These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
Pubmed for Handhelds
PUBMED FOR HANDHELDS
Search MEDLINE/PubMed
Title: 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. Author: Bertolli J, Lee LM, Sullivan PS, AI/AN Race /Ethnicity Data Validation Workgroup. Journal: Public Health Rep; 2007; 122(3):382-92. PubMed ID: 17518310. Abstract: 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.[Abstract] [Full Text] [Related] [New Search]