Impact of Adjustment for Differential Testing by Age and Sex on Apparent Epidemiology of SARS-CoV-2 Infection in Ontario, Canada

Communicable disease surveillance typically relies on case counts for estimates of risk, and counts can be strongly influenced by testing rates. In the Canadian province of Ontario, testing rates varied markedly by age, sex, geography and time over the course of the SARS-CoV-2 pandemic. We applied a standardization-based approach to test-adjustment to better understand pandemic dynamics from 2020 to 2022, and to better understand when test-adjustment is necessary for accurate estimation of risk. Case counts were adjusted for under-testing using a previously published standardization-based approach that estimates case numbers that would have been expected if the entire population was tested at the same rate as most-tested age and sex groups. After adjustment for under-testing, estimated case counts increased threefold and test-adjusted cases correlated better with SARS-CoV-2-attributed death than crude reported cases. Test-adjusted epidemic curves suggested, in contrast to reported case counts, that the first two pandemic waves were equivalent in size, and identified three distinct pandemic waves in 2022, due to the emergence of Omicron variants. Under-reporting was greatest in younger individuals, with variation explained partly by testing rates and prevalence of multigenerational households; test-adjustment resulted in little change in the epidemic curve during time periods when per capita testing rates exceeded 5.5%. We conclude that standardization-based adjustment for differential testing by age and sex results in a different understanding of the epidemiology of SARS-CoV-2 in Ontario. This methodology may offer a means of deriving adjusted estimates of infection incidence from surveillance data, accounting for fluctuations due to changing test practices.