> **Diabetes 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.gov*
>
> **Contract No. 290-00-0004**
>
> The Medstat Group, Inc.
>
> The Council of State Governments
>
> **Prepared by:**
>
> Rosanna M. Coffey, Ph.D.
>
> Trudi L. Matthews, M.A.
>
> Kelly McDermott, M.A.
>
> AHRQ Publication No. 04-0072
>
> September 2004

**Acknowledgments**

This *Resource Guide* was prepared for the Agency for Healthcare
Research Quality (AHRQ) by Rosanna M. Coffey, Ph.D., and Kelly
McDermott, M.A, The Medstat Group, Inc., and Trudi L. Matthews, M.A.,
The Council of State Governments. The idea for the *Resource Guide*
belongs to Denise Remus, Ph.D., R.N. and other AHRQ staff who strive to
move research into the practice of health care and health care policy.
Dwight McNeil, Ph.D., AHRQ Task Leader, and Edward Kelley, Ph.D.,
Director of the National Healthcare Quality Report, guided this work and
set the standard for its quality. Numerous people contributed to the
concept by sharing information, serving on focus groups, and reading
multiple drafts. We acknowledge the generous consultations and
contributions of:

-   Four Partners in the Healthcare Cost and Utilization Project from
    both State hospital associations and State government agencies (Vi
    Naylor, Georgia Hospital Association; Jerry O'Keefe, Massachusetts
    Division of Health Care Finance and Policy and Department of Public
    Health; Mark Sonneborn, Michigan Health and Hospital Association;
    and Gary Blair, Washington State Department of Health).

-   Members of the Council of State Governments, who served as focus
    group members (Representative Mary Skinner, Washington;
    Representative Greg Jolivette, Ohio; Kurt Knickrehm, Director,
    Arkansas Department of Human Services; Assemblyman Felix Ortiz, New
    York; Senator Linda Higgins, Minnesota; Jan Norman, Manager,
    Diabetes Control Program, Washington State Department of Health;
    Senator Larry Salmans, Kansas).

-   Centers for Disease Control and Prevention, Division of Diabetes
    Translation staff who provided suggestions and important insights on
    quality improvement activities of the States and the CDC (Frank
    Vinicor, Michael Engelgau, Russell Sniegowski, and David Guthrie).

-   Centers for Disease Control and Prevention staff who provided
    comments on a prior draft.

-   Representatives of the American Diabetes Association (Stewart Perry,
    Tom Boyer, Nathaniel Clark, M.D., and Ann Albright, Ph.D., R.D.)

-   State and Federal officials who provided personal stories, program
    background and other information (Governor Mike Huckabee, Arkansas;
    Representative Dan Bosley, Massachusetts; Representative Fran
    Wendelboe, New Hampshire; Dr. Kimberlydawn Wisdom, Surgeon General
    of the State of Michigan; Martha Roberts and Laurel Reger of the
    Minnesota Diabetes Prevention and Control Program (DPCP); Jo
    Anderson of the Missouri DPCP; Janet Reese of the North Carolina
    DPCP; Dr. Lawrence Harkless, Chairman of the Texas Diabetes Council;
    Pat Zapp of the Wisconsin DPCP).

-   Sally Sue Brown, formerly of The Council of State Governments, who
    provided research assistance for the *Resource* *Guide.*

**\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_**

This document is in the public domain and may be used and reprinted
without permission except for any copyrighted materials noted for which
further reproduction is prohibited without the specific permission of
copyright holders. AHRQ appreciates citation as to source, and the
suggested format is provided below:

Coffey RM, Matthews TL, McDermott K. Diabetes Care Quality Improvement:
A Resource Guide for State Action. (Prepared by The Medstat Group, Inc.
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; September 2004. AHRQ Pub. No. 04-0072.

**Foreword**

*Diabetes Care Quality Improvement: A Resource Guide for State Action*
and its accompanying *Workbook* were developed by the Agency for
Healthcare Research and Quality (AHRQ) as learning tools for all State
officials who want to improve the quality of health care. Using
State-level data on diabetes care from the 2003 *National Healthcare
Quality Report*, 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,
Diabetes Prevention and Control Programs, Medicaid offices, and
elsewhere---provided comments and feedback throughout the development
and finalization 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 upon existing programs, as an orientation
for new staff, and to share with their partners such as the American
Diabetes Association.

The *Resource Guide* and *Workbook* can serve as a meeting place, where
the creative minds of those who struggle with quality improvement can
share their expertise, ideas, knowledge, and solutions. The various
modules are intended for different users. Senior leaders are responsible
for making the case for diabetes quality improvement and taking action
(Modules 1, 4, and 6) while program staff would need to provide the
information necessary to develop and implement a quality improvement
strategy (Modules 2, 3, and 5). The goal, of course, is that all groups
of people work on these modules as a team. It is within those
discussions and sharing and working together that we hope to achieve
what we set out to do---help States improve the quality of diabetes
care.

If you have any comments or questions on the *Resource* *Guide* or
*Workbook*, please contact AHRQ's Center for Quality Improvement and
Patient Safety, 540 Gaither Road, Suite 3000, Rockville, MD 20850.

**Contents**

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

**Introduction: How and Why To Use This Resource Guide**.... 1

Purpose of the Resource Guide .... 2

Audiences for the Resource Guide .... 3

Structure and Organization of the Resource Guide .... 3

**Module 1: Background -- Making the Case for Diabetes Care Quality
Improvement** 7

The Importance of Diabetes .... 8

The NHQR and NHDR as Resources for State Leaders.... 15

[The Quality Improvement Opportunity](#__RefHeading___Toc59607469)....
18

**Module 2: Data -- Understanding the Foundation of Quality
Improvement**.... 21

Quality Measurement.... 22

Sources of NHQR Data on Diabetes Care.... 25

**Module 3: Information -- Interpreting State Estimates of Diabetes
Quality**... 41

Deriving Information From Data.... 42

Step 1: Identifying Appropriate Metrics and Comparisons... 43

Step 2: Interpreting the Data: What Does It Mean?... 54

**Module 4: Action -- Learning From Activities Currently Underway**...
61

Selected Public/Private Quality Improvement Initiatives... 62

Selected Federal Programs and Resources for Diabetes Care Quality
Improvement... 65

State Approaches to Diabetes Care Quality Improvement ... 69

Selected Local Quality Improvement Efforts... 82

**Module 5: Improvement -- Developing a Strategy for Diabetes Quality
Improvement**... 85

A Model for Quality Improvement... 86

Developing a State Strategy For Improving Diabetes Care Quality... 90

Integrating [Quality Improvement Activities Across
Conditions](#__RefHeading___Toc59607484)... 92

The Importance of Evaluation... 93

**Module 6: The Way Forward -- Promoting Quality Improvement in the
States** 99

[What Can State Leaders Contribute to Quality
Improvement?](#__RefHeading___Toc59607484)\... 99

**References**...\...\...\...\...\...\..... 102

**Appendixes:**

A. Acronyms Used in This Resource Guide... 109

B. List of NHQR Data Sources, Including Those Supporting State
Estimates... 111

C. Additional Data Resources Related to Diabetes Care Quality 112

D. Benchmarks From the NHQR... 127

E. Information on Statistical Significance... 131

F. NHQR Quality Measures for All Conditions by State... 134

G. Index of Diabetes Quality Improvement Initiatives... 148

H. CDC Funding for States' Diabetes Programs, 2003-2004... 152

**Executive Summary**

> *"As rates of diabetes increase across the country, roughly tracking
> with increases in obesity rates, States are quickly approaching a time
> when budgets will not be able to withstand the pressure of treating
> the flood of obesity-related diseases. Consequently, while we search
> for better and more efficient ways of treating diabetes and helping
> people manage the disease so that costly procedures can be prevented,
> we must find more ways to create incentives for people to make healthy
> lifestyle choices. The State that figures out how to do this, while
> respecting and protecting individual liberties, will be the model for
> the Nation."*
>
> --- An Interview with Governor Mike Huckabee, Arkansas

Health care analysts and researchers have documented extensive gaps
between the care that patients receive and what the medical community
has determined to be the most effective care. Despite unrivaled
technological innovation in American health care, too much of the care
that is delivered to patients does not meet the accepted standards of
quality. More alarming, abundant research has demonstrated that these
gaps in quality are responsible for wasteful, ineffective care,
preventable medical complications, avoidable hospitalizations, decreased
quality of life, disability, and premature death.

In an era of rising alarm over the cost of health care, it is
bewildering that so much of the health care that Americans pay for does
not meet accepted standards of quality. When considered in light of the
number of preventable deaths and greater disability due to poor quality
care, it is intolerable. A growing number of health care analysts and
leaders argue that the Nation simply cannot afford to ignore the
widespread quality problems that exist in U.S. health care system.

As the lead Federal agency supporting research into the quality, cost
effectiveness, and safety of health care, the Agency for Healthcare
Research and Quality (AHRQ) is at the forefront of equipping health care
professionals, policymakers and leaders with the information they need
to address the health care quality gap. The *National Healthcare Quality
Report* (NHQR), the *National Healthcare Disparities Report* (NHDR), and
this *Diabetes Care Quality Improvement:* A *Resource Guide for State
Action* are new tools to meet the challenge of improving the quality of
care in America.

**The *National Healthcare Quality Report* & *National Healthcare*
*Disparities Report***

In 2003, AHRQ released the first ever *National Healthcare Quality
Report* and *National Healthcare Disparities Report*. These reports,
mandated by Congress, collected and analyzed national and State-level
data from a variety of reliable sources to measure the state of health
care quality and health disparities in the Nation.

The data in the NHQR and NHDR demonstrate that the gap between health
care research and practice is not just an occasional occurrence but is
pervasive throughout health care. It affects all patient groups, even
those with the most common medical conditions, and every State. The NHQR
and NHDR provide further confirmation that, while in some areas care is
improving, the health care system in America has a long way to go before
it delivers care that is consistent with accepted guidelines and does
not vary significantly by geography, race, ethnicity or socioeconomic
status.

Both reports also called for health policy leaders and health care
professionals to consider ways to improve the quality of care in the
United States and take action to deal with the persistent and costly
gaps in health care quality. Ultimately, quality improvement occurs at
the front lines of health care -- health care professionals and clients
enhancing their understanding and changing their actions to align with
what evidence has revealed as effective care. State leaders can be
catalysts for this change.

**States as Key Contributors to Quality Improvement**

A number of sources have pointed to States as key contributors to
improving the quality of care in America. In two reports, *Crossing the
Quality Chasm: A New Health Care System for the 21^st^ Century* and
*Fostering Rapid Advances in Health Care: Learning from System
Demonstrations*, the Institute of Medicine (IOM, 2001a and 2002)
outlined a variety of strategies to advance public policy around quality
improvement, including attention to care for chronic diseases. The
reports emphasized the role of States along with the Federal Government
in quality improvement. Secretary of Health and Human Services Tommy G.
Thompson has stated that State-level demonstrations are needed to test a
variety of quality improvement approaches, evaluate the effectiveness of
different models, and inform national efforts (IOM, 2003a).

There is a great deal that State leaders can do to support and encourage
quality improvement, and thereby, to improve health outcomes, reduce the
burden of disease, and increase the efficiency of the health care
system. As large health care purchasers, guardians of public health and
health care innovators, States can champion quality improvement and
institute best practices that can transform health care systems. A
number of States have already undertaken ambitious quality improvement
plans, collecting their own data, and developing and implementing
clinical guidelines to help improve quality. The scarcity of reliable
data and quality improvement tools suited to the State context have made
quality improvement in some cases a complex undertaking for pioneering
States.

**The Role of This *Resource Guide***

AHRQ has published this *Resource Guide* to assist States with quality
improvement efforts*.* As the NHQR and the IOM reports make clear,
chronic diseases present unique quality challenges but also have
potential for great improvements in care. Thus, this *Resource Guide*
focuses on diabetes, one of the conditions highlighted in the NHQR.
Using State-level data on diabetes care from the NHQR, 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. AHRQ hopes to catalyze and equip State health care
leaders---governors, State legislators, agency officials, and staff, as
well as nongovernmental leaders at the State level such as professional
associations, business groups, community organizations and others---to
take action to improve the quality of health care in America.

AHRQ, other Federal agencies, national organizations, States, and others
have developed a variety of resources that can assist State leaders in
enhancing their quality improvement efforts. These resources include
clinical research and guidelines for care, measures and data to assess
care quality and document improvements over time, and proven policy
strategies to improve health care quality. Diabetes is an especially
important target for quality improvement efforts because of the current
high cost and rate of preventable complications from diabetes, the
widely accepted guidelines for care and data measures for tracking
improvements in diabetes care, and the variety of promising quality
improvement approaches from State diabetes prevention and control
programs and other diabetes initiatives.

***Resource Guide* Overview**

This *Resource Guide* provides a wealth of information and points to
excellent resources to help States develop quality improvement
strategies. The *Resource Guide* is divided into six modules. Each deals
with a particular component of the quality improvement process. Because
officials in different parts of State government have different roles in
quality improvement, this guide is designed to meet the unique
information needs of a variety of State leaders. Knowing how it is
organized, State leaders can review and use the sections that are most
relevant and appropriate for them.

-   **[Module 1: Background -- Making the Case for Diabetes Care Quality
    Improvement]{.underline}** provides an overview of diabetes and
    quality improvement. It helps to answer the question of why States
    should care about these issues. State leaders should care because of
    the following:

    -   Increasing prevalence of diabetes and its link to obesity.

    -   Seriousness of diabetes complications and their effect on
        quality of life and productivity.

    -   High health care cost of diabetes complications.

    -   Problems with health care disparities for different groups.

    -   Proven effectiveness of interventions to prevent type 2 diabetes
        and delay complications for all types of diabetes.

    -   Potential for a significant return from investments in improving
        diabetes quality of care.

    -   Significant gaps in quality that exist for diabetes care.

    -   Opportunity for States to develop quality improvement strategies
        and document improvements in diabetes care through use of data
        from the NHQR and this guide.

-   **[Module 2: Data -- Understanding the Foundation of Quality
    Improvement]{.underline}** looks at the importance of data
    collection in assessing quality and the role of quality measurement.
    This module will assist State officials by providing:

    -   A listing and explanation of a variety of quality measures from
        the NHQR and NHDR on diabetes care.

    -   Data tables and maps that State leaders can use to assess the
        quality of care in their States

    -   Guidance on selecting reliable measures, collecting good data,
        and the inherent limitations of data sources.

    -   Estimates for all 50 States on the direct and indirect costs of
        diabetes and on the medical care costs related to diabetes for
        Medicaid.

-   **[Module 3: Information -- Interpreting State Estimates of Diabetes
    Quality]{.underline}** takes the next step in the quality
    improvement process by showing State leaders how to turn data into
    information to answer key questions that should be understood before
    action is taken. This module examines:

    -   Different benchmarks that States can use to assess their States'
        performance in providing quality diabetes care.

    -   NHQR data from different States---Georgia, Massachusetts,
        Michigan and Washington---that provide State leaders with
        concrete examples of how one can draw conclusions from the data.

    -   Various factors that affect health care outcomes and the
        delivery of quality care---including socioeconomic factors,
        biological and behavioral differences, and health system
        characteristics---and the role these factors play in assessments
        of health care quality in the States.

-   **[Module 4: Action -- Learning From Activities Currently
    Underway]{.underline}** provides State leaders with a variety of
    national, public-private, Federal, State and local resources and
    best practices in diabetes quality improvement that can inform State
    efforts. The module provides:

    -   Overviews of programs on national diabetes measures, chronic
        care improvement, and disease and self-management.

    -   Overviews of the Federal programs that partner and provide
        funding for diabetes quality improvement efforts in the States.

    -   A catalog of State diabetes quality improvement approaches in
        partnership/planning activities, program development, and
        dissemination, with examples from a variety of States.

    -   More extensive profiles of diabetes quality improvement
        approaches in California, Michigan, Missouri, and North
        Carolina.

    -   A worksheet for analyzing current diabetes quality improvement
        activity in a State.

-   **[Module 5: Improvement -- Developing a Strategy for Diabetes
    Quality Improvement]{.underline}** provides models and tools for
    State leaders to use in crafting a quality improvement strategy for
    a given State. The module examines the Plan-Do-Study-Act (PDSA)
    model, which is used frequently in quality improvement in clinical
    settings, and adapts that model to State policymaking. Some of the
    tools and issues covered in this module include:

    -   The application of the PDSA model to one State program---the
        Wisconsin Collaborative Diabetes Quality Improvement Project.

    -   A worksheet for assembling and analyzing State-specific data
        about diabetes and health care quality.

    -   A PDSA model checklist of steps for designing a State quality
        improvement strategy that fits with and builds upon current
        State activities.

    -   Discussion of the appropriate scope of State quality improvement
        efforts, either focused on diabetes alone or on diabetes in
        connection with other health care conditions.

    -   An overview of the importance of evaluation.

-   **[Module 6: The Way Forward -- Promoting Quality Improvement in the
    States]{.underline}** concludes the *Resource Guide* and examines
    the opportunities for States to contribute to improving diabetes
    care quality, including:

.

-   Providing leadership and shared vision to inspire others to become
    involved in improving health care quality.

-   Fostering partnerships and collaborations between key parties, such
    as health care professionals, providers, patients, purchasers, as
    well as elected and appointed State government leaders and State
    government experts on diabetes..

-   Fostering planning and setting goals that includes specific steps
    and deliverables so that partners move together.

-   Enhancing measurement and reporting to identify the most troublesome
    areas and prioritize resources and attention to those areas that
    most need improvement. .

-   Improving the infrastructure of health care quality through
    attention to professional education, data systems, financing and
    delivery systems, research, and patient education resources, among
    others.

-   Including evaluation and accountability to track how well or poorly
    a quality improvement intervention is working and the health care
    system is performing.

-   Creating incentives to reward the delivery of high quality care.

This *Resource Guide* is designed to demonstrate for State leaders the
need for quality improvement in diabetes. It also provides data,
information, best practices and quality improvement tools that can
assist State leaders in crafting diabetes quality improvement
strategies.

Much has already been done by States, but data from the NHQR show us
that much remains to be done to achieve quality care for all people with
diabetes. By reviewing and analyzing this *Resource* *Guide*, assessing
the local context, and designing a diabetes 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 diabetes care
provide valuable insights into what can be accomplished through
innovative, visionary efforts by State leaders.

**Introduction: How and Why To Use This *Resource Guide***

> *Three and one-half years ago while waiting in an examining room
> during a routine doctor's visit, Representative Dan Bosley of
> Massachusetts was reading a poster on the wall of the doctor's office.
> As he read the poster, a strange thing occurred. He recognized some
> remarkable similarities between the disease described in the poster
> and some symptoms he was experiencing. When his doctor came in to do
> the exam, Rep. Bosley mentioned that he had the symptoms described in
> the poster. His doctor laughed and said that those symptoms could be
> warning signs for a lot of things. Fortunately, the doctor performed a
> blood test. That is how Rep. Bosley found out he had type 2 diabetes.
> At the time of his diagnosis, Rep. Bosley's blood sugar or glucose
> level was 250, significantly above normal.*
>
> *Rep. Bosley has had to make adjustments to his life to deal with his
> diabetes. He takes medication, checks his blood glucose, and monitors
> his eating every day. He has to be cautious about taking other
> medications that may interact with either his diabetes medication or
> affect his blood glucose adversely. Having diabetes also means he has
> to be careful about cuts that do not heal and make sure that his eyes,
> feet and hemoglobin A1c levels, the average blood glucose level over
> the previous 2-3 months, are checked yearly. He also must worry about
> his blood pressure and cholesterol.*
>
> *Rep. Bosley has learned how diabetes affects his life on a daily
> basis. He states, "Although my lifestyle as a legislator makes it
> difficult at times, through changes in my daily routine, an exercise
> regimen, and a better diet, I find that I can control my blood levels
> to the point where I lead a pretty normal life."*

--- An Interview with Representative Daniel Bosley of Massachusetts

For many years, leading health care analysts and researchers have
recognized that the quality of health care delivered by the American
health care system is varied. While producing unrivaled innovation and
new medical treatments, in other ways the U.S. health care system has
difficulty routinely and consistently translating research into
practice, adhering to guidelines for proper care, and improving health
care outcomes. This is particularly true of diabetes care and care for
other chronic diseases (McGlynn, Asch, Adams, et al., 2003).

As the lead Federal agency charged with providing research on health
care quality, outcomes, and efficiency, the Agency for Healthcare
Research and Quality (AHRQ) recently released the first annual *National
Healthcare Quality Report* (NHQR) and the first annual *National
Healthcare Disparities Report* (NHDR). Commissioned by Congress, these
reports provide extensive data on the state of health care quality in
the United States. The NHQR highlighted both important gains and
continuing challenges to health care quality in America. In particular,
the NHQR found strong evidence of wide variation in the quality of care
for many diseases and conditions, including diabetes. The report makes
clear that there is a sizable gap between what experts recognize as the
central elements of quality care and the care that patients actually
receive. The NHDR also found that differences in health care quality
exist across racial, ethnic, geographic, and socioeconomic groups.

The NHQR and NHDR were not the first reports to document significant
gaps in quality in the U.S. health care system. In the groundbreaking
report, *Crossing the Quality Chasm: A New Health Care System for the
21^st^ Century*, the Institute of Medicine (IOM) issued a call to action
to every actor in health care to address this chasm. The IOM
specifically called on AHRQ to identify and foster research on the 15
most expensive medical conditions in order to focus quality improvement
efforts.

To stimulate efforts to improve the quality of care, AHRQ has published
this *Resource Guide* on diabetes quality improvement aimed at a variety
of State and local health care policymakers and leaders. State leaders
in particular can play a key role in championing and fostering health
care quality improvement. This Resource Guide also focuses on diabetes
as a natural target for quality improvement. The high cost of diabetes
complications---their long term effect on individual quality of life,
the high treatment costs, the fact that they are largely preventable,
and the possibility for a sizable return on investment---provide
inherent incentives for State leaders to assess the diabetes care in
their State and identify opportunities for quality improvement.

**Purpose of the *Resource Guide***

The purpose of this Resource Guide is to:

-   Provide an overview of the factors that affect the quality of care
    for diabetes.

-   Present the core elements of health care quality improvement.

-   Provide data from the NHQR on diabetes to inform State
    decisionmaking.

-   Offer a variety of best practices and policy approaches to diabetes
    quality improvement

-   Assist policymakers and others in planning State-level quality
    improvement initiatives.

State leaders may lack State-specific data and research evidence that
can be easily synthesized and presented appropriately to inform
decisionmaking. The NHQR, the NHDR and AHRQ are rich resources for
providing both national and State data on health care quality. Using
data collected from the NHQR and the NHDR, this *Resource Guide* will
help State leaders understand the issues surrounding diabetes quality
improvement, evaluate the quality of diabetes care, and construct
quality improvement plans that are suited to each State's context. AHRQ
has developed this guide to provide States with the resource
information, framework, and guidance to help them understand the issues
involved in implementing a quality improvement program.

**Audiences for the *Resource Guide***

The delivery of high quality care happens in the clinical setting. Thus,
quality improvement efforts ultimately need to affect what happens in a
doctor's office, hospital, or other health care setting. Even so, State
leaders and policymakers can have an enormous impact on health care.
They can create 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 grabs the attention
of health care providers at the local level, just as the NHQR does at
the national and State level. As purchasers and regulators, States can
supply incentives for providers to make the changes necessary to improve
the quality of health care. Through State leadership, health care
improvement strategies can be fashioned more meaningfully for State and
local health care markets.

Thus, the audiences for this *Resource Guide* include:

-   **State elected leaders**---Governors, legislators and their staff
    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, Diabetes Prevention and
    Control Program (DPCP), and State Medicaid officials.

-   **Other nongovernmental State and local health care leaders**---
    Members of professional societies, provider associations, quality
    improvement organizations, voluntary health organizations, health
    plans, business coalitions, community organizations, consumer
    groups, and others who want to stimulate action on health care
    quality improvement at the State level.

**Structure and Organization of the *Resource* *Guide***

Figure I.1 provides a macro-level view of the major components that
State policymakers need to effect quality improvement (McNeill and
Kelley, 2004). The model begins with gathering data and moves through
generating information from those data for specific audiences, then into
appropriate action to effect change, and finally to the intended
outcome---improvement. This conceptual framework shows the links or
stages in the quality improvement process that health system
professionals must navigate to accomplish real change. The *Resource
Guide* is divided into separate modules that tackle each of these stages
in the quality improvement continuum. Each module provides an
explanation of the stages as well as tools that State leaders can use to
move to the next stage in the quality improvement process.

To assist State leaders with finding the information they need in this
guide, the beginning of each module has an outline of the contents and
key ideas. Each module ends with a summary and synthesis to demonstrate
how to use the module to move to the next step. Also, a resource list
for further reading and a discussion of associated appendixes are
included where applicable. []{.mark}

[]{.mark}

**Source**: McNeill and Kelley, 2004.

State leaders in different parts of State government have different
roles in quality improvement. This *Resource Guide* attempts to reach a
variety of State leaders who have key and different contributions to
make to the quality improvement process. Once users know *how* the guide
is organized, they can skip to the sections that are most relevant and
appropriate for them. For instance, the first module provides an
overview of the issues and is designed specifically for senior level
elected and appointed officials. Subsequent modules, by contrast,
provide more in-depth information for specialists and technical staff
such as Diabetes Prevention and Control Program staff, legislative and
policy analysts, quality improvement specialists, and health data
officials.

The modules are organized as follows:

-   **[Module 1: Background]{.underline}** helps to answer the following
    questions: What is diabetes? What is quality improvement? Why should
    States care about these issues? How can States be involved in
    diabetes quality improvement?

-   **[Module 2: Data]{.underline}** looks at the importance of data
    collection in assessing quality and the role of quality measurement
    and examines a variety of data sources on diabetes care quality that
    State leaders can use to assess the quality of care in their States.
    It specifically provides process and outcome measures and estimates
    from the NHQR and NHDR on diabetes care. Module 2 also provides
    guidance on selecting reliable measures and collecting good data and
    discusses the inherent limitations of particular data sources.
    Finally, the module presents data estimates on the direct and
    indirect costs of diabetes, including the cost to Medicaid, for each
    State.

-   **[Module 3: Information]{.underline}** takes the next step in the
    quality improvement chain by showing State leaders how to turn data
    into information to answer key questions that should be understood
    before action is taken. This module examines the different
    benchmarks that States can use to assess their States' performance
    in providing quality diabetes care. An analysis using NHQR data from
    four States---Georgia, Massachusetts, Michigan, and
    Washington---provides State leaders with concrete examples of how
    one can draw conclusions from the data that can motivate local
    action. The module also analyzes various factors that affect health
    care outcomes and the delivery of quality care, including
    socioeconomic factors, biological and behavioral differences, and
    health system characteristics, and the role these factors play in
    assessments of health care quality in the States.

-   **[Module 4: Action]{.underline}** provides State leaders with a
    variety of tools and examples from diabetes care quality initiatives
    that can inform State efforts. The module will provide an overview
    of a variety of national, public-private, Federal, State, and local
    diabetes quality improvement initiatives. Analyzing State action on
    diabetes quality improvement, the module provides a catalog of State
    approaches with brief examples from a variety of States, followed by
    profiles of diabetes quality improvement approaches in California,
    Michigan, Missouri, and North Carolina.

-   **[Module 5: Improvement]{.underline}** provides models, tools and
    checklists for State leaders to use in crafting a quality
    improvement strategy for a given State. The module examines the
    Plan-Do-Study-Act model, which is used frequently in quality
    improvement in clinical settings, and adapts that model to State
    policymaking.

-   [**Module 6: The Way Forward**]{.underline} concludes the *Resource
    Guide* and examines the opportunities for States to contribute to
    improving diabetes care quality.

> []{.mark}

In general, as State leaders begin the process of quality improvement,
they must make several key decisions.  This *Resource Guide* provides
guidance related to each of the following decision points:

1.  **Make quality improvement a priority.** [Module 1:
    Background]{.underline} provides evidence to use in making the case
    for diabetes care quality improvement.

2.  **Decide on a general topic areas for analysis.** This is discussed
    in [Module 2: Data]{.underline}.

3.  **Identify measures that address the topic.** [Module 2:
    Data]{.underline} describes the NHQR measures that address diabetes
    care quality.

4.  **Develop an inventory of data sources for the State or locality.**
    This is pointed out in [Module 2: Data]{.underline}.

5.  **Determine benchmarks for the measures selected.** [Module 3:
    Information]{.underline} explains and identifies benchmarks from the
    NHQR.

6.  **Conduct or commission analyses to create information that
    addresses the questions raised. ** [Module 3:
    Information]{.underline} discusses creation of information from
    data.

7.  **Utilize an existing---or develop a new---advisory group,
    committee, or workgroup focused on quality improvement. ** This is
    reviewed in [Module 4: Action]{.underline}.  An advisory group with
    internal and external members can help refine the topic, design the
    program, identify data and information needs, recommend action, and
    champion the cause.

8.  **Find resources to develop and support the initiative.** Ideas for
    how to find financial support for diabetes quality improvement are
    discussed in [Module 4: Action]{.underline}. Sources for information
    resources are noted throughout the guide.

9.  **Design and take action aimed to improve quality. ** [Module 4:
    Action]{.underline} recounts a wide array of activities that have
    been undertaken by State governments in the area of diabetes care
    quality.

10. **Evaluate the result.** [Module 5: Improvement]{.underline}
    discusses evaluation activities needed to assess the successes and
    challenges of quality improvement efforts.

[Module 6: The Way Forward]{.underline} concludes this *Resource Guide*
by summarizing the key elements necessary in State efforts to promote
diabetes care quality improvement.

**Module 1: Background -- Making the Case for Diabetes Care Quality
Improvement**

[]{.mark}

> *About three years ago, New Hampshire State Representative Fran
> Wendelboe discovered that she had pre-diabetes. She tried controlling
> her diet, losing weight and monitoring her blood glucose on her own,
> but her hectic schedule as an elected official and times of stress
> made this difficult. One morning she experienced trouble seeing and
> knew that she needed to see her doctor.*
>
> *"It was time for me to stop avoiding an official diagnosis and get
> serious, actually past time," stated Representative Wendelboe. "I am
> now on medication twice a day, but I am still struggling with my crazy
> schedule and regular meal times. This is not simple, even knowing that
> the stakes are high."*

-   An Interview with Representative Fran Wendelboe of New Hampshire

[]{.mark}

[]{.mark}

**The Importance of Diabetes**

Diabetes is a serious chronic illness that affects a growing number of
people in the United States every year. More than 18 million people have
diabetes. One of the Nation's leading killers, diabetes is a costly,
chronic disease that, if not diagnosed and treated properly, over the
course of time can lead to serious complications such as heart disease,
stroke, blindness, lower-limb amputation, kidney failure, disability,
and premature death.

For many patients, it is years before they notice the warning signs of
diabetes and are diagnosed. Still others who are diagnosed lack adequate
treatment and do not know how to manage their disease well over time.
Furthermore, the separate care environments that people with diabetes
must navigate due to the nature of their disease -- eye, foot, heart,
and various internal medicine specialists, just to name a few -- mean
that it is difficult for them to consistently receive the most effective
care over time.

**Why Should State Leaders Prioritize Diabetes?**

As protectors of the public's health, State governments play a vital
role in preventing and controlling this disease. Every State has public
resources invested in a Diabetes Prevention and Control Program that is
working to improve care for diabetes, although the level of investment
varies from State to State. As health care purchasers, States are
responsible for ensuring that the health care they pay for on behalf of
State employees, Medicaid clients, and other recipients meets
appropriate standards of quality.

State leaders are called to pay attention to many important issues
during the course of their work. Making critical determinations of the
relative resources and attention that each issue should receive is
vitally important for State leaders. There are a number of reasons why
States may want to take a closer look at diabetes, including:

-   The rising prevalence of the disease (graphically represented in
    Figure 1.1), including increases among children and adolescents,
    driven by an aging and increasingly obese population.

-   The long-term complications that can be prevented if diabetes is
    diagnosed early and treated appropriately over time.

-   The high health care cost of diabetes, primarily its complications
    and the loss of economic productivity when disability or premature
    death occurs.

-   The disparities between various racial and ethnic groups in quality
    of diabetes care.

-   Interventions and treatment that can prevent type 2 diabetes and
    control the development of complications for type 1 and type 2
    diabetes.

-   The potential for return on investment for purchasers and the health
    care system as a whole through diabetes quality improvement.

***Rising Prevalence***

[]{.mark}

According to the Centers for Disease Control and Prevention (CDC),
diabetes currently affects over 18 million people, or 6.3 percent of the
total population (CDC, 2003c). Of those estimated to have the disease,
more than 5 million people do not know they have it (CDC, 2003c).
Another 41 million people are estimated to have prediabetes, a term used
to describe the condition of having an increased risk of developing type
2 diabetes (CDC, 2003b).

Trend data indicate that diabetes is rising at a rate faster than
population growth would alone indicate (CDC, 2003a; Mokdad, Ford,
Bowman, et al., 2000). The development of diabetes has been strongly
linked with obesity, aging, and the increasing racial and ethnic
diversification of the population (Ford, Williamson, Liu, 1997; Resnick,
Valsania, Halter, et al., 2000). Diabetes affects older persons more
frequently than younger populations. Of those over 65 years of age, 16
percent have diabetes, whereas diabetes affects 2 percent of people
between 20 and 44 years of age (Freid, Prager, MacKay, Zia, 2003). The
prevalence of diabetes is also higher among certain racial and ethnic
groups, including blacks and Hispanics (AHRQ, 2003b). Without
intervention now to prevent and control the onset of diabetes, rates
could increase significantly as the large number of baby boomers move
into retirement and live longer.

In addition to the aging of the population, the dramatic rise of obesity
in the U.S. population is also increasing the incidence of diabetes,
especially among children (Mokdad, Ford, Bowman, et al., 2003). Since
1991, obesity rates have grown by 74 percent and diabetes rates have
grown by 61 percent (CDC, 2003). Type 2 diabetes used to be called adult
onset diabetes because it almost never occurred in children and young
people. As childhood obesity has increased, the incidence of type 2
diabetes in children and young people has increased as well. A CDC study
estimates that as many as one in every three children born in 2000 will
develop diabetes, if serious changes do not occur in diet, weight and
exercise in the American population (Narayan, Boyle, Thompson, et al.,
2003). The earlier that diabetes develops the more likely that a patient
will develop complications and die prematurely.

> **Figure 1.1**
>
> ![](media/image2.wmf){width="6.1375in" height="4.2034722222222225in"}

***Long-Term Complications***

Diabetes ranks as the Nation's sixth leading cause of death, at a cost
of 200,000 lives a year (CDC, 2004). Experts believe that this death
rate is underreported because of the number of significant comorbidities
associated with diabetes, such as heart disease, stroke, and kidney
disease that may be coded as the cause of death instead of the diabetes
(CDC, 2003c).

