• Joshua A Salomon and Alyssa Bilinski.
• Department of Health Policy, Stanford University School of Medicine, Stanford, California (J.A.S.).
• Ann. Intern. Med. 2022 Sep 1; 175 (9): 1240-1249.

BackgroundCenters for Disease Control and Prevention (CDC) defines low, medium, and high "COVID-19 community levels" to guide interventions, but associated mortality rates have not been reported.ObjectiveTo evaluate the diagnostic performance of CDC COVID-19 community level metrics as predictors of elevated community mortality risk.DesignTime series analysis over the period of 30 May 2021 through 4 June 2022.SettingU.S. states and counties.ParticipantsU.S. population.MeasurementsCDC "COVID-19 community level" metrics based on hospital admissions, bed occupancy, and reported cases; reported COVID-19 deaths; and sensitivity, specificity, and predictive values for CDC and alternative metrics.ResultsMean and median weekly mortality rates per 100 000 population after onset of high COVID-19 community level 3 weeks prior were, respectively, 2.6 and 2.4 (interquartile range [IQR], 1.7 to 3.1) across 90 high episodes in states and 4.3 and 2.1 (IQR, 0 to 5.4) across 7987 high episodes in counties. In 85 of 90 (94%) episodes in states and 4801 of 7987 (60%) episodes in counties, lagged weekly mortality after onset exceeded 0.9 per 100 000 population, and in 57 of 90 (63%) episodes in states and 4018 of 7987 (50%) episodes in counties, lagged weekly mortality after onset exceeded 2.1 per 100 000, which is equivalent to approximately 1000 daily deaths in the national population. Alternative metrics based on lower hospital admissions or case thresholds were associated with lower mortality and had higher sensitivity and negative predictive value for elevated mortality, but the CDC metrics had higher specificity and positive predictive value. Ratios between cases, hospitalizations, and deaths have varied substantially over time.LimitationsAggregate mortality does not account for nonfatal outcomes or disparities. Continuing evolution of viral variants, immunity, clinical interventions, and public health mitigation strategies complicate prediction for future waves.ConclusionDesigning metrics for public health decision making involves tradeoffs between identifying early signals for action and avoiding undue restrictions when risks are modest. Explicit frameworks for evaluating surveillance metrics can improve transparency and decision support.Primary Funding SourceCouncil of State and Territorial Epidemiologists.

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