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BackgroundShared and divergent predictors of clinical severity across respiratory viruses may support clinical and community responses in the context of a novel respiratory pathogen. MethodsWe conducted a retrospective cohort study to identify predictors of 30-day all-cause mortality following hospitalization with influenza (N=45,749; 2011-09 to 2019-05), respiratory syncytial virus (RSV; N=24,345; 2011-09 to 2019-04), or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; N=8,988; 2020-03 to 2020-12; pre-vaccine) using population-based health administrative data from Ontario, Canada. Multivariable modified Poisson regression was used to assess associations between potential predictors and mortality. We compared the direction, magnitude, and confidence intervals of risk ratios to identify shared and divergent predictors of mortality. Results3,186 (7.0%), 697 (2.9%) and 1,880 (20.9%) patients died within 30 days of hospital admission with influenza, RSV, and SARS-CoV-2, respectively. Shared predictors of increased mortality included: older age, male sex, residence in a long-term care home, and chronic kidney disease. Positive associations between age and mortality were largest for patients with SARS-CoV-2. Few comorbidities were associated with mortality among patients with SARS-CoV-2 as compared to those with influenza or RSV. ConclusionsOur findings may help identify patients at greatest risk of illness secondary to a respiratory virus, anticipate hospital resource needs, and prioritize local prevention and therapeutic strategies to communities with higher prevalence of risk factors.
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BackgroundThere is a growing recognition that strategies to reduce SARS-CoV-2 transmission should be responsive to local transmission dynamics. Studies have revealed inequalities along social determinants of health, but little investigation was conducted surrounding geographic concentration within cities. We quantified social determinants of geographic concentration of COVID-19 cases across sixteen census metropolitan areas (CMA) in four Canadian provinces. MethodsWe used surveillance data on confirmed COVID-19 cases at the level of dissemination area. Gini (co-Gini) coefficients were calculated by CMA based on the proportion of the population in ranks of diagnosed cases and each social determinant using census data (income, education, visible minority, recent immigration, suitable housing, and essential workers) and the corresponding share of cases. Heterogeneity was visualized using Lorenz (concentration) curves. ResultsGeographic concentration was observed in all CMAs (half of the cumulative cases were concentrated among 21-35% of each citys population): with the greatest geographic heterogeneity in Ontario CMAs (Gini coefficients, 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32), and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income, education attainment, and suitable housing; and higher proportion of visible minorities, recent immigrants, and essential workers. Although a consistent feature across CMAs was concentration by proportion visible minorities, the magnitude of concentration by social determinants varied across CMAs. InterpretationThe feature of geographical concentration of COVID-19 cases was consistent across CMAs, but the pattern by social determinants varied. Geographically-prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to SARS-CoV-2s resurgence.
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BackgroundInequities in the burden of COVID-19 observed across Canada suggest heterogeneity within community transmission. ObjectivesTo quantify the magnitude of heterogeneity in the wider community (outside of long-term care homes) in Toronto, Canada and assess how the magnitude in concentration evolved over time (January 21 to November 21, 2020). DesignRetrospective, population-based observational study using surveillance data from Ontarios Case and Contact Management system. SettingToronto, Canada. ParticipantsLaboratory-confirmed cases of COVID-19 (N=33,992). MeasurementsWe generated epidemic curves by SDOH and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 cases by social determinants of health (SDOH) and estimated the crude Gini coefficient. We examined the correlation between SDOH using Pearson correlation coefficients. ResultsThe Gini coefficient of cumulative cases by population size was 0.41 (95% CI: 0.36-0.47) and were estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI: 0.16-0.28); recent immigration (0.12, 95%CI: 0.09-0.16); suitable housing (0.21, 95%CI: 0.14-0.30); multi-generational households (0.19, 95%CI: 0.15-0.23); and essential workers (0.28, 95% CI: 0.23-0.34). Most SDOH were highly correlated. Locally acquired cases were concentrated in higher income neighbourhoods in the early phase of the epidemic, and then concentrated in lower income neighbourhoods. Mirroring the trajectory of epidemic curves by income, the Lorenz curve shifted over time from below to above the line of equality with a similar pattern across SDOH. LimitationsStudy relied on area-based measures of the SDOH and individual case counts of COVID-19. We cannot infer concentration of cases by specific occupational exposures given limitation to broad occupational categories. ConclusionCOVID-19 is increasingly concentrated by SDOH given socioeconomic inequities and structural racism. Primary Funding SourceCanadian Institutes of Health Research.