The presence of too much glucose in the blood causes damage to blood
vessels and, subsequently, to nerves, organs, and tissues; over time
this results in various long-term complications, including:

-   **Heart disease, hypertension, heart attacks, and stroke** -- People
    with diabetes are at increased risk for high blood pressure and
    harmful levels of cholesterol. As a result, they also face increased
    risk of having a heart attack or stroke. Adults with diabetes have
    death rates from heart disease that are 2 to 4 times greater than
    those without diabetes (CDC, 2003c). A person with diabetes has the
    same high risk for a heart attack as a person who has had a previous
    heart attack (Haffner, Lehto, Ronnemaa, et al., 1998).

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-   **Nerve damage** -- Nerve damage can lead to loss of feeling in the
    feet and legs, stomach and digestion problems, sexual dysfunction,
    carpal tunnel syndrome, and other nerve problems. As many as 70
    percent of people with diabetes have some form of nerve damage (CDC,
    2003c).

-   **Ulcers and lower limb amputation** -- Nerve damage and circulation
    problems in the feet and legs can contribute to sores and ulcers
    developing in these areas. Diabetic wounds often have trouble
    healing. Uncontrolled infections in the lower limbs can result in
    the need to amputate toes, a foot, or a leg. More than 60 percent of
    the amputations unrelated to trauma occur in people with diabetes,
    making it the leading cause of nontraumatic amputation (CDC, 2003c).

-   **Eye problems and blindness --** The small blood vessels in the eye
    can become damaged, leading to blurred vision, increased risk for
    glaucoma and cataracts, damage to the retina and blindness. Diabetes
    is the leading cause of new cases of blindness among adults between
    20 and 74 years of age (CDC, 2003c).

-   **Kidney disease and kidney failure --** Damage to the fine blood
    vessels that are responsible for filtering wastes from the body can
    harm the kidneys. If enough damage occurs, the kidneys fail. This
    failure, called end stage renal disease (ESRD), means that
    individuals must undergo dialysis or a kidney transplant to survive.
    Diabetes is responsible for 44 percent of new cases of ESRD, making
    it the leading cause of this disease (CDC, 2003c).

-   **High and low blood glucose levels** -- Glucose levels in the blood
    that are too high or too low can cause people with diabetes to
    experience a number of sudden problems, including shakiness, blurred
    vision, nausea, and vomiting. In serious cases, these imbalances can
    result in coma and death.

-   **Other complications** -- Diabetes also increases the incidence of
    dental disease and skin problems, increases the risk of infection,
    and poses an increased risk for birth defects if pregnant (CDC,
    2003c; CDC, 2004).

None of the complications listed above is an inevitable outcome of
having diabetes. With quality care and proper self-management,
individuals with diabetes can prevent or delay the onset of these
complications (CDC, 2004).

***High Cost of Diabetes***

In 2002, diabetes cost the United States \$132 billion. Of this, \$92
billion was spent directly on medical care, while \$40 billion was the
indirect cost associated with disability, diminished productivity and
premature mortality. Almost 20 percent of health care spending goes to
treat people with diabetes (Hogan, Dall, Nikolov, 2003).

Diabetes is the sixth most expensive condition nationally (Cohen and
Krauss, 2003). On average, medical expenditure for a person with
diabetes in 2002 cost more than \$13,000 per year versus just \$2,500
for the average person without diabetes (Hogan, Dall, Nikolov, 2003).
About half of the lifetime health care costs for patients with diabetes
are related to potentially preventable complications (Herman and
Eastman, 1998).

Low-income populations for which States provide health care assistance
are very vulnerable to the complications of diabetes. Medicaid pays 10.3
percent of the costs for treating diabetes, compared with 6.4 percent
for heart disease and 4.6 percent for cancer, the two most expensive
medical conditions (Cohen and Krauss, 2003). To control Medicaid
spending, States have a financial stake in encouraging providers to give
high quality care to Medicaid recipients with diabetes (Faulkner, 2003).
Recognizing this reality, more than 20 State Medicaid programs are using
disease management as a means to control costs while improving quality
(Brown and Matthews, 2003). [Module 2: Data]{.underline} presents two
data tables with estimates of the total costs of diabetes for all 50
States and also costs just for Medicaid populations in all 50 States.
These estimates are derived from the size of the population and
estimates of diabetes prevalence and costs per person with diabetes
based on judgments from published research.

In addition to Medicaid, private health plans and employers across the
Nation are increasingly looking to wellness programs, disease
management, and case management for diabetes as strategies to control
health care costs. State governments, too, have struggled with rising
health insurance costs for State employees. States, as employers, have
financial incentives to help employees, dependents, and retirees also
avoid the consequences of complications of diabetes. Moreover, for
people with diabetes who are uninsured or who lack drug coverage, the
costs of treating this disease can be a crushing financial burden. As a
result, patients may forgo needed medications or other care, thus
increasing their chances for costly complications later (IOM, 2001c).

[]{.mark}

***Disparities in Health Care***

Significant differences exist between racial, ethnic, and socioeconomic
groups in health outcomes for diabetes (AHRQ, 2003b; IOM, 2003b). For
instance, the NHDR found that blacks, American Indians, and Hispanics
have higher death rates for diabetes than whites. Poor glycemic (or
blood sugar) control, serious complications from diabetes, and
hospitalization for complications were also more common in blacks than
other racial and ethnic groups. People with diabetes who had lower
socioeconomic status were also less likely to receive recommended care,
such as eye exams, and were more likely to be hospitalized for diabetes
complications (AHRQ, 2003b). Such disparities may be due to barriers to
health care access, generally. Overcoming these barriers, such as lack
of insurance coverage or ineligibility for public health programs, is a
substantial challenge for many individuals with diabetes.

States and the Federal Government have actively sought to address health
care disparities as an issue of equity in the health care system.
Disparities also raise questions regarding the effective use of
resources. Care for low-income individuals who are hospitalized due to
diabetes complications is often financed by public sources such as
Medicaid and uncompensated care funds. Ensuring effective care can help
people with diabetes to remain healthy and productive, prevent
complications, and reduce health care costs.

***Effectiveness of Interventions***

Diabetes has tremendous impact on both public and private health care
spending and on the quality of life for those diagnosed with the
disease. Yet type 2 diabetes, the most common form of diabetes, can be
prevented and controlled. It is not inevitable that more Americans
develop diabetes as they age, nor is it inevitable that people with
diabetes experience the long-term complications such as lower limb
amputations, kidney failure, and premature death.

Research indicates that diabetes prevention works. Weight control and
regular exercise can prevent or delay the onset of type 2 diabetes. The
Diabetes Prevention Program was a randomized clinical trial comparing
diet, exercise and treatment with metformin, a drug used to control
blood glucose levels, in 3,234 patients (Knowler, Barrett, Connor, et
al., 2002; Diabetes Prevention Program Group, 2003). Conducted by the
National Institute of Diabetes and Digestive and Kidney Diseases, the
trial demonstrated that changes to diet and a moderate increase in
physical activity reduced the development of diabetes by 58 percent over
3 years; diet and exercise were more effective than drug treatment in
reducing diabetes (Figure 1.2). Similar studies performed in China and
Finland have also demonstrated substantial reductions in the development
of type 2 diabetes through improved diet and exercise among participants
at risk for the disease (Pan, Li, Hu, 1997; Tuomilehto, Lindström,
Eriksson, et al., 2001).

Other studies have shown that proper health care and patient empowerment
can help control and minimize the complications of diabetes for those
who already have the disease. The Diabetes Control and Complications
Trial (DCCT) Research Group studied individuals with type 1 diabetes and
found that intensive treatment for diabetes reduced eye disease by 76
percent, nerve disease by 60 percent, and two forms of kidney problems
by 39 and 54 percent (DCCT, 1993). Another large, longitudinal study
performed in the United Kingdom found that aggressive treatment to lower
blood glucose in patients with type 2 diabetes resulted in the reduction
of eye disease and kidney disease by 25 percent. The same study showed
that reductions in HbA1c levels was associated with a 35 percent
reduction in damage to eyes, kidneys, and nerves and a 25 percent
reduction in the risk of premature death from diabetes (UK Prospective
Diabetes Study Group, 1998).

Patient self-management is particularly important for managing diabetes
and preventing complications. Studies have demonstrated that patient
self-management programs are effective tools for improving patient
outcomes. One Stanford University study funded by AHRQ found that over a
2-year period participants in a chronic disease self-management program
showed reductions in health distress, made fewer visits to the doctor's
office and emergency room, had not experienced any further increases in
disability and had increased self-efficacy (Lorig, Ritter, Stewart, et
al., 2001). Systematic reviews of the literature on self-management
programs for diabetes found positive effects on patients' knowledge,
self-monitoring of blood glucose, diet, and glycemic control (Norris,
Nichols, Caspersen, et al., 2002; Norris, Engelgau, Narayan, 2001).

**Figure 1.2. Results of the Diabetes Prevention Program Study**

![](media/image3.wmf){width="5.125in" height="2.9375in"}

State Diabetes Prevention and Control Programs, funded partially by CDC,
have been associated with noticeable improvements in diabetes prevention
and treatment; State DPCPs raise awareness of diabetes, primary and
secondary prevention, and quality improvement. North Carolina's Project
DIRECT in its first year of operation helped increase diabetes patient
counseling for foot care from 20 to 50 percent. Medical chart reviews
showed improvement in monitoring of blood glucose, recommended
screenings, and diabetes education. In New York State, work with
community and university partners helped to reduce hospitalization rates
for diabetes by 35 percent and lower-extremity amputation by 39 percent
(CDC, 2003d). From 1996 to 2001, Michigan's diabetes program increased
significantly the number of recommended tests and screenings that people
diagnosed with diabetes received. Hemoglobin A1c (HbA1c) tests increased
from 14 to 78 percent, and foot exams increased from 58 to 77 percent.
In addition, patients reported improved exercise and dietary planning
(CDC, 2003e).

Ample research and experience from State DPCPs demonstrate that there
are successful tools for delaying and potentially preventing the
development of type 2 diabetes, managing both type 1 and type 2 diabetes
effectively and preventing the long-term complications that are
responsible for high treatment costs and diminished quality of life for
people with diabetes.

***Potential for Return on Investment***

Because diabetes can result in expensive long-term complications, public
health experts argue that investing in diabetes prevention and control
initiatives today can improve health outcomes and reduce health care
costs. Although the business case for diabetes prevention and quality
improvement is still being developed, a number of studies and the
experience of both public and private payers show promising signs
regarding the return on investment.

A comprehensive economic analysis of the literature on 17 common
diabetes interventions sought to answer whether research has determined
if diabetes prevention and treatment is cost effective for society. The
study ranked diabetes interventions based on whether the interventions
were clearly cost saving, clearly cost effective, possibly cost
effective, not cost effective or unclear. The study determined a number
of areas in which the benefits of diabetes prevention and treatment
provide a clear return on investment, including eye screening and
treatment, prenatal care, kidney disease prevention, and improved
control of blood glucose. The study found no diabetes treatments with
costs that outweighed the benefits (Klonoff and Schwartz, 2000).

Other convincing evidence that quality improvement for diabetes pays off
comes from studies of more intensive and comprehensive treatment. Two
studies analyzed the treatment costs of more intensive versus
conventional care for diabetes, one for type 1 and the other for type 2.
Both studies were based on the Diabetes Control and Complications Trial,
a randomly controlled clinical trial of intensive therapy for type 1
diabetes, compared to traditional, less frequent treatment and contacts.
The trial found that intensive therapy averted complications of the
disease (DCCT Research Group, 1990). The two derivative studies
simulated the lifetime costs of diabetes---one for type 1 (DCCT Research
Group, 1996) and the other for type 2 (Herman and Eastman, 1998). The
researchers reached similar conclusions. Even at two to three times the
expense of conventional therapy, the lifetime costs of improved care
were offset by the lifetime costs of blindness, end-stage renal disease,
and lower extremity amputations.

A study of comprehensive care for diabetes in a managed care environment
demonstrated cost savings in as little as a 3-year period (Sidorov,
Shull, Tomcavage, et al., 2002). The program, designed for six chronic
diseases, found per member per month paid claims averaged \$394.62 per
enrollee with diabetes in the comprehensive care program compared to
\$502.48 per enrollee with diabetes not in the program. That was a total
saving for the health plan of \$4.3 million in paid claims annually for
diabetes care, which compared very favorably with an estimated \$1.81
million cost (including capital expenses) of the disease management
program attributed to diabetes care. These cost reductions were
accompanied by a higher proportion of diabetes patients receiving
recommended tests and monitoring.

Another analysis of the business case for diabetes disease management
conducted by Harvard University for the Commonwealth Fund found that the
two health plans studied were able to cover the costs of their
investment in diabetes disease management programs, but did not save a
significant amount of money. However, each patient enrolled in the
program for 10 years would gain significantly in quality-adjusted life
years (Beaulieu, Cutler, Ho, et al., 2003).  The results of this study
led the authors to conclude:

> ...The magnitude of the difference between costs and patient benefits
> is so great that we believe, at the societal level, the outcomes of
> these comprehensive \[diabetes disease management\] programs will
> always be worth the investment needed (Beaulieu, Cutler, Ho, et al.,
> 2003).

America's Health Insurance Plans, a national trade association,
evaluated eight health plan programs in an analysis of cost savings from
disease management. This analysis found that diabetes disease management
programs reduced hospital inpatient costs, number of days in the
hospital, as well as per member costs and total costs. Disease
management of multiple chronic conditions, including diabetes, also
showed evidence of significant returns. One plan with Medicare,
Medicaid, and commercial enrollees found that it saved \$2.94 for every
dollar invested in disease management for multiple chronic conditions
(AAHP/HIAA, 2003).

From 1999 to 2001, the Washington State Diabetes Collaboratives helped
reduce blood glucose for patients in participating health centers by 10
percent on average; and for patients with poor blood glucose control, it
was reduced from 24 percent to 17 percent, a 7-percentage-point
reduction. The estimated annual cost savings from this improvement is
roughly \$419,000 a year (CDC, 2003a). Other studies have demonstrated
that reducing HbA1c levels from 10 to 9 percent in people with diabetes
can result in savings of more than \$1,200 per patient. The savings can
be as much as \$4,000 in patients with a combination of diabetes, heart
disease, and hypertension, which are common comorbidities of diabetes
(White, 2002).

Other evidence from State disease management programs indicates that
States expect quality improvement for diabetes to help them reduce
health care costs. Washington State hopes to save \$900,000 through its
Medicaid diabetes disease management program. Oregon expects to save
\$1.5 million from its Medicaid disease management that targets
diabetes, asthma, and congestive heart failure (Brown and Matthews,
2003).

A growing body of research indicates that payers, patients, and society
can see a long-term return on investment in diabetes quality
improvement. Yet, more research needs to be conducted on the types of
interventions and resource investments that may yield savings and under
what circumstances. Most studies look at the cost effectiveness of one
treatment or another but do not consider the cost effectiveness of all
interventions together such as the DCCT study did. The challenge of
documenting cost savings from diabetes interventions is that there are
so many potential health problems to address for people with diabetes
and so many combinations of interventions to assess. Tracking and data
management are difficult to do. Cost savings are difficult to calculate
accurately because of measuring savings for people who are unaware that
they have diabetes and for those diagnosed who are *not* using health
care services and are *not* managing their disease. Most importantly,
the available evidence points to the fact that the largest savings from
diabetes interventions can occur many years into the future---a
difficult investment horizon for businesses and legislative budget
analysts who may be looking for short-term savings. While more research
needs to be done, there is reasonable evidence that diabetes
interventions can yield cost savings and little doubt that available
interventions can improve the quality of diabetes care and health
outcomes over the long term.

**The NHQR and NHDR as Resources for State Leaders**

The NHQR and NHDR serve as a snapshot of national health care quality by
providing a means to assess where the health care system is doing well
and where there are areas for improvement. These first reports offer
baseline estimates using current data, and subsequent reports will
compare future years of data against these baselines to assess whether
the United States is improving the quality of health care.

For State leaders, it is important to understand several key findings
from the NHQR. First, on many measures, there is a large gap between
what is recommended care for patients and what the patient often
receives. Further, there is considerable variation in the care that
individuals with the same condition receive from State to State and, for
some measures, region to region. The NHDR also found that there is
considerable variation in care among population groups and socioeconomic
characteristics, such as age, race, ethnicity, education, and income
level.

**Gaps Between Recommended Care and the Care Received**

Clinical guidelines for diabetes care recommend that people with
diabetes receive several important tests and a vaccination for influenza
annually in order to prevent future complications (American Diabetes
Association \[ADA\], 2004a). There is large variation in how often
people with diabetes receive recommended tests and influenza
vaccination. The NHQR reports that:

-   According to AHRQ's Medical Expenditure Panel Survey (MEPS), a
    national data source, the vast majority of patients with
    diabetes---89 percent nationally---receive an HbA1c test within the
    year.

-   According to State data from the CDC's Behavioral Risk Factor
    Surveillance System (BRFSS), nearly half of all people with diabetes
    do not receive a vaccination for influenza annually as recommended
    by diabetes care guidelines. Furthermore, the vaccination rates
    across the States vary tremendously---from 17 percent to 64 percent.

-   According to the same source, nearly one-third of diabetes patients
    do not have a retinal or foot exam annually. Across States, the
    rates range from 50 percent to 83 percent for retinal exams and 50
    percent to 87 percent for foot exams.

-   According to the CDC's National Health and Nutrition Examination
    Survey (NHANES), only 37 percent of adults diagnosed with diabetes
    have HbA1c levels in the optimal range. (There are no State
    estimates for this measure.) (See [Module 2: Data]{.underline} and
    Appendix C for further explanation of these data sources.)

[]{.mark}

These facts highlight where the Nation is doing well and where there is
room for better processes regarding diabetes care. The States with the
highest rates on the diabetes measures above---the best-in-class
States---also provide examples of quality performance that is
achievable.

**Variation in Care Across States**

As the list above indicates, there is considerable variation in diabetes
care from State to State. Yet, diabetes has well-developed national
guidelines for the care that people with diabetes should receive. This
variation suggests considerable room for improvement for some States in
the quality of diabetes care.

Table 1.1 summarizes State-generated estimates for four diabetes care
quality measures from the Behavioral Risk Factor Surveillance System
(BRFSS), collected by States and coordinated by the CDC.

The BRFSS reports that States have a two-fold range of 48 to 89 percent
of their residents with diabetes receiving an annual HbA1c test. A
similar spread between the States occurs for foot exams; a slightly
smaller difference occurs for eye exams. Influenza immunizations,
however, have a four-fold difference between the high and low State
rates.

Table 1.1 also gives the Healthy People 2010 (HP2010) baselines and
goals for objectives similar to the measures used in the NHQR. Comparing
the first column, State averages, with the HP2010 measures, it is
evident that States have made considerable progress from the 1998
baseline estimates for most of these measures. There is room for
improvement on some goals and considerable room for improvement compared
to the performance of the best or top-decile States.

**Variation in Care Across Population Groups**

The NHQR and NHDR also document variation in care across a number of
different population characteristics. The NHQR provides information on
variations in quality measures by:

-   Age

-   Sex

-   Educational level

-   Employment status

-   Health insurance status (public/private/uninsured)

-   Income level

-   Metropolitan/non-metropolitan location

-   Health status

**Table 1.1. Quality measures for diabetes care: All-State average,
top-decile States' average, and State range for 2001, the HP2010
baseline for 1998, and HP2010 goal for 2010**

 

+-----------------------------+-------+-------+-------+-------+------+
| Measure                     | All-  | Top-d | Range | H     | HP   |
|                             | State | ecile | of    | P2010 | 2010 |
|                             | av    | St    | State | bas   | goal |
|                             | erage | ates' | v     | eline | (2   |
|                             |       | av    | alues |       | 010) |
|                             |       | erage |       | \(1   |      |
|                             |       |       |       | 998\) |      |
+-----------------------------+-------+-------+-------+-------+------+
| Process:  percent of adults | 79.4  | 95.6  | 64    | NA    | NA   |
| with diabetes who had a     |       |       | -98.5 |       |      |
| hemoglobin A1c measurement  |       |       |       |       |      |
| at least once in past year  |       |       |       |       |      |
+-----------------------------+-------+-------+-------+-------+------+
| Process:  percent of adults | 61.1  | 83.0  | 47.6  | 59^b^ | TB   |
| with diabetes who had a     |       |       | -89.3 |       | D^c^ |
| hemoglobin A1c measurement  |       |       |       |       |      |
| at least twice in past year |       |       |       |       |      |
| ^a^                         |       |       |       |       |      |
+-----------------------------+-------+-------+-------+-------+------+
| Process:  percent of adults | 66.7  | 79.6  | 50.2  | 47    | 75   |
| with diabetes who had a     |       |       | -82.5 |       |      |
| retinal eye examination in  |       |       |       |       |      |
| past year                   |       |       |       |       |      |
+-----------------------------+-------+-------+-------+-------+------+
| Process:  percent of adults | 64.6  | 81.3  | 47.7  | 55    | 75   |
| with diabetes who had a     |       |       | -87.2 |       |      |
| foot examination in past    |       |       |       |       |      |
| year                        |       |       |       |       |      |
+-----------------------------+-------+-------+-------+-------+------+
| Process:  percent of adults | 37.4  | 59    | 16.5  |       |      |
| with diabetes who had an    |       |       | -64.4 |       |      |
| influenza immunization in   |       |       |       |       |      |
| past year                   |       |       |       |       |      |
+-----------------------------+-------+-------+-------+-------+------+
| ^a^ This measure is not a   |       |       |       |       |      |
| part of the official NHQR   |       |       |       |       |      |
| measure set.  It is the     |       |       |       |       |      |
| revised HP2010 objective    |       |       |       |       |      |
| and is commonly used among  |       |       |       |       |      |
| State DPCPs.  The official  |       |       |       |       |      |
| NHQR measure is the percent |       |       |       |       |      |
| of adults with diabetes who |       |       |       |       |      |
| had a hemoglobin A1c        |       |       |       |       |      |
| measurement at least once   |       |       |       |       |      |
| in the past year and is     |       |       |       |       |      |
| consistent with the         |       |       |       |       |      |
| measures endorsed by the    |       |       |       |       |      |
| National Diabetes Quality   |       |       |       |       |      |
| Improvement Alliance.  This |       |       |       |       |      |
| *Resource Guide* reports    |       |       |       |       |      |
| rates of HbA1c testing for  |       |       |       |       |      |
| both measures whenever      |       |       |       |       |      |
| possible.                   |       |       |       |       |      |
|                             |       |       |       |       |      |
| ^b^ The baseline estimate   |       |       |       |       |      |
| for the HP2010 HbA1c        |       |       |       |       |      |
| objective of tests two or   |       |       |       |       |      |
| more times per year is      |       |       |       |       |      |
| provided by the CDC for the |       |       |       |       |      |
| year 2000 (not for 1998).   |       |       |       |       |      |
|                             |       |       |       |       |      |
| ^c^ The goal for the HP2010 |       |       |       |       |      |
| HbA1c objective has not yet |       |       |       |       |      |
| been determined since the   |       |       |       |       |      |
| change of the measure       |       |       |       |       |      |
| specification from "at      |       |       |       |       |      |
| least once" to "at least    |       |       |       |       |      |
| two times" per year.        |       |       |       |       |      |
|                             |       |       |       |       |      |
| **Source**:  Centers for    |       |       |       |       |      |
| Disease Control and         |       |       |       |       |      |
| Prevention, Behavioral Risk |       |       |       |       |      |
| Factor Surveillance System  |       |       |       |       |      |
| and Healthy People 2010.    |       |       |       |       |      |
+-----------------------------+-------+-------+-------+-------+------+

 

[]{.mark}

The NHDR documents the variation in the quality of and access to health
care across subgroups of race, ethnicity, income, education, and place
of residence.

The data from the NHQR and NHDR, as well as findings from other
research, show that a variety of care for diabetes (AHRQ, 2003a and
2003b). African Americans, American Indians, Asian Americans,
Hispanics/Latinos, and Pacific Islanders are more likely than
non-Hispanic whites to have diabetes (CDC, 2004; AHRQ, 2003b). In
addition, across some measures for diabetes, racial and ethnic
minorities receive less recommended care than whites do and have higher
rates of hospitalization for long-term complications of diabetes (AHRQ,
2003b). However, one study demonstrated that racial and ethnic
disparities are moderated when people are involved in a regular system
of care (Karter, Ferrara, Liu, et al., 2002).

Also, people with incomes below the poverty level and those with less
education are more likely to develop diabetes and its complications.
Individuals with lower incomes and those with less than a college
education also were lower than the national average across most diabetes
quality measures (AHRQ, 2003a and 2003b). All of these findings are
important to recognize as States undertake diabetes quality improvement
initiatives, because the racial, ethnic, and socioeconomic makeup of a
given State influences the underlying factors that affect diabetes care
quality.

The variation in quality across the Nation, across States, and among
various population groups highlight opportunities for improvement.
States with below average rates on a given quality measure have clear
guidance on which areas to address related to diabetes care quality.
Also, low performers may be able to make small changes with big results.
Additionally, States that score highest on a given quality of care
measure can provide a benchmark for other States to aim for and indicate
what is possible.

**The Quality Improvement Opportunity**

In recent years, interest in addressing health care quality has
increased tremendously. The publication of the Institute of Medicine's
(IOM) reports, *To Err is Human* and *Crossing the Quality Chasm,* has
helped spur interest in medical errors, patient safety and quality
improvement. The releases of the NHQR and NHDR have also provided added
attention to health care quality as an issue for Federal and State
policymakers.

In its report, *Fostering Rapid Advances in Health Care*, the IOM
outlined a variety of strategies to advance public policy around quality
improvement, including attention to care for chronic diseases. The
report emphasized the role of States along with the Federal government
in quality improvement. Secretary of Health and Human Services Tommy G.
Thompson has stated that State and local demonstrations are needed to
test a variety of quality improvement approaches, evaluate the
effectiveness of the different models, and inform national efforts (IOM,
2003a). States already have undertaken disease management pilots and
other demonstration projects related to quality improvement using funds
from the CDC, Medicaid, and Medicare (see [Module 4: Action]{.underline}
for more information on the kinds of programs).

States are critical partners in quality improvement with strategic
implications for the future of health care. There is commitment at the
national level to quality improvement. What is needed now is action.

Both the NHQR and IOM's *Crossing the Quality Chasm* report highlight
the importance of improving care for chronic diseases. Diabetes in
particular is recognized as one chronic disease for which quality
improvement efforts could make great strides. Diabetes has widely
respected national guidelines for what constitutes quality care and
well-developed national measures of quality. Despite this fact, the gap
between evidence-based treatment and actual practice and outcomes
continues to be wide. There continues to be a large number of
complications from diabetes that research demonstrates could have been
prevented with high quality care. States can play a key role in
fostering diabetes quality improvement.

**Summary and Synthesis**

[]{.mark}

This module has provided background on diabetes as a disease and its
associated costs, complications and prevalence. This module has also
examined the evidence from both NHQR and NHDR regarding the substantial
gaps in care quality for diabetes that exist across the Nation, between
States, and across population groups.

Because of their roles as health care purchasers for Medicaid and State
employees as well as their role in protecting the public's health,
States have a vested interest in championing prevention of and quality
improvement for diabetes. Particularly in an age of rising health care
costs, States cannot afford simply to pay for business as usual in
health care. Evidence from research indicates that quality improvement
is critical to achieving better health outcomes and closing the gaps
between what we know and what we do in health care. In addition, there
is growing evidence that investments in diabetes quality improvement can
yield a significant return on investment both in terms of cost savings
and improved quality of life for people with diabetes. Fortunately,
there are both existing policy models and new resources that State
leaders can use to assess diabetes care quality in their States and
devise quality improvement plans.

With a background and understanding of the issues related to diabetes
quality improvement, the next step in the quality improvement process is
to formulate a set of questions and gather the data to answer them. The
NHQR and the NHDR are rich data resources for States to use to help
answer questions about the quality of diabetes care in and across
States. [Module 2: Data]{.underline} presents NHQR and NHDR data.
[Module 3: Information]{.underline} analyzes the data and provides
examples of how States can use the data to make comparisons and
assessments of where to focus State efforts to improve diabetes care
quality. [Module 4: Action]{.underline} presents various diabetes
quality improvement approaches that States can use as models for action.
The final modules are designed to help State leaders to devise quality
improvement strategies that are suited to local settings and
circumstances but that draw on national, Federal, and State data and
models for action.

**Resources for Further Reading**

-   *National Healthcare Quality Report* and *National Healthcare
    Disparities Report*, available at:
    http://[www.qualitytools.ahrq.gov](http://www.qualitytools.ahrq.gov/)

-   Institute of Medicine's *Crossing the Quality Chasm: A New Health
    Care System for the 21^st^ 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]{.underline}

**Associated Appendix for Use With This Module**

**Appendix A: Acronyms Used in This Resource Guide**

The acronyms employed to describe the organizations endorsing the NHQR
quality measures are described in Appendix A, along with all other
acronyms used throughout this *Resource Guide*.

**Module 2: Data --- Understanding the Foundation of Quality
Improvement**

> *"Health care is crucial to our quality of life and is one of the
> biggest, and probably the fastest growing financial burdens for
> government, business and individuals. It is complicated, and we are
> learning by experience. Good decisions will make (the State) healthier
> and the State economically competitive, poor decisions will not. We
> need reliable and current data to make good decisions."*
>
> \- Robert Huefner, Ph.D., Member, Utah Health Data Committee
>
> Testimony to the Health and Human Services Appropriations Committee,
> January 10, 2002

A key ingredient to improving health care quality is data. The term
*data* usually refers to values or estimates generated to describe a
concept and to track it over time, space, and populations. Data reveal
the extent of a problem, the subpopulations involved, and the geographic
disparities in outcomes and processes of care. Data are necessary to
make the case for diverting scarce State resources (staff or budgets) to
a quality improvement initiative.

Exploring *available* data is a productive way to begin the process of
identifying quality problems and selecting and defining an improvement
project. Furthermore, the quality improvement process is a cycle
(explained in [Module 5: Improvement]{.underline}) that rests on the
backbone of data. Data are necessary to assess the situation at a
baseline and ultimately to determine whether an intervention is
accomplishing what was intended or whether objectives and actions need
to be changed to improve quality.

The National Healthcare Quality Report, with national and sometimes
State-level data, is a valuable resource for reviewing and comparing
health care quality across the States. It is a source of accepted
measures and benchmarks for comparison. (Benchmarks are explained in
[Module 3: Information]{.underline}.)

This module discusses the basic building blocks of quality improvement
-- measurement and data. The Module describes the diabetes-related data
available in the NHQR and other relevant data sources that States can
use.

Even when data are not readily available, estimates can be generated by
assembling information from various sources. Two practical examples of
this are developed in this module for the Medicaid and State
populations. The results of research studies combined with national and
State databases are used to estimate the Medicaid spending on diabetes
care and the cost burden of diabetes to each State.

What this module does not address are the wide-ranging possibilities,
constrained only by resources, of collecting data through surveys
tailored to planned projects and aimed at measuring the scope of the
quality problem and evaluating the effectiveness of planned
interventions. Each State has a cadre of health statisticians and
analysts who should be recruited to be part of any quality improvement
project aimed at the health care system in the State.

**Quality Measurement**

**Background**

This section reviews the concept of quality measurement, available
diabetes-related measures in the NHQR, and the importance of using
multi-dimensional measure sets. All of this is from the perspective of
State quality improvement programs.

Conceptual design of quality measures is necessary before data
collection can begin. What is to be measured? How should it be measured?
How will it be analyzed?

Fortunately, finding measures of health care quality is not difficult.
Much work has been done over the past 30 years to advance the field of
quality measurement. In fact, the plethora of measures can actually
frustrate health care providers and analysts: Which should be used to
guide and evaluate a quality improvement program? What do the measures
mean? How should individual values be interpreted?

Quality measures cover a large range, from crude measures (e.g.,
unadjusted mortality rates) to more refined measures (e.g., percent of
an at-risk population achieving glycemic control as evidenced by HbA1c
levels). While a full range of measures is essential for a complete
picture of health care quality, specific process measures are needed to
move a health care team toward delivering quality care. For example, the
number of deaths at a hospital can suggest poor quality of treatment at
that hospital, but knowing the number of deaths does not tell the
hospital staff how to improve. Quality measures of processes of care
that are linked to increases or decreases in deaths or other medical
outcomes help medical staff know how to change care in order to improve
patient outcomes.

There is a distinction between *quality* *measures* and *guidelines for
quality care.* The health care quality measures used in the NHQR and
used for State, regional, or local planning for quality improvement
initiatives relate to populations. Such measures are often rates (e.g.,
percentages) which indicate the number achieving a goal (e.g., glycemic
control) relative to a population base (e.g., all people with diabetes
in the Nation).

By contrast, 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 quality care of
individual patients are used as the theoretical underpinning to develop
population-based quality measures.

Most quality improvement efforts focus on process and outcome measures
(see text box below). Process measures often reflect evidenced-based
guidelines of care for specific conditions. Outcome measures often
relate to patient health status. Ideally, improvement in a particular
process measure yields improvement in the associated outcome measure.
Structural measures of the health care infrastructure are a third type
of quality measure, less directly related to quality of care.

**Diabetes-Related Quality Measures in the NHQR**

Although many process measures exist for diabetes care, those listed
below survived an extensive consensus process developed for the NHQR and
could be estimated from national databases. (See Appendix C for more
information on national quality measurement activities and the NHQR
measure selection process). The NHQR uses five process measures and
seven outcome measures; the outcome measures are of two types---test
results and avoidable hospitalizations.

***Process Measures***

-   [HbA1c test]{.underline}**---**Percent of adults with diabetes who
    had a hemoglobin A1c measurement at least once in the past year.

-   [Lipid profile]{.underline}---Percent of patients with diabetes who
    had a lipid profile in the past 2 years.

-   [Eye exam]{.underline}---Percent of adults with diabetes who had a
    retinal eye examination in the past year.

-   [Foot exam]{.underline}---Percent of adults with diabetes who had a
    foot examination in the past year.

-   [Flu vaccination]{.underline}**---**Percent of adults with diabetes
    who had an influenza immunization in the past year.

