American Indian and Alaska Native Communities Face a ‘Disproportionate Burden of Oral Disease’

Published 03/17/2023

Reversing Inequities Involves Challenges and Opportunities

Structural racism and a lack of necessities, such as access to healthy food and adequate housing, have contributed to significant oral health disparities for American Indians and Alaska Natives (AI/AN). While some health outcomes are improving, this white paper ― a collaboration between CareQuest Institute and native-led organizations ― explores the disparities, their causes, and possible solutions. Findings are based on responses from 564 AI/AN participants in the nationally representative State of Oral Health Equity in America survey. 

Key points include: 

  • The prevalence of early childhood caries in AI/AN communities is three times higher than for white children. 
  • AI/AN adults are twice as likely to have untreated decay as the overall US population, and 83% of AI/AN adults report tooth loss, compared with 66% of the overall US population. 
  • Three and a half times as many people who identify as AI/AN report going to the emergency department for dental care or mouth pain in the last year (13.5%) compared with those who do not identify as AI/AN (3.9%). 
  • Researchers and funding organizations need to develop and sustain authentic partnerships with AI/AN communities and organizations to increase representation in research and funding decisions/strategies. 
  • Culturally driven care and greater AI/AN representation in the dental workforce is needed. The number of AI/AN students applying to dental school has decreased dramatically ― from 92 in 2006 to 19 in 2021. 

Solutions to oral health disparities experienced by AI/AN communities, the authors write, must be grounded in diversity, equity, inclusion, and justice. The authors also recommend that accurate data collection regarding the oral health of AI/AN communities must involve clinical examination data, inclusion of AI/AN as an individual category, and the ability to choose more than one racial category in self-reported data to avoid misclassification. 

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