Evidence map›Paper›PMID 37494040›Full record

Observational studyJAMA network open2023

Use of Wastewater Metrics to Track COVID-19 in the US.

Meri R J Varkila, Maria E Montez-Rath, Joshua A Salomon, Xue Yu, Geoffrey A Block, Douglas K Owens, Glenn M Chertow, Julie Parsonnet, Shuchi Anand

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
5.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 28 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Meri R J VarkilaDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Palo Alto, California.
Maria E Montez-RathDivision of Nephrology, Department of Medicine, Stanford University, Palo Alto, California.
Joshua A SalomonDepartment of Health Policy, Stanford University, Stanford, California.
Xue YuDivision of Nephrology, Department of Medicine, Stanford University, Palo Alto, California.
Geoffrey A BlockUS Renal Care, Plano, Texas.
Douglas K OwensDepartment of Health Policy, Stanford University, Stanford, California.
Glenn M ChertowDivision of Nephrology, Department of Medicine, Stanford University, Palo Alto, California.
Julie ParsonnetDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Palo Alto, California.
Shuchi AnandDivision of Nephrology, Department of Medicine, Stanford University, Palo Alto, California.
Stanford University · USRenal Associates P. A. · US

Funding

SARS-COV-2 Screening in Dialysis Facilities: Building an Optimal Strategy to Protect High Risk PopulationsU01AI169477 · NIAID · STANFORD UNIVERSITY · PI ANAND, SHUCHI, CHERTOW, GLENN MATTHEW · 2022 to 2023
$2.3M
NIAID NIH HHS U01 AI169477
6 · The paper itself

Abstract

Importance: Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Objective: To examine the association of county-level wastewater metrics with high case and hospitalization rates nationwide both before and after widespread use of at-home tests. Design, Setting, and Participants: This observational cohort study with a time series analysis was conducted from January to September 2022 in 268 US counties in 22 states participating in the US Centers for Disease Control and Prevention's National Wastewater Surveillance System. Participants included the populations of those US counties. Exposures: County level of circulating SARS-CoV-2 as determined by metrics based on viral wastewater concentration relative to the county maximum (ie, wastewater percentile) and 15-day percentage change in SARS-CoV-2 (ie, percentage change). Main Outcomes and Measures: High county incidence of COVID-19 as evidenced by dichotomized reported cases (current cases ≥200 per 100 000 population) and hospitalization (≥10 per 100 000 population lagged by 2 weeks) rates, stratified by calendar quarter. Results: In the first quarter of 2022, use of the wastewater percentile detected high reported case (area under the curve [AUC], 0.95; 95% CI, 0.94-0.96) and hospitalization (AUC, 0.86; 95% CI, 0.84-0.88) rates. The percentage change metric performed poorly, with AUCs ranging from 0.51 (95% CI, 0.50-0.53) to 0.57 (95% CI, 0.55-0.59) for reported new cases, and from 0.50 (95% CI, 0.48-0.52) to 0.55 (95% CI, 0.53-0.57) for hospitalizations across the first 3 quarters of 2022. The Youden index for detecting high case rates was wastewater percentile of 51% (sensitivity, 0.82; 95% CI, 0.80-0.84; specificity, 0.93; 95% CI, 0.92-0.95). A model inclusive of both metrics performed no better than using wastewater percentile alone. The performance of wastewater percentile declined over time for cases in the second quarter (AUC, 0.84; 95% CI, 0.82-0.86) and third quarter (AUC, 0.72; 95% CI, 0.70-0.75) of 2022. Conclusions and Relevance: In this study, nationwide, county wastewater levels relative to the county maximum were associated with high COVID-19 case and hospitalization rates in the first quarter of 2022, but there was increasing dissociation between wastewater and clinical metrics in subsequent quarters, which may reflect increasing underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments. This study offers a strategy to operationalize county wastewater percentile to improve the accurate assessment of community SARS-CoV-2 infection prevalence when reliability of conventional surveillance data is declining.

Indexed as

COVID-19HumansReproducibility of ResultsSARS-CoV-2WastewaterWastewater-Based Epidemiological MonitoringWastewater

Identifiers

PMID37494040
PMCPMC10372707
OpenAlexW4385264701

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.