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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement (2009)

Chapter: Appendix F: Granular Ethnicities with No Determinate OMB Race Classification

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Suggested Citation:"Appendix F: Granular Ethnicities with No Determinate OMB Race Classification." 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.
×

Appendix F
Granular Ethnicities with No Determinate OMB Race Classification

The Subcommittee recommends collecting an individual’s Hispanic ethnicity, race, and granular ethnicity. Separate questions to collect these data ensure the ability to report Office of Management and Budget (OMB)-compliant data without needing to aggregate granular ethnicity categories back to one of the six OMB categories (e.g., an individual can self-identify as Asian, non-Hispanic, and of Laotian ancestry and all of these data should be retained in a data system). Nevertheless, in some instances, granular ethnicity data may need to be rolled up to one of the OMB categories for purposes of analysis or reporting. For example, an individual may not have responded to the question on race and only responded to the question on granular ethnicity. If the individual’s health plan is required to report data to the state using only the OMB race and Hispanic ethnicity categories, the health plan may want to aggregate the individual’s granular ethnicity to an OMB category, whenever possible.

Aggregating data to the OMB race categories through rollup schemes, though, may inevitably contradict or misrepresent an individual’s self-identification as not all Americans of South African descent are Black, for example. An individual’s granular ethnicity does not automatically determine his or her race; consequently, any rollup scheme may falsely classify some individuals. Certain granular ethnicity categories are more prone to misclassification than others, primarily because several individual races as well as multiracial persons are represented within a single ethnicity.

The subcommittee identified some of these ethnicities by cross-tabulating write-in responses to Census ancestry data by the OMB single-race and Hispanic ethnicity categories. Since many of the ethnicity groups had large proportions of individuals who reported more than one race, the subcommittee then cross-tabulated the ancestry responses with “alone or in combination with one or more other races” variable for each OMB group to see if 90 percent or more in the ethnicity group reported an OMB race either alone or in combination with another race.1 Many of the granular ethnicities that fell short of a 90 percent threshold based on single-race reporting exceeded that threshold when the identification was based on reporting the race group alone or in combination with other races.

Many of the granular ethnicity categories that still could not be assigned to an OMB race category using the 90 percent threshold for responses “alone or in combination” represented populations with long histories of intermarriage and multiracial identity (e.g., Native Hawaiian or Other Pacific Islander, American Indian or Alaska

1

The 90 percent rule used in this analysis is not the only method for identifying granular ethnicity categories that cannot or should not be rolled up to one of the OMB categories. Census 2010, for example, is, when necessary, rolling up write-in responses based on the OMB definitions of each race and Hispanic ethnicity category. Then, all sub-Saharan African ethnicities will be coded as Black, where necessary for analysis.

Suggested Citation:"Appendix F: Granular Ethnicities with No Determinate OMB Race Classification." 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.
×

Native). These granular ethnicity categories could be assigned to the OMB race category of the same name on the basis of the OMB definitions for the Native Hawaiian or Other Pacific Islander and American Indian or Alaska Native categories. However, it is important to note that this assignment misclassifies many individuals based on the OMB race with which they would self-identify given the opportunity.

Additionally, Table F-1 includes granular ethnicities that are rolled up differently by different coding schemes. For example, the Centers for Disease Control and Prevention (CDC)/Health Level 7 (HL7) Race and Ethnicity Code Set 1.0 considers Madagascan in its Asian category while the Massachusetts Superset considers Madagascan under both Asian and African category. Thus, this ethnicity is included in Table F-1 and may be said to have “no determinate OMB race classification.”

The subcommittee suggests that the Department of Health and Human Services (HHS) take into account that some ethnicities do not correspond with one specific OMB race category and that when rollup is necessary, these granular ethnicities be included in a category labeled “no determinate OMB race classification.”

TABLE F-1 Granular Ethnicities That Cannot Be Rolled Up to an OMB Race Category with Greater Than 90 Percent Certainty

Write-in Response to Census Ancestry Question

Population

OMB race categories (% of the population)

White Alone or in Combination

Black or African American Alone or in Combination

AIAN Alone or in Combination

Asian Alone or in Combination

NHOPI Alone or in Combination

Some Other Race Alone or in Combination

Brazilian

177,483

77.3

3.3

0.4

0.2

0.0

30.3

Cape Verdean

76,476

15.7

45.4

2.0

0.8

0.9

58.4

Belizean

38,443

14.4

66.1

3.2

1.2

0.8

27.1

Guyanese

162,170

3.1

58.2

3.1

23.1

1.0

26.6

German from Russia

9,968

75.8

42.7

1.9

0.4

0.1

3.6

Creole

18,821

19.3

73.2

5.1

0.8

0.0

27.5

American Madagascan*

 

 

 

 

 

 

 

Tunisian*

 

 

 

 

 

 

 

Surinam*

 

 

 

 

 

 

 

Trinidadian

160,715

4.4

88.8

1.4

8.0

1.2

9.3

West Indian

152,218

6.9

87.1

3.4

3.1

2.0

17.4

Moroccan

37,219

76.5

17.4

1.3

4.8

0.3

25.7

Dominican

915,208

30.0

14.8

1.5

0.9

0.3

62.3

South African

43,472

86.5

9.0

0.1

2.1

0.0

7.9

Sudanese

13,420

5.6

79.7

0.3

1.1

0.0

28.8

East Indian

61,510

8.5

10.9

2.9

83.1

2.1

15.2

Eurasian

12,473

68.8

0.6

1.5

40.0

2.0

27.3

* The granular ethnicities marked with an asterisk do not include percents of the population because the population size was too small for analysis in the 2000 Public Use Microdata Samples (PUMS).

Suggested Citation:"Appendix F: Granular Ethnicities with No Determinate OMB Race Classification." 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.
×
Page 199
Suggested Citation:"Appendix F: Granular Ethnicities with No Determinate OMB Race Classification." 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.
×
Page 200
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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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