i.e., 

hospital admissions that might have been avoided with high quality ambulatory care) should 

have a substantial impact on Medicaid spending. 

Estimating potential Medicaid savings from asthma disease management—a Virginia 

example. Below are steps for estimating the Medicaid savings from training physicians in the 

Virginia Health Outcomes Partnership program described earlier. Estimates for Virginia are 

below. Using these steps together with State data, it is possible for a State to develop a 

“ballpark” estimate of how much might be saved in Medicaid costs with a similar asthma disease 

management intervention. 

Steps for Estimating Potential Medicaid Savings From an Asthma Disease Management Program 

Step Virginia

 1. Total annual spending for emergency department visits for asthma preintervention for Medicaid recipients $5,056,020 

 2. Total annual number of Medicaid claims for emergency department visits 9,363

 3. Payment per claim: Divide step 1 by step 2 (5,056,020/9,363) $540

 4. Emergency visit reduction factor: Adjusted to four quarters and to exclude 

added costs per physician and added drug prescribing (both included below; 

see steps 7 and 8) 

 0.06

 5. Emergency care visit annual saving after training physicians: Multiply step 

1 by step 4 (5,056,020 X 0.06) $303,361

 6. Number of physicians participating in primary care case management who 

might accept training in asthma management 200

 7. Asthma drug cost: Multiply step 6 by $180 per physician per year (200 X

180) $36,000

 8. Program training costs: Multiply step 6 by $235 per physician (200 X 235) $47,000

 9. Total drug and training costs: Add steps 7 and 8 (36,000 + 47,000) $83,000

10. Total Medicaid savings: Subtract step 9 from step 5 (303,361 − 83,000) $220,361

11. Savings per Medicaid claim: Divide step 10 by step 2 (220,361/9,363) $23.54

Source: Estimates derived from Rossiter et al., 2000. 

Note: See Rossiter et al. for further detail on derivation of the emergency visit reduction factor, asthma drug cost, 

and program training cost. Based on the VHOP experience, step 6 assumed that one-third of Medicaid participating 

physicians in any disease management program would accept training in asthma management. 

People with asthma who have poor asthma management have a high number of repeat ED visits. 

Data from the National Medical Expenditure Survey show that only about 20 percent of all 

asthma patients account for about 80 percent of the total costs of asthma (Weiss et al., 2001; 

Smith et al., 1997). A recent study showed that from a group of more than 3,000 patients, asthma 

patients with 6 or more ED visits accounted for 68 percent of total ED visits (Griswold, 2005).
23

If these asthma patients with multiple ED visits can be identified with State Medicaid data, then 

States can estimate potential cost savings from reducing the number of patients with repeat 

emergency room visits. Multiplying the number of patients who have different numbers of visits 

by the average cost per visit for each group gives an estimate of total ED costs for patients with 

asthma who have frequent ED visits for each group. These costs represent a potential target for 

reducing health care costs for patients with asthma and compare the cost of moderate emergency 

use to high emergency department use for asthma. 

Cost of Excess Hospitalizations 

Rates of avoidable hospitalizations have been developed as indicators of the quality of 

ambulatory care, including care for asthma. Hospitalizations occur because of exacerbations of 

asthma symptoms such as an asthma attack, where a patient cannot breathe and could die without 

medical attention. Some asthma hospitalizations could be avoided with planned care, patient 

education, proper use of long-term controller medications for people with persistent asthma, and 

patient awareness and avoidance of asthma triggers. However, even for patients and physicians 

who comply with the best practices, asthma attacks beyond their control may still occur and 

hospitalization may be necessary for survival. It is the wide variation in asthma admissions rates 

across the country (see Table 1.2) that suggests considerable improvement can be made in 

ambulatory care and self-management that results in reduced hospitalizations and, thus, lower 

costs for asthma care.

A recent study found that about half of admissions for children with asthma in one hospital may 

have been preventable. In a Massachusetts inner-city hospital, 26 percent of parents thought their 

child’s hospitalization for asthma could have been avoided, 38 percent of primary care 

physicians thought an admission could have been avoided, and 43 percent of the inpatient 

attending physicians who saw a child with asthma in the hospital had that view (Flores et al., 

2005). These assessments were independent of each other. The one group without a personal 

stake in the assessment of the chronic care of the children was the inpatient physicians with the 

highest assessment of avoidable admissions. Of all admissions for children with asthma, 54 

percent of admissions were assessed as preventable by any of the three sources. 

Estimating potential cost savings from reducing excess hospitalizations for pediatric 

asthma—a Massachusetts validation. By comparing the Massachusetts hospitalization rate 

with the average for States with the lowest rate of hospitalization for children with asthma, the 

apparent excess (or percent to be reduced in order to achieve best-in-class performance) in 

Massachusetts is 57.5 percent (Table 1.2). This potential for reduction of pediatric asthma 

hospitalizations for Massachusetts is similar to the 54-percent estimate of hospitalizations that 

might have been prevented, based on the judgment of parents, physicians or attending physicians 

at the Boston hospital described above. This supports the use of hospitalization rates above and 

beyond the best-in-class States average rate as a metric to evaluate how much States could save 

with better quality of asthma care. Using Massachusetts as an example, the steps in the following 

calculation show how a State may develop a ballpark estimate of the potential cost savings from 

reducing excess hospital admissions for pediatric asthma. Note that the cost of implementing a 

quality improvement program to reduce hospitalizations is not included in the calculation. 
24 

Steps for Estimating Potential Savings From Reducing Excess Pediatric Asthma Hospitalizations 

* Step 6 was calculated by multiplying the national mean charge per pediatric asthma hospitalization ($5,888) by the 

national cost-to-charge ratio for these hospitalizations (0.44) using data from the 2001 HCUP Nationwide Inpatient 

Sample. (Information on HCUP data and tools is available on the HCUP Web site at http://www.hcup-us.ahrq.gov 

or via email at hcup@ahrq.gov.) 

Summary and Synthesis 

This module provides background on asthma as a disease, its prevalence, complications, and 

associated costs. This module also examines the evidence from both the NHQR and NHDR 

regarding the substantial variation in quality of care for asthma that exists across the Nation, 

between States, and across population subgroups. 

Evidence from research indicates that quality improvement can enhance health outcomes, reduce 

disparities across States and population groups, and provide a return on the investment. The 

return includes both cost savings and improved quality of life for people with asthma and their 

caregivers. 

Resources for Further Reading 

• American Lung Association Trends in Asthma Morbidity and Mortality; available at: 

http://www.lungusa.org/site/pp.asp?c=dvLUK9O0E&b=33347

• National Asthma Education and Prevention Program-- 

http://www.nhlbi.nih.gov/about/naepp/

• Institute for Healthcare Improvement Web resources, available at: 

http://www.ihi.org/IHI/Topics/ChronicConditions/Asthma/ 

Step Massachusetts

 1. Hospital admission rate for pediatric asthma per 100,000 population 

under age 18 (Table 1.2) 

 169.96 

 2. Estimated population under age 18 in State (U.S. Census, 2000; see 

http://www.census.gov/popest/states/asrh/SC-est2004-02.html) 

 1,500,064

 3. Number of pediatric asthma hospital admissions: Multiply step 1 by 

step 2 (169.96 X 1,500,064) 

2,549.51 

 4. Percent of pediatric asthma hospital admissions to be reduced to achieve 

best-in-class (Table 1.2) 

57.5%

 5. Number of hospital admissions for pediatric asthma to reduce (excess 

hospitalizations): Multiply step 3 by step 4 (2,549.51 X 0.575) 

1,465.97 

6. Mean cost for pediatric asthma hospitalization* $2,590.72

7. Total cost of all pediatric asthma hospitalizations in State: Multiply step 

3 by step 6 (2,549.51 X $2,590.72) 

$6,605,066.50

8. Total cost of excess pediatric asthma hospitalizations in State: Multiply 

step 5 by step 6 (1,465.97 X $2,590.72) 

$3,797,917.70

9. Potential cost savings from reducing excess hospitalizations: Subtract 

step 8 from step 7 ($6,605,066.50 − $3,797,917.70) 

$2,807,148.80
25

• Institute of Medicine’s Crossing the Quality Chasm: A New Health Care System for the 21st

Century, available at: http://www.iom.edu/report.asp?id=5432

• Institute of Medicine’s Fostering Rapid Advances in Health Care: Learning from System 

Demonstrations, available at: http://www.iom.edu/report.asp?id=4294

• National Healthcare Quality Report and National Healthcare Disparities Report, available 

at: http://www.qualitytools.ahrq.gov

• National Heart, Lung, and Blood Institute’s Morbidity and Mortality: 2004 Chartbook on 

Cardiovascular, Lung, and Blood Diseases; available at: 

http://www.nhlbi.nih.gov/resources/docs/04_chtbk.pdf

• Boudreaux ED, Emond SD, Clark S, et al. Acute asthma among adults presenting to the 

emergency department: The role of race/ethnicity and socioeconomic status. Chest. 

2003;124:803-812. 

• Griswold SK, Nordstrom CR, Clark S, et al. Asthma exacerbations in North American adults: 

Who are the “frequent fliers” in the emergency department? Chest. 2005; 127(5):1579-1586. 

• Lin S, Fitzgerald E, Hwang S et al. Asthma hospitalization rates and socioeconomic status in 

New York state (1987-1993). Journal on Asthma. 1999;36:239-251. 

• Mayo PH, Richman J, Harris HW. Results of a program to reduce admission for adult 

asthma. Annals of Internal Medicine. 1990;112:864-871. 

• Ray N, Thamer M, Fadillioglu B, et al. Race, income, urbanicity, and asthma hospitalization 

in California: a small area analysis. Chest. 1998;113:1277-1284. 

• Stanton MW, Dougherty D, Rutherford MK. Chronic care for low-income children with 

asthma: strategies for improvement. Rockville (MD): Agency for Healthcare Research and 

Quality; 2005. Research in Action Issue 18. AHRQ Pub. No. 05-0073. 

• Zeiger RS, Heller S, Mellon MH. Facilitated referral to asthma specialist reduces relapse in 

asthma emergency room visits. Journal of Allergy and Clinical Immunology. 1991;87:1160-

1168. 

• Zoratti E, Havstad S, Rodriguez J et al. Health service use by African Americans and 

Caucasians with asthma in a managed care setting. American Journal of Respiratory Critical 

Care Medicine. 1998;158:371-377. 

Associated Appendixes for Use With This Module 

Appendix A: List of Acronyms 

Appendix A lists acronyms of organizations, data sources, and other resources used in this 

Resource Guide. 

Appendix B: Estimates of Medicaid Costs by State 

Appendix B includes data tables with the cost estimates for racial/ethnic subgroups of Medicaid 

eligibles with asthma by State and a flow chart of the methodology used to derive the estimates. 
26 

Module 2: A Framework for State-Led Quality 

Improvement 

States can play a central role in improving the quality of health care for their residents. This 

module presents a framework to help States play this role. 

Quality Health Care and the Quality Improvement Movement 

Health care quality has been defined as “the degree to which health services for individuals and 

populations increase the likelihood of desired health outcomes and are consistent with current 

professional knowledge” (Institute of Medicine [IOM], 1990). Increased attention to quality of 

care in recent years has highlighted the gap between optimal health care and the care that 

Americans typically receive. While producing unrivaled innovation and new medical treatments, 

the U.S. health care system struggles to deliver high quality care consistently. Researchers 

estimate, for example, that nearly 100,000 people die annually in the United Stqtes because of 

medical errors (IOM, 1999). And even when fatal errors are not involved, people receive 

appropriate treatment only about half of the time (McGlynn et al., 2003). 

Compared with other industries in the United States, health care has been slow to embrace 

quality improvement (Chassin, 1998). By contrast, some manufacturing- and service-based 

industries have implemented sophisticated and rigorous quality improvement processes, such as 

the Six Sigma movement adopted by large firms including Motorola and General Electric. This 

movement is named for its goal, “six sigma,” which refers to a measure of extremely low 

tolerance for mistakes. Specifically, six sigma represents 3.4 defects per million events (Spanyi 

and Wurtzel, 2003). The Six Sigma approach thus sets a very ambitious goal for reducing error. 

Health care processes typically operate at a considerably higher tolerance for error—500,000 

defects per million opportunities (based on the conclusion of McGlynn et al., 2003)—or less than 

two sigma rather than six. 

Key Ideas in Module 2: 

• States can play a strategic role in designing, implementing, and assessing health 

care quality improvement. 

• Existing models for quality improvement can be adapted to enable States to play a 

leadership role. 

• The State-led framework is adapted from quality improvement models in other 

industries and incorporates a Plan-Do-Assess approach.. 

• The State-led quality improvement framework contains three stages: 

1. Provide leadership to create a vision. 

2. Work in partnership with key stakeholders. 

3. Implement improvement by creating interventions and assessing their impact. 
27

One of the obstacles to quality improvement in health care has been a lack of rigorous measures 

and data to drive improvement. To help address this gap, the Agency for Healthcare Research 

and Quality (AHRQ) released the first National Healthcare Quality Report (NHQR) and National 

Healthcare Disparities Report (NHDR) designed to establish a baseline of quality measures for 

tracking health care quality in the United States in 2003. The second NHQR and NHDR were 

released in 2004 and began to track health care quality. The 2004 NHQR concluded that quality 

is improving in many areas, but change takes time, the gap between the best possible care and 

actual care remains large, and further improvement in health care is possible (AHRQ, 2004b). 

In addition, AHRQ has begun to develop resource guides and workbooks aimed toward helping 

States take action to improve quality of care for specific chronic conditions (e.g., diabetes and 

asthma). These and other resources from AHRQ and other Federal agencies designed to 

stimulated quality improvement are listed at the end of this module. 

A Strategic Role for States 

Improving quality of care requires active involvement from many participants—providers, 

patients, payers, policymakers, and the public. Among all of these stakeholders, however, State 

governments have a unique leadership role to play. Small networks of providers have developed 

around quality improvement, but strong leadership at the State level is needed to help these 

develop, coalesce, and survive. States also have a span of control over a network of providers 

that they license and can help integrate the efforts of the various networks. Furthermore, they 

have the ability to lead providers in developing a quality improvement process and can muster a 

statewide impetus behind small efforts that might otherwise die for lack of energy. Some parts 

of State government stand to benefit from quality improvement in terms of improved services 

and lower costs. These include Medicaid agencies and mental health and substance abuse 

agencies that also control payments to providers. 

To lead a quality improvement effort, States need a model of how to improve quality and a way 

to target areas for improvement These are discussed in more detail below. 

Developing a Framework for State-Led Quality Improvement 

None of the current models for quality improvement used on the front lines of medical care 

addresses a strategic role for State governments. Therefore, this Resource Guide proposes a 

State-led quality improvement approach that combines general models from product 

manufacturing with specific models developed for health care services. Advocates of quality 

improvement have argued that a quality improvement model adapted from manufacturing can 

work just as well in service industries (Harry 1998, as cited in Chassin 1998). Various quality 

improvement models used in different circumstances are discussed below; then a State-led 

framework, built by borrowing from other models, is presented. 

General Models 

General models of quality improvement are based on the “Plan-Do-Check-Act” or the “Plan-DoStudy-Act” (PDSA) model (Langley et al., 1996). Within a production process, these models 

convey the importance of the following: 
28 

• Planning—Identifying the problem and potential solution. 

• Doing—Actually testing out the proposed solution. 

• Studying—Measuring to see if the solution worked. 

• Acting—Implementing the successful solution. 

Two key features of this model are measurement and the continuousness of the process. 

Organizations measure the effect of a change to know whether the solution is working. A 

familiar mantra in quality circles is: “Without measurement, there can be no improvement.” If 

the test solution did not work, the group starts again to plan a better approach, do another test, 

and assess its effect. Businesses apply this continuous process of planning, doing, and assessing 

until they know they have solved a problem. Then they implement the solution company wide. 

Although this model has stood the test of time in manufacturing circles, it requires special 

application in health care. Unlike centrally controlled manufacturing processes, health care 

delivery is decentralized and resistant to top-down directives from government, corporate 

decisionmakers, or professional organizations. Health care quality improvement happens in 

clinical settings, often one patient at a time. The decentralized nature of health care delivery thus 

creates a substantial obstacle to implementing large-scale quality improvement programs. In 

light of this fact, the components of the process must be carefully adapted to the health care 

setting. 

Three components in particular that need special attention are the composition of the team, the 

plan for measurement and assessment, and the implementation process. First, the quality 

improvement team is as crucial to success as the process. This is true in companies that compose 

their teams of knowledgeable and empowered employees, but more so within complex systems 

of disconnected entrepreneurs, such as in medicine. Highly effective teams are committed to the 

process, champion the cause, apply their energy to implement solutions, and continue the quality 

improvement cycle by moving on to the next problem. Achieving this in health care can be 

particularly challenging. A State’s leadership can influence the composition of the team. 

Second, the plan for measuring and assessing which proposed solutions are likely to work 

requires data collection, careful analysis, and skillful interpretation. While the quality 

improvement objective should be paramount and data and analysis should not paralyze the 

quality improvement team, the complexity of the health care system will present challenges to 

measurement and assessment. Fragmentation of the health care system, financial incentives that 

can discourage change, busy practitioners who may believe they have little time for quality 

improvement, and solo practice or employment arrangements that promote practitioner 

independence are special challenges to instituting change. A State’s experience around data 

collection can be an important asset to the team 

Finally, while implementation within the walls of a manufacturing plant may be straightforward, 

implementation in a complex health care environment may not be. Thus, the plan-do-study part 

of the cycle may be needed to help implement change – plan the change, measure its spread, and 

assess its impact on the goal. A State’s involvement may be essential to advertising and assessing 

the effect of specific interventions statewide.
29

Existing Clinical Models 

The general PDSA model has been applied successfully to the delivery of health care services. 

These applications have focused primarily on clinical processes—i.e., how health care teams of 

physicians, nurses, technicians, managers, and others change specific processes to improve the 

outcome of their service and the health of their consumers, the patients. These applications have 

generally focused on one clinical condition (e.g., diabetes) or one set of procedures (e.g., 

anesthesia services) at a time. The clinical condition or procedure focus is an aspect of clinical 

models that will likely be reflected in State-led quality improvement circles. Furthermore, the 

clinical quality improvement process may be used within the State-led quality improvement 

initiative. 

Institute for Healthcare Improvement. One approach to quality improvement with relevance 

for State-led efforts was developed by the Institute for Healthcare Improvement (IHI). The IHI 

has been working with teams of clinicians from around the country for several years on 

improving systems of care to enhance care processes and outcomes. IHI has developed a twopart model to spur improvement in clinical settings (see box ). 

Chronic Care Model. Another model of quality improvement in the clinical setting is the 

Chronic Care Model. Dr. Edward Wagner and his team at Group Health Cooperative in Seattle, 

with support from the Robert Wood Johnson Foundation, developed the Chronic Care Model 

(see box). The U.S. health care system is oriented more toward care for acute episodes of disease 

rather than prevention and management of chronic conditions. Thus, the Chronic Care Model 

emphasizes a collaborative approach among health care teams, involved patients, and supportive 

communities to develop new and better clinical procedures and systems that support treatment 

and management of chronic illness over time. More information is provided below on 

involvement of State health departments in Diabetes Collaboratives that use the Chronic Care 

Model to achieve rapid advancement in diabetes care at community health centers. More 

information on the Chronic Care Model is available on the Improving Chronic Illness Care 

(ICIC) Web site at: http://improvingchroniccare.org. 

Federal models. None of these models speaks directly to the Federal role in promoting quality 

improvement. To fill the need for such a model, the Centers for Medicare & Medicaid Services 

(CMS) published its own quality roadmap—a strategy for how CMS plans to lead quality 

improvement at the clinical level for its beneficiaries (CMS, 2005). 

The CMS Quality Improvement Roadmap vision is: “The right care for every person every 

time.” Its goals are to: “Make care safe, effective, efficient, patient-centered, timely, and 

equitable.” The strategy, in brief, is to: 

• Work with partnerships to achieve quality goals. 

• Support quality measurement and information. 

• Create the right incentives by paying for quality, not ineffective, health care. 

• Assist practitioners to improve quality. 

• Drive better use of effective health care technologies. 
30 

The Center for Medicaid and State Operations (CMSO) announced a quality initiative for 

Medicaid and the State Children’s Health Insurance Program (SCHIP) in August 2005 that is 

committed to the vision of the CMS Quality Improvement Roadmap for Medicaid and SCHIP 

beneficiaries. The initiative stresses the importance of working in partnership with States and 

external organizations, such as AHRQ, to promote innovation as a strategy for obtaining the best 

value for health care resources invested. The Medicaid SCHIP quality initiative includes a series 

of projects in five key areas: namely (1) evidence-based care and quality measurement, (2) pay 

for performance, (3) health information technology, (4) partnerships, and (5) information 

dissemination. CMSO plans to work with States to encourage Medicaid and SCHIP providers to 

adopt well accepted clinical guidelines with demonstrated effectiveness in improving quality and 

reducing costs for specific conditions in priority areas. 

This initiative has direct implications for States to align quality improvement and incentives to 

provide effective care for beneficiaries. In order to meet the objectives of the Quality 

Improvement Roadmap, it will be necessary for States to implement quality improvement 

strategies for effective care for chronic conditions such as asthma. 

The IHI Methodology 

Part 1 

 Forming the team: This step involves identifying the key players and addressing three 

specific questions as shown below 

o Setting the aims: What are the goals? 

o Establishing measures: How can teams measure whether a change is an 

improvement? 

o Selecting changes: What changes can teams make that will result in improvement? 

Part 2

 Testing changes: This step draws from the Plan-Do-Study-Act cycle. PDSA is a way of 

testing a change in a real work setting—by planning it, trying it, observing the results, and 

acting on them. 

 Implementing changes: After testing changes on a small scale, learning from the tests, 

and refining the change through several PDSA cycles, the team can implement the change 

on a broader scale—for example, for an entire pilot population. 

 Spreading changes: After implementation of a change for a pilot population, the team can 

spread change to other parts of the organization or to other organizations. 

For more information, see 

http://www.ihi.org/IHI/Topics/Improvement/ImprovementMethods/HowTo Improve/. 
31

A New Framework for State-Led Quality Improvement 

This Resource Guide proposes a new tool for State leadership in quality improvement. The Stateled framework draws elements from the models described above. It overlays the PDSA model, 

which here is shortened to Plan-Do-Assess for States because they are not in a position of 

actually changing clinical practice but rather of leading others to improve. States can play a 

central role at three different stages of quality improvement: leading, partnering, and 

implementing improvement. Each stage follows the Plan-Do-Assess cycle with an emphasis on 

measurement and information which States may be in a unique position to support. Each stage is 

described in more detail below. 

Stage 1: Provide leadership. Figure 2.1 depicts the first stage of quality improvement—

leadership. State government is the principal player at this stage. The State’s leadership role can 

be built with the aid of the Plan-Do-Assess tool. For example: 

Specific leadership tasks can be addressed with this framework. For example, an early question 

will be how a State official would initiate a quality improvement project. With the PDA tool, the 

State official would: 

• Plan—A State official leads the process by assigning high-level staff who identify partners 

from among stakeholders and prepare for a kickoff meeting by collecting and assembling 

data—the case for quality improvement, potential targets for improvement, readily available 

data across clinical conditions or settings of care. 

• Do—Staff convene partners, a high-profile State official kicks off the meeting, and staff 

support the partners in a planning process. 

• Assess—Staff assess the partnership (for example, who is attending and contributing at 

meetings) and adjust the partner membership, if necessary. 

Chronic Care Model—The Six Core Components 

 Community - Mobilizing all the available community resources to meet the needs of 

people with chronic illnesses. 

 Health system – Creating organizational cultures, systems and mechanisms that promote 

safe, high quality care throughout the health care system. 

 Self-management support – Empowering and preparing active patients to manage their 

health and navigate the health care system. 

 Delivery system design – Assuring the delivery of effective, efficient clinical care and 

self-management support through appropriate design of the delivery system. 

 Decision support – Promoting appropriate clinical care consistent with scientific evidence 

and patient preferences. 

 Clinical information systems – Organizing patient and population data to facilitate 

efficient and effective care for people with chronic illnesses. 

Source: Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic 

illness: the chronic care model, Part 2. Journal of the American Medical Association. 2002;288(15): 

1909–1914. 
32 

A key component of State government leadership at this stage is championing the need for 

quality improvement. It also is critical to State efforts to identify one or more high placed 

champions from the health care community who can muster support and provide a vision for 

change. 

Figure 2.1. State-led quality improvement—Stage 1 

 

Stage 2: Work in partnership. Figure 2.2 adds the second stage of State-led quality 

improvement, as a ring surrounding the first circle (see Figure 2.1). The second ring focuses on 

the partnership activities, encompasses the core of the improvement process, and relies heavily 

on the partnership to define activities, plan solutions, and assess them before any implementation 

campaigns are undertaken. Many issues will be decided during this stage, usually during a series 

of group meetings. Again, the Plan-Do-Assess tool can be used for each major decision, which at 

this stage might include, for example: 

• Plan—The new partnership will develop a strategy about how the group will function 

(perhaps through consensus) and what clinical condition(s) and/or settings of care will be the 

focus of quality improvement, commit to both the group process and the focus, and design a 

plan for quality improvement and specific solutions for the condition(s) and/or settings 

selected. 

• Do—Team members will test proposed intervention(s) through study of the literature and/or 

a pilot study at a health plan or facility; and during the study or test, they will measure and 

quantify the effect of the intervention(s). 

• Assess—Team members will analyze and interpret the results of the intervention(s) and 

present the results to the partnership. 

Depending on the results, the cycle may begin again with modification of the idea or generation 

of new ones and with the test, measure, analyze, and interpret steps again. Or, the group may be 

ready to move to implementation of the initial idea(s).

These activities rely on a vibrant, committed partnership of stakeholders in the industry (the 

State being one stakeholder/partner) to identify health care problems, propose and test solutions, 
33

measure and analyze results of the test, and assess the promise of statewide implementation for 

improving quality. The solutions will undoubtedly include private-sector and public-sector 

solutions. 

Figure 2.2. State-led quality improvement—Stages 1 (inner circle) and 2 (outer ring)

Stage 3: Implement improvement. Figure 2.3 completes the quality improvement process by 

adding the third and final ring—implementing improvement. This stage is essential for spreading 

success. Because this is where complex partnerships might falter, this ring also uses the Plan-DoAssess process for implementation. The activities might include: 

• Plan—A written plan to spread ideas for change in public and private programs might 

encompass an advertisement campaign and/or new financial incentives or award mechanisms 

for quality improvement. The plan should specify how each partner will contribute to the 

process of bringing about change. A written plan is important to test and coalesce the group’s 

commitment, which will be essential for successful implementation. 

• Do—The implementation begins as the group sets about to spread the change and measure 

the impact of the effort to spread change. The group could falter here by assuming the work 

of the group is finished. However, the measurement step at this point is key to determining 

whether the groups’ ideas are effective, continue to be implemented, and have the desired 

effect. 

• Assess—The group should reconvene periodically to evaluate and discuss the spread of 

change and its outcome—successes and failures. It may be necessary to modify the plan and 

try new approaches, continuing to measure and evaluate the modifications. Or, the group may 

be ready to tackle the next area for improvement. 
34 

Here, the continuous cycle of quality improvement is apparent. The team identifies areas in need 

of improvement, designs a solution, tests it, and plans how to move the solution beyond a 

specific demonstration setting and into the broader practice of health care. The team also 

measures the spread and uptake of the solution in practice, assesses whether the spread has been 

successful and tackles the next problem area. An effective team is committed to ongoing quality 

improvement. 

Figure 2.3. Complete State-led quality improvement framework—Stages 1 (inner circle), 2 (middle 

ring), and 3 (outer ring)

The complete framework. The process of quality improvement may take more than a single 

turn around the circles before results are seen. Furthermore, it will require continuous 

application of the framework to specific quality problems to improve health care quality 

statewide. This means that commitment, leadership, continuity, and the right incentives are 

essential. States, as health care purchasers and leaders in health policy, are well positioned to 

provide these characteristics. 

In this framework, the State is the supporting structure that brings the partnership together and 

nourishes it. State leadership provides energy, facilitates group processes, supplies data when 

available and may collect new data, disseminates evidence-based information (or asks another 

partner to assume that role), and stimulates the group to improve health care quality. The State 

provides an environment for competitors to come together and improve their professional 

services. 

As noted earlier in the discussion of general models, many local and regional quality 

improvement efforts already exist among disconnected groups, and thus a critical aspect of the 

State role will be outreach and education to coordinate and harmonize these diverse efforts. 
35

Selecting Targets for Improvement 

Improving any process requires targeting specific areas or problems. In health care, specific 

conditions or treatments are usually the place to start. Deciding which health conditions or 

procedures to select for improvement is the first challenge facing State leaders and their partners. 

Finding candidates for improvement will be relatively easy; narrowing the list will be difficult. 

Various criteria can be used to identify targets. Answers to a series of questions can help 

determine a priority list of targets for quality improvement. The quality improvement team may 

want to add to or subtract from these: 

• Is there clinical or quality improvement evidence that specific changes will improve health 

care outcomes? 

• What measures of quality health care exist for this targeted area? 

• Are there benchmarks for high quality care? 

• Is there variation across geographic areas, vulnerable subpopulations, or individual providers 

in the quality of care delivered? Is the variation excessive compared to that in underlying 

clinical conditions that clinicians must treat in different ways? 

• Is there a way to assess how a State or smaller geographic area performs in a targeted area? 

• How many lives are affected by the condition or treatment (i.e., prevalence, morbidity, and 

mortality related to the condition)? 

• What is the cost of care and the potential for a return on (or saving from) investment in 

quality improvement? 

Most of these questions take considerable effort to answer. For this reason, AHRQ has begun to 

assemble a set of resources targeted to helping States implement quality improvement initiatives. 

Information Resources for Quality Improvement 

AHRQ-Sponsored Resources for States 

AHRQ provides a number of resources for information and measures on health care quality. 

AHRQ supports research programs and publications intended to provide scientific evidence for 

quality improvement. Other important Federal resources on asthma are noted below also. 

NHQR and NHDR. Two valuable resources are the National Healthcare Quality Report and the 

National Healthcare Disparities Report mentioned above. The former offers benchmarks for 

tracking U.S. health care quality nationally and by State; the latter looks at quality of care and 

access to care for vulnerable subpopulations, such as racial/ethnic minority groups and low 

income groups. The reports, mandated by Congress, were first published in 2003 and are 

produced annually; to date, reports for 2003, 2004, and 2005 are available. All releases of the 

NHQR and NHDR can be accessed at: www.qualitytools.ahrq.gov. 

State resources for selected measures from the NHQR. Measures at the State level from the 

2005 NHQR are available as a user-friendly, interactive Web resource for examining the 

performance of each State and the District of Columbia across various dimensions of quality. 
36 

These dimensions include types of care (preventive, acute, and chronic), settings of care 

(hospital, ambulatory, nursing home, and home health), and total quality (a summary of all Statelevel measures in the NHQR). Also included are breakdowns of the measures that go into 

creating each summary measure. Users can also find quality measures available for specific 

clinical conditions in downloadable tables. These State resources are available at: 

www.qualitytools.ahrq.gov/qualityreport/2005/state. (State resources based on the 2004 NHQR 

are also available; see: www.qualitytools.ahrq.gov/qualityreport/2004/state.) 

HCUP and statewide discharge data systems. Another source for State-level data is the 

statewide discharge data developed within States by State governments, hospital associations, 

and other private data organizations. The Healthcare Cost and Utilization Project is a publicprivate partnership sponsored by AHRQ with 33 participating statewide data organizations that 

accounted for about 90 percent of U.S. discharges in the United States in 2001. As noted in 

Module 1, HCUP provides asthma hospitalization rates for participating States. More 

information on HCUP is available at: http://www.hcup-us.ahrq.gov. 

Evidence reports. AHRQ-sponsored Evidence-based Practice Centers (EPCs) review all 

relevant scientific literature on clinical, behavioral, and organization and financing topics to 

produce evidence reports and technology assessments. These products are used for informing 

and developing coverage decisions, quality measures, educational materials and tools, guidelines, 

and research agendas. EPCs also conduct research on methods of quality improvement. Topics 

are chosen for their relevance to clinical, social science/behavioral, economic, and other health 

care organization and delivery issues—specifically those that are common, expensive, and/or 

significant for the Medicare and Medicaid populations. There are over 120 evidence reports 

assessing various clinical issues (including asthma) and other topics, such as approaches for 

closing the gap in health care quality. A list of these reports is available at: 

http://www.ahrq.gov/clinic/epcix.htm. 

Quality improvement tools. AHRQ also publishes resources for quality improvement for 

specific chronic illnesses. This Resource Guide is the second published by AHRQ to focus on a 

specific chronic illness for States. Diabetes Care Quality Improvement: A Resource Guide for 

State Action and Diabetes Care Quality Improvement: A Workbook for State Action were 

published in 2004. These resources provide measures and benchmarks for States to develop their 

own quality improvement goals and strategies in addition to the ones provided in the National 

Healthcare Quality and Disparities Reports. Copies of the diabetes Resource Guide and

companion Workbook can be downloaded at http://www.ahrq.gov/qual/diabqualoc.htm . 

National Quality Measures ClearinghouseTM (NQMC). AHRQ sponsors the National Quality 

Measures ClearinghouseTM. This online clearinghouse is a database and Web site for 

information on specific evidence-based health care quality measures and measure sets. It 

provides practitioners, health care providers, health plans, integrated delivery systems, 

purchasers, and others a way to get detailed information on quality measures for quality 

improvement. The NQMC is available at: http://www.qualitymeasures.ahrq.gov/. 
37

Other Federal Data Resources for States 

Data sources that can be found within States may include disease registries, hospital discharge 

data programs, etc. After seeking asthma data within the State, States will need to look for 

national asthma data to use for benchmarking their progress. 

Behavioral Risk Factor Surveillance System. BRFSS is a national data source that provides 

data at the State level. Currently, it provides the richest source of asthma data nationwide and by 

State. BRFSS data are based on telephone surveys developed by the CDC but administered by 

each State independently. The survey consists of a core set of questions developed by CDC, 

additional questions developed by the States, and separate, optional modules for States to use. 

The asthma module, which contains the quality-of-care questions, is optional for State use. More 

information about the BRFSS data and methods as well as interactive databases with some State 

and local level asthma data are available at: http://www.cdc.gov/brfss/. 

National Asthma Control Program (NACP). The Centers for Disease Control and Prevention 

also supports a number of Federal programs for States including the National Asthma Control 

Program. (See Appendix C for a list of State interventions for asthma.) This program funds 

States to provide surveillance on asthma and other interventions. Information about the NACP 

can be found at http://www.cdc.gov/asthma/NACP.htm. 

National Asthma Survey (NAS). States should also note that the National Asthma Survey has 

been developed by CDC and other partners as a model for States to use to collect information on 

asthma prevalence and care. The NAS data set includes the BRFSS asthma measures in addition 

to nearly 70 other measures for asthma. (NAS measures are discussed more fully in Module 4 

and in Appendix D.) A pilot data release of NAS data for four States is available at: 

http://www.cdc.gov/nchs/about/major/slaits/nsa.htm. 

National Asthma Education and Prevention Program (NAEPP). Another Federal program 

for States, the NAEPP works with intermediaries including major medical associations, 

voluntary health organizations, and community programs to educate patients, health 

professionals, and the public. NAEPP is coordinated by the National Heart, Lung, and Blood 

Institute. Part of the National Institutes of Health (NIH), NHLBI develops clinical guidelines for 

diagnosis and treatment of asthma. Information about NAEPP can be found at 

http://www.nhlbi.nih.gov/about/naepp/. 

Summary and Synthesis 

States have typically viewed their role in quality improvement from the perspective of the 

guardian of public health, a manager of health care for the poor or disabled, or a buyer of health 

insurance for State employees. However, States can play a more comprehensive leadership role, 

and indeed some States are already doing this, at least in part, with respect to asthma. 

 The framework described in Module 2 envisions three roles for States in quality improvement: 

1. Provide leadership, which entails providing a defining vision for change, identifying 

partners to set goals, and providing an environment that fosters improvement. 
38 

2. Work in partnership, which involves creating a committed partnership of stakeholders 

dedicated to identifying and proposing and testing solutions and developing plans for 

improvement. 

