Bosco Savana, Peng Amy, Tuite Ashleigh R, Simmons Alison, Fisman David N
Dalla Lana School of Public Health, University of Toronto, Room 686, 155 College Street, ON, M5 T 3M7, Toronto, Canada.
Centre for Immunization Programs, Public Health Agency of Canada, Room 686, 155 College Street, ON, M5 T 3M7, Toronto, Canada.
BMC Infect Dis. 2025 Apr 23;25(1):589. doi: 10.1186/s12879-025-10968-6.
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.
传染病监测通常依靠病例数来估计风险，而病例数会受到检测率的强烈影响。在加拿大安大略省，在新冠疫情期间，检测率因年龄、性别、地理位置和时间而有显著差异。我们应用了一种基于标准化的检测调整方法，以更好地了解2020年至2022年的疫情动态，并更好地理解何时需要进行检测调整以准确估计风险。使用先前发表的基于标准化的方法对检测不足进行病例数调整，该方法估计如果整个人口以检测率最高的年龄和性别组相同的速率进行检测时预期的病例数。在对检测不足进行调整后，估计病例数增加了两倍，并且经检测调整后的病例与新冠病毒归因死亡的相关性比原始报告病例更好。与报告的病例数相比，经检测调整后的疫情曲线表明，前两波疫情规模相当，并在2022年识别出三波不同的疫情，这是由于奥密克戎变种的出现。报告不足在年轻人中最为严重，部分差异可由检测率和多代同堂家庭的患病率来解释；在人均检测率超过5.5%的时间段内，检测调整导致疫情曲线变化不大。我们得出结论，基于年龄和性别的差异检测的标准化调整导致对安大略省新冠病毒流行病学有不同的理解。这种方法可能提供一种从监测数据中得出感染发病率调整估计值的手段，同时考虑到检测实践变化引起的波动。
