Chapter 2: Evidence of Disparities Among Ethnicity Groups (continued, 2)

Race, Ethnicity, and Language Data: Standardization for Health Care Qu

American Indian or Alaska Native Groups

The number and proportion of persons in the American Indian or Alaska Native (AIAN) racial group is heavily influenced by whether the numbers are for AIAN alone or AIAN in combination with other racial groups. In Census 2000, 2.4 million persons (0.87 percent) in the U.S. population, fell in the AIAN alone group, but AIAN in combination with other races numbered 4.3 million (1.5 percent of the U.S. population).12 The Census and the Office of Management and Budget (OMB) define the term AIAN as referring to persons with origins in the indigenous persons of North, Central, or South America (Ogunwole, 2006), while the Indian Health Service (a U.S. Department of Health and Human Services agency responsible for providing federal health services to AIAN persons) uses its own narrower definition, which is confined to those enrolled in any of the federally or state-recognized tribes within the United States.13 To accommodate these identifications, Census 2000 provided space for a respondent to write in the name of his or her enrolled or principal tribe or affiliation.

Demographic Characteristics 

As in the previous sections, Table 2-7 presents the larger population figures for the AIAN population alone and in combination with other races, along with variations in English proficiency and poverty rates for selected tribes. Not displayed in the table is the place of residence of the AIAN populations; one-third of American Indians live in tribal areas, 2.4 percent in Alaska Native villages, and the remaining 64.1 percent outside of tribal areas. Outside of tribal areas, 27.2 percent of AIAN individuals over age of 25 have less than a high school education, compared with 31.8-33.1 percent living in tribal areas (Ogunwole, 2006).

Health-Related Differences Among American Indian or Alaska Native Tribal Groups

In the literature, the AIAN group has been subdivided primarily based on tribal affiliation and/or geographic location. The available literature has examined health differences among these groups by measures of cancer, end-stage renal disease (ESRD), type II diabetes, and metabolic syndrome.


Cancer rates among AIAN populations vary and are often misreported because of misclassification of race/ ethnicity data in national AIAN cancer registries (Wiggins et al., 2008). This has posed problems for cancer surveillance, research, and overall public health practice (Johnson et al., 2009; Wiggins et al., 2008). Using population-based cancer registries, Wiggins and colleagues (2008) examined the incidence rates of cancer in AIAN and non-Hispanic Whites during 1999-2004 and found that national data masks regional and thereby tribal variation. When combining incidence rates for all cancer sites, AIAN rates were found to be higher than non-Hispanic White rates in the Northern Plains (538.1 versus 464.8 per 100,000), Southern Plains (492.6 versus 461.2), and Alaska (511.0 versus 486.8). Rates in the Southwest, Pacific Coast, and the East, however, were found to be lower in AIANs than non-Hispanic Whites (218.3-308.9 per 100,000 vs. 398.9-574.4 per 100,000, respectively). When separating by cancer type, lung cancer, and colorectal cancer rates were found to be higher in AIANs than non- Hispanic Whites in Alaska and the Northern Plains. Stomach, gallbladder, kidney, and liver cancer rates were also found to be higher among AIANs than among non-Hispanic Whites overall, in Alaska, in the Plains regions, and in the Southwest (Wiggins et al., 2008). The analyses were limited to persons living within the Contract Health Service Delivery Areas of the Indian Health Service.

Kelly and colleagues (2006) found subgroup differences when comparing the cancer incidence rates of American Indians from New Mexico and Alaska.14 Between 1993 and 2002, Alaska Indians had a higher incidence rate for all cancer sites combined than either New Mexico Indians or U.S. Whites; in-fact, the overall cancer incidence rate of Alaska Indians was 2.5 times higher than that of New Mexico Indians. The largest variations between the two Indian groups were found in rates of oral cavity/pharynx, esophagus, colon and rectum, pancreas, larynx, lung, prostate, and bladder cancer. Differences in esophageal, larynx, prostate, and bladder cancer were found only in men, while both Alaska Indian men and women had 7 to 10 times higher rates of lung cancer and approximately two-fold rates of all cancers. Cultural use of tobacco was credited as a major factor in these differences (Kelly et al., 2006). No data were collected on income in the different populations.

