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1.
Prev Med Rep ; 30: 101993, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36157712

RESUMO

The long-term dynamics of COVID-19 disease incidence and public health measures may impact individuals' precautionary behaviours as well as support for measures. The objectives of this study were to assess longitudinal changes in precautionary behaviours and support for public health measures. Survey data were collected online from 1030 Canadians in each of 5 cycles in 2020: June 15-July 13; July 22-Aug 8; Sept 7-15; Oct 14-21; and Nov 12-17. Precautionary behaviour increased over the study period in the context of increasing disease incidence. When controlling for the stringency of public health measures and disease incidence, mixed effects logistic regression models showed these behaviours did not significantly change over time. Odds ratios for avoiding contact with family and friends ranged from 0.84 (95% CI 0.59-1.20) in September to 1.25 (95% CI 0.66-2.37) in November compared with July 2020. Odds ratios for attending an indoor gathering ranged from 0.86 (95% CI 0.62-1.20) in August to 1.71 (95% CI 0.95-3.09) in October compared with July 2020. Support for non-essential business closures increased over time with 2.33 (95% CI 1.14-4.75) times higher odds of support in November compared to July 2020. Support for school closures declined over time with lower odds of support in September (OR 0.66 [95% CI 0.45-0.96]), October (OR 0.48 [95% CI 0.26-0.87]), and November (OR 0.39 [95% CI 0.19-0.81]) compared with July 2020. In summary, respondents' behaviour mirrored government guidance between July and November 2020 and supported individual precautionary behaviour and limitations on non-essential businesses over school closures.

2.
Infect Dis Model ; 6: 123-132, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33313456

RESUMO

While surveillance can identify changes in COVID-19 transmission patterns over time and space, sections of the population at risk, and the efficacy of public health measures, reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases. Our primary objective was to estimate the percentage of cases reported in the general community, considered as those that occurred outside of long-term care facilities (LTCFs), in specific provinces and Canada as a whole. We applied a methodology using the delay-adjusted case fatality ratio (CFR) to all cases and deaths, as well as those representing the general community. Our second objective was to assess whether the assumed CFR (mean = 1.38%) was appropriate for calculating underestimation of cases in Canada. Estimates were developed for the period from March 11th, 2020 to September 16th, 2020. Estimates of the percentage of cases reported (PrCR) and CFR varied spatially and temporally across Canada. For the majority of provinces, and for Canada as a whole, the PrCR increased through the early stages of the pandemic. The estimated PrCR in general community settings for all of Canada increased from 18.1% to 69.0% throughout the entire study period. Estimates were greater when considering only those data from outside of LTCFs. The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07% on March 11th, 2020 to 2.00% on September 16th, 2020. Therefore, the true CFR in the general community in Canada was likely less than 2% on September 16th. According to our analysis, some provinces, such as Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatchewan reported a greater percentage of cases as of September 16th, compared to British Columbia, Ontario, and Québec. This could be due to differences in testing rates and criteria, demographics, socioeconomic factors, race, and access to healthcare among the provinces. Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study. The estimates provide context to the summative state of the pandemic in Canada, and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

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