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Objective To evaluate and adjust for American Indian and Alaska Native ( AI/ AN) racial misclassification in two hospital discharge datasets in the Pacific Northwest. Data Sources/Study Setting Oregon (2010-2011) and Washington (2011) hospital discharge datasets were linked with the Northwest Tribal Registry ( NTR), a registry of AI/ AN individuals who accessed services at Indian health facilities in the Northwest. Study Design Record linkage was used to match state hospital records to the NTR. A state record was considered misclassified if it matched the NTR and was coded as non- AI/ AN or missing race data. Effect of misclassification was evaluated by comparing prelinkage and postlinkage, age-adjusted hospital discharge rates. Data Collection/Extraction Methods Researchers used Link Plus 2.0 software (Atlanta, GA, USA) for linkages and SAS 9.4 (Cary, NC, USA) for statistical analyses. Principal Findings In Oregon, 55.4 percent of matching records were misclassified (66.5 percent miscoded white, and 22.1 percent were missing race information). In Washington, 44.9 percent of matching records were misclassified (61.8 percent miscoded white, and 32.7 percent were missing race information). Linkage increased ascertainment of AI/ AN hospitalizations by 31.8 percent in Oregon and 33.9 percent in Washington. Linkage increased the rate ratio ( RR) for AI/ AN hospitalizations in comparison to non-Hispanic whites ( NHW) from 0.81 to 1.07 in Oregon, and from 1.21 to 1.62 in Washington. Conclusion Correction of race in hospital discharge datasets through linkage with a reference file of known AI/ AN individuals is an important first step before analytic research on AI/ AN health care in the Pacific Northwest can be accomplished with administrative datasets. [ABSTRACT FROM AUTHOR]

Objective: To compare rates and risk factors of severe COVID-19-related outcomes between American Indian/Alaska Native (AI/AN) and non-Hispanic White people (NHW). Methods: Aggregate Social Vulnerability Index (SVI), COVID-19-related risk factor, hospitalization, and mortality data were obtained from 16 states for January 1, 2020-March 31, 2021. Generalized estimating equation Poisson regression models calculated age-adjusted cumulative incidences, incidence ratios (IR), and 95% confidence intervals (CI) comparing AI/AN and NHW persons by age, sex, and county-level SVI status. Results: Race data were missing for 42.7% of COVID-19 cases, 24.7% of hospitalizations, and 10.1% of deaths. Risk of AI/AN COVID-19 mortality was 2.6 times that of NHW persons (IR 2.6, 95% CI: 1.7 - 3.4); risk of COVID-19-related hospitalization among AI/AN persons was 3.5 times that of NHW (IR: 3.5, 95% CI: 2.7 - 4.3). Severe COVID-19 outcomes were significantly higher for AI/AN persons compared to NHW persons across all age and sex groups. There was no statistically significant difference in COVID-19 outcomes by SVI status. Associations between severe COVID-19 outcomes and co-morbid risk factors were inconsistent. Conclusions: Results describe increased risk of severe COVID-19 outcomes for AI/AN persons compared to NHW persons despite quality issues in public health surveillance data. Data linkages and improved ascertainment reduce race/ethnicity misclassification and improve data quality. COVID-19-related health burdens among AI/AN persons warrant improved access for AI/AN communities to medical countermeasures and healthcare resources. [ABSTRACT FROM AUTHOR]