Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.

Background: Identification of hypertensive disorders in pregnancy research often uses hospital International Classification of Diseases v. 10 (ICD-10) codes meant for billing purposes, which may introduce misclassification error relative to medical records. We estimated the validity of ICD-10 codes for hypertensive disorders during pregnancy overall and by subdiagnosis, compared with medical record diagnosis, in a Southeastern United States high disease burden hospital. Methods: We linked medical record data with hospital discharge records for deliveries between 1 July 2016, and 30 June 2018, in an Atlanta, Georgia, public hospital. For any hypertensive disorder (with and without unspecified codes) and each subdiagnosis (hemolysis, elevated liver enzymes, and low platelet count [HELLP] syndrome, eclampsia, preeclampsia with and without severe features, chronic hypertension, superimposed preeclampsia, and gestational hypertension), we calculated positive predictive value (PPV), negative predictive value (NPV) sensitivity, and specificity for ICD-10 codes compared with medical record diagnoses (gold standard). Results: Thirty-seven percent of 3,654 eligible pregnancies had a clinical diagnosis of any hypertensive disorder during pregnancy. Overall, ICD-10 codes identified medical record diagnoses well (PPV, NPV, specificity >90%; sensitivity >80%). PPV, NPV, and specificity were high for all subindicators (>80%). Sensitivity estimates were high for superimposed preeclampsia, chronic hypertension, and gestational hypertension (>80%); moderate for eclampsia (66.7%; 95% confidence interval [CI] = 22.3%, 95.7%), HELLP (75.0%; 95% CI = 50.9%, 91.3%), and preeclampsia with severe features (58.3%; 95% CI = 52.6%, 63.8%); and low for preeclampsia without severe features (3.2%; 95% CI, 1.4%, 6.2%). Conclusions: We provide bias parameters for future US-based studies of hypertensive outcomes during pregnancy in high-burden populations using hospital ICD-10 codes.

Background: Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data. Methods: We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification. Results: The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons. Conclusions: These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.

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.

Abortion funds are key actors in mitigating barriers to abortion access, particularly in contexts where state-level abortion access restrictions are concentrated. Using 2017–2019 case management data from a regional abortion fund in the southeastern U.S., we described the sociodemographic and service use characteristics of cases overall (n = 9585) and stratified by state of residence (Alabama, Florida, Georgia, Mississippi, South Carolina, and Tennessee). Overall, cases represented people seeking abortion fund assistance who predominately identified as non-Hispanic Black (81%), 18–34 years of age (84%), publicly or uninsured (87%), having completed a high school degree or some college (70%), having one or more children (77%), and as Christian (58%). Most cases involved an in-state clinic (81%), clinic travel distance under 50 miles (63%), surgical abortion (66%), and pregnancy under 13 weeks’ gestation (73%), with variation across states. The median abortion fund contribution pledge was $75 (interquartile range (IQR): 60–100), supplementing median caller contributions of $200 (IQR: 40–300). These data provide a unique snapshot of a population navigating disproportionate, intersecting barriers to abortion access, and abortion fund capacity for social care and science. Findings can inform abortion fund development, data quality improvement efforts, as well as reproductive health, rights and justice advocacy, policy, and research.