Assessment of Racial Misclassification Among American Indian and Alaska Native Identity in Cancer Surveillance Data in the United States and Considerations for Oral Health: A Systematic Review

• Published in: Health equity Vol. 8; no. 1; pp. 376 - 390
• Main Authors: Llaneza, Amanda J, Holt, Alex, Seward, Julie, Piatt, Jamie, Campbell, Janis E
• Format: Journal Article
• Language: English
• Published: United States Mary Ann Liebert, Inc., publishers 01-06-2024; Mary Ann Liebert
• Subjects: American Indian/Alaska Native; cancer surveillance; health equity; oral health; racial misclassification; Review; cancer surveillance; American Indian/Alaska Native; oral health; health equity; racial misclassification
• ISSN: 2473-1242; 2473-1242
• DOI: 10.1089/heq.2023.0252

Introduction:
Misclassification of American Indian and Alaska Native (AI/AN) peoples exists across various databases in research and clinical practice. Oral health is associated with cancer incidence and survival; however, misclassification adds another layer of complexity to understanding the impact of poor oral health. The objective of this literature review was to systematically evaluate and analyze publications focused on racial misclassification of AI/AN racial identities among cancer surveillance data.
Methods:
The PRISMA Statement and the CONSIDER Statement were used for this systematic literature review. Studies involving the racial misclassification of AI/AN identity among cancer surveillance data were screened for eligibility. Data were analyzed in terms of the discussion of racial misclassification, methods to reduce this error, and the reporting of research involving Indigenous peoples.
Results:
A total of 66 articles were included with publication years ranging from 1972 to 2022. A total of 55 (83%) of the 66 articles discussed racial misclassification. The most common method of addressing racial misclassification among these articles was linkage with the Indian Health Service or tribal clinic records (45 articles or 82%). The average number of CONSIDER checklist domains was three, with a range of zero to eight domains included. The domain most often identified was Prioritization (60), followed by Governance (47), Methodologies (31), Dissemination (27), Relationships (22), Participation (9), Capacity (9), and Analysis and Findings (8).
Conclusion:
To ensure equitable representation of AI/AN communities, and thwart further oppression of minorities, specifically AI/AN peoples, is through accurate data collection and reporting processes.