2014
DOI: 10.2105/ajph.2014.301933
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Racial Misclassification of American Indians and Alaska Natives by Indian Health Service Contract Health Service Delivery Area

Abstract: Limiting presentation and analysis to CHSDA counties helped mitigate the effects of race misclassification of AI/AN persons, although a portion of the population was excluded.

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Cited by 160 publications
(173 citation statements)
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“…An algorithm was applied to Hispanic ethnicity data to reduce misclassification of Hispanic persons as being of unknown ethnicity (16). To reduce misclassification of AI/AN race, first the Indian Health Service (IHS) patient registration database, which contains records of individuals who are members of federally recognized tribes, was linked to incidence data, and then rates for AI/AN were based on cases in counties covered by IHS Contract Health Service Delivery Area (CHSDA) because linkage studies have identified less misclassification of AI/AN race in CHSDA counties (17). Gallbladder cancer incidence and death rates among AI/AN were further analyzed by IHS region; because of small numbers of cases and deaths, annual rates were averaged over the 10-year period 1999-2011 (17).…”
Section: Resultsmentioning
confidence: 99%
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“…An algorithm was applied to Hispanic ethnicity data to reduce misclassification of Hispanic persons as being of unknown ethnicity (16). To reduce misclassification of AI/AN race, first the Indian Health Service (IHS) patient registration database, which contains records of individuals who are members of federally recognized tribes, was linked to incidence data, and then rates for AI/AN were based on cases in counties covered by IHS Contract Health Service Delivery Area (CHSDA) because linkage studies have identified less misclassification of AI/AN race in CHSDA counties (17). Gallbladder cancer incidence and death rates among AI/AN were further analyzed by IHS region; because of small numbers of cases and deaths, annual rates were averaged over the 10-year period 1999-2011 (17).…”
Section: Resultsmentioning
confidence: 99%
“…Particularly, rates may be underestimated for API, AI/AN, and Hispanics; however, efforts were made to ensure that this information was as accurate as possible. Analyses among AI/AN were restricted to those residing in CHSDA counties, which has been found to decrease misclassification of race and thus provide more accurate reflect rates (17). Fourth, analyses based on ethnicity are limited because grouping Hispanics as one ethnicity may mask important differences by country of origin (29).…”
Section: Discussionmentioning
confidence: 99%
“…In addition, misclassification of race or ethnicity in cancer registry records could have affected our results; for example, if Native Americans were misclassified as nonHispanic white or unknown, IRs and IRRs for American Indians/ Alaskan natives would be underestimates. 30 However, the case-case ORs and survival analyses use data only from patient cases and, therefore, should be less influenced by misclassification of race. Therefore, although the IRs and IRRs for American Indians/ Alaskan natives and possibly other minorities may be interpreted with caution, we believe that when taken together with the comparisons of case proportions and survival by subtype, our results are robust and informative.…”
Section: Discussionmentioning
confidence: 99%
“…Less frequently, investigators have attempted to validate the accuracy of the values reported for selected data variables. Two studies looking at the prevalence of cancers and sexually transmitted diseases among American Indians linked surveillance data to Indian Health Service registries to determine the accuracy of race and ethnicity data and the degree of misclassification (40,86). In both studies, American Indian race was misclassified and underreported, resulting in underestimation of the burden of these health conditions among American Indians and limiting the usefulness of these data for monitoring progress towards addressing health disparities.…”
Section: Wwwannualreviewsorg • Public Health Surveillance Systemsmentioning
confidence: 99%