Use of wastewater metrics to track COVID-19 in the U.S.: a national time-series analysis over the first three quarters of 2022.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
08 Feb 2023
Historique:
pubmed: 18 2 2023
medline: 18 2 2023
entrez: 17 2 2023
Statut: epublish

Résumé

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.

Sections du résumé

Background UNASSIGNED
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.
Methods UNASSIGNED
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.
Results UNASSIGNED
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).
Conclusion UNASSIGNED
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.

Identifiants

pubmed: 36798337
doi: 10.1101/2023.02.06.23285542
pmc: PMC9934789
pii:
doi:

Types de publication

Preprint

Langues

eng

Déclaration de conflit d'intérêts

Declaration of Interests. Ascend Clinical Laboratory and Abbott Laboratory provide COVID-19 testing materials, supplies and personnel for 5U01AI169477. Dr Anand reports consulting fees from Vera Therapeutics.

Auteurs

Meri Varkila (M)

Departments of Medicine (Infectious Diseases and Geographic Medicine), Stanford University.

Maria Montez-Rath (M)

Department of Medicine (Nephrology), Stanford University.

Joshua Salomon (J)

Department of Health Policy, Stanford University.

Xue Yu (X)

Department of Medicine (Nephrology), Stanford University.

Geoffrey Block (G)

US Renal Care, Plano, Texas.

Douglas K Owens (DK)

Department of Health Policy, Stanford University.

Glenn M Chertow (GM)

Department of Medicine (Nephrology), Stanford University.
Epidemiology and Population Health, Stanford University.

Julie Parsonnet (J)

Departments of Medicine (Infectious Diseases and Geographic Medicine), Stanford University.
Epidemiology and Population Health, Stanford University.

Shuchi Anand (S)

Department of Medicine (Nephrology), Stanford University.

Classifications MeSH