End-Stage Renal Disease

Using data from the U.S. Renal Data System, Hochman and colleagues (2007) examined the prevalence and incidence of ESRD in 200,000 adult members of the Navajo Nation in Arizona, New Mexico, and Utah. Prevalence and incidence rates are compared for ESRD among all adults in the United States; all Native Americans in the country; and Native Americans living in Arizona, New Mexico, and Utah and Colorado (outside of the Navajo Reservation). After adjusting for age, they found that the prevalence of ESRD in the Navajo Nation was 0.63 percent, higher than that in all U.S. adults (0.19 percent) and Native American adults (0.36 percent). However, this rate was lower than the prevalence among other Native American adults in the Southwest (0.89 percent) (Hochman et al., 2007). Incidence rates followed the same pattern. The study did not control for socioeconomic status.

Type II Diabetes

Type II diabetes affects a disproportionate number of AIANs; the highest rates in the country are among the Pima Indians of Arizona (Knowler, 1978). From 1990 to 1997, the number of AIANs diagnosed with diabetes increased dramatically, from 43,262 to 64,474 (Burrows et al., 2000). While documentation of specific tribal differences is limited, Burrows and colleagues found prevalence to vary by region (3.0 percent in the Alaska region vs. 17.4 percent in the Atlantic region), suggesting tribal differences in population rates of diabetes (Burrows et al., 2000). Since no socioeconomic data were analyzed in this study, it is difficult to determine whether the regional differences alone are the underlying cause of the perceived tribal differences in diabetes rates, or regional location is correlated with other factors that could influence these rates.

Metabolic Syndrome

Often a predictor of diabetes, metabolic syndrome varies widely in prevalence across different AIAN adult populations. Shumacher and colleagues examined the prevalence of metabolic syndrome15 among four groups, including the Navajo Nation from the southwestern United States and three within Alaska (Schumacher et al., 2008). Rates were age-adjusted to the 2000 U.S. adult population and compared with the rates of U.S. Whites, using NHANES data. Among those from the Navajo Nation, 43.2 percent of men and 47.3 percent of women had metabolic syndrome. These were much higher than rates in Alaska, where prevalence varied by region among men from 18.9 percent in western Alaska to 35.1 percent in southeast Alaska, and among women from 22.0 percent in western Alaska to 38.4 percent in southeast Alaska.


Studies have shown that disparities exist among AIAN groups. For conditions such as cancer, for which disparities appear to be even greater when one adjusts for misclassification of race/ethnicity, standardized collection of tribal identification as a granular ethnicity could provide the basis for better, more tailored health care responses.


The available evidence on health and heath care disparities among granular ethnic groups in the U.S. population is limited primarily to those groups for which discrete categorization on national survey instruments currently exists. Many studies include large data sets, often national ones, pooled over multiple years that usually provide information that is sufficiently powered to support reliable inferences and conclusions. Evidence of health and heath care disparities among population subgroups is only beginning to emerge and our gaps in knowledge from the published literature are substantial. This is especially true for groups not captured in national data sets that may be of interest to local quality improvement efforts. However, the research reviewed in this chapter consistently finds significant variation across some of subgroups under each of the OMB categories, confirming the utility of collection and reporting of racial and ethnic data at a group level. Indeed, the need for further disaggregation beyond OMB race and ethnicity categories was emphasized by authors of many of the studies reviewed (Bilheimer and Sisk, 2008; Borrell and Crawford, 2008; Canino et al., 2006; Davis et al., 2006; Hayes et al., 2008; Huang and Carrasquillo, 2008; Jerant et al., 2008; Kagawa-Singer et al., 2007; Lancaster et al., 2006; Read et al., 2005b). After controlling for socioeconomic conditions, many of these differential effects remain.

The scientific findings in this chapter demonstrate the existence of disparities in health and health care at a level of categorization that is more detailed than the OMB categories of race and Hispanic ethnicity. Therefore, the subcommittee concludes that use of the broad OMB categories alone can mask identification of disparities at the more granular level.

