Centers for Disease Control and Prevention, Atlanta, GA, United States.
United States Public Health Service, Rockville, MD, United States.
JMIR Public Health Surveill. 2022 Jun 2;8(6):e34296. doi: 10.2196/34296.
In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important.
We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19.
We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data.
We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states.
Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.
在美国，COVID-19 是一种全国性应报告疾病，这意味着各州将病例和住院情况报告给疾病控制与预防中心（CDC）。从美国的每一个设施中识别和报告每一个病例可能在长期内不可行。从现有的监测系统中创建可持续的方法来估计 COVID-19 的负担变得越来越重要。
我们旨在提供一种利用监测数据的方法，为估计 COVID-19 的每月住院率创建一个长期解决方案。
我们使用 COVID-19 相关住院监测网络（COVID-NET）的监测数据和贝叶斯分层模型进行外推，从 2020 年 5 月至 2021 年 4 月，为 50 个州估算 COVID-19 的每月住院率。住院率是根据住院期间或入院前 14 天内进行的实验室确诊 SARS-CoV-2 检测的患者计算得出的。我们为 6 个年龄组（0-17、18-49、50-64、65-74、75-84 和≥85 岁）分别创建了一个模型。我们从多个数据源中确定了随年龄、州和月份而变化的协变量，并根据两种方法（最小绝对收缩和选择算子（LASSO）和尖峰和板条选择方法）为每个年龄组进行协变量选择。我们通过检查模型估计对协变量选择和模型外推的敏感性以及将我们的结果与外部数据进行比较，来验证我们的方法。
我们估计，从 2020 年 5 月至 2021 年 4 月，美国 COVID-19 的累计发病率为每 10 万人中有 1093.9（992.4-1204.6）例住院，累计发病率为 358.31 万（90%可信区间[CrI]325.05 万至 394.54 万）。各州之间的累计发病率从 359 至 1856 例/每 10 万人不等。累计发病率最高的年龄组是≥85 岁的人群（5575.6；90%CrI 5066.4-6133.7）。每月住院率最高的是 12 月（183.7；90%CrI 154.3-217.4）。我们按州估算的每月住院率显示了各州之间峰值幅度、峰值数量和峰值时间的变化。
我们估算 COVID-19 住院率的新方法有可能为监测 COVID-19 负担提供可持续的估计，并为利用监测数据提供灵活的框架。
