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Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
×

RACE, ETHNICITY, AND LANGUAGE DATA

STANDARDIZATION FOR HEALTH CARE QUALITY IMPROVEMENT

Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement

Board on Health Care Services

Cheryl Ulmer, Bernadette McFadden, and David R. Nerenz, Editors

INSTITUTE OF MEDICINE OF THE NATIONAL ACADEMIES

THE NATIONAL ACADEMIES PRESS

Washington, D.C.
www.nap.edu

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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This study was supported by Task No. #HHSP233200800005T between the National Academy of Sciences and the Agency for Healthcare Research and Quality. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the organizations or agencies that provided support for this project.

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Suggested citation: IOM (Institute of Medicine). 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press.

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
×

“Knowing is not enough; we must apply.

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—Goethe

INSTITUTE OF MEDICINE OF THE NATIONAL ACADEMIES


Advising the Nation. Improving Health.

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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THE NATIONAL ACADEMIES

Advisers to the Nation on Science, Engineering, and Medicine


The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished scholars engaged in scientific and engineering research, dedicated to the furtherance of science and technology and to their use for the general welfare. Upon the authority of the charter granted to it by the Congress in 1863, the Academy has a mandate that requires it to advise the federal government on scientific and technical matters. Dr. Ralph J. Cicerone is president of the National Academy of Sciences.


The National Academy of Engineering was established in 1964, under the charter of the National Academy of Sciences, as a parallel organization of outstanding engineers. It is autonomous in its administration and in the selection of its members, sharing with the National Academy of Sciences the responsibility for advising the federal government. The National Academy of Engineering also sponsors engineering programs aimed at meeting national needs, encourages education and research, and recognizes the superior achievements of engineers. Dr. Charles M. Vest is president of the National Academy of Engineering.


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The National Research Council was organized by the National Academy of Sciences in 1916 to associate the broad community of science and technology with the Academy’s purposes of furthering knowledge and advising the federal government. Functioning in accordance with general policies determined by the Academy, the Council has become the principal operating agency of both the National Academy of Sciences and the National Academy of Engineering in providing services to the government, the public, and the scientific and engineering communities. The Council is administered jointly by both Academies and the Institute of Medicine. Dr. Ralph J. Cicerone and Dr. Charles M. Vest are chair and vice chair, respectively, of the National Research Council.


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Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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SUBCOMMITTEE ON STANDARDIZED COLLECTION OF RACE/ETHNICITY DATA FOR HEALTHCARE QUALITY IMPROVEMENT

DAVID R. NERENZ (Chair), Director,

Center for Health Services Research, Henry Ford Health System, Detroit, MI

MARGARITA ALEGRÍA, Professor,

Department of Psychiatry, Harvard Medical School, and

Director,

Center for Multicultural Mental Health Research, Cambridge Health Alliance, Boston, MA

JOHN Z. AYANIAN, Professor of Medicine and Health Care Policy,

Harvard Medical School and Brigham and Women’s Hospital, Boston, MA

IGNATIUS BAU, Program Director,

The California Endowment, Oakland, CA

RODERICK J. HARRISON, Senior Research Scientist,

Office of the Vice President for Research and Compliance, Howard University, Washington, DC

ROMANA HASNAIN-WYNIA, Director,

Center for Healthcare Equity and

Associate Professor,

Research, Feinberg School of Medicine, Northwestern University, Chicago, IL

NINEZ PONCE, Associate Professor,

Department of Health Services, UCLA School of Public Health, Los Angeles, CA

WAYNE S. RAWLINS, National Medical Director,

Aetna Government Health Plans, Aetna, Hartford, CT

PAUL M. SCHYVE, Senior Vice President,

The Joint Commission, Oakbrook Terrace, IL

ALAN M. ZASLAVSKY, Professor of Health Care Policy (Statistics),

Harvard Medical School, Boston, MA

Study Staff

Cheryl Ulmer, Study Director

Bernadette McFadden, Research Associate

Michelle Bruno, Research Associate

Adam Schickedanz,

Mirzayan Science and Technology Fellow1

Cassandra Cacace, Senior Program Assistant

Roger Herdman, Board Director,

Board on Health Care Services

1

Served through May 2009.

