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Introduction: American Indian/Alaska Native (AI/AN) pregnant people face barriers to health and healthcare that put them at risk of pregnancy complications. Rates of severe maternal morbidity (SMM) among Indigenous pregnant people are estimated to be twice that of non-Hispanic White (NHW) pregnant people. Methods: Race-corrected Oregon Hospital Discharge and Washington Comprehensive Hospital Abstract Reporting System data were combined to create a joint dataset of births between 2012 and 2016. The analytic sample was composed of 12,535 AI/AN records and 313,046 NHW records. A multilevel logistic regression was used to assess the relationship between community-level, individual and pregnancy risk factors on SMM for AI/AN pregnant people. Results: At the community level, AI/AN pregnant people were more likely than NHW to live in mostly or completely rural counties with low median household income and high uninsured rates. They were more likely to use Medicaid, be in a high-risk age category, and have diabetes or obesity. During pregnancy, AI/AN pregnant people were more likely to have insufficient prenatal care (PNC), gestational diabetes, and pre-eclampsia. In the multilevel model, county accounted for 6% of model variance. Hypertension pre-eclampsia, and county rurality were significant predictors of SMM among AI/AN pregnant people. High-risk age, insufficient PNC and a low county insured rate were near-significant at p < 0.10. Discussion: Community-level factors are significant contributors to SMM risk for AI/AN pregnant people in addition to hypertension and pre-eclampsia. These findings demonstrate the need for targeted support in pregnancy to AI/AN pregnant people, particularly those who live in rural and underserved communities. Significance: What is already known on this subject? American Indian and Alaska Native pregnant people face higher rates of severe maternal morbidity and mortality, and the risk is exacerbated for rural Indigenous pregnant people. What this study adds? This publication uses a multilevel model to assess the contribution of community-level factors in severe maternal morbidity risk for American Indian and Alaska Native pregnant people. This analysis highlights the important role that rurality, prenatal care adequacy and access to insurance play in maternal morbidity risk and discusses how those risks are disproportionately felt by American Indian and Alaska Native pregnant people in the Pacific Northwest. [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]