Evidence map›Paper›PMID 36030667›Full record

ArticleWater research2022

Biomarkers selection for population normalization in SARS-CoV-2 wastewater-based epidemiology.

Shu-Yu Hsu, Mohamed Bayati, Chenhui Li, Hsin-Yeh Hsieh, Anthony Belenchia, Jessica Klutts, Sally A Zemmer, Melissa Reynolds, Elizabeth Semkiw, Hwei-Yiing Johnson and 5 more

Open access · hybridAbstract read
In one paragraph

Article in Water research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
7.3field-weighted citation impact, top 2% 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

35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 77 citations in OpenAlex.

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  17. Tracking epidemic viruses in wastewaters.Microbial biotechnology · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 3 institutions in 1 country.

Shu-Yu HsuSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA; Center for Agroforestry, University of Missouri, Columbia, MO 65201, USA.
Mohamed BayatiSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Chenhui LiSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Hsin-Yeh HsiehSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Anthony BelenchiaBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Jessica KluttsWater Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA.
Sally A ZemmerWater Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA.
Melissa ReynoldsBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Elizabeth SemkiwBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Hwei-Yiing JohnsonBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Trevor FoleyMissouri Department of Corrections, Jefferson City, MO, USA.
Chris G WiebergWater Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA.
Jeff WenzelBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Marc C JohnsonDepartment of Molecular Microbiology and Immunology, University of Missouri, School of Medicine and the Christopher S. Bond Life Sciences Center, Columbia, MO 65201, USA.
Chung-Ho LinSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA; Center for Agroforestry, University of Missouri, Columbia, MO 65201, USA. Electronic address: linchu@missouri.edu.
University of Missouri · USMissouri Department of Health and Senior Services · USMissouri Department of Natural Resources · US

Funding

Wastewater Detection of COVID-19U01DA053893 · NIDA · MISSOURI STATE DEPT/ HEALTH & SENIOR SRV · PI WENZEL, JEFF · 2021 to 2022
$4.0M
NIDA NIH HHS U01 DA053893
6 · The paper itself

Abstract

Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) levels in the communities since the coronavirus disease 2019 (COVID-19) outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater is critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (PMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated and validated for their utility in normalizing the SARS-CoV-2 loads through two normalizing approaches using the data from 64 wastewater treatment plants (WWTPs) in Missouri. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performing candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuating population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population of the given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation (rho=0.5878, p < 0.05) between SARS-CoV-2 load per capita and case numbers per capita. This chemical biomarker complements the current normalization scheme recommended by CDC and helps us understand the size, distribution, and dynamics of local populations for forecasting the prevalence of SARS-CoV2 within each sewershed.

Indexed as

COVID-19SARS-CoV-2BiomarkersCaffeineCreatinineHumansHydroxyindoleacetic AcidRNA, ViralWastewaterWastewater-Based Epidemiological MonitoringBiomarkersCaffeineCreatinineHydroxyindoleacetic AcidRNA, ViralWastewaterParaxanthinePopulation biomarkerPopulation normalizationSARS-CoV-2Wastewater-based epidemiology

Identifiers

PMID36030667
PMCPMC9376872
OpenAlexW4292018397

What OpenQuestion holds

Textmetadata
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.