Race, Ethnicity, and Language Data Standardization for Health Care Quality Improvement (2009) / Chapter Skim
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1 Introduction
1 Introduction
Pages 13-30
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Categories for collection and methods of aggregation for reporting race, ethnicity, and language data vary. Challenges to improving data quality include nonstandardized categories, a lack of understanding of why data are collected, health information technology (HIT)
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The detection phase requires organizations to systemati cally collect relevant demographic data and to link these data to measures of quality. This phase brings health systems one step closer to understanding where the disparities (or differential health care needs)
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. This chapter provides background on key issues and challenges surrounding the categorization and collection of race, ethnicity, and language data for health care quality improvement.
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CHALLENGES TO COLLECTING RACE, ETHNICITY, AND LANGUAGE DATA A variety of entities, such as states, health plans, health professionals, hospitals, community health centers, nursing homes, and public health departments -- as well as the public -- play roles in obtaining, sharing, and using race, ethnicity, and language data. All of these entities, though, have different reasons for and ways of categorizing, collecting, and aggregating these data.
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Terms such as "Haitian" groups of Africa, including, for example, Black, or "Negro" can be used in addition to "Black or African American, Negro, Nigerian, or Haitian African American" Hispanic or Latino A person of Mexican, Puerto Rican, Cuban, South A person of Mexican, Puerto Rican, Cuban, South or Central American, or other Spanish culture or Central American, or other Spanish culture or or origin, regardless of race. The term, "Spanish origin, regardless of race origin," can be used in addition to "Hispanic or Latino" Native Hawaiian or A person having origins in any of the original People having origins in any of the original peoples Other Pacific Islander peoples of Hawaii, Guam, Samoa, or other Pacific of Hawaii, Guam, Samoa, or other Pacific Islands, Islands including people who identify as Native Hawaiian, Chamorro, Tahitian, Mariana Islander, or Chuukese White A person having origins in any of the original People having origins in any of the original peoples peoples of Europe, the Middle East, or North of Europe, the Middle East, or North Africa, Africa including Irish, German, Italian, Lebanese, Near Easterner, Arab, or Polish Some Other Race All other responses not classifiable in the White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander race categories; respondents providing write-in entries such as multiracial, mixed, interracial, "American," or a Hispanic/ Latino group (e.g., Mexican, Puerto Rican, Cuban)
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(Coltin, 2009) , and the fact that the current OMB categories are not sufficiently descriptive of locally relevant population groups (Friedman et al., 2000; NRC, 2004b)
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. Because the standard was not designed with regard to health or health care specifically, the groups identified by the OMB categories may not be the only analytic groups useful for advancing health care quality improvement.
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• Preference for self-reported race and Hispanic ethnicity Use of the Standards • Used at a minimum for all federally sponsored statistical data collections that include data on race and ethnicity An Approach to Improving the Categorization and Aggregation of Data The OMB categories are not sufficiently descriptive to distinguish among locally relevant ethnic populations that face unique health problems and may have dissimilar patterns of care and outcomes (Hasnain-Wynia and Baker, 2006)
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9 regulations, to collecting, sharing, and reporting these data. HIPAA restricts the use and disclosure of identifiable health information, but does not limit the collec tion of demographic data for quality improvement purposes (Kornblet et al., 2008)
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STUDY CHARGE AND APPROACH The IOM, under a contract with the Agency for Healthcare Research and Quality (AHRQ) , formed the Sub committee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement to report on the issue of standardization of race, ethnicity, and language variables; define a standard set of race, ethnicity, and language categories; and define methods of obtaining race, ethnicity, and language data (Box 1-3)
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New national standards have been set for birth and death records, incorporating categories beyond those set by OMB; states and localities are free to use additional categories and are encouraged to do so along the lines of the subcommittee's recommendations. The subcommittee was formed in conjunction with the Committee on Future Directions for the National Healthcare Quality and Disparities Reports.
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The subcommittee developed principles to guide its deliberations, including the need for: • Nomenclature for each variable and its categories that would maximize individuals' ease and consistency of identification with those categories; • Local decision making about categories that would be useful given the size and diversity of the popula tion served or surveyed, as well as the consideration that quality improvement activities tend to be locally based; • A framework that would allow some flexibility in approaches to collection but retain uniform categories, in recognition of the different capacities of information systems; and • Fostering comparability across the variety of actors that collect and use these data. Building on Previous Studies In developing its rationale and framework for standardization, the subcommittee considers previous research on the categorization, collection, and use of race, ethnicity, and language data in health care settings.
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The present report addresses these data collection challenges and proposes a framework for moving forward with standardized data collection across all health and health care entities, not just within HHS agencies or by recipients of federal funds. Previous reports have reiterated the importance of collecting more detailed ethnicity data than are captured by the OMB standard categories; this report proposes a template of categories so that entities wishing to collect detailed data can do so in systematic, uniform ways.
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Chapter 3 addresses the utility of the OMB categories in capturing important cultural and social groups for statistical reporting before considering the collection of more granular ethnicity data and how standard coding of categories can allow for the sharing of data beyond a single service site. The chapter examines the geographic distribution of racial and ethnic groups across the United States and the need for balance between nationally uniform categories for data collection and flexibility in how different subsets of categories are used for local quality improvement.
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UCLA School of Public Health. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, March 12, 2009.
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Wellpoint. Presentation to the IOM Commit tee on Future Directions for the National Healthcare Quality and Disparities Reports, March 12, 2009.
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In Racial, ethnic and primary language data collection: An assessment of federal policies, practices and perceptions, volume 2. Washington, DC: National Health Law Program (NHeLP)
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