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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Reviewers

This report has been reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise, in accordance with procedures approved by the National Research Council’s Report Review Committee. The purpose of this independent review is to provide candid and critical comments that will assist the institution in making its published report as sound as possible and to ensure that the report meets institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We wish to thank the following individuals for their review of this report:

OLIVIA CARTER-POKRAS, Department of Epidemiology and Biostatistics, College of Health and Human Performance, University of Maryland, College Park, MD

SIMON P. COHN, Kaiser Permanente Medical Care Program, Oakland, CA

HAROLD P. FREEMAN, National Cancer Institute, Rockville, MD and Columbia University, New York, NY

DEEANA L. JANG, Asian & Pacific Islander American Health Forum, Washington, DC

JENNIE R. JOE, Native American Research and Training Center, University of Arizona, Tucson, AZ

ERIC B. LARSON, Center for Health Studies, Group Health Cooperative of Puget Sound, University of Washington, Seattle, WA

DENISE LOVE, National Association of Health Data Organizations, Salt Lake City, UT

JOHN LUMPKIN, Health Care Group, Robert Wood Johnson Foundation, Princeton, NJ

MARY A. PITTMAN, Public Health Institute, Oakland, CA

KENNETH PREWITT, School of International and Public Affairs, Columbia University, New York, NY

Although the reviewers listed above have provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations nor did they see the final draft of the report before its release. The review of this report was overseen by Faith Mitchell, Grantmakers In Health, and Edward B. Perrin, University of Washington. Appointed by the National Research Council and Institute of Medicine, they were responsible for making certain that an independent examination of this report was carried out in accordance with institutional procedures and that all review comments were carefully considered. Responsibility for the final content of this report rests entirely with the authoring committee and the institution.

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Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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Foreword

The Institute of Medicine report Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare (2002) called attention to poorer access to health care and worse health outcomes among certain racial and ethnic groups. According to reports from the Agency for Healthcare Research and Quality and others, disparities in the quality of care and in health outcomes persist. Accelerating progress toward eliminating these disparities depends in part on our ability to identify and track experiences in health care among individuals from a variety of racial and ethnic backgrounds and who speak a variety of languages other than English.

This report offers an approach to identifying racial, ethnic, and language categories that bear on disparities in health care and health outcomes. Extending beyond the broad racial and ethnic categories used by the Office of Management and Budget, this report provides a more granular classification of ethnicity and language needs. This standardized approach to classification will both help measure progress in eliminating disparities and assure that comparisons across different settings are based on similar groupings of individuals.

I want to express my appreciation to the subcommittee and staff for the tremendous effort that has gone into this report. Their work represents another positive step toward the goal of high quality health care for everyone.


Harvey V. Fineberg, M.D., Ph.D.

President, Institute of Medicine

August 2009

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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Preface

Calling attention to the need for improvement in quality of care has been a central theme for many Institute of Medicine (IOM) reports. Crossing the Quality Chasm: A New Health System for the 21st Century noted significant shortcomings in the nation’s health care delivery system in terms of safety, effectiveness, timeliness, efficiency, patient-centeredness, and equity, while Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare documented that in a variety of organizational settings and clinical domains, members of racial and ethnic minority groups receive poorer quality care than their White counterparts.

While many studies published since the 2003 release of Unequal Treatment have shown similar patterns, there is evidence of some progress. Disparities in some domains (e.g., process of care measures such as use of beta blockers or aspirin after heart attack) have been shown to be shrinking over time for some populations but not others. Individual health plans, hospitals, and medical groups have organized quality improvement projects aimed at reducing disparities and have succeeded in doing so. The underlying reasons for disparities are increasingly understood so that initiatives to address disparities can be focused on factors that are likely to have the greatest positive effect. The impact of language, culture, and socioeconomic status, along with race and ethnicity, are also more clearly understood. Yet studies reveal that disparities remain on both process of care and outcome measures.

