How Should Epidemiologists Respond to Data Genocide?

Data quality for and about American Indian/Alaska Native (AI/AN) people is undermined by deeply entrenched, colonial practices that have become standard in US federal data systems. This article draws on cases of maternal mortality and COVID-19 to demonstrate the ethical and clinical need for inclusive, diverse, and accurate data when researching AI/AN health trends. This article further argues that epidemiologists specifically must challenge implicit bias, question methods and practices, and recognize colonial, racist reporting practices about AI/AN people that have long undermined data collection, analytical, and dissemination practices that are fundamental to epidemiological research.

Good Health and Wellness: Reflections on Implementing Health Promotion in Indian Country.

In 2014, the Centers for Disease Control and Prevention (CDC) launched "A Comprehensive Approach to Good Health and Wellness in Indian County" (GHWIC) to promote health and chronic disease prevention in tribal communities while facilitating cross-cultural learning and relationship-building. Through GHWIC, CDC aimed to work with American Indian and Alaska Native communities to identify effective health promotion strategies to address chronic disease disparities. Tribal sovereignty, community context, and consideration of tribal histories (e.

Understanding Risk and Protective Factors Influencing Urban American Indian /Alaska Native Youth Graduation Expectations.

Utilizing data collected by the Monitoring the Future project between 2005-2015, this study assesses the effect of risk and protective factors in shaping the graduation expectations of urban American Indian/Alaska Native (AI/AN) students as compared to their non-Hispanic White (NHW) peers. The responses of nearly 150,000 8th- and 10th-grade students reveal that single race and multi-race AI/AN students experienced 13 of 15 risk factors at higher proportions than NHW students, and 12 of 15 risk factors corresponded to single race AI/AN students and a third of risk factors corresponded to multi-race AI/AN students having higher odds of expecting not to graduate. Additionally, for the majority of the 14 protective factors analyzed, both single race and multi-race AI/AN students showed lower odds of expecting to graduate compared to their NHW peers.