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.
Table of Contents
|2 Evidence of Disparities Among Ethnicity Groups||31-60|
|3 Defining Categorization Needs for Race and Ethnicity Data||61-92|
|4 Defining Language Need and Categories for Collection||93-126|
|5 Improving Data Collection Across the Health Care System||127-146|
|Appendix A: Acronyms and Abbreviations||163-166|
|Appendix B: Legislation Cited in Report||167-170|
|Appendix C: Workshop Agendas||171-176|
|Appendix D: Subcommittee Member and Staff Biographies||177-182|
|Appendix E: Subcommittee Template: Developing a National Standard Set of Granular Ethnicity Categories and a Rollup Scheme||183-198|
|Appendix F: Granular Ethnicities with No Determinate OMB Race Classification||199-200|
|Appendix G: Kaiser Permanente: Evolution of Data Collection on Race, Ethnicity, and Language Preference Information||201-220|
|Appendix H: Contra Costa Health Plan Language Assistance Database and Ethnicity Categories||221-236|
|Appendix I: Subcommittee Template: Developing a National Standard Set of Spoken Language Categories and Coding||237-264|
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