***Outcome Measures***

-   **Test results---**The NHQR uses the three measures listed below:

    -   [HbA1c levels]{.underline}---Percent of adults with diagnosed
        diabetes with HbA1c levels \> 9.5 percent (poor control); \< 9.0
        percent (needs improvement); and \< 7.0 percent (optimal
        control)

    -   [Cholesterol levels]{.underline}--- Percent of adults with
        diagnosed diabetes with most recent LDL-C level \< 130 mg/dL
        (needs improvement); \<100 (optimal)

    -   [Blood pressure]{.underline}---Percent of adults with diagnosed
        diabetes with most recent blood pressure \<140/90 mm/Hg

```{=html}
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```
-   **Avoidable hospitalizations---**The NHQR uses the four measures
    listed below:

    -   Hospital admissions for adults with [uncomplicated, uncontrolled
        diabetes]{.underline} per 100,000 population

    -   Hospital admissions for adults with [short-term
        complications]{.underline} of diabetes per 100,000 population

    -   Hospital admissions for adults with [long-term
        complications]{.underline} of diabetes per 100,000 population

    -   Hospital admissions for [lower extremity
        amputations]{.underline} for patients of all ages with diabetes
        per 1,000 population

Ideally, improvement in a process measure will yield improvement in an
associated outcome measure. An example of this, used by the NHQR is the
diabetes process measure of an annual HbA1c test to monitor blood
glucose levels. Control of blood glucose in people with diabetes has
been connected with the delay of complications. Such complications often
result in hospitalization. Hospitalizations for uncontrolled (long-term
and short-term) complications of diabetes are outcome measures used in
the NHQR. In this case, improvement in the process of monitoring HbAlc
is expected to decrease the number of such hospitalizations, as
diagramed in Figure 2.1. Of course, the connections are never that
simple or direct. An HbA1c test does not necessarily mean that a patient
will self-manage the disease sufficiently or the clinician will provide
the appropriate intervention to lower an HbA1c level and decrease
long-term complications. Effective patient and provider education is a
crucial link.

**Figure 2.1. Relationship of a diabetes process and outcome measure**

**Sources of NHRQ Data on Diabetes Care**

This section describes actual estimates for the diabetes measures above
from the NHQR as well as other sources of data that may help States
generate estimates or analyze factors related to the quality of diabetes
care. The *quality of the data* itself 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.

The NHQR uses many different data sources (see Appendix B for a complete
list). 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.

The diabetes data in the NHQR come from five data sources:

-   Behavioral Risk Factor Surveillance System, a telephone survey
    designed by the CDC and conducted by individual States. BRFSS data
    are the only diabetes-related data reported by State in the NHQR
    (except for a special analysis using HCUP data discussed in [Module
    3: Information]{.underline}). BRFSS provides State-level estimates
    for four of the five process measures.

-   Medical Expenditure Panel Survey--Household Survey, a national
    in-person survey, conducted by AHRQ. MEPS data are used for all five
    process measures and report data by national population subgroup.

-   National Health and Nutrition Examination Survey, a physical
    examination survey conducted by clinicians who staff a
    tractor-trailer clinic that travels to sampled communities under the
    auspices of the National Center for Health Statistics (NCHS/CDC).
    NHANES is used for two laboratory value-related outcome measures
    that require clinical data from physical examinations.

-   Healthcare Cost and Utilization Project (HCUP), a census of hospital
    discharge records for States (29 in 2000) in a
    Federal-State-Industry partnership, sponsored by AHRQ. HCUP data are
    used to report on three outcome measures of avoidable
    hospitalizations.

-   National Hospital Discharge Survey (NHDS), a national sample of
    hospitals and a sample of their discharges, conducted by NCHS. NHDS
    is used for one outcome-related avoidable hospitalization.

General information on each data source and its limitations are
presented next. The most detail is presented on BRFSS because it is the
only NHQR diabetes data that reports by State. Following those
discussions, Table 2.1 presents the State-by-State rates for the four
BRFSS process measures. Appendix C includes a more in-depth discussion
of each data source and other NHQR data tables. Data tables in Appendix
C from sources other than BRFSS present national rates and data by
subgroup.

**Process Measures---BRFSS and MEPS Data**

***Behavioral Risk Factor Surveillance System***

BRFSS data used in the NHQR are from 2001; in that year, 41 States
collected data for three of the five diabetes process measures in the
NHQR. Those measures include annual HbA1c testing, foot exams, and eye
exams. All 50 States collected data on receipt of influenza vaccination
in the past year.

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 diabetes
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 diabetes data are
available at: <http://www.cdc.gov/brfss/>.

**Limitations of BRFSS data:** Every data source has limitations. They
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/2001/codebook_01.rtf>).
    For respondents with diabetes the sample is even smaller, generally
    around 200 (Mukhtar, Murphy, Mitchell, 2003; Safran, Mukhtar, Murphy
    2003). 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.

-   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 diabetes---will not be
    represented in the estimates that come from the survey (Nelson,
    Holtzman, Bolen, et al. 2001). 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 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 don't 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 about
    socially unacceptable behaviors. Furthermore, cultural and language
    barriers and limited health knowledge can affect the quality of
    self-reported data (Nelson, Holtzman, Bolen, et al. 2001). 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 State which vary considerably in the
    levels of their budgets 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 (e.g., extent of probing for answers).

**Addressing small sample size limitations:** One way to deal with small
samples is by pooling data over two or three years. In 1999, when the
CDC incorporated evaluation and program accountability requirements for
the State diabetes control programs, it provided baseline estimates of
State rates for HbA1c testing, eye exams, foot exams, and self-monitored
blood glucose by pooling the data from 1997 through 1999. A more stable
baseline facilitated comparisons among the States and enabled States to
monitor improvements (Safran, Mukhtar, Murphy, 2003). (Tables C.6
throughC.10 in Appendix C include these baseline estimates and BRFSS
trends for various years

Because the NHQR uses data from only one year, [Module 3:
Information]{.underline} takes sample size into account when
interpreting the data on diabetes quality measures from BRFSS.

Despite limitations, BRFSS diabetes data are widely used by State DPCP
coordinators. Seventy percent of State coordinators surveyed reported
that they used those data for program evaluation, publications, or
program implementations. When rating the usefulness of the questions in
the diabetes module, State coordinators rated HbAlc testing, eye exams,
foot exams, self-monitoring of blood glucose, and diabetes education as
"highly useful" (Mukhtar, Murphy, Mitchell, 2003).

**BRFSS estimates for diabetes care quality:** Table 2.1 shows estimates
for the four NHQR measures derived from BRFSS and includes estimates for
the revised HP2010 objective for HbA1c testing at least twice annually.
These estimates are reported nationally (over all 41 contributing
States) and by individual State. Each of the four measures includes the
estimate of the rate per 100 people (or percent) and the standard error
of the rate (which is affected by the sample size).

Table 2.1 also indicates statistical significance for each State
compared to the national average and the top decile of States. (The top
decile or "best in class" benchmark is explained in [Module 3:
Information]{.underline}.) Two different statistical significance tests
are represented in symbols as follows:

-   [Test of difference from the national average]{.underline}---For
    this test, the symbols + and − represent the State rate that is
    statistically above (+) or below (−) the national average. States
    with no adjacent symbol are not statistically different from the
    national average.

-   [Test of difference from the average of the best-in-class
    States]{.underline}---For tests of difference from the top-decile
    States, the symbol ‡ indicates States that are indistinguishable
    from the best-in-class States. States without the ‡ symbol are
    statistically different from the best-in-class average.

> The maps in Figure 2.2 summarize the five BRFSS measures found in
> Table 2.1 in relation to the national average rates. The hues show
> which States are statistically significantly below or above the
> average, those that are not different from the average statistically,
> and those that do not collect data.
>
> **Table 2.1. Percent of non-institutionalized adults 18 and over with
> diabetes who reported having important diabetes tests or health
> services in the past year, age adjusted, by State, 2001**

![](media/image4.wmf){width="6.035416666666666in"
height="8.708333333333334in"}

**Figure 2.2. States above, below, and at the national average for
important clinical processes for the noninstitutionalized population
with diabetes**

  ---------------------------------------------------- -----------------------------------------------------
  ![](media/image5.wmf){width="2.990972222222222in"    ![](media/image6.wmf){width="3.0743055555555556in"
  height="2.7840277777777778in"}                       height="2.75in"}

  ![](media/image7.wmf){width="2.9277777777777776in"   ![](media/image8.wmf){width="3.0118055555555556in"
  height="2.6666666666666665in"}                       height="2.69375in"}

  ![](media/image9.wmf){width="3.095833333333333in"    ![](media/image10.wmf){width="2.8118055555555554in"
  height="2.625in"}                                    height="2.386111111111111in"}
  ---------------------------------------------------- -----------------------------------------------------

***Medical Expenditure Panel Survey***

The NHQR uses data from the Medical Expenditure Panel Survey to report
national rates by national subgroup for five process measures. Four
measures are the same as those from BRFSS---HbA1c testing, eye exams,
foot exams, and influenza immunizations. The fifth measure is lipid
profile---the percentage of people with diabetes who reported receiving
a test for lipid profiles in the past 2 years.

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,
in-person interviews. The diabetes component is collected via a separate
paper and pencil questionnaire distributed to respondents who report
that they have been diagnosed with diabetes. More information about MEPS
data and methods are available at
<http://www.meps.ahrq.gov/WhatIsMEPS/Overview.HTM>.

**Differences between MEPS and BRFSS:** MEPS reports on the same process
measures as BRFSS nationally but does not produce State-level estimates.
Notable differences exist between MEPS and BRFSS national rates for
HbA1c testing and influenza immunization. The HbA1c MEPS-BRFSS
difference (90 percent versus 79 percent) is due to different survey
response options and the order of the questions. The MEPS-BRFSS
influenza immunization difference (55 percent versus 37 percent) is due
to different age-group definitions between the two surveys; the MEPS
rate is for adults age 18 and over; the BRFSS rate is for adults age 18
to 64. Since flu shots are less likely to be given to younger than to
elderly people, the BRFSS rate is lower than the MEPS rate. More
information on differences between MEPS and BRFSS is provided in
Appendix C.

[]{.mark}

**Outcome Measures---NHANES, HCUP, and NHDS Data**

***National Health and Nutrition Examination Survey***

The NHQR uses data from the National Health and Nutrition Examination
Survey for two outcome measures related to diabetes---the average blood
glucose level over the prior 2 to 3 months and blood pressure at
examination. NHANES, which uses a relatively small sample size because
of the costliness of conducting physical examinations in communities,
does not support State-level estimates. NHANES does provide estimates
for the Nation that could be used as benchmarks over time. These
benchmarks would be valuable to a State that has the same clinical
measures for some population within the State (such as health systems
with electronic medical records) or if the State establishes special
data collection through health care providers for such measures. (Note:
To be comparable to data from providers, the NHANES HbA1c and blood
pressure values would have to be recalculated to exclude people who do
not use the health care system during a year.) Additional information on
NHANES is available at:
<http://www.cdc.gov/nchs/about/major/nhanes/NHANES99_00.htm>.

***Healthcare Cost and Utilization Project***

The NHQR uses inpatient discharge data from the Healthcare Cost and
Utilization Project for national estimates of three outcome measures of
avoidable hospitalizations related to diabetes. HCUP is a public-private
partnership sponsored by AHRQ with 29 participating States that covers
about 80 percent of U.S. discharges in the United States in 2000, the
time for which data are included in the first NHQR. While national
diabetes estimates from HCUP are included in the NHQR, State-level data
are not, except for one special analysis of admissions for uncomplicated
uncontrolled diabetes (discussed in [Module 3:
Information]{.underline}). Additional information on HCUP data is
available at: [http://www.hcup-us.ahrq.gov/overview.jsp]{.underline}.

AHRQ also has developed the Quality Indicators (AHRQ QIs) for use with
HCUP and other hospital administrative data (AHRQ, 2001, 2002, 2003).
The AHRQ QIs use sophisticated clinical algorithms of inclusions and
exclusions to define patient groups at low risk of poor health outcomes
and then calculate the outcomes of these low risk groups across
different settings and populations. The algorithms have been tested,
reviewed, and hewn by clinical consensus panels under AHRQ sponsorship.
The AHRQ QIs include the Prevention Quality Indicators, which estimate
rates of avoidable admissions, including diabetes admissions, as an
indirect measure of the quality of ambulatory diabetes care in the
United States. As tools for local quality improvement, the AHRQ QIs 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 Prevention
Quality Indicators are part of the first NHQR and NHDR. Additional
information on the AHRQ QIs is available at:
<http://www.qualityindicators.ahrq.gov/>.

**Limitations of HCUP data:** The main limitation of HCUP data (or any
administrative billing data) is that the data are collected for the
purpose of payment, and what is coded as clinical diagnoses and
procedures can be affected by reimbursement incentives (Keating,
Landrum, Landon, 2003). Such incentives can encourage or discourage
coding of specific types of conditions or treatments. 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.

***National Hospital Discharge Survey***

The NHQR used the National Hospital Discharge Survey for one outcome
measure---lower extremity amputations. The NCHS 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>.

**Limitations of NHDS data:** 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 sample size
than HCUP. As a result, many subgroup estimates that can be made with
HCUP cannot be supported with NHDS data.

**Filling Local Data Gaps**

Finding data is a challenge for quality improvement programs. Two
avenues can be used to locate relevant data: 1) developing an inventory
of local data sources and 2) using published research to generate local
estimates. The latter (generating local estimates) is acceptable for
planning purposes, until better local sources are located and analyzed.
One of the best sources for filling the data gaps will be the State DPCP
staff.

***Developing an Inventory of Local Data Sources***

Local data (whether State, county, municipal, or individual health care
provider data) are essential for quality improvement programs to have an
impact locally. Local leaders and health care professionals must see
their own data in comparison to other providers and to State, regional,
and national benchmarks in order to appreciate the importance of their
work.

Health care quality improvement programs should develop a complete
inventory of data systems available at the State and local level. Doing
so may reduce data-related costs and avoid duplicate data collection.
Also, a review of local data in the context of the NHQR and NHDR should
make clear where existing local surveys or data systems could be
modified to add information comparable to the concepts used in those
reports and, thus, to provide the raw materials for insights into health
care and its quality at the local level.

Most States have data systems that can contribute to a review of health
care quality at the State or local levels. Some of those data systems
include:

-   BRFSS data, available at the State level through the State health
    department.

-   Statewide inpatient hospital discharge systems, for which HCUP and
    NHDS data can provide uniform national comparisons.

-   State vital statistics, which include mortality rates by cause of
    death and for which the National Vital Statistics System can provide
    uniform national comparisons.

-   Special disease registries, some of which are focused on diabetes.

-   Other special data collection of State departments of health
    statistics and other State programs.

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 reimbursement from Medicaid.

-   State employee health benefit claims for reimbursement.

-   Patient records from State- or county-run programs, such as mental
    health and substance abuse programs or school health programs.

Some examples of State-level data sources are available at the National
Association of Health Data Organizations' Web site
([http://www.nahdo.org/soa/soalist1.asp?Category=State%20Agency](http://www.nahdo.org/soa/soalist1.asp?Category=State Agency)).

Other Federal or national systems compile data that describe State and
local populations or health resources. These include:

-   The CDC's Division of Diabetes Translation Web site, a valuable
    starting place to identify data and become familiar with the network
    of organizations and individuals associated with diabetes data
    collection at the State and national level
    (<http://www.cdc.gov/diabetes/statistics/index.htm>).

-   Census population data by State, maintained by the U.S. Bureau of
    the Census (<http://eire.census.gov/popest/data/states.php>)

-   The Area Resource File*,* a county- and State-level database of
    health care resources from several surveys and data sources,
    compiled by the Health Resources and Services Administration (HRSA).

-   Quality of care in managed care organizations, provided through the
    National Committee for Quality Assurance (see:
    [http://www.ncqa.org/](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's 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.

***Using Published Studies and Readily Available Data To Develop State
or Local Estimates***

Before resources are invested in data collection targeted to an
improvement goal, some information can be assembled from existing
sources and published research studies. Sometimes published studies on a
topic can be used to derive estimates at the State or local level. These
"ballpark estimates" should be replaced by more accurate local data when
they are available.

To assess the impact of diabetes on the State, studies of diabetes
nationally might be used. For example, if a national study shows how
subgroups differ in diabetes prevalence or costs and provides estimates
by those general subpopulations (e.g., age groups), then those general
subpopulation characteristics in the State (or locale) can be applied to
the national rates, thus resulting in State (or local) estimates for
diabetes.

The more detailed and compatible the data are across sources, the better
the estimate will be. However, existing data details are seldom
sufficient, which limits the confidence of estimates that can be made
from existing tables and published estimates. When this is the case,
original analyses of the underlying data may be necessary. When actual
data are available from State agencies for all or part of the
information components, they are preferable to estimations from national
data.

Two examples of deriving State estimates from national data and studies
are presented here: 1) Medicaid spending on diabetes care, and 2) total
cost burden of diabetes, by State.

**Example One---Medicaid Spending on Diabetes Care:** This example
derives estimates of spending on diabetes care for State Medicaid
agencies using the following components:

-   National diabetes prevalence by age and by race/ethnicity
    separately.

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

-   National expenditures related to diabetes for a younger and older
    adult population from a published study to derive the estimates.

+-------------------------+--------------------------------------------+
| **Components**          | **Location of Data**                       |
+-------------------------+--------------------------------------------+
| Diabetes prevalence     | CDC National Diabetes Fact Sheet available |
| rates for 2002          | at:                                        |
|                         | <http:/                                    |
|                         | /www.cdc.gov/diabetes/pubs/factsheet.htm>) |
+-------------------------+--------------------------------------------+
| Medicaid populations    | CMS Web site:                              |
| for each State, by age  |                                            |
| and separately by race  | <ht                                        |
| for 1998                | tp://www.cms.gov/medicaid/msis/mstats.asp> |
+-------------------------+--------------------------------------------+
| Change in Medicaid      | CMS Web site:                              |
| enrollment between 1998 | <http://www.cms.hhs                        |
| and 2002                | .gov/medicaid/managedcare/enrolstats.asp>. |
+-------------------------+--------------------------------------------+
| Expenditures per person | American Diabetes Association funded       |
| with diabetes by age    | article:                                   |
| group for 2002          |                                            |
|                         | Hogan, Dall, and Nikolov, 2003             |
+-------------------------+--------------------------------------------+

Table 2.2 shows the *estimated* expenditures. They are ballpark
estimates of such spending likely occurring across State Medicaid
agencies. (Figure C.1 in Appendix C charts the flow of data,
assumptions, and calculations made to devise the Medicaid spending
estimates for diabetes.)

Although the Medicaid population is primarily women and children, the
diabetes population is disproportionately elderly. Data from each source
were reconfigured to reflect the same underlying population and adjusted
to reflect the same year of reference to make data compatible across
sources. Because prevalence and cost are so different by age, the
estimates were first generated separately for the adult nonelderly
population and the elderly population and then were reassembled.
Children and youth under 20 were excluded because certain pieces of
information were unavailable for them and because prevalence of diabetes
(type 1 and type 2) among them is small (0.25 percent).

Another consideration for diabetes is its higher prevalence among
certain racial and ethnic groups. Prevalence rates by race/ethnicity
were applied to those respective subgroups of Medicaid. Also, Medicaid
enrollees of unknown age or race/ethnicity were distributed in
proportion to the known age or known race/ethnicity subgroups. Finally,
data from different years were adjusted to be compatible.

The estimates in Table 2.2 have limitations. The obvious limitations in
these estimates include omission of spending for children and the
institutionalized population. First, although spending for children and
youth under age 20 is omitted, only 0.25 percent of this population has
diabetes and the effect is likely to be small. Second, the omission of
the institutionalized population is a more serious downward bias on
spending estimates, because people with advanced stages of diabetes are
more likely to be hospitalized or to reside in nursing homes and their
care is costly. Third, however, for people dually eligible for Medicaid
and Medicare (which is most of this Medicaid population over 60 years of
age), some of the expenditures for diabetes will be paid for by Medicare
and not by Medicaid, which results in higher estimates here than should
be the case. The net effect of these latter two offsetting biases cannot
be determined from these data. Fourth, the inclusion of spending for
*all* medical care for people with diabetes 20 years of age and over is
included in these estimates (rather than only the spending related to
diabetes) because medical expenditures by type and age could not be
identified readily. This overestimates expenditures related to diabetes
care. The net effect of all of these limitations is unclear. What is
clear is that a State's Medicaid data will be a more accurate source for
calculating expenses for Medicaid related to diabetes.

One should note that the estimates presented in Table 2.2 are
approximations to State Medicaid spending on diabetes. Estimates
calculated from State Medicaid information systems for diabetes
prevalence and actual Medicaid payments would be more accurate.

The estimates here can be useful for understanding the implications of
diabetes for health care costs and the possible returns from investment
in diabetes care quality. States governments (e.g., State Medicaid
Directors) may have *actual* costs of diabetes for their population. If
so, then these actual costs would be preferable to estimates based on
national averages from various data sources. Corroboration from external
sources can increase the confidence in State and local estimates based
on different methods.

**Table 2.2. Medicaid eligible population and their estimated diabetes
prevalence and expenditures for medical care, for people 20 to 60 and
over 60 years of age, 2002**

![](media/image11.wmf){width="5.826388888888889in"
height="8.499305555555555in"}

**Example Two---Estimates of the cost burden of diabetes for each
State:** This example estimates the total cost of diabetes care for each
State's total population. The total cost of diabetes care includes its
direct and indirect costs. *Direct costs* are directly associated with
treatment of the disease, including medical expenditures for routine
services, treatment of complications, and the increase in general
medical conditions attributable to diabetes. *Indirect costs* are dollar
estimates associated with decreased productivity, disability, and
premature death. At the end of this section is an exercise for
calculating a State's costs with different assumptions that might be
generated from State data.

Table 2.3 shows estimates of the cost of diabetes for each State's total
population using readily available data and following the methods of
Hogan, Dall, and Nikolov (2003). This is a more direct calculation than
the Medicaid calculation because a State's total population is more
likely to have characteristics similar to the total U.S. population than
is the Medicaid population.

Table 2.4 is a step-by-step exercise that shows how the estimates were
generated; it provides a guide to States who want to use different
assumptions. The data needed include: the size of the State population,
the prevalence of diabetes in the State, and estimates of the cost
burden. For the estimates in Table 2.3, the State populations are from
the U.S. Bureau of the Census (see:
[http://eire.census.gov/popest/data/states/tables/NST-EST2003-01.php]{.underline}).
State-level diabetes prevalence is available through the CDC at:
[http://www.cdc.gov/diabetes/statistics/prev/state/table15.htm]{.underline}.

The direct and indirect costs of medical care for individuals with and
without diabetes were estimated for the Nation by Hogan, Dall, and
Nikolov (2003). Although they used diabetes prevalence estimates from
the National Health Interview Survey (NHIS), the estimates in Table 2.3
use the CDC's BRFSS prevalence data because they were available by
State. Thus, the estimates of State-level direct and indirect costs when
summed across all States differ slightly from the Hogan and colleagues'
national estimate of cost burden.

For direct cost per person with diabetes, estimates from Hogan et al.
are used. Their total direct cost burden per person *with* diabetes in
2002 is \$13,243. The age-adjusted estimate of the direct cost of care
per person *without* diabetes is \$5,642. The \$7,601 difference is used
in Table 2.3 to net out the regular medical care costs for patients with
diabetes (that is, cost unrelated to diabetes and its sequelae). The
\$7,601 cost is then multiplied by the State diabetes prevalence to
derive the State estimate for the direct cost of care for diabetes.

For indirect cost per person with diabetes, the Hogan et al. estimate
(\$3,289 annually) is multiplied by the State diabetes prevalence to
derive the State indirect cost estimate. The total cost burden is the
sum of the direct and the indirect diabetes costs for each State.

![](media/image12.wmf){width="6.5in" height="8.973611111111111in"}

  --------------------------------------------------------- ----------------------------
  **Table 2.4: Estimating the cost burden of diabetes for a 
  State in 2002**                                           

  **Step 1:** **Total prevalence:** Find the total diabetes **1.\_\_\_\_\_\_\_\_\_\_**
  prevalence for the State in 2002, using CDC data.         

  **Step 2: Direct cost of diabetes care:** Multiply the    **2.\_\_\_\_\_\_\_\_\_\_**
  answer from step 1 by \$10,683, which is the estimated    
  excess direct medical cost per person with diabetes for   
  diabetes-related medical care. The resulting number is    
  the direct cost for all people with diabetes in the State 
  in 2002.                                                  

  **Step 3: Indirect cost of diabetes care:** Multiply the  **3.\_\_\_\_\_\_\_\_\_\_**
  answer from step 1 by \$3,289, which is the estimated     
  indirect cost per person with diabetes. The resulting     
  number is the indirect cost for all people with diabetes  
  in the State in 2002.                                     

  **Step 4:** **Total cost burden for people with           **4.\_\_\_\_\_\_\_\_\_\_**
  diabetes:** Add the answers from step 2 and step 3. The   
  result is the total cost burden of diabetes in the State. 
  --------------------------------------------------------- ----------------------------

Source for dollar multipliers: Hogan, Dall, and Nikolov (2003).

**Summary and Synthesis**

This module orients users to the importance of data as the foundation of
the quality improvement cycle. Data are essential for assessing the
situation and measuring the impact of a quality improvement project.
Without it, State leaders could spend effort and resources without
accomplishing the most important goal---improving the health outcomes of
their residents. Data, used effectively, should guide the quality
improvement process and enhance a team's effectiveness in focusing on
the right goal and making the right decisions.

Module 2 describes two components of data collection for quality
improvement: 1) measurement and 2) data sources. The *National
Healthcare Quality Report* and the *National Healthcare Disparities
Report* now provide easy access to the health care quality measures and
related data sources that are national (and sometimes State-level) in
scope. This module highlights the diabetes-related measures and data in
those reports.

Important considerations when using data include data limitations and
making certain that comparison data are truly comparable to the
State-level data. Taking an inventory of existing State and local data
sources and using existing national data and studies can help to fill in
gaps in local data, at least in the planning stages of a quality
improvement program.

Once data have been identified or collected, the next step is analyzing
and translating those data into information that can be used to make
policy-level decisions. [Module 3: Information]{.underline} interprets
the data from a State perspective and begins to explore its meaning.

**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/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.

-   **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.

-   **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 data**

> <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.

-   **DATA2010**

<http://wonder.cdc.gov/data2010/>

> This Web site includes data from a number of different State and
> national data sources and can be used to monitor the objectives for
> Healthy People 2010.

**Diabetes 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 disease registries to track the treatments
received by people with diabetes and other chronic conditions 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.chcf.org/documents/chronicdisease/ComputerizedRegistriesInChronicDisease.pdf>

> This Web site offers a primer on the use of disease registries for a
> variety of chronic conditions, including diabetes.

**Other Useful Web Sites**

  -------------------------------------- ---------------------------------------------------------------------------------
  Agency for Healthcare Research and     [http://www.ahrq.gov/](http://www.ahcpr.gov/)
  Quality:                               

  AHRQ Quality Indicators                [http://www.qualityindicators.ahrq.gov](http://www.qualityindicators.ahrq.gov/)

  National Committee on Quality          [http://www.ncqa.org/](http://www.ncqa.org/index.asp)
  Assurance:                             

  National Diabetes Quality Improvement  <http://www.nationaldiabetesalliance.org/>
  Alliance:                              

  National Quality Forum:                <http://www.qualityforum.org/>

  National Guideline Clearinghouse:      [http://www.guideline.gov/](http://www.guidelines.gov/)
  -------------------------------------- ---------------------------------------------------------------------------------

**Associated Appendixes for Use With This Module**

**Appendix A: Acronyms Used in This Resource Guide**

The acronyms employed to describe the organizations endorsing the NHQR
quality measures are described in Appendix A, along with all other
acronyms used throughout this *Resource Guide*.

**Appendix B: List of All NHQR Data Sources, Including Those Supporting
State Estimates**

Appendix B lists the 25 data sources used in the NHQR and highlights the
10 data sources that provided State-level data in the NHQR.

**Appendix C: Additional Data Resources Related to Diabetes Care
Quality**

Appendix C lists additional data resources that may be helpful in
studying diabetes care in a State. It includes separate sections, with
accompanying tables, on the NHQR measures selection process (see Table
C.1), details on data source description and limitations (Tables
C.2-C.10), and steps for estimating Medicaid spending on diabetes care
by State (Figure C.1). Details on notable differences between MEPS and
BRFSS national rates are included, as well as further information on the
data sources for the process and outcome measures discussed in this
module.

**Module 3: Information -- Interpreting State Estimates of Diabetes
Quality**

**Deriving Information From Data**

Data do not necessarily convey information. Information comes from data
that have been collected, analyzed and arranged to answer a question.
Deriving information from data usually requires original data collection
designed to answer the question. However, "secondary" use of data
collected for another purpose can often lead to powerful information,
obtained efficiently.

Both original and secondary data collection require strategies for
summarizing and interpreting the results. For example, to determine how
well the health care system has educated and motivated people with
diagnosed diabetes to control their blood glucose levels requires
original data collection of HbA1c laboratory values from clinical
records. The resulting values of HbA1c levels must be summarized (e.g.,
using overall averages), explored by relevant subgroups (e.g., managed
care versus private practice to determine how well providers in
different settings educate and motivate their patients), and interpreted
in terms of how well the assembled database answers the question and
represents the total population (e.g., data collected from clinical
records miss people without access to health care with undiagnosed
diabetes).

Secondary data assembled from various sources for the NHQR address the
overarching question of how well the U.S. health care system provides
health care for U.S. residents. Although State-specific estimates are
provided in the NHQR for many measures, they are not fully analyzed
there from a State perspective.

Steering committees for State quality improvement programs need
information to answer many questions on the State's health care quality
performance. Among them are:

-   What measures should the State use to assess health care quality?

-   What metrics and comparisons for each measure should be used to
    compare with the State?

-   What does the State's position among other States mean?

-   What goals should be set for quality improvement?

While *all* the questions that a quality improvement committee might
raise will not be answerable from data in the NHQR, it is a valuable
source for identifying readily available and consensus-based measures,
for locating national averages, for deriving other benchmarks, and for
selecting achievable targets for improvement. This module shows how to
do these things from a State viewpoint. Module 2 presented a minimum set
of measures from the NHQR that can be used for assessing diabetes
quality within the State. Module 3 uses that measure set to describe two
steps:

> Step 1: Identifying appropriate metrics and comparisons
>
> Step 2: Interpreting the State's position among other States

While the specific questions that State leaders ask about the quality of
health care in the State will determine the comparisons to be made,
below is a general guide to thinking about and using the data in the
NHQR to create information for State quality improvement programs.

**Step 1: Identifying Appropriate Metrics and Comparisons**

**Benchmark Metrics for States**

The NHQR provides a national set of estimates and some State estimates
that can be used as benchmarks for quality improvement. A benchmark is
an external marker for assessing how one entity, for example a State,
compares. The benchmark can represent the best performer or the average
performer. How the State fares depends on what the benchmark is.

Several types of metrics or benchmarks can be used for assessing a
State. From more to less stringent, they include:

-   The **theoretic limit** of aiming for 100-percent achievement (or
    0-percent occurrence for avoidable events), which is an ideal but
    often impractical goal

-   A **best-in-class estimate** of the top State or top tier of States
    (the top 10 percent of States is used in this *Resource Guide*),
    which shows what has been achieved

-   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 will be less aggressive than the best-in-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

Most of these benchmarks can be found in or derived from the NHQR. The
best-in-class estimate is not reported in the NHQR, nor is the regional
norm based on BRFSS data. Both, however, can be derived from data in the
NHQR. Detail on how the best-in-class estimate and other benchmarks are
derived is given in Appendix D.

These benchmarks for each of the diabetes care-related measures in the
NHQR are reported in Table D.1 in Appendix D. These benchmarks for four
measures---HbA1c test, eye exam, foot exam, and flu vaccinations---are
graphically displayed in Figure 3.1.

For HbA1c testing, for example, Figure 3.1 shows a range of benchmark
values. Though the theoretic limit may be difficult to achieve for many
valid reasons, the best-in-class estimate has been achieved by some
States. The national average is often used to assess a State's
performance. However, Figure 3.1 makes it clear that the national
average is not a very difficult level to achieve; about half of the
States are above and about half are below that average. The same is true
for regional estimates that take into account the practice patterns in
different regions of the country.

**Figure 3.1. Benchmarks of important tests for adults\* who have
diabetes, 2001**

![](media/image13.wmf){width="9.840277777777779in"
height="4.971527777777778in"}

For the eye and foot exam process measures in Figure 3.1, the
best-in-class average is above the national Healthy People 2010 goal,
which itself still exceeded the national average in 2001. The influenza
vaccinations for adults with diabetes have the lowest rates of these
process measures partially because adults over age 64 are excluded from
this measure (while they are included in the other three measures).
Moreover, Healthy People 2010 did not set a goal for influenza
immunization of this population.

**Understanding State Variation**

Although comparing the State's rate to a benchmark shows how far or
close the State's rate is from the benchmark, it gives few clues as to
the State's position among all other States. If a State's rate is below
the national average, is it the lowest of the low States? Or, is it
doing better than all the other States that are below the national norm?
Knowing this ranking can help a State understand how much effort might
be needed to catch up with health care quality in other States.

Average benchmark values do not reveal the degree of variation that
exists on any one measure across the Nation. Variation among States can
be seen on a scatter diagram, where each State represents one point on
the graph. Other indicators can be added to and identified on that
scatter diagram with different symbols. Figure 3.2 shows (as gray
diamonds) the distribution of State rates for important tests that
should be performed each year for people with diabetes. It superimposes
the national average as a black square and the best-in-class average as
a black triangle.

> **Figure 3.2. Percent of adults age 18 and over with diabetes who had
> an important test at least once in past year, age adjusted, all States
> and benchmarks, 2001**

![](media/image14.wmf){width="6.459027777777778in"
height="4.339583333333334in"}

Figure 3.2 reveals that immunization for influenza for people with
diabetes has the most State variation among the four measures and it has
the lowest rates --- providers in one State vaccinated only 17 percent
of adults aged 18 to 64 with diabetes. The spread among the States is
nearly fourfold, from 17 to 64 percent. Also, the other tests are
performed with wide variation --- spread from about 50 percent to 80 or
90 percent of adults with diabetes, across the States. Such variation
indicates considerable room for improvement for many of the States.

Figure 3.2 was modified to track an individual State (State A) across
all the measures of diabetes care quality. For example, in Figure 3.3,
State A is represented as a black diamond when it is statistically
different from the national average or as a black-bordered white diamond
when it is not statistically different from the national average.

**Figure 3.3. Tracking your State: Percent of adults age 18 and over
with diabetes who had an important test at least once in past year, age
adjusted, for State A, all States, and benchmarks, 2001**

![](media/image15.wmf){width="6.45in" height="5.061805555555556in"}

The solid black diamond in Figure 3.3 shows that State A's rate compared
to the national average is considered a statistically significant
difference and probably is not attributable to just random variation
that appears among the States and within each State. It may well
represent some practice difference in State A that is not common
nationally. What that difference is caused by cannot be deciphered from
these data. Local insights and exploration are needed to understand
underlying factors that might influence State A's rate of HbA1c testing.

The black-bordered white diamond in Figure 3.3 indicates that State A's
rate compared to the national average is not considered a statistically
significant difference and could as easily occur because of random
variation as because of any specific practice by health care providers
in State A. (Statistical significance and how it is determined is
explained in Appendix E.)

[]{.mark}

In general (although not shown for all States, here), when individual
States are tracked across diabetes measures, it becomes apparent that