3. Implement improvement, which means creating interventions within a strong partnership, 

measuring and analyzing the results of changes, and applying successful improvements on a 

broader scale. The solutions will undoubtedly include those from both the private and public 

sectors. 
39

Module 3: Learning From Current State Quality Improvement 

Efforts 

States are currently involved in many efforts to improve the quality of asthma care. These 

actions can inform other State efforts. This module provides examples of current State efforts to 

improve the quality of asthma care within the context of a State-led model of quality 

improvement. 

The Introduction to this Resource Guide described a new, strategic role for States in leading 

quality improvement for asthma. A State-led quality improvement framework that States could 

use in playing this role was described in Module 2. Building on lessons from industrial and 

clinical models for quality improvement, it adapted the Plan-Do-Study-Act cycle of quality 

improvement for the policymaking context to a Plan-Do-Assess approach illustrated in Figures 

2.1- 2.3. 

This framework identifies the three stages in which States can play a key role—provide 

leadership, work in partnership, and implement improvement. Many States are already active in 

these areas, so Module 3 also provides specific examples of what States are doing currently at 

each of the three stages presented above. 

Current State Efforts To Improve the Quality of Asthma Care 

States have typically viewed their role in quality improvement from a public health perspective 

or, more narrowly, as a buyer of health insurance for state employees. However, as outlined in 

the introduction, States can play a broader and more strategic role. Some States are already 

doing this, at least in part, with respect to asthma. 

States have undertaken a variety of asthma initiatives over the years. Many of these have been 

funded by the Centers for Disease Control and Prevention. States have used CDC funding to 

establish creative programs to address asthma prevention and control. As attention to health care 

quality has increased, State asthma programs have also adopted quality improvement aims. 

States also initiate other programs. States have established asthma disease management programs 

in Medicaid and have partnered with the private sector on quality improvement for asthma care. 

Many States also have tried to integrate CDC-funded efforts with private sector and Medicaid 

efforts. 

Key Ideas in Module 3: 

• A variety of quality improvement initiatives at the State level are sparking change 

in health systems across the Nation. 

• States can use this module to identify examples and resources for asthma care 

quality improvement. 
40 

Appendix C lists over 100 separate programs in 48 States that target improvement in some aspect 

of asthma care and include efforts at the Federal and State/local levels, joint private/public 

efforts, and efforts by private national organizations with Internet links to more information. 

 A significant number of these programs are targeted to specific populations—such as children, 

minority communities, or Medicaid recipients—or on public health approaches toward asthma 

mitigation. However, it is difficult to generalize about these programs, given their diversity and 

heterogeneity. Therefore these programs are divided into 12 categories that relate their relevance 

to some of the important aspects of quality improvement: 

• Advisory bodies and councils. 

• Coalitions. 

• Collaboratives. 

• Cross-agency work. 

• Data measurement and reporting. 

• Developing and enforcing guidelines. 

• Disease management. 

• Minority and rural outreach. 

• Public service/education efforts. 

• Self-management (of asthma). 

• Provider training. 

• Use of technology. 

Note that these categories cut across the three stages of activity highlighted in the State-led 

model. The number and range of State activities make it difficult to present all instances. 

Therefore this module presents some examples of specific programs in States that fit these 

stages. In stage 1, States are providing leadership by championing quality, convening 

partnerships, and providing support. In stage 2, States are working in partnership on various 

activities—planning for quality improvement, developing and complying with asthma 

guidelines, and supporting measurement and data collection. In stage 3, States are implementing

improvement by supporting activities that implement asthma care quality interventions of various 

types, evaluating their effectiveness, and spreading success. 

Most of the information provided below and in Appendix C was derived from a review of State 

health department Web sites, CDC resources, Internet research, and in-person interviews with 

State agency officials. Examples below provide a sampling of State efforts that reflect regional, 

size, and funding differences among States. Although not an exhaustive list, it demonstrates a 

range of State efforts related to asthma quality improvement. 

Stage 1: Provide Leadership 

A Champion for Quality 

Having a champion for quality improvement is critical to the success of any asthma quality 

improvement initiative. Champions provide consistent leadership, give greater visibility to 
41

issues, and spur others toward greater strides in quality improvement. In some cases, asthma 

quality improvement initiatives have received recognition and support from the highest leaders 

of State government. The involvement of an influential, recognizable leader, such as a governor 

or other high-level elected official, can enhance stakeholder engagement in the process and 

heighten the attention given to quality improvement initiatives in the media and within the public 

and private sectors. 

In other cases, dedicated staff within an executive agency, such as the asthma control program 

staff in the State’s health department, are instrumental in providing the leadership needed to pull 

together diverse stakeholders and influential elected officials to promote quality. For example: 

• In New York, Governor George Pataki has championed improving asthma care. Following 

his 1999 State-of-the-State speech, New York launched an aggressive asthma prevention and 

control agenda. New York has developed clinical treatment guidelines for asthma, provided 

funding to regional coalitions to improve asthma care, and implemented a Medicaid disease 

management and quality improvement initiative related to asthma. 

• Staff in Oregon’s Asthma Program was established a Workgroup on Improving Asthma Care 

with representatives from health plans, health care providers, medical professional groups, 

and advocates. Through a consensus process, the workgroup developed guidelines for asthma 

care in the State based on NHLBI guidelines that were then published and distributed by 

Oregon’s Asthma Program to providers, medical professionals, and others. 

Convene Partners and Develop Support 

Quality improvement leaders cannot accomplish their task alone. Creating networks of support 

has been critical for State programs that address asthma quality improvement. States have used 

various methods to convene parties interested in improving asthma care, including creating or 

assembling broad coalitions or networks of multiple stakeholders as well as using advisory 

bodies, councils, or State workgroups that are smaller and authorized by statute or regulation. 

State advisory bodies, councils, and workgroups. A number of States have established 

through legislation or executive action, advisory boards, councils, or workgroups on asthma that 

assist with statewide asthma planning and quality improvement efforts. Advisory bodies, 

councils, and workgroups are generally led by State officials and typically include a variety of 

experts and stakeholder groups from the public and private sectors, such as the American Lung 

Association, State health professional associations, hospital associations, and provider 

organizations. Other stakeholders may include large businesses, employer groups, and other 

community leaders. These advisory bodies, usually housed within the State’s health department, 

are often supported through CDC Asthma Prevention and Control Programs. 

 

• The Minnesota Department of Health developed the Commissioner’s Asthma Advisory 

Workgroup to provide direction and assistance in forming a statewide plan to address the 

rising health and economic burden of asthma in Minnesota. 
42 

• Connecticut established its Asthma Advisory Council in 2004 to assist in the 

implementation of the State asthma plan. The council consists of 15 members appointed by 

the State Commissioner of the Department of Public Health. 

Coalitions and networks. Coalitions and networks are broad-based, voluntary efforts, in 

contrast to advisory bodies or other State-sanctioned entities. These groups are generally formed 

by private initiative; elected State officials or executive agency staff may participate. Coalitions 

and networks bring together a broad variety of stakeholders in a State to work together to 

identify areas of strength, common objectives, and gaps in services. They also develop plans to 

assure that the essential treatment and educational services for managing asthma are in place in a 

community. 

Coalitions may also include community representatives and nontraditional partners such as the 

corner grocery store owner, faith communities, health organizations, social service agencies, and 

more. Coalitions and networks can be important allies in quality improvement efforts in the State 

because of their broad membership and natural interest in improving the quality of care for 

asthma. For example: 

• Ohio’s Asthma Coalition is an association of medical and public health professionals, 

business leaders, various government agencies, community activists, and others dedicated to 

improving the quality of life for people with asthma through information sharing, 

networking, and advocacy. 

• The Colorado Asthma Coalition is a group of health care professionals and community 

members committed to working together to improve public awareness and education, data 

collection and research, and provider education. 

Stage 2: Work in Partnership 

Planning for Quality Improvement 

Convening a State advisory body or a coalition of stakeholders or both is just the first step in the 

improvement process. The group must then develop a strategy and plan of action for asthma 

quality improvement. 

In many States, the State’s asthma plan will be the guiding document for group action. A State 

asthma plan is required by the CDC for States that receive funding from its National Asthma 

Control Program. These plans provide an overview of the asthma prevention and control issues 

within the State that need to be addressed. The plan also articulates goals and identifies 

strategies the State will use to achieve the goals. Thirty-five States received fiscal year 2004 

funding from CDC for asthma prevention and control. (More information on State asthma plans 

is available on the CDC Web site at www.cdc.gov/asthma. The CDC Web site provides links to 

State asthma programs and their State plans.) 

State asthma plans generally include most of the traditional public health activities such as 

education and awareness, data collection, disease surveillance, and partnership activities with 
43

State and community groups. Some State asthma plans also include improving the quality of care 

for asthma, although this varies. States have used the State asthma plan process to develop 

evidence-based asthma treatment guidelines, collect data on quality of care measures, improve 

asthma self-management education and practice, and train providers on asthma management 

training. 

In other cases, asthma quality improvement initiatives may develop apart from the State’s asthma 

program, such as with Medicaid disease management or pay-for-performance initiatives. 

Provided below are examples of different ways that States have worked in partnership with 

others to develop and implement asthma quality improvement plans. 

Developing and Complying With Asthma Guidelines 

To help translate research-based evidence into practice, several States are promoting the use of 

evidence-based clinical guidelines for asthma care. Like Oregon, many States have adopted 

guidelines established by the National Heart, Lung, and Blood Institute, while others have 

worked through the process of developing State-specific asthma treatment guidelines. 

• New York released its Clinical Guidelines for the Diagnosis, Management, and Evaluation 

of Adults and Children with Asthma – 2003 along with a call to action to the State’s health 

care professionals to participate in regional conferences for physician education. 

• Missouri’s Center for Asthma Treatment at Children's Mercy Hospital has worked to fully 

implement the 1997 NHLBI guidelines for diagnosis and management of asthma by creating 

an integrated program for asthma treatment and standardizing the education and medical 

treatment of people with asthma. 

• The Texas Medicaid Managed Care Asthma Project is a pediatric asthma pilot program for 

Medicaid enrollees. The program provides standardized patient and family asthma 

management education and supplies best practice guidelines to providers. 

Quality Measurement and Data Collection 

The development of quality measures and data collection and analysis are fundamental steps in 

quality improvement. States have used CDC asthma program funding to improve data 

collection, including information about prevalence, death rates, and other statistics. Other data 

sources include managed care plan or Medicaid program data. (More information on identifying 

and using asthma data sources for quality improvement plans is presented in Module 4.) 

• The Colorado Asthma Program is developing a statewide surveillance data system to 

determine the prevalence, mortality, and morbidity of asthma in the State and to assess any 

associated morbidity and mortality. 

• Wisconsin’s Asthma Plan includes objectives to use (by 2007) the NHLBI guidelines for 

diagnosis and management of asthma statewide and to build the capacity of health care 

organizations in the State to monitor and measure asthma care quality. 
44 

Partnerships Beyond Health Care 

Effective asthma interventions can leverage partnerships beyond the health care setting. Asthma 

interventions can involve leaders in businesses, employer groups, schools and day care centers, 

and organizations of caretakers, social workers, and others. Comprehensive asthma interventions 

should address environmental issues that affect people with asthma and support self-management 

for patients in the context of their communities and daily lives. 

• The National Cooperative Inner-City Asthma Study Intervention is a social-workerbased education program that focuses on environmental control. Social workers are trained as 

asthma counselors and work with the child’s caretaker to improve communications between 

family and physician (Sullivan et al., 2002). 

Stage 3: Implement Improvement 

Implementing Asthma Care Quality Interventions 

States use a variety of interventions to affect asthma care. Some examples of ways that States 

seek to improve the quality of care for asthma are listed below. 

Self-management/patient education. Patient self-management is critical for good asthma 

outcomes. Patient education programs can be conducted in a variety of settings that are 

accessible to target populations, including: churches, neighborhood associations, schools, and 

community-based organizations that are well recognized in a community. These programs can be 

conducted in small groups, or one-on-one, based on the identified needs of the population. 

• Alabama’s Inner-City Asthma Intervention program at the University of Alabama at 

Birmingham provides patients with individualized treatment plans and education based on 

evaluation from physicians, nurses, and educators to improve self-management skills. 

• In North Carolina, the Inner-City Asthma Intervention program provides individualized and 

group educational sessions on asthma for children. The sessions provide a basic 

understanding of asthma, its triggers, environmental control, warning signs, and 

medications. 

Collaboratives. Improving the quality of care for asthma is a systemic issue. The entire health 

care system and all its actors need to be mobilized to improve the quality of care received by 

persons with asthma. Building on the successes of the Health Disparities Collaboratives funded 

by the Health Resources and Services Administration (HRSA), a number of States have started 

or participated in collaboratives that bring together teams of practitioners to develop quality 

improvement strategies for clinical settings. Collaboratives typically use a PDSA model to bring 

together teams over a period of time, develop an improvement idea, test it on a limited basis, 

study the effect, and then implement the change more broadly. 
45

• California Medi-Cal officials and a group of health plans, providers and community-based 

organizations that serve the Medi-Cal population have participated in the Plan/Practice 

Improvement Partnership, a quality improvement effort aimed at developing clinical and 

administrative approaches to improve asthma care. The collaborative is funded through the 

Center for Health Care Strategies Best Clinical and Administrative Practices program. 

• The New York State Medicaid Asthma Disease Management and Quality Improvement 

Initiative promotes disease management interventions in the treatment of asthma. 

Community Health Centers in the greater NYC region provide patient education to improve 

health outcomes for Medicaid recipients. 

Provider training. Because health care providers are a key element in improving asthma quality 

care, many States have actively sought their involvement in developing programs. In addition, 

States are providing outreach, training, and support to health care professionals as they seek to 

implement new evidence-based care guidelines. 

• Arkansas Asthma Coalition offers primary care physicians, health care providers, school 

nurses, and physician office staff training that spotlights diagnosis, evaluation, and treatment 

of asthma as well as skills for managing, educating, and communicating with asthma 

patients. In addition, approximately 1,200 staff of public schools receive the American Lung 

Association's Asthma In-Service Training. 

State disease management programs. Because States are looking for ways to control Medicaid 

costs while maintaining or improving quality, 38 States are implementing disease management 

programs, many of them targeting asthma. Medicaid disease management programs seek to 

increase patient knowledge and self-management skills, improve provider adherence to clinical 

guidelines, and implement computer technology to track patients more effectively in clinical 

settings for provider awareness and for system-wide evaluations of the effectiveness of the 

intervention. Improved care management for asthma helps patients get their asthma under control 

and ensures that care provided meets accepted standards. 

• The Virginia Health Outcomes Partnership established a training program for physicians in 

the Medicaid Primary Care Case Management program that focused on reducing emergency 

care for asthma through better education for physicians on disease management and 

communication skills. The program resulted in overall savings for Medicaid, even when the 

training costs and higher drug costs were factored in. 

• The Florida Agency for Health Care Administration’s Medicaid Disease Management 

Program has contracted with experienced disease management organizations to provide 

disease management services to Medicaid recipients who have been diagnosed with asthma. 

• The Indiana Chronic Disease Management Program (ICDMP) was developed after 

legislation required the Office of Medicaid Policy and Planning to implement a disease 

management program for people with asthma and other chronic diseases. The ICDMP 

provides information on asthma for Medicaid recipients as well as all other patients. 
46 

• The Missouri State Medicaid Disease Management program is targeted toward patients 

enrolled in the fee-for-service Medicaid program. This program focuses on disease 

management tactics for asthma patients determined to be at high risk for adverse outcomes. 

The goal is to slow the progression of asthma and avoid medical crises.

Pay-for-performance initiatives. Quality improvement experts have long recognized that 

providers have little incentive to improve health care quality in an environment where every 

health care organization is paid for quantity of services rather than quality. While the 

effectiveness of pay-for-performance on quality improvement is still being studied, an increasing 

number of private and public payers are exploring use of financial incentives to spur quality 

improvement (Dudley, 2005). CMS is conducting a number of Medicare demonstration projects 

that include pay-for-performance. As of January 2006, CMS has a new voluntary program on 

quality measure reporting to help providers assess their performance in anticipation of the trend 

to implement pay-for-performance in both the public and private sector. Some private insurers 

have already begun implementing pay-for-performance to improve quality, including Blue 

Cross/Blue Shield, Wellpoint Health Networks, and others. (More information can be found at 

The Leapfrog Group Web site at http://www.leapfroggroup.org/leapfrog_compendium.) 

States, too, are implementing pay-for-performance initiatives, particularly through Medicaid 

managed care contracts. Pay-for-performance initiatives are still in early stages of development. 

Currently, most pay-for-performance initiatives have a broad focus and use quality measures for 

several chronic conditions including asthma. 

• Iowa and Massachusetts have included financial incentives to contractors that deliver 

behavioral health services. One study found that the contractors involved in pay-forperformance initiatives showed improvement in the specific areas where financial incentives 

were provided (Dyer et al., 2002). 

• Medical groups in California have agreed to use common data and performance measures 

for their quality improvement incentives. The pay-for-performance initiative is a 

collaboration of seven California health plans which use the same survey instrument for 

patient satisfaction and some HEDIS® measures for cancer screening, asthma, diabetes, 

coronary artery disease, and immunizations. More information on this initiative can be 

found at http://www.iha.org. 

Evaluating Effectiveness 

Interventions to improve quality of care need to be evaluated for clinical effectiveness as well as 

cost effectiveness. State leaders, however, need a clear definition of success. Demonstrating real 

cost savings in a short time frame can be difficult. What may be more readily demonstrated is 

improved quality of care, cost avoidance, and improved quality of life for patients. With 

diligence, careful planning, and longer time to evaluate a program, States can also evaluate the 

cost effectiveness of quality improvement efforts. 

One study (Rossiter et al., 2000) offers a good example of how to design and evaluate an asthma 

intervention program. The Virginia Health Outcomes Partnership targeted low income asthma 
47

patients in a Medicaid primary case-management program. The program aimed to reduce the rate 

of emergency care visits for asthma. An intervention group and a control group were used to 

assess the effectiveness of the intervention in a real world setting where other factors can be 

expected to change. The study provided statistics on the reduction in emergency care visits as 

well as the projected direct savings to Medicaid that accounted for the cost of the physician 

training and the costs of increased prescribing of drugs to control the asthma symptoms. (See 

Module 1 for information about estimating potential cost savings from quality improvement.) 

Spreading Success 

The experience of success should not be isolated to a single program or clinic. Successful 

programs and strategies need to be disseminated in order for quality of care to be improved 

overall. It is important to adapt existing models for quality improvement and to spread their 

success to other communities and health care settings. 

• The federally sponsored Health Disparities Collaboratives, developed by HRSA with the 

Institute for Healthcare Improvement, aim to transform the delivery of care in community 

health centers. The program improves the care for certain chronic conditions by targeting 

providers, patients, and communities to support provider-patient partnerships. Participants in 

the Health Disparities Collaboratives have worked to improve care for their patients with 

asthma and spread the changes and improvements throughout their health center. 

• Organizations such as the National Initiative for Children’s Healthcare Quality (NICHQ)

have formed other learning collaboratives to spread success in asthma care improvement as 

well (http://www.nichq.org/nichq). States can use this model with other programs to spread 

the success of these programs. 

• In North Carolina the Center for Children’s Healthcare Improvement (CCHI) has worked 

with the State’s Division of Medical Administration and Area Health Education Centers 

(AHECs) on a variety of quality improvement projects aimed at improving the care for 

asthma among the Medicaid population. Building on its past successes working with clinical 

practices, CCHI plans a multi-tiered policy, community, and clinical approach to spread 

quality improvement statewide, beginning first in two AHEC regions. 

Summary and Synthesis 

Successful programs of current asthma quality improvement activities provide State leaders with 

examples, useful resources, lessons learned, and approaches for enhancing initiatives and 

partnerships. State programs have been successful in improving asthma care. Even so, much 

remains to be done. States have a unique role to play in championing improvement for asthma 

care, forging partnerships to address approaches to change, and implementing those approaches 

to help providers deliver the best care and to help people with asthma enjoy optimal quality of 

life. Whether a State is building the infrastructure for improving asthma care or already has a 

well developed set of partnerships in place, there are a variety of approaches in place that can 

inform State efforts. 
48 

Resources for Further Reading 

• Institute for Healthcare Improvement stories for asthma; available at: 

http://www.ihi.org/IHI/Topics/ChronicConditions/Asthma/ImprovementStories/. 

• Rossiter LF et al. The impact of disease management on outcomes and cost of care: A study 

of low-income asthma patients, Inquiry. 2000;37:188-202. 

• Rust GS, Murray V, Octaviani H, et al. Asthma care in community health centers: A study by 

the Southeast regional clinicians’ network. Journal of the National Medical Association. 

1999;91(7):398-403. 

• Stanton MW, Dougherty D, Rutherford MK. Chronic care for low-income children with 

asthma: strategies for improvement. Rockville, MD: Agency for Healthcare Research and 

Quality; 2005. Research in Action Issue 18. AHRQ Pub. No. 05-0073. 

Associated Appendix for Use With This Module 

Appendix C: National and State Asthma Programs 

Appendix C lists asthma quality improvement programs by State and Web site links for further 

information. 
49

Module 4: Measuring Quality of Care for Asthma 

This module discusses the basic building blocks of quality improvement—measures and data. 

The module describes the asthma-related data available in the NHQR and NHDR and from other 

sources that States can use. Each State has a cadre of health statisticians and analysts who should 

be recruited as part of any quality improvement project aimed at the health care system in the 

State because they will be familiar with local health data and because they know how to use and 

interpret data. 

Quality Measurement 

This section reviews the concept of quality measurement, available asthma-related measures in 

the NHQR and other sources, and the importance of using multi-dimensional measure sets. All of 

this is examined from the perspective of States and their role in initiating quality improvement 

programs. 

The Concept of Quality Measurement 

The Institute of Medicine defines health care quality as “the degree to which health services for 

individuals and populations increase the likelihood of desired health outcomes and are consistent 

with current professional knowledge” (IOM, 2001). That definition suggests a distinction 

between quality measures and guidelines for quality care: 

• Quality measures relate to populations. They include rates that indicate how many members 

of a population achieved a goal (for example, low emergency room visits for asthma 

nationwide) relative to a population base (for example, all people with asthma in the United 

States). 

Key Ideas in Module 4: 

• Quality improvement begins with measurement, which requires good measures and 

data for measuring quality of care. 

• Process and outcome measures should be considered together to assess asthma care 

quality. 

• The NHQR is a starting point for accessing consensus-based measures. Although a 

consensus on a small core of key asthma measures has not yet evolved, this Resource 

Guide identifies measures that are available for local quality improvement programs. 

• Before undertaking any extensive data collection, State agencies should identify the 

questions to be answered and the data available to answer them. There are national and 

local data sources that can provide relevant data for creating estimates of State 

performance. 

• State-level baseline estimates for asthma care afford State leaders a broad view of 

asthma care quality in their State. 

• Analysis of data can answer some key questions for States: 

o What measures should be used to set goals for quality asthma care? 

o What goals should be set as targets for specific measures? 

o What factors influence a State’s position among other States? 
50 

• Guidelines for quality care are recommendations devised via consensus processes of clinical 

experts that describe standards of care for individual patients. In general, guidelines for 

individual patient care prescribe what clinicians can do to improve the care that they deliver 

to their patients with a specific disease or condition. These guidelines also are used as the 

basis for developing population-based measures that enable analysts to assess and track 

change in the treatment of a population. 

With a specific population in mind, a quality improvement program should consider the 

dimensions to be measured before embarking on data collection. What is to be measured? What 

change will be instituted? What quality measure will track the spread of that change? What is the 

ultimate outcome to be improved and how is that changed measured? What special populations 

are to be targeted and how will their improvement be documented? 

Types of Quality Measures 

Quality measures cover a large range, from crude measures (for example, unadjusted mortality 

rates) to more refined measures (for example, percent using asthma medications to achieve better 

asthma control). Although a full range of measures is essential for a complete picture of health 

care quality, specific process measures are needed to guide a health care team in improving 

quality of care. For example, the number of deaths related to asthma at a hospital can suggest 

poor quality of treatment at that hospital and in the community, but knowing the number of 

deaths does not tell the hospital staff or community providers how to improve. Metrics that 

measure processes of care that reduce deaths or improve other medical outcomes help medical 

staff know how to change care so that they provide better care. 

Most quality improvement efforts focus on two types1

 of measures—process and outcome: 

• Process measures often reflect evidence-based guidelines of care for specific conditions. 

Process measures are generally considered to be within the control of the provider and, 

therefore, are performance indicators. They also are more likely to reveal actions that can be 

taken to improve quality (for example, whether a necessary test or medication is given). 

• Outcome measures frequently relate to patient health status. Better outcomes are the 

ultimate objective of quality improvement—for example, lower mortality, lower 

hospitalization rates, or better test results. 

 

Ideally, improvements in processes yield improvements in associated outcomes, and measures 

should reflect that. However, the connections may not be that direct. For example, the asthma 

process measure for inhaled corticosteroid use is included in the NHQR because the evidencebased NHLBI clinical guidelines for asthma care recommend daily use of such medications for 

asthma patients with persistent asthma. Use of such asthma medications can help control and 

prevent asthma attacks and thus prevent the need for emergency care and hospitalizations. 

The NHQR also monitors the outcome measure of hospitalizations for asthma. In this case, 

 

1

 A third type of measure is less directly related to quality of care. Structural measures reflect aspects of the health 

care infrastructure that generally are broad in scope, system wide, and difficult to link to short-term quality 

improvement (for example, a hospital’s staff-to-bed ratio). The NHQR does not use structural measures. 
51

improvement in the process of prescribing inhaled corticosteroids and proper use by patients is 

expected to decrease the number of such hospitalizations, as diagrammed below. However, other 

factors (discussed more fully later in this module) are also important. Effective provider and 

patient education and self-management are crucial components. Without these, improved 

outcomes might never occur. 

 Relationship of Process Improvements to Outcomes 

Selection of Quality Measures for the NHQR 

The selection of quality measures for the NHQR was based on criteria that include the clinical 

significance of the measure, reliability of available data, and consensus of the experts. The first 

NHQR, published in 2003, used a consensus process for determining which measures to include 

in the national tracking of health care quality. That process included issuing a public call for 

measures and assembled an interagency task force that reviewed and selected measures 

according to criteria developed by the Institute of Medicine and adopted by the Interagency 

Work Group for the NHQR (see box). 

Other Sources of Asthma Measures 

The NHQR currently includes only a few asthma measures, but others are available. Some of the 

major developers of asthma measures are: 

• The National Asthma Survey, funded by CDC and tested in 2003, is a 15-minute survey that 

States can use to provide a comprehensive assessment of asthma in the State. The NAS 

includes questions found in the BRFSS asthma supplement as well as a more comprehensive 

set of questions on asthma care. (More information is available at 

http://www.cdc.gov/nchs/about/major/slaits/nsa.htm). 

• The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) DiseaseSpecific Care Certification Program provides an implementation guide for asthma 

performance measures for hospitals. A module for children’s hospitalizations for asthma is in 

development. (More information is available at http://www.jcaho.org.) 

• The HRSA Bureau of Primary Health Care supports Health Disparities Collaboratives for 

disease-specific conditions, including asthma, for primary health care centers to participate in 

Daily use of 

medications for 

patients with 

persistent asthma 

Reduced number of 

hospitalizations for 

asthma in the total 

population 
52 

learning networks to improve quality of care. These learning collaboratives maintain a 

registry of asthma patients and monitor care for asthma patients on a monthly basis. (More 

information is available at http://www.healthdisparities.net.) 

• The National Initiative for Children’s Healthcare Quality also develops learning 

collaboratives for asthma care for children based on the chronic care model for quality 

improvement. 

Multiple Dimensions of Quality for Asthma Care 

One challenge of initiating quality improvement for asthma care from the perspective of a State 

or local quality improvement team is selecting from measures that assess the process and 

outcomes of improved care. There are many measures that could be used to assess different 

aspects of asthma care. Table 4.1 shows important dimensions of asthma quality of care and the 

measures that have been developed to assess these dimensions for improving care for asthma. 

The dimensions include provider processes of care, patient self-care processes, and outcomes of 

care such as quality-of-life factors. In addition, insurance coverage and prevalence and severity 

of asthma among the population are important factors that will influence the various measures of 

asthma care quality in any population. 

Appendix D lists over 100 measures that are used throughout the country to measure asthma care 

quality and shows that different organizations evaluate different dimensions of asthma and 

define measures in different ways. Such variability in measurement makes it difficult, if not 

impossible, to compare across organizations, settings, and geography. CDC’s National Asthma 

Survey addresses nearly all of the dimensions of quality asthma care, and the Behavioral Risk 

Factor Surveillance System surveys address the questions of influenza vaccination and smoking 

cessation counseling. Only a measure of whether the physician assessed the patient’s asthma 

severity appropriately is missing. As noted earlier, the NAS was implemented in 2003 and tested 

Criteria for Selecting Asthma Measures: 

Importance 

• Impact on health: What is the impact on the patient? 

• Meaningfulness: Are providers and patients concerned about this area? 

• Susceptibility to influence by the health care system: Can the health care system meaningfully address 

this aspect or problem? 

Scientific soundness 

• Validity: Does the measure actually measure what it is intended to measure? 

• Reliability: Does the measure provide stable results across various populations and circumstances? 

• Explicitness of evidence: Is scientific evidence available to support the measure? 

Feasibility and usefulness

• Existence of prototypes: Is the measure in use? 

• Availability of required data across the system: Can information needed for the measure be collected in 

the scale and time frame required? 

• Cost or burden of measurement: How much will it cost to collect the data needed for the measure? 

• Capacity of data and measure to support subgroup analyses: Can the measure be used to compare 

different groups of the population? 

Source: Adapted from Institute of Medicine, Envisioning the National Health Care Quality Report, 2001. 
53

in a few States. It has been adapted for use as a call-back survey in the BRFSS; the call-back data 

will be merged with the BRFSS core data so that all the measures in BRFSS will be available for 

analysis with the asthma-specific data. States can use Table 4.1 as a guide to understand how the 

measures can be used to assess asthma care quality. 

Table 4.1. Dimensions of asthma care measurement 

Category Measure description Importance

Provider Care (Process Measures) 

A.1. Asthma 

severity assessment 

Asthma severity is assessed by 

health professional during a 

patient visit. 

Treatment strategies for asthma involve a stepwise approach in 

which the level of therapy increases with the asthma severity (see 

Module 1 for severity classifications). An adequate assessment of 

severity is thus a key step in determining appropriate management 

and treatment plans for patients with asthma (JCAHO, 2004). 

Asthma severity can be assessed by a health professional using a 

spirometer and taking a history of symptoms. Assessments are 

important for adjusting appropriate therapy and medication for 

long-term control of asthma. 

A.2. Asthma 

medications 

Use of anti-inflammatory 

medications (such as inhaled 

corticosteroids) to control asthma 

for patients with persistent 

asthma. 

There are two types of medications used for asthma: Antiinflammatory long-term controller medication and quick-acting 

relief medication for asthma attacks (bronchodilators). Daily antiinflammatory medications (or long-term controller medications) 

can prevent exacerbations and chronic symptoms for patients with 

persistent asthma. Inhaled corticosteroids are the most effective 

anti-inflammatory medication available for treating the underlying 

inflammation of persistent asthma (CDC & NHLBI, 2003). They 

do not have the serious side effects of oral steroids, especially 

when properly inhaled. Use of specific asthma medication and 

frequency of use are measures that show what percentage of 

asthma patients use medication and how well they understand how 

to use their medication. However, measures of medication use 

should be interpreted with knowledge of the severity level. 

A.3. Asthma 

management plans 

Patients with asthma who are 

given a written/documented 

asthma management plan. 

The management goals for controlling asthma can vary for 

different asthma patients. This is especially important for patients 

with persistent asthma. Therefore, it is important for providers and 

patients to discuss goals and how to control asthma. Writing a 

management plan helps clarify expectations for treatment and 

provides patients with an easy reference for remembering how to 

manage their asthma (CDC & NHLBI, 2003). 

A.4. Selfmanagement 

support or patient 

education 

Patients and their families have 

discussed with their doctors how 

to manage their asthma and avoid 

asthma triggers. 

Patient education is a key component of asthma care. Because 

management of asthma generally occurs outside of the doctor’s 

office after assessment and acute care, it is important for asthma 

patients and their caregivers to be informed about their asthma. 

The aim is to help patients manage their asthma in the context of 

their daily lives. Patients and their families should know how to 

recognize symptoms, how to avoid triggers, when and how to use 

asthma medication and delivery devices, and when to seek care. At 

a minimum, competent asthma education enlists and encourages 

family support, includes instructions on self-management skills, 

and is integrated with routine ongoing care (CDC & NHLBI, 

2003). 
54 

Planned care visits for asthma are 

completed at least every 6 months, 

or more frequently for more 

severely ill patients or those with 

comorbidities. 

Patients with asthma should seek care at least every 1-6 months 

depending on asthma severity and ability to control symptoms. 

Patients with asthma may experience varying symptoms and 

severity, which may require adjustments in therapy. Because of the 

nature of asthma, variable exposure to allergens and irritants, or 

insufficient adherence to a medication regimen, regular followup is 

recommended (CDC & NHLBI, 2003). 

Asthma patients are given 

influenza vaccines. 

A.5. Planned care 

for asthma 

Asthma patients are given 

smoking cessation counseling. 

During planned care visits, persons with asthma may require 

preventive care for other common conditions since they are more 

vulnerable to other health complications due to their condition. Flu 

vaccination is recommended for persons with asthma to prevent 

asthma exacerbation due to influenza. Smoking is also a trigger for 

many asthma patients since smoke (first- or second-hand) can 

exacerbate difficulty breathing. 

Patient/Parent Self-Care (Process Measures) 

Percent of asthma population that 

has been advised by a health 

professional to change things in 

home, school, or work to reduce 

asthma triggers.

B.1. Environmental 

modifications 

Percent of asthma population 

exposed to environmental tobacco 

smoke.

Environmental and occupational factors contribute to illness and 

disability from asthma. Decreases in lung function and a 

worsening of asthma have been associated with exposure to 

allergens, indoor pollutants (for example, tobacco smoke), and 

ambient air pollutants (for example, ozone, sulfur dioxide, nitrogen 

dioxide, acid aerosols, and particulate matter). The patient’s or 

caregiver’s awareness of environmental triggers is an important 

part of their ability to manage their asthma and prevent asthma 

attacks. There are numerous ways to reduce asthma attacks by 

making changes in the home, school or work such as reducing 

exposure to dust by removing carpeting or using special linens in 

the bedroom, removing pets, not smoking, etc. However, the 

extent to which these changes can be made depends on the 

patient’s ability to control these environments. Because not all 

changes are feasible, health providers must understand their 

patients’ environments and circumstances to give advice. 

Outcome Measures 

Number of days in the past month 

with limited activity due to 

asthma.

Number of school/work days 

missed in the past month due to 

asthma.

Number of days with sleeping 

difficulty in the past month due to 

asthma.

Number of days with (or free of) 

asthma symptoms in the past 

month. 

C.1. Daily symptom 

burden 

Frequency of use of beta-agonists 

for people with asthma. 

Asthma attacks and symptoms are indicators of the ineffectiveness 

of treatment and management of the disease. Also, asthma attacks 

or symptoms can have a significant impact on a person’s ability to 

participate in normal daily activities. Sensitivity to environmental 

triggers can keep a person with asthma from going to work or 

school. Assessing the number of days with limited activity helps to 

evaluate the burden of the disease on the population. Also, 

frequent use of beta-agonists for relief of asthma attacks is an 

indicator of ineffective long-term control of asthma. By 

monitoring the frequency of asthma attacks, symptoms, and use of 

quick-relief medications, access to and effectiveness of treatment 

can be assessed across the population diagnosed with asthma. 

Rate of asthma hospitalizations in 

the State. 

C.2. Acute 

avoidable events 

due to asthma 

(exacerbations) Rate of emergency or urgent care 

visits for asthma in the State. 

Hospitalization for asthma can often be prevented when the 

condition is properly managed. Hospitalizations, emergency 

department visits, or urgent care visits may reflect poor asthma 

management by patients and their health care providers. 

