Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study

The emergence of COVID-19 in Canada has led to over 4.9 million cases and 59,000 deaths by May 2024. Traditional clinical surveillance metrics (hospital admissions and clinical laboratory-positive cases) were complemented with wastewater and environmental monitoring (WEM) to monitor SARS-CoV-2 incidence. However, challenges in public health integration of WEM persist due to perceived limitations of WEM data quality, potentially driving inconsistent correlations variability and lead times. This study investigates how factors like population size, WEM measurement magnitude, site isolation status, hospital admissions, and clinical laboratory-positive cases affect WEM data correlations and variability in Ontario. The analysis uncovers a direct relationship between clinical surveillance data and the population size of the surveyed sewersheds, while WEM measurement magnitude was not directly impacted by population size. Higher variability in clinical surveillance data was observed in smaller sewersheds, likely reducing correlation strength for inferring COVID-19 incidence. Population size significantly influenced correlation quality, with thresholds identified at ∼66,000 inhabitants for strong WEM-hospital admissions correlations and ∼68,000 inhabitants for WEM-laboratory-positive cases during waned vaccination periods in Ontario (the Omicron BA.1 wave). During significant vaccination immunization (the Omicron BA.2 wave), these thresholds increased to ∼187,000 and 238,000, respectively. These findings highlight the benefit of WEM for strategic public health monitoring and interventions, especially in smaller communities. This study provides insights for enhancing public health decision making and disease monitoring through WEM, applicable to COVID-19 and potentially other diseases.

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Publication Details

Journal
ACS ES&T Water
Published
2025-03-07
DOI
https://doi.org/10.1021/acsestwater.4c00958
Citations
2
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
2.79

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article

Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study

Denina Simmons, Marc Habash, Art F. Y. Poon, Katy Peng et al.
2 citations
ACS ES&T Water
SARS-CoV-2 detection and testing
2.79
article

Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study

Denina Simmons, Marc Habash, Art F. Y. Poon, Katy Peng, David Bulir, C. B. Dean, Kimberley Gilbride, Claire Oswald, Andrea E. Kirkwood, Lawrence Goodridge, Nada Hegazy, Patrick M. D’Aoust, Jean‐Paul Desaulniers, Banu Örmeci, Gustavo Ybazeta, Hadi A. Dhiyebi, Christopher T. DeGroot, R. Michael L. McKay, Christopher J. Kyle, Elizabeth Renouf, Chandler H. Wong, Minqing Ivy Yang, Mark R. Servos, Md Pervez Kabir, Sarah Jane Payne, Eric J. Arts, Trevor C. Charles, Elizabeth A. Edwards, Robert Delatolla, Lakshmi Pisharody, Nathan T. Ramsay, Shen Wan, Hui Peng, Stephen Brown, Emma Tomalty, Tram Bich Nguyen, Élisabeth Mercier, Joan Hu, Felix Gyawu Addo
article en
2 citations

Abstract

The emergence of COVID-19 in Canada has led to over 4.9 million cases and 59,000 deaths by May 2024. Traditional clinical surveillance metrics (hospital admissions and clinical laboratory-positive cases) were complemented with wastewater and environmental monitoring (WEM) to monitor SARS-CoV-2 incidence. However, challenges in public health integration of WEM persist due to perceived limitations of WEM data quality, potentially driving inconsistent correlations variability and lead times. This study investigates how factors like population size, WEM measurement magnitude, site isolation status, hospital admissions, and clinical laboratory-positive cases affect WEM data correlations and variability in Ontario. The analysis uncovers a direct relationship between clinical surveillance data and the population size of the surveyed sewersheds, while WEM measurement magnitude was not directly impacted by population size. Higher variability in clinical surveillance data was observed in smaller sewersheds, likely reducing correlation strength for inferring COVID-19 incidence. Population size significantly influenced correlation quality, with thresholds identified at ∼66,000 inhabitants for strong WEM-hospital admissions correlations and ∼68,000 inhabitants for WEM-laboratory-positive cases during waned vaccination periods in Ontario (the Omicron BA.1 wave). During significant vaccination immunization (the Omicron BA.2 wave), these thresholds increased to ∼187,000 and 238,000, respectively. These findings highlight the benefit of WEM for strategic public health monitoring and interventions, especially in smaller communities. This study provides insights for enhancing public health decision making and disease monitoring through WEM, applicable to COVID-19 and potentially other diseases.

ACS ES&T WaterVol. 5(4)
Western University (CA), University of Waterloo (CA), University of Ottawa (CA), Simon Fraser University (CA), University of Toronto (CA), Queen's University (CA), Health Sciences North (CA), Ontario Tech University (CA), Canadian Institute for Public Safety Research and Treatment (CA), Abiola Ajimobi Technical University Ibadan (NG), Great Lakes Institute for Environmental Research, Toronto Metropolitan University (CA), Trent University (CA), Carleton University (CA), University of Windsor (CA), University of Guelph (CA), Great Lakes Institute of Management (IN), McMaster University (CA)
Government of Ontario, Ministère de l’Environnement, de la Protection de la nature et des Parcs, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada
Clean water and sanitation
Openalex Percentile: Top 12%
SARS-CoV-2 detection and testing
2.79
DOIOpen Access PDFOpenAlex
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