COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
General Dynamics Information Technology, Atlanta, Georgia, USA.
Influenza Other Respir Viruses. 2023 Jan;17(1):e13089. doi: 10.1111/irv.13089. Epub 2023 Jan 10.
The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) required a sampling methodology that allowed for production of timely population-based clinical estimates to inform the ongoing US COVID-19 pandemic response.
We developed a flexible sampling approach that considered reporting delays, differential hospitalized case burden across surveillance sites, and changing geographic and demographic trends over time. We incorporated weighting methods to adjust for the probability of selection and non-response, and to calibrate the sampled case distribution to the population distribution on demographics. We additionally developed procedures for variance estimation.
Between March 2020 and June 2021, 19,293 (10.4%) of all adult hospitalized cases were sampled for chart abstraction. Variance estimates for select variables of interest were within desired ranges.
COVID-NET's sampling methodology allowed for reporting of robust and timely, population-based data on the clinical epidemiology of COVID-19-associated hospitalizations and evolving trends over time, while attempting to reduce data collection burden on surveillance sites. Such methods may provide a general framework for other surveillance systems needing to quickly and efficiently collect and disseminate data for public health action.
COVID-19 相关住院监测网络（COVID-NET）需要一种抽样方法，以便及时提供基于人群的临床估计数据，为美国正在进行的 COVID-19 大流行应对工作提供信息。
我们开发了一种灵活的抽样方法，考虑了报告延迟、监测点之间住院病例负担的差异以及随时间变化的地理和人口趋势。我们采用了加权方法来调整选择和无应答的概率，并根据人口统计学数据对抽样病例分布进行校准。我们还制定了方差估计程序。
2020 年 3 月至 2021 年 6 月，对 19293 例（10.4%）成年住院病例进行了图表摘录抽样。选定感兴趣变量的方差估计值在预期范围内。
COVID-NET 的抽样方法允许及时报告 COVID-19 相关住院的临床流行病学和随时间演变的趋势的稳健、基于人群的数据，同时试图减轻监测点的数据收集负担。这些方法可能为其他需要快速高效收集和传播数据以采取公共卫生行动的监测系统提供一个通用框架。
