Data Disaggregation with American Indian/Alaska Native Population Data

More than any other racial group, American Indian/Alaska Natives (AIAN) face the risk of imprecise survey estimates due to survey processes regarding the classification, tabulation, and weighting of race/ethnicity. Variations in approaches to classifying racial and ethnic populations in federal and state health statistics have substantial implications for how we measure health status, access to healthcare, healthcare quality, and health equity. We identify strategies to improve data capacity for AIAN in federal health surveys by exploring current approaches to collecting and coding of AIANs across eight population-based health surveys (seven federal surveys and the California Health Interview Survey). Our analysis assesses how different coding and weighting decisions affect the classification and measurement of the AIAN population by comparing single-race non-Hispanic/Latino AIAN to more expansive classifications that include not only those reporting AIAN race alone, but also individuals reporting AIAN race in combination with other races and/or in combination with Hispanic/Latino ethnicity. Our results provide insight into the representativeness of each survey on the AIAN population and our ability to draw conclusions about the health of the AIAN population and the health disparities they face. The results show considerable variation across surveys in their measurement of the AIAN population based on survey classification, tabulation, and weighting approaches.

Automated summary

This study investigates the reasons and strategies for inaccurate data on American Indian/Alaska Natives in federal health surveys, highlighting significant variations in the impact of different survey methods on the calculation and measurement of this population.