Hospitalizations are also highly disruptive to patients and families 

and increase the cost of asthma care for State Medicaid agencies 

and State employee benefits programs. Avoidable hospitalization 

measures are shown in Module 1, Table 1.2. 
55

Enabling Factor 

D.1. Access to care People with asthma who have 

health insurance coverage in the 

State. 

Health insurance coverage influences the propensity of patients to 

seek health care in the management of a chronic disease. Without 

health insurance, families are likely to cut down on routine 

medications and/or doctor visits for monitoring the condition and 

to have poorer results in managing it. 

Other Factors 

Percent of population that has ever 

been told they have asthma by a 

doctor or health professional. 

Percent of population that 

currently has asthma. 

D.2. Prevalence 

Percent of population that has had 

asthma attack in past 12 months. 

Though not modifiable (i.e., primary prevention of asthma is 

poorly understood), prevalence information provides an indication 

of the burden of disease on the population and health system. 

Data Sources for Asthma Quality of Care 

Once States have identified the appropriate measures, the next step is locating sources of data for 

assessing the health system’s performance in delivering quality care for asthma. This section 

describes three data sources for assessing asthma quality of care: the NHQR, the Behavioral 

Risk Factor Surveillance System (BRFSS), and local data sources. 

Asthma-Related Data in the NHQR: Avoidable Hospitalizations 

The NHQR asthma-related measures are primarily national or regional in geographic scope. At 

the State level, one asthma measure for outcomes of three age groups (under 18, 18-64, and 65 

and over) appears in the NHQR—avoidable hospitalizations related to asthma. As shown in 

Module 1, Table 1.2 lists that measure by age group, available for 33 States that have statewide 

hospital discharge data systems and participated in the Healthcare Cost and Utilization Project 

(HCUP) in 2001. HCUP is a Federal-State-Industry partnership, sponsored by AHRQ, which 

standardizes data across States. Table 1.2 shows: 

• The State’s hospitalization rate adjusted for age and sex differences among the States. 

• The difference between the State’s rate and the average of the “best-in-class” States—the 

10 percent of States that have the lowest admission rates. 

By examining the State rate and the difference from the best-in-class rate, a State can determine 

how far it has to go to reduce hospitalizations to become a top performer. 

Hospitalization rates are affected by demographic characteristics of the population such as age, 

socioeconomic status and race/ethnicity. Although quality improvement efforts do not modify 

these characteristics, quality improvement initiatives can target subgroups that experience 

disparities to improve their asthma care quality and improve outcomes such as reducing 

hospitalizations for asthma. 
56 

The NHQR contains State-level rates only for this outcome measure of avoidable hospital 

admissions. The NHQR currently excludes State-level asthma process measures because no 

national consensus has, as yet, established the key asthma measures out of the many that have 

been developed and used by various organizations. As noted previously, over 100 measures for 

approximately 50 topics related to asthma care quality are listed in Appendix D. Also, results 

from the new National Asthma Survey, designed by the CDC to overcome limitations in the 

BRFSS asthma supplement, were not available in 2003 and 2004 for the first two releases of the 

NHQR and NHDR. Future releases are expected to include asthma measures from the NAS 

when available nationwide. 

Six Asthma Measures in CDC’s Behavioral Risk Factor Surveillance System 

Currently, the richest source of asthma data nationwide by State is CDC’s Behavioral Risk 

Factor Surveillance System. However, data from BRFSS should be interpreted with care. Due to 

sample size limitations, estimates may have large standard errors. The estimates reported here 

are from the most recent data year, but several years of data could be pooled together for more 

reliable estimates. Despite limitations, BRFSS asthma data are a valuable starting point for 

viewing the national landscape of asthma quality care by State. 

Table 4.2 summarizes estimates for six measures derived from BRFSS listed in Table 4.1. Each 

measure is displayed with the three estimates—the national average (reporting States weighted to 

a national average), the best-in-class average (the 10 percent of States with the best value), and 

the poorest performing average (the 10 percent of States with the poorest values). 

Table 4.2. Six quality measures for asthma: National average, best-in-class average, and 

poorest performing average, 2003 

National 

Average Best-in-class average 

Poorest performing 

average 

Measure 

Category 

(as described 

in Table 4.1) Measure description 

Percent of 

people 

Standard 

Error 

Percent of 

people 

Standard 

Error 

Percent of 

people 

Standard 

Error 

Number of 

States 

reporting 

Process Measures 

A.2 Medications (in the 

past month) 71.1 0.9 75.3 1.8 62.1 2.8 19 

A.5 

Planned care visits 

(2 or more in the 

past 12 months) 

28.3 0.9 40.4 3.0 17.4 1.9 19 

Smoking 

(counseling in the 

past 12 months) 

A.5 82.2 1.6 87.9 3.0 75.8 4.1 15 

Flu shots (in the past 

12 months) 40.3 0.6 53.3 1.5 27.9 1.8 54 

Outcome Measures 

Urgent care visits 

(in the past 12 

months) 

28.1 0.9 19.4 2.0 35.5 1.9 19 

C.2 

Emergency room 

visits (in the past 12 

months) 

17.7 0.8 12.2 1.5 22.3 2.1 19 

Source: Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System, 2003. 
57

Table 4.2 shows that the gap between the best-performing and poorest-performing States varies 

by type of measure.2

 Process measures are practices that clinicians can directly influence. For 

the four process-of-care measures, for example, the largest gap is for planned care visits in the 

past year—a measure that reflects whether the asthma is being monitored routinely by the 

respondent’s health care provider. The difference between the best- and poorest-performing 

States on planned care visits is more than double. The gap for provision of flu shots is also nearly 

twofold. By contrast, the spread for the two other care processes—medication use and smoking 

cessation counseling—is small, about 15 to 20 percent. Thus, these latter two activities are more 

uniformly applied across the country than are planned care visits and flu shots. 

Outcome measures are necessary for State programs and policymakers to assess the effects of 

changes in processes of care on the outcome of patients with asthma, and thus on the success of 

the quality improvement program. For outcome measures, the proportion of people using urgent 

and emergency services for acute crises for asthma is 80 percent higher in the worst performing 

States than in the best. The variation in the outcomes of care for people with asthma is probably 

influenced by the variation in the quality of care they receive. 

Local Data Sources 

Finding appropriate data can be a challenge for quality improvement programs. To stimulate 

interest and start the quality improvement process, States can develop an inventory of local data 

sources. (See Appendix F for a summary of asthma-related data sources.) 

Local data (whether by State, county, municipality, or individual health care provider) are 

essential for quality improvement programs to have a local impact. Local leaders and health care 

professionals must see their own data compared with those from other providers and with State, 

regional, and national benchmarks in order to appreciate the importance of their work. By 

developing a complete inventory of data systems available at the State and local level, States can 

avoid duplicate data collection and reduce data-related costs. Also, a review of local data in the 

context of national data should clarify where existing local surveys or data systems could be 

improved. 

Some possible local data sources to consider are listed below. These data sources may or may 

not be health care specific, but they may afford important opportunities for collaborations with 

various State or local agencies. It may be possible to add questions to ongoing local surveys to 

inform quality improvement activities for asthma. 

Children and Youth: 

• State school health surveys, administered before entering public schools to assess youth 

health risk behavior, may include questions about asthma prevalence, activity limitations 

due to asthma, etc. 

 

2

 As shown in Table 4.2, the number of States (including DC and U.S. territories) reporting on each measure varies. 

For more detail on BRFSS estimates and individual State estimates of BRFSS measures, see Appendix E. 
58 

• The Youth Tobacco Survey, administered by State health departments, may include 

questions on asthma prevalence to assess health risks and health behavior related to 

tobacco use. 

• The Youth Risk Behavior Surveillance System, administered by CDC and State and local 

health and education agencies, monitors health-risk behaviors that contribute to 

unintentional injuries and violence, tobacco use, alcohol and other drug use, unintended 

pregnancy and sexually transmitted diseases, unhealthy dietary behaviors, and physical 

inactivity. 

Adults: 

• Occupational health surveys may provide data on work environment and triggers for 

asthma, activity limitation, and number of work days missed due to asthma. 

Community/Environmental Assessment: 

• Community surveys may provide local data on environmental factors that affect asthma 

and may compare asthma prevalence and outcomes by county, city, or neighborhood 

levels. 

Most States also have ongoing surveys or health data systems that collect data at the State, 

county, and sometimes provider level. Some of those data systems include: 

• State-level BRFSS data, available through the State health department. 

• Statewide inpatient hospital discharge systems that collect data on individual discharges 

from hospitals and can provide county-level and, sometimes, hospital-level data. National 

benchmarks are available for these types of data through the NHQR. 

• State vital statistics include mortality rates by cause of death and race/ethnicity. The 

National Vital Statistics System, which compiles these State data, can provide uniform 

national estimates and State estimates. 

• Special disease registries focused on asthma may exist within the State, and these provide 

a rich source of patient-level information on severity and adherence to tracked treatments. 

• Other special data collection of State departments of health statistics and other State 

programs may be modified to address asthma. 

Specific data systems for populations that the State supports are also available in most States. 

These include: 

• Medicaid information systems based on health care provider claims for Medicaid 

reimbursement. 

• State employee health benefit claims for reimbursement. 

• Patient records from State- or county-operated programs, such as mental health and 

substance abuse programs, public assistance, or justice systems. 

Examples of State-level data sources are available at the National Association of Health Data 

Organizations Web site at: http://www.nahdo.org/soa/soalist1.asp?Category=State%20Agency. 
59

Other Federal or national asthma surveillance systems compile data that describe State and local 

populations or health resources. These include: 

• NHLBI Web site, a valuable starting place to identify data and become familiar with the 

network of organizations and individuals associated with asthma data collection on the 

State and national level (http://www.nhlbi.nih.gov).

• Census population data by State, maintained by the U.S. Bureau of the Census. These 

data are helpful for describing the demographic characteristics and wealth of local areas 

(http://eire.census.gov/popest/data/states.php). 

• The Area Resource File, a county-level database of health care resources from several 

surveys and data sources, compiled by the Health Resources and Services 

Administration. This resource might be helpful in analyzing the health resources 

available on a county level. 

• Quality of care in managed care organizations, provided through the National Committee 

for Quality Assurance (see: http://www.ncqa.org/index.asp). Local managed care 

organizations can be an important source of local data on health care quality.

• The Henry J. Kaiser Family Foundation Web site (http://kff.org/statepolicy/index.cfm), a 

rich source of health and other information at the State-level compiled from many public 

databases and published studies. This may help identify differences among State 

environments that would explain asthma prevalence or treatment differences across 

States.

• American Lung Association Web site, which contains patient education materials and 

tools as well as research on asthma (http://www.lungusa.org).

Using Benchmarks To Develop State Performance Estimates 

Once States have identified measures and acquired relevant data, analysts must develop 

estimates that gauge State performance. 

Benchmarks

Benchmarks are external markers or values against which States can measure performance. The 

benchmark can represent the national average or best performers. How the State fares depends 

on where the State estimate falls compared with the benchmark. The NHQR provides a national 

set of estimates and State estimates that can be used as benchmarks for quality improvement. 

Several types of metrics or benchmarks can be used for assessing a State. From more to less 

stringent, they include: 

• The theoretic limit of 100 percent achievement (or 0 percent occurrence for avoidable 

events), which is an ideal, but often impractical or even impossible goal. 

• A best-in-class estimate of the top State or top tier of States that shows what has been 

achieved (e.g., the top 10 percent of States is used in this Resource Guide). 
60 

• A national consensus-based goal, such as Healthy People 2010, set by a consensus of 

experts; such goals may be set more or less stringently than other benchmarks. 

• A national average over all States, which shows the norm of practice nationwide but, 

being an average estimate, will represent a weaker goal than the best-in-class estimate. 

• A regional average, which a State can use to compare itself to other States that are more 

likely to face similar environments; but, as a goal, it may be less aggressive than the bestin-class goal. 

• An individual State rate, which itself can be used as a baseline against which to evaluate 

State-level interventions and progress over time within the State or to offer as a norm for 

local provider comparisons. 

Some of these benchmarks can be found in the NHQR—the national and regional averages. The 

best-in-class estimate, not reported in the NHQR, can be derived from data in it. See Appendix G 

for details on the best-in-class estimate and other benchmarks that can be derived from the 

NHQR. Appendix H describes how to conduct statistical significance testing to determine 

whether or not comparisons of estimates show significant differences. 

Asthma Benchmarks for States 

A focused and limited set of measures for tracking quality nationally on an ongoing basis has not 

yet been specified for asthma. Thus, the NHQR has not yet settled on a complete set of 

consensus-based measures for asthma. As noted above, the National Asthma Survey is expected 

to inform that process in the future. 

For this Resource Guide, benchmarks were calculated for asthma measures that were chosen 

based on availability of BRFSS data at the State level, current clinical guidelines, advice from an 

expert steering committee, and measures that will have a direct link to State budgets. They 

include: 

• Process measures—services important for controlling asthma and preventing complications: 

o Routine checkups for patients with asthma (two or more planned doctor visits in the past 

12 months). 

o Medications (use of medication to control asthma). 

o Advice to quit smoking (for asthma patients who smoke). 

o Flu shots (recommended for patients with asthma). 

• Outcome measures—avoidable health care use: 

o Urgent care visits for asthma. 

o Emergency room visits for asthma. 

Table 4.2 includes benchmarks—the national and best-in-class averages—for these measures. 

Figure 4.1 shows regional variations and the extent of the spread between States for each 

measure. The State analyses which follow illustrate four of these measures in more detail. 

BRFSS has limitations for establishing benchmarks for State performance, including limited 

questions on asthma and other technical issues. These are discussed further in Appendix E. 
Figure 4.1. Six quality measures for asthma: national average, best-in-class average, and State variation, by region, Source: Calculated from BRFSS data, 2003; see Appendix Table E.1. For State estimates for adults by age group, see Appendix E as follows: urgent c

emergency room visit, Table E.6; routine care, Table E.10; medications, Table E.12; smoking cessation counseling, Table E.15; flu shot, Table E.16. 
62 

Studying Individual States Against Benchmarks 

This section compares four States, as examples, to the key benchmarks for the asthma measures. 

The four States were chosen because they show variation across the measures in how States rank 

against the benchmarks. In Figures 4.2-4.5, States are compared to a national average and a bestin-class State average for each measure. 

Though the theoretic limit may be difficult to achieve for many valid reasons, some States have 

already achieved the best-in-class estimate. Although the average over all States is often used to 

assess a State’s performance, aiming for it means the State aims to be average rather than the 

best. Also, in some cases, a quality improvement team may set goals higher than the bestperforming States because they may view all States as poor performers. 

Figure 4.2. Percent of adults with asthma with routine checkups, medications, urgent care visits, 

and emergency room visits, 2003: Maryland compared to benchmarks 

Source: Calculated from BRFSS 2003 data. 

Maryland. Figure 4.2 reveals the following about asthma care in Maryland compared with 

national benchmarks and based on statistical tests. The marked “goal” on the vertical axis 

indicates the direction of improvement rather than an achievable value. 

• Maryland is close to the national average benchmark on two measures of asthma care 

quality—routine checkups and urgent care visits. Maryland has room for improvement on 

these dimensions to become a best-performing State. 

• Maryland is below the national average for percentage of asthma patients who take 

medications for asthma. Given the importance of medication use to control asthma 

Routine checkups (2 or more per year): Maryland 

compared to benchmarks

28.3

40.4

24.2

0

20

40

60

80

100

National average Best in class Maryland

Goal

Medications (all severity levels reported): 

Maryland compared to benchmarks

75.3

62.9

71.1

0

20

40

60

80

100

National average Best in class Maryland

Goal

Urgent care visits: Maryland compared to 

benchmarks

28.1

19.4

28.1

0

10

20

30

40

National average Best in class Maryland

Goal

Emergency room visits: Maryland compared to 

benchmarks

17.7

12.2

18.9

0

10

20

30

40

National average Best in class Maryland

Goal

Percent

of

people

with

asthma

Percent

of

people

with

asthma
63

symptoms and prevent asthma attacks and their position on this measure, Maryland may 

want to investigate asthma drug therapy within the State, determine the locales or 

subpopulations for which such therapy is lacking, and develop a targeted program to 

improve the use of prescription drugs in the State for residents with asthma. 

• Maryland appears to be statistically no different from the national average and the bestin-class average for emergency room visits. Small samples interfere with the ability of the 

data to distinguish between average and best-in-class in this case. Because of the 

weakness of this test and because the percent of people with emergency visits is higher in 

Maryland than nationally, Maryland might want to determine this rate more precisely. 

Maryland statewide hospital emergency department data system may want to address this 

issue. 

Maryland can improve the treatment of asthma in the community and reduce the number of 

expensive emergency services in the State. Also Maryland has an opportunity to reduce its 

hospitalizations of patients with asthma for all age groups (see Table 1.2). 

Figure 4.3. Percent of adults with asthma with routine checkups, medications, urgent care visits, 

and emergency room visits, 2003: Michigan compared to benchmarks 

Source: Calculated from BRFSS 2003 data. 

Michigan. Figure 4.3 reveals that: 

• Michigan is at the national average benchmark for two measures—routine checkups and 

urgent care visits. Thus, Michigan has room for improvement on these dimensions to 

become a best-performing State. 

Urgent care visits: Michigan compared to 

benchmarks

28.1

19.4

26.3

0

10

20

30

40

National average Best in class Michigan

Goal

Emergency room: Michigan compared to 

benchmarks

17.7

12.2

16.7

0

10

20

30

40

National average Best in class Michigan

Goal

Routine checkups (2 or more per year): Michigan 

compared to benchmarks

28.3

40.4

29.0

0

20

40

60

80

100

National average Best in class Michigan

Goal

Medications (all severity levels reported): 

Michigan compared to benchmarks

75.3 71.1 70.9

0

20

40

60

80

100

National average Best in class Michigan

Goal

Percent

of

people

with

asthma

Percent

of

people

with

asthma
64 

• Michigan appears to be statistically no different from the national and best-in-class 

averages for the other two measures—use of asthma medications and emergency room 

visits. Small samples impede a robust test between average and best-in-class for these 

important measures, which points out the need for better assessment methods. Larger 

samples for BRFSS may be a relatively inexpensive solution for better statistics. 

• Michigan was not among the below-average States for any of these four asthma 

measures. This suggests that Michigan’s efforts toward disease prevention and control 

may have contributed to this positive result. Michigan also is helped by its average 

sociodemographic characteristics, especially an average poverty rate. 

Thus, Michigan may want to improve its strategy for measuring asthma care quality and 

currently could justify focusing attention on improving the frequency of checkups for people 

with asthma in order to become a best-performing State. If that strategy is done well, it could 

reduce the cost of expensive urgent/emergency care. Also, by improving outpatient care 

Michigan has the opportunity to in turn reduce costly hospitalizations related to asthma (see 

Table 1.2). 

Figure 4.4. Percent of adults with asthma with routine checkups, medications, urgent care visits, 

and emergency room visits, 2003: New Jersey compared to benchmarks 

Source: Calculated from BRFSS 2003 data. 

New Jersey. Figure 4.4 shows that: 

• New Jersey is among the best-in-class States for routine checkups for people with 

asthma. Although this is excellent performance among all States, the best performers only 

Urgent care visits: New Jersey compared to 

benchmarks

28.1

19.4

34.0

0

10

20

30

40

National average Best in class New Jersey

Goal

Emergency room visits: New Jersey compared to 

benchmarks

17.7

12.2

17.7

0

10

20

30

40

National average Best in class New Jersey

Goal

Routine checkups (2 or more per year): New 

Jersey compared to benchmarks

28.3

40.4 35.8

0

20

40

60

80

100

National average Best in class New Jersey

Goal

Percent 

of 

people 

with 

asthma

Medications (all severity levels reported): New 

Jersey compared to benchmarks

75.3 71.1 71.0

0

20

40

60

80

100

National average Best in class New Jersey

Goal

Percent 

of 

people 

with 

asthma
65

reach the 40-percent mark for the percent of people with asthma who have planned care 

visits two or more times per year. Thus, New Jersey may want to aim for higher checkup 

rates for its population with asthma. 

• New Jersey’s estimate for asthma medication use is statistically no different from the 

national average or best-in-class average. Again, the small samples blur the ability to 

make the distinction, but the value of the estimate is closer to the all-States average than 

the best-in-class States estimate. Another factor with this measure is that the spread of the 

values across the States is very narrow, suggesting that medication use in asthma care is 

relatively uniform across the States. 

• New Jersey is worse than or at the national average on the use of urgent and emergency 

care, respectively. 

Despite New Jersey’s excellent performance on checkups and reasonable performance on 

medication therapy, its poorer performance on use of expensive urgent care services raises a 

question. How effective are community-based checkups for people with asthma if they use a 

high level of urgent care services? New Jersey may want to explore the nature of checkups for 

people with asthma and determine whether health care providers are using the best asthma 

management practices (see Module 1) with their patients. 

Figure 4.5. Percent of adults with asthma with routine checkups, medications, urgent care visits, 

and emergency room visits, 2003: Vermont compared to benchmarks 

Source: Calculated from BRFSS 2003 data. 

Routine checkups (2 or more per year): Vermont 

compared to benchmarks

28.3

40.4

17.8

0

20

40

60

80

100

National average Best in class Vermont

Goal

Medications (all severity levels reported): 

Vermont compared to benchmarks

75.3 69.4 71.1

0

20

40

60

80

100

National average Best in class Vermont

Goal

Urgent care visits: Vermont compared to 

benchmarks

28.1

19.4 19.6

0.0

10.0

20.0

30.0

40.0

National average Best in class Vermont

Goal

Emergency room visits: Vermont compared to 

benchmarks

17.7

12.2 12.1

0.0

10.0

20.0

30.0

40.0

National average Best in class Vermont

Goal

Percent

of

people

with

asthma

Percent

of

people

with

asthma
66 

Vermont. Figure 4.5 shows that: 

• Vermont is among the poorest-performing-States for routine checkups for people with 

asthma. 

• Vermont appears to be statistically no different than the national average and best-inclass average for use of asthma medications. Again this distinction cannot be made 

definitely due to small sample size. 

• Vermont is among the best-in-class States for the two outcome measures related to 

expensive emergency medical care. Vermont has low rates of urgent care visits and 

emergency department visits for asthma. 

This result—reasonable outcomes for emergency services use, but poor processes for checkups 

and medication, which appear to be inconsistent with each other—suggests that these measures 

by themselves are not the strongest determinants of patient outcomes and that other underlying 

factors are at work. The next section discusses some of the external factors that can affect the 

quality of care in communities. 

Factors That Affect Quality of Asthma Care 

The State data presented above raise several questions for anyone involved in quality 

improvement. What does a State’s position on the continuum of quality measures mean? What 

factors influence that position and the variability among the States? What factors can be 

influenced through State policy change and local efforts? 

A number of factors influence quality and outcomes of health care for any disease, as Figure 4.6 

shows. Some factors may be difficult to change, such as biologically inherited traits; income, 

education, and social status; and general population characteristics. Others may be changeable in 

the medium or long term, but not in the short term, such as the supply of health care 

professionals, the makeup and mission of health care organizations, and the disease prevalence 

of the population (which represents ingrained patterns of personal behaviors and health system 

effectiveness). All of these factors influence the process and outcome of health care. 

Although State government and community leaders do not have control over all factors, State 

actions can influence some important factors to promote positive change. These include 

educating people with asthma about the risks of uncontrolled asthma, raising awareness among 

professionals about health care processes that can improve outcomes for people with asthma, 

raising awareness in schools and communities about mitigating risk factors that can trigger 

asthma attacks, and creating financial incentives to encourage providers to manage diseases with 

their patients. Some States, for example, target programs to affect patient self-management and 

other external causes toward minority populations that are disproportionately affected by asthma. 

To better understand what influences a State’s position and how it compares with other States, 

some of the factors presented in Figure 4.6 are discussed in more detail below. 
67

Figure 4.6. Factors that affect disease process and outcome measures 

Racial, Ethnic, and Socioeconomic Factors 

The socioeconomic makeup of a State will likely play a role in how it compares to national 

norms on process and outcome measures. States with a higher proportion of individuals living in 

poverty, lower average education, and a more diverse racial and ethnic population, for instance, 

will likely find poorer outcomes for their population compared to the national population (IOM, 

2003). 

The NHDR (AHRQ, 2003a; 2004a) summarizes the racial, ethnic, and socioeconomic 

differences in asthma across the Nation (but not by State). Nationwide, minority or lower 

socioeconomic status is associated with higher asthma prevalence, higher asthma death rates, 

higher rates of serious asthma complications, and poorer asthma outcomes. (Blacks, for example, 

are much more likely than Whites to be hospitalized for asthma; see Table 4.3). 

The socioeconomic makeup of a State, thus, should play a role in the strategies that the State 

uses to improve asthma care quality. For instance, States that target efforts to improve asthma 

care at population groups particularly at risk for asthma complications should also be able to 

improve their overall performance on asthma care quality. 

Patient Risk of Disease:

•Biological

•Socioeconomic

Quality of Care:

•Process Measures

•Outcome Measures

Provider Ability to Manage 

Disease:

•Incentives

•Knowledge

•Educational tools

•Referral networks

External Environment:

•Disease prevalence

•Health care system

Difficult-to-Change Factors Feasible-to-Change Factors 

Patient Ability to 

Self-Manage:

•Behavior

•Knowledge

•Compliance

External Environment:

•Access to care

•Health care resources

•Government resources

•Government leadership

•QI programs

Measures

Patient Risk of Disease:

•Biological

•Socioeconomic

Quality of Care:

•Process Measures

•Outcome Measures

Provider Ability to Manage 

Disease:

•Incentives

•Knowledge

•Educational tools

•Referral networks

External Environment:

•Disease prevalence

•Health care system

Difficult-to-Change Factors Feasible-to-Change Factors 

Patient Ability to 

Self-Manage:

•Behavior

•Knowledge

•Compliance

External Environment:

•Access to care

•Health care resources

•Government resources

•Government leadership

•QI programs

Measures

Patient Risk of Disease:

•Biological

•Socioeconomic

Quality of Care:

•Process Measures

•Outcome Measures

Provider Ability to Manage 

Disease:

•Incentives

•Knowledge

•Educational tools

•Referral networks

External Environment:

•Disease prevalence

•Health care system

Difficult-to-Change Factors Feasible-to-Change Factors 

Patient Ability to 

Self-Manage:

•Behavior

•Knowledge

•Compliance

External Environment:

•Access to care

•Health care resources

•Government resources

•Government leadership

•QI programs

Measures
68 

Table 4.3. Asthma hospitalizations by race/ethnicity and community income, United States, 2001 

Race/ethnicity & Income1 Estimate

Standard 

error P-value Estimate

Standard 

error P-value Estimate

Standard 

error P-value

All Income

Total 197.213 13.937 113.221 3.384 164.407 5.254

White Non Hispanic 138.531 8.417 86.794 2.234 134.421 4.770

Black Non Hispanic 450.500 52.994 0.000 289.492 22.208 0.000 350.680 30.096 0.000

Hispanic 187.549 23.640 0.051 131.084 14.294 0.002 269.780 31.601 0.000

Asian and Pacific Islander 82.070 11.078 0.000 81.168 11.327 0.626 265.633 41.565 0.002

Other 479.712 54.078 0.000 287.163 29.021 0.000 570.597 61.084 0.000

Less than $25,000

Total 320.780 61.432 247.247 34.825 259.808 32.743

White Non Hispanic 168.288 18.127 140.991 11.653 155.496 18.086

Black Non Hispanic 491.382 96.659 0.001 471.882 86.934 0.000 471.283 90.216 0.001

Hispanic 295.895 115.834 0.276 213.124 70.749 0.314 360.829 94.130 0.032

Asian and Pacific Islander 32.194 14.549 0.000 70.704 26.821 0.016 169.517 91.263 0.88

Other 222.329 54.795 0.349 240.679 62.787 0.119 369.948 114.972 0.065

$25,000-$34,999

Total 263.164 21.849 149.112 6.753 194.038 9.592

White Non Hispanic 180.069 11.659 115.857 4.911 161.635 9.042

Black Non Hispanic 535.166 72.011 0.000 318.593 28.612 0.000 350.476 35.187 0.000

Hispanic 222.134 33.903 0.241 156.160 23.101 0.088 336.245 60.625 0.004

Asian and Pacific Islander 72.055 12.197 0.000 65.869 12.952 0.000 206.572 50.218 0.378

Other 353.319 58.711 0.004 219.008 30.510 0.001 428.836 81.411 0.001

$35,000-$44,999

Total 190.585 15.653 110.463 4.773 155.947 7.552

White Non Hispanic 138.866 10.679 87.823 3.895 128.315 6.663

Black Non Hispanic 433.132 61.370 0.000 263.376 23.811 0.000 331.170 36.243 0.000

Hispanic 157.770 21.546 0.432 116.240 14.537 0.059 253.628 34.592 0.000

Asian and Pacific Islander 59.070 11.869 0.000 81.666 18.631 0.746 290.330 65.565 0.014

Other 529.759 71.266 0.000 298.926 50.589 0.000 693.164 134.304 0.000

$45,000 or more

Total 152.433 13.900 85.672 3.920 158.891 8.595

White Non Hispanic 120.982 10.700 72.609 3.354 136.513 7.994

Black Non Hispanic 359.443 45.173 0.000 208.308 20.511 0.000 330.666 43.173 0.000

Hispanic 158.914 25.643 0.172 107.003 11.603 0.004 248.074 31.343 0.001

Asian and Pacific Islander 92.105 15.234 0.121 84.922 14.822 0.418 321.229 63.161 0.004

Other 818.261 128.185 0.000 370.707 40.786 0.000 869.244 119.059 0.000

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, State Inpatient Databases, disparities 

analysis file, 2001. This file is designed to provide national estimates on disparities using weighted records from a sample of hospitals 

from the following 22 states: AZ, CA, CO, CT, FL, GA, HI, KS, MD, MA, MI, MO, NJ, NY, PA, RI, SC, TN, TX, VA, VT, and WI. 

1 Median household income is based on ZIP Code data obtained from Claritas linked to patient ZIP Code in the HCUP database. 

Pediatric admissions per 100,000 

population less than 18 years

PQI 4

Adult admissions per 100,000 

population

age 18 and over

PQI 15

Adult admissions per 100,000 

population

age 65 and over

PQI 15

Denominators were obtained from ZIP-Code-level population counts by age, gender, race, and ethnicity based on U.S. censuses for

2000 and 1990 and Claritas methods to deal with race/ethnicity coding changes between those years.

P values to test race category compared to white category.
69

Biological and Behavioral Factors 

Understanding biological and behavioral influences on asthma should help in developing 

assessment tools and interventions for preventing or reducing the burden of asthma. Risk factors 

for asthma include (King et al., 2004): 

• Parental history of asthma. 

• Early-life stressors and infections. 

• Obesity. 

• Exposure to indoor allergens, tobacco smoke, and outdoor pollutants. 

• Work-related exposures. 

Socioeconomic factors may be related to underlying biological factors or behavioral factors. The 

accumulated stress of poverty, low levels of control in jobs and relationships, low job and life 

satisfaction, and societal discrimination against minority groups can influence health status 

(Williams, 1999). 

Physical Environment 

The physical environment in which asthma patients live is an important contributor to their 

asthma severity. The presence of poor air quality, dust, pets, cockroaches, and other allergens 

can affect how well a patient is able to control his or her asthma. A recent study released by the 

National Institutes of Health shows the connection between decayed bacteria in bedrooms and 

other rooms of a house and asthma prevalence (Thorne et al., 2005). 

External Environment

In addition to individual characteristics (some of which are amenable to change with personal 

motivation), each State has a different infrastructure and different environmental factors over 

which policymakers may or may not have control. These factors include the collective health 

status of the population, the distribution of health care services within locales, the distribution of 

wealth and tax resources among communities, and government programs and leadership. 

State leaders will face different health care system challenges, including: 

• Health system infrastructure—Availability of health professionals, emergency rooms, and 

hospitals beds. 

• Uninsured populations—Presence of vulnerable and uninsured populations and the need for 

special State programs to cover the cost of health care for them. 

• Safety net infrastructure—Availability of a safety net of health care providers as a last 

resort for those who cannot afford health insurance and private health care. 

• Provider knowledge—Providers who have sufficient state-of-the-art knowledge to manage 

asthma effectively and to educate their patients in asthma self-management. 
70 

• Public education—Public education programs that raise patient awareness of the warning 

signs of the disease, its potential complications, the importance of diet and exercise, and the 

effectiveness of personal self-management, including knowing when to consult a doctor. 

• Government resources—Funds, in a time of tight State budgets, to stimulate quality 

improvement activities related to asthma care. 

• Leaders to champion quality improvement—Leaders who can draw attention to the 

problems associated with asthma and harness the commitment of health professionals to 

change practices and monitor results. 

• Knowledge of what to do—Identification of effective quality improvement programs that 

are based on scientific evidence. 

• Adequate data systems to assess progress—Availability of data systems that can provide 

comparable measures across providers, communities, and States. 

The inter-relationship among all of the factors in Figure 4.6, then, affects how a State compares 

with other States on measures of asthma care quality. 

It is difficult to measure all of these factors at the State level. An attempt was made to analyze 

the BRFSS measures in Table 4.2 against individual State-level environmental factors—

prevalence of asthma, emphysema and chronic bronchitis in the population, the percent of the 

population below poverty level, racial/ethnic makeup of the population, the HMO penetration 

rate, the supply of hospital beds, and air quality in the State. The findings were not consistent 

enough across measures and factors to be believable. Again, the small sample sizes and 

imprecision of the asthma estimates themselves may be the limiting factor. Moreover, survey 

averages (e.g., percent having planned care visits) related to State aggregates from other sources 

(e.g., percent of the population that is uninsured) do not provide a direct test of these 

relationships. 

With large databases, it is possible to assess asthma care quality at not only the State but also 

local levels for some measures. For example, HCUP data and the statewide discharge data 

systems that are the source of HCUP data (with its hundreds of thousands of discharge records 

per State per year) support analyses at the county or other market areas. County-level data related 

to health care resources are generally available, although county data on health risk behaviors of 

the population generally are not. State analysts could use their county-level databases to compare 

asthma outcome measures based on HCUP data—e.g., asthma hospitalizations—or on data from 

their statewide data organization with other county characteristics. AHRQ’s Prevention Quality 

Indicator software can be applied to a State’s discharge data to produce county-level statistics. 

Summary and Synthesis 

Local leaders and health care professionals must see their own data in comparison with other 

provider data and with State, regional, and national benchmarks in order to appreciate the 

importance of their work. Assessing State quality of care for asthma begins with identifying 

quality measures. These fall into two main groups: process measures, which reflect the quality 

of care delivered, and outcome measures, which reflect patient health status. The former are 

needed to guide health care providers on how to change, the latter are needed to know whether 
71

the changed processes have had the intended effect. Data (whether State, county, municipal, or 

individual health care provider data) are essential for quality improvement programs to have an 

impact locally. Ideally, improvements in particular processes yield improvements in the 

associated outcomes. 

The NHQR provides a starting point for accessing consensus-based measures. The NHQR 

provides estimates for asthma hospitalizations by State. In addition, BRFSS estimates are used to 

assess asthma care quality by State. Although consensus on a few key measures of asthma care 

quality has not yet evolved, this Resource Guide provides an inventory of some measures. 

Data are essential to improve quality. States need performance data on asthma care to gauge their 

own performance against national benchmarks and to focus quality improvement efforts by 

identifying potential problem areas. This Resource Guide provides a list of national, State, and 

local sources for estimates for asthma, asthma care, and other related information. 

This module also shows how data can be analyzed and interpreted to answer the global question: 

How does my State compare with other States and national benchmarks on health care quality 

for asthma? State-level baseline estimates across all conditions studied in the NHQR afford 

State leaders a broad view of health care quality in their State. More refined questions about 

areas within the State will require local data and analysis. 

Resources for Further Reading 

Data and Data Tools on the Internet 

Many data resources are available on the Internet, including many sources used in the NHQR 

and NHDR. Some Web sites allow users to manipulate the data to produce tables and other 

useful outputs. Such resources include: 

• HCUPnet 

http://www.ahrq.gov/data/hcup/hcupnet.htm

HCUPnet allows users to select national statistics, or detailed statistics for certain States, for 

various conditions and procedures. The interactive program also allows users to compare 

types of patients and types of hospitals. These statistics are based on data received from 

Statewide hospital discharge data programs for inclusion in HCUP. 

