Predictors of all-cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS-CoV-2.

BACKGROUND: Shared and divergent predictors of clinical severity across respiratory viruses may support clinical and community responses in the context of a novel respiratory pathogen. METHODS: We conducted a retrospective cohort study to identify predictors of 30-day all-cause mortality following hospitalization with influenza (N = 45,749; 2010-09 to 2019-05), respiratory syncytial virus (RSV; N = 24 345; 2010-09 to 2019-04), or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; N = 8988; 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. RESULTS: A total of 3186 (7.0%), 697 (2.9%), and 1880 (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 with those with influenza or RSV. CONCLUSIONS: Our 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.

COVID-19 Cases Among Congregate Care Facility Staff by Neighborhood of Residence and Social and Structural Determinants: Observational Study.

BACKGROUND: Disproportionate risks of COVID-19 in congregate care facilities including long-term care homes, retirement homes, and shelters both affect and are affected by SARS-CoV-2 infections among facility staff. In cities across Canada, there has been a consistent trend of geographic clustering of COVID-19 cases. However, there is limited information on how COVID-19 among facility staff reflects urban neighborhood disparities, particularly when stratified by the social and structural determinants of community-level transmission. OBJECTIVE: This study aimed to compare the concentration of cumulative cases by geography and social and structural determinants across 3 mutually exclusive subgroups in the Greater Toronto Area (population: 7.1 million): community, facility staff, and health care workers (HCWs) in other settings. METHODS: We conducted a retrospective, observational study using surveillance data on laboratory-confirmed COVID-19 cases (January 23 to December 13, 2020; prior to vaccination rollout). We derived neighborhood-level social and structural determinants from census data and generated Lorenz curves, Gini coefficients, and the Hoover index to visualize and quantify inequalities in cases. RESULTS: The hardest-hit neighborhoods (comprising 20% of the population) accounted for 53.87% (44,937/83,419) of community cases, 48.59% (2356/4849) of facility staff cases, and 42.34% (1669/3942) of other HCW cases. Compared with other HCWs, cases among facility staff reflected the distribution of community cases more closely. Cases among facility staff reflected greater social and structural inequalities (larger Gini coefficients) than those of other HCWs across all determinants. Facility staff cases were also more likely than community cases to be concentrated in lower-income neighborhoods (Gini 0.24, 95% CI 0.15-0.38 vs 0.14, 95% CI 0.08-0.21) with a higher household density (Gini 0.23, 95% CI 0.17-0.29 vs 0.17, 95% CI 0.12-0.22) and with a greater proportion working in other essential services (Gini 0.29, 95% CI 0.21-0.40 vs 0.22, 95% CI 0.17-0.28). CONCLUSIONS: COVID-19 cases among facility staff largely reflect neighborhood-level heterogeneity and disparities, even more so than cases among other HCWs. The findings signal the importance of interventions prioritized and tailored to the home geographies of facility staff in addition to workplace measures, including prioritization and reach of vaccination at home (neighborhood level) and at work.

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study.

BACKGROUND: Inequities in the burden of COVID-19 were observed early in Canada and around the world, suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time. PURPOSE: To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January to November 2020 using a retrospective, population-based observational study using surveillance data. METHODS: We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients. RESULTS: Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]:0.36-0.47) and 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); multigenerational households (0.19, 95%CI:0.15-0.23); and essential workers (0.28, 95%CI:0.23-0.34). CONCLUSIONS: There was rapid epidemiologic transition from higher- to lower-income neighborhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.