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Shelter-in-place mandates and closure of non-essential businesses have been central to COVID-19 response strategies including in Toronto, Canada. Approximately half of the working population in Canada are employed in occupations that do not allow for remote work suggesting potentially limited impact of some of the strategies proposed to mitigate COVID-19 acquisition and onward transmission risks and associated morbidity and mortality. We compared per-capita rates of COVID-19 cases and deaths from January 23, 2020 to January 24, 2021, across neighborhoods in Toronto by proportion of the population working in essential services. We used person-level data on laboratory-confirmed COVID-19 community cases (N=74,477) and deaths (N=2319), and census data for neighborhood-level attributes. Cumulative per-capita rates of COVID-19 cases and deaths were 3-fold and 2.5-fold higher, respectively, in neighborhoods with the highest versus lowest concentration of essential workers. Findings suggest that the population who continued to serve the essential needs of society throughout COVID-19 shouldered a disproportionate burden of transmission and deaths. Taken together, results signal the need for active intervention strategies to complement restrictive measures to optimize both the equity and effectiveness of COVID-19 responses.
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BackgroundOptimizing the public health response to reduce coronavirus disease 2019 (COVID-19) burden necessitates characterizing population-level heterogeneity of COVID-19 risks. However, heterogeneity in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing may introduce biased estimates depending on analytic design. MethodsWe explored the potential for collider bias and characterized individual, environmental, and social determinants of testing and diagnosis using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those diagnosed, we used separate analytic designs to compare predictors of: 1) individuals testing positive versus negative; 2) symptomatic individuals only testing positive versus testing negative; and 3) individuals testing positive versus individuals not testing positive (i.e., testing negative or not being tested). Analyses included tests conducted between March 1 and June 20, 2020. ResultsOf a total of 14,695,579 individuals, 758,691 were tested for SARS-CoV-2, of whom 25,030 (3.3%) tested positive. The further the odds of testing from the null, the more variability observed in the odds of diagnosis across analytic design, particularly among individual factors. There was less variability in testing by social determinants across analytic designs. Residing in areas with highest household density (adjusted odds ratio [aOR]: 1.86; 95%CI: 1.75-1.98), highest proportion of essential workers (aOR: 1.58; 95%CI: 1.48-1.69), lowest educational attainment (aOR: 1.33; 95%CI: 1.26-1.41), and highest proportion of recent immigrants (aOR: 1.10; 95%CI: 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. InterpretationWhere testing is limited, risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation, and structural racism.
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BackgroundWe compared the risk of, testing for, and death following COVID-19 infection across three settings (long-term care homes (LTCH), shelters, the rest of the population) in the Greater Toronto Area (GTA), Canada. MethodsWe sourced person-level data from COVID-19 surveillance and reporting systems in Ontario, and examined settings with population-specific denominators (LTCH residents, shelters, and the rest of the population). We calculated cumulatively, the diagnosed cases per capita, proportion tested for COVID-19, daily and cumulative positivity, and case fatality proportion. We estimated the age- and sex-adjusted relative rate ratios for test positivity and case fatality using quasi-Poisson regression. ResultsBetween 01/23/2020-05/25/2020, we observed a shift in the proportion of cases: from travel-related and into LTCH and shelters. Cumulatively, compared to the rest of the population, the number of diagnosed cases per 100,000 was 59-fold and 18-fold higher among LTCH and shelter residents, respectively. By 05/25/2020, 77.2% of LTCH residents compared to 2.4% of the rest of the population had been tested. After adjusting for age and sex, LTCH residents were 2.5 times (95% confidence interval (CI): 2.3-2.8) more likely to test positive. Case fatality was 26.3% (915/3485), 0.7% (3/402), and 3.6% (506/14133) among LTCH residents, shelter population, and others in the GTA, respectively. After adjusting for age and sex, case fatality was 1.4-fold (95%CI: 1.1-1.9) higher among LTCH residents than the rest of the population. InterpretationHeterogeneity across micro-epidemics among specific populations in specific settings may reflect underlying heterogeneity in transmission risks, necessitating setting-specific COVID-19 prevention and mitigation strategies.
