Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

Binyam N Desta; Sylvia Ota; Effie Gournis; Sara M Pires; Amy L Greer; Warren Dodd; Shannon E Majowicz

doi:10.1177/22799036231174133

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

Binyam N Desta
, Sylvia Ota
, Effie Gournis
, Sara M Pires
, Amy L Greer
, Warren Dodd
, Shannon E Majowicz

University of Waterloo
Toronto Public Health
University of Guelph

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada.
Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.
Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.
Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Original language	English
Journal	Journal of Public Health Research
Volume	12
Issue number	2
Number of pages	9
ISSN	2279-9036
DOIs	https://doi.org/10.1177/22799036231174133
Publication status	Published - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
Surveillance
Disease
Disease notification
Report
Public reporting of healthcare data
Ontario
Canada

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10.1177/22799036231174133Licence: CC BY

FulltextFinal published version, 231 KBLicence: CC BY

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title = "Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020",

abstract = "Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.",

keywords = "COVID-19, Surveillance, Disease, Disease notification, Report, Public reporting of healthcare data, Ontario, Canada",

author = "Desta, \{Binyam N\} and Sylvia Ota and Effie Gournis and Pires, \{Sara M\} and Greer, \{Amy L\} and Warren Dodd and Majowicz, \{Shannon E\}",

year = "2023",

doi = "10.1177/22799036231174133",

language = "English",

volume = "12",

journal = "Journal of Public Health Research",

issn = "2279-9036",

publisher = "SAGE Publications",

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TY - JOUR

T1 - Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

AU - Desta, Binyam N

AU - Ota, Sylvia

AU - Gournis, Effie

AU - Pires, Sara M

AU - Greer, Amy L

AU - Dodd, Warren

AU - Majowicz, Shannon E

PY - 2023

Y1 - 2023

N2 - Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

AB - Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

KW - COVID-19

KW - Surveillance

KW - Disease

KW - Disease notification

KW - Report

KW - Public reporting of healthcare data

KW - Ontario

KW - Canada

U2 - 10.1177/22799036231174133

DO - 10.1177/22799036231174133

M3 - Journal article

C2 - 37197719

SN - 2279-9036

VL - 12

JO - Journal of Public Health Research

JF - Journal of Public Health Research

IS - 2

ER -

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

Abstract

UN SDGs

Keywords

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