Continued work in addressing disparities requires the collection and use of data on race, ethnicity, and language in all health and health care data systems, as called for in 2004 by the National Research Council report, Eliminating Health Disparities: Measurement and Data Needs. These data provide the opportunity to monitor and analyze disparities, and are informative in identifying individuals and groups to whom quality improvement or other interventions can be directed. Across a range of organizational levels, from the Agency for Healthcare Research and Quality National Healthcare Disparities Report at one end, to the work carried out by individual physician offices and community health centers at the other, the collection and use of data on race, ethnicity, and language are key parts of the process of identifying health care needs and eliminating disparities.

Quality improvement can be organized as a collaborative effort at a local, regional, statewide, or even national level. Even when projects are carried out by individual organizations, the process of benchmarking involves sharing information from organization to organization. For some quality improvement projects, literal data sharing is important, as an entity collecting race, ethnicity, or language data (e.g., a multispecialty group practice) may provide that information to another entity (e.g., a managed care plan) in order for the second entity to use the information for analyses of quality of care data. Additionally, regional, state, and national health care agencies may wish to pool data from individual organizations to address disparities in a broader geographic context.

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
×

The collection of data on race, ethnicity, and language will, in principle, have the greatest impact if it is done according to standards that allow for comparison of data across organizations, sharing of individual-level data from one to another, and combining of data from multiple sources. The Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement was asked to examine the issue of how data on race, ethnicity, and language are collected in various contexts associated with health care, and to offer recommendations on standardization of the categories for these variables. This report addresses data collection challenges and proposes a framework for moving forward with standardized data collection across health care entities. Previous reports have reiterated the importance of collecting more detailed ethnicity data than are captured by the Office of Management and Budget (OMB) standard categories; this report proposes templates of granular ethnicity and language categories for national adoption so that entities wishing to collect detailed data can do so in systematic, uniform ways. The recommendations presented here provide guidance to entities on data collection to support their efforts to improve quality and eliminate disparities.


David R. Nerenz, Chair

Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement

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Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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Acknowledgments

The subcommittee and staff are grateful to many individuals and organizations who contributed to this study. Most specifically, the subcommittee would like to thank members of the Institute of Medicine Committee on Future Directions for the National Healthcare Quality and Disparities Reports for their guidance and comments on the report. The committee members include:1

SHEILA BURKE (Chair), Faculty Research Fellow, Malcolm Weiner Center for Social Policy, John F. Kennedy School of Government, Harvard University, Boston, MA

ANNE BEAL, Assistant Vice President, The Commonwealth Fund, New York, NY

E. RICHARD BROWN, Professor, UCLA School of Public Health and Director, UCLA Center for Health Policy Research, Los Angeles, CA

MARSHALL H. CHIN, Professor of Medicine, University of Chicago, Chicago, IL

JOSE J. ESCARCE, Professor of Medicine, Division of General Internal Medicine and Health Services Research, UCLA School of Medicine, Los Angeles, CA

KEVIN FISCELLA, Associate Professor, University of Rochester, Rochester, NY

ELLIOT S. FISHER, Professor of Medicine and Community and Family Medicine, Dartmouth Medical School, and Director, Center for Health Policy Research, Dartmouth Institute for Health Care Policy and Clinical Practice, Lebanon, NH

DAWM M. FITZGERALD, CEO, QSource, Memphis, TN

FOSTER GESTEN, Medical Director, Office of Health Insurance Programs, New York State Department of Health, Albany, NY

BRENT C. JAMES, Chief Quality Officer and Executive Director, Intermountain Health Care, Inc. Institute for Health Care Delivery Research, Salt Lake City, UT

JEFFREY KANG, Chief Medical Officer and Senior Vice President for Medical Strategy and Policy, CIGNA Corporation, Hartford, CT

SHARON-LISE T. NORMAND, Professor, Department of Health Care Policy, Harvard Medical School, Boston, MA

CHRISTOPHER QUERAM, President/CEO, Wisconsin Collaborative for Healthcare Quality, Middleton, WI

1

Subcommittee members Ignatius Bau, David Nerenz, and Paul Schyve are also members of the Committee.

