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1.
Influenza Other Respir Viruses ; 2022 May 24.
Article in English | MEDLINE | ID: covidwho-1861366

ABSTRACT

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.

2.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-332273

ABSTRACT

ABSTRACT Background Identification of shared and divergent predictors of clinical severity across respiratory viruses may support clinical decision-making and resource planning in the context of a novel or re-emergent 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;2011-09 to 2019-05), respiratory syncytial virus (RSV;N=24,345;2011-09 to 2019-04), or 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. Results 3,186 (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. Common 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. Conclusions Our findings may help identify patients at highest risk of illness secondary to a respiratory virus, anticipate hospital resource needs, and prioritize local preventions and therapeutics to communities with high prevalence of risk factors. Summary In this study of patients hospitalized with influenza, respiratory syncytial virus, and SARS-CoV-2, common predictors of mortality included: older age, male sex, residence in long-term care homes and chronic kidney disease. These predictors may support clinical- and systems-level decision making.

3.
CMAJ ; 194(6): E195-E204, 2022 02 14.
Article in English | MEDLINE | ID: covidwho-1686132

ABSTRACT

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (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 and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, 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 the resurgence of SARS-CoV-2.


Subject(s)
COVID-19/epidemiology , Demography/statistics & numerical data , Social Determinants of Health/statistics & numerical data , COVID-19/economics , Canada/epidemiology , Cities/epidemiology , Cross-Sectional Studies , Demography/economics , Humans , SARS-CoV-2 , Social Determinants of Health/economics , Socioeconomic Factors
4.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296345

ABSTRACT

Background There 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. Methods We 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. Results Geographic concentration was observed in all CMAs (half of the cumulative cases were concentrated among 21-35% of each city’s 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 Québec (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. Interpretation The 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-2’s resurgence.

5.
CMAJ ; 193(32): E1261-E1276, 2021 08 16.
Article in French | MEDLINE | ID: covidwho-1538242

ABSTRACT

CONTEXTE: Optimiser la réponse de la santé publique pour diminuer le fardeau de la COVID-19 nécessite la caractérisation de l'hétérogénéité du risque posé par la maladie à l'échelle de la population. Cependant, l'hétérogénéité du dépistage du SRAS-CoV-2 peut fausser les estimations selon le modèle d'étude analytique utilisé. Notre objectif était d'explorer les biais collisionneurs dans le cadre d'une vaste étude portant sur les déterminants de la maladie et d'évaluer les déterminants individuels, environnementaux et sociaux du dépistage et du diagnostic du SRAS-CoV-2 parmi les résidents de l'Ontario, au Canada. MÉTHODES: Nous avons exploré la présence potentielle de biais collisionneurs et caractérisé les déterminants individuels, environnementaux et sociaux de l'obtention d'un test de dépistage et d'un résultat positif à la présence de l'infection au SRAS-CoV-2 à l'aide d'analyses transversales parmi les 14,7 millions de personnes vivant dans la collectivité en Ontario, au Canada. Parmi les personnes ayant obtenu un diagnostic, nous avons utilisé des études analytiques distinctes afin de comparer les prédicteurs pour les personnes d'obtenir un résultat de test de dépistage positif plutôt que négatif, pour les personnes symptomatiques d'obtenir un résultat de test de dépistage positif plutôt que négatif et pour les personnes d'obtenir un résultat de test de dépistage positif plutôt que de ne pas obtenir un résultat positif (c.-à-d., obtenir un résultat de test de dépistage négatif ou ne pas obtenir de test de dépistage). Nos analyses comprennent des tests de dépistage réalisés entre le 1er mars et le 20 juin 2020. RÉSULTATS: Sur 14 695 579 personnes, nous avons constaté que 758 691 d'entre elles ont passé un test de dépistage du SRAS-CoV-2, parmi lesquelles 25 030 (3,3 %) ont obtenu un résultat positif. Plus la probabilité d'obtenir un test de dépistage s'éloignait de zéro, plus la variabilité généralement observée dans la probabilité d'un diagnostic était grande parmi les modèles d'études analytiques, particulièrement en ce qui a trait aux facteurs individuels. Nous avons constaté que la variabilité dans l'obtention d'un test de dépistage était moins importante en fonction des déterminants sociaux dans l'ensemble des études analytiques. Les facteurs tels que le fait d'habiter dans une région ayant une plus haute densité des ménages (rapport de cotes corrigé 1,86; intervalle de confiance [IC] à 95 % 1,75­1,98), une plus grande proportion de travailleurs essentiels (rapport de cotes corrigé 1,58; IC à 95 % 1,48­1,69), une population atteignant un plus faible niveau de scolarité (rapport de cotes corrigé 1,33; IC à 95 % 1,26­1,41) et une plus grande proportion d'immigrants récents (rapport de cotes corrigé 1,10; IC à 95 % 1,05­1,15), étaient systématiquement corrélés à une probabilité plus importante d'obtenir un diagnostic de SRAS-CoV-2, peu importe le modèle d'étude analytique employé. INTERPRÉTATION: Lorsque la capacité de dépister est limitée, nos résultats suggèrent que les facteurs de risque peuvent être estimés plus adéquatement en utilisant des comparateurs populationnels plutôt que des comparateurs de résultat négatif au test de dépistage. Optimiser la lutte contre la COVID-19 nécessite des investissements dans des interventions structurelles déployées de façon suffisante et adaptées à l'hétérogénéité des déterminants sociaux du risque, dont le surpeuplement des ménages, l'occupation professionnelle et le racisme structurel.

6.
Ann Epidemiol ; 65: 84-92, 2022 01.
Article in English | MEDLINE | ID: covidwho-1525672

ABSTRACT

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.