Standardization of categories of granular ethnicity would enable valid comparisons across settings, across geographic locations, and over time. The level of granularity necessary for analysis will vary according to the composition of the population being served or studied, whether the size of subgroups is sufficiently large to make statistically reliable comparisons, and whether the pattern of differences experienced by subgroups identifies distinct needs that are not already revealed by data aggregated into broader categories. A recommendation regarding how ethnicity data should be collected to help inform improvements in health and health care quality among racial and ethnic subgroups is discussed in the next chapter.


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1 In the first session of the 111th Congress, bills were introduced to include check-off boxes on Census Bureau questionnaires for Dominican ethnicity (HR 1504 and SB 1084) and for Caribbean ethnicity in general (HR 2071 and SB 1083).
2 Personal communication, O. Carter-Pokras, University of Maryland School of Public Health, April 13, 2009.
3 To identify relevant evidence on health and health care for this chapter, Medline articles were queried using keywords "subgroup," "sub-population," "health disparities," "racial," "ethnic," "Hispanic," "Latino," "African," "Black," "White," and "Asian" in various combinations. Literature since 1997 was scanned and culled, first by title, then abstract, then full text. Reference sections of relevant articles were also scanned to find other relevant literature.
4 The form for this survey had check-off boxes for three specific categories (Mexican, Puerto Rican, Cuban), followed by a check-off box for "Other Spanish, Hispanic/Latino," accompanied by a space for writing in another specific Hispanic origin group. The numerous other identified subgroups are based on the "other" responses.
5 The Census Bureau allows people of Brazilian heritage to self-identify whether they are Hispanic or not, but the Census does not automatically classify Brazilians who speak Portuguese as Hispanics. About half of Brazilians identified as non-Hispanic in both Census 2000 and the Current Population Survey (del Pinal and Schmidley, 2000).
6 A survey of 2,554 Latinos aged 18 years and older, half monolingual Spanish, 868 Mexican, 495 Puerto Rican, 577 Cuban, and 614 other Hispanics. The NLAAS population was similar to the Census 2000 population distribution by gender, age, education, marital status, and geographic distribution, but differed in terms of nativity and household income.
7 Acculturation was measured using a modified Marin Short Acculturation Scale.
8 12.2 percent reported Black alone with the remainder reporting more than one race; of those checking more than one race, the largest combinations in order were 784,764 reporting both Black and White, followed by 417,249 reporting Black and "Some other race," generally Hispanic, and then 182,494 reporting Black and American Indian/Alaska Native
9 3.6 percent reported Asian alone; Hispanic Asians make up about 1.0 percent of the Asian population. Of those checking more than one race, the largest combinations in order were Asian and White (0.9 million, 0.3 percent of the total population), Asian and "Some other race" (0.2 million, 0.1 percent), Asian and NHOPI (0.1 million), and Asian and Black or African American (0.1 million).
10 0.13 percent (378,782) reported being NHOPI alone.
11 The number identifying as White alone or in combination was 216.9 million, 211.5 of which were White alone, followed by White in combination with "Some other race" at 2.2 million, White and AIAN at 1.1 million, White and Asian at 0.9 million, and White and Black at 0.8 million.
12 The most frequent combinations reported are AIAN and White (1.0 million), AIAN and Black (0.18 million).
13 The Indian Healthcare Improvement Act, Public Law 94-437, 25 U.S.C. 1603(c)-(d).
14 Alaska Native people comprise three major ethnic groups: Eskimo, Indian, and Aleut.
15 The National Cholesterol Education Program defines metabolic syndrome "by a group of metabolic risk factors in one person.... Abdominal obesity (excessive fat tissue in and around the abdomen); Atherogenic dyslipidemia (blood fat disorders—high triglycerides, low HDL cholesterol and high LDL cholesterol—that foster plaque buildups in artery walls); Elevated blood pressure; Insulin resistance or glucose intolerance (the body can't properly use insulin or blood sugar); Prothrombotic state (e.g., high fibrinogen or plasminogen activator inhibitor-1 in the blood); Proinflammatory state (e.g., elevated C-reactive protein in the blood)" (American Heart Association, 2009).

Current as of March 2010
Internet Citation: Chapter 2: Evidence of Disparities Among Ethnicity Groups (continued, 2): Race, Ethnicity, and Language Data: Standardization for Health Care Qu. March 2010. Agency for Healthcare Research and Quality, Rockville, MD.