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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SARAH SCHOLLE, Assistant Vice President for Research and Analysis, National Committee for Quality Assurance, Washington, DC

BRUCE SIEGEL, Director, Center for Health Care Quality, The George Washington University School of Public Health and Health Services, Washington, DC

The subcommittee acknowledges Constance Citro, director of the Committee on National Statistics, National Research Council of the National Academies, for her insight and contacts within the statistical community as well as the help of Thomas Plewes in locating materials on limited English proficiency.

In addition, the subcommittee benefited from the testimony before the committee and subcommittee during public workshops: Karen Adams (National Quality Forum), Donald Berwick (Institute for Healthcare Improvement), Andrew Bindman (UCSF and CA Medicaid Research Institute), Carolyn Clancy (Agency for Healthcare Research and Quality), Kathryn L. Coltin (Harvard Pilgrim Health Care), Brenda Edwards (Division of Cancer Control & Population Sciences, National Cancer Institute), Marc Elliott (RAND Corporation), Irene Fraser (Agency for Healthcare Research and Quality), Allen Fremont (RAND Corporation), Ron Hays (Division of General Internal Medicine and Health Services Research, UCLA), Karen Humes (U.S. Census Bureau), Deeana Jang (Asian & Pacific Islander American Health Forum, speaking on behalf of Out of Many, One’s Health Data Taskforce), Marjorie Kagawa-Singer (UCLA School of Public Health), Karen Kmetik (American Medical Association and The Physician Consortium for Performance Improvement), David Lansky (Pacific Business Group on Health), Nicole Lurie (RAND Corporation, Center for Population Health and Health Disparities), Jennifer Madans (National Center for Health Statistics), Paul McGann (Office of Clinical Standards and Quality, Centers for Medicare and Medicaid Services), Ernest Moy (Agency for Healthcare Research and Quality), Marsha Regenstein (The George Washington University), Thomas Reilly (Office of Research, Development and Information, Centers for Medicare and Medicaid Services), Michael Rodriguez (Department of Family Medicine, David Geffen School of Medicine, UCLA), Patrick Romano (Divisions of General Medicine and General Pediatrics, Center for Healthcare Policy and Research, UC Davis), Joachim Roski (Engelberg Center for Health Care Reform, The Brookings Institution), Maribeth Shannon (Market and Policy Monitor Program, California HealthCare Foundation), Gayle Tang (National Diversity, Kaiser Permanente), Kalahn Taylor-Clark (Engelberg Center for Healthcare Reform, The Brookings Institution), Grace Ting (Health Equities Programs, Wellpoint, Inc.), Katherine K. Wallman (U.S. Office of Management and Budget), Thomas Williams (Integrated Healthcare Association), and Mara Youdelman (National Health Law Program).

Many others provided valuable advice on the issues under study; these include Mona L. Bormet (Asian & Pacific Islander American Health Forum), Erin Bowman (California Health Care Safety Net Institute), Rita Carreón (America’s Health Insurance Plans), Olivia Carter-Pokras (University of Maryland), Coralie Chan (Kaiser Permanente), Francis Frasier (Indian Health Service), Sundak Ganesan (Centers for Disease Control and Prevention Vocabulary and Messaging Team), Sheldon Greenfield (University of California, Irvine), Kirk Greenway (Indian Health Service), Brady Hamilton (National Center for Health Statistics), William E. Hammond (Duke University), George Isham (HealthPartners), Wendy Jameson (California Health Care Safety Net Institute), Charles Jarvis (NextGen, Executive Team HIMSS EHRA), Ashish Jha (Harvard School of Public Health), Sherrie Kaplan (University of California, Irvine), Theodore Klein (Klein Consulting), Diane Louise Leach (Indian Health Service), Mark Leavitt (Certification Commission for Healthcare Information Technology), Denise Love (National Association of Health Data Organizations), Martin Martinez (California Pan-Ethnic Health Network), Vickie M. Mays (Department of Health Services, UCLA), Mark McClellan (The Brookings Institution), JeanHee Moon (Center for Health Care Strategies), Jeannette Noltenius (Out of Many, One), Edna Paisano (Indian Health Service), Ruth Perot (Summit Health Institute for Research and Education), Daniel Pollack (Centers for Disease Control and Prevention), Kenneth Prewitt (School of International and Public Affairs, Columbia University), Richard Pride (University of Mississippi Medical Center), Alisa Ray (Certification Commission for Healthcare Information Technology), C. Sue Reber (Certification Commission for Healthcare Information Technology), Bob Rehm (America’s Health Insurance Plans), Yvette Roubideaux (University of Arizona College of Medicine), Barbara Rudolph (The Leapfrog Group), Hyon Shin (U.S. Census Bureau), Jane Sisk (National Center for Health Statistics, Centers for Disease Control and Prevention), Brian Smedley (Joint Center for Political and Economic Studies), Benjamin P. Smith (Indian Health Service), Phillip L. Smith (Indian Health Service), Benjamin Steffen (Maryland Health Care

