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BACKGROUND: United States’ federal and subfederal immigrant-related policy activity has increased in recent years. We hypothesize that these policies are structural determinants of health for Latinx communities, operating through access to resources, discriminatory enforcement, and stress. METHODS: We searched seven databases for quantitative studies, published as of September 2021, examining the association between the presence of federal, state, or local immigrant-related policy(ies), over time or cross-sectionally, and mental or physical health outcomes among immigrant or US-born Latinx adults. We rated studies on methodologic quality. RESULTS: Eleven studies were included. Policies included federal and state policies. Health outcomes included mental health (7 studies), self-rated health (n=6), and physical disability (n=1). Among immigrant, noncitizen, or Spanish-preferring Latinx adults, exclusionary policies were associated with poor self-rated health, physical disability, and poor mental health. Inclusive policies were associated with better health, though null findings were more common than among studies of exclusionary policies. Only three studies separately examined policy effects on US-born or citizen Latinx adults and these findings were often null. All studies received a weak overall study quality rating; Among quality domains, studies were strongest in confounding control and weakest in outcome information bias and reporting missing data approaches. CONCLUSIONS: These results support the hypothesis that immigrant-related policies, especially exclusionary policies, are structural drivers of health for immigrant or noncitizen Latinx adults. However, evidence is scant among US-born or citizen Latinx adults. Studies of policies and physical health outcomes besides disability are lacking, as are results disaggregated by nativity and/or citizenship status.

Background The use of billing codes (ICD-10) to identify and track cases of gestational and pregestational diabetes during pregnancy is common in clinical quality improvement, research, and surveillance. However, specific diagnoses may be misclassified using ICD-10 codes, potentially biasing estimates. The goal of this study is to provide estimates of validation parameters (sensitivity, specificity, positive predictive value, and negative predictive value) for pregestational and gestational diabetes diagnosis using ICD-10 diagnosis codes compared with medical record abstraction at a large public hospital in Atlanta, Georgia. Methods This study includes 3,654 deliveries to Emory physicians at Grady Memorial Hospital in Atlanta, Georgia, between 2016 and 2018. We linked information abstracted from the medical record to ICD-10 diagnosis codes for gestational and pregestational diabetes during the delivery hospitalization. Using the medical record as the gold standard, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for each. Results For both pregestational and gestational diabetes, ICD-10 codes had a high-negative predictive value (>99%, Table 3) and specificity (>99%). For pregestational diabetes, the sensitivity was 85.9% (95% CI = 78.8, 93.0) and positive predictive value 90.8% (95% CI = 85, 97). For gestational diabetes, the sensitivity was 95% (95% CI = 92, 98) and positive predictive value 86% (95% CI = 81, 90). Conclusions In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.