期刊
JOURNAL OF POPULATION RESEARCH
卷 39, 期 4, 页码 527-531出版社
SPRINGER
DOI: 10.1007/s12546-021-09261-5
关键词
American Indian and Alaska Native populations; Indigenous Peoples; Indigenous data representation; Indigenous data sovereignty; COVID-19
类别
Indigenous Peoples in the United States are facing disproportionate impacts of COVID-19, with higher infection and mortality rates. The lack of reliable data likely underestimates the true impact. The importance of Indigenous data sovereignty in data collection and management has been emphasized, calling for systemic efforts to improve COVID-19 data availability and quality.
Indigenous Peoples in the United States have been experiencing disproportionate impacts of COVID-19. American Indian and Alaska Native persons are more likely to be infected, experience complications, and die from coronavirus. Evidence suggests that Indigenous persons have 3.5 times the incidence rate of non-Hispanic/Latinx whites. Unfortunately, this is likely a gross underestimate because of a lack of reliable and accurate COVID-19 data for American Indian and Alaska Native populations. Multiple factors contribute to poor data quality including the lack of Indigenous representation in the data and rampant racial misclassification at both the individual and group levels. The current pandemic has shed light on multiple pre-existing issues related to Indigenous data sovereignty in data collection and management. We discuss the importance of centring Indigenous data sovereignty in the systemic efforts to increase COVID-19 data availability and quality. The federal and state governments must support and promote Tribes' rights to access data. Federal and state governments should also focus on bolstering their data availability and quality for aggregated data on AIAN populations and for providing disaggregated Tribal data to Tribes. Given the pivotal moment in the United States with ongoing and parallel pandemics of coronavirus and racism, we urge demographers and population scientists to reflect on the role of structural racism in data, data collection and analysis.
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