there is uneven performance across measures. No single State
consistently ranks highest or lowest across all measures. Thus, all
States have some room for improvement compared to national benchmarks
and clinical guidelines for the treatment of people with diabetes.

**Four States Compared to Benchmarks**

To show how States may want to examine their own estimates, four States
are compared to national, regional, and best-in-class State-average
benchmarks, below. The national average and best-in-class average, when
used together, summarize key information across the States and enable
graphical comparisons to be simplified as bar charts (see Figures
3.4.A-3.4.D).

In the bar charts, statistical significance from the national average is
represented by a **bold** value at the top of the State bar. State
values that are not bold are not statistically distinguishable from the
national average. (For a discussion of how to consider statistical
significance, see the previous section on Understanding State
Variation.) Also, statistical tests have been performed to compare the
State average to the best-in-class average. These test results are
presented in Table 2.1 in [Module 2: Data]{.underline}.

The example States were chosen because each comes from a different
region of the country and, at the same time, they represent a range of
experiences --- States performing above or below the national average on
individual measures and States with longstanding and relatively new
quality improvement programs in diabetes prevention and control. By
using four States, it is easier to show the nuances of making
comparisons with limited data. The graphical and statistical analysis
below can be applied to any State collecting these measures through the
BRFSS.

Each of these States is described in terms of the four diabetes care
process measures explored generally above. Descriptions of their quality
improvement activities can be found in the [Module 4:
Action]{.underline}.

**Georgia:** Figure 3.4.A reveals two facts about diabetes care in
Georgia compared to national norms:

-   **People with diabetes in Georgia are more likely than the national
    norm to report having had two or more HbA1c test in the past year.**
    This is a statistically significant finding and suggests that
    Georgia health care professionals are aware of the importance of
    glycemic control, are testing their patients, and may be educating
    their patients about glycemic control. Whether they are successful
    in helping their patients control their blood glucose cannot be
    determined from these data. It is possible that Georgia physicians
    see a more advanced stage of diabetes among their patients and are
    therefore more concerned about their patients and are testing them
    more frequently. Special data collection would be necessary to
    evaluate the blood glucose levels of people with diabetes in the
    State and the effectiveness of the better-than-average HbA1c testing
    in Georgia.

-   **Georgia does not differ statistically from the national average on
    the three process measures that relate to eye exams, foot exams, and
    influenza vaccination for adults with diabetes**. The absence of a
    statistically significant difference has to be tempered with the
    fact that BRFSS samples for individual States are often quite small
    and probably too small to have the power to detect a difference of
    the size measured here, even if it exists. Thus, the higher rates of
    eye exams (statistically insignificant) are simply inconclusive.
    When compared to the more stringent benchmark of the best-in-class
    rates for these measures, however, Georgia is not one of the top 10
    percent of States (see Table 2.1). This is especially true for
    immunization against influenza where there is almost a
    30-percentage-point difference between Georgia's rate and the
    best-in-class average (30 percent versus 59 percent).

**Massachusetts:** Figure 3.4.B reveals the following about
Massachusetts compared to national norms:

-   **Massachusetts appears to be close to the national average on all
    of the NHQR process measures for diabetes care quality, using the
    test of statistical significance.** However, because of the small
    sample sizes of the BRFSS, consider the magnitude of the differences
    from the national average. Massachusetts' average for HbA1c testing
    two or more times per year is 8 percentage points higher than the
    average State and for flu immunizations its average is 7 points
    higher --- notable differences. In both cases, however, the amount
    of variation among the States and within Massachusetts makes these
    statements equivocal, and the higher values could as likely be due
    to chance as to better performance.

-   **Massachusetts is not among the top decile States when compared to
    best-in-class estimates.** Massachusetts' lower values are
    statistically different (see Table 2.1), which means the differences
    are unlikely to be chance occurrences compared to the top 10 percent
    of States. This suggests that Massachusetts may want to focus
    system-wide efforts on improving diabetes care quality.

**Michigan:** Figure 3.4.C shows that:

-   **Michigan is similar to the national average across all States for
    three of four process measures.** For HbA1c testing two or more
    times per year, and annual eye exams and foot exams, Michigan rates
    are not statistically different from the national average and the
    differences are within 5 percentage points.

-   **Michigan is below the best-in-class average.** Michigan is below
    the best-in-class average on all four measures and the differences
    are statistically significant (Table 2.1). For two measures in
    particular, the differences are large. Rates for HbA1c testing two
    or more times per year in Michigan are 27 percentage points lower
    than the best-in-class average and rates for influenza immunizations
    are 32 percentage points lower. This result calls for local study
    and possibly identifies an opportunity for Michigan to focus
    activities more widely on HbA1c testing and influenza vaccinations
    for people with diabetes.

**Washington State:** Figure 3.4.D shows the following for Washington:

-   **Washington State performs better than the national average on
    influenza immunizations for people with diabetes.** The Washington
    rate (49.6 percent) is 12 percentage points higher than the national
    average, and the difference is statistically significant.
    Furthermore, although Washington is not one of the top decile States
    (with values averaging 59 percent and ranging from 56 to 64 percent,
    Table 2.1), Washington did test as not statistically significant
    from the top decile, given the amount of variation among and within
    the States. Thus, Washington is doing relatively well in vaccinating
    its diabetes population. However, given that rates of immunization
    are low in all States, benefits are possible from activities aimed
    at improving immunization of people with diabetes against influenza.

-   **Washington is similar to the national average on the other three
    process measures.** Rates for the State are similar to the national
    average for HbA1c tests two or more times per year, and annual eye
    exams, and foot exams. Washington's rates are higher than, but
    within 5 percentage points, of the national average. Washington,
    however, is not among the top decile States when compared with
    best-in-class averages (see Table 2.1).

Keep in mind that data on diabetes process measures provide a partial
picture of diabetes care in each State. Outcome measures would be a
valuable addition for understanding the impact of care processes in each
State. The NHQR provided one of its diabetes outcome measures ---
avoidable admissions for uncontrolled, uncomplicated diabetes --- by 14
States including the four example States reviewed above. These data are
discussed in Step 2.

**Figure 3.4.A. Percent of adults\* with diabetes, who had an important
test at least once in past year, age adjusted, for Georgia compared to
benchmarks, 2001**

![](media/image16.wmf){width="9.840277777777779in"
height="4.971527777777778in"}

**Figure 3.4.B. Percent of adults\* with diabetes, who had an important
test at least once in past year, age adjusted, for Massachusetts
compared to benchmarks, 2001**

![](media/image17.wmf){width="9.840277777777779in"
height="4.971527777777778in"}

**Figure 3.4.C. Percent of adults\* with diabetes, who had an important
test at least once in past year, age adjusted, for Michigan compared to
benchmarks, 2001**

![](media/image18.wmf){width="9.816666666666666in" height="4.66875in"}

**Figure 3.4.D. Percent of adults\* with diabetes, who had an important
test at least once in past year, age adjusted, for Washington State
compared to benchmarks, 2001**

![](media/image19.wmf){width="9.840277777777779in"
height="4.971527777777778in"}

**Step 2: Interpreting the Data---What Does It Mean?**

The data presented in Step 1 raise a number of 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 controlled
through decisionmaking and local efforts?

**Factors That Affect the Quality of Diabetes Care**

A number of factors affect the quality and outcomes of health care, as
Figure 3.5 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 unchangeable 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 or ineffectiveness).

> **Figure 3.5. Factors That Affect Diabetes Process and Outcome
> Measures**

![](media/image20.wmf){width="5.520833333333333in"
height="3.5097222222222224in"}

Although State government and community leaders do not have control over
many of these factors, there are some areas where implementing action at
the State level can increase awareness and promote positive change.
These include educating people with diabetes, targeting campaigns about
the risks of obesity and sedentary lifestyles to the general public,
raising awareness among professionals about health care processes that
can improve outcomes for people with diabetes, and creating financial
incentives to encourage providers to improve management of the disease.

For example, the CDC's Division of Nutrition and Physical Activity began
funding 20 State programs on prevention of obesity in 2003. These
programs focus on education of people at risk of diabetes and supportive
environments for healthy eating and physical activity. (Information on
specific State programs can be found at:
<http://www.cdc.gov/nccdphp/dnpa/obesity/state_programs/index.htm>.)

Other States target minority populations that are disproportionately
affected by diabetes in an effort to affect individual self-management
and other external causes. Also, many States have passed legislation to
secure and regulate insurance coverage for people with diabetes because
absence of health care coverage can delay diagnosis, evaluation,
education, and proper monitoring and management of the disease with
disastrous consequences (see information at
<http://www.ncsl.org/programs/health/diabetes.htm>).

To better understand what influences a State's position and how it
compares with other States, some factors that are presented in Figure
3.5 are discussed in more detail below.

**Racial, Ethnic, and Socioeconomic Factors:** As previously noted, 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, 2003b).

The NHDR (AHRQ, 2003b) summarizes the racial, ethnic, and socioeconomic
differences in diabetes across the entire Nation, where minority or
lower socioeconomic status is associated with higher diabetes
prevalence, higher diabetes death rates, higher rates of serious
complications (including end stage renal disease and amputations).
Nevertheless, process-of-care measures generally do not differ greatly
among white and minority racial and ethnic groups at the national level
(see Table D.2, Appendix D). Absence of differences at the national
level does not mean that such differences are nonexistent at the State
and local level. Outcomes do differ among racial and income groups at
the national level. For example, many more hospitalizations for
long-term complications of the disease, including amputation related to
diabetes, are seen for blacks compared to whites (Table D.2).

The socioeconomic makeup of a State should also play a role in the
strategies that a State uses to improve diabetes care quality. For
instance, States may be able to improve diabetes care quality through
efforts targeted at population groups particularly at risk for diabetes
complications. (The section on Dissemination: Minority and Rural
Outreach in [Module 4: Action]{.underline} describes approaches being
used in some States.)

**Biological and Behavioral Factors:** The likelihood of developing the
most common form of diabetes, type 2, is influenced by both biology and
behavior (National Diabetes Education Program \[NDEP\], undated \[a\]).
Risk factors for type 2 diabetes include:

-   **Family history of diabetes---**Particularly, in the immediate
    family.

-   **Gestational diabetes**---Women who develop gestational diabetes
    during pregnancy, children whose mother had gestational diabetes
    while carrying them (NDEP, undated \[b\]), and women who gave birth
    to at least one baby weighing nine pounds or more.

-   **Age ---**Risk of diabetes increases with age.

-   **Overweight/obesity---**A known risk factor for diabetes.
    Overweight is defined as a body mass index ≥ 25 (\>23 if Asian
    American and ≥ 26 if Pacific Islander) and obesity is a body mass
    index of ≥ 30.

-   **Lack of exercise ---**Exercise less than three times a week is
    associated with developing diabetes and its future complications.

-   **Diet and nutrition---**High calorie intake (proteins,
    carbohydrates, or fat) increases the risk of developing diabetes and
    its complications.

Some additional factors that contribute to developing complications in
people who already have been diagnosed with diabetes include (NDEP,
undated \[a\]):

-   **High blood pressure---**Pressure greater than 140/90 mm/Hg is
    associated with increased risk of complications for people with
    diabetes.

-   **Abnormal lipid levels---**HDL (high density lipoprotein, or
    "good") cholesterol less than 40 mg/dL for men and less than 50
    mg/dL for women and triglyceride level greater than or equal to 250
    mg/dL are danger signs of complications for people with diabetes.

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).

**External Environment:** In addition to individual characteristics
(some of which are amenable to change with personal motivation), each
State has different infrastructure and other environmental factors over
which policy-makers may or may not have control. These factors include
the collective health status of the population, the distribution of
health care services within locales, 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---**The 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---**The 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 are not up to date with
    state-of-the-art knowledge to manage diabetes effectively and of
    patient education programs to help patients learn to manage their
    diabetes.

-   **Public education---**The need for 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**---The funds, in a time of tight State
    budgets, to stimulate quality improvement activities related to
    diabetes care.

-   **Leaders to champion quality improvement---** Those leaders who can
    draw attention to the problems associated with diabetes and harness
    the commitment of health professionals to change practices and
    monitor results.

-   **Knowledge of what to do---**The 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 comparisons across providers,
    communities, and even with other States.

The inter-relationship between all of the factors in Figure 3.5, then,
affects how a State compares with other States on measures of diabetes
care quality. It is difficult to measure all of these factors at the
State or local level and to analyze and show their effect with data.[^1]
One analysis of the NHQR compares hospital admissions for uncomplicated,
uncontrolled diabetes to State environmental factors that are readily
available --- measures of poverty, obesity, and diabetes prevalence.[^2]
This analysis was possible because 14 States in the Healthcare Cost and
Utilization Project [^3] provided their State discharge data for
inclusion in this analysis for the NHQR.

Figure 3.6 shows the resulting associations among admissions for
uncontrolled, uncomplicated diabetes and rates of obesity, poverty, and
diabetes prevalence. Diabetes prevalence does not vary much across the
States, but obesity and poverty rates do. Admission rates also vary
greatly across these States; most of these State admission rates are
significantly different from the national average, and the low-to-high
rates differ fourfold in magnitude. Furthermore, States that have very
high admission rates have higher obesity and poverty rates than the
States with lower admission rates.

Yet, as noted earlier in this module, poverty and obesity alone do not
account for all the differences between States in rates of avoidable
hospitalizations for diabetes. Other factors certainly play a role. The
health system infrastructure, rate of the uninsured, provider knowledge
and incentives, public education, funding and leadership, knowledge of
what to do, and information systems---all will affect the challenges
that State leaders face in leading communities to improve health care
for people with diabetes.

**Interpreting Process and Outcome Measures Together**

The four States presented earlier in the State-level comparison are
included in Figure 3.6. Examining these States in terms of process
measures, this one outcome measure, and underlying population
characteristics is instructive.

**Figure 3.6**

![](media/image21.wmf){width="4.709722222222222in"
height="3.7895833333333333in"}

-   Georgia, which had better HbA1c testing rates for two or more times
    per year than the other four States, also has very high rates of
    avoidable hospitalizations for uncomplicated, uncontrolled diabetes.
    This suggests the need to examine the adequate of ambulatory care;
    perhaps HbA1c testing is not translating into improved glycemic
    control for patients. Georgia has one of the highest rates of
    poverty (usually correlated with lower education) among the States;
    perhaps additional targeted patient education would be beneficial.
    Furthermore, Georgia ranks third among States in medically
    underserved or health personnel shortage areas (Hawkins and Proser,
    2004). This suggests that less access to ambulatory care in some
    areas may lead to more hospitalizations for early stage diabetes.
    Whenever process and outcomes measures do not agree, they should be
    examined critically in the context of the State environment.

-   Massachusetts, which had process rates that were not distinguishable
    statistically from the national average but that were notably higher
    on HbA1c testing rates for two or more times per year and influenza
    immunization, has one of the lowest rates of uncontrolled diabetes
    hospitalizations among the 14 States. Massachusetts' population also
    has lower rates of the underlying problems of obesity and poverty
    compared to other States.

-   Michigan, which had process-of-care rates indistinguishable from the
    national average and on the lower end of diabetes care quality, had
    a moderately low rate of these avoidable hospitalizations. This is
    despite the fact that Michigan has a population with high obesity
    rates (but not high poverty rates).

-   Washington, which had process measures that were fairly similar to
    the national average with the exception of its high immunization
    rate, had one of the lowest rates of these avoidable
    hospitalizations. Washington's population has one of the lowest
    poverty and obesity rates.

These combined views of diabetes care in the States suggest that the
underlying populations and personal risk behaviors and perhaps
self-management of the disease have more of an effect on the outcomes of
care than whether or not a particular test is given. The test itself is
not sufficient for improving diabetes outcomes. Complicated interactions
of many factors influence diabetes outcomes. Furthermore, often the
results on one measure are not consistent with findings on another
measure, even when the measures are related. This indicates the
importance of improving information systems that can track problems and
enhance understanding of the effectiveness of quality improvement
programs.

None of the above analysis tracks State results with their diabetes care
quality improvement programs. No full-scale evaluations have yet been
published of State interventions in health care quality improvement.
Through interviews with State officials, this *Resource* *Guide*
identifies a few programs that likely have influenced the quality
measures discussed here. They are described in detail in [Module 4:
Action]{.underline}.

## Summary and Synthesis

This module shows how data from the NHQR can be analyzed and interpreted
to answer the question of how a State compares to other States and
national benchmarks on health care quality for one disease --- diabetes.
Maps and charts can be used to help State leaders and quality
improvement teams, whether or not they are trained in statistics and
analysis, understand where their State stands in terms of diabetes
quality.

A key question for all States is: What goals should the State set as
targets for specific diabetes care quality measures? The NHQR can be
used to identify consensus-based measures, as shown in [Module 2:
Data]{.underline}. States may identify and define other measures as
well. The advantage of the NHQR measures is that the best-in-class State
estimate, which can be derived easily from the NHQR, shows what has
already been achieved by some States. It is a reasonable target for most
measures. However, some measures might be so crucial to good diabetes
outcomes that the target should not be limited by what other communities
have achieved to date. Improvements above and beyond the best-in-class
States may be warranted. Experts in diabetes care and local community
leaders can help make these types of judgments.

Another key question is: Are all States able to meet the challenge of
the best-in-class States? The answer depends on the measure, the factors
that relate to that measure (health system versus consumer actions), and
the current health and socioeconomic status of the State population. The
analyses in this module reveal that many factors influence diabetes
care, making the assessment between diabetes outcomes and processes of
care difficult to affirm. Nevertheless, State-level baseline estimates
of diabetes care enable States to assess their starting point and to
evaluate their progress over time.

Some States may be able to assemble better data than are available
nationally to understand the quality of care in their State. This has
been done in some States (see for example Michigan in [Module 4:
Action]{.underline}. State leaders can assess the quality of diabetes
care using the NHQR data to obtain an idea of where their State stands
in comparison to other States and the Nation. One thing is clear from
the NHQR data and the information this module derives from it---no State
measures up to all the guidelines for diabetes care completely. The next
module provides insights on what actions some States have taken to
improve diabetes quality.

Finally, diabetes is only one of many conditions that warrant
improvements in health care quality. While this *Resource Guide* focuses
only on diabetes, State leaders will ask: What other conditions are ripe
for improvement? The answer will differ by State. Tables F.1 and F.2 in
Appendix F present the quality of care measures for all diseases
examined in the numerous tables of the NHQR by State. Thus, assembled in
one place, State leaders can scan the list of measures to see how their
own State compares to the national average across all NHQR measures.
Once diabetes quality improvement is on track, State leaders may want to
start with Appendix F to inspire their next campaign to improve health
care quality.

**Associated Appendixes for Use With This Module**

**Appendix D. Benchmarks From the NHQR**

Appendix D provides additional detail on benchmarks and how they were
developed and defined for this *Resource* *Guide*. It also explains the
best benchmarks for stimulating quality improvement. This appendix notes
that methods used to generate the benchmarks must be understood to
ensure they are compatible with a State's estimates.

**Appendix E. Information on Statistical Significance**

Appendix E 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 and
when estimates, and thus standard errors, must be derived from the data
provided.

**Appendix F: NHQR Quality Measures for All Conditions by State**

Appendix F lists quality measures for all conditions and topics in the
NHQR. It includes the national estimate and then an indicator for
whether or not the State estimate (not shown due to space limitation) is
statistically greater, lower, or no different from the national average.
The measures for which State-level data are not reported in the NHQR are
excluded from the table. This resource can help State leaders identify
which diseases, in addition to diabetes, are in need of quality
improvement. Many of the same data issues related to diabetes are
applicable to other disease topics, although different data sources and
limitations may apply to them.

**Module 4: Action -- Learning From Activities Currently Underway**

> *Discussing the role of State government in improving diabetes care,
> Dr. Lawrence Harkless, Chairman of the Texas Diabetes Council, had
> advice for State leaders interested in diabetes quality improvement.
> "Poor quality diabetes care is not about bad people, it's about bad
> systems. In the fifteen minutes a doctor has with a patient, he/she
> will address the most pressing health concerns. So many of these
> patients have multiple conditions that create competing priorities for
> doctors."*
>
> *"Until research provides a cure for diabetes, constant efforts to
> increase awareness, knowledge and skills -- in our health sciences
> schools, our private and community health centers, our schools and our
> local and State government - are crucial to our success in controlling
> this destructive disease," he added. "The economic costs from lost
> productivity, the health care costs of life-threatening complications,
> and the personal costs of limited fulfillment -- these are costs our
> State can ill afford to pay."*
>
> *---* Dr. Lawrence Harkless, Chairman of the Texas Diabetes Council,
> and Professor, Department of Orthopedic Surgery, and Louis Bogey
> Professor of Podiatric Medicine and Surgery, University of Texas
> Health Sciences, San Antonio, Texas

In [Module 1: Background]{.underline}, readers had the opportunity to
learn about diabetes, its consequences in terms of cost and its toll on
human life, and the need for quality improvement in health care,
particularly as it relates to diabetes. [Module 2: Data]{.underline}
introduced readers to data from the NHQR and how these data can be
useful to States. [Module 3: Information]{.underline} demonstrated how
State leaders could use data to make accurate comparisons and
assessments of diabetes care quality in their States. This module will
examine various models of quality improvement, ranging from private
efforts, not directly related to government, to Federal, State, and
local government initiatives.

Within health care, many organizations and individuals play a role in
efforts to improve quality. Rather than reinvent the wheel, State
leaders have the opportunity to learn and draw lessons from initiatives
that are ongoing at both the national and State levels in both the
public and private arenas. These initiatives also have publications,
guidelines, and other resources that can assist the development of new
initiatives. Listed below are selected programs that are central to
States and their ongoing diabetes quality improvement efforts. Appendix
G gives a more extensive listing of various diabetes quality improvement
efforts involving national non-governmental organizations and Federal
agencies with Internet links for more information for State leaders.

**Selected Public/Private Quality Improvement Initiatives**

There are a wide range of public and private quality improvement
initiatives active at different stages of quality improvement. Although
the components of quality improvement are numerous, the examples given
below illustrate the action at stages most important for State leaders,
including the collection of measurement data and the implementation of
quality improvement programs. Some organizations focus on one stage of
quality improvement while many play a part at all stages. This list is
by no means exhaustive, but it provides examples of how national
organizations and partnerships are related to State efforts. These
strategies are being widely implemented and fine tuned for various
populations and organizations.

**National Diabetes Quality Improvement Alliance**

One of the most important advances in quality improvement is the
development of the consensus-based measures to assess health care
quality. Organized by leading diabetes stakeholder groups in 1998, the
Diabetes Quality Improvement Project (DQIP) was a voluntary coalition of
public and private organizations that have cooperated to develop a
national set of diabetes-specific performance and outcome measures.
Comprised of the American Diabetes Association, the Centers for Medicare
& Medicaid Services (CMS), the Foundation for Accountability, the
National Committee for Quality Assurance, the American Academy of Family
Physicians, the American College of Physicians, and the Department of
Veterans Affairs, DQIP was the first successful collaboration to develop
a single set of performance measures to determine the appropriateness
and effectiveness of diabetes care. The data collected included measures
for HbA1c, blood pressure, lipid profiles, eye and foot exams, and
smoking cessation counseling, among others.

In 2001, the DQIP partners joined other leading organizations, including
the American Medical Association and the Joint Commission on
Accreditation of Healthcare Organizations, to form the National Diabetes
Quality Improvement Alliance (NDQIA). The Alliance agreed to work on
developing one national performance measurement set for diabetes
accepted by all major stakeholders. In October 2002, the newly formed
Alliance developed national, uniform consensus standards from all
parties -- purchaser, provider, and consumer groups. The NHQR includes a
subset of these measures for which national data exist. Some States have
used the DQIP and NDQIA measures as the basis for developing local
diabetes guidelines and reporting. Further information on the Alliance
and its national measures is available at
<http://www.nationaldiabetesalliance.org/>.

**The Chronic Care Model**

One model of quality improvement particularly applicable to diabetes
care quality 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. 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 to develop new and better
clinical procedures and systems that support providers and patients in
treating and managing 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]{.underline}.

**IHI Breakthrough Series Collaboratives**

The Institute for Healthcare Improvement (IHI) created the Breakthrough
Series Collaboratives to assist health care organizations with making
rapid advances in lowering costs and improving quality for a variety of
conditions in a variety of health care settings. A collaborative brings
together quality improvement experts and practice teams from many
different health care organizations that work together for 6 to 8 months
to achieve quality improvement in a specific area. Since 1995 when IHI
held the first Collaborative, more than 700 trained teams from over 450
U.S. and Canadian health care organizations have participated in these
programs. By capitalizing on the collective wisdom of participating
organizations, expert faculty, and improvement advisors, these
organizations have dramatically improved outcomes and reduced costs in a
variety of areas, including:

-   Reduced waste in the form of shorter intensive care unit stays and
    less waiting time.

-   Dramatic reductions in defects such as adverse drug events, long
    waits for pain medications, and unnecessary hospitalizations for
    chronic conditions.

-   New levels of performance achieved including enhanced control of
    blood sugar and access to primary care.

IHI combined efforts with the Group Health Cooperative's Improving
Chronic Illness Care program to train health care providers and others
in using the Chronic Care Model to accomplish real change in the way
chronic diseases, including diabetes, are treated and tracked (Institute
for Healthcare Improvement, 2002). Hundreds of health care teams around
the country are currently using the Breakthrough approach combined with
the Chronic Care Model in Health Disparities Collaboratives sponsored by
HRSA's Bureau of Primary Health Care. (Health Disparities Collaboratives
are discussed in further detail later in this module.) The short-term
evaluations of Collaboratives showed improvements in blood sugar control
for diabetes patients; dramatic increase in followup for patients with
depression; decreased rates of blood pressure among patients with
cardiovascular disease; success in providing asthmatic patients with
daily preventive medicines; and decreasing health care costs even while
increasing the number of patient visits (Wagner, Austin, Davis, et al.,
2001). More information about IHI, its Breakthrough Series, and diabetes
programs can be found on the following Web sites:

-   Breakthrough Collaboratives general information:
    [http://www.ihi.org/IHI/Programs/CollaborativeLearning/]{.underline}

-   Improving care for people with chronic conditions -- diabetes:

> [http://www.ihi.org/IHI/Topics/ChronicConditions/Diabetes/HowToImprove/]{.underline}

-   Report from the Health Disparities Collaborative on Diabetes:
    [http://www.healthdisparities.net/Diabetes_Apr2002.pdf]{.underline}

**Other Strategies**

***Self-Management Programs***

Diabetes is one chronic condition whose treatment and outcomes are
heavily dependent on how well the patient monitors and manages the
disease outside the health care setting. An important approach to
quality improvement for diabetes is improving patient self-management.
Self-management programs emphasize and focus on patient education and
behavior modification. Health care professionals work with patients to
build their confidence in managing their own disease, in working within
the health care system and the community to have their needs met, and in
managing the emotional effects of their illness. Patients are informed
about their disease and trained using evidence-based information in how
they should manage their condition. A variety of educational tools are
used to assist the patient (for example, classes, Internet information,
and toll-free hotlines). There are national standards for diabetes
self-management and patient education. Because of the critical role of
patient self-management in diabetes care, some purchasers are beginning
to provide reimbursement for certified diabetes educators to interact
with diabetes patients.

One AHRQ-sponsored study conducted by Stanford University researchers
showed that 2 years after participating in a self-management program,
study participants showed reductions in health distress, made fewer
visits to the doctor's office and emergency room, had not experienced
any further increases in disability, and had increased self-efficacy
(Lorig, Ritter, Stewart, et al., 2001). More information on the Chronic
Disease Self-management Program at Stanford University is available at:
<http://patienteducation.stanford.edu/programs/>.

***Disease Management Programs***

Another model for quality improvement that is capturing attention
nationwide is disease management. Disease management is a term that
refers to a variety of programs and interventions that seek to:

-   Identify patients with a particular chronic condition or set of
    conditions.

-   Establish a coordinated system of interventions and
    information-sharing for enrolled patients and their providers.

-   Encourage doctors and other health care providers to use
    evidence-based practice guidelines to treat chronic illnesses.

-   Educate and train patients in self-management so that they avoid
    disease complications.

-   Monitor interventions and outcomes over time to evaluate the
    effectiveness of the disease management program.

Disease management has grown rapidly over the past five years and is now
used widely by employer-sponsored health plans to manage costs and
improve clinical care for many chronic conditions, including diabetes.
More recently, State Medicaid programs and Medicare also have begun to
use disease management for their populations. Initial assessments from
State Medicaid disease management programs are promising in terms of
cost control and quality improvement (Brown and Matthews, 2003; Wagner,
Austin, Davis, et al., 2001; AAHP/HIAA, 2003). Table 4.1 below lists
Medicaid diabetes disease management efforts that are underway. More
information on disease management programs in general is available from
the Disease Management Association of America's Web site at
[http://www.dmaa.org](http://www.dmaa.org/). Further information on
diabetes disease management programs is available at the Council of
State Governments' (CSG) Web site at
[http://www.csg.org/CSG/Policy/health/chronic+illness/default.htm](http://www.csg.org/CSG/Policy/health/chronic+illness+.htm).

**Selected Federal Programs and Resources for Diabetes Care Quality
Improvement**

[]{.mark}

In addition to public/private quality improvement efforts, State
diabetes efforts are also linked with Federal programs. There are a
variety of programs at the Federal level that address diabetes and
quality improvement; some of these are partnering with States, and
others have useful resources for State efforts.

**CDC's Diabetes Prevention and Control Program**

The Centers for Disease Control and Prevention currently helps to fund
the Diabetes Prevention and Control Program (DPCP) in every State. The
DPCP model began as a small number of demonstration projects in the late
1970s. In response to the growing burden of diabetes in the United
States, the program has evolved into a nationwide, joint State-Federal
effort with the CDC spending \$20 million annually throughout the 50
States, the District of Columbia, and 8 U.S. territories and island
jurisdictions. These programs and the people who staff them are rich
information resources on diabetes. (See
http://www.cdc.gov/diabetes/states/index.htm for links to each State
DPCP.)

  ------------- -------------------------------------------------------------
  **Table 4.1.  
  State         
  diabetes      
  disease       
  management    
  (DM)          
  programs**    

  Colorado      In 2002, Colorado partnered with Eli Lilly to fund a pilot
                program to improve access to quality care for beneficiaries
                with diabetes and schizophrenia.

  Delaware      In 2003, the State passed a bill to create a task force to
                evaluate how implementing a statewide DM program could impact
                quality and cost.

  Florida       Florida\'s Disease Management Initiative program is 7 years
                old and covers a number of diseases including diabetes.

  Illinois      In 2003, Illinois authorized a pilot DM program to evaluate
                the effect DM has on health outcomes and costs.

  Indiana       In Spring 2003, Indiana launched the Coordinated Care
                Management program to help Medicaid enrollees diagnosed with
                chronic conditions, including diabetes.

  Iowa          In 2003, Iowa authorized a pilot DM program for Medicaid
                beneficiaries suffering from a range of chronic conditions
                including diabetes.

  Kentucky      Medicaid managed care enrollees with diabetes are identified
                to receive services ranging from patient education to access
                to a 24-hour nurse hotline.

  Maine         Maine's new Dirigo Health Insurance Plan promotes DM along
                with disease prevention and quality improvement programs.

  Maryland      From 1991-1997 Maryland ran a Diabetes Care Program that
                provided DM services to Medicaid beneficiaries with diabetes.
                These services are now provided through the State's Medicaid
                managed care providers.

  Mississippi   For Medicaid beneficiaries with diabetes, Mississippi offers
                DM services including patient and provider education and case
                management.

  Missouri      Missouri's Disease State Management Program serves
                fee-for-service Medicaid enrollees with chronic conditions
                at-risk of negative health outcomes.

  New Jersey    Through its Medicaid managed care programs, enrollees have
                access to DM services for a number of diseases including
                diabetes.

  New Mexico    New Mexico requires Medicaid managed care providers to offer
                DM services to beneficiaries with chronic conditions. The
                2003 law also directs the State to pilot a DM program for the
                fee-for-service population.

  North         Through its Medicaid managed care program -- Carolina ACCESS
  Carolina      -- North Carolina provides case management services for
                enrollees with chronic diseases.

  Oregon        Oregon's DM program is targeted to save \$1.5 million net in
                the first 6 months of operation.

  South         Since 2001, South Carolina has offered an adult diabetes DM
  Carolina      program through its Medicaid managed care program.

  Texas         Recently, Texas expanded its DM program to include
                fee-for-service Medicaid beneficiaries and enrollees in the
                State's children's health insurance program.

  Virginia      In 1997, Virginia's successful pilot DM program was expanded
                to incorporate other diseases, including diabetes.

  Washington    In 2002, Washington rolled out a statewide DM program for
                Medicaid beneficiaries. In its first year, the diabetes
                program served 8,468 clients and is estimated to have saved
                the State \$900,000.

  West Virginia The West Virginia Health Initiative Project (WVHIP) works to
                promote evidence-based best practices in diabetes management.
  ------------- -------------------------------------------------------------

**Source**: The Council of State Governments (CSG). This table is
derived from a review of State and Federal Web sites and published
literature on Medicaid disease management by CSG staff. Published source
material included Brown and Matthews, 2003; Wheatley, 2001; Faulkner,
2003; NCSL, 2003, U.S. Department of Health and Human Services, 2003b.

The CDC's DPCP has developed two types of programs: capacity building
and basic implementation. Twenty-six States currently have capacity
building grants with an average award of \$270,000 to:

-   Develop initial expertise in diabetes control.

-   Provide a focal point for diabetes control.

-   Establish systems to define the scope of the diabetes problem..

-   Identify gaps in diabetes care, for both patient access and
    quality-of-care issues.

-   Develop and evaluate limited intervention projects.

-   Identify external supporters for diabetes control activities.

DPCP's basic implementation program awards an average of \$725,000 to
State health departments. Twenty-four States currently have basic
implementation grants. The implementation grants are to:

-   Build on expertise in program, science, and policy areas to control
    and prevent diabetes.

-   Coordinate statewide diabetes control and prevention.

-   Expand systems to define and analyze the scope of the diabetes
    problem.

-   Improve access to diabetes care for all people and raise the quality
    of that care..

-   Use statewide public health projects to reduce diabetes-related
    problems.

-   Inform, educate, and empower external supporters to control and
    prevent diabetes.

To qualify for CDC funds, State governments are required to provide
matching support through State funds or in-kind commitments of personnel
or other resources. The amount of State funding varies. Some States
provide a significant level of their own funding for diabetes efforts,
surpassing CDC funding by two or three times. Texas, for example,
appropriated more than \$6 million in State funding for diabetes in
fiscal year 2003-2004. Other States provide more modest support or no
additional support. In addition, money is often provided through private
grants. States with higher incidence of diabetes also do not necessarily
spend more on their diabetes prevention and control programs. CDC aims
to assist State health departments in developing programs to address the
disease; CDC funding amounts to approximately \$1.27 per American with
diabetes (see Appendix H for a breakdown of CDC and State DPCP funding
for all 50 States).

In 1999, the CDC required each State to establish measurement and
evaluation procedures to track and promote program success. DPCP uses a
"model of influence" approach. As such, the DPCP acts as a mechanism for
