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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249132PMC
http://dx.doi.org/10.1089/heq.2023.0252DOI Listing

Publication Analysis

Top Keywords

racial misclassification
24
cancer surveillance
12
oral health
12
misclassification
8
misclassification american
8
american indian
8
indian alaska
8
alaska native
8
identity cancer
8
surveillance data
8

Similar Publications

Importance: Understanding exposure to air pollution is important to public health, and disparities in the spatial distribution of regulatory air quality monitors could lead to exposure misclassification bias.

Objective: To determine whether racial and ethnic disparities exist in Environmental Protection Agency (EPA) regulatory air quality monitor locations in the US.

Design, Setting, And Participants: This national cross-sectional study included air quality monitors in the EPA Air Quality System regulatory monitoring repository, as well as 2022 American Community Survey Census block group estimates for racial and ethnic composition and population size.

View Article and Find Full Text PDF

Objectives: The original STONE score was designed to predict the presence of uncomplicated renal colic and the corresponding absence of alternate serious etiologies. It was retrospectively derived and prospectively validated and resulted in five variables: Sex (male gender), Timing (acute onset of pain), "Origin" (non-Black race), Nausea/vomiting (present), and Erythrocytes (microscopic hematuria). With recent increased awareness of the potential adverse impacts of including race (a socially constructed identity) in clinical prediction rules, we sought to determine if a revised STONE score without race could be constructed with similar diagnostic accuracy.

View Article and Find Full Text PDF

Introduction: Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities.

Methods: We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry.

View Article and Find Full Text PDF

Measuring age-specific, contextual exposures is crucial for lifecourse epidemiology research. Longitudinal residential data offers a "golden ticket" to cumulative exposure metrics and can enhance our understanding of health disparities. Residential history can be linked to myriad spatiotemporal databases to characterize environmental, socioeconomic, and policy contexts that a person experienced throughout life.

View Article and Find Full Text PDF

Background: Fall-related mortality has increased rapidly over the past two decades in the USA, but the extent to which mortality varies across racial and ethnic populations, counties, and age groups is not well understood. The aim of this study was to estimate age-standardised mortality rates due to falls by racial and ethnic population, county, and age group over a 20-year period.

Methods: Redistribution methods for insufficient cause of death codes and validated small-area estimation methods were applied to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual fall-related mortality.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!