Varkila Meri, Montez-Rath Maria, Salomon Joshua, Yu Xue, Block Geoffrey, Owens Douglas K, Chertow Glenn M, Parsonnet Julie, Anand Shuchi
Departments of Medicine (Infectious Diseases and Geographic Medicine), Stanford University.
Department of Medicine (Nephrology), Stanford University.
medRxiv. 2023 Feb 8:2023.02.06.23285542. doi: 10.1101/2023.02.06.23285542.
Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Using nationwide data available through the US National Wastewater Surveillance System, we examined the performance of two wastewater metrics in predicting high case and hospitalizations rates both before and after widespread use of at-home tests.
We performed area under the receiver operating characteristic (ROC) curve analysis (AUC) for two wastewater metrics-viral concentration relative to the peak of January 2022 ("wastewater percentile") and 15-day percent change in SARS-CoV-2 ("percent change"). Dichotomized reported cases (≥ 200 or <200 cases per 100,000) and new hospitalizations (≥ 10 or <10 per 100,000) were our dependent variables, stratified by calendar quarter. Using logistic regression, we assessed the performance of combining wastewater metrics.
Among 268 counties across 22 states, wastewater percentile detected high reported case and hospitalizations rates in the first quarter of 2022 (AUC 0.95 and 0.86 respectively) whereas the percent change did not (AUC 0.54 and 0.49 respectively). A wastewater percentile of 51% maximized sensitivity (0.93) and specificity (0.82) for detecting high case rates. A model inclusive of both metrics performed no better than using wastewater percentile alone. The predictive capability of wastewater percentile declined over time (AUC 0.84 and 0.72 for cases for second and third quarters of 2022).
Nationwide, county wastewater levels above 51% relative to the historic peak predicted high COVID rates and hospitalization in the first quarter of 2022, but performed less well in subsequent quarters. Decline over time in predictive performance of this metric likely reflects underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments.
新冠病毒居家检测的广泛使用妨碍了社区新冠发病率的测定。利用通过美国国家废水监测系统获取的全国性数据,我们研究了两种废水指标在居家检测广泛使用前后预测高病例率和住院率方面的表现。
我们对两种废水指标进行了受试者操作特征(ROC)曲线下面积分析(AUC),这两种指标分别是相对于2022年1月峰值的病毒浓度(“废水百分位数”)和新冠病毒的15天变化百分比(“变化百分比”)。将报告病例(每10万人≥200例或<200例)和新住院病例(每10万人≥10例或<10例)进行二分法划分作为我们的因变量,并按日历季度分层。我们使用逻辑回归评估了结合废水指标的表现。
在22个州的268个县中,废水百分位数在2022年第一季度检测到了高报告病例率和住院率(AUC分别为0.95和0.86),而变化百分比则未检测到(AUC分别为0.54和0.49)。废水百分位数为51%时,检测高病例率的灵敏度(0.93)和特异性(0.82)达到最大值。包含两种指标的模型表现并不优于单独使用废水百分位数。废水百分位数的预测能力随时间下降(2022年第二和第三季度病例的AUC分别为0.84和0.72)。
在全国范围内,相对于历史峰值,县废水水平高于51%预测了2022年第一季度的高新冠发病率和住院率,但在随后的季度中表现较差。该指标预测性能随时间下降可能反映了病例报告不足、检测减少,以及可能由于疫苗和治疗导致的感染毒力降低。