 

• HCUP User Support (HCUP-US) 

http://www.hcup-us.ahrq.gov/home.jsp

This Web site is designed to answer HCUP-related questions; provide detailed information 

on HCUP databases, tools, and products; and offer assistance to HCUP users. 

• AHRQ Quality Indicators 

http://www.qualityindicators.ahrq.gov/

The AHRQ Quality Indicators are measures of health care quality that make use of readily 

available hospital inpatient administrative data. Asthma measures can be found in the 

Prevention Quality Indicators module. 
72 

• MEPSnet 

http://www.meps.ahrq.gov/MEPSNet/IC/MEPSnetIC.asp 

This Web site offers users statistics and trends about health care expenditures, utilization, and 

health insurance, including national and regional health insurance estimates. 

• BRFSS – Annual Survey 

http://www.cdc.gov/brfss/technical_infodata/index.htm 

This Web site has detailed technical information about the survey in addition to 

downloadable data sets in ASCII and SAS formats. 

• BRFSS 

http://www.cdc.gov/brfss/ 

This Web site provides useful background information about the BRFSS implementation, 

technical information, and documentation. 

• CDC Faststats – Asthma 

http://www.cdc.gov/nchs/fastats/asthma.htm

 The Faststats Web site provides easy access to statistics on topics of public health 

importance. The asthma page has a general overview of asthma statistics and links to specific 

data sources for more information on national data for asthma. 

 

Asthma Registries 

Some additional Web sites offer links to useful tools and information to facilitate data collection 

at the local level. Two Web sites that offer instruction for implementing asthma registries to 

track the treatments given to people with asthma are: 

• http://www.healthdisparities.net/training_manuals_and_tools.html

This Web site, associated with the HRSA Health Disparities Collaboratives, offers a number 

of useful tools, including helpful information for creating and assessing computer registries. 

• http://www.improvingchroniccare.org/improvement/docs/ICIC_Registry_Comparison_Octob

er02.xls 

This Web site offers a comparison of asthma registries.

Other Useful Web Sites 

Agency for Healthcare Research and Quality — http://www.ahrq.gov/ 

National Asthma Control Program —http://www.cdc.gov/asthma/NACP.htm

National Committee for Quality Assurance — http://www.ncqa.org/index.asp 

National Asthma Quality Improvement Alliance — http://www.nationalasthmaalliance.org/ 

National Quality Forum — http://www.qualityforum.org/ 
73

National Guideline Clearinghouse — http://www.guidelines.gov/ 

National Asthma Education and Prevention Program— http://www.nhlbi.nih.gov/about/naepp/

Associated Appendixes for Use With This Module 

Appendix D: Asthma Measures 

Appendix D is an inventory of available national (Table D.1) and local (Table D.2) asthma 

measures and sources. Measures are categorized by topics related to asthma care. 

Appendix E: BRFSS Measures, Data, and Benchmarks 

Appendix E provides the results of significance tests for BRFSS State estimates compared to the 

national average of each measure and compared to the best-in-class estimates for each measure. 

P-values less than 0.05 are considered statistically significant. State estimates that have p-value 

less than 0.05 are statistically different from the comparison estimate (national average or bestin-class). State estimates that have p-value greater than 0.05 are not statistically different from 

the comparison estimate. 

Appendix F: Other Data Sources 

Appendix F summarizes data sources used in this Resource Guide other than BRFSS data. This 

appendix includes descriptions and tables, where available, of national data sources (HCUP, 

HEDIS®, MEPS, and NHDS) and local data sources available from some States. 

Appendix G: Benchmarks From the NHQR 

Appendix G provides additional detail on benchmarks that can be derived from the NHQR and 

explains how they were developed and defined for this Resource Guide. This appendix discusses 

the best benchmarks for stimulating quality improvement, emphasizing that methods used to 

generate the benchmarks must be understood to ensure they are compatible with a State’s 

estimates. 

Appendix H: Information on Statistical Significance 

Appendix H shows how to compare State estimates to benchmarks using statistical significance 

and p-values that take into account the expected random variation in estimates. This appendix 

also shows how to calculate p-values when estimates and standard errors are provided. 
74 

Module 5: Moving Ahead – Implications for State 

Action 

This module draws implications for States to move forward with their own asthma care quality 

improvement initiatives. States need to go beyond generic resources and tailor efforts to their 

individual needs; specifically, they need to gather more localized data and involve key partners. 

Asthma is a chronic condition that presents a compelling case for quality improvement: 

• Asthma is becoming more prevalent. Estimates show that the number of Americans with 

asthma nearly doubled between 1980 and 1996. In 2001, more than 30 million Americans 

reported suffering from asthma at some time in their lives. 

• Substantial disparities exist in diagnoses and quality of asthma care. Data from the NHQR 

and NHDR show wide variations in quality for asthma across States and also across different 

socioeconomic, racial, and ethnic groups. 

• Asthma cannot be cured but is highly treatable. It can be controlled and managed through a 

range of interventions and treatments that prevent attacks and allow people with asthma to 

function normally. Often the disease can be self managed, allowing people with asthma to 

avoid costly hospitalizations or procedures. 

• Asthma is costly to treat. Families with an asthmatic child reported spending nearly three 

times as much annually on health care as families with a non-asthmatic child. In 2001, 

asthma’s total estimated cost for the total burden of the illness (including health care services 

and other lost abilities) was about $14 billion. 

Thus, there is potential for a substantial return on investment for purchasers and the health care 

system as a whole through asthma quality improvement. 

Essential Elements in State-Led Quality Improvement 

This Resource Guide offers a model for States to undertake a systematic effort to improve the 

quality of asthma care within their jurisdictions. State leaders can contribute essential elements 

to the process of asthma care quality improvement. These elements include the following 

• Providing leadership and vision. Quality improvement requires leadership. Whether 

initiatives are developing locally, statewide or nationally, effective leadership is essential to 

quality improvement. It will not emerge without a champion who can provide leadership to 

help organizations and individuals develop a shared vision and common goals for health care 

quality. Leadership must be a catalyst for others to become involved in developing shared 

vision and goals for improving health care quality. 

• Forming partnerships and collaborations. In addition to leadership and vision, 

partnerships and collaborations are vital to improving quality. Health care quality is the 

product of many different parts of the health care system but ultimately must affect what 

happens in the community, the patient environment, and the clinical setting. Thus, all groups 

that affect patient care should participate in quality improvement efforts. Health care 
75

professionals and providers need to establish systems that deliver appropriate, quality care 

consistently; patients need to demand and participate in the best available care; and 

purchasers must demand and pay for the highest quality, most cost-effective delivery of care. 

Consumer groups with interest in asthma can be powerful allies for change and a source of 

expertise. State health department staff and other asthma care experts from private-sector 

organizations can provide support and expertise for State initiatives. 

• Initiating measurement and reporting. A key step for State action is measuring quality. 

This step involves defining quality standards, identifying measures of those standards, 

finding available benchmarks, locating data that pertain to the State, perhaps collecting new 

data, and using data to track how well the health care system is performing and how well 

pilot interventions are working. Benchmarks and comparison data provide a mechanism for 

assessing how well the State is doing and how well any given health plan or provider is doing 

in a selected area of care. 

In order to improve, the health care system must have data robust enough to estimate a given 

set of measures of health care quality. As part of a systematic improvement initiative, States 

will need to go beyond generic resources and develop a comprehensive, State-specific picture 

of asthma care. Doing so will enable States to identify specific quality problems in their own 

communities and tailor specific solutions. Results then must be made available to enable 

purchasers and consumers to make meaningful decisions based on the performance of 

various providers. 

• Assisting planning and goal setting. State quality improvement initiatives should involve 

development of an action plan with specific goals for quality improvement in the State. The 

action plan must include timelines for specific steps and deliverables to help ensure that all 

partners move together. The plan should include specific responsibilities and benefits for as 

many project partners as possible to ensure commitment and continued involvement. 

• Assuring evaluation and accountability. After establishing partnerships with committed 

leaders and a common vision and goals, developing measures, collecting and analyzing data, 

and setting goals, there also is a need for evaluation of both health system performance and 

accountability for health care quality. Evaluation allows partners to identify the most 

troublesome areas and devote resources and attention to those areas where improvement is 

needed. Evaluation also enables recognition of areas where there is solid performance or 

conduct improvement over time. It may require some technical input and expertise, but it is 

an important component of the quality improvement process. Without evaluation, impact of 

the program will be unknown and future direction for the program will be haphazard. 

• Creating incentives. Although reporting data on performance is often enough to spur low 

performers toward improvement, tying rewards to high performance is also needed. The 

American health care system typically pays providers for the level, not the quality, of 

services delivered. However, States—as large health care purchasers—are in a position to 

offer financial incentives for providers to deliver quality care. For example, State programs 

can include bonuses and rewards for physicians who follow evidence-based guidelines in 

delivering asthma care. 
76 

• Spreading the change statewide. It is important for States to develop and implement ways 

to spread quality improvement in asthma statewide. This involves planning an effective 

tracking system, collecting and analyzing data, and drawing conclusions. State leaders must 

differentiate between sound conclusions and inconclusive findings and use this information 

to further the asthma quality improvement effort and to address other health care issues. 

States Have a Way Forward—A Final Note 

This Resource Guide has attempted to demonstrate for State leaders the need for quality 

improvement in asthma. Much has been done, but data from the NHQR show that much remains 

to be done to achieve quality care for all people with asthma. 

By reviewing and analyzing the information in this Resource Guide, assessing the local context, 

and designing an asthma quality improvement strategy, State leaders can identify opportunities 

to make a difference in the quality of care their constituents receive. The experiences of States 

that have implemented quality improvement for asthma care provide valuable insights into what 

can be accomplished through innovative, visionary efforts by State leaders. 
77

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82 
83

Appendix A: Acronyms Used in This Resource Guide 

AHRQ = Agency for Healthcare Research and Quality 

ALA = American Lung Association 

AMA-PCPI = American Medical Association Physicians Consortium for Performance 

Improvement 

BRFSS = Behavioral Risk Factor Surveillance System 

CASI = Chicago Asthma Surveillance Initiative 

CDC = Centers for Disease Control and Prevention 

CMS = Centers for Medicare and Medicaid Services 

ED = Emergency department 

EPA = Environmental Protection Agency 

HHS = U.S. Department of Health and Human Services 

HCUP = Healthcare Cost and Utilization Project 

HEDIS® = Health Plan Employer Data and Information Set 

HRSA = Health Resources and Services Administration 

IOM = Institute of Medicine 

JCAHO = Joint Commission on Accreditation of Healthcare Organizations 

JCAHO-APMIG = Joint Commission on Accreditation of Healthcare Organizations, Asthma 

Performance Measurement Implementation Guide 

MEPS = Medical Expenditure Panel Survey 

MQIC = Michigan Quality Improvement Initiative Guideline 

NACP = National Asthma Control Program 

NAEPP = National Asthma Education and Prevention Program 

NAS = National Asthma Survey 

NICHQ = National Initiative for Children’s Healthcare Quality 

NCHS = National Center for Health Statistics 

NCQA = National Committee for Quality Assurance 

NHDR = National Healthcare Disparities Report 

NHIS = National Health Interview Survey 

NHLBI = National Heart, Lung, and Blood Institute 

NHQR = National Healthcare Quality Report 
84 

NIH = National Institutes of Health 

NQF = National Quality Forum 

NYCCAI = New York City Childhood Asthma Initiative 

PDSA = Plan-Do-Study-Act 

SAMHSA = Substance Abuse and Mental Health Services Administration 

SCHIP = State Children’s Health Insurance Program 

VHOP = Virginia Health Outcomes Partnership 

YRBSS = Youth Risk Behavior Surveillance System 
85

Appendix B: Medicaid Spending on Asthma by State 

Table B.1. Medicaid: White eligibles by State and age group and estimated spending, 2003 

State

White Medicaid 

eligibles 20031

White Medicaid 

eligibles 0-18 with 

asthma1, 2

White Mediciad 

eligibles 19-64 with 

asthma1, 2

White Medicaid 

eligibles 65+ with 

asthma1, 2

Estimated Medicaid 

spending on asthma for 

White eligibles3

Total US 22,701,341 905,503 628,876 144,054 $1,773,516,649

Alabama 52,494 2,694 1,102 182 4,203,511

Alaska 390,559 15,584 9,909 3,284 30,407,709

Arizona 382,262 16,916 9,224 2,362 30,115,828

Arkansas 389,480 15,732 12,152 1,273 30,807,735

California 2,158,045 68,237 79,403 11,297 167,939,872

Colorado 205,897 9,268 4,731 1,349 16,217,833

Connecticut 229,485 9,082 6,121 1,741 17,903,805

Delaware 64,562 2,431 2,076 292 5,071,376

District of Columbia 3,198 120 99 18 250,200

Florida 1,025,871 43,552 24,455 7,952 80,262,621

Georgia 601,570 29,499 12,418 3,285 47,763,132

Hawaii 41,581 1,563 1,269 246 3,252,530

Idaho 160,519 8,431 3,120 617 12,857,672

Illinois 845,443 35,852 18,839 7,665 65,888,417

Indiana 615,960 29,596 13,402 3,256 48,874,374

Iowa 263,902 10,992 6,763 1,819 20,682,943

Kansas 203,229 9,495 4,375 1,314 16,044,371

Kentucky 633,173 26,228 16,100 4,576 49,561,289

Louisiana 349,746 17,382 6,656 2,199 27,723,546

Maine 337,986 8,541 11,165 4,411 25,483,521

Maryland 256,981 11,751 6,125 1,366 20,332,490

Massachusetts 598,052 19,428 20,202 4,220 46,335,517

Michigan 872,297 39,047 21,817 4,423 68,986,122

Minnesota 417,376 16,366 11,079 3,324 32,511,398

Mississippi 244,342 11,068 5,144 1,935 19,175,111

Missouri 780,751 32,970 21,004 4,229 61,500,747

Montana 79,485 3,352 2,072 489 6,247,087

Nebraska 182,149 8,618 3,808 967 14,152,135

Nevada 115,861 4,956 2,969 701 9,115,153

New Hampshire 107,956 5,100 2,269 701 8,527,540

New Jersey 346,068 14,100 8,483 2,936 26,964,964

New Mexico 117,937 5,953 2,496 466 9,421,091

New York 1,262,842 39,950 40,713 8,845 94,579,159

North Carolina 613,633 25,674 15,088 4,667 48,001,670

North Dakota 54,556 2,029 1,525 454 4,234,852

Ohio 1,154,160 49,814 30,741 5,702 91,143,667

Oklahoma 425,416 21,923 7,705 2,397 33,839,063

Oregon 473,718 15,823 17,187 2,048 37,043,782

Pennsylvania 1,031,052 40,852 27,771 7,565 80,504,753

Rhode Island 93,912 3,584 2,710 643 7,330,424

South Carolina 362,941 15,791 9,435 1,888 28,649,907

South Dakota 69,319 3,397 1,363 436 5,490,238

Tennessee 1,132,878 37,946 39,367 6,241 88,287,686

Texas 863,652 42,286 16,132 6,155 68,231,736

Utah 165,232 7,983 3,964 539 13,192,476

Vermont 337,070 15,000 7,195 2,788 26,398,676

Virginia 93,460 3,240 2,868 744 7,240,497

Washington 716,287 32,393 18,331 3,047 56,817,714

West Virginia 338,437 14,172 9,197 1,847 26,644,736

Wisconsin 382,666 14,569 10,213 3,325 29,700,163

Wyoming 55,890 2,696 1,259 252 4,445,604

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp were projected to 2003, using a 

rate of increase for Medicaid enrollment published in A Profile of Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf.

2 Calculations of prevalence rates based on national prevalence rates from the Centers for Disease Control and Prevention, National Center for Health Statistics, 

Asthma on Demand. National Health Interview Survey 1999-2003 accessed at http://209.217.72.34/hdaa/ReportFolders/ReportFolders.aspx, applied to weighted 

average population estimates for each State from US Census for 2003 accessed at http://www.census.gov/popest/counties/CO-EST2005-01.html.

3 Calculations of direct cost per person, are based on Weiss et al. (1994), inflated by medical care component of CPI to 2003 dollars. Indirect cost per person, are 

based on Weiss et al. (1994) inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in 

the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while NHIS prevalence rates are 

0-17, 18-64, and 65+ years. 
86 

Table B.2. Medicaid: Black eligibles by State and age group and estimated spending, 2003 

State

Black Medicaid 

eligibles 20031

Black Medicaid 

eligibles 0-18 with 

asthma1, 2

Black Medicaid 

eligibles 19-64 with 

asthma1, 2

Black Medicaid 

eligibles 65+ with 

asthma1, 2

Estimated Medicaid 

spending on asthma for 

Black eligibles3

Total US 12,404,969 776,656 367,509 81,385 $1,294,977,870

Alabama 6,338 511 142 23 713,818

Alaska 418,059 26,183 11,344 3,635 43,493,286

Arizona 196,087 13,620 5,060 1,253 21,061,666

Arkansas 62,707 3,976 2,092 212 6,635,453

California 952,618 47,279 37,484 5,156 95,013,418

Colorado 32,235 2,277 792 218 3,474,304

Connecticut 109,760 6,818 3,131 861 11,422,282

Delaware 63,970 3,781 2,200 299 6,636,004

District of Columbia 182,772 10,725 6,050 1,080 18,865,945

Florida 816,028 54,377 20,804 6,540 86,350,426

Georgia 743,095 57,196 16,405 4,195 82,203,158

Hawaii 3,003 177 98 18 310,194

Idaho 1,562 129 32 6 176,995

Illinois 787,269 52,402 18,761 7,380 82,991,693

Indiana 199,882 15,075 4,651 1,092 21,997,566

Iowa 28,273 1,848 775 201 2,984,812

Kansas 51,659 3,789 1,189 345 5,624,690

Kentucky 98,325 6,393 2,674 735 10,356,629

Louisiana 580,108 45,253 11,807 3,771 64,277,453

Maine 5,182 206 183 70 484,547

Maryland 401,392 28,810 10,231 2,207 43,584,276

Massachusetts 128,515 6,553 4,643 938 12,820,957

Michigan 525,261 36,906 14,050 2,754 56,751,877

Minnesota 106,521 6,556 3,024 877 11,049,201

Mississippi 428,730 30,483 9,653 3,510 46,118,170

Missouri 286,330 18,979 8,238 1,604 30,452,958

Montana 737 49 21 5 78,234

Nebraska 33,129 2,460 741 182 3,574,530

Nevada 38,034 2,554 1,042 238 4,051,209

New Hampshire 2,052 152 46 14 224,118

New Jersey 309,887 19,818 8,123 2,719 32,396,836

New Mexico 10,488 831 237 43 1,174,223

New York 907,734 45,073 31,297 6,574 87,642,841

North Carolina 575,275 37,779 15,127 4,523 60,682,486

North Dakota 1,381 81 41 12 141,323

Ohio 534,784 36,229 15,233 2,731 57,264,105

Oklahoma 113,018 9,142 2,189 658 12,668,399

Oregon 27,560 1,445 1,069 123 2,786,817

Pennsylvania 474,799 29,528 13,677 3,602 49,458,519

Rhode Island 18,177 1,089 561 129 1,879,295

South Carolina 485,752 33,173 13,504 2,613 52,081,919

South Dakota 2,369 182 50 15 261,510

Tennessee 475,787 25,015 17,681 2,710 47,978,362

Texas 612,626 47,082 12,238 4,514 67,449,808

Utah 4,863 369 125 16 538,825

Vermont 338,641 23,655 7,731 2,896 36,223,030

Virginia 1,033 56 34 9 104,229

Washington 69,439 4,929 1,900 305 7,539,128

West Virginia 18,761 1,233 545 106 1,990,949

Wisconsin 131,634 7,867 3,757 1,183 13,531,945

Wyoming 1,328 101 32 6 146,617

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp were projected to 2003, using a 

rate of increase for Medicaid enrollment published in A Profile of Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf.

2 Calculations of prevalence rates based on national prevalence rates from the Centers for Disease Control and Prevention, National Center for Health Statistics, 

Asthma on Demand. National Health Interview Survey 1999-2003 accessed at http://209.217.72.34/hdaa/ReportFolders/ReportFolders.aspx, applied to weighted 

average population estimates for each State from US Census for 2003 accessed at http://www.census.gov/popest/counties/CO-EST2005-01.html.

3 Calculations of direct cost per person, are based on Weiss et al. (1994), inflated by medical care component of CPI to 2003 dollars. Indirect cost per person, are 

based on Weiss et al. (1994) inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in 

the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while NHIS prevalence rates are 

0-17, 18-64, and 65+ years. 
87

Table B.3. Medicaid: American Indian/Alaska Native eligibles by State and age group and 

estimated spending, 2003 

State

American Indian/ 

Alaska Native 

Medicaid eligibles 

20031

American 

Indian/Alaska 

Native Medicaid 

eligibles age 0-18 

with asthma1, 2

American 

Indian/Alaska 

Native Medicaid 

eligibles age 19-64 

with asthma1, 2

American 

Indian/Alaska 

Native Medicaid 

eligibles age 65+ 

with asthma1, 2

Estimated Medicaid 

spending on asthma for 

American Indian/Alaska 

Native eligibles3

Total US 751,396 296,410 26,019 -- $340,695,223

Alabama 44,832 148 1,177 -- 1,399,399

Alaska 2,226 9,152 71 -- 9,745,459

Arizona 4,892 4,527 148 -- 4,939,895

Arkansas 138,602 1,709 5,405 -- 7,517,339

California 43,745 23,966 2,012 -- 27,449,563

Colorado 3,308 721 95 -- 862,260

Connecticut 955 2,506 32 -- 2,681,107

Delaware 291 1,675 12 -- 1,782,261

District of Columbia 25 4,573 1 -- 4,833,019

Florida 1,284 17,859 38 -- 18,910,665

Georgia 1,095 16,266 28 -- 17,217,454

Hawaii 474 75 18 -- 97,816

Idaho 5,133 34 125 -- 168,196

Illinois 3,790 16,087 106 -- 17,109,339

Indiana 585 4,519 16 -- 4,791,460

Iowa 1,893 653 61 -- 753,621

Kansas 4,114 1,124 111 -- 1,304,231

Kentucky 300 2,240 10 -- 2,376,498

Louisiana 1,798 11,865 43 -- 12,582,248

Maine 3,035 94 125 -- 231,267

Maryland 1,297 9,460 39 -- 10,036,644

Massachusetts 2,672 3,050 113 -- 3,341,752

Michigan 7,857 12,717 246 -- 13,696,808

Minnesota 27,795 2,397 922 -- 3,507,774

Mississippi 2,930 8,841 77 -- 9,423,814

Missouri 2,226 7,034 75 -- 7,511,730

Montana 24,215 18 789 -- 852,194

Nebraska 9,411 709 246 -- 1,008,572

Nevada 3,396 902 109 -- 1,067,513

New Hampshire 96 44 3 -- 49,234

New Jersey 3,296 6,692 101 -- 7,178,188

New Mexico 87,910 242 2,326 -- 2,713,929

New York 52,913 20,020 2,132 -- 23,406,887

North Carolina 24,093 12,797 740 -- 14,304,669

North Dakota 16,066 31 561 -- 625,854

Ohio 1,806 13,294 60 -- 14,111,083

Oklahoma 88,214 2,281 1,997 -- 4,520,590

Oregon 14,303 722 649 -- 1,448,536

Pennsylvania 1,981 10,957 67 -- 11,648,562

Rhode Island 330 433 12 -- 470,030

South Carolina 1,307 11,880 42 -- 12,598,073

South Dakota 40,391 49 992 -- 1,100,759

Tennessee 3,641 11,975 158 -- 12,820,952

Texas 11,718 12,116 274 -- 13,091,593

Utah 10,336 122 310 -- 456,313

Vermont 1,136 6,956 30 -- 7,382,571

Virginia 246 24 9 -- 35,107

Washington 29,925 1,738 957 -- 2,847,833

West Virginia 182 462 6 -- 494,329

Wisconsin 11,820 2,893 394 -- 3,473,168

Wyoming 5,511 31 155 -- 196,938

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp were projected to 2003, using a 

rate of increase for Medicaid enrollment published in A Profile of Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf.

2 Calculations of prevalence rates based on national prevalence rates from the Centers for Disease Control and Prevention, National Center for Health Statistics, 

Asthma on Demand. National Health Interview Survey 1999-2003 accessed at http://209.217.72.34/hdaa/ReportFolders/ReportFolders.aspx, applied to weighted 

average population estimates for each State from US Census for 2003 accessed at http://www.census.gov/popest/counties/CO-EST2005-01.html.

3 Calculations of direct cost per person, are based on Weiss et al. (1994), inflated by medical care component of CPI to 2003 dollars. Indirect cost per person, are 

based on Weiss et al. (1994) inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in 

the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while NHIS prevalence rates are 

0-17, 18-64, and 65+ years. 
88 

Table B.4. Medicaid: Asian eligibles by State and age group and estimated spending, 2003 

State

Asian Medicaid 

eligibles 20031

Asian Medicaid 

eligibles 0-18 with 

asthma1, 2

Asian Medicaid 

eligibles age 19-64 

with asthma1, 2

Asian Medicaid 

eligibles age 65+ 

with asthma1, 2

Estimated Medicaid 

spending on asthma for 

Asian eligibles3

Total US 1,234,931 35,541 14,729 8,235 $61,819,532

Alabama 5,752 213 52 21 302,136

Alaska 3,688 106 40 33 189,172

Arizona 5,203 166 54 34 268,323

Arkansas 12,303 359 165 42 598,122

California 464,610 10,600 7,360 2,556 21,677,971

Colorado 4,307 140 43 30 224,147

Connecticut 10,650 304 122 85 540,296

Delaware 2,062 56 29 10 99,747

District of Columbia 1,584 43 21 10 77,488

Florida 13,926 427 143 113 721,624

Georgia 13,722 485 122 79 725,070

Hawaii 57,698 1,565 758 359 2,833,813

Idaho 870 33 7 4 46,226

Illinois 51,435 1,574 493 490 2,702,151

Indiana 3,205 111 30 18 167,929

Iowa 3,539 106 39 26 180,732

Kansas 2,723 92 25 18 143,208

Kentucky 1,932 58 21 15 98,874

Louisiana 3,459 124 28 23 185,174

Maine 2,297 42 33 32 112,052

Maryland 20,757 685 213 116 1,071,235

Massachusetts 39,429 924 573 292 1,891,487

Michigan 22,363 722 241 119 1,143,593

Minnesota 44,958 1,272 514 376 2,284,450

Mississippi 2,814 92 26 23 148,867

Missouri 7,097 216 82 40 358,049

Montana 439 13 5 3 22,333

Nebraska 2,873 98 26 16 147,924

Nevada 6,794 210 75 43 346,458

New Hampshire 840 29 8 6 44,360

New Jersey 20,667 608 218 184 1,067,145

New Mexico 2,653 97 24 11 139,308

New York 156,054 3,562 2,166 1,149 7,266,339

North Carolina 12,603 380 133 101 649,398

North Dakota 297 8 4 3 14,939

Ohio 8,489 264 97 44 428,767

Oklahoma 6,033 224 47 36 324,465

Oregon 17,539 423 274 80 820,297

Pennsylvania 32,557 931 378 251 1,647,679

Rhode Island 5,096 140 63 37 253,954

South Carolina 1,775 56 20 10 90,101

South Dakota 658 23 6 4 35,040

Tennessee 11,901 288 178 69 564,851

Texas 41,827 1,478 336 313 2,247,778

Utah 9,775 341 101 33 502,094

Vermont 18,787 603 173 163 992,390

Virginia 429 11 6 4 21,133

Washington 52,943 1,728 583 237 2,691,899

West Virginia 726 22 8 4 36,563

Wisconsin 20,523 564 236 187 1,042,941

Wyoming 270 9 3 1 14,032

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp were projected to 2003, using a 

rate of increase for Medicaid enrollment published in A Profile of Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf.

2 Calculations of prevalence rates based on national prevalence rates from the Centers for Disease Control and Prevention, National Center for Health Statistics, 

Asthma on Demand. National Health Interview Survey 1999-2003 accessed at http://209.217.72.34/hdaa/ReportFolders/ReportFolders.aspx, applied to weighted 

average population estimates for each State from US Census for 2003 accessed at http://www.census.gov/popest/counties/CO-EST2005-01.html.

3 Calculations of direct cost per person, are based on Weiss et al. (1994), inflated by medical care component of CPI to 2003 dollars. Indirect cost per person, are 

based on Weiss et al. (1994) inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in 

the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while NHIS prevalence rates are 

0-17, 18-64, and 65+ years. 
89

Table B.5. Medicaid: Hispanic eligibles by State and age group and estimated spending, 2003 

State

Hispanic or Latino 

Medicaid eligibles 

20031

Hispanic or Latino 

Medicaid eligibles 

age 0-17 with 

asthma1, 2

Hispanic or Latino 

Medicaid eligibles 

age 18-64 with 

asthma1, 2

Hispanic or Latino 

Medicaid eligibles 

age 65+ with 

asthma1, 2

Estimated Medicaid 

spending on asthma for 

Hispanic eligibles3

Total US 10,801,183 381,752 1,598,946 60,408 $2,156,734,295

Alabama 4,493 204 382 14 634,228

Alaska 13,702 484 1,701 102 2,417,058

Arizona 21,371 838 2,400 116 3,544,421

Arkansas 440,646 15,771 82,743 1,269 105,435,515

California 4,920,033 137,847 1,284,846 22,700 1,527,273,821

Colorado 165,526 6,602 16,861 956 25,802,310

Connecticut 141,820 4,973 19,464 948 26,823,676

Delaware 17,353 579 3,462 69 4,342,576

District of Columbia 11,978 397 2,213 60 2,821,562

Florida 558,278 21,001 61,199 3,814 90,886,344

Georgia 12,802 556 1,052 62 1,764,785

Hawaii 5,941 198 1,067 31 1,369,474

Idaho 30,080 1,400 2,191 102 3,902,616

Illinois 387,338 14,554 37,098 3,095 57,849,264

Indiana 61,476 2,617 5,614 286 9,000,024

Iowa 8,486 313 1,075 52 1,521,275

Kansas 36,768 1,522 3,287 209 5,302,943

Kentucky 11,986 440 1,495 76 2,125,154

Louisiana 5,594 246 391 31 706,073

Maine 1,413 32 297 16 364,865

Maryland 51,725 2,096 5,668 242 8,459,697

Massachusetts 189,959 5,468 41,814 1,182 51,208,930

Michigan 80,763 3,203 9,746 361 14,064,103

Minnesota 2,161 75 294 15 405,789

Mississippi 5,092 204 435 36 713,595

Missouri 355 13 49 2 68,103

Montana 2,359 88 309 13 433,403

Nebraska 96 4 8 0 13,272

Nevada 41,199 1,562 5,218 220 7,396,383

New Hampshire 3,375 141 288 19 473,695

New Jersey 202,015 7,293 23,413 1,511 34,041,772

New Mexico 238,835 10,683 20,644 832 33,980,107

New York 655,432 18,372 131,411 4,046 162,543,979

North Carolina 95,923 3,556 11,186 643 16,257,062

North Dakota 0 0 0 0 0

Ohio 51,827 1,982 7,093 226 9,827,228

Oklahoma 51,885 2,369 3,283 258 6,244,762

Oregon 103,128 3,052 26,187 393 31,311,009

Pennsylvania 130,864 4,594 18,314 846 25,100,644

Rhode Island 37,469 1,267 6,020 226 7,939,106

South Carolina 13,447 518 1,753 62 2,465,091

South Dakota 2,141 93 160 12 279,412

Tennessee 37,292 1,107 8,686 181 10,539,438

Texas 1,667,224 72,331 112,215 10,472 206,065,722

Utah 41,953 1,796 4,658 121 6,946,577

Vermont 37,711 1,487 3,315 275 5,364,307

Virginia 305 9 55 2 70,705

Washington 149,602 5,995 18,900 561 26,898,176

West Virginia 709 26 101 3 138,119

Wisconsin 42,284 1,426 5,811 324 7,989,138

Wyoming 6,970 298 682 28 1,065,015

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp were projected to 2003, using a 

rate of increase for Medicaid enrollment published in A Profile of Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf.

2 Calculations of prevalence rates based on national prevalence rates from the Centers for Disease Control and Prevention, National Center for Health Statistics, 

Asthma on Demand. National Health Interview Survey 1999-2003 accessed at http://209.217.72.34/hdaa/ReportFolders/ReportFolders.aspx, applied to weighted 

average population estimates for each State from US Census for 2003 accessed at http://www.census.gov/popest/counties/CO-EST2005-01.html.

3 Calculations of direct cost per person, are based on Weiss et al. (1994), inflated by medical care component of CPI to 2003 dollars. Indirect cost per person, are 

based on Weiss et al. (1994) inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in 

the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while NHIS prevalence rates are 

0-17, 18-64, and 65+ years. 
90 

Table B.6. Medicaid: Other eligibles by State and age group and estimated spending, 2003 

State

Other Medicaid 

eligibles 20031

Other Medicaid 

eligibles age 0-18 

with asthma1, 2

Other Medicaid 

eligibles age 19-64 

with asthma1, 2

Other Medicaid 

eligibles age 65+ 

with asthma1, 2

Estimated Medicaid 

spending on asthma for 

Other eligibles3

Total US 3,397,257 228,135 58,820 19,000 $323,286,711

Alabama 5,896 509 77 18 639,094

Alaska 25,343 1,702 402 188 2,421,989

Arizona 0 0 0 0 0

Arkansas 20,400 1,387 398 59 1,948,204

California 349,105 18,584 8,028 1,611 29,821,517

Colorado 31,405 2,380 451 181 3,182,905

Connecticut 198 13 3 1 18,822

Delaware 327 21 7 1 30,249

District of Columbia 3,900 245 75 20 359,800

Florida 303,030 21,658 4,515 2,070 29,843,607

Georgia 101,943 8,416 1,315 491 10,801,002

Hawaii 11,469 726 219 60 1,061,371

Idaho 0 0 0 0 0

Illinois 21,633 1,544 301 173 2,132,951

Indiana 9,655 781 131 45 1,011,482

Iowa 45,239 3,172 725 275 4,408,080

Kansas 8,526 671 115 49 881,228

Kentucky 31,809 2,218 506 203 3,092,177

Louisiana 59,011 4,937 702 327 6,304,430

Maine 0 0 0 0 0

Maryland 27,240 2,097 406 128 2,779,468

Massachusetts 257,727 14,096 5,441 1,603 22,337,478

Michigan 34,363 2,590 537 154 3,466,237

Minnesota 35,466 2,341 588 249 3,358,611

Mississippi 31,157 2,376 410 217 3,173,668

Missouri 32,288 2,295 543 154 3,162,008

Montana 56 4 1 0 5,441

Nebraska 5,204 414 68 24 535,544

Nevada 0 0 0 0 0

New Hampshire 2,352 187 31 13 244,572

New Jersey 110,570 7,584 1,694 827 10,677,712

New Mexico 9,684 823 128 34 1,040,620

New York 1,146,321 61,052 23,098 7,077 96,394,222

North Carolina 81,824 5,763 1,257 548 7,998,152

North Dakota 0 0 0 0 0

Ohio 20,857 1,515 347 91 2,064,162

Oklahoma 0 0 0 0 0

Oregon 6,825 384 155 26 596,494

Pennsylvania 29,985 2,000 505 194 2,851,708

Rhode Island 51,852 3,331 935 313 4,839,131

South Carolina 39,599 2,901 643 182 3,936,545

South Dakota 186 15 2 1 19,703

Tennessee 55,889 3,152 1,214 271 4,899,550

Texas 37,146 3,062 434 233 3,940,151

Utah 3,328 271 50 10 348,840

Vermont 1,716 129 23 13 173,255

Virginia 63,053 3,681 1,209 442 5,634,250

Washington 93,866 7,147 1,501 352 9,509,846

West Virginia 7,072 499 120 34 689,684

Wisconsin 182,211 11,679 3,040 1,395 17,027,225

Wyoming 531 43 7 2 55,726

1 Centers for Medicare & Medicaid Services, MSIS State Summary FY 2002. http://www.cms.hhs.gov/medicaid/msis/tables2002.asp were projected to 2003, using a 

rate of increase for Medicaid enrollment published in A Profile of Medicaid 2000 Chartbook available at http://www.cms.hhs.gov/charts/medicaid/2Tchartbk.pdf.