improving diabetes care through strategic partnering and programmatic
interventions. The purpose is to ultimately affect broad change in the
health system and the health of the community. The evaluation and
accountability requirements are meant to stimulate such activity on the
part of the DPCP (Safran, Mukhtar, Murphy, 2003). The evaluation
framework of this policy shift is detailed in the discussion on
evaluation in [Module 5: Improvement.]{.underline}

**Diabetes Detection Initiative and Steps to a HealthierUS**

Under the leadership of Secretary Tommy G. Thompson, the U.S. Department
of Health and Human Services has developed a new initiative, the
"Diabetes Detection Initiative: Finding the Undiagnosed." More than 5
million of the 18.2 million people with diabetes in the United States do
not know they have the disease. The Diabetes Detection Initiative (DDI)
is a community-based effort to identify individuals with type 2 diabetes
who have not been diagnosed. The DDI is designed to raise awareness of
diabetes risk factors, increase blood testing of individuals at risk for
diabetes, and increase diagnosis and treatment for those people who do
not know they have the condition.

Ten communities around the Nation with high risk populations are serving
as DDI pilot sites, including Oakland, California; Wichita and Sedgwick
County, Kansas; Springfield/Holyoke, Massachusetts; Flint, Michigan;
East Harlem, New York, Choctaw Nation, Oklahoma; Orangeburg County,
South Carolina; Seattle, Washington; Fayette and Greenbrier Counties,
West Virginia; and Wind River Indian Reservation, Wyoming. Future plans
calls for the DDI to expand to other locations across the country. The
Diabetes Detection Initiative is aligned with other Federal health
initiatives, including the Secretary's Steps to a HealthierUS and the
President\'s HealthierUS programs, which are aimed at encouraging
physical activity, improved nutrition, and a more prevention-oriented
society. More information on the DDI is available at
[http://www.ndep.nih.gov/ddi]{.underline}; additional information on
Steps to a HealthierUS is available at
[http://www.healthierus.gov/steps/index.html]{.underline}.

**HRSA's Health Disparities Collaboratives**

HRSA's Bureau of Primary Heath Care (BPHC) and the CDC's Diabetes
Prevention and Control Program sponsor Health Disparities
Collaboratives, a unique partnership with community health centers
(CHCs) across the country aimed at improving chronic illness care for
underserved and minority communities. CHCs are the key safety net
providers for low-income, uninsured patients throughout the country. The
low-income and ethnically and racially diverse populations at community
health centers have an increased risk of complications from chronic
illness (NACHC, 2003). In an ambitious program to reduce health
disparities, HRSA began the first Diabetes Collaborative in 1999 with 85
CHCs. The CHCs developed registries and enrolled 16,000 people with
diabetes in the collaboratives. In 2000, another 120 health centers
participated in a second Diabetes Collaborative.

The Health Disparities Collaboratives incorporate the change model
created by the IHI Breakthrough Series and the Chronic Care Model for
diabetes care improvement. This program has allowed CHCs to participate
in team training to apply best practice models of care for chronic
disease. The population-based model of care relies on identifying which
patients have an illness and ensuring that they receive evidence-based
care. The model helps patients to participate and manage their
conditions. Over the course of the 1-year collaborative, the CHC teams
participate in learning sessions and set goals, such as data collection
on certain outcomes (for example, blood tests). Then they develop, test,
and implement evidence-based strategies for a specific clinical area
(for example, diabetes) and for a specific community. Between meetings,
CHC teams focus on implementing goals and measuring changes in their
health centers. The team collects data to measure the impact of the
changes and additional learning opportunities allow teams to improve
processes over time. The teams share information and learn from national
experts and each other through a Listserv, regular site visits, monthly
progress reports, and conference calls. Results indicate the rate of
HbA1c testing for people with diabetes increased significantly at the
participating centers over the first year (Health Disparities
Collaborative, 2004).

Additional information on the Bureau of Primary Care and the Health
Disparities Collaboratives is available at
<http://bphc.hrsa.gov/programs/HDCProgramInfo.htm> and
[http://www.healthdisparities.net/]{.underline}.

**National Diabetes Education Program**

The National Diabetes Education Program (NDEP) is a national
collaboration sponsored jointly by the National Institutes of Health
(NIH) and the CDC. The NDEP includes over 200 partners at all levels of
government and society. Many State DPCPs use NDEP materials and partner
on NDEP initiatives. The goal of the NDEP program is to improve
prevention and treatment of diabetes, thereby reducing illness and death
from this disease. Because so many of the complications from diabetes
are preventable, the NDEP seeks to help educate the public about
diabetes, promote better patient self-management, improve the quality of
care for diabetes, address health policies that may improve quality and
access to care, and reduce disparities among racial and ethnic
populations that are disproportionately affected by diabetes. A variety
of resources on diabetes quality of care improvement as well as
education and awareness campaigns and other resources are available at
the NDEP Web site at [http://ndep.nih.gov](http://ndep.nih.gov/).
Another part of the NDEP is a Web site devoted to improving diabetes
care at
[http://]{.underline}[www.betterdiabetescare.org](http://www.betterdiabetescare.org/).
The site has information, resources and tools for providers, educators,
and organizations on how to participate in and advance quality
improvement in diabetes care.

**CMS' Quality Improvement Organizations**

Under Titles 11 and 18 of the Social Security Act, Quality Improvement
Organizations (QIOs) are designated as the guardians of quality,
cost-effective care for both Medicare and Medicaid. The 37 QIOs in the
United States, also known as peer review organizations, are non-profit
organizations that operate under the direction of the Centers for
Medicare & Medicaid Services. QIOs are responsible for using medical
reviews, data collection and analysis, and other functions authorized by
CMS as a means to achieve national, State, and local quality improvement
goals. QIOs are vital partners in CMS' Health Care Quality Improvement
Program due to their collaborative relationships with local networks of
hospitals and providers. QIOs have been involved in several State and
local quality improvement projects related to diabetes. Qualis, a QIO in
Washington State, was a partner with the Washington State Department of
Health and the Improving Chronic Illness Care program of the Robert Wood
Johnson Foundation in the Washington State Diabetes Collaborative.
Missouri's QIO, MissouriPRO, participated in the HRSA Health Disparities
Collaborative for diabetes in community health centers in the State.
North Dakota's QIO has assisted clinics with implementing diabetes flow
sheets, increasing preventive care and screening and establishing a
diabetes care tracking system that generates reminders for routine
diabetes checks. General information on the role of QIOs is available at
[http://www.cms.hhs.gov/qio]{.underline}/. Specific information on QIO
diabetes quality improvement initiatives is available at
[http://www.medqic.org/content/nationalpriorities/topics/projectdes.jsp?topicID=477&showMeasures=yes&showSteps=yes]{.underline}.
Examples of QIO initiatives in various States are available on the
American Health Quality Association Web site at
[http://www.ahqa.org/pub/quality/161_689_2974.cfm]{.underline}.

**State Approaches to Diabetes Care Quality Improvement**

> []{.mark}

The following sections summarize various kinds of State diabetes quality
improvement approaches relating to partnership/planning activities,
program development, and dissemination. A few States are highlighted
under each type of approach to illustrate examples of best practices.

[]{.mark}

States have undertaken a variety of diabetes initiatives over the years,
most of which have been spearheaded by State DPCPs. Although the mandate
of State DPCPs covers many aspects of diabetes prevention and control,
States DPCPs have included quality improvement as a part of their
diabetes work. States have used CDC funding to establish creative
programs to address diabetes quality improvement, ranging from using the
Chronic Care Model in collaboratives to developing diabetes guidelines.

[]{.mark}

There are also stand-alone State initiatives that are not directly
connected to CDC and State DPCP efforts. States have established
diabetes disease management programs in Medicaid and have partnered with
the private sector on quality improvement related to diabetes. Many
States have also tried to integrate CDC-funded efforts with
private-sector and Medicaid efforts.

The range of State activities makes it difficult to present all of the
possibilities. Instead, various activities and programs States have used
to address diabetes care quality are listed below. Except where other
citations are provided, the information provided below was derived from
a review of State health department Web sites, CDC resources, Internet
research, and in-person interviews with State agency officials. A focus
group of State officials and diabetes experts also assisted with
formulating the categories for State diabetes quality improvement
approaches. State examples were selected in order to provide a sampling
of State efforts that reflects regional, size and funding differences
between States. Also, the uniqueness of the State efforts in relation to
similar programs was taken into consideration. Although not an
exhaustive list, it demonstrates a range of efforts States have
undertaken related to diabetes quality improvement. These efforts may be
cataloged as follows:

> **Partnership/Planning Activities**

-   Coalitions

-   Advisory bodies and councils

-   Working across State agency lines

> **Program Development**

-   Developing and complying with diabetes guidelines

-   Data measurement and reporting

-   Use of technology

-   Self-management/patient education

-   Collaboratives

-   Provider training

-   State disease management programs

> **Dissemination**

-   Raising awareness through public relations

-   Minority and rural outreach

**Partnership/Planning Activities**

***Coalitions***

Creating networks of support has been critical for States that have
established far reaching programs addressing diabetes quality
improvement. Coalitions bring together a broad variety of stakeholders
in a State to work together to identify areas of strength, common
objectives, and gaps in service. They also develop plans to assure that
the essential treatment and educational services for managing diabetes
are in place in a community. Coalitions also include community
representatives and nontraditional partners such as the corner grocery
store owner, faith communities, health organizations, social service
agencies, and more. Coalitions can develop strategic nontraditional
plans and establish measures and processes for determining community
success.

-   **California's** Diabetes Coalition, which includes representatives
    from the general public, the State DPCP, local health departments,
    universities, volunteer organizations, pharmaceutical companies, and
    community-based organizations, has developed evidence-based
    guidelines, a patient survey, and a model patient record.

***Advisory Bodies and Councils***

A number of States also have advisory boards and councils that assist
with statewide diabetes planning and quality improvement efforts.
Whereas coalitions are broad-based, voluntary efforts, advisory bodies
are usually smaller, more formalized entities with objectives and
structure that are established by law. Advisory bodies and councils
include a variety of experts and stakeholder groups such as the American
Diabetes Association, State professional associations, and provider
organizations, among others.

-   **Florida's** Diabetes Advisory Council advises the Governor and the
    Secretary of the Department of Health on emerging diabetes issues
    affecting care, treatment, and quality of life.

-   The **Texas** Diabetes Council was created by the Texas legislature
    in 1983 to promote diabetes prevention and awareness, to work with
    private and public health care organizations, and advise the
    legislature on laws needed to further education and health services
    for people with diabetes.

***Working Across State Agency Lines***

State programs often operate in isolation from one another. However,
several States have recognized that their diabetes prevention and
control program can work with other agencies within State government to
reduce diabetes and its complications. This approach can be highly
efficient and effective in reaching targeted groups for prevention and
disease management.

-   **Maryland's** Medicaid program adapted the Maryland DPCP's Model
    for Comprehensive Diabetes Management, paying for a package of
    preventive services, equipment, and supplies for diabetes care.
    Although the program was later handed over to Medicaid managed
    health plans, an independent evaluation found that the diabetes care
    program saved an average of almost \$4,600 a year per program
    participant.

-   **Massachusetts'** Diabetes Program partnered with the Division of
    Medical Assistance to implement a patient education and provider
    training initiative incorporating the Massachusetts Guidelines for
    Adult Diabetes Care. This involved quality improvement and
    measurement initiatives related to health outcomes for people with
    diabetes.

-   **California** worked with the training division of the Department
    of Motor Vehicles to educate officers in evaluating people who come
    to the department's attention due to diabetes.

-   **West Virginia's** DPCP has established a worksite health promotion
    program for State employees that facilitates lifestyle changes to
    improve the health and self-care practices of people with diabetes.

**Program Development Activities**

***Developing and Complying With Diabetes Guidelines***

To close the gap between research and practice, several States are
promoting the use of evidence-based clinical guidelines for diabetes
care. Many States have adopted existing guidelines established by the
National Quality Forum, HEDIS^®^ Comprehensive Diabetes Care Measures or
the American Diabetes Association, while others have worked through the
process of developing their own.

-   **Massachusetts** DPCP convened a Diabetes Guidelines Work Group to
    develop the Massachusetts Guidelines for Adult Diabetes Care and
    accompanying tools for primary care settings.

-   **Nebraska's** Medicaid program has established a Diabetes
    Subcommittee that is developing consensus guidelines and working
    with health plans and providers to ensure implementation of the
    guidelines among those covered by Medicaid.

***Data Measurement and Reporting***

As Module 2: Data indicated earlier, data measurement and analysis is a
fundamental step in quality improvement. State DPCP and others
organizations have come together to agree on consensus measures on
diabetes quality and used the data to compare quality performance among
health plans and providers.

-   **Michigan's** DPCP established its Diabetes Core Measures
    Initiative in collaboration with the Michigan Associate of Health
    Plans. The measures were developed to ensure that all patients with
    diabetes receive evidenced-based care.

-   The **New Jersey** DPCP developed and implemented diabetes care
    performance measures and integrated them into routine clinical
    practice in several managed care and community health care settings.
    The performance measures are published in a State newsletter and on
    the Internet at <http://www.state.nj.us/health/fhs/diabnews.htm>.

***Use of Technology***

States are taking advantage of new technologies to improve diabetes care
through better communication and more efficient services.

-   **California** created a series of electronic seminars on
    diabetes-related issues for DPCPs around the country and coalition
    members throughout the State.

-   **Maine's** Consortium for Clinical Office System Improvement has
    worked to implement an array of tools for primary care practices
    aimed at quality improvement, prevention and chronic disease
    management, including the Cardiovascular & Diabetes Electronic
    Management System.

-   **Oklahoma** partnered with the University of Oklahoma's
    Ophthalmology Department to enable rural Oklahomans to receive
    diabetic retinopathy screening in their own communities using a
    state-of-the-art fiber optic telemedicine design.

***Self-Management/Patient Education***

Patient self-management is critical for good diabetes outcomes. Several
States have established certification programs for diabetes
self-management educators. By requiring this training, States can set a
high standard based on the latest evidence-based information. Patient
education programs are best conducted in a variety of settings that are
easily accessible to target populations, including: churches,
neighborhood associations, and other 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. For more information on diabetes education programs, visit
the ADA's Web site at
[http://www.diabetes.org/education/edustate2.asp]{.underline}.

-   **Rhode Island** created a statewide initiative called *My Diabetes
    Record,* which is aimed at improving self-management of diabetes and
    meeting the national HP2010 objectives for eye care, foot care,
    HbA1c tests, lipid profiles, and influenza and pneumonia
    vaccinations. All third-party insurers use this standard tool.

-   **Utah's** DPCP has a State-sponsored certification process for
    outpatient diabetes self-management programs. The voluntary program
    uses national guidelines and evaluates diabetes clinical quality
    improvement.

-   **Arkansas**' Medicaid and DPCP have partnered with the Eli Lilly
    and Company to provide diabetes self-management education in
    underserved areas of the State.

***Collaboratives***

Improving the quality of care for diabetes is a systemic issue. The
entire health care system and all its actors need to be mobilized to
deal with diabetes. Thus, a number of States established their own
statewide collaboratives or have worked with community health centers on
the HRSA Health Disparities Collaboratives to achieve diabetes quality
improvement.

-   **New Mexico** was one of several States to participate in the first
    HRSA Health Disparities Collaborative focused on diabetes. Eleven
    clinics or practices participated and used an electronic diabetes
    patient registry to ensure people with diabetes received recommended
    care with the State health department serving as technical advisor
    to the participants.

-   The State of **Washington** leads the way in establishing
    State-based diabetes collaboratives. Since 1999, Washington's three
    diabetes collaboratives have reached 65 clinical practice teams and
    accomplished significant clinical improvements, such as reductions
    in HbA1c levels, cholesterol, and blood pressure. There was also
    improvement of 35-50 percent in the number of patients who received
    foot examinations, blood pressure screenings, and cholesterol tests
    (Daniel, Norman, Davis, et al., 2004).

-   **Wisconsin** developed a unique public-private initiative in
    conjunction with managed care plans in the State. The Wisconsin
    Collaborative Diabetes Quality Improvement Project monitors and
    evaluates plan performance on diabetes measures and works together
    on quality improvement initiatives. More in-depth information about
    this initiative is provided in [Module 5: Improvement.]{.underline}

***Provider Training***

Because health care providers are a key element in improving diabetes
quality care, many States have actively sought their involvement in
developing programs. In addition, States are providing outreach and
support to health care professionals as they seek to implement new
evidence-based care guidelines.

-   **New York** established three diabetes centers of excellence.
    Medical centers in the State competed for the recognition and
    additional funding available for the designated centers of
    excellence. The centers conduct research and provide health care
    professionals, providers, and patients with information and
    resources aimed at improving diabetes prevention and treatment
    (Cornell, 2003).

-   **New Hampshire** offers an annual statewide multi-track
    professional training conference targeted to primary care health
    care professionals, insurers, legislators, podiatrists, school
    nurses, occupational health nurses, and other health and human
    service professionals.

-   **North Carolina** provides scholarships for local health department
    staff to attend the East Carolina University School of Medicine's
    Clinical Fellowship in Diabetes. The week-long continuing education
    program is led by a diverse group of faculty who address everything
    from quality clinical care for diabetes patients to increasing the
    cultural competencies of providers. The health care professionals
    who attend are then required to train colleagues in their local
    communities.

***State Disease Management Programs***

Because States are looking for ways to control costs while maintaining
or improving quality in Medicaid, more than 20 States are implementing
disease management programs, many of them targeting diabetes (Smith,
Ellis, Gifford, et. al., 2002; see table 4.1 below). Medicaid disease
management programs seek to increase patient knowledge and
self-management skills, improve provider adherence to clinical
guidelines, and implement technology to track patients more effectively.
Improved care management for diabetes is aimed at decreasing preventable
complications, thereby controlling costs and potentially improving long
term health outcomes.

-   **Florida**'s Disease Management Initiative has the longest running
    Medicaid disease management program in the Nation, addressing a
    variety of chronic illnesses, including diabetes (Brown and
    Matthews, 2003).

-   **Indiana** launched the Coordinated Care Management program, a
    voluntary disease management program for Medicaid patients with
    diabetes, chronic heart failure, asthma, and other costly conditions
    for Medicaid. The program will hire 80 new nurse managers over a
    2-year period to perform assessments and conduct patient education
    (U.S. Department of Health and Human Services, 2003).

-   **Kentucky's** Medicaid Managed Care plan, Passport**,** identifies
    members with diabetes through claim review, the nurse advice line,
    and referrals from doctors. The plan uses patient education and
    provider interventions to improve self-management and compliance
    with treatment guidelines. Since its inception in 1999, enrollees
    are doing better than the national average in monitoring symptoms
    and controlling the disease, and patient adherence and performance
    has improved each year of the program (Atkins, 2003).

**Dissemination Activities**

***Raising Awareness Through Public Relations***

An important component of addressing diabetes care involves raising
awareness. Surprisingly, while there are 13 million diagnosed cases of
diabetes in the U.S. in 2002, there were 5.2 million undiagnosed cases
of diabetes (CDC, 2003c). If the diabetes goes too long without proper
diagnosis, lasting damage to an individual's health can occur. Thus, it
is important that the general public and providers be aware of the
disease and its symptoms. States use a variety of methods to spread the
word about diabetes.

-   **Wyoming's** DPCP published a brochure, \"What Wyoming Should Know
    about Diabetes,\" and distributed it to 21,000 Medicaid-eligible
    households and to more than 50,000 other citizens.

-   **Tennessee's** DPCP collaborated with the ADA, the Tennessee
    Academy of Ophthalmology, and the University of Tennessee
    Agricultural Extension Service to bring the National Eye Institute's
    traveling vision exhibit to Tennessee.

***Minority and Rural Outreach***

The prevalence of type 2 diabetes is increasing most rapidly among
minority populations. In addition, millions of people living in rural
area have diabetes, and special attention must be given to ensure they
are receiving quality health care. The NHDR reveals that minority
racial/ethnic groups and lower socioeconomic groups receive fewer
services for diabetes care, and that African Americans and Hispanics
have higher hospitalization rates for complications of diabetes (AHRQ,
2003b).

Several States have developed innovative programs to target these
groups. A first step in addressing this concern is making patient
information available in an understandable format. This could involve
using pictorial representations or providing documents in languages
other than English. Reaching these groups also involves tailoring the
message or targeting the delivery.

> *Removing the Language Barrier*

-   **Florida** has made their entire DPCP Web site available in
    Spanish.

-   **Washington** has 20 self-management educators who are specially
    trained in delivering diabetes programs in Spanish.

> *Targeting the Message*

-   **North Carolina** partnered with the General Baptist State
    Convention and the State's Office of Minority Health, to conduct
    programs for African American congregations throughout the State
    that focus on awareness, risk factors, complications, and prevention
    strategies. The program provides educational presentations,
    workshops, and materials and develops public service announcements
    to radio stations with a predominantly African American listening
    audience.

-   **Minnesota's** DPCP coordinates with the Office of Minority Health
    to address diabetes among minorities. Funds earmarked for reducing
    disparities in the minority and Native American population pay the
    salaries of two staff members who work on diabetes efforts targeted
    at these groups.

> *Rural Outreach Efforts*

-   **Colorado** implemented the Rural Diabetes Project that promotes
    diabetes preventive practices through a tracking and followup system
    with private eye care and primary care providers for eye disease
    screening and blood pressure control. Colorado also coordinates the
    *Buddy System*, a network of health professionals that provide
    diabetes education in hospitals, clinics, and public health
    agencies. Rural educators are matched with certified diabetes
    educators for one-on-one consultations.

**Profiles of Selected Best Practice States**

This section examines the mix of programs that four different States
DPCPs use to improve diabetes care quality. The States profiled here
were selected based on a variety of criteria. A list of high-performing
States was developed based on rankings in the NHQR on diabetes care
quality. This list was supplemented by information on best practices
from the CDC, the Assistant Surgeon General's office, and research on
other innovative diabetes quality improvement programs. From this list a
cross section of States was selected that represented different areas of
the country, geographic and population differences, and baselines for
diabetes care quality. Some the States listed below score well on
diabetes quality of care measures in the NHQR. Others are below national
averages according to NHQR data. Thus, these States demonstrate
real-world approaches to improving diabetes care that attempt to
surmount the challenges that States face.

***California***

California uses a variety of approaches and partnerships to address
diabetes in the State. The State's Medicaid program, MediCal, identified
diabetes as a high-cost disease. The DPCP investigated the effect of
case management on the Medicaid population. The 4-year study showed that
case management resulted in a significant reduction in HbA1c levels
(California Medi-Cal Type 2 Diabetes Study Group, 2004). Now that the
DPCP has shown that this strategy works, it is working on funding for a
study that will help determine whether this effort would be cost
effective and feasible throughout the Medicaid population.

> The California Cooperative Healthcare Reporting Initiative (CCHRI**)**
> is an innovative public-private partnership that has developed a
> program for measuring diabetes quality of care (see box below).
> California is part of the HRSA Health Disparities Collaborative which
> works with community clinics on diabetes care. The California Primary
> Care Association expanded the program to include more clinics with the
> funding support of the California Health Care Foundation. California
> is also one of five States involved in the HRSA sponsored diabetes
> collaboratives that focused on identifying pre-diabetes. California
> also uses NDEP educational materials and is participating in the
> national Diabetes Detection Initiative (DDI) of the U.S. Department of
> Health and Human Services. The primary focus of this initiative is to
> help people understand their diabetes risk by knowing the risk factors
> and assist high-risk people in linking with various health care
> systems and health care professionals to discuss testing.

California has evaluated the success of its efforts through multiple
methods that identify short-term, long- term and process outcomes. The
State is following CDC's logic modeling, looking at data to inform and
help guide future efforts. They are using measures that are already
being collected and then deriving ways to fill in the gaps. In addition
to quantitative data, California is collecting qualitative information
through focus groups and surveys of partners about the effectiveness of
communications and messages.

As part of its DPCP, the State of Michigan has set up a statewide
network aimed at ensuring comprehensive diabetes management.

The program, established as part of the Michigan Department of Community
Health, Division of Chronic Disease and Injury Control, gained more
resources in 1994 after receiving a CDC comprehensive grant and funds
from the new State tobacco tax revenues. The State's Upper Peninsula
region staff developed a model for working with health care providers on
providing quality care and professional education aimed at improving
diabetes care in the Finnish population. When the data showed that
people served in the region had better outcomes, DPCP established six
regional Diabetes Outreach Networks (DONs) statewide. Michigan's DON
Diabetes Care Improvement Project was recognized as a Best Practice
Initiative in 2002 by the Assistant Secretary for Health at the U.S.
Department of Health and Human Services.

Each DON in Michigan develops collaborative partnerships with health
care delivery agencies, sponsoring and providing professional education,
and coordinating and developing diabetes resources within their service
region. Such collaboration of diabetes care resources is aimed at
increasing awareness and ensuring that persons with diabetes and at risk
for diabetes are identified and receive ongoing diabetes care and
education. While the regional networks have some efforts that are unique
to their area, much work is done on a statewide basis. The entire staff
meets three times a year and holds conference calls on a monthly basis
to coordinate efforts and develop programs.

Results from the Michigan DON demonstrate that working with health care
agencies and providers through a statewide Diabetes Care Improvement
Project can result in improved outcomes for persons with diabetes.
Trends in follow-up data from fiscal year 1996-2001 show a significant
increase in the number of persons with diabetes receiving important
tests. Individualized data analysis from the regional DONs also shows a
positive downward trend in the levels of HbA1c, which is associated with
significantly reduced risk of complications. The program has
demonstrated local reductions in diabetes-related hospitalizations,
amputations, and mortality. The program began seeing these results
relatively soon after implementation and has begun to close the gap
between Michigan's diabetes averages and the national average.

Michigan's Diabetes Policy Advisory Council works with DON directors and
provides an opportunity for sharing information, best practices, and
networking. In an effort to increase awareness and gain more support,
regional directors meet regularly with area legislators to share
information, identify gaps, and discuss how the legislature can help
reduce the burden of diabetes. In addition, they meet with citizens and
inform them about how they can seek legislative support.

Involvement by elected officials in diabetes related events has created
momentum for the effort. Michigan was one of a group of States that
participated in the Chronic Disease Academy sponsored by the CDC and
hosted by the National Governors' Association. A team of agency
directors, legislators, and advocacy groups attended this 3-day session
and created a strategic plan for addressing chronic disease issues in
the State. One of the ideas the State has implemented is a prevention
caucus for legislators. Recently the group launched a challenge among
State officials to adopt healthier habits. People in all branches of
State government are now clocking their steps when they exercise and
competing to be the most active. Even the Governor is wearing a
pedometer. Involvement by elected leaders has attracted press attention
and is raising public awareness.

The appointment of the State's first Surgeon General, Dr. Kimberlydawn
Wisdom, has served to identify synergies between various efforts. In
October 2003, Michigan presented its new Michigan Diabetes Strategic
Plan. Developed by the Michigan Department of Community Health, the
Michigan Diabetes Prevention and Control Program, and the Michigan
Diabetes Strategic Plan Task Force and its Steering Committee, the plan
addresses issues related to diabetes care and prevention. It also
establishes a unified course of action among health care providers,
public and private health officials, researchers, businesses, community
groups, and people with diabetes to implement the most promising
diabetes prevention and control strategies in the most cost-effective
ways. Some highlights of the report include:

-   Expanding diabetes primary prevention activities

-   Developing an ongoing public awareness campaign

-   Developing a Statewide diabetes consumer advisory group

-   Reducing diabetes-related health disparities among minority
    populations

-   Providing quality diabetes pregnancy-related care and education to
    women

Dr. Wisdom and U.S. Surgeon General Dr. Richard Carmona recently
announced the involvement of Flint, Michigan in the DDI. In Michigan,
the DDI will concentrate on the undiagnosed populations with a paper
risk assessment test that can be followed up by a blood test and further
treatment as necessary. Materials for the paper assessment are available
through a variety of community channels such as social services,
faith-based establishments, retail outlets, and fraternal organizations.

***Missouri***

The State's DPCP has identified diabetes as a serious public health
problem (Missouri Department of Health and Senior Services, 2002).
Citing studies showing that interventions can prevent or delay diabetes
complications, the State's DPCP has led the effort to implement the
Chronic Care Model.

The department has collaborated with federally qualified health centers
(FQHCs) and one National Health Service Corp site in HRSA's Health
Disparities Collaborative for diabetes. Participating clinics were
chosen strategically in an effort to align disease impact with a service
provider who was ready and willing to work on the project. Each center
implemented the Chronic Care Model in one or more clinics, forming teams
of diabetes-related health care specialists. Each center established an
initial registry of patients with diabetes. Additional provider and/or
site registries were added as the year progressed. The electronic
registries were used to monitor indicators of health behavior, health
status, and services received. Monthly summary registry reports were
sent to the State's DPCP, where the data were aggregated. The State's
DPCP provided FQHCs with financial support, a local learning session,
technical assistance on registry development, maintenance, health system
redesign, monthly reports, and evaluation skills.

From June 2000 to May 2003, preliminary results indicated health centers
significantly improved 12 of 16 diabetes-related care measures,
including increases in the prevalence of at least two HbA1c tests at
least 3 months apart (an increase of 15 percent), dilated eye exams (190
percent), foot exams (47 percent), influenza vaccinations (76 percent),
and whether the patient set self-management goals (37 percent). Future
efforts will focus on maintaining these improvements and extending
Collaborative activities to other health care sites.

The DPCP has tapped other resources by working closely with the State's
cardiovascular disease program and MissouriPRO, which has a contract
from CMS to manage quality improvement on behalf of beneficiaries.
MissouriPRO helped lead the training and implementation process for
expanding the Collaborative to include 10 additional health providers.
DPCP sees this partnership as a strategic alignment, expecting changes
by CMS providers ultimately to have an impact on the rest of the State.

***North Carolina***

North Carolina's DPCP initiated a number of diabetes initiatives
including a unique community-based program, Project DIRECT, that targets
diabetes prevention and care efforts in the African-American community.
Using a comprehensive approach, Project DIRECT (Diabetes Interventions
Reaching and Educating Communities Together) encouraged exercise and
improved nutrition, promoted awareness of diabetes, and increased
screening for diabetes (CDC, 2003e).

Early on, the DPCP pulled together a statewide diabetes advisory council
that included all stakeholders. The group became active in advocacy and
policy issues. Their support was crucial in helping secure matching
funds from the State legislature that allowed them to gain more
resources from CDC as a basic implementation program. In 1996, members
of the council successfully pushed legislation mandating that insurance
cover diabetes education and testing strips. The most recent legislative
action was the 2002 Care for School Children with Diabetes Act. At the
time of passage in 2002 only three States had this kind of legislation
for children in the public school system. The law ensures that the needs
of students are addressed through an individualized diabetes care plan
that includes provisions for snacks, testing, and assistance from an
adult. The advisory council was very active in getting the bill passed.

North Carolina has reached 91 percent of its diabetes goals in the last
4 years, seeing increases in foot exams, eye exams, flu shots and HbA1c
tests. The State has also met its goals in improvement among minority
groups. The DPCP has begun working with the State QIO to examine
Medicaid reimbursement claims for diabetes care, especially information
on children.

**Selected Local Quality Improvement Efforts**

In addition to national, Federal, and State quality improvement
approaches, there are also local efforts to improve diabetes care
quality. Local quality improvement initiatives are a crucial part of
overall efforts because they are closer to and have more direct contact
with providers and local health systems. Quality improvement programs
and models, such as the Breakthrough Collaboratives or the
Plan-Do-Study-Act model (discussed further in [Module 5:
Improvement]{.underline}), are best implemented at the local level. Yet,
State-level support is critical to local efforts because payment
structures, as well as the legal and regulatory structure of the health
care market, are largely a State responsibility.

There are any number of local quality improvement initiatives that exist
for diabetes, too many to list here. In addition to the Health
Disparities Collaboratives (which involve the Federal, State, and local
levels), two additional examples of local diabetes projects are included
as illustrations of the links between national, Federal, State, and
local contexts:

-   The St. Louis Diabetes Coalition is a voluntary network of health
    plans, provider groups, and other community organizations and
    companies that are working together to improve diabetes awareness,
    education and adherence to standards of care in the St. Louis area.
    The Coalition has worked together on a number of initiatives,
    including its Diabetes Screening and Treatment Guidelines**.** The
    treatment guidelines were endorsed by all of the major health plans
    in the St. Louis region, giving providers a single source for
    diabetes guidelines acceptable to all major payers. The Missouri
    Department of Health and Senior Services worked alongside the
    Coalition members to distribute the guidelines to more than 5,000
    physicians in St. Louis and other parts of Missouri.  More
    information about this and other the St. Louis Diabetes Coalition
    initiatives is available at
    [http://www.diabetescoalition.org]{.underline}.

-   The Niagara Health Quality Coalition (NHQC) is a local organization
    of employers, providers, physicians and insurers in western New York
    dedicated to working together to achieve quality, affordable health
    care. NHQC is affiliated with the Buffalo Niagara Partnership, the
    largest employer organization in the Niagara area representing 3,300
    firms with more than 200,000 employees. Stating that data are
    national but change is local, the NHQC provides links to both State
    and national data that can help local organizations, companies, and
    individuals become informed about the quality performance of various
    health care sectors. The NHQC's Web site,
    [http://www.myhealthfinder.com](http://www.myhealthfinder.com/),
    links to hospital, health plan, physician, and long term-care
    quality data. The site also has links to clinical care guidelines
    for diabetes that were developed by the State diabetes coalition.

**Summary and Synthesis**

The breadth of diabetes quality improvement activities both nationally
and across the States provides State leaders with a variety of proven
experiences, useful resources, lessons learned, and best practices for
enhancing initiatives and partnerships in their own States. State
programs have been successful in making inroads in diabetes prevention
and quality improvement.

Yet, there is still much that can be done. Despite the efforts of
States, national organizations, the Federal Government and a host of
local and community efforts, there is still room for improvement.
Diabetes rates continue to rise, substantial gaps in care for diabetes
exist, preventable complications occur all too frequently, and the
Nation is paying the price in higher health care costs and lower
productivity and quality of life.

State leaders may also wonder which quality improvement strategies are
the most promising approaches to achieving real improvements in diabetes
care quality. While this question cannot be answered conclusively for
the public policy arena, a recent research analysis of diabetes quality
improvement strategies in clinical settings provides some evidence for
prioritizing certain approaches. A systematic review of the literature
on clinical diabetes quality improvement strategies found that provider
education (i.e., meetings or conferences, outreach visits, and
distribution of educational materials) and disease management were the
most effective strategies in achieving significant improvements in
patient HbA1c levels. However, the study also found multiple quality
improvement interventions achieved more significant improvements in
HbA1c levels and provider adherence to clinical guidelines than single
interventions (Shojania, McDonald, Wachter, et al., 2004).

Before embarking on any particular public policy approach, however,
State leaders will need to assess what is already being done to address
diabetes care quality in their State. Talking with DPCP officials in the
State health department, Medicaid directors, State employee benefit
officials, State and community stakeholder group leaders, provider
associations and professional societies can help State leaders assess
what is already underway in the State, which efforts have been most
successful and where additional efforts are needed. The State Diabetes
Quality Improvement (QI) Inventory, presented in Table 4.2, is designed
to assist State leaders in assessing the range of diabetes programming
and determining the appropriate stage of development of an activity.

Based on this inventory, State leaders are ready to move to the next
stage in the quality improvement process -- actually developing a
quality improvement strategy for a State. The next module of the
*Resource Guide* is designed to assist State leaders with planning and
implementing diabetes quality improvement action strategies by using
information from previous modules in applying the PDSA model of quality
improvement to the public policy setting. [Module 5:
Improvement]{.underline} also discusses important components of
