Public health reports (Washington, D.C. : 1974) · 2007
Racial misidentification of American Indians/Alaska Natives in the HIV/AIDS Reporting Systems of five states and one urban health jurisdiction, U.S., 1984-2002.
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Bertolli Jeanne, Lee Lisa M, Sullivan Patrick S, AI/AN Race /Ethnicity Data Validation Workgroup
Аннотация
OBJECTIVES: We examined racial misidentification of American Indians/Alaska Natives (AI/AN) reported to the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) Reporting Systems (HARS) of five U.S. states and one county.
METHODS: To identify AI/AN records with misidentified race, we linked HARS data from 1984 through 2002 to the Indian Health Service National Patient Information and Reporting System (NPIRS), excluding non-AI/AN dependents, using probabilistic matching with clerical review. We used chi-square tests to examine differences in proportions and logistic regression to examine the associations of racial misidentification with HARS site, degree of AI/AN ancestry, mode of exposure to HIV, and urban or rural location of residence at time of diagnosis.
RESULTS: A total of 1,523 AI/AN individuals was found in both NPIRS and HARS; race was misidentified in HARS for 459 (30%). The percentages of racially misidentified ranged from 3.7% (in Alaska) to 55% (in California). AI/AN people were misidentified as white (70%), Hispanic (16%), black (11%), and Asian/Pacific Islander (2%); for 0.9%, race was unspecified. Logistic regression results (data from all areas, all variables) indicated that urban residence at time of diagnosis, degree of AI/AN ancestry, and mode of exposure to HIV were significantly associated with racial misidentification of AI/AN people reported to HARS.
CONCLUSIONS: Our findings add to the evidence that racial misidentification of AI/AN in surveillance data can result in underestimation of AI/AN HIV/AIDS case counts. Racial misidentification must be addressed to ensure that HIV/ AIDS surveillance data can be used as the basis for equitable resource allocation decisions, and to inform and mobilize public health action.
METHODS: To identify AI/AN records with misidentified race, we linked HARS data from 1984 through 2002 to the Indian Health Service National Patient Information and Reporting System (NPIRS), excluding non-AI/AN dependents, using probabilistic matching with clerical review. We used chi-square tests to examine differences in proportions and logistic regression to examine the associations of racial misidentification with HARS site, degree of AI/AN ancestry, mode of exposure to HIV, and urban or rural location of residence at time of diagnosis.
RESULTS: A total of 1,523 AI/AN individuals was found in both NPIRS and HARS; race was misidentified in HARS for 459 (30%). The percentages of racially misidentified ranged from 3.7% (in Alaska) to 55% (in California). AI/AN people were misidentified as white (70%), Hispanic (16%), black (11%), and Asian/Pacific Islander (2%); for 0.9%, race was unspecified. Logistic regression results (data from all areas, all variables) indicated that urban residence at time of diagnosis, degree of AI/AN ancestry, and mode of exposure to HIV were significantly associated with racial misidentification of AI/AN people reported to HARS.
CONCLUSIONS: Our findings add to the evidence that racial misidentification of AI/AN in surveillance data can result in underestimation of AI/AN HIV/AIDS case counts. Racial misidentification must be addressed to ensure that HIV/ AIDS surveillance data can be used as the basis for equitable resource allocation decisions, and to inform and mobilize public health action.