CONTEXT: Efforts to address disparities experienced by American Indians/Alaska Natives (AI/AN) have been hampered by a lack of accurate and timely health data. One challenge to obtaining accurate data is determining who "counts" as AI/AN in health and administrative data sets.
OBJECTIVE: To compare the effects of definition and misclassification of AI/AN on estimates of all-cause and cause-specific mortality for AI/AN in Washington during 2015-2016.
DESIGN: Secondary analysis of death certificate data from Washington State. Data were corrected for AI/AN racial misclassification through probabilistic linkage with the Northwest Tribal Registry. Counts and age-adjusted rates were calculated and compared for 6 definitions of AI/AN. Comparisons were made with the non-Hispanic white population to identify disparities. SETTING: Washington State. PARTICIPANTS: AI/AN and non-Hispanic white residents of Washington State who died in 2015 and 2016. MAIN OUTCOME MEASURES: Counts and age-adjusted rates for all-cause mortality and mortality from cardiovascular diseases, cancer, and unintentional injuries.
RESULTS: The most conservative single-race definition of AI/AN identified 1502 AI/AN deaths in Washington State during 2015-2016. The least conservative multiple-race definition of AI/AN identified 2473 AI/AN deaths, with an age-adjusted mortality rate that was 48% higher than the most conservative definition. Correcting misclassified AI/AN records through probabilistic linkage significantly increased mortality rate estimates by 11%. Regardless of definition used, AI/AN in Washington had significantly higher all-cause mortality rates than non-Hispanic whites in the state.
CONCLUSIONS: Reporting single-race versus multiple-race AI/AN had the most consequential effect on mortality counts and rates. Correction of misclassified AI/AN records resulted in small but statistically significant increases in AI/AN mortality rates. Researchers and practitioners should consult with AI/AN communities on the complex issues surrounding AI/AN identity to obtain the best method for identifying AI/AN in health data sets.