evaluation plans for State efforts.

**Associated Appendixes for Use With This Module**

**Appendix G: Index of Diabetes Quality Improvement Initiatives**

Appendix G provides brief descriptions and links to further information
for a variety of national and federal diabetes quality improvement
initiatives. State leaders may want to review and consider these
programs as models or resources for State action.

**Appendix H: CDC and State Funding for DPCP, by State, 2003-2004**

Appendix H shows the funding provided by the CDC to each State for the
DPCP and each State's contribution. State contributions are shown by
"general funds" and "in-kind" resources.

+----------------------------------+----------+------------+----------+
| **Table 4.2**                    |          |            |          |
|                                  |          |            |          |
| **State Diabetes Quality         |          |            |          |
| Improvement (QI) Inventory**     |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  | **Stage  |            |          |
|                                  | of       |            |          |
|                                  | Devel    |            |          |
|                                  | opment** |            |          |
+----------------------------------+----------+------------+----------+
| **State QI Actions**             | **Pl     | **Implem   | **Eval   |
|                                  | anning** | entation** | uation** |
+----------------------------------+----------+------------+----------+
| **PARTNERSHIP/PLANNING           |          |            |          |
| ACTIVITIES**                     |          |            |          |
+----------------------------------+----------+------------+----------+
| **Coalition/Advisory Board**     |          |            |          |
+----------------------------------+----------+------------+----------+
| **Collaborative**                |          |            |          |
+----------------------------------+----------+------------+----------+
| **Cross-Agency Initiatives**     |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  |          |            |          |
+----------------------------------+----------+------------+----------+
| **PROGRAM DEVELOPMENT            |          |            |          |
| ACTIVITIES**                     |          |            |          |
+----------------------------------+----------+------------+----------+
| **Diabetes Care Guidelines**     |          |            |          |
+----------------------------------+----------+------------+----------+
| **Data Measurement & Reporting** |          |            |          |
+----------------------------------+----------+------------+----------+
| **Information Technology**       |          |            |          |
+----------------------------------+----------+------------+----------+
| **Self-Management/Patient        |          |            |          |
| Education**                      |          |            |          |
+----------------------------------+----------+------------+----------+
| **Provider Training**            |          |            |          |
+----------------------------------+----------+------------+----------+
| **Collaborative**                |          |            |          |
+----------------------------------+----------+------------+----------+
| **Disease Management**           |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  |          |            |          |
+----------------------------------+----------+------------+----------+
| **DISSEMINATION ACTIVITIES**     |          |            |          |
+----------------------------------+----------+------------+----------+
| **Raising Awareness**            |          |            |          |
+----------------------------------+----------+------------+----------+
| **Minority & Rural Outreach**    |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  |          |            |          |
+----------------------------------+----------+------------+----------+
| **OTHER STATE DPCP ACTIVITIES**  |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  |          |            |          |
+----------------------------------+----------+------------+----------+
|                                  |          |            |          |
+----------------------------------+----------+------------+----------+
| **Other QI Action in my State**  |          |            |          |
+----------------------------------+----------+------------+----------+
| **Non-Governmental Initiatives** |          |            |          |
+----------------------------------+----------+------------+----------+
| **Federal Initiatives**          |          |            |          |
+----------------------------------+----------+------------+----------+
| **Local Initiatives**            |          |            |          |
+----------------------------------+----------+------------+----------+

**Module 5: Improvement - Developing a Strategy for Diabetes Quality
Improvement**

> *"As rates of diabetes increase across the country, roughly tracking
> with increases in obesity rates, States are quickly approaching a time
> when budgets will not be able to withstand the pressure of treating
> the flood of obesity-related diseases. Consequently, while we search
> for better and more efficient ways of treating diabetes and helping
> people manage the disease so that costly procedures can be prevented,
> we must find more ways to create incentives for people to make healthy
> lifestyle choices. The State that figures out how to do this, while
> respecting and protecting individual liberties, will be the model for
> the Nation."*

-- An Interview with Governor Mike Huckabee, Arkansas

Quality health care is a goal that all health care professionals and
policymakers can achieve, yet many do not know where to begin.

The challenge of the health care system is to define what is "quality
health care" and lead participants in the health care system to increase
quality, reduce mistakes, and attain quality results for every patient
every time. Some may view this as impossible. Others can point to the
great strides that have been made in manufacturing and other services by
applying the principles of quality improvement. And some can point to
dramatic improvements in reducing deaths in U.S. hospitals from applying
the principles of quality improvement (Gabor, 2004). Additionally, a
number of States today can point to gains that they have made in
diabetes outcomes for their citizens to confirm that quality improvement
in health care is possible.

[Module 1: Background]{.underline} provided an overview of diabetes and
quality improvement. [Module 2: Data]{.underline} provided a variety of
data sources with State-specific data on diabetes quality of care.
[Module 3: Information]{.underline} helped State leaders understand how
data must be examined to make comparisons and create information for
guiding decisions and leading change. [Module 4: Action]{.underline}
offered a variety of national, Federal, State, and local approaches,
resources, and best practices that can inform State quality improvement
efforts.

This module aims to assist State leaders to develop diabetes quality
improvement strategies suited to State contexts. [Module 5:
Improvement]{.underline} provides models for quality improvement,
presents a case study of how one State -- Wisconsin -- undertook an
ambitious quality improvement effort, and discusses how State leaders
can begin to develop their own State-specific strategies to improve
diabetes care quality.

**A Model for Quality Improvement**

While local contexts differ, models of quality improvement give the
common elements needed to stimulate change and improvement in any
situation. As State leaders embark on new initiatives or revitalize
existing ones, quality improvement models can inform those efforts. The
key is to find a suitable model for an individual State and its
partners, and then pick and choose the components that are most useful
for a specific local context. Explained below is a model that may be
useful for State leaders developing quality improvement strategies.

**Plan-Do-Study-Act (PDSA) Model**

A time-tested quality improvement tool still useful today is the
"Plan-Do-Check-Act" or the "Plan-Do-Study-Act" model for guiding quality
enhancement projects of all types (see Figure 5.2). The PDSA model
conceptualizes the continuing cycle of improvement.

Its steps for effective quality improvement include:

-   **Plan** --- Set the goals of the quality improvement
    cycle---questions, predictions, data to be collected, and the who,
    what, when, where of the project.

-   **Do** --- Carry out the plan and document problems and unexpected
    observations.

-   **Study** --- Complete the analysis of the data, compare to
    predictions, and summarize lessons.

-   **Act** --- Determine changes to be made and decide what will happen
    in the next cycle (Langley, Nolan, Nolan, et al., 1996).

> **Figure 5.1**
>
> ![](media/image22.wmf){width="4.7347222222222225in"
> height="3.004166666666667in"}
>
> **Source**: Adapted from Langley G, Nolan K, Nolan T, et al. The
> Improvement Guide: A Practical Approach to Enhancing Organizational
> Performance. San Francisco: Jossey-Bass Publishers, 1996.

The PDSA cycle usually applies at the point of production, in this case
to the front-line of health care at the point of care. The concept also
can be applied to the quality improvement role of State leaders. Drawing
on insights from State quality improvement activities around diabetes
care, State leaders might consider a "Partner-Plan-Do-Study-Act" model.

[]{.mark}

-   **Partner** --- Decide who are strategic partners of quality
    improvement and recruit them to the project --- champions in health
    care production, stakeholders (e.g., consumer/patient groups, health
    care professionals, purchasers, health plans, and topic experts,
    among others), and key State leaders and agencies (e.g., visible
    champions, diabetes experts, program planning/evaluation staff and
    quality improvement experts). Is the group large enough to include
    key leaders and perspectives, yet small enough to be productive?

-   **Plan** --- The goals of a project will be broad in the context of
    statewide activities because many partners and processes will need
    to be involved. What does the group predict are the current
    obstacles to quality care? How will the goals be put into action?
    What data need to be collected to prove that the changes are indeed
    improvements?

-   **Do** --- Test the plan and document problems and unexpected
    observations as data are collected. Initial plans seldom produce
    desired results the first time. Pilot test the ideas of the group
    with front-line health care programs, providers, and consumers.
    Reconvene the partners and discuss successes and problems.

-   **Study** --- Complete the data analysis, compare the results to
    predictions, and summarize lessons learned. Do the test results
    convince the partners that full-scale implementation will be
    successful? Because the scope of activities may be broad and costs
    may be involved, the planned action should be based on reasonable
    data and results.

-   **Act** --- Determine the changes to be made. Implement the changes
    State- or district-wide. Continually assess those changes through
    data collection and analysis. Are the changes working? What will
    happen in the next cycle?

The PDSA is one model of quality improvement that has withstood the test
of time. There are other tools and methodologies for quality improvement
to suit the various stages at which States find themselves. Following
are additional resources that States might want to use to facilitate
quality improvement wherever they are along the continuum.

-   The Quality Assurance Project, sponsored by the United States Agency
    for International Development (USAID), presents models based on
    quality improvement on an international scale. These models are
    useful and easily translated for States (information available at
    <http://www.qaproject.org/resourcesintro.html>).

-   The IHI breakthrough series focuses on change at the provider level,
    but is an important approach that State leaders should understand
    for developing change agents (information available at
    [http://www.ihi.org](http://www.ihi.org/)[/ihi]{.underline}).

Numerous tools are also available to further assist quality improvement
projects. Quality improvement tools suited for policymakers are
available on AHRQ's Quality Tools Web site at:
[http://www.qualitytools.ahrq.gov/channels/channel.aspx?mode=3&inc=browse.policy_makers.inc]{.underline}.
For another quality toolbox, see Tague (1995).

The PDSA model can be applied to the context of State leadership in
quality improvement. The actual approaches and actions that States will
take will be as varied as the examples that appear in [Module 4:
Action]{.underline} of this *Resource Guide*. One State's experience, in
particular, can help illustrate how the PDSA model can be applied to an
actual quality improvement project.

**PDSA Case Study: Wisconsin Collaborative Diabetes Quality Improvement
Project**

Wisconsin's DPCP, part of the Wisconsin Department of Health and Family
Services, received CDC funding in 1994 and other funding since then.
Over time the DPCP developed an ambitious strategy to improve diabetes
care quality for clients of managed care organizations. Wisconsin's
diabetes quality improvement efforts in many ways mirror the stages of
the PDSA model described above.

-   **Partner** --- In 1996, the DPCP formed the **Diabetes Advisory
    Group** comprised of over 50 diverse groups and organizations**.**
    Wisconsin's health maintenance organizations (HMOs) were key
    partners in the advisory group.

-   **Plan** --- One of the first plans developed by the Diabetes
    Advisory Group was the development of **Diabetes Mellitus Care
    Guidelines** and supporting documents for use by all health care
    providers in the State. Released in 1998, these guidelines were
    endorsed by the Advisory board members, and members promoted the use
    of the guidelines throughout the health care system blanketing the
    State with a common message about quality diabetes care. The DPCP
    used materials from other States and also worked with the University
    of Wisconsin-Madison to use data to assess the status of diabetes
    care in the State and adherence to the Diabetes Mellitus Care
    Guidelines.

-   **Do** --- Out of this successful effort, the **Wisconsin
    Collaborative Diabetes Quality Improvement Project** was established
    in 1999. The goal of the Diabetes Quality Improvement Project is to
    improve the quality of diabetes care for people who receive services
    through Wisconsin's HMOs and two other large health systems by:

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-   Evaluating implementation of the Wisconsin Essential Diabetes
    Mellitus Care Guidelines.

-   Sharing data issues, strategies, initiatives and lessons learned.

-   Improving diabetes care through collaborative quality improvement
    initiatives.

> Collaborators included university experts, Wisconsin's QIO, the State
> Medicaid program, and other health care industry partners. The
> department used a two-pronged approach to convince the HMOs to sign
> on. First, they leveraged the support of a well-connected spokesperson
> to discuss the guidelines and the possibility of forming the
> collaborative. Secondly, they participated in ongoing discussions
> about quality improvement in the private sector. They presented the
> collaboration as a potential win-win opportunity. HMOs would get value
> from the project through access to information, tools and ongoing
> support as well as receiving good media coverage for their work with
> the State. The DPCP would reach the 68 percent of the State's
> population served by the participating HMOs.

-   **Study** --- Collaborators used the Health Plan Employer Data and
    Information Set **(HEDIS^®^) comprehensive diabetes care measures**
    to track progress in improving diabetes care. The State's DPCP
    contracted with the University of Wisconsin to provide confidential
    analysis and reporting. Each HMO was given a confidential
    identifying number so it could see how its performance compared with
    other organizations. The project was careful to use these data
    results cooperatively, not competitively, with a goal of improving
    diabetes care in Wisconsin. Participants in the Collaborative
    continued to discuss issues and strategies such as registry
    development, data collection, and provider profiles.

> The quality improvement plan included evaluative efforts to assess
> improvements in diabetes care. An evaluation of the HEDIS^®^ data
> showed that the project:

-   Increased eye exams for people with diabetes from 62 to 69 percent.

-   Increased cholesterol screening and control from 72 to 78 percent
    and 45 to 51 percent, respectively.

-   Increased kidney disease monitoring from 47 to 52 percent.

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-   **Act** --- With data collection and reporting in place, the
    Wisconsin Collaborative Diabetes Quality Improvement Project took
    further action by focusing its quality improvement efforts. With
    HbA1c rates already at 90 percent in the State but eye examination
    rates much lower, the partners determined that the project should
    establish a statewide Diabetes Eye Care Initiative. In 2001, this
    project began with the goal of increasing eye examination rates and
    enhancing communication among specialists and primary care providers
    related to diabetes eye care.

> In October 2003, the project released a compendium of the diabetes
> quality initiatives implemented in the 5years since the project began.
> In addition to describing the interventions used, the compendium
> provided information on barriers, ongoing challenges, and lessons
> learned. Some of the lessons and strategies used to achieve the
> encouraging results of the project were:

-   More inclusive quality improvement teams over time

-   Increased use of diabetes care teams, champions, and case management
    services

-   More in-depth barrier analysis and intervention evaluation

-   Community collaboration

-   More advanced information systems for developing, tracking, and
    feedback on "targeted" interventions

-   Support for providers and clinics

-   An increased focus on the role of the consumer

-   Increased use of technology to enhance communication and outreach
    (Wisconsin Department of Health and Family Services, 2003).

More information on the Wisconsin Collaborative Diabetes Quality
Improvement Project, is available at:
[http://www.dhfs.state.wi.us/Health/diabetes/Diabetes_Collaborative_Improvement_Project.htm]{.underline}

As this case study demonstrates, quality improvement can take many years
and iterations before actual change and quality improvement can be
documented. However, the reward is that once the partnerships and
processes are in place there is the opportunity to see measurable
advances in care quality and in health outcomes.

**Developing a State Strategy for Improving Diabetes Care Quality**

The PDSA quality improvement model described above can be combined with
previous modules of this *Resource Guide* to build a strategy for
improving diabetes care quality. AHRQ has also developed a companion
*Workbook* that can assist State leaders through a step-by-step process
for using the data, information and resources from this *Resource Guide*
to develop the case for diabetes quality improvement in a particular
State, examine strategic areas for improvement, and develop a detailed
strategy.

Described below are tools that can help State leaders develop a State
quality improvement strategy. These tools can be used in conjunction
with the *Workbook* exercises. The first tool is the State Diabetes
Quality Improvement Worksheet (see Figure 5.2) that can assist State
leaders with assembling the data about their State and diabetes. Another
tool is the PDSA checklist that provides the common steps State leaders
need to take to build a quality improvement strategy. In working through
these tools, State leaders are advised to work closely with State DPCP
officials to plan and develop their State's diabetes quality improvement
strategy.

**Building the Case for Diabetes Quality Improvement**

One step in the process of developing a quality improvement strategy is
for State leaders to gather information about diabetes in their State.
The worksheet below helps State leaders to assemble State-specific
information on diabetes prevalence, cost and quality of care to assess
opportunities for improvement. This worksheet information combined with
the inventory of programmatic activities related to diabetes assembled
at the end of [Module 4: Action]{.underline} allows State leaders to
assess the current condition of diabetes care and public policy in their
State. Using this information, they can make the case that diabetes
quality improvement is important for their State.

**[Figure 5.2. State Diabetes Quality Improvement
Worksheet]{.underline}**

**From Table 2.3 -- State-Specific Estimate of Cost Burden of
Diabetes:**

-   Number of people with diabetes

-   Percent of the population with diabetes

-   Direct cost of diabetes in the State

-   Indirect cost of diabetes in the State

**From Table 2.1 -- NHQR Diabetes Quality Measures**

-   HbA1c testing rate (two or more times per year) in the State

    -   national average 61

    -   HP 2010 goal 50

    -   Best in class State average 82

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-   Retinal eye examination rate in the State

    -   national average 67

    -   HP 2010 goal 75

    -   Best in class State average 81

-   Foot examination rate in the State

    -   national average 65

    -   HP 2010 goal 75

    -   Best in class State average 82

-   Flu vaccination rate in the State

    -   national average 37

    -   HP 2010 goal n/a

    -   Best in class State average 58

**From Appendix H -- CDC and State Funding for Diabetes Program**

-   CDC funding

-   State in-kind and general funding

-   State funding in States with similar disease burden

-   State funding ranges in surrounding States

**From Appendix F -- NHQR Quality Measures for Various Conditions**

Measures on which state is below average (indicated by a minus sign in
the column for the state)

-   
-   
-   
-   

**Putting the PDSA Model To Work**

The adapted PDSA model is a general model intended to capture the most
important components of quality improvement; but State leaders may
wonder how to put it to use. Provided below is a checklist of PDSA
quality improvement steps. This checklist outlines the common steps that
State leaders need to take to develop a quality improvement strategy.
Using the checklist as a framework, State leaders can fill in the State
and local background, data, information, public policy approaches and
other resources to develop a strategy suited to their particular
context.

As State leaders do this, one of the most important factors to bear in
mind is the cyclical nature of quality improvement. Improving health
care quality is not a one-time activity but must be ongoing. Sustained
improvement usually occurs over many years. Thus, the most effective
action plans will include not only short-term goals but long-range ones
as well.

**Integrating Quality Improvement Activities Across Conditions**

Diabetes is one of several chronic illnesses with demonstrated
opportunities for quality of care improvements. Care for asthma, cancer,
heart disease, and other common chronic conditions affecting millions of
Americans too often falls short of what research has indicated to be the
most effective treatments. Diabetes and other chronic conditions
combined account for 78 percent of all health care spending and 7 out of
10 deaths.

State leaders can use this information to help decide how broadly or
narrowly to focus their quality improvement efforts. Diabetes may be
just one of several costly health care conditions that are appropriate
areas to invest in quality improvement efforts in a given State. In
addition, there may be advantages to integrating quality improvement
efforts across several conditions, such as stretching scarce resources
by using economies of scale across programs, minimizing investment costs
in infrastructure, and maximizing the effect of systemic changes in
health care delivery.

Thus, some States may choose to expand on existing diabetes quality
improvement efforts while other States may want to establish
comprehensive quality improvement efforts that target several diseases
at once. For example, several States, including Wisconsin, have expanded
their efforts with diabetes to address heart disease since the two are
related. On the other hand, Vermont has initiated a broader chronic care
initiative to improve the quality of care for all chronic diseases but
has chosen diabetes as the first focus area for the initiative. (See the
Vermont Chronic Care Initiative Web site at
[http://www.healthyvermonters.info/hi/chronic/chroniccare.shtml]{.underline}
for more information.)

**The Importance of Evaluation**

Evaluation is essential to understand whether a quality improvement
activity is accomplishing planned goals, whether goals and actions are
ultimately improving the health outcomes of the population, and what
adjustments are necessary. Evaluation in quality improvement can be done
quickly, as often suggested by facilitators, to maintain momentum of the
quality improvement activity. Evaluation can also look at longer term,
underlying components of the program. One program that can serve as a
resource for State leaders in developing an evaluation plan for diabetes
quality improvement efforts is the CDC's accountability efforts for
State DPCPs.

In 1999, the CDC began addressing the need for more systematic
State-level programmatic evaluation and accountability in the National
Diabetes Prevention and Control Program. The CDC devised seven national
objectives for diabetes care, including increasing the percentages of
people with diabetes receiving HbA1c testing, eye exam, foot exam, and
influenza vaccination. In addition, the national objectives include
reducing health disparities and establishing linkages to other wellness
programs, such as: physical activity or smoking cessation programs for
people with diabetes.

The CDC asked States to devise their own State objectives for improved
diabetes population health in order to address the uniqueness of each
State's population. A critical objective that States usually include is
to establish measurement procedures to track progress. The CDC focus on
evaluation emphasizes the importance of measurement. The State DPCP has
become a catalyst for statewide improvements through partnering and
accountability on various operational levels.

**CDC Model for Program Evaluation**

The CDC employs a model of evaluation that includes four groupings of
standards for program evaluation, and six repeating steps in the
evaluation process as illustrated in Figure 5.3.

**Figure 5.3. CDC model of evaluation**

![](media/image23.jpeg){width="3.2354166666666666in"
height="2.939583333333333in"}

> **Source**: Centers for Disease Control and Prevention. Framework for
> Program Evaluation in Public Health. MMWR 1999;48(No. RR-11).
> Available at: <http://www.cdc.gov/eval/framework.htm> (accessed March
> 3, 2004).

The CDC provides 30 standards under the four subgroups of utility,
feasibility, propriety, and accuracy. These standards are guidelines for
conducting sound and fair evaluations and may be briefly described as
follows:

-   **Utility** ensures that the evaluation serves the needs of intended
    users.

-   **Feasibility** results in evaluations that are realistic and
    sensible.

-   **Propriety** ensures ethical integrity in the conduct of the
    evaluation.

-   **Accuracy** leads to information that is technically sound.

**Steps in the Evaluation Process**

The six steps in the evaluation process may vary as to when they are
carried out, though one step usually lays a foundation for the next.
Steps will be repeated as results become clear and new issues arise.
Each step serves to ensure the effectiveness of the evaluation.

-   **Engaging stakeholders** is essential to ensure that the evaluation
    addresses the important elements of the program and that the
    evaluation is used. Stakeholders include those served by the
    program, those planning and directing the program, and those
    involved in program operations.

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-   **Describing the program** serves two functions. First, it lays out
    in detail the program's goals and strategies so that everyone
    involved understands them. Second, it provides an opportunity for
    consensus building around the goals and strategies.

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-   **Focusing the evaluation design** addresses the greatest issues of
    concern. This step includes identifying the purpose of the
    evaluation; defining the users and usefulness of the evaluation;
    listing stakeholders' questions that need to be addressed;
    establishing methods to ascertain information upon which the
    evaluation will be based; and developing consensus around particular
    roles and responsibilities pertaining to the evaluation.

-   **Gathering credible evidence** contributes to the robustness of the
    evaluation. Developing credible evidence involves defining
    appropriate indicators, identifying legitimate sources of
    information; ensuring the quality of data gathered; and aligning the
    infrastructure for collecting evidence with the environment (and
    individuals) from which the information is gathered.

-   **Justifying conclusions** is important to ensure that the
    evaluation will be used. When consensus is reached regarding the
    goals and strategies of the program, when the values of the
    evaluation are aligned, and when the evidence gathered is credible,
    then conclusions will naturally be justified. At this point,
    conclusions and recommendation can be made.

-   **Ensuring use and sharing lessons learned** includes designing
    mechanisms for feedback and dissemination of the information gained
    in the evaluation.

Employing an evaluation specialists or, at the least, assembling an
evaluation team with a designated leader will help facilitate the
process. Issues regarding internal bias and external influences must be
addressed to ensure integrity of the analytic work and a trusted
evaluation of a program or project.

To be effective, however, evaluation strategies must be timely and
useful. They should be considered at the beginning of the project and
they should have a reasonable deadline for completion. Including an
experienced evaluator on the quality improvement team can help ensure
that the evaluation will be sound, useful, and timely. The evaluation
should feed back to the quality improvement cycle and direct future
actions.

**Summary and Synthesis**

Module 5 has provided a model of quality improvement, a case study of
one State's innovative quality improvement strategy and some tools and
considerations to help State leaders develop their own quality
improvement strategy. This module does not provide States with one
approach or answer. Instead, decisions about the kind of quality
improvement strategies that a State should pursue are the responsibility
of State leaders and their partners, who are positioned to know what is
best suited for their State context.

There are common elements to quality improvement that can inform the
development of State strategies. The PDSA model adapted for the
policymaking context is one approach that can assist State leaders. It
is also important that State innovators examine the current condition of
diabetes care and what diabetes programs are underway. State quality
improvement efforts can then build on and fill in the gaps to develop a
more comprehensive, coordinated approach to improving care. Because
quality improvement occurs over a long time frame, evaluation is crucial
to determine what effects the strategy has had and to justify continued
efforts and resources over time.

**Resources for Further Reading**

Langley G, Nolan K, Nolan T, Norman C, Provost L. The Improvement Guide:
A Practical Approach to Enhancing Organizational Performance. San
Francisco: Jossey-Bass Publishers, 1996.

Tague NR. The Quality Toolbox. Milwaukee, WI: ASQC Quality Press, 1995.

**Associated Appendix for Use With This Module**

**Appendix F: NHQR Quality Measures for All Conditions by State**

Appendix F provides quality measures for all conditions and topics in
the NHQR. It includes the national estimate and an indicator for whether
or not the State estimate (not shown due to space limitation) is
statistically greater, lower, or no different from the national average.
This resource can help States identify which diseases and their
treatments may be in need of attention.

**Module 6: The Way Forward -- Promoting Quality Improvement in the
States**

> *"Quality health care means doing the right thing at the right time in
> the right way for the right person and having the best results
> possible."* (AHRQ, 2003c)

Health care analysts and researchers have long documented extensive gaps
in the quality of care delivered to Americans. Despite unrivaled
technological innovation in American health care, too much of the care
that is delivered to patients does not meet the accepted standards of
quality. The findings of the *National Healthcare Quality Report* and
the *National Healthcare Disparities Report* provided further
confirmation that, while in some areas care is improving, the health
care system in America has a long way to go before it delivers care that
is consistent with accepted guidelines and does not vary significantly
by geography, race, ethnicity, or socioeconomic status.

There is a great deal that State leaders can do to support and encourage
quality improvement, and thereby, to improve health outcomes, reduce the
burden of disease, and possibly increase the efficiency of the health
care system. As guardians of public health and health care innovators,
States can champion quality improvement and best practices that can
transform health care systems. In most cases, State government is also
one of the largest health care purchasers in a State due to Medicaid and
State employee health insurance programs. With States experiencing
budget problems and high growth in health care costs, States cannot
afford to pay for a product that does not meet accepted standards of
quality. Alone or in concert with other purchasers, State governments
can help steer the health care system toward the consistent delivery of
high quality care.

Diabetes is one chronic condition that has a compelling case for quality
improvement for States. The disease burden from diabetes is great in
terms of the number of people affected, the cost of complications, its
effect on quality of life, and the disparities in care between racial
and ethnic groups. Despite its prevalence and cost, research has
demonstrated that type 2 diabetes can be prevented, and complications
from both type 1 and type 2 diabetes can be prevented or significantly
delayed with appropriate treatment. Diabetes also has widely accepted,
evidence-based guidelines for care and a strong case for a return on
investment for purchasers and society from diabetes quality improvement
efforts.

The NHQR provides an array of national and State-level data that can
help States focus their quality improvement efforts. This *Resource
Guide* has taken the data from the NHQR on diabetes and placed it within
a model of quality improvement to assist States with improving diabetes
care. State leaders now have the opportunity to contribute to the
growing momentum to improve the quality of care in America through State
initiatives.

**What Can State Leaders Contribute to Quality Improvement?**

As State leaders examine how they can be involved in improving the
health care quality, there are a number of essential elements that State
leaders can contribute to the process. These elements include: providing
leadership and shared vision, fostering partnerships and collaborations
between key parties, planning and setting goals, enhancing measurement
and reporting, improving the infrastructure of health care quality,
assuring evaluation and accountability, and creating incentives.

-   **Providing leadership and vision---**Quality improvement requires
    leadership. It will not emerge without a champion who can provide
    leadership for organizations and individuals to work together to
    develop a shared vision and common goals for health care quality.
    Whether initiatives are developing locally, statewide or nationally,
    effective leadership is essential to quality improvement. However,
    health care quality cannot be accomplished by one champion, be it a
    person or an organization. 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 of the groups that have an effect on
    patient care should participate in quality improvement efforts for
    an initiative to be successful, including health care professionals,
    providers, patients, and purchasers. Health care 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 diabetes can be powerful allies for change
    and a source of expertise. State health department staff and other
    diabetes care experts from private sector organizations can provide
    support and expertise for State initiatives.

-   **Assisting planning and goal setting---**Once partnerships and
    collaborations are initiated, the quality improvement group must
    develop 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 buy-in
    and continued involvement.

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-   **Enhancing measurement and reporting---**Another essential piece
    for understanding quality is defining quality standards and
    developing measures to track how well or poorly the quality
    improvement intervention is working and the health care system is
    performing. In addition to assessing quality-improvement activities,
    measurement and reporting provide a mechanism for comparing how well
    any given health plan or provider is doing in a selected area of
    care. Quality measurement can involve counting the number of
    patients who received a needed immunization or screening or how
    often patients had to be hospitalized due to complications after a
    surgery by a given provider. Health care providers and companies
    must have data systems that are robust enough to estimate a given
    set of measures of health care quality. Results then must be made
    available for purchasers and consumers to be able to make meaningful
    comparisons of the performance of various providers. As discussed
    earlier in the *Resource Guide*, there is a well developed set of
    measures for diabetes care quality and many existing data collection
    and reporting systems that are available to States. There is a wide
    array of public measures and publicly available data sources
    reported in the NHQR, as well as other important measures that
    States may want to incorporate into a quality improvement and
    evaluation strategy.

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-   **Improving the health care infrastructure**---Part of quality
    improvement is the ability to make necessary adjustments in the
    infrastructure of health care. Infrastructure in health care can
    include professional education, data systems, financing and delivery
    systems, research, and patient education resources, among others.
    Health care quality evaluation by its nature highlights areas for
    improvement, thus drawing attention to areas where health care
    professionals may need additional education or training, where
    providers may need to redesign systems of care, where payers need to
    improve financial incentives, and where purchasers need to allocate
    more resources to address quality concerns and reward superior
    performance. Evaluation of health care quality can also reveal areas
    for further research and ways that patients can be actively engaged
    in managing their care and changing behavior such as smoking
    cessation, nutritional improvements, or other areas that may
    contribute to health care problems.

-   **Assuring evaluation and accountability---**After establishing
    partnerships with solid leadership and common vision and goals,
    measuring and collecting data on quality, and reporting it for
    comparison, there also is the need for evaluation of both health
    system performance and accountability for health care quality.
    Evaluation allows partners to identify the most troublesome areas
    and prioritize resources and attention to those areas that most need
    improvement. Evaluation promotes the opportunity to celebrate 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---**While reporting data on performance is
    often enough to spur low performers toward improvement, there is
    also the need to tie rewards to high performance. Currently, the
    American health care system essentially pays all providers the same
    regardless of the level of quality that they deliver. Without
    adequate rewards for improvement, health care quality will languish.
    As one of the largest health care purchasers in any given State,
    State governments can have a tremendous influence over incentives
    for quality improvement in the health care system through their
    payment structures.

This *Resource Guide* has attempted to demonstrate for State leaders the
need for quality improvement in diabetes. Much has been done, but data
from the NHQR show that much remains to be done to achieve quality
diabetes care for all people with diabetes. The number of people newly
diagnosed with diabetes and the number suffering its complications are
still growing.

By reviewing and analyzing this *Resource Guide*, assessing their local
context, and designing a diabetes 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 diabetes care provide valuable
insights into what can be accomplished through innovative, visionary
efforts by State leaders.

**References**

Agency for Health Care Administration (AHRQ, 2000). The Florida Medicaid
Disease Management Initiative: A report on the Florida Medicaid Disease
Management Program -- Historical perspective, start-up activities,
current operations, future operations and expectations. Available at:
<http://www.statecoverage.net/statereports/fl2.pdf> (accessed February
27, 2004).

Agency for Healthcare Research and Quality (AHRQ, 2001). AHRQ Quality
Indicators---Guide to Prevention Quality Indicators: Hospital Admission
for Ambulatory Care Sensitive Conditions, AHRQ Pub. No. 02-R0203.
Rockville, MD: Agency for Healthcare Research and Quality, 2001.
Available at:
[http://www.qualityindicators.ahrq.gov](http://www.qualityindicators.ahrq.gov/)
(accessed February 20, 2004).

Agency for Healthcare Research and Quality (AHRQ, 2002). AHRQ Quality
Indicators---Guide to Inpatient Quality Indicators: Quality of Care in
Hospitals---Volume, Mortality and Utilization, AHRQ Pub. No. 02-R0204.
Rockville, MD: Agency for Healthcare Research and Quality, 2002.
Available at:
[http://www.qualityindicators.ahrq.gov](http://www.qualityindicators.ahrq.gov/)
(accessed February 20, 2004).

Agency for Healthcare Research and Quality (AHRQ, 2003). AHRQ Quality
Indicators---Guide to Patient Safety Indicators, AHRQ Pub. No. 03-R203.
Rockville, MD: Agency for Healthcare Research and Quality, 2002.
Available at:
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(accessed April 9, 2004).

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**Appendix A: Acronyms Used in This Resource Guide**

AAHP/HIAA = American Association of Health Plans/Health Insurance
Association of America

ADA = American Diabetes Association

AHCA = Agency for Health Care Administration (of Florida)

AHRQ = Agency for Healthcare Research and Quality

AHRQ QIs = AHRQ Quality Indicators

AMA = American Medical Association

BRFSS = Behavioral Risk Factor Surveillance System

BPHC = Bureau of Primary Health Care (of HRSA)

CDC = Centers for Disease Control and Prevention

CHC = Community Health Center

CMS = Centers for Medicare & Medicaid Services

CSG = Council of State Governments

CPS = Current Population Survey

DCCT = Diabetes Control and Complications Trial

DDI = Diabetes Detection Initiative (of Michigan)

DHHS = (U.S.) Department of Health and Human Services

DON = Diabetes Outreach Network (of Michigan)

DPCP = Diabetes Prevention and Control Program (joint CDC/State program)

DQIP = Diabetes Quality Improvement Program

FQHC = Federally Qualified Health Centers

HDL = High density lipoproteins

HCUP = Healthcare Cost and Utilization Project

HEDIS^®^ = Health Plan and Employer Data and Information Set

HP2010 = Healthy People 2010 (of NIH and CDC)

HRSA = Health Resources and Services Administration

ICIC = Improving Chronic Illness Care

IHI = Institute for Healthcare Improvement

IHS = Indian Health Services

IOM = Institute of Medicine

JCAHO = Joint Commission on Accreditation of Healthcare Organizations

MEPS = Medical Expenditure Panel Survey

MSA = Metropolitan Statistical Area

NCSL = National Conference of State Legislatures

NCQA = National Committee for Quality Assurance

NDCP = National Diabetes Control Program

NDPCP = National Diabetes Prevention and Control Program

NDEP = National Diabetes Education Program

