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OBJECTIVES: Black, Indigenous, and People of Color have borne a disproportionate incidence of COVID-19 cases in the United States. However, few studies have documented the completeness of race and ethnicity reporting in national COVID-19 surveillance data. The objective of this study was to describe the completeness of race and ethnicity ascertainment in person-level data received by the Centers for Disease Control and Prevention (CDC) through national COVID-19 case surveillance. METHODS: We compared COVID-19 cases with "complete" (ie, per Office of Management and Budget 1997 revised criteria) data on race and ethnicity from CDC person-level surveillance data with CDC-reported aggregate counts of COVID-19 from April 5, 2020, through December 1, 2021, in aggregate and by state. RESULTS: National person-level COVID-19 case surveillance data received by CDC during the study period included 18 881 379 COVID-19 cases with complete ascertainment of race and ethnicity, representing 39.4% of all cases reported to CDC in aggregate (N = 47 898 497). Five states (Georgia, Hawaii, Nebraska, New Jersey, and West Virginia) did not report any COVID-19 person-level cases with multiple racial identities to CDC. CONCLUSION: Our findings highlight a high degree of missing data on race and ethnicity in national COVID-19 case surveillance, enhancing our understanding of current challenges in using these data to understand the impact of COVID-19 on Black, Indigenous, and People of Color. Streamlining surveillance processes to decrease reporting incidence and align reporting requirements with an Office of Management and Budget-compliant collection of data on race and ethnicity would improve the completeness of data on race and ethnicity for national COVID-19 case surveillance.

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CONTEXT: Despite attention to federal and state governments' response to the US opioid crisis, few studies have systematically examined local governments' role in tackling this problem. OBJECTIVES: To determine what opioid policy and programmatic activities local governments are implementing, which activities are more challenging and require a greater latent ability to implement, and what community, environmental, and institutional factors shape such ability. DESIGN: A cross-sectional survey and multistage sampling procedure. SETTING/PARTICIPANTS: Of all 358 county governments in 5 purposively selected states (Colorado, North Carolina, Ohio, Pennsylvania, and Washington) surveyed, 171 counties (response rate = 47.8%) with complete data on self-reported policy and programmatic activities and predictor variables were eligible for analysis. MAIN OUTCOME MEASURES: Nineteen opioid policy and programmatic activities were analyzed individually and combined into a latent implementation ability index using empirical Bayes means estimates. RESULTS: Item response theory and bivariate analysis were applied. Item response theory estimates suggested that having police officers carry naloxone and establishing a task force of community leaders were easier to implement than more challenging activities such as establishing needle exchanges and allowing arrest alternatives for opioid offenses. Covering individuals' treatment costs was predicted to involve the highest ability. County population size (r = 0.34; 95% confidence interval [CI], 0.20-0.47), population density (r = 0.35; 95% CI, 0.21-0.47), and being a Pennsylvania county (r = 0.45; 95% CI, 0.32-0.56) showed the strongest associations with latent implementation ability. CONCLUSIONS: Counties appear engaged in opioid policy and programmatic activity, although some activities are likely more difficult and may require greater ability to implement than others. More sparsely populated counties appear more disadvantaged in implementing activities for tackling the opioid crisis and may need additional assistance to leverage their ability to build a comprehensive policy and programmatic infrastructure.

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Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision. Further, we build models that predict the likelihood of bacterial susceptibility to sets of antibiotics. These models contain decision thresholds that separate subgroups of patients whose susceptibility rates to narrow-spectrum antibiotics equal overall susceptibility rates to broader-spectrum drugs. Retroactively analyzing these thresholds on our one year test set, we find that 14% of patients infected with Escherichia coli and empirically treated with piperacillin/tazobactam could have been treated with ceftriaxone with coverage equal to the overall susceptibility rate ofpiperacillin/tazobactam. Similarly, 13% of the same cohort could have been treated with cefazolin - a first generation cephalosporin.