2 Calculations of prevalence rates based on national prevalence rates from the Centers for Disease Control and Prevention, National Center for Health Statistics, 

Asthma on Demand. National Health Interview Survey 1999-2003 accessed at http://209.217.72.34/hdaa/ReportFolders/ReportFolders.aspx, applied to weighted 

average population estimates for each State from US Census for 2003 accessed at http://www.census.gov/popest/counties/CO-EST2005-01.html.

3 Calculations of direct cost per person, are based on Weiss et al. (1994), inflated by medical care component of CPI to 2003 dollars. Indirect cost per person, are 

based on Weiss et al. (1994) inflated by average annual wage percent change to 2003. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in 

the United States, 1985-1994. J Allergy Clin Immunol. 2000 Sep;106(3):493-9.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while NHIS prevalence rates are 

0-17, 18-64, and 65+ years. 
Figure B.1. Methods for deriving Medicaid spending for asthma by State, age, and race/ethnicity, 2003 

Original

source

data

Adjustments

Final

Estimate

National annual

average asthma 

prevalence 2003, by 

age 0-17, 18-44, 

45-64, 65+ within 

race/ethnicity 

(NHIS, 1999-2003)

Population estimates

for 2003 by age

(U.S. Census data, 2004) 

Medicaid eligibles

population by age

2002 (CMS, 

MSIS data, 2002)

Medicaid eligibles

population by race

2002 (CMS,

MSIS data, 2002)

Direct 

med

expendi

person wi

for 1994 

et al, 

Calculate a weighted average

of age 18-44 and 45-64 to get

prevalence rate for age 18-64

within race

Apply age group proportion

of Medicaid population in each

State to each subgroup

(assumes age distribution of

each racial subgroup is the

same within the State

Medicaid population)

Inflate 

dollar

Medic

compone

National prevalence

rates by age (0-17,

18-64, 65+) within

race/ethnicity

Multiply prevalence

rate by Medicaid

population age and

race/ethnicity subgroup

to get number of

Medicaid eligibles by

State, age, and

race/ethnicity

Medicaid

eligibles by

State, age

within

race/ethnicity

Apply

.01%

change 1

Medicaid

eligibles by

State, age

within

race/ethnicity

Direc

per p

with a

20

Number of Medicaid eligibles

with asthma in each age/race

subgroup for each State

Multiply number in each

age/race subgroup by total

annual expenditures per

person with asthma

Total spending in 2003 for Medicaid

eligibles with asthma age/race

subgroup by each State

1 Projections to 2003 Medicaid eligibles based on A Profile of Medicaid 2000 Chartbook available at http://www.hhs.gov/charts/medicaid/2Tchartbk.pd

Original

source

data

Adjustments

Final

Estimate

National annual

average asthma 

prevalence 2003, by 

age 0-17, 18-44, 

45-64, 65+ within 

race/ethnicity 

(NHIS, 1999-2003)

Population estimates

for 2003 by age

(U.S. Census data, 2004) 

Medicaid eligibles

population by age

2002 (CMS, 

MSIS data, 2002)

Medicaid eligibles

population by race

2002 (CMS,

MSIS data, 2002)

Direct 

med

expendi

person wi

for 1994 

et al, 

Calculate a weighted average

of age 18-44 and 45-64 to get

prevalence rate for age 18-64

within race

Apply age group proportion

of Medicaid population in each

State to each subgroup

(assumes age distribution of

each racial subgroup is the

same within the State

Medicaid population)

Inflate 

dollar

Medic

compone

National prevalence

rates by age (0-17,

18-64, 65+) within

race/ethnicity

Multiply prevalence

rate by Medicaid

population age and

race/ethnicity subgroup

to get number of

Medicaid eligibles by

State, age, and

race/ethnicity

Medicaid

eligibles by

State, age

within

race/ethnicity

Apply

.01%

change 1

Medicaid

eligibles by

State, age

within

race/ethnicity

Direc

per p

with a

20

Number of Medicaid eligibles

with asthma in each age/race

subgroup for each State

Multiply number in each

age/race subgroup by total

annual expenditures per

person with asthma

Total spending in 2003 for Medicaid

eligibles with asthma age/race

subgroup by each State

1 Projections to 2003 Medicaid eligibles based on A Profile of Medicaid 2000 Chartbook available at http://www.hhs.gov/charts/medicaid/2Tchartbk.pd
Appendix C: National and State Asthma Programs 

This appendix lists State asthma programs in existence in the spring of 2005. Program 

information can be obtained from the Web linkages provided in the table. The programs are 

classified by the following typology: 

• Advisory Bodies and Councils 

• Coalitions 

• Collaboratives 

• Cross-Agency Work 

• Data Measurement and Reporting 

• Developing and Enforcing Guidelines 

• Disease Management 

• Minority and Rural Outreach 

• Public Service/Education Efforts 

• Self-Management 

• Provider Training 

• Use of Technology 

92 
Location Program Classification & Year Begun For More Information 

National American Lung Association Education 

1904 

www.lungusa.org 

Association of Asthma Educators Provider Training 

2002 

www.asthmaeducators.org 

Asthma and Allergy Foundation of America Education 

1953 

http://aafa.org/index.cfm 

National Asthma Education and Prevention Program 

Developing Guidelines March 1989 

www.nhlbi.nih.gov 

National Asthma Education Certification Board 

Provider Training 2000 

http://www.naecb.org/ 

National Respiratory Training Center Provider Training 

2002 

http://www.nrtc-usa.org/ 

Alabama Asthma Indicator Screenings Disease Management http://www.lungusa2.org/alaba

Inner-City Asthma Intervention – University of Alabama at Birmingham, School of Medicine (CDC funded) 

Self-Management April 2001 

http://www.health.uab.edu/sho

Assessing Alaska's Asthma Burden (CDC funded) 

Coalition October 2003 

Alaska http://www.aklung.org/asthma

Statewide Asthma Education Programs (CDC funded) 

Self-Management Patient Education October 2003 

http://aafa.org/display.cfm?id=

Inner-City Asthma Intervention – St. Joseph’s Hospital and Medical Center (CDC funded) 

Self-Management April 2001 

Arizona lparrar@chw.edu 

Inner-City Asthma Intervention – El Rio Health Center (CDC funded) 

Self-Management April 2001 

http://www.elrio.org/ http://www.elrio.org/initiative

Arkansas Arkansas Asthma Coalition – Arkansas 

Children's Hospital Research Institute 

Coalition Minority and Rural Outreach 1997 

http://achri.ach.uams.edu/ http://hp2010.nhlbihin.net/ast_

California Better Asthma Care for California Kids Education 

Provider Training 2005 

http://www.betterasthmacare.ohttp://www.dhs.ca.gov/ps/cdic
Location Classification Program For More Information & Year Begun 

Inner-City Asthma Intervention – Children’s Asthma Consortium (CDC funded) 

Self-Management April 2001 

http://www.mssm.edu/peds 

California Breathing – California Department of Health Services 

Coalition Self-Management Patient Education 

http://www.dhs.ca.gov/cdic/cahttp://www.californiabreathing

Mobile Asthma Care – Children's Hospital Central California (CDC funded) 

Self-Management Patient Education August 2002 

http://www.cdc.gov/asthma/co

Replication and Implementation of Scientifically Proven Asthma Interventions – Chula Vista Elementary School District (CDC funded) 

Self-Management Disease Management April 2001 

http://www.cvesd.k12.ca.us 

Controlling Asthma in American Cities – University of California, Berkeley (CDC funded) 

Collaboratives September 2001 

http://www.berkeley.edu 

CalAsthma Disease Management 

February 2001 

 http://www.calasthma.org/ 

California Statewide Collaborative for Achieving Better Care for Asthma (Medicaid contract) 

Coalition Collaborative 1995 

Center for Health Care Strateghttp://www.chcs.org/ 

The Colorado Asthma Program (CDC funded) Collaboratives 

Disease Management Self-Management Data Measurement and 

Reporting 

September 2001 

cdphe.psdrequests@state.co.us http://www.cdphe.state.co.us/p

Colorado Asthma Coalition Coalition 

October 2000 

asthmacoalition@alacolo.orghttp://www.asthmacolorado.or

Colorado 

National Jewish Pilot Disease Management Program (Medicaid contract) 

Disease Management February 2004 

http://www.nationaljewish.orgnews/y2004/medicaid-co-resu

Connecticut Addressing Asthma from a Public Health 

Perspective – Connecticut Department of Public Health and Addiction Services (CDC funded) 

Cross-Agency Work 

Data Measurement September 2001 

http://www.dph.state.ct.us/BCforce.htm 

94 
Location Classification Program For More Information & Year Begun 

Delaware No programs found 

Inner-City Asthma Intervention  Health Choice Network (CDC funded) 

Self-Management April 2001 

http://www.cdc.gov/asthma/co

Population-Based Models To Establish Surveillance for Asthma Incidence in Defined Geographic Areas – Miami-Dade County Health Department (CDC funded) 

Data Measurement 

/Reporting 

Disease Management September 2001 

http://www.dadehealth.org/ 

Florida 

Medicaid Disease Management Programs (Medicaid funded) 

Disease Management http://www.fdhc.state.fl.us/Me

/index.shtml 

Asthma in Older Women – Kaiser Permanente Georgia (CDC funded) 

Data Measurement /Reporting October 2000 

http://www.cdc.gov/asthma/co

Addressing Asthma from a Public Health Perspective – Georgia Department of Human Resources (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://health.state.ga.us/pdfs/eppdf http://health.state.ga.us/pdfs/epn_2004.pdf 

Georgia 

Viruses and Asthma – Emory University School of Medicine (CDC funded) 

Coalition September 2003 

http://medicine.emory.edu/ http://www.sph.emory.edu/zap

Assessment of Health Effects Associated with Volcanic Emissions – The Hawaii State Department of Health (CDC funded) 

Data Measurement 

/Reporting 

Disease Management September 1998 

http://www.state.hi.us/doh/ 

Addressing Asthma from a Public Health Perspective – The Hawaii State Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2002 

http://www.state.hi.us/doh/ 

Hawaii 

Childhood Rural Asthma Project, Phase 2 – The Hawaii State Department of Health (CDC funded) 

Minority and Rural 

Outreach 

September 2003 

http://www.state.hi.us/doh/ 
Location Classification Program For More Information & Year Begun 

Idaho Addressing Asthma from a Public Health 

Perspective – Idaho Department of Health and Welfare (CDC funded) 

Other programs: • Healthy Homes, Head Start • School Asthma Management Model for Idaho (SAMMI) 

Cross-Agency Work Data Measurement September 2001 

http://www2.state.id.us/dhw/a

Addressing Asthma in Illinois-Statewide program 

Coalition Developing Guidelines July 2000 

http://www.idph.state.il.us/pdf

Chicago Asthma Consortium Coalition 

1996 

http://www.chicagoasthma.org

Replication and Implementation of Scientifically Proven Asthma Interventions American Lung Association of Metropolitan Chicago (CDC funded) 

Disease Management Self-Management September 2001 

http://www.lungchicago.org/ 

Inner-City Asthma Intervention – Cook County Hospital, Department of Pediatrics, Pediatric Allergy Division Office (CDC funded) 

Self-Management April 2001 

http://www.bronx-leb.org

Replication and Implementation of Scientifically Proven Asthma Interventions – PCC Community Wellness Center (CDC funded) 

Disease Management Self-Management September 2002 

http://www.cdc.gov/asthma/c

http://www.pccwellness.org/co

Replication and Implementation of Scientifically Proven Asthma Interventions – Southern Illinois Healthcare Foundation (CDC funded) 

Disease Management Self-Management September 2002 

http://www.cdc.gov/asthma/co

Illinois 

Controlling Asthma in American Cities – University of Illinois (CDC funded) 

Collaborative September 2001 

 http://www.uic.edu/ 

Indiana Addressing Asthma from a Public Health 

Perspective – Indiana State Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2002 

http://www.state.in.us/isdh/ind

96 
Location Classification Program For More Information & Year Begun 

Indiana Chronic Disease Management Program 

Disease Management Early 2004 

http://www.indianacdmprograhttp://www.indianacdmprogra

Iowa Addressing Asthma from a Public Health 

Perspective – Iowa Department of Public Health (CDC funded) 

Coalition Data Measurement August 2000 

http://www.idph.state.ia.us/hphma_surveillance_plan.pdf 

Kansas American Lung Association of Kansas Disease Management http://www.kslung.org/program

Good Health Kentucky Asthma Resource – Good Samaritan Foundation 

Public Service/ Education Efforts 

Kentucky http://www.goodhealthky.org/

The Metropolitan Asthma Coalition (MAC) – American Lung Association, Louisville, Kentucky 

Developing and 

Enforcing Guidelines 

 http://www.kylung.org/mac.h

Louisiana American Lung Association of Louisiana Public Service and 

Education Efforts 

http://www.louisianalung.org/

Maine Addressing Asthma from a Public Health 

Perspective – Maine Asthma Prevention and Control Program (CDC funded) 

Cross-Agency Work Data Measurement August 2000 

http://www.maine.gov/dhhs/bo

Maryland Maryland Childhood Asthma Program, Dept. 

of Health and Mental Hygiene 

Addressing Asthma from a Public Health Perspective (CDC funded) 

Cross-Agency Work Data Measurement Developing Guidelines September 2001

http://www.fha.state.md.us/mchttp://www.fha.state.md.us/mc_2003.pdf 

Baystate Medical Center – Inner-City Asthma Intervention (CDC funded) 

Self-Management April 2001 

Massachusetts http://www.baystatehealth.com

Massachusetts Department of Public Health Addressing Asthma from a Public Health Perspective (CDC funded) 

Cross-Agency Work Data Measurement September 2003 

 http://www.state.ma.us/dph/ 

Addressing Asthma from a Public Health Perspective – Michigan Department of Community Health (CDC funded) 

Cross-Agency Work Data Measurement August 2000 

http://www.GetAsthmaHelp.o

Asthma Surveillance and Interventions in Hospital Emergency Departments Program (CDC funded) 

Collaborative August 2001 

http://www.msu.edu/

Michigan 

Enhanced Surveillance of Asthma Deaths Michigan Department of Community Health (CDC funded) 

Collaborative September 2000 

http://www.GetAsthmaHelp.o
Location Classification Program For More Information & Year Begun 

Controlling Asthma in American Cities American Lung Association of Minnesota (CDC funded) 

Collaborative April 2001 

http://www.alamn.org/InfoCenhttp://www.alamn.org/InfoCen

Inner-City Asthma Intervention – American Lung Association of Minnesota (CDC funded)

Self-Management April 2001 

http://www.alamn.org/InfoCenhttp://www.alamn.org/InfoCen

Minnesota 

Addressing Asthma from a Public Health Perspective – Minnesota Department of Health (CDC funded) 

Advisory Board. Cross-Agency Work Data Measurement September 1999 

http://www.health.state.mn.us/

Inner-City Asthma Intervention – JacksonHinds Comprehensive Health Center Self-Management April 2001 Mississippi (601) 362-5321 

Addressing Asthma from a Public Health Perspective – Mississippi Department of Health

Cross-Agency Work Data Measurement September 2003 

http://www.msdh.state.ms.us/m

Inner-City Asthma Intervention Children’s Mercy Hospitals and Clinics 

Self-Management April 2001 

http://www.childrensmercy.or

Inner-City Asthma Intervention – Washington University School of Medicine 

Self-Management April 2001 

http://medicine.wustl.edu/

Addressing Asthma from a Public Health Perspective – Missouri Department of Health and Senior Services (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://www.dhss.mo.gov/asthm

Controlling Asthma in American Cities – St. Louis Regional Asthma Consortium (CDC funded)

Collaborative Fall 2000 

http://www.asthma-stlouis.org

Missouri 

Missouri State Medicaid Disease Management Program (Medicaid funded) 

Disease Management November 2002 

http://www.heritageinfo.com/mocaidrx/files/dm/Pr

Montana Montana Environmental Public Health 

Tracking Program -- Montana Department of Public Health and Human Services (CDC National EPHT Program) 

Data Measurement and Reporting 

 http://www.dphhs.mt.gov/eph

Nebraska Addressing Asthma from a Public Health 

Perspective – Nebraska Health and Human Services System (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://www.hhs.state.ne.us/epi

98 
Location Classification Program For More Information & Year Begun 

Nevada American Lung Association of Nevada Public service and 

education effort 

http://www.lungs.org/ 

New Hampshire 

Addressing Asthma from a Public Health Perspective – New Hampshire Department of Health and Human Services (CDC funded) 

Cross-Agency Work Data Measurement 

http://www.dhhs.nh.gov/DHH

Interdepartmental Report and Strategic Plan for Asthma – New Jersey Department of Health and Senior Services and others 

Collaborative/ Coalition Minority and rural outreach 

http://www.state.nj.us/health/cstrategicplan.pdf 

Replication and Implementation of Scientifically Proven Asthma Interventions –Babyland Family Services (CDC funded) 

Self-Management Disease Management September 2001 

http://www.nccic.org/ccpartne

Inner-City Asthma Intervention – Children’s Hospital of New Jersey at Newark Beth Israel Medical Center, Saint Barnabas Health Care System (CDC funded) 

Self-Management Rural and Minority outreach April 2001 

http://www.sbhcs.com/hospita

Replication and Implementation of Scientifically Proven Asthma Interventions – PBI Regional Medical Center (CDC funded) 

Self-Management Disease Management September 2002 

 http://www.cdc.gov/asthma/chttp://www.pbih.org 

New Jersey 

Addressing Asthma from a Public Health Perspective – New Jersey Department of Health and Senior Services (CDC funded) 

Cross-Agency Work Data Measurement 

http://www.state.nj.us/health/f

New Mexico Addressing Asthma from a Public Health 

Perspective – New Mexico Department of Health (CDC funded) 

Cross-Agency Work Data Measurement August 2000 

http://www.health.state.nm.us/ 

New York New York's Action Against Asthma: 

• Regional Asthma Coalitions • Healthy Neighborhoods Program • Minority Asthma Partnerships • Medicaid • Child Health Plus • Family Health Plus • School Based Health Centers 

Cross-Agency Work Data Measurement Coalition Public Service and Education 

http://www.health.state.ny.us/ http://www.health.state.ny.us/nm http://www.health.state.ny.us/n
Location Classification Program For More Information & Year Begun 

Replication and Implementation of Scientifically Proven Asthma Intervention – Bronx-Lebanon Hospital Center (CDC funded) 

Self-Management Disease Management September 2001 

http://www.bronx-leb.org/ http://www.bronxleb.org/Centers_of_Excellence

Controlling Asthma in American Cities –Columbia University (CDC funded) 

Collaborative September 2001 

http://www.columbia.edu/ http://www.cumc.columbia.edage/Clinical%20Centers/Webs

Inner-City Asthma Intervention – Mount Sinai School of Medicine Department of Pediatrics (CDC funded) 

Self-Management April 2001 

http://www.mssm.edu/peds/ 

Inner-City Asthma Intervention – University at Buffalo, SUNY (CDC funded) 

Self-Management  April 2001 

http://www.smbs.buffalo.edu/f

Inner-City Asthma Intervention – WakeMed (CDC funded) 

Self-Management April 2001 

North Carolina http://www.wakemed.org 

Assessing Asthma-Related School and Work Absences – University of North CarolinaChapel Hill, School of Public Health (CDC funded) 

Data Measurement and Reporting October 2001 

http://www.hpdp.unc.edu/ReseCFID=50420&CFTOKEN=52

North Dakota No programs listed 

Inner-City Asthma Intervention Rainbow Babies and Children’s Hospital (CDC funded) 

Self-Management April 2001 

Ohio http://www.uhrainbow.com/ 

The Ohio Asthma Coalition Coalition http://www.ohiolung.org/ohio-

Oklahoma Addressing Asthma from a Public Health 

Perspective – Oklahoma State Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2002 

http://www.health.state.ok.us/ 

Oregon Asthma Program – Oregon DHS Oregon Asthma Network 

Coalition Self-Management Public Service and Education 2000 

http://www.dhs.state.or.us/pubhttp://www.dhs.state.or.us/puber.cfm http://oregon.gov/DHS/ph/asth

Oregon 

Inner-City Asthma Intervention – CareOregon (CDC funded) 

Self-Management April 2001 

 http://www.careoregon.org/m

100
Location Classification Program For More Information & Year Begun 

Addressing Asthma from a Public Health Perspective – Oregon Department of Human Services (CDC funded) 

Cross-Agency Work Data Measurement October 1999 

http://oregon.gov/DHS/ph/asth

Asthma in Older Women – Kaiser Northwest/Kaiser Colorado 

Data Masurement and Reporting 

http://www.cdc.gov/asthma/co

Incidence of Occupational Asthma within the Northwest Division of Kaiser Permanente – Kaiser Foundation Research Institute 

Data Measurement and Reporting 

http://www.cdc.gov/asthma/co(503) 335-6755 

Population-Based Models To Establish Surveillance for Asthma Incidence in Defined Geographic Areas – Kaiser Foundation Research Institute 

Developing and Enforcing Guidelines 

http://www.cdc.gov/asthma/co(503) 335-6755 

Controlling Asthma in American Cities –Children’s Hospital of Philadelphia (CDC funded) 

Collaborative http://www.chop.edu/consume

Addressing Asthma from a Public Health Perspective – Pennsylvania Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://webserver.health.state.p

Replication and Implementation of Scientifically Proven Asthma Interventions – Philadelphia Department of Public Health 

Self-Management Disease Management September 2003 

http://www.phila.gov/health/ 

Pennsylvania 

National Jewish Medical and Research Center Disease Management http://www.nationaljewish.org

info/diseases/asthma/index.asp

Rhode Island Rhode Island Asthma Control Program – 

Rhode Island Department of Health (CDC 

funded) 

Developing and Enforcing Guidelines September 1999 

http://www.health.state.ri.us/d

South Carolina Asthma Surveillance and Interventions in 

Hospital Emergency Departments Program – 

University of South Carolina (CDC funded) 

Coalition Public Service and Education Data Measurement and Reporting September 2000 

http://www.sc.edu/ 

South Dakota No programs listed 
Location Classification Program For More Information & Year Begun 

Tennessee Asthma Care Management Program –

Tennessee Department of Finance & Administration

Public Service and Education Self-Management Disease Management 

http://www.state.tn.us/tenncare

The Asthma Coalition of Texas Coalition http://www.texasasthma.org/ Replication and Implementation of Scientifically Proven Asthma Interventions – Harris County Hospital District (CDC funded) 

Self-Management Disease Management September 2001 

http://www.hchdonline.com/ 

Inner-City Asthma Intervention – University of Texas Health Science Center at San Antonio (CDC funded) 

Self-Management April 2001 

http://www.uthscsa.edu 

Addressing Asthma from a Public Health Perspective – Texas Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://www.dshs.state.tx.us/ http://www.dshs.state.tx.us/ch

Texas 

Texas Medicaid Managed Care Asthma Project 

Self-Management Disease Management 

http://www.hhsc.state.tx.us/mema.html 

Utah Addressing Asthma from a Public Health 

Perspective – Utah Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://health.utah.gov/asthma/ 

Vermont Addressing Asthma from a Public Health 

Perspective – Vermont Dept. of Health Asthma Program (CDC funded) 

Cross-Agency Work Data Measurement August 2000 

http://healthvermont.gov/preve

Controlling Asthma in Richmond Metropolitan Area (CARMA) (CDC funded) 

Collaborative September 2001 

Virginia http://www.carmakids.org/ 

Addressing Asthma from a Public Health Perspective – Virginia Department of Health (CDC funded) 

Cross-Agency Work Data Measurement September 2001 

http://www.vahealth.org/asthm

Replication and Implementation of Scientifically Proven Asthma Interventions – Asthma and Allergy Foundation of America Washington State Chapter (CDC funded) 

Self-Management Disease Management 

Washington http://www.aafawa.org/ 

Addressing Asthma from a Public Health Perspective – Washington Department of Health (CDC funded) 

Cross-Agency Work Data Measurement 

http://www.alaw.org/asthma/w/ 

102
Location Classification Program For More Information & Year Begun 

Breathe Easy Washington Program –Washington Department of Health 

Public Service and Education 

http://www.alaw.org/air_quali

West Virginia Addressing Asthma from a Public Health 

Perspective – West Virginia Department of Health and Human Resources 

Cross-Agency Work Data Measurement 

http://www.wv.gov/offsite.asp/bph 

Wisconsin Addressing Asthma from a Public Health 

Perspective – Wisconsin Department of Health and Family Services (CDC funded) 

Cross-Agency Work Data Measurement 

http://www.dhfs.state.wi.us/ 

Wyoming Wyoming Department of Health 

General list of asthma resources 

Public Service and Education 

http://wdh.state.wy.us/asthma/
104 

Appendix D: Asthma Measures 

Table D.1. Inventory and comparison of asthma measures from 12 national sources,1 grouped by 

type of measure 

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Provider Care (Process)

Asthma severity classification done during asthma visit. Children National NICHQ

Percent of patients with severity assessment for asthma at last visit. All National HRSA

Rate of asthma patients with a documented asthma severity level. All Hospitals JCAHOAPMIG

Percentage of patients who were evaluated during at least one office visit for 

the frequency (numeric) of daytime and nocturnal asthma symptoms.

All National AMA PCPI 

(NQF 

approved)

Severity Assessment - Spirometer measurements used in diagnosis. All Hospitals JCAHO

Establishment of a personal best peak flow measurement. All Hospitals JCAHO

Percent of moderate and severe persistent asthma population older than five 

years who have established a personal best Peak Flow through multiple 

readings.

All National HRSA

Percent of asthma population that uses a peak flow meter at home. All National MEPS

Medications - timing Time since last asthma medication taken. All State/National NAS

Percent of asthma population that takes any prescription medication for 

asthma.

All National MEPS

Percentage of all patients with mild, moderate, or severe persistent asthma 

who were prescribed either the preferred long-term control medication (inhaled 

corticosteroid) or an acceptable alternative treatment.

All National AMA PCPI 

(NQF 

approved)

Children National NICHQ

All Hospitals JCAHOORYX

Percent of asthma patients who use asthma medications. All National HRSA

Rate of medications use for those with a severity assessment of having 

persistent asthma.

All Hospitals JCAHOAPMIG

Use of systemic corticosteroids in patients with an acute exacerbation. All Hospitals JCAHO

Percent of asthma patients who use corticosteroids for long-term control of 

asthma.

All National HEDIS

Percent of asthma patients who use corticosteroids In inpatient hospital 

setting.

Children Hospitals JCAHOORYX

Medications - frequency Frequency of use of asthma medication in the past 30 days. Adults State/National BRFSS

Number of puffs taken each time. All State/National NAS

Number of times per week. All State/National NAS

Percent of asthma population that has ever use a prescription inhaler. All State/National NAS

Number of full canisters used in the past 3 months. All State/National NAS

Use before exercising. All State/National NAS

Use any steriod inhalers for asthma. All National MEPS

Percent of population that has had a health professional show them how to 

use an inhaler.

All State/National NAS

Medications - specific 

type

Percent of asthma population that takes [NAME OF MEDICATION] for their 

asthma (self assessment).

All State/National NAS

Medications - nebulizer Percent of asthma population that uses a nebulizer with their asthma 

medication.

All State/National NAS

Medications - OTC Percent of asthma population that has ever used over-the-counter medication 

for asthma.

All State/National NAS

Percent of asthma population taking asthma medications in pill form. All State/National NAS

Percent of asthma population taking asthma medications in syrup form. All State/National NAS

Length of time used fpr each medication. All State/National NAS

Medications - relievers Use of relievers for inpatient childhood asthma. Children Hospitals JCAHOORYX

Rate of asthma patients with a documented self-management plan. All Hospitals JCAHOAPMIG

Percent that has taken a class on how to manage asthma. All State/National NAS

Asthma management plan given to family. Children National NICHQ

Asthma management plan is discussed with and understood by patient/family. Children Hospitals JCAHOORYX

Percent with a printed asthma management plan. All State/National NAS

Patients with self-management goals during asthma visit. Children National NICHQ

Percent with self-management goal at last visit. All National HRSA

Severity Assessment

Medications

Use of anti-inflammatory medications to control asthma.

Medications - inhaler

Severity Assessment - 

peak flow

Medications - 

corticosteroids

Medications - forms

Documented asthma 

management plan

Medications - NSAIDS 
 105

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Health professional discussed how to better control asthma at time of hospital 

discharge.

All State/National NAS

Rate of asthma patients who have been educated on asthma triggers and 

avoidance.

All Hospitals JCAHOAPMIG

Percent of asthma population whose doctor or health professional taught them 

what to do during an asthma attack.

All State/National NAS

Percent of asthma population whose doctor or health professional taught them 

how to use a peak flow meter.

All State/National NAS

Percent of asthma population whose doctor or health professional taught them 

how to recognize early signs or symptoms of an asthma episode.

All State/National NAS

Percent of patients with planned care visits. Children National NICHQ

Adults State/National BRFSS

All State/National NAS

Doctor visits Time since last talked to a doctor or other health professional about your 

asthma.

All State/National NAS

Prevention - depression Percent of asthma population screened for depression. All National HRSA

Percent of asthma population that has had a flu shot in the past 12 months. Adults State/National BRFSS

One dose of flu vaccine administered in past 15 months. All HRSA

Percent of asthma population that has been advised by a health professional 

to quit smoking.

Adults State/National BRFSS

Rate of smoking cessation advice/counseling with caregivers of children with 

asthma.

Children Hospitals JCAHOAPMIG

Patient satisfaction Patients satisfaction with asthma care. Children National NICHQ

Patient/Parent Self-Care (Process)

Percent of asthma population exposed to environmental tobacco smoke. All National HRSA

Percent of asthma population evaluated on triggers other than environmental 

tobacco smoke (dust, mites, cats, dogs, molds/fungi, cockroaches).

All National HRSA

Percent of asthma population whose work environment worsens their asthma. All State/National NAS

Percent of asthma population that has seen cockroaches in the home. All State/National NAS

Percent of asthma population with mold or musty odor inside home in the past 

30 days.

All State/National NAS

Percent of asthma population that uses gas for cooking. All State/National NAS

Percent of asthma population with unvented gas logs or gas fireplace or gas 

stove in the home.

All State/National NAS

Percent of asthma population that has smoking in the home in the past week. All State/National NAS

Percent of asthma population that has a wood burning stove in the home. All State/National NAS

Percent of asthma population that uses air cleaner or purifier inside the home. All State/National NAS

Uses a bathroom fan that vents to the outside. All State/National NAS

Uses a dehumidifier to reduce moisture inside the home. All State/National NAS

Uses an exhaust fan when cooking. All State/National NAS

Percent of asthma population that has been advised by a health professional 

to change things in home, school or work to improve asthma.

All State/National NAS

Percent of asthma population that have carpeting or rugs in the bedroom. All State/National NAS

Percent of asthma population that use mattress covers made for controlling 

dust mites.

All State/National NAS

Percent of asthma population that use pillow covers made for controlling dust 

mites.

All State/National NAS

Percent of asthma population with indoor pets. All State/National NAS

Percent of asthma population that allows pets in the bedroom. All State/National NAS

Percent of asthma population that washes sheets and pillowcases in hot 

water.

All State/National NAS

Percent of asthma population that has smoked at least 100 cigarettes in their 

entire life.

All State/National NAS

Percent of asthma population that now smokes every day, some days or not at 

all.

All State/National NAS

Prevention smoking 

cessation counseling

Routine checkups

Behavior - environmental 

modification

Behavior - smoking

Prevention - flu

Behavior - environmental 

assessment

Behavior - environmental 

modification devices

Patient education

Average number of doctor visits for routine checkup for asthma.
106 

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Daily Symptom Burden (Outcome)

All State/National NAS

Adults State/National BRFSS

Number of school/work days missed. All State/National NAS

Average number of days lost at work or school in the past 30 days. All National HRSA

Average number of missed school days in the past two months. Children National NICHQ

Percent of asthma population that experiences limitation in usual activities due 

to asthma.

All State/National NAS

All State/National NAS

Adults State/National BRFSS

All National MEPS

All National NHIS

All State/National NAS

Adults State/National BRFSS

Number of asthma attacks in the past 3 months. All State/National NAS

Percent of people with asthma who have symptoms all the time. All State/National NAS

Time since last had symptoms of asthma. All State/National NAS

Duration of most recent asthma attack. All State/National NAS

Percent of people with asthma who had a longer/shorter recent attack 

compared to last attack.

All State/National NAS

All State/National NAS

All National HRSA

Number of symptom-free days among patients with persistent asthma. Children National NICHQ

All State/National NAS

Adults State/National BRFSS

Avoidable Events 

All State/National NAS

Adults State/National BRFSS

Percent of children with asthma who have had an acute asthma visit in the 

past two months.

Children National NICHQ

Return to hospital with same asthma diagnosis within 7 days following 

emergency department visit or observation stay.

Children Hospitals JCAHOORYX

Children National NICHQ

All National HRSA

All National HRSA

All State/National NAS

Adults State/National BRFSS

Return to hospital with same asthma diagnosis within 7 days following 

inpatient discharge.

Children Hospitals JCAHOORYX

Return to hospital with same asthma diagnosis with 30 days following inpatient 

discharge.

Children Hospitals JCAHOORYX

Return to hospital with same asthma diagnosis within 30 days following 

emergency department visit or observation stay.

Children Hospitals JCAHOORYX

Average length of stay for discharges from short-stay hospitals for asthma. All National NHDS

Risk-adjusted length of stay (LOS) for childhood asthma patients. Children Hospitals JCAHOORYX

Have stayed overnight in a hospital due to asthma in the past 12 months. All State/National NAS

Children State/National HCUP

Adults State/National HCUP

Percent of children with asthma who have had a hospitalization for asthma in 

the past six months.

Children National NICHQ

Rate of discharge from short-stay hospitals for asthma. All National NHDS

Number of symptom-free days in the past two weeks.

Activity limitation days

Number of days unable to work or carry out usual activities due to asthma in 

the past 12 months.

Hospital admissions for asthma per 100,000 population.

Emergency/Urgent care

Symptom - sleep

Symptom - free

Number of emergency department visits in the past 12 months. 

Symptom - frequency Number of days with symptoms of asthma in the past 30 days.

Symptom - duration

Number of days when symptoms of asthma make it difficult to stay asleep in 

the past 30 days.

Number of visits for urgent treatment of worsening asthma symptoms or an 

asthma episode or attack besides ED or urgent care.

Percent of asthma patients who have had a visit to an ED/Urgent care office 

for asthma in the past 6 months.

Hospitalizations

Percent of people with asthma who have had an episode of asthma or an 

asthma attack during the past 12 months.
 107

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Other Dimensions (Prevalence)

Children State/National BRFSS

Adults State/National BRFSS

All State/National NAS

All National MEPS

All National NHIS

Age at diagnosis. Adults State/National BRFSS

Children State/National BRFSS

Adults State/National BRFSS

All State/National NAS

Children National NICHQ

All National HRSA

Percent of asthma population with a parent who has ever been told he or she 

has asthma.

All State/National NAS

With a sibling who has ever been told he or he has asthma. All State/National NAS

With a grandparent who has ever been told he or she has asthma. All State/National NAS

Other Dimensions (Demographic)

Highest level of school completed. All State/National NAS

Zip code. All State/National NAS

Total combined income in household. All State/National NAS

Height. All State/National NAS

Weight. All State/National NAS

Birth weight. All State/National NAS

Percent of asthma population with low birth weight. All State/National NAS

Percent of asthma population of Hispanic or Latino origin. All State/National NAS

Percent of asthma population that is white, black, American Indian/Alaska 

Native, or Asian/Pacific Islander.

All State/National NAS

Percent of asthma population whose household has been without telephone 

service for 1 week or more in the past 12 months.

All State/National NAS

Percent of people with asthma that have any kind of health care coverage, 

including health insurance, prepaid plans such as HMOs, or government plans 

such as Medicare.

All State/National NAS

Percent of people with asthma that did not have any health insurance or 

coverage.