NDIC = National Diabetes Information Clearinghouse

NDQIA = National Diabetes Quality Improvement Alliance;

> the NDQIA includes AHRQ, American Academy of Family Physicians;
> American Association of Clinical Endocrinologists; American College of
> Physicians; American Diabetes Association; American Medical
> Association; Centers for Disease Control and Prevention; Centers for
> Medicare & Medicaid Services; Joint Commission on Accreditation of
> Healthcare Organizations; National Committee for Quality Assurance;
> National Institute of Diabetes and Digestive and Kidney Disease; The
> Endocrine Society; U.S. Department of Veterans Affairs

NHIS = National Health Interview Survey

NHANES = National Health and Nutrition Examination Survey

NHDR = National Healthcare Disparities Report

NHDS = National Hospital Discharge Survey

NHQR = National Healthcare Quality Report

NIH = National Institutes of Health

PDSA = Plan-Do-Study-Act

QIO = Quality Improvement Organization (of CMS)

**Appendix B: List of NHQR Data Sources, Including Those Supporting
State Estimates**

The following is a list of the data sources assembled and used in the
NHQR. Those sources in **bold** collect data and support analyses at the
State level, although they may or may not be available in the NHQR:

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   

-   **Behavioral Risk Factor Surveillance System (BRFSS)** Dialysis
    Facility Compare (DFC) **Healthcare Cost and Utilization Project
    (HCUP)** Health Plan Employer Data and Information Set (HEDIS^®^)
    HIV/AIDS Surveillance SystemMedical Expenditure Panel Survey (MEPS)
    Medicare Quality Monitoring System (MQMS) **Minimum Data Set (MDS)**
    National Ambulatory Medical Care Survey (NAMCS) **National CAHPS®
    Benchmarking Database (NCBD)** National Health and Nutrition
    Examination Survey (NHANES) National Health Interview Survey (NHIS)
    National Home and Hospice Care Survey (NHHCS) National Hospital
    Ambulatory Medical Care Survey (NHAMCS) National Hospital Discharge
    Survey (NHDS) **National Immunization Survey (NIS)** National
    Nosocomial Infections Surveillance (NNIS) National Nursing Home
    Survey (NNHS) National TB Surveillance System (NTBSS) **National
    Vital Statistics System ---Linked Birth and Infant Death Data
    (NVSS-I)National Vital Statistics System, Mortality (NVSS-M)Outcome
    and Assessment Information Set (OASIS) Quality Improvement
    Organization (QIO)** Surveillance, Epidemiology, and End Results
    Program (SEER) **United States Renal Data System (USRDS)**

# Appendix C: Additional Data Resources Related to Diabetes Care Quality

# 

This appendix contains additional information and detailed tables on the
following:

-   *National Healthcare Quality Report measures selection process*

> Table C.1: Diabetes measure set for the NHQR with endorsing
> organizations, data sources, and level of data

-   *Descriptions of data sources for the process and outcome measures
    discussed in this [Resource Guide]{.underline} (including notable
    differences between MEPS and BRFSS national rates) and data tables
    from the NHQR*

> Table C.2: Percent of non-institutionalized adults over age 18 saying
> they were diagnosed with diabetes who reported having important tests
> in the past year (or two years in one case), by national population
> subgroup, United States, 2000
>
> Table C.3: Percent of adults age 18 and over with diagnosed diabetes
> who have specific HbA1c levels and who have specific blood pressure
> levels, United States, 1999-2000
>
> Table C.4: Hospital admission for adults over age 18 for specific
> diabetes complications (excluding obstetric and neonatal admissions
> and transfers from other institutions) per 100,000 population age 18
> and older, Healthcare Cost and Utilization Project (HCUP), United
> States, 2000
>
> Table C.5: Lower extremity amputations in persons with diabetes per
> 1,000 population (all ages), National Hospital Discharge Survey,
> United States, 1997-2000

# Table C.6: CDC Three-Year Baseline: Percent of adults age 18 and over with diabetes who had recommended diabetes tests in the past year, pooled 1997-1999

# Table C.7: CDC Annual Trends: Percent who had a dilated-eye examination in the past year per 100 adults with diabetes, crude rates and age-adjusted rates, by State, 1995-2002

> Table C.8: CDC Annual Trends: Percent who had a foot examination in
> the past year per 100 adults with diabetes, crude rates and
> age-adjusted rates, by State, 1995-2002
>
> Table C.9: CDC Annual Trends: Percent who had an influenza vaccination
> in the past year per 100 adults with diabetes, crude rates and
> age-adjusted rates, by State, 1995-2002

-   *Flow chart of steps for estimating State Medicaid spending on
    diabetes care*

Figure C.1: Estimation steps for Medicaid spending on diabetes care

***National Healthcare Quality Report* Measures Selection Process**

Researchers have developed health care quality measures based on
scientific evidence, practice guidelines, and consensus processes.
Consensus building around measure sets has been used recently to narrow
the list of measures and increase their acceptance.

*Consensus building* is a process by which stakeholders and experts in a
field identify a connection between a measure and quality health care.
The process generally includes expert judgment and evaluation, rigorous
testing of the measure in the field to ensure that improved performance
is linked to improved health, and review and agreement of the experts.
Several organizations are involved in developing national quality
measure sets, including:

-   The National Diabetes Quality Improvement Alliance (discussed in
    [Module 4: Action]{.underline})

-   The National Quality Forum

-   The National Committee on Quality Assurance, the accrediting body
    for managed care health plans, with its HEDIS^®^ performance
    measures

In the first NHQR, AHRQ pursued a careful process to define the first
set of NHQR measures. The underlying framework for selecting the NHQR
measure set was developed by the Institute of Medicine in *Envisioning
the National Health Care Quality Report*. Their matrix framework crosses
components of health system quality (effectiveness, safety, timeliness,
and patient centeredness) with consumers' health care needs (staying
healthy, getting better, living with illness or disability, and
end-of-life care). Measures were chosen to fill the cells of this matrix
so that all areas of health care quality would be addressed. The measure
selection process:

1.  Invited organizations with consensus-based measures (developed with
    experts and often rigorous testing) to submit them for review.

2.  Issued a public call for measures and data sources.

3.  Convened a Federal Interagency Workgroup to evaluate (based on
    importance, scientific soundness and feasibility) the 600 measures
    submitted and to select the final set of 147 measures.

4.  Invited public review of the final measure set.

Out of the set of 147 measures in the NHQR, 12 are diabetes measures.
All diabetes measures were developed through consensus processes of the
endorsing organizations. Table C.1 below lists the NHQR diabetes
measures, the organizations that endorse them through a consensus
process, the data sources, and the analytic level (State or national)
supported by the data.

> **Table C.1. Diabetes measure set for the NHQR with endorsing
> organizations, data sources and level of data**

+------------------------------------+--------------+---------+-------+
| **Diabetes measure**               | **O          | **Data  | **Ana |
|                                    | rganizations | s       | lytic |
|                                    | that endorse | ource** | le    |
|                                    | through      |         | vel** |
|                                    | consensus    |         |       |
|                                    | process**    |         |       |
+------------------------------------+--------------+---------+-------+
| Process: Percent of adults with    | AHRQ, AMA,   | MEPS    | Nat   |
| diabetes who had a hemoglobin A1c  | HP2010,      |         | ional |
| measurement at least once in past  | JCAHO, NCQA, | BRFSS   | and   |
| year                               | NDQIA        |         | State |
+------------------------------------+--------------+---------+-------+
| Process: Percent of patients with  | AHRQ, JCAHO, | MEPS    | Nat   |
| diabetes who had a lipid profile   | NCQA, NDQIA  |         | ional |
| in past 2 years                    |              |         |       |
+------------------------------------+--------------+---------+-------+
| Process: Percent of adults with    | AHRQ,        | MEPS    | Nat   |
| diabetes who had a retinal eye     | HP2010,      |         | ional |
| examination in past year           | JCAHO, NCQA, | BRFSS   | and   |
|                                    | NDQIA        |         | State |
+------------------------------------+--------------+---------+-------+
| Process: Percent of adults with    | AHRQ, JCAHO, | MEPS    | Nat   |
| diabetes who had a foot            | NCQA, NDQIA  |         | ional |
| examination in past year           |              | BRFSS   | and   |
|                                    |              |         | State |
+------------------------------------+--------------+---------+-------+
| Process: Percent of adults with    | AHRQ, JCAHO, | MEPS    | Nat   |
| diabetes who had an influenza      | NCQA, NDQIA  |         | ional |
| immunization in past year          |              | BRFSS   | and   |
|                                    |              |         | State |
+------------------------------------+--------------+---------+-------+
| Outcome: Percent of adults with    | NDQIA        | NHANES  | Nat   |
| diagnosed diabetes with HbA1c      |              |         | ional |
| level \> 9.5 percent (poor         |              |         |       |
| control); \< 9.0 percent (needs    |              |         |       |
| improvement); \< 7.0 percent       |              |         |       |
| (optimal)                          |              |         |       |
+------------------------------------+--------------+---------+-------+
| Outcome: Percent of adults with    | NDQIA        | n/a     | n/a   |
| diagnosed diabetes with most       |              |         |       |
| recent LDL-C level \< 130          |              |         |       |
| mg/dL(needs improvement); \<100    |              |         |       |
| (optimal)                          |              |         |       |
+------------------------------------+--------------+---------+-------+
| Outcome: Percent of adults with    | NDQIA        | NHANES  | Nat   |
| diagnosed diabetes with most       |              |         | ional |
| recent blood pressure \<140/90     |              |         |       |
| mm/Hg                              |              |         |       |
+------------------------------------+--------------+---------+-------+
| Outcome: Hospital admissions for   | AHRQ,        | HCUP    | Nat   |
| uncontrolled diabetes per 100,000  |              |         | ional |
| population                         |              |         |       |
+------------------------------------+--------------+---------+-------+
| Outcome: Hospital admissions for   | AHRQ         | HCUP    | Nat   |
| short term complications of        |              |         | ional |
| diabetes per 100,000 population    |              |         |       |
+------------------------------------+--------------+---------+-------+
| Outcome: Hospital admissions for   | AHRQ         | HCUP    | Nat   |
| long term complications of         |              |         | ional |
| diabetes per 100,000 population    |              |         |       |
+------------------------------------+--------------+---------+-------+
| Outcome: Hospital admissions for   | AHRQ,        | NHDS    | Nat   |
| lower extremity amputations in     | HP2010,      |         | ional |
| patients with diabetes per 1,000   | JCAHO, NCQA  |         |       |
| population                         |              |         |       |
+------------------------------------+--------------+---------+-------+

**Key**: AHRQ=Agency for Healthcare Research and Quality; AMA = American
Medical Association; BRFSS=Behavioral Risk Factor Surveillance System;
HCUP=Healthcare Cost and Utilization Project; HP2010=Healthy People
2010; JCAHO = Joint Commission on Accreditation of Healthcare
Organizations; NCQA=National Committee for Quality Assurance;
NDQIA=National Diabetes Quality Improvement Alliance; NHANES=National
Health and Nutrition Examination Survey; NHDS = National Hospital
Discharge Survey.

**Data Source Description, Limitations, and Data Tables From the NHQR**

**Notable Differences Between MEPS and BRFSS National Rates**

Some of the MEPS and BRFSS measures on diabetes are the same and both
are used in the NHQR. However, only one MEPS measure is used in this
*Resource* *Guide* because not only does MEPS not give State-level
estimates, the methods used to derive the MEPS and BRFSS estimates for
the same measures differ. As a result, the NHQR diabetes estimates from
MEPS and BRFSS show notable differences for the HbA1c and immunization
measures. MEPS reports that 90 percent of people with diabetes get one
or more HbA1c test per year; BFRSS reports 79 percent. MEPS reports 55
percent of people with diabetes receive a flu vaccination; BRFSS reports
37 percent. MEPS and BRFSS are very close on the eye and foot
examination rates, 67 versus 67 percent and 66 versus 65 percent,
respectively.

The difference for the HbA1c test rate is in part due to the structure
of the survey questions and in part due to the treatment of respondents
who "have not heard of HbA1c." While BRFSS allows for this distinction
to be made in the response options, MEPS does not. These respondents are
counted as though they answered "no" in BRFSS, and potentially not
included in MEPS. The percentage of these responses in BRFSS is fairly
low, at about 5% in 2001, but will still affect the final rate.

The difference for influenza immunizations is due to definitional
differences between BRFSS and MEPS. The BRFSS rate is for adults age 18
to 64; the MEPS rate is for adults age 18 and over. Since flu shots are
more often given to the elderly, the BRFSS rate is lower than the MEPS
rate.

There are other differences between the two data sources that can
contribute to the differences between estimates of the same measures. 
The surveys relate to different time periods, use different sampling
approaches, and use different interview techniques, to name the
obvious.  Because of the differences in the estimates of the same
measures and because only BRFSS permits State estimates, only BRFSS
estimates are discussed in [Module 3: Information]{.underline} of this
*Resource Guide*.

**Process Measures---MEPS data**

The MEPS data provide national benchmarks by important segments of the
population. Its breakdowns identify subgroups for whom diabetes care
quality can be problematic and for whom solutions need to be targeted.

Table C.2 shows estimates for the five MEPS diabetes-related measures in
the NHQR. The estimates are provided by national subgroups related to
race, ethnicity, sex, age, education, employment status, income, health
insurance status, respondent's location, and perceived health status.
Table C.2 shows the rate per 100 respondents (or percent) and the
standard error for each measure and subgroup. Only estimates that have a
standard error that is less than 30 percent of the estimate (relative
standard error \< 30 percent) are shown on Table C.2. No statistical
comparison tests were performed in Table C.2 but the estimates and
standard errors can be used to make such comparisons (see [Appendix
E]{.underline} for how to do this).

  ----------------------- -------------- ----------- ------------ ----------- ------------ --------------- ------------ --------------- ------------ ---------------- ------------
  **Table C.2. Percent of                                                                                                                                             
  non-institutionalized                                                                                                                                               
  adults over age 18                                                                                                                                                  
  saying they were                                                                                                                                                    
  diagnosed with diabetes                                                                                                                                             
  who reported having                                                                                                                                                 
  important tests in the                                                                                                                                              
  past year (or two years                                                                                                                                             
  in one case), by                                                                                                                                                    
  national population                                                                                                                                                 
  subgroup, United                                                                                                                                                    
  States, 2000**                                                                                                                                                      

  ** **                   ** **          **HbA1c                  **Lipid                  **Retinal eye                **Foot                       **Influenza      
                                         test a/**                profile in               examination**                examination**                immunization**   
                                                                  past two                                                                                            
                                                                  years**                                                                                             

  **Population group**    **Population   **Percent   **Standard   **Percent   **Standard   **Percent d/**  **Standard   **Percent d/**  **Standard   **Percent d/**   **Standard
                          total**        d/**        error**      d/**        error**                      error**                      error**                       error**

  **Total**               12,608,034     89.85       1.27         94.32       0.87         66.51           1.76         66.40           1.73         54.79            2.20

  **Race**                                                                                                                                                             

  White                   9,900,291      90.35       1.36         94.06       0.92         66.71           2.04         67.60           2.06         57.38            2.31

  Black                   2,168,837      85.08       3.92         94.94       1.61         64.30           4.63         60.36           4.11         42.90            4.79

  Asian/Pacific Islander  DSU            DSU         DSU          DSU         DSU          DSU             DSU          DSU             DSU          DSU              DSU

  Aleut/Eskimo/American   DSU            DSU         DSU          DSU         DSU          DSU             DSU          DSU             DSU          DSU              DSU
  Indian                                                                                                                                                              

  **Hispanic ethnicity**                                                                                                                                               

  Hispanic                1,633,326      85.72       3.17         91.22       2.12         58.34           4.33         66.19           4.06         46.58            3.61

  Non-Hispanic            10,974,708     90.49       1.32         94.78       0.89         67.71           1.88         66.43           1.99         56.00            2.46

  **Ethnicity**                                                                                                                                                        

  Non-Hispanic, White     8,414,743      91.07       1.42         94.62       0.95         67.79           2.25         67.88           2.37         59.18            2.55

  Non-Hispanic, Black     2,080,623      86.00       3.67         94.76       1.67         65.75           4.67         60.40           4.36         43.41            4.86

  Non-Hispanic, Other     DSU            DSU         DSU          DSU         DSU          DSU             DSU          DSU             DSU          DSU              DSU

  **Gender**                                                                                                                                                           

  Male                    5,872,199      90.04       1.81         95.57       1.08         69.18           2.72         68.67           2.39         56.64            2.93

  Female                  6,735,836      89.68       1.79         93.22       1.19         64.17           2.16         64.40           2.29         53.18            2.64

  **Age**                                                                                                                                                              

  18-44                   2,072,046      83.41       3.92         87.63       3.47         53.81           4.90         59.45           3.73         29.94            4.77

  45-64                   5,308,843      92.28       1.63         94.82       1.21         66.05           2.78         66.54           2.74         48.70            2.85

  65 and over             5,227,145      90.24       1.99         96.46       1.07         72.10           2.58         69.13           2.60         70.82            2.88

  **Education b/**                                                                                                                                                     

  Less than high school   3,985,853      87.26       2.51         93.74       1.48         63.73           2.76         66.69           2.88         54.05            3.25
  (\<12 years )                                                                                                                                                       

  High school graduate    4,210,763      89.81       2.29         94.56       1.35         60.99           3.37         64.08           2.72         51.65            2.82
  (=12 years)                                                                                                                                                         

  At least some college   4,245,485      92.02       1.73         95.09       1.44         74.48           2.46         67.44           3.05         58.39            4.03
  (\>12 years)                                                                                                                                                        

  **Employment status                                                                                                                                                  
  c/**                                                                                                                                                                

  Employed                4,812,613      87.44       1.97         93.70       1.41         60.24           3.30         62.07           2.76         42.37            3.36

  Not employed            2,537,705      94.29       2.18         91.01       2.10         67.17           3.45         70.13           2.99         46.04            3.55

  **Insurance**                                                                                                                                                        

  Age 18-64                                                                                                                                                            

  Any private             5,329,710      90.78       1.85         94.04       1.28         62.92           3.00         65.69           2.54         44.52            3.10

  Public only             DSU            DSU         DSU          88.67       3.62         68.27           4.33         64.73           5.38         42.96            5.37

  Uninsured               DSU            DSU         DSU          DSU         DSU          DSU             DSU          DSU             DSU          DSU              DSU

  Age 65 and over                                                                                                                                                      

  Medicare only           DSU            DSU         DSU          95.60       1.72         76.68           4.68         68.87           4.82         65.15            5.82

  Medicare and private    2,749,323      91.40       2.58         97.79       1.09         71.29           3.82         69.48           3.94         75.96            3.79

  Medicare and other      DSU            DSU         DSU          DSU         DSU          DSU             DSU          DSU             DSU          DSU              DSU
  public                                                                                                                                                              

  No insurance/private    DSU            DSU         DSU          DSU         DSU          DSU             DSU          DSU             DSU          DSU              DSU
  only                                                                                                                                                                

  **Family income**                                                                                                                                                    

  Negative or poor(\<100% 1,778,855      86.81       3.39         86.69       2.94         63.44           3.44         60.28           4.33         48.12            5.26
  of poverty level)                                                                                                                                                   

  Near poor/low           3,021,438      88.85       2.83         93.95       1.61         64.43           3.47         68.18           3.15         57.57            3.72
  income(100-\<200% of                                                                                                                                                
  poverty level)                                                                                                                                                      

  Middle                  3,722,916      90.14       2.13         94.08       1.41         60.94           3.75         65.41           2.80         51.26            3.78
  income(200-\<400% of                                                                                                                                                
  poverty level)                                                                                                                                                      

  High income(\>=400% of  4,084,825      91.41       2.19         98.00       0.89         74.49           2.60         68.66           2.95         58.75            3.19
  poverty level)                                                                                                                                                      

  **MSA status**                                                                                                                                                       

  MSA                     9,671,403      89.54       1.54         94.80       0.99         67.19           2.04         67.24           1.99         54.86            2.60

  Non-MSA                 2,936,632      90.91       2.28         92.74       1.41         64.27           3.18         63.61           3.35         54.55            3.91

  **Perceived health                                                                                                                                                   
  status**                                                                                                                                                            

  Excellent/very          7,588,200      90.22       1.48         94.91       1.17         68.02           1.93         63.32           2.35         53.87            2.44
  good/good                                                                                                                                                           

  Fair/poor               5,019,834      89.22       2.15         93.42       1.21         64.22           2.99         71.12           2.64         56.20            3.04

  a/ See page preceding                                                                                                                                               
  page for a discussion                                                                                                                                               
  of the differences in                                                                                                                                               
  denominators for BRFSS                                                                                                                                              
  data and MEPS data                                                                                                                                                  

  b/ Persons 18 and over                                                                                                                                              
  with unknown education                                                                                                                                              
  status not included.                                                                                                                                                

  c/ Employment status                                                                                                                                                
  for persons 18-64 only.                                                                                                                                             

  d/ For all values, RSE                                                                                                                                              
  \< 30 percent.                                                                                                                                                      

  DSU - Data do not meet                                                                                                                                              
  the criteria for                                                                                                                                                    
  statistical                                                                                                                                                         
  reliability, data                                                                                                                                                   
  quality or                                                                                                                                                          
  confidentiality.                                                                                                                                                    

  Source: Agency for                                                                                                                                                  
  Healthcare Research and                                                                                                                                             
  Quality, Center for                                                                                                                                                 
  Financing, Access and                                                                                                                                               
  Cost Trends, Medical                                                                                                                                                
  Expenditure Panel                                                                                                                                                   
  Survey.                                                                                                                                                             
  ----------------------- -------------- ----------- ------------ ----------- ------------ --------------- ------------ --------------- ------------ ---------------- ------------

**Outcome Measures---NHANES, HCUP, and NHDS Data**

***NHANES Data***

The NHQR uses data from the National Health and Nutrition Examination
Survey for two outcome measures related to diabetes---the HbA1c, a
measure of blood glucose level over the prior two to three months, and
blood pressure at examination. NHANES does not support State-level
estimates but does provide clinical outcome estimates for the total
national population that could be used as benchmarks. \[Note: To be
comparable to data from providers, the NHANES HbA1c and blood pressure
values would have to be recalculated to exclude people who do not use
the health care system during a year.\]

The NHANES collects data from in-person interviews, physical
examinations, and medical tests from a mobile vehicle which is set up as
a medical office. With this survey method, NHANES is able to collect
data that are detailed clinically, including laboratory results. Because
of the expense of the NHANES (e.g., the cost of the mobile clinic and
staff), the sample size on the NHANES is small, 9,965 participants, and
does not support either State-level estimates or national subgroup
estimates within the population of people with diabetes. Additional
information on NHANES is available at:
<http://www.cdc.gov/nchs/about/major/nhanes/NHANES99_00.htm>.

Table C.3 shows the percent of adults with diabetes by specific test
values. The percent and standard error are provided.

  -------------------------------------------- ------------- -------------
  **Table C.3. Percent of adults age 18 and                  
  over with diagnosed diabetes who have                      
  specific HbA1c levels and who have specific                
  blood pressure levels, United States,                      
  1999-2000**                                                

  **Test Results**                             **Percent**   **Standard
                                                             error**

  **HbA1c Levels:**                                          

  \> 9.5 percent (poor control)                13.5          2.6

  \< 9.0 percent (needs improvement)           79.1          2.7

  \< 7.0 percent (optimal)                     37.0          3.8

                                                             

  **Average blood pressure at exam**                         

  \<140/90 mm/Hg                               59.3          3.5

                                                             

  Source: Centers for Disease Control and                    
  Prevention, National Center for Health                     
  Statistics, National Health and Nutrition                  
  Examination Survey.                                        
  -------------------------------------------- ------------- -------------

In addition to the HbA1c and blood pressure values, NHANES can provide
LDL-C levels (\< 130 mg/dL (needs improvement) and \<100 (optimal)).
Those LDL estimates were not available in time for publication of the
first NHQR. The measure remains part of the official NHQR measure set
and is to be included in the future.

***HCUP Data***

The NHQR uses inpatient discharge abstract data from the Healthcare Cost
and Utilization Project for national estimates of three outcome measures
that provide a window on the quality of ambulatory care---avoidable
hospitalizations related to diabetes. While the national estimates are
included in the NHQR, State-level data are not, except for one special
analysis of admissions for uncomplicated uncontrolled diabetes
(discussed in [Module 3: Information]{.underline}). For year 2000 data
used in the NHQR, 29 States contributed and more States had statewide
discharge data systems maintained by State data agencies, State hospital
associations, or statewide data consortia. These data systems can be
used to generate these three outcome measures.

HCUP is a public-private partnership sponsored by AHRQ with 29
participating States in 2000, the time for which data are included in
the first NHQR. The data are from statewide historical administrative
databases going back to 1988. In 2000, HCUP included the:

-   Nationwide Inpatient Sample (NIS) --- all hospitals and all of their
    inpatient discharges (6 to 7 million records per year) across the 29
    States, weighted so that national estimates can be derived from it.

-   State Inpatient Databases (SID) --- a census of inpatient discharge
    records for each participating State covering nearly 80 percent of
    the 36 million U.S. hospital discharges per year in 2000.

-   State Ambulatory Surgery Databases (SASD) --- all discharge records
    for ambulatory surgery centers (hospital based and freestanding).

-   Kids' Inpatient Database (KID) --- A sample of children's discharges
    from over 2,500 community hospitals.

In addition, AHRQ developed the Quality Indicators, which are measures
of health care quality that make use of readily available hospital
inpatient administrative data and available as public software to help
analysts evaluate quality of care in hospitals and, indirectly, in
ambulatory care settings. The AHRQ QIs are organized into three
categories, the Prevention Quality Indicators, the Inpatient Quality
indicators, and the Patient Safety Indicators. Additional information on
HCUP data is available at:
[http://www.hcup-us.ahrq.gov/databases.jsp](http://www.ahcpr.gov/data/hcup/hcup-pkt.htm).
Additional information on the AHRQ QIs is available at:
<http://www.qualityindicators.ahrq.gov/>.

The NHQR and NHDR used selected Prevention Quality Indicators to examine
hospital admissions that evidence suggests could have been avoided, at
least I part, through high-quality outpatient care. Table C.4 shows
rates of three of these indicators related to diabetes---hospital
admissions for three complications of diabetes that should be
avoidable---1) uncontrolled uncomplicated diabetes, 2) serious
short-term complications, and 3) serious long-term complications. The
rates are defined relative to 100,000 people in the population of the
State. Results are presented by patient characteristics (age, sex,
income, and location of patient residence) and by hospital
characteristics (region of the country). The rate and its standard error
are shown.

The main limitation of HCUP data (or any other administrative data
source) is that the data are collected for one purpose and used for
another. Many State-level discharge data systems use data from hospital
billing data and are thus collected for reimbursement purposes. However,
these data are so valuable that they are used for many other purposes,
such as cost tracking, quality monitoring, or health policy evaluations.
Reimbursement incentives affect what data are collected and how they are
collected. Thus, while mining these data for clues to quality, analysts
should constantly be on the alert for data problems---incomplete or
inaccurate entries or lack of adequate clinical detail.

+----------------------+-------+------+-------+------+-------+------+
| **Table C.4.         |       |      |       |      |       |      |
| Hospital admission   |       |      |       |      |       |      |
| for adults over age  |       |      |       |      |       |      |
| 18 for specific      |       |      |       |      |       |      |
| diabetes             |       |      |       |      |       |      |
| complications        |       |      |       |      |       |      |
| (excluding obstetric |       |      |       |      |       |      |
| and neonatal         |       |      |       |      |       |      |
| admissions and       |       |      |       |      |       |      |
| transfers from other |       |      |       |      |       |      |
| institutions) per    |       |      |       |      |       |      |
| 100,000 population   |       |      |       |      |       |      |
| age 18 and older,    |       |      |       |      |       |      |
| Healthcare Cost and  |       |      |       |      |       |      |
| Utilization Project, |       |      |       |      |       |      |
| United States,       |       |      |       |      |       |      |
| 2000**               |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
|                      |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| ** **                | **For |      | **For |      | **For |      |
|                      | un    |      | dia   |      | dia   |      |
|                      | contr |      | betes |      | betes |      |
|                      | olled |      | with  |      | with  |      |
|                      | dia   |      | short |      | long  |      |
|                      | betes |      | -term |      | -term |      |
|                      | wi    |      | com   |      | com   |      |
|                      | thout |      | plica |      | plica |      |
|                      | co    |      | tions |      | tions |      |
|                      | mplic |      | b/**  |      | c/**  |      |
|                      | ation |      |       |      |       |      |
|                      | a/**  |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| **Population group** | **Est | **   | **Est | **   | **Est | **   |
|                      | imate | Stan | imate | Stan | imate | Stan |
|                      | d/**  | dard | d/**  | dard | d/**  | dard |
|                      |       | err  |       | err  |       | err  |
|                      |       | or** |       | or** |       | or** |
+----------------------+-------+------+-------+------+-------+------+
| **Total**            | 2     | 1    | 5     | 1    | 12    | 2    |
|                      | 8.518 | .056 | 1.202 | .437 | 0.810 | .922 |
+----------------------+-------+------+-------+------+-------+------+
| **Patient            |       |      |       |      |       |      |
| characteristic**     |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| Age groups           |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| 18-44                | 1     | 0    | 5     | 1    | 3     | 1    |
|                      | 4.698 | .658 | 4.179 | .749 | 3.484 | .203 |
+----------------------+-------+------+-------+------+-------+------+
| 45-64                | 3     | 1    | 4     | 1    | 15    | 4    |
|                      | 5.765 | .570 | 8.693 | .560 | 8.036 | .663 |
+----------------------+-------+------+-------+------+-------+------+
| 65 and over          | 5     | 2    | 4     | 1    | 33    | 7    |
|                      | 9.172 | .037 | 5.275 | .362 | 8.630 | .922 |
+----------------------+-------+------+-------+------+-------+------+
| Age groups for       |       |      |       |      |       |      |
| elderly              |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| 65-69                | 4     | 2    | 4     | 1    | 29    | 8    |
|                      | 9.147 | .428 | 0.031 | .931 | 3.116 | .094 |
+----------------------+-------+------+-------+------+-------+------+
| 70-74                | 5     | 2    | 4     | 1    | 33    | 8    |
|                      | 7.050 | .673 | 0.686 | .932 | 6.307 | .931 |
+----------------------+-------+------+-------+------+-------+------+
| 75-79                | 6     | 2    | 4     | 2    | 37    | 10   |
|                      | 6.926 | .988 | 6.638 | .165 | 1.110 | .177 |
+----------------------+-------+------+-------+------+-------+------+
| 80-84                | 6     | 3    | 5     | 2    | 38    | 11   |
|                      | 8.099 | .339 | 2.712 | .705 | 8.835 | .263 |
+----------------------+-------+------+-------+------+-------+------+
| 85 and over          | 6     | 3    | 5     | 3    | 33    | 11   |
|                      | 6.566 | .643 | 5.424 | .290 | 9.067 | .185 |
+----------------------+-------+------+-------+------+-------+------+
| Gender               |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| Male                 | 2     | 1    | 5     | 1    | 13    | 3    |
|                      | 8.542 | .072 | 3.180 | .746 | 5.980 | .215 |
