Department of Health Policy, Stanford University School of Medicine, Stanford, California (J.A.S.).
Departments of Health Services, Policy & Practice & Biostatistics, Brown University School of Public Health, Providence, Rhode Island (A.B.).
Ann Intern Med. 2022 Sep;175(9):1240-1249. doi: 10.7326/M22-0803. Epub 2022 Aug 2.
Centers 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.
To evaluate the diagnostic performance of CDC COVID-19 community level metrics as predictors of elevated community mortality risk.
Time series analysis over the period of 30 May 2021 through 4 June 2022.
U.S. states and counties.
U.S. population.
CDC "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.
Mean 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.
Aggregate 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.
Designing 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.
Council of State and Territorial Epidemiologists.
疾病控制与预防中心 (CDC) 定义了低、中、高“COVID-19 社区级别”，以指导干预措施，但尚未报告相关死亡率。
评估 CDC COVID-19 社区级别指标作为预测社区死亡率升高风险的指标的诊断性能。
2021 年 5 月 30 日至 2022 年 6 月 4 日期间的时间序列分析。
美国各州和各县。
美国人口。
基于住院、床位占用和报告病例的 CDC“COVID-19 社区级别”指标；报告的 COVID-19 死亡人数；以及 CDC 和替代指标的敏感性、特异性和预测值。
在高 COVID-19 社区级别出现前 3 周，90 个高级别事件中每 10 万人的平均和中位数每周死亡率分别为 2.6 和 2.4（四分位距 [IQR]，1.7 至 3.1），而在 7987 个高级别事件中每 10 万人的死亡率分别为 4.3 和 2.1（IQR，0 至 5.4）。在 90 个州中的 85 个（94%）和 7987 个县中的 4801 个（60%）事件中，发病后每周死亡率超过 0.9 人/10 万，在 90 个州中的 57 个（63%）和 7987 个县中的 4018 个（50%）事件中，发病后每周死亡率超过 2.1 人/10 万，相当于全国人口每天约有 1000 人死亡。基于较低的住院或病例阈值的替代指标与较低的死亡率相关，并且对死亡率升高具有更高的敏感性和阴性预测值，但 CDC 指标具有更高的特异性和阳性预测值。病例、住院和死亡之间的比例随时间有很大变化。
综合死亡率未考虑非致命结果或差异。病毒变体、免疫力、临床干预和公共卫生缓解策略的不断演变使未来波次的预测变得复杂。
为公共卫生决策制定指标涉及在识别早期行动信号和在风险适度时避免过度限制之间的权衡。评估监测指标的明确框架可以提高透明度和决策支持。
州和地区流行病学家理事会。