All State/National NAS

Percent of asthma population that is currently employed/unemployed. All State/National NAS

Percent of asthma population unable to work for health reasons/disabled. All State/National NAS

1

Sources:

AMA PCPI = American Medical Association Physician Consortium for Performance Improvement, NQF Ambulatory Care Measures, 2005

Demographics

Prevalence - family 

history

Prevalence - lifetime

Prevalence - registry

Prevalence - current

Percent of people that has ever been told they have asthma by a doctor or 

health professional that still have asthma.

Percent of population that has ever been told they have asthma by a doctor or 

health professional.

Access

NHIS = National Health Interview Survey, CDC, 2001

Employment

Number of asthma patients in the registry.

MEPS = Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality, 2000

NICHQ = National Initiative for Children's Health Care Quality monthly measures for children with asthma for practices participating in initiative

HRSA = Health Resources and Services Administration, Bureau of Primary Health Care, Disparities Collaboratives - Asthma

HCUP = Health Care Utilization Project, Agency for Healthcare Research and Quality, 2001

NHDS = National Hospital Discharge Survey, CDC, 2001

JCAHO-APMIG = Joint Commission on Accrediation of Healthcare Organizations, Asthma Performance Measurement Implementation Guide

NAS = National Asthma Survey, CDC, 2003 

JCAHO-ORYX = JCAHO ORYX initiative on hospital performance for implementation in 2006

BRFSS = Behavioral Risk Factor Surveillance Survey, CDC, 2003

HEDIS = Healthplan Employer Data and Information Set, National Committee for Quality Assurance, 2003
108 

Table D.2. State and local asthma measures: four selected quality improvement initiatives 

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Provider Care (Process)

Patients with newly diagnosed asthma (moderate or severe) 

reported to have spirometry as part of their evaluation

All City CASI

Physicians monitoring spirometry or peak flow during office 

visits

All City CASI

Percent of people with asthma who have had a spirometry 

measurement.

All State Oregon

Physicians reporting access to spirometry

- Spirometer in office

- Referral to an asthma specialist who performs spirometry

- No access to spirometry

All City CASI

For specialists:

Patients with newly diagnosed asthma (moderate or severe) 

reported to have selected diagnostic techniques as part of 

their evaluation (percents). Techniques listed: spirometry, 

chest radiograph, skin testing or radioallergosorbent testing, 

sinus radiographs, trial of daily peak flow monitoring, sputum 

examination and stain for eosinophilia

All City CASI

Patients with newly diagnosed asthma (moderate or severe) 

reported to have daily peak flow as part of their evaluation

All City CASI

Peak flow measurement at ED discharge All City CASI

Physicians monitoring: 

Techniques listed: spirometry or peak flow during office 

visits, frequency of wheeze/cough, frequency of beta 2-

agonist use, activity levels, frequency of disturbed sleep due 

to asthma symptoms, loss of work/school days due to 

asthma, spirometry or peak flow, direct observation of inhaler 

technique, peak flow diary review

Percent of physicians using peak flow or PFTI

- Acutely symptomatic patient (never, rarely, sometime, 

often)

- Asymptomatic patient (never, rarely, sometimes, often)

Patients with newly diagnosed asthma (moderate or severe) 

reported to have selected diagnostic techniques as part of 

their evaluation. 

Techniques listed: spirometry, chest radiograph, skin testing 

or radioallergosorbent testing, sinus radiographs, trial of daily 

peak flow monitoring, sputum examination and stain for 

eosinophilia, sinus radiographs, CT of the sinuses, MRI of 

the sinuses, nasal speculum examination, rhinolaryngoscopy, 

upper GI for gastroesophagel reflux disease (GERD), 

esophogeal pH testing for GERD

All City CASI

Percent of people with persistent asthma who have been 

seen by a medical practitioner for asthma in the last 12 

months

All State Oregon

Percent of members with persistent asthma who have at 

least one preventive/ambulatory visit with a primary care 

physician, pulmonologist, or allergist

All State health 

plans

MQIC

Severity Assessment - 

spirometry access

Severity Assessment - 

peak flow et al.

Severity Assessment - 

spirometry

Doctor visit
 109

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Physicians prescribing inhaled steroid (for patients <5 years 

old, for patients > 5 years old) for patients with moderate 

persistent symptoms

All City CASI

Others listed:

- Oral beta-agonist (for patients <5 years old, for patients > 5 

years old)

- Inhaled beta-agonist (for patients <5 years old, for patients 

> 5 years old)

- Theophylline (for patients <5 years old, for patients > 5 

years old)

- Systemic steroid (for patients <5 years old, for patients > 5 

years old)

- Inhaled steroid (for patients <5 years old, for patients > 5 

years old)

- Cromolyn or nedocromil (for patients <5 years old, for 

patients > 5 years old)

All City CASI

Medications after ED 

visit

After ED visit percentage of patients given:

- Prescription for systemic steroids

- Prescription for inhaled steroids/cromolyn

- Prescription for antibiotics

All City CASI

Patients with moderate or severe asthma prescribed a 

corticosteroid inhaler

All City CASI

Patients with asthma for whom any type of metered-dose 

inhaler is prescribed

All City CASI

During ED visit, formal training in use of metered-dose 

inhaler, spacer

All City CASI

Percent of people with persistent asthma who have at least 

one filled prescription for a daily inhaled anti-inflammatory 

medication

All State Oregon

Percent of people with persistent asthma who use more than 

one canister of a short-acting inhaled bronchodilator every 

two months for one year.

All State Oregon

Patients with moderate or severe persistent asthma for 

whom written treatment plans are routinely developed

All City CASI

Percent of people with asthma who have a written asthma 

action plan

All State Oregon

Percent of people with asthma who have documentation of 

asthma education

All State Oregon

Physicians' approach to asthma education.

Techniques listed: form education program, informal 

education delivered by nurse or physician, other, do not 

provide asthma education

All City CASI

During ED visit:

- Formal asthma education by physician or nurse

- Written asthma educational material

All City CASI

Percent of people with persistent asthma who have received 

education about their triggers and how to reduce their 

exposure to them

All State Oregon

Asthma education

Medications - 

corticosteroids

Medications - inhaler

Written asthma plans
110 

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

NAEPP guidelines

Physicians and NAEPP guidelines

- Heard of NAEPP guidelines (yes/no)

- Think NAEPP guidelines are useful (extremely useful, 

somewhat useful, not very useful, no use at all)

All City CASI

Physician’s likelihood of initiating a consultation with an 

asthma specialist based on the following event or criteria: 

hospitalization for asthma, an emergency department visit for 

asthma, multiple medications with continued symptoms, a lifethreatening asthma episode, all patients with mild persistent 

asthma, all patients with moderate persistent asthma, all 

patients with severe persistent asthma, atypical signs or 

symptoms, for a diagnosis in child < 3 years old

All City CASI

Percent of people hospitalized for asthma who are seen by 

an asthma specialist within one month of the hospital 

discharge date

All State Oregon

Acute exacerbations

Patients who call practice for an acute (not life-threatening) 

exacerbation are usually:

- Told to go to the emergency department

- Provided with a same-day office appointment

- Scheduled for an appointment within the week

- Other

All City CASI

Preventive care - flu 

vaccine

Percent of people with persistent asthma who have received 

an influenza immunization in the last 12 months

All State Oregon

Preventive care - 

allergen testing

Percent of people with persistent non-seasonal asthma who 

have received allergen testing

All State Oregon

Hospital Care (Process)

Standards of care - 

hospital

Whether a hospital is using the following hospital-wide:

- Currently using NAEPP guidelines

- Currently using critical pathways

All City CASI

Standards of care - ICU

Whether the hospital is using the following in ICU:

- Currently using guidelines

- Currently using critical pathways

All City CASI

Standards of care - 

bedside

Whether care at bedside includes: nebulization, peak flow 

monitoring, peak flow instruction, evaluation of inhaler 

technique, inhaled anti-inflammatories, asthma education

All City CASI

Community-based care 

at hospitals

Percent of hospitals that have:

1. Formal asthma education in outpatient setting

2. Utilization review for asthma

3. Clinical case management program for asthma

4. Home visits as part of asthma management

5. Community-based asthma screening

6. Community-based adult asthma education programs

7. Community-based pediatric asthma education programs

8. School-based asthma education programs

All City CASI

Emergency Department Care (Process)

Assessment in ED

Whether assessment in the ED includes: 

1. PEFR measurement as part of initial assessment

2. PEFR measurement to document improvement after 

treatment

3. Pulse oximetry as part of initial assessment

4. Pulse oximetry to document improvement after treatment

5. Arterial blood gas as part of initial assessment

6. Arterial blood gas as part of assessment of severe cases

7. Chest radiograph for patients wheezing for the first time

8. Chest radiograph for patients with wheezing and fever

9. Chest radiograph when diagnosis of asthma is in doubt

All City CASI

Treatment in ED

1. Average time asthma patients spent in ED

2. Average time asthma patients spent in ED before 

disposition

3. Percentage of patients receiving:

- IV or po steroids used within the first hour

- IV or po steroids used at any time during ED care

- Theophylline therapy at any time during ED care

- Supplemental oxygen at any time during ED care

- Treatment for >4 hours

4. Percentage of EDs reporting:

- Availability of respiratory therapy, both day and night

- The first medication given for asthma attack (beta-agonist 

by nebulizer, beta-agonist by metered-dose inhaler)

All City CASI

Consultation with asthma 

specialist
 111

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Percent of patients given a specific follow up appointment All City CASI

Percent of people with one emergency department visit for 

asthma who are seen by a medical practitioner within one 

month of the emergency department visit date

All State Oregon

Percent of people with two emergency department visits for 

asthma in 12 months who are seen by an asthma specialist 

within one month of the most recent emergency department 

visit

All State Oregon

Managed Care Organizations (Enabling factors)

Management

1. MCOs offering an asthma education program

2. MCOs offering an asthma case management program

3. MCOs offering an asthma disease management program

All City CASI

Benefits

Asthma-specific covered benefits

- Medications

- Spacer devices

- Peak flow meters

- Nebulizers

- Asthma education

- Pillow/mattress covers

- Smoking cessation programs

- Smoking cessation medication

- HEPA filter/cleaner

- Dehumidifier

- Home assessment

All City CASI

Patient Care - Process 

Reported as percentage sampled who answered the 

question correctly

1. Asthma cannot be cured

2. Vaporizer is good treatment

3. Asthma limits exercise

4. Need for asymptomatic asthma visits

5. Common reason for school absences

6. Asthma runs in families

7. Asthma is mainly an emotional illness

8. Asthma resolves if attacks stop

9. Where to go for treatment

10. Asthma onset always in childhood

11. Signs: shortness of breath

12. Signs: chest tightness

13. Signs: severe headaches

14. Signs: nocturnal cough

15. Signs: wheezing with exercise

16. Triggers: furry pets

17. Triggers: mosquito bites

18. Triggers: dampness

19. Triggers: cockroaches

20. Triggers: poor diet

21. Triggers pollen

22. Hospitalizations are preventable

23. Symptoms are preventable

24. Adequacy of OTC medications

25. Asthma is a serious disease

26. Asthma care is expensive

27. See doctor immediately for attack

28. Appropriateness of ED for treatment

29. Addiction to asthma medicines

30. Overprotective mothers and asthma

Percent of people with asthma who has knowledge of 

asthma medication use and what do in case of an 

exacerbation.

All State Oregon

Percent of people with asthma who affirm receipt of 

information about asthma and treatment techniques.

All State Oregon

Percent of people with asthma who report high levels of 

confidence in understanding and using this information.

All State Oregon

Percent of people with asthma who report behavior 

consistent with having received and understood this 

information.

All State Oregon

Asthma knowledge

Follow up after ED visit
112 

 

Type of measure Variants of the measure definition

Age 

group

Geographic 

scope Source1

Environmental triggers

Percent of people with persistent asthma with documentation 

they have been asked at least once about home and

occupational exposures to:

- Dust-mites

- Animal allergens

- Tobacco smoke

- Exercise-induced bronchospasm

All State Oregon

Mortality (Outcome)

Mortality rates

Mortality rates

- By age and gender

- By borough

Children City NYCCAI

Avoidable events (Outcome)

Emergency/Urgent care Emergency department visits per 1000 members with

asthma.

All State health 

plans

MQIC

Relapse rate Percentage of asthma patients estimated to relapse within 7 

days.

All City CASI

Hospitalizations

Hospitalization rates

- By age

- Comparison of New York City to New York State

- Trends 1990-2000

- Distribution by age group

- Leading causes of hospitalizations in children 0-14

- By age and gender

- By income (ZIP code areas)

- Distribution by payer

- Total charges by payer

- Average length of stay

- By borough

- By neighborhood

- By ZIP code

- By month and age

Children City NYCCAI

Other dimensions (Prevalence)

Self-reported lifetime prevalence for adults age 18 and over

- By age

- By race/ethnicity

- By borough

- By neighborhood

Children City NYCCAI

School-based prevalence

- By gender

- By income (ZIP code areas)

- By borough

- By neighborhood

Children City NYCCAI

Other dimensions (Behavior)

Percent of people with asthma who currently do not smoke 

cigarettes.

All State Oregon

Percent of non-smokers with asthma who are not exposed to 

tobacco smoke in the home.

All State Oregon

NYCCAI = New York City Childhood Asthma Initiative (Garg et al, 2003)

Key to Sources: 

Prevalence

Smoking 

MQIC = Michigan Quality Improvement Initiative Guideline: Management of Persistent Asthma (MQIC, 2005; 

http://www.mqic.org/meas.htm)

CASI = Chicago Asthma Surveillance Initiative (Weiss KB, Grant EN. The Chicago Asthma Surveillance Initiative: As 

Oregon = Guide to Improving Asthma Care in Oregon (Oregon Health Division, 2005; 

http://www.dhs.state.or.us/publichealth/asthma/guideor.cfm) 
 113

Appendix E: BRFSS Measures, Data, and Benchmarks 

In 2003, asthma data were collected under the Behavioral Risk Factor Surveillance System for 5 

process measures, 7 outcome measures, and 3 prevalence measures. Those measures for adults 

with asthma include asthma history, routine check ups, doctor visits for asthma, limited activity 

due to asthma, medications for asthma, asthma symptoms, asthma episodes, emergency 

department visits, urgent care visits, and sleep difficulty due to asthma. The number of entities 

reporting varied from 15 to 54 depending on the measure. All 50 States, DC, and 3 U.S. 

Territories collected data on receipt of influenza vaccination in the past year. In our analysis, 

adult smokers with asthma were studied to determine the prevalence of smoking and asthma. 

The BRFSS data are based on telephone surveys developed by the CDC but administered by 

each State independently. The survey consists of a core set of questions developed by CDC, 

additional questions developed by the States, and separate, optional modules for States to use. 

The asthma module, which contains the quality-of-care questions, is optional for State use. More 

information about the BRFSS data and methods as well as interactive databases with some State 

and local level asthma data are available at: http://www.cdc.gov/brfss/. 

Limitations of BRFSS Data 

Every data source has limitations that can relate to the population represented, methods used to 

collect the data, definitions, and analytic approaches. These factors affect the estimates 

generated from a data set. When similar measures from two data sets differ, the cause can 

usually be traced to the limitations of the data sets. By understanding the limitation of a data set, 

the strengths and weakness of estimates from the data set can be assessed and the estimates can 

be used more responsibly. 

Limitations of BRFSS data include the following: 

• BRFSS samples are kept small to minimize survey costs for States. The State BRFSS 

samples for the year 2001 range from 1,888 to 8,628 respondents (see: 

http://www.cdc.gov/brfss/technical_infodata/surveydata/2003.htm). Small samples 

increase the variance of estimates and decrease the size of the difference between two 

subpopulations that can be detected through the survey responses. In fact, among the 

asthma measures, the small sample sizes impeded statistical tests of differences, as 

discussed below. 

• The BRFSS survey excludes people without a residential phone and people who are 

institutionalized. This means that the total population of interest—all people with 

asthma—will not be represented in the estimates that come from the survey.1

 This 

weakness can be dealt with by carefully discussing BRFSS results in relation to the 

population it represents. 

• BRFSS data are self-reported and reflect the perceptions of respondents. An advantage 

 

1 See: Nelson D, Holtzman D, Bolen J, Stanwyck C, Mack K. Reliability and validity of measures from the 

behavioral risk factor surveillance system (BRFSS). Sozial un Praventivmedizin. 2001;46(Supp 1):S3-42. 
114 

of self-reports is that they can reveal information that cannot be obtained from other 

sources; for example, the receipt of flu vaccinations for people who do not see a doctor 

during the year. A disadvantage of self-report data is that respondents may have 

difficulty recalling events, understanding or interpreting questions, or responding 

truthfully to questions such as about compliance with advice. Furthermore, cultural and 

language barriers and limited health knowledge can affect the quality of self-reported 

data.2

 These problems may occur with different propensity for different subgroups. 

BRFSS data, like most surveys, are limited by budget constraints. Because BRFSS is funded by 

States which vary considerably in resources allocated to health surveys, these fiscal disparities 

may affect the quality of the data across States. Such data quality shortcomings can include bias 

from differential response rates, varying followup periods, and variations in interviewer 

protocols or skills (for example, extent of probing for answers). 

Small Sample Size in BRFSS 

Table E.1 shows that small sample sizes in the BRFSS supplemental asthma survey result in tests 

that are unreliable. For example for smoking cessation counseling, 15 of 15 reporting States 

could not be distinguished from the average of the top 2 States (or top10 percent of States). This 

is partly because smoking cessation counseling is commonly provided across all States (the 

distribution of percent counseled is narrow), in combination with the small numbers of 

individuals interviewed in BRFSS. The smaller the difference to be detected, the greater the 

sample needed. The same issues are apparent for the measure “average number of symptom-free 

days in the past 2 weeks.” Fourteen of 19 estimates are indistinguishable from the top decile, 

again a problem of small sample size. 

By contrast, “flu shots in the past 12 months” is a measure collected from the core BRFSS 

survey and thus more reliable estimates result. Eleven of 54 entities represent States comparable 

to the best-in-class average of 5 States in the top 10 percent. 

The issue of sample size is the main reason that the NHQR, which produces annual estimates, 

did not include State-level BRFSS data. For State estimates, multiple years of BRFSS should be 

used. 

Estimates for individual BRFSS measures by State (including the District of Columbia and U.S. 

Territories) are presented in Tables E.2-E.16. 

 

2

 Ibid. 
 115

Table E.1. Selected quality measures for asthma by State, District of Columbia, and U.S. Territory, 2003 

SE SE SE SE SE SE SE SE SE

Total US average 28.3 0.861 82.2 1.613 40.3 0.635 71.1 0.867 10.9 0.903 53.9 1.126 9.7 0.096 28.1 0.888 17.7 0.754

Best-in-class 

average

40.4 2.964 87.9 3.005 53.3 1.547 75.3 1.844 3.3 0.857 63.6 2.486 10.5 0.295 19.4 1.952 12.2 1.464

Worst performing 

States average

17.4 1.910 75.8 4.069 27.9 1.804 62.1 2.782 18.2 2.028 39.6 2.549 9.0 0.197 35.5 1.863 22.3 2.107

Number of States 

reporting

19 15 54 19 19 19 19 19 19

Alabama 41.3 3.741

Alaska 45.7 † 4.699

Arizona 42.5 4.371

Arkansas 75.5 † 5.771 46.8 † 3.142

California 36.8 2.853

Colorado 83.6 † 5.261 43.7 3.079

Connecticut 33.2 † 2.691 89.6 † 4.633 45.3 2.648 73.2 † 2.413 16.1 − 2.063 58.8 † 3.292 10.2 † 0.274 30.1 2.579 17.3 2.037

Delaware 41.1 † 3.666 39.6 3.519 74.2 † 3.121 8.1 † 2.661 64.4 † 4.001 10.1 † 0.359 33.2 3.517 18.8 2.984

District of Columbia 38.9 † 5.035 31.8 4.370 69.2 † 4.504 18.4 5.484 42.0 5.731 10.5 † 0.485 27.6 † 4.500 26.5 4.737

Florida 41.6 4.027

Georgia 32.7 † 3.166 36.0 3.503 76.1 † 2.895 11.2 3.472 44.0 − 4.194 9.9 † 0.368 26.1 2.778 20.8 2.577

Hawaii 29.4 + 3.543 43.4 3.929 69.7 † 3.851 4.1 † 0.881 45.7 4.838 10.4 † 0.372 40.0 − 4.044 13.2 † 2.544

Idaho 37.5 2.930

Illinois 37.6 2.678

Indiana 34.8 † 2.506 40.0 2.521 74.3 † 2.404 8.0 1.881 48.9 2.920 9.1 − 0.287 31.2 2.402 20.4 2.099

Iowa 22.0 − 2.661 83.3 † 6.397 40.5 3.244 74.3 † 3.108 6.8 † 3.242 58.2 † 3.880 9.1 0.364 24.0 † 3.003 12.5 † 2.035

Kansas 37.4 2.980

Kentucky 81.0 † 4.532 37.5 2.522

Louisiana 77.1 † 6.601 43.1 3.208

Maine 46.4 † 3.691

Maryland 24.2 3.103 43.7 3.540 62.9 − 3.712 13.7 3.117 55.0 † 4.787 10.1 † 0.399 28.1 3.259 18.9 † 3.289

Massachusetts 43.1 2.312

Michigan 29.0 2.888 39.5 3.062 70.9 † 2.990 9.9 † 4.138 61.4 † 3.579 9.3 0.323 26.3 † 2.955 16.7 † 2.511

Minnesota 21.3 − 2.987 45.8 † 3.640 71.4 † 3.366 3.0 † 1.112 59.8 † 4.413 9.8 † 0.356 19.1 † 3.154 15.3 † 2.914

Mississippi 35.6 3.286

Missouri 38.9 3.557

Montana 52.9 † 3.677

Nebraska 76.3 † 5.698 51.6 † 3.004

Nevada 32.7 4.415

New Hampshire 21.9 − 2.234 81.2 † 7.810 43.3 2.751 69.2 † 2.739 11.3 2.001 63.0 † 3.163 10.0 † 0.264 25.1 † 2.466 16.8 † 2.271

New Jersey 35.8 † 2.088 82.0 † 4.411 37.6 2.004 71.0 † 2.018 18.2 − 2.174 52.0 2.714 10.3 † 0.205 34.0 2.086 17.7 1.771

New Mexico 23.8 2.806 46.6 † 3.085 66.5 3.110 14.1 3.056 59.2 † 3.699 9.7 † 0.357 20.0 † 2.336 13.7 † 2.193

New York 44.3 2.689

North Carolina 84.5 † 5.238 41.3 2.813

North Dakota 49.6 † 3.749

Ohio 36.9 3.446

Oklahoma 30.0 2.300 87.2 † 3.822 44.1 2.472 74.5 † 2.328 10.4 2.599 39.1 − 2.849 9.0 − 0.269 27.8 2.241 21.2 2.349

Oregon 40.5 2.904

Pennsylvania 40.4 3.289

Rhode Island 86.5 † 6.718 46.1 2.938

South Carolina 48.7 † 3.000

South Dakota 56.0 † 3.603

Tennessee 37.1 3.736

Texas 25.2 2.783 83.9 † 4.479 38.0 2.848 73.6 † 2.631 8.6 2.149 53.3 3.385 9.6 0.289 31.3 2.994 17.3 † 2.378

Utah 16.9 − 3.199 35.6 3.474 61.2 − 4.150 8.8 † 3.809 51.4 4.600 10.0 † 0.401 27.3 † 4.046 12.6 † 2.450

Vermont 17.8 − 2.199 36.9 3.055 69.4 † 3.006 3.5 † 1.257 57.4 † 3.782 9.7 † 0.321 19.6 † 2.460 12.1 † 2.096

Virginia 29.8 2.947 80.5 † 6.180 39.4 3.391 67.1 2.908 17.2 3.099 55.8 3.562 9.7 † 0.376 28.8 2.817 20.0 2.814

Washington 41.7 1.465

West Virginia 76.3 † 5.923 45.9 3.225

Wisconsin 23.6 2.958 43.5 3.377 65.8 3.533 8.7 † 4.640 56.0 † 4.086 9.9 † 0.364 22.5 † 2.915 15.7 † 2.651

Wyoming 55.0 † 3.198

Guam 31.3 6.900

Puerto Rico 25.6 − 2.482

Virgin Islands 18.8 − 6.433

Source: BRFSS, CDC, 2003.

† Indicates that the State estimate is not significantly different from the best in class average (P>0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

− Indicates that the State estimate is statistically worse than the national average (P<0.05).

No symbol indicates that State estimate is no different than the national average.

Routine care (in 

the past 12 

months)

Smoking 

(counseling in 

the past 12 

months)

Flu shots (in the 

past 12 months)

Medications (in 

the past month)

Percent Percent Average Average 

Emergency room 

visits (in the past 

12 months)

Percent Percent Percent Percent Percent 

Process Measures

Outcome Measures- Avoidable 

Outcome Measures- Daily life health care use

Limited activity 

days (in the past 

12 months)

Sleep difficulty 

(none in the past 

month)

Symptom-free 

(in the past 2 

weeks)

Urgent care 

visits (in the 

past 12 months)
116 

Table E.2. Lifetime asthma prevalence: Percent of people who were ever told by a health professional that 

they have asthma by State, District of Columbia, and U. S. Territory, 2003 

P value 

compared 

to national 

average

P value 

compared 

to top 

decile 

average

Total U.S. 257312 11.9 0.121 200657 12.3 0.138 54666 10.03 0.238

Top decile average 21099 10.1 0.270 12935 10.2 0.350 3861 7.2 0.452

Bottom decile 

average

38392 14.8 0.232 30176 15.6 0.266 3788 13.3 0.728

 

Alabama 3339 11.6 0.688 0.626 0.048 2568 11.9 0.796 742 10.31 1.274

Alaska 2658 13.3 0.942 0.143 0.001 2357 12.9 0.991 278 15.58 3.063

Arizona 3233 12.5 0.845 0.497 0.007 2369 12.4 0.980 833 13.01 1.616

Arkansas 4199 11.3 0.569 0.336 0.049 3210 12.3 0.672 961 7.67 0.936

California 4475 13.4 − 0.606 0.016 0.000 3580 13.6 0.678 880 12.36 1.268

Colorado 4064 12.4 0.621 0.402 0.001 3360 12.4 0.675 681 12.62 1.614

Connecticut 5310 12.2 0.512 0.608 0.000 4143 13.3 0.600 1068 7.69 0.918

Delaware 4038 11.7 0.668 0.814 0.023 3212 12.2 0.762 797 9.27 1.310

District of Columbia 2038 12.7 0.925 0.368 0.006 1661 13.5 1.047 333 9.57 2.071

Florida 5034 10.1 ‡ 0.644 0.006 1.000 3552 10.4 0.764 1431 9.19 1.191

Georgia 7633 11.8 0.580 0.919 0.007 6138 12.1 0.654 1429 10.36 1.033

Hawaii 4332 11.6 0.609 0.595 0.027 3423 12.5 0.710 882 7.67 1.044

Idaho 4992 11.7 0.551 0.710 0.010 3944 12 0.629 1020 10.06 1.020

Illinois 5264 11.1 0.492 0.129 0.066 4261 11.5 0.552 999 8.81 0.992

Indiana 5474 12.0 0.495 0.844 0.001 4344 12.5 0.563 1104 9.83 0.977

Iowa 4999 10.3 ‡ 0.556 0.004 0.808 3707 10.9 0.666 1269 7.82 0.857

Kansas 4613 11.5 0.582 0.545 0.025 3535 12.4 0.687 1038 7.9 0.907

Kentucky 7622 12.6 0.582 0.267 0.000 5690 12.6 0.673 1900 12.48 1.016

Louisiana 5072 10.2 ‡ 0.508 0.001 0.848 4064 10.4 0.576 984 9.18 1.019

Maine 2384 13.4 0.814 0.068 0.000 1881 14.1 0.955 477 10.55 1.435

Maryland 4433 12.3 0.660 0.531 0.002 3587 12.7 0.746 775 10.44 1.317

Massachusetts 7569 14.4 − 0.532 0.000 0.000 6059 15.3 0.610 1372 10.67 1.095

Michigan 3546 13.6 0.678 0.015 0.000 2703 14.2 0.779 824 10.6 1.173

Minnesota 3874 10.5 ‡ 0.596 0.024 0.511 3042 11.2 0.691 832 6.97 0.897

Mississippi 4416 10.9 ‡ 0.569 0.089 0.198 3434 11.2 0.651 951 9.4 1.060

Missouri 4250 11.9 0.712 0.978 0.019 3130 12.9 0.846 1083 7.91 1.075

Montana 4018 11.1 0.670 0.217 0.184 3104 11.2 0.762 882 10.5 1.405

Nebraska 4970 10.3 ‡ 0.507 0.002 0.702 3720 10.6 0.588 1225 9.39 0.929

Nevada 2969 11.4 0.825 0.573 0.125 2365 12 0.941 599 8.24 1.374

New Hampshire 5036 12.9 0.567 0.094 0.000 4041 13.7 0.655 921 8.92 1.004

New Jersey 11293 10.9 ‡ 0.365 0.009 0.078 8621 11.5 0.426 2479 8.57 0.653

New Mexico 5490 10.5 ‡ 0.495 0.005 0.500 4245 10.6 0.561 1224 9.83 1.005

New York 5535 11.7 0.507 0.687 0.006 4350 12.4 0.588 1113 8.39 0.920

North Carolina 9446 11.3 0.524 0.281 0.038 7166 11.3 0.600 2210 11.26 0.981

North Dakota 3021 10.1 ‡ 0.618 0.004 0.965 2264 10.2 0.716 730 9.66 1.207

Ohio 3821 10.8 0.641 0.104 0.287 3049 11.4 0.731 733 8.22 1.309

Oklahoma 7624 11.8 0.458 0.816 0.001 5670 12.5 0.538 1918 8.54 0.721

Oregon 4010 14.7 − 0.633 0.000 0.000 3112 15.2 0.727 873 12.14 1.181

Pennsylvania 3665 11.9 0.616 0.987 0.007 2807 12.2 0.710 827 10.85 1.223

Rhode Island 4042 14.4 0.690 0.000 0.000 3161 15.9 0.821 830 8.22 1.017

South Carolina 5921 10.2 ‡ 0.473 0.000 0.927 4651 10.1 0.529 1213 10.41 1.077

South Dakota 5257 10.7 ‡ 0.572 0.035 0.368 3826 10.7 0.677 1397 10.56 0.959

Tennessee 2586 11.8 0.804 0.941 0.040 2064 12.2 0.912 511 9.59 1.411

Texas 6022 11.3 0.491 0.206 0.038 4891 11.4 0.550 1083 10.18 1.020

Utah 4048 11.3 ‡ 0.692 0.377 0.112 3326 11.4 0.768 703 10.25 1.373

Vermont 4243 12.2 0.605 0.638 0.002 3308 13.2 0.707 910 7.14 0.890

Virginia 5435 12.1 0.606 0.722 0.002 4379 12.1 0.666 1018 12.88 1.471

Washington 18605 13.9 − 0.325 0.000 0.000 14661 14.3 0.368 3920 11.58 0.624

West Virginia 3346 11.8 0.623 0.900 0.011 2533 12.1 0.727 801 10.68 1.147

Wisconsin 4049 11.0 ‡ 0.623 0.143 0.200 3217 11.3 0.710 802 9.63 1.262

Wyoming 3999 11.3 ‡ 0.572 0.266 0.069 3172 11 0.639 801 12.24 1.265

Guam 805 10.3 ‡ 1.153 0.170 0.859 733 10.2 1.210 65 12.24 4.061

Puerto Rico 4166 20.7 − 0.849 0.000 0.000 3183 22 0.979 978 13.72 1.300

Virgin Islands 2051 9.2 ‡ 0.964 0.006 0.385 1735 10 1.098 276 4.92 1.515

DNC=Data Not Collected

DNS=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease 

Prevention and Health Promotion.

