Inclusive data practices: disaggregating race and assessing comorbidities among American Indian and Alaska Native individuals in PRAMS

包容性数据实践:在PRAMS项目中对美国印第安人和阿拉斯加原住民进行种族细分并评估其合并症

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Abstract

IMPORTANCE: To assess potential misclassification or exclusion of American Indian and Alaska Native (AI/AN) individuals within the Pregnancy Risk Assessment Monitoring System (PRAMS) Phase 8, we compared differences between aggregated and self-reported race variables and their impact on maternal comorbidities. METHODS/DESIGN: We utilized several CDC-provided ethnoracial identity variables alongside a disaggregated variable we created. We then estimated comorbidity prevalences between these groupings to determine the impact of these methodological differences. RESULTS: PRAMS variables, MRACE_AMI and MAT_RACE_PU, included 13,341 (no distinction between AI and AN) and 7,494 AI (excluded AN altogether), respectively. Our constructed variable (n = 13,383) included 19 ethnoracial-subgroups and 42 tribal members not selecting AI/AN race. We found significant differences in the prevalence of comorbidities by these variables. For instance, the prevalence for diabetes with MAT_RACE_PU was 4.93%, with MRACE_AMI was 4.04%, but our subgroup AI (alone) was 5.46%, and AN (alone) was 1.37%. CONCLUSION: Our results highlight significant disparities in maternal comorbidities among AI/AN women when different racial classification strategies are employed. Disaggregating these data revealed differences that are crucial for understanding the unique health challenges faced by various subgroups.

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