Subject(s)
COVID-19 , Geography , Humans , Ontario/epidemiology , Retrospective Studies , SARS-CoV-2 , Socioeconomic Factors
7.
Ann Epidemiol ; 63: 63-67, 2021 11.
Article in English | MEDLINE | ID: covidwho-1326908

ABSTRACT

Shelter-in-place mandates and closure of nonessential businesses have been central to COVID19 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 and deaths, and census data for neighborhood-level attributes. Cumulative per-capita rates of COVID-19 cases and deaths were 3.3-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.


Subject(s)
COVID-19 , Epidemics , Canada , Humans , Occupations , SARS-CoV-2
8.
CMAJ ; 193(20): E723-E734, 2021 05 17.
Article in English | MEDLINE | ID: covidwho-1238783

ABSTRACT

BACKGROUND: Optimizing the public health response to reduce the burden of COVID-19 necessitates characterizing population-level heterogeneity of risks for the disease. However, heterogeneity in SARS-CoV-2 testing may introduce biased estimates depending on analytic design. We aimed to explore the potential for collider bias in a large study of disease determinants, and evaluate individual, environmental and social determinants associated with SARS-CoV-2 testing and diagnosis among residents of Ontario, Canada. METHODS: We explored the potential for collider bias and characterized individual, environmental and social determinants of being tested and testing positive for SARS-CoV-2 infection using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those with a diagnosis, we used separate analytic designs to compare predictors of people testing positive versus negative; symptomatic people testing positive versus testing negative; and people testing positive versus people not testing positive (i.e., testing negative or not being tested). Our analyses included tests conducted between Mar. 1 and June 20, 2020. RESULTS: Of 14 695 579 people, we found that 758 691 were tested for SARS-CoV-2, of whom 25 030 (3.3%) had a positive test result. The further the odds of testing from the null, the more variability we generally observed in the odds of diagnosis across analytic design, particularly among individual factors. We found that there was less variability in testing by social determinants across analytic designs. Residing in areas with the highest household density (adjusted odds ratio [OR] 1.86, 95% confidence interval [CI] 1.75-1.98), highest proportion of essential workers (adjusted OR 1.58, 95% CI 1.48-1.69), lowest educational attainment (adjusted OR 1.33, 95% CI 1.26-1.41) and highest proportion of recent immigrants (adjusted OR 1.10, 95% CI 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. INTERPRETATION: Where testing is limited, our results suggest that risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment in and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation and structural racism.


Subject(s)
COVID-19 Testing/methods , COVID-19/epidemiology , Pandemics , Population Surveillance , RNA, Viral/analysis , SARS-CoV-2/genetics , Social Determinants of Health/statistics & numerical data , Adolescent , Adult , COVID-19/diagnosis , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Ontario/epidemiology , Young Adult
9.
CMAJ ; 193(20): E723-E734, 2021 05 17.
Article in English | MEDLINE | ID: covidwho-1206209

ABSTRACT

BACKGROUND: Optimizing the public health response to reduce the burden of COVID-19 necessitates characterizing population-level heterogeneity of risks for the disease. However, heterogeneity in SARS-CoV-2 testing may introduce biased estimates depending on analytic design. We aimed to explore the potential for collider bias in a large study of disease determinants, and evaluate individual, environmental and social determinants associated with SARS-CoV-2 testing and diagnosis among residents of Ontario, Canada. METHODS: We explored the potential for collider bias and characterized individual, environmental and social determinants of being tested and testing positive for SARS-CoV-2 infection using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those with a diagnosis, we used separate analytic designs to compare predictors of people testing positive versus negative; symptomatic people testing positive versus testing negative; and people testing positive versus people not testing positive (i.e., testing negative or not being tested). Our analyses included tests conducted between Mar. 1 and June 20, 2020. RESULTS: Of 14 695 579 people, we found that 758 691 were tested for SARS-CoV-2, of whom 25 030 (3.3%) had a positive test result. The further the odds of testing from the null, the more variability we generally observed in the odds of diagnosis across analytic design, particularly among individual factors. We found that there was less variability in testing by social determinants across analytic designs. Residing in areas with the highest household density (adjusted odds ratio [OR] 1.86, 95% confidence interval [CI] 1.75-1.98), highest proportion of essential workers (adjusted OR 1.58, 95% CI 1.48-1.69), lowest educational attainment (adjusted OR 1.33, 95% CI 1.26-1.41) and highest proportion of recent immigrants (adjusted OR 1.10, 95% CI 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. INTERPRETATION: Where testing is limited, our results suggest that risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment in and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation and structural racism.


Subject(s)
COVID-19 Testing/methods , COVID-19/epidemiology , Pandemics , Population Surveillance , RNA, Viral/analysis , SARS-CoV-2/genetics , Social Determinants of Health/statistics & numerical data , Adolescent , Adult , COVID-19/diagnosis , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Ontario/epidemiology , Young Adult
10.
CMAJ Open ; 8(4): E627-E636, 2020.
Article in English | MEDLINE | ID: covidwho-840782

ABSTRACT

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.


Subject(s)
COVID-19/diagnosis , COVID-19/transmission , Disease Outbreaks/prevention & control , SARS-CoV-2/genetics , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/virology , COVID-19 Testing/methods , COVID-19 Testing/statistics & numerical data , Canada/epidemiology , Female , Homeless Persons/statistics & numerical data , Humans , Long-Term Care/statistics & numerical data , Male , Middle Aged , Outcome Assessment, Health Care , Prospective Studies , Travel/statistics & numerical data , Travel-Related Illness
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