Standard 

Sample Size Percent Error Sample Size Percent

Standard 

Sample Size Percent Error

Standard 

Error

All Adults Adults Age 18-64 Adults Age 65+
 117

Table E.3. Current asthma prevalence: Percent of people who were ever told they have asthma who still have 

asthma by State, District of Columbia, and U.S. Territory, 2003 

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average

Total U.S. 256651 7.7 0.099 200105 7.8 0.112 54562 7.2 0.200

Top decile average 22299 5.9 0.207 17387 5.9 0.233 3960 4.6 0.362

Bottom decile 

average

25719 10.0 0.247 19921 10.3 0.284 4815 10.1 0.577

Alabama 3337 7.5 0.543 0.717 0.006 2567 7.6 0.620 741 7.3 1.108

Alaska 2653 9.1 0.826 0.092 0.000 2353 8.7 0.870 277 12.0 2.667

Arizona 3223 8.3 0.724 0.412 0.001 2364 8.1 0.834 828 9.4 1.440

Arkansas 4189 7.3 0.449 0.384 0.005 3202 7.8 0.523 959 5.4 0.823

California 4469 8.4 0.497 0.167 0.000 3575 8.3 0.556 879 8.6 1.059

Colorado 4054 8.3 0.504 0.243 0.000 3350 8.4 0.561 681 7.2 1.036

Connecticut 5290 8.3 0.430 0.174 0.000 4128 9.0 0.503 1063 5.1 0.779

Delaware 4033 7.5 0.531 0.711 0.005 3208 7.8 0.602 796 6.1 1.100

District of Columbia 2035 7.8 0.720 0.891 0.011 1658 8.1 0.802 333 6.8 1.796

Florida 5020 6.1 ‡ 0.502 0.002 0.713 3540 6.1 0.597 1429 6.0 0.924

Georgia 7610 7.0 0.470 0.145 0.032 6119 7.1 0.530 1425 6.5 0.854

Hawaii 4328 5.6 ‡ 0.439 0.000 0.537 3419 6.1 0.517 882 3.9 0.687

Idaho 4974 7.9 0.468 0.676 0.000 3927 8.0 0.531 1019 7.5 0.914

Illinois 5254 7.4 0.396 0.462 0.001 4253 7.4 0.437 997 7.1 0.916

Indiana 5461 8.1 0.401 0.333 0.000 4334 8.3 0.453 1101 6.9 0.834

Iowa 4996 6.2 ‡ 0.418 0.000 0.520 3704 6.5 0.495 1269 5.3 0.702

Kansas 4606 7.5 0.455 0.668 0.001 3530 7.8 0.528 1036 6.2 0.829

Kentucky 7600 9.8 0.507 0.000 0.000 5674 9.6 0.582 1894 10.8 0.952

Louisiana 5066 6.2 ‡ 0.398 0.000 0.504 4059 6.2 0.446 983 6.6 0.869

Maine 2375 9.9 − 0.700 0.002 0.000 1873 10.1 0.815 476 9.0 1.322

Maryland 4420 7.8 0.537 0.855 0.001 3575 8.0 0.610 774 7.2 1.038

Massachusetts 7548 9.9 − 0.442 0.000 0.000 6041 10.4 0.510 1369 7.7 0.864

Michigan 3538 9.3 − 0.566 0.005 0.000 2695 9.4 0.645 824 8.2 1.062

Minnesota 3852 6.8 ‡ 0.479 0.066 0.085 3022 7.2 0.552 830 5.0 0.782

Mississippi 4405 6.9 ‡ 0.468 0.094 0.051 3424 7.0 0.536 950 6.6 0.901

Missouri 4242 8.0 0.582 0.611 0.001 3125 8.6 0.693 1080 5.5 0.865

Montana 4009 7.9 0.562 0.726 0.001 3095 7.8 0.628 882 8.6 1.276

Nebraska 4958 7.1 0.411 0.156 0.009 3710 7.1 0.470 1223 7.2 0.829

Nevada 2954 6.6 ‡ 0.638 0.088 0.297 2351 6.8 0.724 598 5.6 1.155

New Hampshire 5014 8.5 0.457 0.087 0.000 4024 8.8 0.522 917 7.0 0.913

New Jersey 11257 7.1 0.292 0.052 0.001 8592 7.3 0.337 2473 6.1 0.562

New Mexico 5486 6.7 ‡ 0.408 0.017 0.080 4242 6.8 0.461 1223 6.7 0.849

New York 5518 7.6 0.413 0.814 0.000 4337 8.1 0.477 1110 5.8 0.768

North Carolina 9433 7.1 0.400 0.145 0.008 7156 6.7 0.448 2207 8.6 0.872

North Dakota 3016 7.0 0.511 0.179 0.046 2262 6.7 0.580 727 8.0 1.097

Ohio 3813 7.1 0.507 0.245 0.028 3043 7.2 0.571 731 6.4 1.120

Oklahoma 7607 7.6 0.368 0.793 0.000 5659 7.9 0.432 1912 6.1 0.583

Oregon 3999 9.3 − 0.524 0.003 0.000 3103 9.5 0.598 871 8.4 1.015

Pennsylvania 3652 8.3 0.533 0.268 0.000 2796 8.3 0.607 825 8.6 1.134

Rhode Island 4030 9.6 − 0.549 0.001 0.000 3150 10.4 0.651 830 6.0 0.862

South Carolina 5913 6.1 ‡ 0.359 0.000 0.629 4643 5.8 0.387 1213 7.8 0.960

South Dakota 5246 7.3 0.491 0.425 0.009 3816 7.1 0.578 1396 8.3 0.859

Tennessee 2580 7.9 0.641 0.758 0.003 2058 8.0 0.720 511 7.7 1.309

Texas 6009 6.9 0.401 0.053 0.027 4880 6.8 0.446 1081 7.0 0.866

Utah 4040 7.4 0.578 0.609 0.015 3322 7.4 0.641 699 6.5 1.130

Vermont 4233 8.4 0.523 0.188 0.000 3299 9.0 0.612 909 5.2 0.781

Virginia 5411 7.6 0.497 0.844 0.002 4357 7.4 0.543 1016 9.3 1.261

Washington 18529 9.1 − 0.266 0.000 0.000 14595 9.2 0.299 3911 8.1 0.541

West Virginia 3341 8.1 0.509 0.440 0.000 2529 8.1 0.586 800 8.0 1.005

Wisconsin 4039 7.5 0.503 0.696 0.003 3207 7.5 0.564 802 7.4 1.129

Wyoming 3986 7.5 0.469 0.676 0.002 3160 7.0 0.513 800 9.7 1.164

Guam 803 6.5 ‡ 0.950 0.209 0.537 731 6.6 1.009 65 6.4 2.896

Puerto Rico 4166 10.8 − 0.619 0.000 0.000 3183 11.3 0.715 978 8.1 0.985

Virgin Islands 2042 4.5 ‡ 0.761 0.000 0.076 1726 4.8 0.873 276 2.4 0.979

DNC=Data Not Collected

DNS=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

Sample Size Percent

Standard 

Error Sample Size Percent

Standard 

Sample Size Percent Error

Standard 

Error

All Adults Adults Age 18-64 Adults Age 65+
118 

Table E.4. Age at asthma diagnosis: Percent of adults with asthma 

who were diagnosed before age 10 by State, District of Columbia, and 

U.S. Territory, 2003 

Sample Size Percent

Standard 

Error

P value 

compared to 

national 

average

P value 

compared to 

top decile 

average

Total U.S. 10695 38.5 0.798

Top decile average 1423 47.0 1.872

Bottom decile average 1062 31.0 1.702

Alabama N/C N/C N/C -- --

Alaska N/C N/C N/C -- --

Arizona N/C N/C N/C -- --

Arkansas N/C N/C N/C -- --

California N/C N/C N/C -- --

Colorado N/C N/C N/C -- --

Connecticut 612 32.1 ‡ 2.238 0.007 0.699

Delaware 461 39.0 3.098 0.883 0.024

District of Columbia 227 42.2 4.024 0.370 0.011

Florida N/C N/C N/C -- --

Georgia 791 46.7 + 2.748 0.004 0.000

Hawaii 465 44.7 + 2.880 0.039 0.000

Idaho N/C N/C N/C -- --

Illinois N/C N/C N/C -- --

Indiana 617 35.7 ‡ 2.249 0.235 0.098

Iowa 455 42.0 3.014 0.259 0.001

Kansas N/C N/C N/C -- --

Kentucky N/C N/C N/C -- --

Louisiana N/C N/C N/C -- --

Maine N/C N/C N/C -- --

Maryland 477 33.9 ‡ 2.926 0.129 0.392

Massachusetts N/C N/C N/C -- --

Michigan 450 29.5 ‡ 2.624 0.001 0.636

Minnesota 378 33.5 ‡ 3.054 0.114 0.474

Mississippi N/C N/C N/C -- --

Missouri N/C N/C N/C -- --

Montana N/C N/C N/C -- --

Nebraska N/C N/C N/C -- --

Nevada N/C N/C N/C -- --

New Hampshire 604 32.1 ‡ 2.490 0.015 0.703

New Jersey 1130 33.0 ‡ 1.861 0.007 0.428

New Mexico 571 33.8 ‡ 2.538 0.076 0.363

New York N/C N/C N/C -- --

North Carolina N/C N/C N/C -- --

North Dakota N/C N/C N/C -- --

Ohio N/C N/C N/C -- --

Oklahoma 821 45.4 + 2.159 0.003 0.000

Oregon N/C N/C N/C -- --

Pennsylvania N/C N/C N/C -- --

Rhode Island N/C N/C N/C -- --

South Carolina N/C N/C N/C -- --

South Dakota N/C N/C N/C -- --

Tennessee N/C N/C N/C -- --

Texas 632 47.4 2.438 0.001 0.000

Utah 460 33.7 ‡ 3.133 0.139 0.445

Vermont 473 35.8 ‡ 2.686 0.340 0.129

Virginia 625 39.7 2.775 0.667 0.007

Washington N/C N/C N/C -- --

West Virginia N/C N/C N/C -- --

Wisconsin 446 33.8 ‡ 2.999 0.129 0.418

Wyoming N/C N/C N/C -- --

Guam N/C N/C N/C -- --

Puerto Rico N/C N/C N/C -- --

Virgin Islands N/C N/C N/C -- --

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Diagnosed at age < 10 years

Source: Medstat calculations from Behavioral Risk Factor Surveillance System 2003, Centers for Disease 

Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Sample size varies across asthma measures because of varying applicability of questions to respondents 

and refusals.
 119

Table E.5. Urgent care visits: Percent of adults currently with asthma who had at least one urgent care visit 

for asthma with their provider in the past 12 months by State, District of Columbia and U.S. Territory, 2003 

Sample Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average Sample Size Percent

Standard 

Error Sample Size Percent

Standard 

Error

Total U.S. 7239 28.1 0.888 5923 29.2 1.012 1263 21.9 1.685

Top decile average 586 19.4 1.952 0.000 579 19.6 1.843 123 16.2 3.371

Bottom decile 

average

1028 35.5 1.863 828 36.5 2.089 173 33.6 4.503

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 431 30.1 2.579 0.463 0.001 374 30.2 2.744 52 29.1 7.851

Delaware 322 33.2 3.517 0.160 0.001 265 33.2 3.811 54 33.2 9.334

District of Columbia 153 27.6 ‡ 4.500 0.913 0.095 130 28.8 4.941 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 525 26.1 2.778 0.493 0.048 432 25.7 3.060 89 26.6 6.110

Hawaii 255 40.0 − 4.044 0.004 0.000 215 40.3 4.430 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 447 31.2 2.402 0.226 0.000 372 32.8 2.702 74 20.2 5.224

Iowa 299 24.0 ‡ 3.003 0.190 0.199 230 25.0 3.478 68 19.0 5.306

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 320 28.1 3.259 1.000 0.022 260 29.6 3.634 58 18.3 6.791

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 321 26.3 ‡ 2.955 0.560 0.051 255 28.0 3.347 63 17.0 4.886

Minnesota 256 19.1 ‡ 3.154 0.006 0.936 212 21.1 3.535 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 430 25.1 ‡ 2.466 0.252 0.070 361 26.7 2.762 60 15.3 4.669

New Jersey 773 34.0 2.086 0.009 0.000 613 35.1 2.356 149 27.6 4.576

New Mexico 368 20.0 ‡ 2.336 0.001 0.844 295 18.6 2.532 73 27.4 5.925

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 559 27.8 2.241 0.901 0.005 439 26.6 2.459 119 33.8 5.021

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 401 31.3 2.994 0.306 0.001 325 33.1 3.436 73 21.0 4.874

Utah 305 27.3 ‡ 4.046 0.847 0.079 252 27.8 4.434 50 24.9 7.728

Vermont 330 19.6 ‡ 2.460 0.001 0.949 284 20.6 2.687 N/S N/S N/S

Virginia 425 28.8 2.817 0.813 0.006 344 31.2 3.566 80 18.5 5.164

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 319 22.5 ‡ 2.915 0.066 0.377 265 21.3 3.010 53 28.5 8.202

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
120 

Table E.6. Emergency room visits: Percent of adults with asthma who have had at least one visit to the 

emergency room for asthma in the past 12 months by states, District of Columbia and U.S. Territory, 2003 

Sample Size Percent 

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average Sample Size Percent 

Standard 

Error Sample Size Percent

Standard 

Error

Total U.S. 7290 17.7 0.754 5955 18.7 0.854 1281 12.4 1.356

Top decile average 635 12.2 1.464 0.001 536 12.1 1.712 127 7.0 2.497

Bottom decile 

average

712 22.3 2.107 482 22.8 2.668 145 22.2 4.637

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 437 17.3 2.037 0.838 0.044 377 17.5 2.170 55 16.8 6.615

Delaware 322 18.8 2.984 0.725 0.048 265 21.0 3.412 54 7.8 4.239

District of Columbia 155 26.5 4.737 0.067 0.004 131 25.3 4.865 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 539 20.8 2.577 0.252 0.004 441 20.2 2.877 94 22.2 5.952

Hawaii 258 13.2 ‡ 2.544 0.093 0.722 218 13.2 2.777 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 446 20.4 2.099 0.226 0.001 370 21.5 2.338 75 14.5 4.653

Iowa 301 12.5 ‡ 2.035 0.016 0.918 230 13.4 2.445 70 8.0 3.550

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 321 18.9 ‡ 3.289 0.729 0.064 261 20.5 3.705 58 8.1 3.470

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 324 16.7 ‡ 2.511 0.697 0.123 258 17.1 2.806 63 14.7 5.325

Minnesota 257 15.3 ‡ 2.914 0.423 0.344 212 16.0 3.247 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 433 16.8 ‡ 2.271 0.717 0.086 362 16.7 2.493 62 18.8 5.854

New Jersey 776 17.7 1.771 0.989 0.017 615 19.1 2.043 150 10.4 3.194

New Mexico 370 13.7 ‡ 2.193 0.082 0.580 297 14.2 2.448 73 10.8 4.601

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 557 21.2 2.349 0.160 0.001 439 21.6 2.662 117 19.0 4.026

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 403 17.3 ‡ 2.378 0.876 0.067 327 19.5 2.757 73 6.4 3.030

Utah 304 12.6 ‡ 2.450 0.046 0.894 250 11.5 2.565 51 22.1 7.392

Vermont 334 12.1 ‡ 2.096 0.011 0.957 286 12.7 2.300 N/S N/S N/S

Virginia 433 20.0 2.814 0.425 0.014 351 21.9 3.185 81 11.7 3.709

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 320 15.7 ‡ 2.651 0.462 0.251 265 16.2 2.834 53 13.4 7.056

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
 121

Table E.7. Asthma attacks/episodes: Percent of adults with asthma who had an asthma episode in the past 12 

months by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size

Percent 

with 

Asthma 

Episode

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average Sample Size

Percent 

with 

Asthma 

Episode

Standard 

Error

Sample 

Size

Percent 

with 

Asthma 

Episode

Standard 

Error

Total U.S. 7292 56.0 0.945 5958 58.9 1.046 1280 39.2 2.078

Top decile average 761 49.3 2.402 795 52.4 2.775 108 27.6 5.067

Bottom decile 

average

972 63.4 1.846 773 66.1 2.050 185 52.2 4.023

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 434 54.6 ‡ 2.765 0.633 0.148 375 57.5 2.971 54 31.8 7.329

Delaware 321 54.7 ‡ 3.564 0.717 0.212 264 60.5 3.829 54 23.3 7.020

District of Columbia 156 54.7 ‡ 4.860 0.792 0.320 131 54.6 5.225 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 541 53.3 ‡ 3.519 0.465 0.343 445 53.8 3.898 92 48.5 6.883

Hawaii 258 56.2 ‡ 4.014 0.970 0.143 218 58.4 4.377 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 447 56.6 2.614 0.829 0.040 371 60.4 2.855 75 34.4 6.047

Iowa 300 53.2 ‡ 3.325 0.420 0.340 229 57.4 3.855 70 33.1 6.630

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 317 55.7 ‡ 3.688 0.937 0.146 257 57.8 4.094 58 41.6 7.638

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 323 53.1 ‡ 3.210 0.380 0.348 257 55.9 3.560 63 38.4 6.767

Minnesota 255 60.1 3.543 0.260 0.011 211 64.6 3.823 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 434 56.0 ‡ 2.816 0.989 0.069 362 56.9 3.097 63 49.3 6.915

New Jersey 777 52.3 ‡ 2.187 0.118 0.359 619 55.5 2.440 147 33.8 4.630

New Mexico 370 52.6 ‡ 3.261 0.323 0.409 297 54.5 3.642 73 42.7 6.852

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 567 64.1 − 2.426 0.002 0.000 444 66.0 2.722 122 53.8 4.961

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 405 62.3 − 2.847 0.036 0.000 329 66.1 3.119 73 41.5 6.376

Utah 306 58.0 ‡ 4.165 0.633 0.069 251 60.8 4.576 52 39.5 8.696

Vermont 329 51.6 ‡ 3.346 0.209 0.572 283 55.7 3.579 N/S N/S N/S

Virginia 432 47.6 ‡ 3.378 0.016 0.674 350 50.6 3.900 81 34.2 7.050

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 320 56.2 ‡ 3.583 0.954 0.109 265 58.2 3.960 53 45.4 8.227

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

Adults- total Adults Age 18-64 Adults age 65 and over
122 

Table E.8. Limited activity due to asthma: Average number of days adults with asthma were unable to work 

or carry out usual activities in the past 12 months by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size

Average 

Number of 

Days

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average

Sample 

Size

Average 

Number of 

Days

Standard 

Error

Sample 

Size

Average 

Number of 

Days

Standard 

Error

Total U.S. 7063 10.9 0.903 5814 10.1 0.925 1200 15.8 3.082

Top decile average 572 3.3 0.857 0.000 424 2.8 0.555 132 5.7 2.492

Bottom decile average 913 18.2 2.028 734 18.5 2.232 128 28.3 8.891

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 432 16.1 − 2.063 0.021 0.000 375 15.7 2.147 53 20.5 7.161

Delaware 312 8.1 ‡ 2.661 0.316 0.087 258 6.5 2.340 51 19.2 12.991

District of Columbia 153 18.4 5.484 0.179 0.007 130 20.0 6.302 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 500 11.2 3.472 0.936 0.027 416 10.7 3.888 80 12.9 5.988

Hawaii 253 4.1 ‡ 0.881 0.000 0.537 214 3.7 0.897 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 431 8.0 1.881 0.163 0.023 359 8.2 2.107 71 7.0 3.800

Iowa 289 6.8 ‡ 3.242 0.219 0.302 227 7.2 3.782 61 4.2 3.109

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 316 13.7 3.117 0.386 0.001 260 14.6 3.506 54 7.3 3.866

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 314 9.9 ‡ 4.138 0.811 0.119 253 9.7 4.652 58 11.5 7.315

Minnesota 252 3.0 ‡ 1.112 0.000 0.850 210 1.9 0.645 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 416 11.3 2.001 0.864 0.000 351 9.2 1.794 57 27.5 10.076

New Jersey 760 18.2 − 2.174 0.002 0.000 604 18.1 2.351 147 19.2 5.902

New Mexico 364 14.1 3.056 0.312 0.001 293 11.3 2.460 71 29.0 13.906

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 543 10.4 2.599 0.861 0.009 427 9.8 2.833 115 14.1 6.921

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 384 8.6 2.149 0.330 0.021 316 6.8 1.872 65 20.0 9.854

Utah 301 8.8 ‡ 3.809 0.598 0.156 250 9.7 4.263 N/S N/S N/S

Vermont 320 3.5 ‡ 1.257 0.000 0.878 278 3.8 1.384 N/S N/S N/S

Virginia 406 17.2 3.099 0.050 0.000 330 17.0 3.345 75 18.5 7.539

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 317 8.7 ‡ 4.640 0.643 0.252 263 4.8 1.891 53 27.2 23.898

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
 123

Table E.9. No sleep difficulty due to asthma: Percent of adults with asthma who had no difficulty sleeping due 

to asthma during the past month by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Total U.S. 5286 53.9 1.126 4331 52.0 1.263 912 64.2 2.300

Top decile average 544 63.6 2.486 0.000 457 62.8 2.737 115 72.7 4.436

Bottom decile 

average

528 39.6 2.549 675 40.0 2.793 154 51.8 4.799

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 298 58.8 ‡ 3.292 0.156 0.248 257 58.6 3.542 N/S N/S N/S

Delaware 229 64.4 ‡ 4.001 0.012 0.872 191 62.6 4.434 N/S N/S N/S

District of Columbia 103 42.0 5.731 0.041 0.001 83 45.9 6.435 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 407 44.0 − 4.194 0.023 0.000 338 43.6 4.587 66 46.0 8.124

Hawaii 173 45.7 4.838 0.098 0.001 143 43.9 5.545 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 362 48.9 2.920 0.110 0.000 305 46.5 3.198 57 63.6 6.898

Iowa 240 58.2 ‡ 3.880 0.283 0.245 181 54.9 4.471 59 74.7 6.378

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 208 55.0 ‡ 4.787 0.828 0.109 167 56.8 5.291 N/S N/S N/S

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 255 61.4 ‡ 3.579 0.047 0.608 200 60.0 4.016 52 67.5 6.933

Minnesota 181 59.8 ‡ 4.413 0.195 0.454 148 57.9 4.895 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 315 63.0 ‡ 3.163 0.007 0.880 266 63.0 3.467 N/S N/S N/S

New Jersey 506 52.0 2.714 0.527 0.002 401 50.4 3.051 96 61.0 5.840

New Mexico 258 59.2 ‡ 3.699 0.167 0.329 208 58.9 4.137 50 61.0 8.156

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 425 39.1 − 2.849 0.000 0.000 337 36.3 3.178 88 56.1 5.778

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 307 53.3 3.385 0.859 0.014 248 49.5 3.789 56 70.6 6.198

Utah 238 51.4 4.600 0.592 0.019 193 49.6 5.250 N/S N/S N/S

Vermont 246 57.4 ‡ 3.782 0.372 0.172 220 56.1 4.012 N/S N/S N/S

Virginia 304 55.8 3.562 0.612 0.072 254 51.9 4.301 N/S N/S N/S

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 231 56.0 ‡ 4.086 0.615 0.113 191 54.7 4.494 N/S N/S N/S

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
124 

Table E.10. Routine care for asthma: Percent of adults with asthma who had 2 or more planned care visits 

for asthma during the past 12 months by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Total U.S. 7194 28.3 0.861 5903 26.0 0.946 1238 41.7 2.123

Top decile average 473 40.4 2.964 0.000 393 37.0 3.149 197 59.6 4.212

Bottom decile 

average

634 17.4 1.910 535 16.1 2.016 128 34.5 4.544

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 431 33.2 ‡ 2.691 0.082 0.072 372 31.8 2.865 54 40.5 7.918

Delaware 320 41.1 ‡ 3.666 0.001 0.888 263 37.8 3.926 54 63.7 8.358

District of Columbia 153 38.9 ‡ 5.035 0.039 0.792 130 35.3 5.265 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 521 32.7 ‡ 3.166 0.182 0.075 431 30.0 3.404 86 54.7 7.183

Hawaii 255 29.4 + 3.543 0.759 0.017 215 28.1 3.844 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 441 34.8 ‡ 2.506 0.014 0.150 369 33.4 2.735 71 43.7 6.446

Iowa 296 22.0 − 2.661 0.024 0.000 226 19.4 2.947 69 33.6 6.214

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 319 24.2 3.103 0.199 0.000 259 22.3 3.362 58 37.1 7.559

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 322 29.0 2.888 0.816 0.006 257 26.7 3.162 62 41.7 6.776

Minnesota 255 21.3 − 2.987 0.024 0.000 212 20.7 3.269 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 430 21.9 − 2.234 0.008 0.000 362 20.1 2.370 59 35.6 6.716

New Jersey 759 35.8 ‡ 2.088 0.001 0.209 605 31.7 2.263 143 58.0 4.881

New Mexico 366 23.8 2.806 0.122 0.000 295 20.9 2.960 71 38.8 7.146

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 558 30.0 2.300 0.479 0.006 440 28.1 2.511 117 42.5 5.066

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 397 25.2 2.783 0.284 0.000 325 23.1 3.101 69 38.4 6.386

Utah 303 16.9 − 3.199 0.001 0.000 251 15.1 3.401 N/S N/S N/S

Vermont 331 17.8 − 2.199 0.000 0.000 284 16.9 2.325 N/S N/S N/S

Virginia 420 29.8 2.947 0.627 0.011 343 27.8 3.423 76 39.1 7.936

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 317 23.6 2.958 0.125 0.000 264 20.9 2.964 52 36.6 8.337

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
 125

Table E.11. Doctors visits for asthma: Percent of adults with asthma who had a physician visit for asthma in 

the past 12 months by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Total U.S. 7294 61.4 0.953 5958 60.8 1.061 1283 64.7 2.038

Top decile average 1212 71.1 1.510 992 69.6 1.692 204 80.1 2.998

Bottom decile 

average

871 56.1 2.719 548 55.3 2.861 131 53.0 4.983

 

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 435 68.7 ‡ 2.477 0.006 0.402 376 67.5 2.686 54 74.7 6.498

Delaware 322 68.4 3.460 0.051 0.476 265 66.0 3.894 54 81.9 5.068

District of Columbia 154 66.6 4.593 0.272 0.347 130 64.2 5.051 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 538 56.6 3.890 0.232 0.001 442 55.6 4.194 92 62.7 6.819

Hawaii 258 63.3 ‡ 3.926 0.647 0.062 218 63.1 4.299 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 448 64.9 2.508 0.191 0.034 372 65.1 2.748 75 63.4 6.131

Iowa 300 63.7 ‡ 3.500 0.532 0.051 230 62.1 3.953 69 71.4 7.062

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 321 61.5 3.534 0.987 0.012 261 63.4 3.892 58 48.9 7.748

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 323 60.8 3.178 0.866 0.004 257 58.6 3.557 63 72.5 5.998

Minnesota 256 61.7 3.511 0.929 0.014 212 63.0 3.835 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 433 61.4 2.814 0.997 0.002 362 61.1 3.096 62 63.9 6.988

New Jersey 777 72.5 ‡ 1.904 0.000 0.567 616 70.9 2.177 150 79.4 3.655

New Mexico 368 56.9 3.281 0.184 0.000 295 55.2 3.651 73 65.8 6.241

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 570 58.9 2.570 0.355 0.000 446 57.2 2.893 123 68.2 4.480

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 402 59.4 2.934 0.507 0.000 326 60.2 3.263 73 56.2 6.510

Utah 307 57.1 4.097 0.308 0.001 253 55.5 4.512 51 67.6 7.922

Vermont 333 55.4 3.335 0.081 0.000 285 55.9 3.602 N/S N/S N/S

Virginia 429 58.5 3.515 0.422 0.001 347 57.1 4.026 81 64.5 6.699

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 320 58.1 3.579 0.378 0.001 265 56.6 3.978 54 65.5 7.249

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
126 

Table E.12. Medications for asthma: Percent of adults with asthma who took asthma medication in the past 

month by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compared 

to top 

decile 

average

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Total U.S. 7202 71.1 0.867 5885 68.9 0.986 1264 84.1 1.463

Top decile average 1093 75.3 1.844 0.039 800 75.9 2.024 116 93.6 2.135

Bottom decile 

average

615 62.1 2.782 501 59.5 3.084 164 72.7 4.825

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 425 73.2 ‡ 2.413 0.402 0.499 367 71.4 2.640 53 85.1 5.646

Delaware 320 74.2 ‡ 3.121 0.338 0.763 264 71.0 3.560 53 90.9 3.500

District of Columbia 155 69.2 ‡ 4.504 0.683 0.212 130 65.5 5.028 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 527 76.1 ‡ 2.895 0.096 0.808 433 77.4 3.007 90 70.0 7.409

Hawaii 251 69.7 ‡ 3.851 0.730 0.193 214 68.2 4.203 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 442 74.3 ‡ 2.404 0.209 0.744 367 74.2 2.625 74 76.1 5.791

Iowa 301 74.3 ‡ 3.108 0.318 0.786 230 72.4 3.539 70 83.9 6.690

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 307 62.9 − 3.712 0.031 0.003 248 60.5 4.126 57 79.6 6.330

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 322 70.9 ‡ 2.990 0.952 0.212 256 66.6 3.403 63 95.9 2.596

Minnesota 254 71.4 ‡ 3.366 0.925 0.313 209 70.1 3.728 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 426 69.2 ‡ 2.739 0.499 0.063 358 66.5 3.052 59 85.5 5.031

New Jersey 766 71.0 ‡ 2.018 0.947 0.112 607 67.7 2.329 148 86.3 2.788

New Mexico 367 66.5 3.110 0.158 0.015 295 64.3 3.537 72 78.3 5.212

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 566 74.5 ‡ 2.328 0.167 0.795 444 72.4 2.646 121 87.2 3.047

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 396 73.6 ‡ 2.631 0.367 0.597 322 71.9 3.001 71 84.4 4.302

Utah 308 61.2 − 4.150 0.020 0.002 253 58.5 4.582 52 83.9 5.858

Vermont 330 69.4 ‡ 3.006 0.590 0.095 283 68.5 3.258 N/S N/S N/S

Virginia 421 67.1 2.908 0.191 0.018 341 63.9 3.647 79 80.7 5.353

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 318 65.8 3.533 0.143 0.017 264 60.7 3.954 53 89.8 4.315

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over
 127

Table E.13. Asthma symptom-free days: Average number of days adults with asthma were free of asthma 

symptoms in past 2 weeks by State, District of Columbia and U.S. Territory, 2003 

Sample 

Size Average

Standard 

Error

P value 

compared 

to national 

average

P value 

compare

d to top 

decile 

average

Sample 

Size Average

Standard 

Error

Sample 

Size Average

Standard 

Error

Total U.S. 7135 9.7 0.096 5858 9.9 0.104 1225 8.3 0.248

Top decile average 398 10.5 0.295 0.014 339 10.8 0.313 139 9.6 0.522

Bottom decile 

average

999 9.0 0.197 808 9.3 0.212 129 6.3 0.561

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 423 10.2 ‡ 0.274 0.081 0.466 366 10.5 0.278 52 8.4 0.968

Delaware 314 10.1 ‡ 0.359 0.323 0.351 262 10.4 0.368 N/S N/S N/S

District of Columbia 152 10.5 ‡ 0.485 0.090 0.945 129 11.1 0.513 N/S N/S N/S

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 521 9.9 ‡ 0.368 0.570 0.216 434 9.9 0.405 83 9.8 0.688

Hawaii 246 10.4 ‡ 0.372 0.061 0.866 210 10.6 0.395 N/S N/S N/S

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 441 9.1 − 0.287 0.046 0.001 368 9.2 0.304 72 8.1 0.798

Iowa 297 9.1 0.364 0.115 0.003 228 9.7 0.382 68 5.9 0.797

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 308 10.1 ‡ 0.399 0.352 0.399 250 10.2 0.447 56 9.2 0.805

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 318 9.3 0.323 0.182 0.004 254 9.6 0.349 61 6.7 0.790

Minnesota 257 9.8 ‡ 0.356 0.755 0.139 212 10.0 0.385 N/S N/S N/S

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 418 10.0 ‡ 0.264 0.288 0.205 352 10.4 0.270 58 7.7 0.842

New Jersey 757 10.3 ‡ 0.205 0.015 0.487 605 10.6 0.216 141 8.6 0.592

New Mexico 363 9.7 ‡ 0.357 0.894 0.067 293 9.9 0.389 70 8.2 0.870

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 558 9.0 − 0.269 0.014 0.000 440 9.3 0.294 117 7.2 0.619

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 397 9.6 0.289 0.810 0.034 321 9.8 0.313 73 8.7 0.748

Utah 303 10.0 ‡ 0.401 0.540 0.272 248 10.1 0.436 52 8.5 0.994

Vermont 327 9.7 ‡ 0.321 0.960 0.061 282 9.6 0.343 N/S N/S N/S

Virginia 418 9.7 ‡ 0.376 0.904 0.077 340 9.8 0.404 77 8.9 0.822

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 317 9.9 ‡ 0.364 0.610 0.194 264 10.1 0.386 52 8.9 0.941

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Number of symptom-free days: 

 Symptoms Less than once a week = 13 Free Days 

 1 or 2 times per week = 11 Free Days 

 More than 2 Times per week = 6 Free Days 

 Everyday, not all the time = 0 Free Days 

 Everyday, all the time = 0 Free Days 

Symptoms of asthma include cough, wheezing, shortness of breath, chest tightness and phlegm production when you do not have a cold or respiratory 

infection.

All Adults Adults age 18-64 Adults age 65 and over

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.
128 

Table E.14. Asthma symptoms: Percent of adults with asthma who experienced asthma symptoms every day 

in past 2 weeks by State, District of Columbia, and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compared 

to top 

decile 

average

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Total U.S. 257659 0.4 0.018 200858 0.4 0.020 54799 0.6 0.045

Top decile average 13347 0.9 0.092 5092 0.7 0.141 1795 0.9 0.214

Bottom decile average 9032 1.8 0.150 7055 1.6 0.162 1845 2.7 0.443

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Connecticut 5317 1.2 ‡ 0.168 0.000 0.100 4147 1.2 0.182 1070 1.4 0.436

Delaware 4042 1.2 ‡ 0.203 0.000 0.224 3215 1.1 0.208 797 1.8 0.627

District of Columbia 2042 0.8 ‡ 0.261 0.108 0.773 1663 0.7 0.293 334 1.6 0.643

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia 7651 1.1 ‡ 0.199 0.000 0.331 6152 1.1 0.227 1433 1.2 0.291

Hawaii 4339 0.7 ‡ 0.136 0.039 0.186 3429 0.7 0.154 883 0.8 0.301

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana 5481 1.7 − 0.188 0.000 0.000 4348 1.7 0.202 1107 2.1 0.507

Iowa 5003 1.4 − 0.184 0.000 0.018 3710 1.2 0.196 1270 2.2 0.485

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Louisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland 4435 1.3 ‡ 0.235 0.000 0.092 3589 1.3 0.268 775 1.5 0.415

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan 3551 1.8 − 0.246 0.000 0.000 2707 1.6 0.269 825 3.0 0.636

Minnesota 3883 1.1 ‡ 0.185 0.000 0.350 3048 1.0 0.203 835 1.5 0.449

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 5042 1.4 − 0.168 0.000 0.012 4045 1.2 0.175 923 2.2 0.492

New Jersey 11305 0.9 ‡ 0.098 0.000 1.000 8630 0.8 0.102 2481 1.6 0.294

New Mexico 5494 1.4 0.191 0.000 0.029 4246 1.2 0.209 1227 2.0 0.479

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 7633 1.6 − 0.165 0.000 0.000 5674 1.5 0.188 1922 2.1 0.343

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 6035 1.0 ‡ 0.140 0.000 0.477 4899 0.9 0.146 1086 1.9 0.448

Utah 4054 1.2 ‡ 0.213 0.000 0.233 3329 1.1 0.230 706 1.7 0.582

Vermont 4250 1.4 − 0.207 0.000 0.034 3313 1.5 0.243 912 0.9 0.305

Virginia 5442 1.4 − 0.199 0.000 0.034 4382 1.2 0.208 1020 2.5 0.615

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wisconsin 4054 1.3 ‡ 0.210 0.000 0.068 3219 1.2 0.223 805 1.9 0.589

Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Symptoms of asthma include cough, wheezing, shortness of breath, chest tightness and phlegm production when you do not have a cold or respiratory 

infection.

All Adults Adults age 18-64 Adults age 65 and over

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.
 129

Table E.15. Smoking cessation counseling: Percent of adults with asthma who were advised to quit smoking 

by a health professional by State, District of Columbia, and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

error

P value 

compared 

to national 

average

P value 

compared 

to top 

decile 

average

Sample 

Size Percent

Standard 

error

Sample 

Size Percent

Standard 

error

Total U.S. 1483 82.2 1.613 1316 82.1 1.708 167 84.0 4.374

Top decile average 175 87.9 3.005 0.095 161 87.9 3.101 -- -- --

Bottom decile 

average

143 75.8 4.069 128 74.7 4.340 -- -- --

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Alaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Arkansas 79 75.5 ‡ 5.771 0.264 0.057 71 74.3 6.164 N/S N/S N/S

California N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Colorado 50 83.6 ‡ 5.261 0.801 0.476 N/S N/S N/S N/S N/S N/S

Connecticut 53 89.6 ‡ 4.633 0.134 0.765 51 89.3 4.740 N/S N/S N/S

Delaware N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

District of Columbia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Georgia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Hawaii N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Illinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Indiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Iowa 52 83.3 ‡ 6.397 0.868 0.515 N/S N/S N/S N/S N/S N/S

Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Kentucky 243 81.0 ‡ 4.532 0.807 0.206 208 80.3 4.955 N/S N/S N/S

Louisiana 58 77.1 ‡ 6.601 0.457 0.138 50 76.2 7.182 N/S N/S N/S

Maine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Maryland N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Michigan N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Minnesota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Missouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Montana N/S N/S N/S -- -- N/S N/S N/S N/S N/S N/S

Nebraska 64 76.3 ‡ 5.698 0.317 0.071 57 75.2 6.064 N/S N/S N/S

Nevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New Hampshire 75 81.2 ‡ 7.810 0.896 0.420 68 81.0 8.286 N/S N/S N/S

New Jersey 121 82.0 ‡ 4.411 0.969 0.270 107 81.6 4.689 N/S N/S N/S

New Mexico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

North Carolina 160 84.5 ‡ 5.238 0.676 0.573 140 86.1 5.525 N/S N/S N/S

North Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Ohio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Oklahoma 122 87.2 ‡ 3.822 0.229 0.883 110 87.2 3.983 N/S N/S N/S

Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Pennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Rhode Island 79 86.5 ‡ 6.718 0.534 0.849 73 85.8 7.047 N/S N/S N/S

South Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

South Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Tennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Texas 71 83.9 ‡ 4.479 0.727 0.454 61 84.0 4.771 N/S N/S N/S

Utah N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Vermont N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virginia 80 80.5 ‡ 6.180 0.791 0.282 74 78.9 6.615 N/S N/S N/S

Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

West Virginia 89 76.3 ‡ 5.923 0.337 0.081 82 77.2 6.118 N/S N/S N/S

Wisconsin N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Wyoming N/S N/S N/S -- -- N/S N/S N/S N/S N/S N/S

Guam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Puerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

Virgin Islands N/S N/S N/S -- -- N/S N/S N/S N/S N/S N/S

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for 

Chronic Disease Prevention and Health Promotion.

