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.

Impact of COVID-19 on Tuberculosis Prevention and Treatment in Canada: A Multicenter Analysis of 10 833 Patients.

We assessed the COVID-19 pandemic's impact on treatment of latent tuberculosis, and of active tuberculosis, at 3 centers in Montreal and Toronto, using data from 10 833 patients (8685 with latent tuberculosis infection, 2148 with active tuberculosis). Observation periods prior to declarations of COVID-19 public health emergencies ranged from 219 to 744 weeks, and after declarations, from 28 to 33 weeks. In the latter period, reductions in latent tuberculosis infection treatment initiation rates ranged from 30% to 66%. At 2 centers, active tuberculosis treatment rates fell by 16% and 29%. In Canada, cornerstone measures for tuberculosis elimination weakened during the COVID-19 pandemic.

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.

Heterogeneity in testing, diagnosis and outcome in SARS-CoV-2 infection across outbreak settings in the Greater Toronto Area, Canada: an observational study.

BACKGROUND: Congregate settings have been disproportionately affected by coronavirus disease 2019 (COVID-19). Our objective was to compare testing for, diagnosis of and death after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across 3 settings (residents of long-term care homes, people living in shelters and the rest of the population). METHODS: We conducted a population-based prospective cohort study involving individuals tested for SARS-CoV-2 in the Greater Toronto Area between Jan. 23, 2020, and May 20, 2020. We sourced person-level data from COVID-19 surveillance and reporting systems in Ontario. We calculated cumulatively diagnosed cases per capita, proportion tested, proportion tested positive and case-fatality proportion for each setting. We estimated the age- and sex-adjusted rate ratios associated with setting for test positivity and case fatality using quasi-Poisson regression. RESULTS: Over the study period, a total of 173 092 individuals were tested for and 16 490 individuals were diagnosed with SARS-CoV-2 infection. We observed a shift in the proportion of cumulative cases from all cases being related to travel to cases in residents of long-term care homes (20.4% [3368/16 490]), shelters (2.3% [372/16 490]), other congregate settings (20.9% [3446/16 490]) and community settings (35.4% [5834/16 490]), with cumulative travel-related cases at 4.1% (674/16490). Cumulatively, compared with the rest of the population, the diagnosed cases per capita was 64-fold and 19-fold higher among long-term care home and shelter residents, respectively. By May 20, 2020, 76.3% (21 617/28 316) of long-term care home residents and 2.2% (150 077/6 808 890) of the rest of the population had been tested. After adjusting for age and sex, residents of long-term care homes were 2.4 (95% confidence interval [CI] 2.2-2.7) times more likely to test positive, and those who received a diagnosis of COVID-19 were 1.4-fold (95% CI 1.1-1.8) more likely to die than the rest of the population. INTERPRETATION: Long-term care homes and shelters had disproportionate diagnosed cases per capita, and residents of long-term care homes diagnosed with COVID-19 had higher case fatality than the rest of the population. Heterogeneity across micro-epidemics among specific populations and settings may reflect underlying heterogeneity in transmission risks, necessitating setting-specific COVID-19 prevention and mitigation strategies.

Estimated surge in hospital and intensive care admission because of the coronavirus disease 2019 pandemic in the Greater Toronto Area, Canada: a mathematical modelling study.

BACKGROUND: In pandemics, local hospitals need to anticipate a surge in health care needs. We examined the modelled surge because of the coronavirus disease 2019 (COVID-19) pandemic that was used to inform the early hospital-level response against cases as they transpired. METHODS: To estimate hospital-level surge in March and April 2020, we simulated a range of scenarios of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread in the Greater Toronto Area (GTA), Canada, using the best available data at the time. We applied outputs to hospital-specific data to estimate surge over 6 weeks at 2 hospitals (St. Michael's Hospital and St. Joseph's Health Centre). We examined multiple scenarios, wherein the default (R0 = 2.4) resembled the early trajectory (to Mar. 25, 2020), and compared the default model projections with observed COVID-19 admissions in each hospital from Mar. 25 to May 6, 2020. RESULTS: For the hospitals to remain below non-ICU bed capacity, the default pessimistic scenario required a reduction in non-COVID-19 inpatient care by 38% and 28%, respectively, with St. Michael's Hospital requiring 40 new ICU beds and St. Joseph's Health Centre reducing its ICU beds for non-COVID-19 care by 6%. The absolute difference between default-projected and observed census of inpatients with COVID-19 at each hospital was less than 20 from Mar. 25 to Apr. 11; projected and observed cases diverged widely thereafter. Uncertainty in local epidemiological features was more influential than uncertainty in clinical severity. INTERPRETATION: Scenario-based analyses were reliable in estimating short-term cases, but would require frequent re-analyses. Distribution of the city's surge was expected to vary across hospitals, and community-level strategies were key to mitigating each hospital's surge.