+----------------------+-------+------+-------+------+-------+------+
| Female               | 2     | 1    | 4     | 1    | 10    | 2    |
|                      | 8.399 | .112 | 9.119 | .313 | 8.278 | .853 |
+----------------------+-------+------+-------+------+-------+------+
| Median income of     |       |      |       |      |       |      |
| patient zip          |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| Less than \$25,000   | 6     | 5    | 9     | 7    | 19    | 15   |
|                      | 2.728 | .709 | 1.142 | .913 | 3.319 | .735 |
+----------------------+-------+------+-------+------+-------+------+
| \$25,000-\$34,999    | 4     | 2    | 6     | 3    | 15    | 6    |
|                      | 2.783 | .184 | 6.742 | .123 | 0.143 | .530 |
+----------------------+-------+------+-------+------+-------+------+
| \$35,000-\$44,999    | 3     | 1    | 5     | 2    | 12    | 5    |
|                      | 0.324 | .720 | 6.531 | .586 | 6.912 | .240 |
+----------------------+-------+------+-------+------+-------+------+
| \$45,000 or more     | 1     | 0    | 3     | 1    | 9     | 3    |
|                      | 6.604 | .872 | 7.990 | .626 | 0.913 | .692 |
+----------------------+-------+------+-------+------+-------+------+
| Location of patient  |       |      |       |      |       |      |
| residence            |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| MSA e/               | 2     | 1    | 5     | 1    | 11    | 3    |
|                      | 5.126 | .180 | 1.353 | .714 | 6.966 | .349 |
+----------------------+-------+------+-------+------+-------+------+
| Non-MSA e/           | 3     | 1    | 5     | 2    | 12    | 5    |
|                      | 9.697 | .895 | 1.859 | .056 | 1.337 | .099 |
+----------------------+-------+------+-------+------+-------+------+
| **Hospital           |       |      |       |      |       |      |
| characteristic**     |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| Location of          |       |      |       |      |       |      |
| inpatient treatment  |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| Northeast            | 3     | 3    | 4     | 2    | 13    | 8    |
|                      | 0.106 | .489 | 8.082 | .826 | 5.812 | .645 |
+----------------------+-------+------+-------+------+-------+------+
| Midwest              | 2     | 2    | 4     | 2    | 10    | 5    |
|                      | 7.987 | .266 | 5.444 | .948 | 9.735 | .310 |
+----------------------+-------+------+-------+------+-------+------+
| South                | 3     | 1    | 6     | 2    | 13    | 4    |
|                      | 5.652 | .543 | 1.359 | .690 | 3.628 | .595 |
+----------------------+-------+------+-------+------+-------+------+
| West                 | 1     | 1    | 4     | 2    | 9     | 5    |
|                      | 5.610 | .158 | 3.313 | .806 | 6.239 | .363 |
+----------------------+-------+------+-------+------+-------+------+
| a/ Without           |       |      |       |      |       |      |
| short-term           |       |      |       |      |       |      |
| (ketoacidosis,       |       |      |       |      |       |      |
| hyperosmolarity,     |       |      |       |      |       |      |
| coma) or long-term   |       |      |       |      |       |      |
| (renal, eye,         |       |      |       |      |       |      |
| neurological,        |       |      |       |      |       |      |
| circulatory, other   |       |      |       |      |       |      |
| unspecified)         |       |      |       |      |       |      |
| complications.       |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| b/ Ketoacidosis,     |       |      |       |      |       |      |
| hyperosmolarity, or  |       |      |       |      |       |      |
| coma.                |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| c/ Renal, eye,       |       |      |       |      |       |      |
| neurological,        |       |      |       |      |       |      |
| circulatory, or      |       |      |       |      |       |      |
| other unspecified    |       |      |       |      |       |      |
| complications.       |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| d/ Rates are         |       |      |       |      |       |      |
| adjusted by age and  |       |      |       |      |       |      |
| sex using the total  |       |      |       |      |       |      |
| U.S. as the standard |       |      |       |      |       |      |
| population; when     |       |      |       |      |       |      |
| reporting is by age, |       |      |       |      |       |      |
| the adjustment is by |       |      |       |      |       |      |
| sex only; when       |       |      |       |      |       |      |
| reporting is by sex, |       |      |       |      |       |      |
| the adjustment is by |       |      |       |      |       |      |
| age only.            |       |      |       |      |       |      |
|                      |       |      |       |      |       |      |
| e/ Metropolitan      |       |      |       |      |       |      |
| Statistical Areas    |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+
| Source: Agency for   |       |      |       |      |       |      |
| Healthcare Research  |       |      |       |      |       |      |
| and Quality, Center  |       |      |       |      |       |      |
| for Delivery,        |       |      |       |      |       |      |
| Organization, and    |       |      |       |      |       |      |
| Markets, Healthcare  |       |      |       |      |       |      |
| Cost and Utilization |       |      |       |      |       |      |
| Project, Nationwide  |       |      |       |      |       |      |
| Inpatient Sample     |       |      |       |      |       |      |
| (2000).              |       |      |       |      |       |      |
+----------------------+-------+------+-------+------+-------+------+

***NHDS Data***

Table C.5 shows the rate of lower extremity amputations for people with
diabetes per 1,000 population, for two time periods---1997 through 1999
and 1998 through 2000. NHDS pools data over several years, which is why
the tables reflect an overlap in years. The estimates are provided by
national subgroups when the size of the database supports the subgroup
estimate (i.e., for age, sex, and black-white subgroups). The rate and
standard error of the rate are provided. Each State with discharge data
can generate estimates for all of the subgroups reported.

  ---------------------------------- --------------- -------------- --------------- --------------
  **Table C.5. Lower extremity                                                      
  amputations in persons with                                                       
  diabetes per 1,000 population (all                                                
  ages), National Hospital Discharge                                                
  Survey, United States, 1997-2000**                                                

  ** **                              **1997-1999**                  **1998-2000**   

                                                     **Standard**   ** **           **Standard**

  **Population group**               **Rate**        **error**      **Rate**        **error**

  **Total**                          5.0             0.4            4.8             0.4

  **Age (not age adjusted)**                                                         

  0-17                               DSU             DSU            DSU             DSU

  18-44                              3.3             0.6            3.5             0.6

  45-64                              7.1             0.6            6.4             0.6

  65 and over                        10.4            0.7            9.8             0.7

  **Race a/**                                                                        

  American Indian or Alaska Native   DSU             DSU            DSU             DSU

  Asian or Pacific Islander          DSU             DSU            DSU             DSU

  Asian                              DSU             DSU            DSU             DSU

  Native Hawaiian or Other Pacific   DSU             DSU            DSU             DSU
  Islander                                                                          

  Black or African American          7.3             1.5            7.0             1.5

  White                              3.4             0.3            3.5             0.4

  **Ethnicity**                                                                      

  Hispanic                           DSU             DSU            DSU             DSU

  Non-Hispanic                       DSU             DSU            DSU             DSU

  Black or African American          DSU             DSU            DSU             DSU

  White                              DSU             DSU            DSU             DSU

  **Gender**                                                                         

  Female                             2.9             0.3            3.2             0.3

  Male                               7.3             0.8            6.6             0.8

  **Expected payment source b/**                                                     

  Medicaid                           DSU             DSU            DSU             DSU

  Medicare                           DSU             DSU            DSU             DSU

  Private/other insurance            DSU             DSU            DSU             DSU

  Uninsured                          DSU             DSU            DSU             DSU

  Unknown (weighted count)           DSU             DSU            DSU             DSU

  a/ Race categories changed in                                                     
  2000. Data for 2000 may not be                                                    
  comparable to those used in                                                       
  previous years.                                                                   

  b/ Rates may be overestimated due                                                 
  to undercount in the denominators                                                 
  of some payment sources from the                                                  
  Current Population Survey.                                                        

  DSU - Data do not meet the                                                        
  criteria for statistical                                                          
  reliability, data quality, or                                                     
  confidentiality.                                                                  

  Source: Centers for Disease                                                       
  Control and Prevention, National                                                  
  Center for Health Statistics,                                                     
  National Hospital Discharge                                                       
  Survey.                                                                           
  ---------------------------------- --------------- -------------- --------------- --------------

**Table C.6. CDC Three-year baseline: Percent of adults age 18 and over
with diabetes who had recommended diabetes tests in the past year,
pooled 1997-1999 a/**

# ![](media/image24.wmf){width="4.3284722222222225in" height="8.247222222222222in"} 

# Table C.7. CDC annual trends: Percent who had a dilated-eye examination in the past year per 100 adults with diabetes, crude rates and age-adjusted rates, by State, 1995-2002

# ![](media/image25.wmf){width="6.499305555555556in" height="7.911111111111111in"} 

# Table C.8. CDC annual trends: Percent who had a foot examination in the past year per 100 adults with diabetes, crude rates and age-adjusted rates, by State, 1995-2002

# ![](media/image26.wmf){width="6.495833333333334in" height="7.377777777777778in"} 

**Table C.9. CDC annual trends: Percent who had an influenza vaccination
in the past year per 100 adults with diabetes, crude rates and
age-adjusted rates, by State, 1995-2002**

![](media/image27.wmf){width="5.695138888888889in"
height="8.498611111111112in"}

**Estimation Steps for Medicaid Spending on Diabetes Care by State**

This section describes methods for estimating Medicaid spending on
medical care for people with diabetes by State.

Estimates for Medicaid spending on diabetes care had to be constructed
from multiple public data sources, as described in [Module 2:
Data]{.underline}. Because the estimation involved many assumptions, the
method used is described in a flow chart (Figure C.1). The top level of
the flow chart represents the original data sources; the middle levels
show assumptions, adjustments, and calculations made with the original
data; and the final level (at the bottom) of the flow sheet is the
result. The adjustments were necessary to make different sources
compatible with respect to population and time frame. This method was
applied to data on Medicaid eligibles to get an estimate of the
potential cost for Medicaid of medical care for diabetes patients.

These are only "ball-park" estimates because of the assumptions that had
to be made to work with available data. Obvious limitations in these
estimates include omission of spending for children and the
institutionalized population. Furthermore spending on medical care
unrelated to diabetes is included when it should be excluded. Although
spending for children and youth under age 20 is omitted, only 0.25
percent of this population has diabetes and the effect is likely to be
small. The omission of the institutionalized population is a more
serious downward bias on spending estimates because people with advanced
stages of diabetes are more likely to be hospitalized or to reside in
nursing homes, and their care is costly. The inclusion of spending for
*all* medical care for people with diabetes 20 years of age and over is
included in these estimates (rather than only the spending related to
diabetes) because medical expenditures by type and age could not be
identified readily. This overestimates expenditures related to diabetes
only.

The resulting ball-park estimates are shown in Table 2.2 of the
*Resource* *Guide*. Clearly, a better approach to deriving State
Medicaid costs for diabetes care would be to use Medicaid claims, if
they were readily available for all States.

**Figure C.1. Estimation steps for Medicaid spending on diabetes care**

# Appendix D: Benchmarks From The NHQR

**More Details on Benchmarks**

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 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-in-class 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 National Center for Health Statistics of 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. The current goals (see
    inset of diabetes-related topics for Healthy People goals), now
    called Healthy People 2010 (HP2010, U.S. Department of Health and
    Human Services, 2000), also are included in the NHQR when relevant.

-   **The 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 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.

-   **The 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 (Wennberg and Cooper, 1999),
    regional estimates may be weak goals for regions where practice
    should change to enhance the health care quality for people with
    diabetes. 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.

-   **The State rate:** As noted in the [Module 3:
    Information]{.underline}, the State's own rate may serve as a
    benchmark for various purposes---tracking changes over time,
    evaluating the effect of a statewide intervention to improve
    quality, or reporting the norm for local communities and providers
    to compare to 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.

**The Best Benchmarks**

Best-in-class estimates are the best way to view the opportunities for a
State to improve. Basing a best-in-class measure on a group of best
States (rather than the single top State) mitigates the effects of an
extreme that other States might find unreasonable to emulate.

Table D.1 shows values for the best-in-class benchmarks (as simple
averages of the top 10 percent of States) and for other benchmarks. The
other benchmarks include Healthy People 2010 goals (when available for a
measure), the national norm, regional average benchmarks, and State
rates for the four example States used in this *Resource Guide*. These
benchmarks are provided for all of the diabetes-related measures in the
NHQR. Four of the measures---HbA1c test, eye exam, foot exam, and flu
vaccinations in the past year (in addition to HbA1c test two times in
the past year)---are displayed graphically in the [Module 3:
Information.]{.underline}

Benchmarks related to diabetes care for different socioeconomic groups
are available from the NHDR. Those benchmarks are national averages and
are not available by State. However, individual States may have data
that can be analyzed by socioeconomic group (e.g., avoidable
hospitalizations by racial, educational, or income group). Table D.2
shows values for the national averages for diabetes process and outcome
measures by socioeconomic characteristics of the national population.

**Methods Matter**

Methods of measurement and data quality can have a large impact on the
value of a benchmark. For this reason, it is crucial that the methods
and data used to derive various benchmarks are similar. For example,
when comparing the State to the Nation, the same methods and data
sources should be used to calculate the estimates. That is why this
*Resource Guide* presents only the BRFSS estimates to compare States and
the Nation. Other sources (for example MEPS) were used for national
estimates of the same measures in the NHQR. However, MEPS and BRFSS use
different survey methods and present different measures; the impact of
the former is apparent in the HbA1c rates---90 percent (MEPS) versus 79
percent (BRFSS)---and the impact of the latter is seen in the influenza
vaccination rates for people with diabetes---55 percent for those age 18
and over (MEPS) versus 37 percent for those age 18 to 64 (BRFSS).

![](media/image28.wmf){width="5.99375in" height="6.956944444444445in"}

**Appendix E: Information on Statistical Significance**

This section is provided for data analysts who want 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
respondents' 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.

**P-Values**

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 pre-specified 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.

**Judgments About Comparisons**

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?

-   **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-to-benchmark difference large enough
    to warrant efforts to rectify it? A 1- or 2-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
    stakeholders who understand the environment of a State can help
    make.

**How To Calculate P-Values**

P-values are used in this *Resource Guide* to determine whether the
estimate of a given State is statistically different---above or below a
given average (e.g., the national average or the average of the top
decile States). Calculating the p-value is straightforward when the
standard errors (SEs) of the estimates are provided, as in the case of
the national rate and individual State rates in the first formula and
example below. However, when the standard error has not been provided,
as is the case for the mean of the top decile of States, then the
calculation is more complicated and may require additional data, such as
sample sizes. The method for the p-value calculation for the top-decile
States is also provided (see second formula and example).

**Calculating P-Value To Compare States to the National Average**

[]{.mark}

For an individual State estimate compared to the national average, the
following formula shows how to derive a t-test statistic, which is a
statistical test for whether the State average is likely to come from a
distribution different from the national average. From the t-test, a
p-value can be derived; and if the p value is less than 0.05, it can be
concluded with 95-percent confidence that the mean from the State
distribution is statistically different from the mean from the national
distribution (see example for one State). Rates and standard errors are
provided for most measures in the NHQR tables.

![](media/image29.png){width="1.4270833333333333in"
height="0.8645833333333334in"}Two-sided t-test:

where:

> R~1~ = a State rate
>
> R~2~ = national rate
>
> SE^2^~1~ = square of the standard error of the State rate (or its
> variance)
>
> SE^2^~2~ = square of the standard error of the national rate (or its
> variance)

This formula is more conveniently calculated using SAS or EXCEL with the
following commands:

> SAS: p = 2 \* (1 - PROBNORM(ABS(t)))
>
> EXCEL: p = 2\*(1 - NORMDIST(ABS(t),0,1,TRUE))

**Example**: How does Georgia compare to the national average for annual
retinal exams for adults with diabetes? The national rate and standard
error for adults with diabetes receiving annual retinal exams are 66.7
and 1.2, respectively. Georgia's rate and standard error for annual
retinal exams are 70.4 and 3.7, respectively. Following is the EXCEL
statement for the p-value, which encompasses the t-test formula with the
Georgia and national values.

[]{.mark}

> p =
> 2\*(1-NORMDIST(ABS(70.4-66.7)/SQRT((3.7\*3.7)+(1.2\*1.2)),0,1,TRUE))
>
> p = 0.34

Because the p-value is greater than 0.05, we cannot conclude that
Georgia is statistically different from the national average. Our
confidence is that this would be true 95 percent of the time in repeated
tests.

**Calculating P-Value To Compare States to the Top Decile Average**

To compare individual States to the top decile average, both the top
decile rate and its standard error must be estimated, which is done
using the fundamental equation of analysis of variance and weighting
individual State values by their respective samples sizes. (The NHQR
tables do not provide sample sizes; but this information is available
from the CDC Web site.)

Let us assume that the top decile is comprised of three States. Using
the three top States, the formula determines the three-State sample
size, the weighted mean for the three States, and the total sum of
squares about the three-State mean. The latter 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 mean. The
within-State sum of squares (SS) is obtained by squaring the State's SE
and multiplying by the sample size times the sample size minus one. The
between-State sum of squares is obtained by summing the sample-weighted
squared difference between the State mean and the overall three-State
mean. Here is the formula (note: 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.
>
> Within State SS = n1\*(n1-1)\*s1^2^ + n2\*(n2-1)\*s2^2^ +
> n3\*(n3-1)\*s3^2^, represents the simplified sum of squared deviations
> of values within the State from its mean.
>
> Between State SS = n1\*(m1-M) ^2^ + n2\*(m2-M) ^2^ + n3\*(m3-M)^2^, is
> the sum of squared deviations of means between the three States
> weighted by sample size.
>
> Total SS= Within State SS + Between State SS
>
> VAR = SS/(N-1), is the estimated variance for the three-State mean
>
> SE = sqrt(VAR/N), is the estimated standard error for the three-State
> mean.

Using the estimated standard error and weighted mean for the top decile
of States, a p-value can be calculated that reflects how a State
compares to the average of top decile States.

**Example**: How does Georgia compare to the top decile of States for
rates of annual retinal exams for adults with diabetes? First, determine
the number of States in the top decile. If all States are considered,
then the top decile would be the top five States, however, not all
States report for all data sources. In the case of BRFSS data for
diabetes, 41 States and the District of Columbia reported; therefore,
the top decile is the top four States.

[]{.mark}

The four States with the highest rates for retinal eye exams are
Wisconsin with a rate of 82.5, SE=3.1, and sample size of 201; Maine
with a rate of 82.3, SE=3.5, and a sample size of 172; Nebraska with a
rate of 80.4, SE=5.6, and a sample size of 214; and Connecticut with a
rate of 77.1, SE=4.3, and a sample size of 492. The overall sample size
is 1,079 and the overall weighted average is 79.6. The within State
SS=6,642,737, the between State SS=6,156, and the total SS=6,648,893.
From the total SS, the weighted SE can be determined for the top decile
average and the calculation for p-values can be used to compare States
to that top decile average.

[]{.mark}

The p-value for Georgia compared to the top decile average is 0.03.
Because the p-value is less than 0.05, it can be concluded that Georgia,
which is below the top-decile average, is significantly different from
the top decile and, thus, there is opportunity for improvement in annual
retinal exams.

**Appendix F: NHQR Quality Measures for All Conditions by State**

This appendix lists quality measures for all conditions and topics in
the NHQR. It includes the national estimate and then an indicator for
whether or not the State estimate (not shown due to space limitation) is
statistically greater, lower, or no different from the national average.
The measures for which State-level data are not reported in the NHQR are
noted as not available (n/a). This resource can help States identify
which diseases and treatments, including and outside of diabetes, may be
in need of attention. Many of the same data issues described in [Module
2: Data]{.underline} and [Module 3: Information]{.underline} are
applicable to other disease topics, although different data sources and
limitations may apply to them.

> **Table F.1. NHQR quality measures for all conditions when available
> by State, alphabetically Alabama through Missouri**

![](media/image30.wmf){width="9.793055555555556in"
height="6.034027777777778in"}

![](media/image31.wmf){width="9.952777777777778in"
height="5.742361111111111in"}

![](media/image32.wmf){width="9.952777777777778in"
height="5.884722222222222in"}

![](media/image33.wmf){width="9.952777777777778in"
height="5.570833333333334in"}

![](media/image34.wmf){width="9.952777777777778in"
height="6.305555555555555in"}

![](media/image35.wmf){width="9.080555555555556in"
height="6.2756944444444445in"}

> See Footnotes at the end of Table F.2.
>
> **Table F.2. NHQR quality measures for all conditions when available
> by State, alphabetically Montana through Wyoming**

![](media/image36.wmf){width="9.75in" height="6.0875in"}

![](media/image37.wmf){width="9.717361111111112in"
height="5.558333333333334in"}

![](media/image38.wmf){width="9.717361111111112in"
height="5.868055555555555in"}

![](media/image39.wmf){width="9.717361111111112in"
height="5.519444444444445in"}

![](media/image40.wmf){width="9.717361111111112in"
height="5.999305555555556in"}

![](media/image41.wmf){width="8.959722222222222in"
height="6.261805555555555in"}

a.  Symbols for significance test are:

> **+** = greater than national average and statistically significant at
> the p\<0.05 level
>
> **-** = less than national average and statistically significant at
> the p\<0.05 level
>
> **ns** = not significant (i.e., not statistically different than the
> national average)
>
> **n/a** = either the national or State rate or standard error was not
> available

b.  Measure is age adjusted to the 2000 standard population.

c.  1990-based postcensal population estimates were used to calculate
    death rates; future reports will present rates based on intercensal
    population estimates for 1998 and 1999 and bridged-race population
    estimates for 2000 and subsequent years

d.  Population includes males only

e.  Population includes females only

f.  Prevalent dialysis patients on list on 12/31/YR divided by prevalent
    dialysis patients on 12/31/YR

g.  All Medicare dialysis patients who initiated therapy in the given
    year were included

h.  Patients with prior kidney transplants and patients over the age of
    69 were excluded from the measure

i.  Percents are estimated using the Kaplan-Meier methodology

j.  Follow-up is censored at removal from the list, death, or the end of
    the three year period

k.  Patient survival rate is measured as standardized mortality ratio
    (SMR) by source of the data

l.  Population is Medicare patients only.

m.  Time in minutes from arrival to initiation of a thrombolytic agent
    in patients with ST segment elevation or left bundle branch block
    (LBBB) on the electrocardiogram (ECG) performed closest to hospital
    arrival time.

n.  Median time in minutes from arrival to percutaneous transluminal
    angioplasty (PTCA) in patients with ST segment elevation or left
    bundle branch block (LBBB) on the electrocardiogram (ECG) performed
    closest to hospital arrival time.

o.  Includes only those with liveborn infants

p.  Percent of children, age 19 to 35 months, receiving at least four
    doses of diphtheria-tetanus-acellular pertussis (DTaP), at least
    three doses of polio, at least one dose of measles-mumps-rubella
    (MMR), at least three doses of Haemophilus influenzae B (Hib), and
    at least three doses of hepatitis B antigens.

q.  Pain during a 7 day period that was excruciating at any time or
    moderate, among residents experiencing daily pain.

r.  For period 4/1/02 to 6/30/02.

s.  A facility had to have at least 20 residents in the denominator for
    a post-acute measure to be calculated and 30 residents in the
    denominator for a chronic care measure to be calculated. Therefore
    the number of facilities may vary for each measure reported in a
    State.

t.  At least 1 of 4 late-loss ADLs (bed mobility, transfers, toilet use
    and eating).

u.  This percentage is composite of data from the quarterly and annual
    MDS assessment forms completed for residents. The annual MDS form
    contains data on multiple types of infections.

v.  U.S. estimate reflects the average of the States with measures.

w.  For period 1/1/02 to 6/30/02.

x.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who get better at getting
    dressed.

y.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who get better at taking their
    medicines correctly (by mouth).

z.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who get better at bathing.

a.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who stay the same or don\'t get
    worse at bathing.

b.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who get better at getting in and
    out of bed.

c.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who get better at walking or
    moving around.

d.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who get better at getting to and
    from the toilet.

e.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who have less pain when moving
    around.

f.  Consumer language for this measure is: Percentage of patients who
    are short of breath less often. The CMS report results of testing
    this language is available at
    <http://www.cms.hhs.gov/quality/hhqi/OASISPhaseI.pdf>.

g.  Consumer language for this measure is: Percentage of patients who
    are having less of a problem with urinary incontinence or wetting
    themselves. The CMS report results of testing this language is
    available at <http://www.cms.hhs.gov/quality/hhqi/OASISPhaseI.pdf>.

h.  Consumer language used on the Home Health Compare Web site for this
    measure is: Percentage of patients who are confused less often.

i.  Consumer language used on the CMS Home Health Compare Web site for
    this measure is: Percentage of patients who had to be admitted to
    the hospital.

j.  The national average includes Puerto Rico.

k.  The national average includes the Virgin Islands.

l.  The national average includes Guam

**Appendix G:** **Index of Diabetes Quality Improvement Initiatives
[]{.mark}**

[]{.mark}

Listed below are a number of national and Federal quality improvement
programs related to diabetes that State leaders may find useful as
templates for State initiatives or for additional resources.

[]{.mark}

[]{.mark}

**Public/Private Quality Improvement Initiatives**

There are a wide range of public and private quality improvement
initiatives active at different stages of quality improvement. While
there are numerous components of quality improvement, the examples given
below illustrate the quality improvement activities aimed at measurement
and incentives. While the list is by no means exhaustive of
public/private quality improvement initiatives, it provides examples of
what organizations are doing specific to diabetes.

**Measurement**

***National Committee for Quality Assurance and HEDIS***^®^
***Measures***

The National Committee for Quality Assurance is a national, nonprofit
organization founded in 1991 that is dedicated to improving the quality
of health care. NCQA is well known for its accreditation of managed care
organizations and performance measurement initiatives. NCQA's Health
Plan Employer Data and Information Set (or HEDIS^®^) is used by more
than 90 percent of health plans in the United States to report
performance on a wide variety of quality of care indicators, ranging
from child immunization rates to waiting time for appointments to member
satisfaction measures. HEDIS^®^ also includes measures for diabetes care
quality. NCQA reports on health plan performance in its annual
publication, the *State of Health Care Quality* (NCQA, 2003). In
addition, NCQA's Quality Compass, a national database of HEDIS^®^ and
accreditation information from health plans, is a resource for health
plans, employers, and governments to assess and compare health care
quality. NCQA in collaboration with the American Diabetes Association
also has a program called the Diabetes Physician Recognition Program
that recognizes physicians based on the quality of diabetes care they
provide using its diabetes measures. Consumers can check online for a
listing of physicians who are recognized for the quality of the diabetes
care they provide. More information about NCQA and its programs is
available at [http://www.ncqa.org/]{.underline}.

***National Diabetes Quality Improvement Alliance***

Organized by leading diabetes stakeholder groups in 1998, the Diabetes
Quality Improvement Project was a voluntary coalition of public and
private organizations that have cooperated to develop a national set of
diabetes-specific performance and outcome measures. In 2001, the DQIP
partners joined other leading organizations to form the National
Diabetes Quality Improvement Alliance. The Alliance agreed to work on
developing one national performance measurement set for diabetes
accepted by all major stakeholders. In October 2002, the newly formed
Alliance developed national, uniform consensus standards from purchaser,
provider, and consumer groups. Further information is available on the
Alliance Web site at
[http://www.nationaldiabetesalliance.org/.]{.underline}

**Incentives**

***Bridges to Excellence Project***

Pay-for-performance initiatives have gained momentum in recent years as
health care analysts have recognized that a disincentive for quality
improvement exists in the U.S. health care system because all providers
receive the same reimbursement regardless of the quality of their
product (Leatherman, Berwick, Iles, et al., 2003). In its report,
*Crossing the Quality Chasm*, the IOM recommended that payments for care
should be redesigned to encourage providers to make positive changes to
their care processes. Ideally, this shift will begin with purchasers and
insurers and filter down through the delivery system to help encourage
improvements at all levels.

In response to this challenge, a group of employers, physicians, health
plans and patients has come together to create Bridges to Excellence
focused on realigning incentives around higher quality. The program has
created incentives through two programs, Diabetes Care Link and
Physicians Office Link. The Diabetes Care Link requires certification or
recognition under NCQA's Diabetes Physician Recognition Program and then
grants 1- or 3-year recognition through a cash bonus system for
participating physicians delivering quality diabetes care. The Diabetes
Care Link program also focuses on helping people with diabetes engage in
their own care and achieve better outcomes. The program estimates a
savings of \$350 and a cost of \$175 per patient per year (Bridges to
Excellence, 2004). More information on the Bridges to Excellence project
is available at [http://www.bridgestoexcellence.com/bte]{.underline}.

***JCAHO Codman Award***

The Joint Commission on Accreditation of Healthcare Organizations is the
Nation's leading accreditor of hospitals and other health care
facilities. JCAHO has established the Ernest A. Codman Award to
recognize health care organizations that use process and outcomes
measures to improve organization performance and, ultimately, the
quality of care provided to the public. The Codman Award was created in
1996 to showcase the effective use of performance measures, and enhance
knowledge and encourage the use of performance measurement to improve
the quality of health care. Information on this program is available at
[http://www.jcaho.org/accredited+organizations/codman+award/codman_overview.htm]{.underline}.

**Federal Programs and Resources for Diabetes Quality Improvement**

[]{.mark}

In addition to public/private quality improvement efforts, State leaders
can also use Federal quality improvement programs and resources for
State efforts. There are a variety of programs at the Federal level that
address diabetes and quality improvement, some of which are partnering
with States, and others that have useful resources for State efforts.

**Quality Interagency Coordination Task Force**

In addition to preparing the first annual NHQR and subsequent reports,
AHRQ is also involved in diabetes care by overseeing the day-to-day
operations of the Federal Quality Interagency Coordination Task Force
(QuIC). The purpose of the QuIC is to ensure that all Federal agencies
involved in purchasing, providing, studying, or regulating health care
services are working in a coordinated manner toward the common goal of
improving quality care. This group has selected diabetes and depression
as the first two areas for which it will mount an effort to improve
clinical quality of care. For diabetes, the work group is focusing its
efforts on having all Federal programs agree to use the DQIP measures of
care and then to improve health care provider performance based on these
indicators. More information on this task force is available at
<http://www.quic.gov/>.

**CDC Diabetes Prevention and Control Program**

The Centers for Disease Control and Prevention currently funds the
Diabetes Prevention and Control Program in every State. This program is
discussed extensively in [Module 4: Action]{.underline}; for further
information, see this section of the *Resource Guide*.

**National Public Health Initiative on Diabetes and Women\'s Health**

[]{.mark}

CDC, the American Diabetes Association, the American Public Health
Association (APHA), and the Association of State and Territorial Health
Officials (ASTHO)cosponsor the National Public Health Initiative on
Diabetes and Women\'s Health. Part of a comprehensive program to improve
women's health, the CDC-lead initiative has three phases. In Phase I,
the CDC prepared *Diabetes & Women\'s Health Across the Life Stages: A
Public Health Perspective*. Published in 2001, this report examined why
diabetes is a serious public health problem for women and analyzed the
various factors that affect diabetes in women. This report also explored
the impact of diabetes on women\'s lives using the various life stages
as a framework---adolescence, reproductive years, middle age, and elder
years.  A copy of this publication is available on CDC\'s Web site at
[http://www.cdc.gov/diabetes/projects/women.htm]{.underline}.

In 2001 during Phase II, CDC joined the ADA, APHA, and ASTHO to turn the
report into action. The four groups convened a task force in November
2001, with representatives of over 40 organizations from the public,
private, and nonprofit sectors.  Proposed recommendations that emerged
from this meeting were published as the *Interim Report: Proposed
Recommendations for Action* and are also available on CDC\'s Web site at
[http://www.cdc.gov/diabetes/pubs/interim/index.htm]{.underline}. In
Phase III, currently ongoing, multidisciplinary agencies---including
government, academic, voluntary, business, community-based, and
professional organizations---selected recommendations of highest
priority and identified appropriate strategies for implementation. This
national agenda represents the result of their deliberations for action.
Additional information is available at
[http://www.cdc.gov/diabetes/pubs/action/index.htm]{.underline}.

**Healthy People 2010**

Healthy People 2010 is a national prevention program lead by the U.S.
Department of Health and Human Services in partnership with other
Federal agencies, States, businesses, communities, and consumers. HP2010
outlines a broad range of objectives in health care with the goal of
increasing the quality and length of life and eliminating health
disparities in the United States. Diabetes is one of the focus areas of
HP2010.

State leaders can use HP2010 objectives to assess health care quality.
Some NHQR measures that relate to the HP2010 objectives still show room
for improvement. Further information on HP2010 goals related to diabetes
is available at
<http://www.healthypeople.gov/document/HTML/Volume1/05Diabetes.htm>.

**HRSA's Health Disparities Collaboratives**

HRSA's Bureau of Primary Heath Care and the CDC's Diabetes Prevention
and Control Program sponsor Health Disparities Collaboratives, a unique
partnership with community health centers across the country aimed at
improving chronic illness care for underserved and minority communities.
This program is discussed in [Module 4: Action]{.underline}; for further
information, see this section of the *Resource Guide*.

**National Diabetes Program of the Indian Health Service**

The National Diabetes Program of the Indian Health Service (IHS) is a
public health effort to improve the prevention and treatment of diabetes
among American Indian and Alaska Native populations. This segment of the
U.S. population suffers disproportionately from high rates of type 2
diabetes. The IHS uses the following to track and improve diabetes care
quality among American Indian and Alaska Native populations:

-   Quality measures from the Indian Health Diabetes Care and Outcomes
    Audit, which are similar to national (DQIP measures.

-   Case management to coordinate care and provide followup.

-   Information management to identify patients and assure timely and
    appropriate care.

-   Practice teams to deliver multidisciplinary care and education.

-   Systems of care that are clearly defined and close gaps in care.

-   Patient education to assist patients with managing their diabetes.

-   Provider training to assure continuing education and competency.

-   Protocol-based practice to ensure that evidence-based guidelines are
    followed.

-   Provision of specialty exams and services to ensure access to
    necessary specialist services.

-   Staging of populations to manage differing needs of various ages and
    stages of disease progression.

More information is available at
<http://www.ihs.gov/MedicalPrograms/Diabetes/index.asp>.

**National Diabetes Education Program**

The National Diabetes Education Program is a national collaboration
sponsored jointly by the NIH and the CDC. This program is discussed in
[Module 4: Action]{.underline}; for further information, see this
section of the *Resource Guide*.

**CMS' Quality Improvement Organizations**

Quality Improvement Organizations are designated as the guardians of
quality, cost-effective care for both Medicare and Medicaid. This
program is discussed in [Module 4: Action]{.underline}; for further
information, see this section of the *Resource Guide*.

**Appendix H: CDC Funding for States' Diabetes Programs, 2003-2004**

![](media/image42.wmf){width="5.197916666666667in"
height="8.739583333333334in"}

[^1]: This State-level analysis is feasible because of information
    collected at the State level. Similar analyses may be possible for
    smaller geographic areas within States. For example, the HCUP data,
    described below, permit analyses at the county or finer market
    areas. Data related to health care resource are generally available
    at the county level, although data on health risk behaviors of the
    population generally are not. State analysts could use their county
    level databases to compare diabetes quality measures based on HCUP
    data with other characteristics of counties.

[^2]: Diabetes prevalence, poverty and obesity rates were selected
    because they were most closely related to admissions for these
    avoidable hospitalizations among a set of other factors studied
    (including age of the population, insurance coverage, and health
    resources).

[^3]: HCUP Partners providing their data for this analysis were: Arizona
    Department of Health Services, Colorado Health & Hospital
    Association, Georgia Hospital Association, Hawaii Health Information
    Corporation, Iowa Hospital Association, Kentucky Department for
    Public Health, Maine Health Data Organization, Massachusetts
    Division of Health Care Finance and Policy, Michigan Health and
    Hospital Association, Missouri Hospital Association, Texas Health
    Care Information Council, Washington State Department of Health,
    West Virginia Health Care Authority, Wisconsin Department of Health
    and Family Services.