Current Smokers age 18-64

Current Smokers age 65 and 

Adults-Current Smokers over
130 

Table E.16. Percent of all adults who received flu shots and percent of adults with asthma who received flu 

shots by State, District of Columbia, and U.S. Territory, 2003 

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

P value 

compared 

to national 

average

P value 

compared 

to top 

decile 

average

Sample 

Size Percent

Standard 

Error

Total U.S. 257022 33.1 0.169 20807 40.3 0.635 200380 25.6 0.177

Top decile average 26217 41.2 0.362 1578 53.3 1.547 0.000 18391 33.3 0.427

Bottom decile 

average

15180 24.2 0.436 979 27.9 1.804 15032 19.7 0.432

Alabama 3330 35.2 0.983 266 41.3 3.741 0.785 0.003 2560 27.4 1.077

Alaska 2646 34.2 1.297 221 45.7 ‡ 4.699 0.251 0.126 2343 30.7 1.343

Arizona 3228 33.0 1.216 297 42.5 4.371 0.615 0.020 2366 24.8 1.322

Arkansas 4195 37.8 0.845 322 46.8 ‡ 3.142 0.042 0.064 3204 29.9 0.919

California 4471 30.1 0.804 395 36.8 2.853 0.235 0.000 3577 22.6 0.816

Colorado 4061 35.3 0.838 348 43.7 3.079 0.284 0.005 3355 29.4 0.882

Connecticut 5305 36.8 0.757 453 45.3 2.648 0.068 0.009 4137 27.9 0.805

Delaware 4037 35.0 0.984 323 39.6 3.519 0.837 0.000 3210 27.2 1.036

District of Columbia 2038 33.4 1.357 161 31.8 4.370 0.054 0.000 1660 27.3 1.434

Florida 5026 31.9 0.995 370 41.6 4.027 0.758 0.006 3546 21.5 1.041

Georgia 7631 29.0 0.718 562 36.0 3.503 0.230 0.000 6133 23.1 0.756

Hawaii 4323 42.4 0.959 259 43.4 3.929 0.431 0.020 3417 34.8 1.057

Idaho 4989 32.7 0.800 413 37.5 2.930 0.353 0.000 3943 25.5 0.851

Illinois 5262 28.0 0.692 428 37.6 2.678 0.331 0.000 4260 22.0 0.715

Indiana 5461 32.7 0.701 468 40.0 2.521 0.903 0.000 4332 25.7 0.735

Iowa 4997 38.4 0.811 308 40.5 3.244 0.955 0.000 3706 28.3 0.866

Kansas 4609 35.4 0.811 345 37.4 2.980 0.347 0.000 3532 27.4 0.872

Kentucky 7620 32.3 0.811 838 37.5 2.522 0.288 0.000 5687 24.7 0.874

Louisiana 5070 32.3 0.759 325 43.1 3.208 0.399 0.004 4059 25.3 0.797

Maine 2387 37.6 1.099 235 46.4 ‡ 3.691 0.102 0.086 1885 28.6 1.150

Maryland 4430 35.2 0.890 340 43.7 3.540 0.347 0.013 3588 29.8 0.956

Massachusetts 7552 34.5 0.704 764 43.1 2.312 0.239 0.000 6043 25.5 0.740

Michigan 3547 31.1 0.874 327 39.5 3.062 0.799 0.000 2703 24.1 0.937

Minnesota 3867 38.0 0.859 258 45.8 ‡ 3.640 0.138 0.057 3035 29.5 0.912

Mississippi 4405 33.1 0.825 310 35.6 3.286 0.161 0.000 3423 25.6 0.881

Missouri 4247 35.5 1.030 342 38.9 3.557 0.699 0.000 3128 27.5 1.149

Montana 4018 36.8 1.025 314 52.9 ‡ 3.677 0.001 0.926 3104 28.6 1.090

Nebraska 4971 38.1 0.770 361 51.6 ‡ 3.004 0.000 0.607 3716 29.8 0.839

Nevada 2967 25.6 1.120 198 32.7 4.415 0.088 0.000 2363 19.3 1.132

New Hampshire 5033 33.7 0.753 442 43.3 2.751 0.280 0.002 4038 25.6 0.786

New Jersey 11266 31.3 0.514 825 37.6 2.004 0.193 0.000 8605 23.4 0.546

New Mexico 5482 34.6 0.762 389 46.6 ‡ 3.085 0.046 0.052 4236 26.9 0.819

New York 5527 32.9 0.732 439 44.3 2.689 0.150 0.004 4343 25.4 0.771

North Carolina 9423 33.9 0.770 734 41.3 2.813 0.719 0.000 7147 27.0 0.848

North Dakota 3021 37.4 0.977 221 49.6 ‡ 3.749 0.014 0.365 2265 28.5 1.056

Ohio 3817 31.1 0.921 300 36.9 3.446 0.339 0.000 3049 23.0 0.933

Oklahoma 7602 40.0 0.650 579 44.1 2.472 0.140 0.002 5653 31.9 0.713

Oregon 4004 33.4 0.827 382 40.5 2.904 0.946 0.000 3105 25.7 0.879

Pennsylvania 3659 35.0 0.884 306 40.4 3.289 0.985 0.000 2797 27.0 0.952

Rhode Island 4038 39.3 0.914 417 46.1 2.938 0.055 0.030 3154 30.6 0.989

South Carolina 5910 35.0 0.723 392 48.7 ‡ 3.000 0.006 0.171 4643 27.9 0.770

South Dakota 5257 46.0 0.816 370 56.0 ‡ 3.603 0.000 0.495 3824 38.0 0.924

Tennessee 2585 36.1 1.084 209 37.1 3.736 0.399 0.000 2062 30.5 1.162

Texas 6010 32.5 0.692 428 38.0 2.848 0.431 0.000 4879 26.7 0.735

Utah 4044 34.5 0.986 311 35.6 3.474 0.183 0.000 3321 28.9 1.039

Vermont 4242 33.9 0.828 342 36.9 3.055 0.282 0.000 3307 25.6 0.872

Virginia 5434 34.7 0.893 450 39.4 3.391 0.789 0.000 4378 28.5 0.960

Washington 18596 35.4 0.441 1780 41.7 1.465 0.368 0.000 14648 28.5 0.471

West Virginia 3342 36.6 0.925 300 45.9 3.225 0.089 0.038 2529 28.2 0.997

Wisconsin 4051 36.1 0.911 328 43.5 3.377 0.357 0.008 3216 28.3 0.968

Wyoming 3989 36.0 0.841 312 55.0 ‡ 3.198 0.000 0.633 3166 28.8 0.893

Guam 800 27.5 1.814 58 31.3 6.900 0.194 0.002 729 24.6 1.845

Puerto Rico 4114 20.1 0.802 485 25.6 − 2.482 0.000 0.000 3140 16.1 0.843

Virgin Islands 2037 19.6 1.229 77 18.8 − 6.433 0.001 0.000 1723 17.1 1.290

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chro

N/C=Data Not Collected

N/S=Data Not Sufficient for Release (sample less than 50)

Note: Estimates and standard errors have been weighted either to state or nation as appropriate. 

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).

+ Indicates that the State estimate is statistically better than the national average (P<0.05).

- Indicates that the State estimate is statistically worse than the national average (P<0.05).

All Adults All Adults with Asthma Adults-age 18-64
 131

Table E.16. Percent of all adults who received flu shots and percent of adults with asthma who received flu 

shots by State, District of Columbia and U.S. Territory, 2003 (continued) 

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Sample 

Size Percent

Standard 

Error

Total U.S. 16554 33.5 0.678 54651 69.6 0.373 4108 76.1 1.160

Top decile average 1352 44.4 1.654 5211 77.7 0.656 305 88.6 1.727

Bottom decile 

average

796 23.8 1.870 2904 51.8 1.203 335 58.6 3.323

Alabama 215 33.8 4.115 741 70.2 1.820 50 76.4 6.083

Alaska 186 38.6 4.947 280 66.5 3.755 N/S N/S N/S

Arizona 209 33.9 4.849 831 68.9 2.286 85 74.1 7.060

Arkansas 264 40.2 3.377 963 71.0 1.608 57 87.5 4.070

California 311 28.7 2.955 879 72.5 1.931 83 80.8 4.494

Colorado 292 38.9 3.315 683 74.2 1.997 53 78.7 6.370

Connecticut 390 39.5 2.791 1068 74.3 1.509 56 84.1 5.496

Delaware 266 34.8 3.716 797 70.0 2.089 54 67.6 9.339

District of Columbia 135 24.7 4.052 333 63.0 3.137 N/S N/S N/S

Florida 254 28.8 4.440 1429 65.9 1.864 110 83.2 4.527

Georgia 460 31.6 3.890 1432 67.0 1.684 98 65.4 6.602

Hawaii 219 41.0 4.287 879 76.4 1.815 N/S N/S N/S

Idaho 328 31.3 3.129 1018 70.3 1.691 85 72.1 5.639

Illinois 355 33.8 2.888 998 62.2 1.807 73 60.2 6.655

Indiana 391 33.6 2.693 1103 66.1 1.552 76 76.6 5.324

Iowa 233 31.3 3.549 1268 77.5 1.344 74 84.8 4.709

Kansas 277 30.4 3.146 1038 70.8 1.546 66 77.1 5.715

Kentucky 604 29.5 2.817 1900 69.1 1.496 232 72.0 4.232

Louisiana 259 36.6 3.533 987 68.3 1.668 65 74.7 5.909

Maine 184 39.3 4.047 476 74.8 2.194 50 80.4 6.153

Maryland 275 38.4 3.900 771 68.4 2.119 63 80.5 5.490

Massachusetts 641 36.5 2.462 1371 74.9 1.472 112 84.7 3.421

Michigan 259 34.3 3.339 825 67.5 1.848 65 70.9 6.101

Minnesota 213 40.1 3.931 832 80.3 1.428 N/S N/S N/S

Mississippi 244 30.4 3.558 951 69.0 1.643 65 60.8 6.845

Missouri 271 31.9 3.830 1082 69.9 1.897 69 86.7 5.526

Montana 245 46.2 4.166 881 72.8 1.928 68 80.8 5.749

Nebraska 273 43.1 3.357 1230 73.6 1.352 87 88.2 3.335

Nevada 156 27.8 4.698 599 60.0 2.995 N/S N/S N/S

New Hampshire 368 36.7 2.960 922 73.9 1.606 65 84.8 4.847

New Jersey 655 31.3 2.128 2469 67.2 1.136 159 70.6 4.224

New Mexico 310 39.7 3.292 1225 72.4 1.416 79 81.7 4.514

New York 365 38.6 2.917 1112 68.0 1.730 70 79.8 5.731

North Carolina 543 34.0 3.117 2206 68.8 1.457 183 70.8 4.740

North Dakota 161 38.7 4.186 729 73.0 1.768 57 87.2 4.410

Ohio 247 30.4 3.608 729 68.0 2.159 50 72.7 8.449

Oklahoma 450 37.3 2.664 1914 75.8 1.067 125 83.0 3.548

Oregon 304 34.4 3.191 874 70.5 1.702 75 71.4 5.879

Pennsylvania 237 33.6 3.602 831 69.1 1.751 69 68.4 6.415

Rhode Island 351 42.0 3.202 833 76.2 1.672 59 76.3 6.125

South Carolina 303 38.9 3.337 1210 69.3 1.532 84 85.2 4.286

South Dakota 257 45.8 4.176 1399 77.9 1.216 111 89.8 2.791

Tennessee 168 32.8 4.039 512 69.1 2.293 N/S N/S N/S

Texas 349 31.5 3.049 1082 67.7 1.546 75 75.9 5.286

Utah 256 30.7 3.718 704 74.8 2.141 52 77.6 7.450

Vermont 291 30.7 3.156 911 74.1 1.539 50 88.0 4.293

Virginia 365 32.9 3.777 1017 69.6 1.879 84 67.8 6.722

Washington 1438 36.4 1.594 3924 73.4 0.864 338 75.9 3.164

West Virginia 231 37.5 3.576 801 69.1 1.778 68 79.1 5.369

Wisconsin 270 34.3 3.576 805 72.1 1.842 56 85.1 4.700

Wyoming 226 46.2 3.743 797 72.6 1.729 82 87.3 4.200

Guam 53 28.2 7.040 64 59.7 6.909 N/S N/S N/S

Puerto Rico 385 22.2 2.645 969 40.2 2.081 99 49.5 6.259

Virgin Islands 67 18.3 6.791 274 34.9 3.865 N/S N/S N/S

Adults with Asthma age 18-64 Adults Age 65 and over

Adults With Asthma age 65 and 

over
132

Appendix F: Other Asthma-Related Data Sources 

This appendix provides information on national and local data sources for asthma noted in this 

Resource Guide to further assist States in generating estimates or analyzing factors related to the 

quality of asthma care. The quality of the data is discussed throughout this section, because 

State leaders in quality improvement must understand issues that will be raised in the 

improvement process. Health care providers may argue that the data, due to limitations, do not 

reflect reality. They may say: “The data are the problem and not the health care system.” 

Understanding data limitations leads to responsible use of data. 

For the purposes of this Resource Guide, only data sources that are able to provide information 

that is nationally representative and available by State are used. Different sources use different 

methods, definitions, and classifications. Some sources produce estimates by State and some by 

national population subgroup, such as race/ethnicity, gender, age, and income. 

Sources of Asthma Data in the NHQR 

The asthma data in the NHQR come from two data sources: the Healthcare Cost and Utilization 

Project (provided to AHRQ by statewide discharge data organizations) and NCQA’s Health Plan 

Employer Data and Information Set. 

Healthcare Cost and Utilization Project 

HCUP is a public-private partnership sponsored by AHRQ with 33 participating States that 

covered about 90 percent of U.S. discharges in the United States in 2001. The participating 

statewide data organizations (government, hospital association, or other private organization) 

provide their statewide hospital discharge data to HCUP for reformatting into standardized files. 

While national asthma estimates from HCUP are included in the NHQR, State-level data are 

reported in the NHQR only for one special analysis of admissions for asthma. 

The following HCUP Partners provided data for the 2001 HCUP Nationwide Inpatient Sample: 

Arizona Arizona Department of Health Services 

California Office of Statewide Health Planning & Development 

Colorado Colorado Health & Hospital Association 

Connecticut Chime, Inc. 

Florida Agency for Health Care Administration 

Georgia GHA: An Association of Hospitals & Health Systems 

Hawaii Hawaii Health Information Corporation 

Illinois Illinois Department of Public Health 

Iowa Iowa Hospital Association 

Kansas Kansas Hospital Association 

Kentucky Kentucky Department for Public Health 

Maine Maine Health Data Organization 

Maryland Maryland Health Services Cost Review Commission 

Massachusetts Massachusetts Division of Health Care Finance and Policy 
133

Michigan Michigan Health & Hospital Association 

Minnesota Minnesota Hospital Association 

Missouri Hospital Industry Data Institute 

Nebraska Nebraska Hospital Association 

New Jersey New Jersey Department of Health & Senior Services 

New York New York State Department of Health 

North Carolina North Carolina Department of Health and Human Services 

Oregon Oregon Association of Hospitals & Health Systems 

 Pennsylvania Pennsylvania Health Care Cost Containment Council 

Rhode Island Rhode Island Department of Health 

South Carolina South Carolina State Budget & Control Board 

Tennessee Tennessee Hospital Association 

Texas Texas Department of State Health Services 

Utah Office of Health Care Statistics, Utah Department of Health 

Vermont Vermont Association of Hospitals and Health Systems 

Virginia Virginia Health Information 

Washington Washington State Department of Health 

West Virginia West Virginia Health Care Authority 

Wisconsin Wisconsin Department of Health & Family Services 

Contact information for these statewide data organizations is available at: http://www.hcupus.ahrq.gov/partners.jsp?SID. Additional information on HCUP data can be found at: 

http://www.ahrq.gov/data/hcup/hcup-pkt.htm. 

The main limitation of HCUP data (or any administrative billing data) is that the data are 

collected primarily for the purpose of reimbursement, and what is coded as clinical diagnoses 

and procedures can be affected by reimbursement incentives.1 Such incentives can encourage or 

discourage coding of specific types of conditions or treatments. In addition, the data do not 

include detailed clinical information (e.g., physiologic measures) beyond diagnoses and 

procedures, which are useful in determining patient severity of illness. Nevertheless, HCUP data 

can be used for many purposes, provided that the bias of coding is considered and ruled out as 

inconsequential. Thus, while administrative hospital data can be mined for clues to quality of 

care, analysts should be alert for whether the data contain incomplete entries or inadequate 

clinical detail. 

AHRQ has developed the Quality Indicators for use with HCUP and other hospital 

administrative data. These indicators use sophisticated clinical algorithms of inclusions and 

exclusions to define patients with similar characteristics and then calculate the outcomes of these 

groups of patients across different settings and populations. The algorithms have been tested, 

reviewed, and hewn by clinical consensus panels under AHRQ sponsorship. The AHRQ Quality 

Indicators include the Prevention Quality Indicators, which estimate rates of avoidable hospital 

admissions, including separate indicators for pediatric and adult asthma admissions, as an 

indirect measure of the quality of ambulatory asthma care in the United States. As tools for local 

 

1

 See: Keating N, Landrum M, Landon B, Ayanian J, Borbas C, Guadagnoli E. Managing chronic illness in managed 

care settings: Measuring the quality of diabetes care using administrative data: Is there a bias? Health Services 

Research. 2003;38(6):1529-45. 
134

quality improvement, the AHRQ Quality Indicators can be used as screens for quality problems 

that call for more in-depth local study; they are not considered definitive measures of local 

quality of care. As national measures they capture trends in quality as well as coding of 

diagnoses. National estimates of the asthma Prevention Quality Indicators are part of the 2003 

and 2004 NHQR and NHDR; State estimates are in the 2004 NHQR. Additional information on 

the AHRQ Quality Indicators is available at: http://www.qualityindicators.ahrq.gov/. 

Health Plan Employer Data and Information Set 

HEDIS® collects data from health plans across the country. HEDIS® is a set of standardized 

performance measures designed to ensure that purchasers and consumers have the information 

they need to reliably compare the performance of managed health care plans. The performance 

measures in HEDIS® are related to many significant public health issues such as cancer, heart 

disease, smoking, asthma and diabetes. HEDIS® also includes a standardized survey of 

consumers' experiences that evaluates plan performance in areas such as customer service, access 

to care and claims possessing. HEDIS® is sponsored, supported, and maintained by the National 

Committee for Quality Assurance. 

Because HEDIS® data are collected at the health plan level, State estimates cannot be made. To 

provide regional estimates, each health plan is assigned to the State in which the health plan 

headquarters are located, but these are not necessarily where the practices are located. HEDIS 

data are also limited in that they are relevant to care provided only under managed health care 

plans. 

Other Sources of Data on Asthma Care 

Asthma-related measures from the following sources discussed in this Resource Guide are not 

yet included in the NHQR. (For detailed information on State-level BRFSS measures not yet 

included in the NHQR, see Appendix E.)

National and Setting-Specific Data Sources 

Medical Expenditure Panel Survey 

MEPS is a family of surveys, including a Household Survey and surveys of related health care 

providers. Information is collected annually on health care utilization, expenditures, and health 

insurance coverage. For the most part, MEPS data are collected using computer-assisted, inperson interviews. The asthma component is collected via a separate paper and pencil 

questionnaire distributed to respondents who report that they have been diagnosed with asthma. 

More information about MEPS data and methods is available at: 

http://www.meps.ahrq.gov/WhatIsMEPS/Overview.HTM. 

MEPS reports national rates by national subgroup for the percentages of asthma patients who 

used prescription asthma medications, inhaled steroids, and peak flowmeters. Other measures of 

asthma process of care are not captured in this data set. The following table shows MEPS data 

on these measures for 2000: 
135

Asthma medication and peak flowmeter use for patients with asthma by age group, 2000 

_____________________________________________________________ 

Measure Total Under Age 18 

 age 18 and older 

Percent who used: 

Asthma medication 86.5 91.2 84.3 

Inhaled steroids 49.8 42.3 53.5 

Peak flowmeter in home 31.3 27.5 33.1 

_________________________________________________________________________________ 

Source: Agency for Healthcare Research and Quality (2003). MEPS Statistical Brief #13, 

Asthma Treatment: Use of Medications and Devices, 2000. 

MEPS collects expenditure data from a national sample but does not collect data by State; thus 

State-level estimates are not available. The following table from MEPS shows total expenses 

for the category of “COPD, asthma” by site of service: 

Total expenses, in millions of dollars, for COPD, asthma by site of service, 1996-2002 

_____________________________________________________________________________________ 

Year Total 

Outpatient and 

office-based 

medical provider 

visits 

Hospital 

inpatient stays 

Emergency 

room visits 

Prescribed 

medicines 

Home health 

care 

2002 45,262.78 11,923.53 12,464.81 1,642.40 15,150.31 4,081.72 

2001 44,404.43 9,825.75 16,324.51 1,612.99 13,327.22 3,313.96 

2000 36,487.99 7,225.14 13,929.82 1,119.92 8,750.69 5,460.57 

1999 33,651.40 7,115.61 11,982.60 1,197.51 8,239.30 5,116.38 

1998 31,707.10 6,820.20 14,489.72 915.37 6,719.09 2,762.73 

1997 28,973.39 6,356.35 13,256.91 1,090.62 6,100.09 2,169.43 

1996 28,594.88 6,895.89 12,702.23 942.14 5,630.05 2,424.57 

_____________________________________________________________________________________________ 

Source: Agency for Healthcare Research and Quality. MEPS Compendium of Tables-Medical Expenditures by 

Condition (1996-2002). Total expenses for conditions by site of service: United States, 1997. March 3, 2003. 

Medical Expenditure Panel Survey Component Data. 

National Hospital Discharge Survey 

The NHQR uses the National Hospital Discharge Survey for one outcome measure—estimated 

annual rate of hospitalizations for asthma. The National Center for Health Statistics at CDC uses 

a national sample of hospitals and a sample of their discharges to collect administrative hospital 

records for the NHDS (similar to HCUP). The sample consists of about 270,000 inpatient records 

from about 500 hospitals and is representative of inpatient discharges nationally. Additional 

information on NHDS data is available at: 

http://www.cdc.gov/nchs/about/major/hdasd/nhdsdes.htm. 

The limitation of NHDS data are similar to those for HCUP data (described above) because 

NHDS also uses discharge records or inpatient claims for reimbursement. In addition, although 

NHDS is a true probability sample, it has a much smaller size than HCUP. As a result, many 

subgroup estimates that can be made with HCUP cannot be supported with NHDS data. The 

NHDS cannot produce State-level estimates. 
136

National Asthma Survey 

The National Asthma Survey, a national sample of households interviewed by phone, was 

conducted by CDC beginning in 2004. The survey contains questions that can be used to develop 

measures similar to BRFSS asthma measures in addition to other measures related to processes 

of asthma care including asthma education, peak flow meter use, spirometer use, demographic 

information of persons with asthma, and others. Pilot survey data were released in 2005. (Results 

of pilot tests in four States are available at: http://www.cdc.gov/nchs/about/major/slaits/nas.htm.) 

National Health Interview Survey 

Conducted by CDC’s National Center for Health Statistics, the National Health Interview Survey 

collects data on asthma prevalence for all ages and for children only. Twelve-month prevalence 

data were collected for children from 1980 to 1996 and for all ages from 1982 to 1996. 

Beginning in 1997, the survey asks questions on lifetime diagnosis and 12-month attack 

prevalence; a question on current prevalence (i.e., “Do you still have asthma?”) was added in 

2001. The NHIS also includes questions on the number of school days missed by children and 

the number of workdays missed by adults due to asthma. 

National Ambulatory Medical Care Survey/National Hospital Ambulatory Medical Care 

Survey (NAMCS/NHAMCS)

 

These surveys are conducted annually by CDC’s National Center for Health Statistics. NAMCS 

surveys office-based physicians who are randomly assigned to a 1-week reporting period. The 

survey form includes questions on reason for the visit and physician diagnosis as well as whether 

the patient has various chronic diseases, including asthma, regardless of diagnosis. NHAMCS 

collects similar data for hospital emergency and outpatient departments over a 4-week reporting 

period. Recent findings related to asthma from these and other NCHS surveys can be found at: 

http://www.cdc.gov/nchs/products/pubs/pubd/hestats/asthma/asthma.htm. 

Health Care Setting-Specific Data Sources 

The following data sources collect asthma care quality data for specific health care settings rather 

than nationally representative or state level data. Therefore, data from these sources are useful 

for informing initiatives or policies in the appropriate health care setting but not necessarily for 

broad statewide programs. However, the availability of these data point to important 

opportunities for collaborations with other health providers and sectors to improve the quality of 

asthma care. 

• The Joint Commission on Accreditation of Healthcare Organizations, an organization 

that oversees the quality of hospitals and other health care organizations, collects hospital 

data on disease-specific care, including asthma. 

• Health Disparities Collaboratives, learning processes of HRSA’s Bureau of Primary Health 

Care, are disease specific (diabetes, heart disease, and asthma) and include community health 

centers across the Nation. 
137

• National Institute for Children’s Healthcare Quality Learning Collaboratives are 

partnerships and learning networks that collect quality data on care for specific diseases in 

primary care practices. NICHQ efforts aim to measure and improve quality of care and build 

structural support for quality improvement. 

Local Data Sources 

Below are summaries of some local data sources that have been developed to assess more closely 

the processes of asthma care for specific populations in specific geographic areas. (See Appendix 

D for descriptions of measures from local and other health care setting data sources.) 

Chicago Asthma Surveillance Initiative (CASI) 

The goal of CASI is to develop a community-wide surveillance program that characterizes and 

monitors asthma care in the Chicago area in greater detail than other public health surveillance. 

To accomplish this, CASI surveyed Chicago-area hospitals, emergency departments, primary 

care physicians, specialty care physicians, pharmacists, managed care organizations, the general 

public, and persons or families affected by asthma to learn about asthma care and its outcomes. 

Seven surveys are included: emergency department, hospital, managed care, primary care 

physician, specialty care physician, pharmacist, and asthma survey of the general population. 

The CASI surveys were designed to assist the Chicago Asthma Consortium in setting program 

priorities and to evaluate the impact of these programs over time. The first promising effect 

stemming from the CASI surveys was the creation of the Chicago Emergency Department 

Asthma Collaborative in 1997 in which 28 EDs agreed to participate in a 1-year communitybased collaborative aimed at improving ED asthma care.2

 

Guide to Improving Asthma Care in Oregon 

The goal of this Oregon Asthma Program guide is to steer efforts to improve asthma 

management and to define appropriate indicators for monitoring the quality of medical care 

provided to Oregonians with asthma. The guide establishes nine priority areas including: 

periodic assessment and monitoring of asthma; spirometry; coordination of care; written asthma 

action plan; asthma education; pharmacology; influenza immunization; assessment, education, 

management, and treatment of allergens and irritants; and asthma recommendations for health 

systems. The guide was developed through a consensus process and includes population-based 

goals and indicators. The guide does not address all the care a patient with asthma may need. 

Rather, it is based on a set of procedures that are measurable for defined populations and 

therefore lend themselves to systematic monitoring. The guide can be accessed at: 

http://www.dhs.state.or.us/publichealth/asthma/guideor.cfm. 

Michigan Quality Improvement Consortium (MQIC) 

The goal of the Michigan Quality Improvement Consortium is to establish and implement a core 

set of clinical practice guidelines and performance measures for Michigan health plans. The 

 

2 See: Weiss KB, Grant EN. The Chicago Asthma Surveillance Initiative: a community-based approach to 

understanding asthma care. Chest. 1999;116:141S-145S. 
138

interventions designed and implemented by each plan to improve consistent delivery of services 

are at the discretion of individual plans; but guidelines, performance goals, measurement 

methodology, and performance reporting are standardized. The MQIC asthma guideline 

recommends provision of specific services at least annually including a written action plan for 

self-management and education regarding use of peak flowmeter, inhaler, spacer and medication, 

recognition/treatment of symptoms and when to seek medical attention, identification, and 

avoidance of triggers and smoking cessation counseling. 

New York City Childhood Asthma Initiative (NYCCAI) 

The New York City Department of Health and Mental Hygiene’s Childhood Asthma Initiative is 

a public health effort to reduce asthma morbidity among children 0 to18 years of age. Expected 

outcomes of the NYCCAI include reductions in hospitalizations, ED visits, and school absences 

due to asthma and improvements in management of childhood asthma among families. The 

NYCCAI is building on existing research and educational and clinical efforts, resulting in a 

coordinated and comprehensive effort to understand, treat, and prevent asthma in New York 

City.3

 

 

3

 See: Garg R, Karpati A, Leighton J, Perrin M, Shah M. Asthma Facts, Second Edition. New York City 

Department of Health and Mental Hygiene, May 2003 (available at: http://nyc.gov/html/doh/pdf/asthma/facts.pdf). 
 139

Appendix G: Benchmarks From the NHQR 

The NHQR provides a national set of estimates and, 

often, State estimates that can be used as 

benchmarks for quality improvement. A 

benchmark can be a baseline or point from which 

you start, not necessarily representing a goal or 

target; or it can be the best current rate, something 

achievable; or it can represent a consensus of what 

should be achieved. It is a basis for making 

comparisons. 

Several types of benchmarks can be derived from 

the NHQR: 

• Theoretic limit benchmark—The theoretic limit refers to the maximum or minimum level 

that a measure can take on. For example, 100 percent for positive outcomes or 0 percent for 

negative, avoidable events. In an ideal world, these would be achievable, but in a world 

where so many factors are involved in achieving a maximum result, those benchmarks may 

be unrealistic. Also, some concepts might feasibly come closer to the theoretic limit than 

others. 

• Best-in-class benchmark—The rate for the top State or top tier of States can be used for 

what manufacturers call a “best in class” benchmark. (The top tier can be defined as the top 

5 or 10 percent of States averaged together.) Using influenza vaccination as an example, the 

highest rate of flu vaccination for people with diabetes across the States (64 percent) may be 

assumed to be a feasible goal for States to achieve. However, some may view the top State 

rate as an impractical target given their population and circumstances. Others may view that 

goal as inadequate depending on the value of the rate and the state of medical knowledge and 

practice, and they may view the 100-percent goal as their target. These judgments will vary 

across States because States face different circumstances and environments. This Resource

Guide uses the top 10 percent of States, combined in a simple average, to derive the best-inclass estimate. A simple average, rather than weighted average, was used because the 

denominators from the BRFSS estimates were not available in the NHQR. 

• A national consensus-based goal—Some organizations propose targets that should be 

achieved to improve the health status of the overall population and vulnerable subgroups. 

For example, two decades ago, the Centers for Disease Control and Prevention developed 

diabetes-related goals for a healthier U.S. population. Each decade those goals are reviewed 

and reestablished. 

• National average—The overall average indicates where the average member of a group 

stands. For example, the average of influenza vaccination rates for people with diabetes in 

States (37 percent according to the BRFSS data source) is the “norm” for States or is the rate 

Key Messages on Benchmarks: 

A benchmark: 

• Is a point for comparison. 

• Is a place to start. 

• May be inadequate or impractical from 

different vantage points. 

Methods matter: 

They can have a large impact on comparisons. 
 140

for the “average” State. States with rates below the average would prefer to be at or above 

the average. But the average may not be an indicator of quality health care. 

• Regional norm—States may prefer a regional estimate for comparison because they want to 

see how they perform compared to medical practice within the region. Given the wide 

regional variation in U.S. medical practice, regional estimates may be weak goals for regions 

where practice should change to enhance the health care quality for people with asthma. For 

this Resource Guide, the regional averages are calculated for the four Census regions, 

Northeast, Midwest, South, and West. (The averages are simple averages because the 

denominators for BRFSS estimates were not available from the NHQR.) 

• State rate—The State’s own rate may serve as a benchmark for various purposes, such as 

tracking changes over time, evaluating the effect of a statewide intervention to improve 

quality, or reporting the norm for local communities and providers to use as a comparison 

with their own performance. Concerns noted above about using national or regional averages 

as goals also apply to State rates. For provider-level estimates, the best-in-class providers 

may be a better indication of what is achievable and should be used as a goal rather than the 

State average rate. Severity adjustments are an important issue at the provider level, where 

populations of patients with varying severity and comorbidity levels are unlikely to be 

distributed evenly across providers. 
 141

Appendix H: Information on Statistical Significance 

This section is provided for data analysts who wish to generate other statistics and/or perform 

statistical tests for other comparisons than those that are provided in the NHQR and NHDR. 

Comparing State and Average Estimates Using P-Values 

When comparing an individual State estimate to another estimate, such as the all-State average 

or the average for the top tier of States, every measure has error associated with it. The error is 

associated with sampling (size of the sample or sampling methods), accuracy of respondent 

recall and responses, data entry processes, and many other factors. When comparing estimates it 

is important to take this error (which can be estimated with statistical assumptions) into account. 

A common statistic for comparing two rates to determine whether they differ is the t-test based 

on a normal distribution. The t-test can be compared to a normal distribution with a prespecified 

level of significance or acceptable error in conclusions about whether or not two statistics come 

from the same distribution or population. The p-value, a statistic for a normal distribution, can 

be calculated to determine whether two measures are likely from the same or from different 

distributions. 

Statistical significance and magnitude of the difference should be considered together when 

comparing two estimates. The first check should be: Is the difference statistically different? 

The second check should be: Are the differences large enough to be meaningful for policy 

purposes? These questions are addressed below: 

• Is the difference statistically different? Are the p-values less than 0.05? If so, you can 

assume that the underlying distributions come from different populations or experiences. But 

there are some other considerations. The statistical test of differences is affected by the 

number of observations from which the measures were generated. For example, if the 

measures were generated from hundreds of thousands of records then summary measures 

(such as averages) have less variance and lower p-values, which imply “statistical 

significance” even when the magnitude of the differences might be tiny. Alternatively, when 

differences are large and the number of observations is few, the absence of statistical 

significance might simply mean that the data set does not have enough observations for a 

powerful test. This happens frequently with the BRFSS measures because the annual sample 

sizes of the State surveys are small—from about 2,000 to 8,500 observations.

• Are the differences large enough to be meaningful for policy purposes? Because of the 

relationship between the statistical test and the number of observations, some judgment must 

be used to assess the meaning of the differences between State estimates. Thus, in addition 

to statistical significance it is important to ask the second question: Is the State-tobenchmark difference large enough to warrant efforts to rectify it? A one or two percentage 

point difference in a measure may not be worth the effort to improve it. A 5 or 10 percentage 

point difference may mean that a substantial number of State residents are affected by poor 

health care quality in the State. These are judgments that local experts and stake holders who 

understand the environment of a State can help make. 
 142

Calculating P-Values 

Calculating the p-value is straightforward when the standard errors (SEs) of the estimate are 

provided. For example, standard errors are provided for the national average and for individual 

States. Thus, the test for statistical significance between those two estimates is straightforward 

(and provided first). However, calculating another average (say, the top decile average) for 

which the standard error has not been provided is more complicated. In fact, the top decile 

comparisons in this work are evaluated for statistical significance because the population 

denominators were not readily available in time for publication of this Resource Guide. 

Nevertheless, the method for that calculation is presented below. 

 

Calculating the p-value when the relevant standard errors are provided. For an individual 

State estimate compared to the all-State average, the appropriate standard errors have been 

provided in the NHQR tables. To assess whether or not a State rate is statistically different from 

the average, calculate the p-value, as follows. 

Two-sided t-test: 

Where: 

R1 = a State rate 

R2 = national rate 

SE2

1 = square of the standard error of the State rate (or its variance) 

SE2

2 = square of the standard error of the national rate (or its variance) 

If the p value is smaller than 0.05, then a State can conclude, with 95 percent confidence, that the 

State rate is statistically different from the all-State average rate. 

The p-value can be calculated using SAS or EXCEL with the following data elements and 

formula functions: 

SAS: p = 2 * (1 - PROBNORM(ABS(t))); 

EXCEL: p= 2*(1- NORMDIST(ABS(t),0,1,TRUE)) 
 143

Calculating the p-value when the relevant standard errors are not provided. The 

fundamental equation of analysis of variance can be used to calculate p-values for other 

comparisons. For example, comparing a State rate to the average of the top three States would 

involve the following. The total sum of squares about the overall three-State mean is the sum of 

the within-State sum of squared deviations from the State mean and the between-State sum of 

squared deviations from the three-State pooled mean. The within-State sum of squares is 

obtained by squaring the State’s standard error and multiplying by the sample size. The 

between-State sum of squares is obtained by summing the sample-weighted squared difference 

between the State average and the overall three-State average. The formula is below (note: x**2 

= x squared and sqrt(x) = square root of x): 

Let n1, n2, and n3 be the sample sizes for each State. 

Let m1, m2, and m3 be the means for each State. 

Let s1, s2, and s3 be the standard errors for each State. 

N = n1 + n2 + n3, is the overall three-State sample size. 

M = (n1*m1 + n2*m2 + n3*m3) / N, is the overall three-State mean. 

SS = n1*(n1-1)*s1**2 + n2*(n2-1)*s2**2 + n3*(n3-1)*s3**2 + n1*(m1-M)**2 

+ n2*(m2-M)**2 + n3*(m3-M)**2 

VAR = SS / (N-1) 

SE = sqrt(VAR), which is the estimated standard error for the three-State mean. 

Now suppose you have a mean m0 and standard error s0 from a State and you want to test 

whether m0 is significantly different from M. The test statistic is: 

Z = (m0 – M) / sqrt(SE**2 + s0**2), 

which can be compared to 1.96 to test the difference at the 5-percent significance level. Or 

alternatively the p-value can be calculated as in the previous section.