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
×

Commission), Otilia Tiutin (Contra Costa Health Plan), Alan Trachtenberg (Indian Health Service), William Vega (David Geffen School of Medicine, UCLA), Lucie Vogel (Indian Health Service), Robin Weinick (Institute of Health Policy, Massachusetts General Hospital), and Ellen Wu (California Pan-Ethnic Health Network).

California state government representatives were generous in their time discussing issues with respect to collection of data and implementation of SB 853. These included Cindy Ehnes and Hattie Hanley of the Department of Managed Health Care; Shelley Rouillard and Ernesto Sanchez, Managed Risk Medical Insurance Board; David Carlisle, Candace Diamond, Serena Beltran, and Ron Spingarn, Office of Statewide Health Planning and Development; Sandra Perez and Ed Mendoza, California Office of the Patient Advocate; and Rita Marowitz, Medi-Cal Managed Care Division.

Many within the IOM were helpful throughout the study process, including Karen Anderson, Lyla Hernandez, Laura Levitt, Rose Martinez, and Sharyl Nass. In addition, we would like to thank Clyde Behney, Linda Kilroy, Abbey Meltzer, Vilija Teel, Lauren Tobias, Jackie Turner, and Jordan Wyndelts for their continuing support throughout the project to ensure release of this expedited report. We also wish to acknowledge the editing provided by Rona Briere.

Funding for this study was provided by the Agency for Healthcare Research and Quality (AHRQ). The subcommittee appreciates its support for the project as well as substantive support from AHRQ staff, particularly Roxanne Andrews, Carolyn Clancy, and Ernest Moy. The California Endowment has provided additional funding to ensure widespread distribution of this report’s summary brief in Spanish and Chinese.

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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Boxes, Figures, and Tables

Summary Box

S-1

 

Statement of Task: Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement,

 

2

Figure

S-1

 

Recommended variables for standardized collection of race, ethnicity, and language need,

 

3

Chapter 1 Boxes

1-1

 

Barriers to Collection of Race, Ethnicity, and Language Data,

 

18

1-2

 

The 1997 OMB Revisions to the Standards for the Classification of Federal Data on Race and Hispanic Ethnicity,

 

20

1-3

 

Statement of Task: Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement,

 

23

Figures

1-1

 

A framework for reducing disparities in health care systems,

 

14

1-2

 

Williams, Lavizzo-Mourey, and Warren’s framework for understanding the relationships between race, medical/health care, and health,

 

18

1-3

 

Overview of purposes and uses of race, ethnicity, and language data to guide subcommittee’s investigation of issues of categorization and collection,

 

24

Table

1-1

 

Categories and Definitions Promulgated by the OMB and the U.S. Bureau of the Census,

 

17

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
×

Chapter 2 Tables

2-1

 

Census 2000 Population by Race and Hispanic Ethnicity,

 

33

2-2

 

Selected Characteristics of the Hispanic/Latino/Spanish Population in the United States,

 

37

2-3

 

Selected Characteristics of the Black Population in the United States,

 

42

2-4

 

Selected Characteristics of the Asian Population in the United States,

 

45

2-5

 

Selected Characteristics of the NHOPI Population in the United States,

 

49

2-6

 

Selected Characteristics of the White Population in the United States,

 

51

2-7

 

Selected Characteristics of the American Indian or Alaska Native (AIAN) Population in the United States,

 

54

Chapter 3 Boxes

3-1

 

Race and Ethnicity Categories in the HCUP Databases,

 

68

3-2

 

The Use of Data Linkages to Improve Data Coverage and Quality in Cancer Registries,

 

69

3-3

 

The Challenge of Categorizing Filipino Respondents,

 

71

3-4

 

Realizing the Necessity of Collecting Data: The University of Mississippi Medical Center,

 

80

Figures

3-1

 

Reproduction of questions of race and Hispanic origin from Census 2000,

 

71

3-2

 

Geographic distribution of the Asian population,

 

76

3-3

 

CDC ethnicities rolled up to the OMB minimum categories for race and Hispanic ethnicity with subcommittee annotations,

 

82

3-4

 

Models for data collection instruments to collect race, Hispanic ethnicity, and granular ethnicity data,

 

88

Tables

3-1

 

OMB Race and Hispanic Ethnicity Categories According to a One- and Two-Question Format,

 

62

3-2

 

Race and Ethnicity Categories Collected by Various Data Sources,

 

64

3-3

 

Race and Hispanic Ethnicity Categories Used by State Medicaid and CHIP Programs,

 

67

3-4

 

Hispanic and Non-Hispanic Population Distribution by Race for the United States: 2000,

 

72

3-5

 

Comparison of Granular Ethnicity Categorization and Coding Systems,

 

78

3-6

 

Examples of Instructions, Phrasing, and Terminology to Capture Race and Ethnicity Data,

 

86

Chapter 4 Boxes

4-1

 

Language Concordance Between Patients and Providers,

 

95

4-2

 

Assessing Whether Language Assistance Needs Are Met,

 

96

Figures

4-1

 

Census 2000 questions about language,

 

100

4-2

 

Karliner algorithm,

 

104

4-3

 

Most spoken languages in North Dakota, Minnesota, Texas, and Maine, 2005,

 

112

4-4

 

Number of languages spoken in each state,

 

114

Suggested Citation:"Front Matter." Institute of Medicine. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press. doi: 10.17226/12696.
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Tables

4-1

 

Summary of Question Types and Categories,

 

103

4-2

 

Correlations Between Self-Reported English Ability in Speaking, Reading, and Writing,

 

106

4-3

 

Relationship of Speaking and Reading Ability,

 

107

4-4

 

Language Categories in Selected Collection Instruments,

 

110

Chapter 5 Boxes

5-1

 

Statewide Race and Ethnicity Data Collection: Massachusetts,

 

131

5-2

 

Collecting and Using Data: The Alliance of Chicago Community Health Services,

 

132

5-3

 

Collecting Data in Small Physician Practices,

 

133

5-4

 

Successful Collection of Data by a Health Plan: Aetna,

 

134

5-5

 

Standardizing Direct Data Collection,

 

138

5-6

 

The Use of Indirectly Collected Data by a Health Plan: Wellpoint, Inc.,

 

142

Figures

5-1

 

A snapshot of data flow in a complex health care system,

 

129

5-2

 

Opportunities to collect data within the health care system,

 

130

Chapter 6 Figure

6-1

 

Recommended variables for standardized collection of race, ethnicity, and language need,

 

149

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The goal of eliminating disparities in health care in the United States remains elusive. Even as quality improves on specific measures, disparities often persist. Addressing these disparities must begin with the fundamental step of bringing the nature of the disparities and the groups at risk for those disparities to light by collecting health care quality information stratified by race, ethnicity and language data. Then attention can be focused on where interventions might be best applied, and on planning and evaluating those efforts to inform the development of policy and the application of resources. A lack of standardization of categories for race, ethnicity, and language data has been suggested as one obstacle to achieving more widespread collection and utilization of these data.

Race, Ethnicity, and Language Data identifies current models for collecting and coding race, ethnicity, and language data; reviews challenges involved in obtaining these data, and makes recommendations for a nationally standardized approach for use in health care quality improvement.

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