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1.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335181

ABSTRACT

Evidence from early observational studies suggested negative vaccine effectiveness for the SARS-CoV-2 Omicron variant. Using transmission modeling, we illustrated how increased contact between vaccinated individuals, vaccinated contact heterogeneity, paired with lower vaccine efficacies could produce negative measurements and how we can identify this mechanism via a key temporal signature.

2.
International Journal of Infectious Diseases ; 2022.
Article in English | ScienceDirect | ID: covidwho-1804272

ABSTRACT

Background : Epidemic of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and lengths of stay in hospital and intensive cares units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. Methods : We used population-based provincial hospitalization data from the epicenters of Canada (Ontario and Québec). Adjusted estimates were obtained using marginal standardization of logistic regression models, accounting for patient-level and hospital-level determinants. Results : Using all hospitalizations from Ontario (N=26,541) and Québec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after adjustment. The odds of in-hospital mortality in the highest patient load quintile was 1.2 (95%CI: 1.0-1.4;Ontario) and 1.6 (95%CI: 1.3-1.9;Québec) times that of the lowest quintile. Mean hospital and ICU lengths of stay decreased over time but ICU stays were consistently higher in Ontario than Québec. Conclusion : In-hospital mortality risks and lengths of ICU stay declined over time, despite changing patient demographics. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.

3.
MethodsX ; 9: 101614, 2022.
Article in English | MEDLINE | ID: covidwho-1796315

ABSTRACT

Infectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form contacts if they meet in patch C. We build upon their approach to address three potential gaps that remain, outlined in the bullets below. We describe the steps required to implement our approach in detail, and present step-wise results of an example application to generate contact matrices for SARS-CoV-2 transmission modelling in Ontario, Canada. We also provide methods for deriving the mobility matrix based on GPS mobility data (appendix).•Our approach includes a distribution of contacts by age that is responsive to the underlying age distributions of the mixing populations.•Our approach maintains different age mixing patterns by contact type, such that changes to the numbers of different types of contacts are appropriately reflected in changes to overall age mixing patterns.•Our approach distinguishes between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch, and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch.

4.
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.

5.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-330356

ABSTRACT

Importance: Social determinants of health (SDOH) play an important role in COVID-19 outcomes. More research is needed to quantify this relationship and understand the underlying mechanisms. Objectives: To examine differential patterns in COVID-19-related mortality by area-level SDOH accounting for confounders;and to compare these patterns to those for non-COVID-19 mortality, and COVID-19 case fatality (COVID-19-related death among those diagnosed). Design, setting, and participants: Population-based retrospective cohort study including all community living individuals aged 20 years or older residing in Ontario, Canada, as of March 1, 2020 who were followed through to March 2, 2021. Exposure: SDOH variables derived from the 2016 Canada Census at the dissemination area-level including: median household income;educational attainment;proportion of essential workers, racialized groups, recent immigrants, apartment buildings, and high-density housing;and average household size. Main outcomes and measures: COVID-19-related death was defined as death within 30 days following, or 7 days prior to a positive SARS-CoV-2 test. Cause-specific hazard models were employed to examine the associations between SDOH and COVID-19-related mortality, treating non-COVID-19 mortality as a competing risk. Results: Of 11,810,255 individuals included, 3,880 (0.03%) died related to COVID-19 and 88,107 (0.75%) died without a positive test. After accounting for demographics, baseline health, and other SDOH, the following SDOH were associated with increased hazard of COVID-19-related death (hazard ratios [95% confidence intervals]) comparing the most to least vulnerable group): lower income (1.30[1.09-1.54]), lower educational attainment (1.27[1.10-1.47]), higher proportion essential workers (1.28[1.10-1.50]), higher proportion racialized groups (1.42[1.16-1.73]), higher proportion apartment buildings (1.25[1.11-1.41]), and larger vs. medium household size (1.30[1.13-1.48]). In comparison, areas with higher proportion racialized groups were associated with a lower hazard of non-COVID-19 mortality (0.88[0.85-0.92]). With the exception of income, SDOH were not independently associated with COVID-19 case fatality. Conclusions and relevance: Area-level social and structural inequalities determine COVID-19-related mortality after accounting for individual demographic and clinical factors. COVID-19 has reversed the pattern of lower non-COVID-19 mortality by racialized groups. Pandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin disproportionate acquisition and transmission risks and shape barriers to the reach of, and access to prevention interventions.

6.
Int J Infect Dis ; 118: 73-82, 2022 Feb 23.
Article in English | MEDLINE | ID: covidwho-1700024

ABSTRACT

BACKGROUND: Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out. METHODS: We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains. RESULTS: Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth. CONCLUSIONS: The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.

7.
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
8.
MethodsX ; 2021.
Article in English | EuropePMC | ID: covidwho-1602137

ABSTRACT

Graphical Infectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form contacts if they meet in patch C. We build upon their approach to address three potential gaps that remain, outlined in the bullets below. We describe the steps required to implement our approach in detail, and present step-wise results of an example application to generate contact matrices for SARS-CoV-2 transmission modelling in Ontario, Canada. We also provide methods for deriving the mobility matrix based on GPS mobility data (appendix).

9.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-297081

ABSTRACT

Background: Epidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in intensive cares units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. Methods: We used population-based provincial hospitalization data from Ontario and Québec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. Results: Using all hospitalizations from Ontario (N=26,541) and Québec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4;Ontario) and 1.6 (95%CI: 1.3-1.9;Québec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Québec by 3-6 days. Conclusion: In-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.

10.
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.

11.
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.

12.
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
13.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-292101

ABSTRACT

Background: Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and a stringency index across Canada prior to accelerated vaccine roll-out.Methods: We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed effects model accounting for stringency period, province, and mobility in public domains.Results: Case growth declined, rapidly, by 37–50% and began plateauing within the first two weeks of the first wave, irrespective of the starting values of the stringency index. Across individual stringency periods, there was a lag of 11·3 days, on average, to observe the largest cumulative decline in relative growth. The largest decreasing trends from our mixed effects model occurred over the first stringency period in each province, at a mean index value of 25·2 out of 100.Conclusions: There was a negative correlation between NPI stringency and growth of SARS-CoV-2 that attenuated throughout the course of Canada’s epidemic. We suggest that individual- and network-level risk factors need to guide the use of NPIs in future epidemics.

15.
Lancet Infect Dis ; 21(11): 1472-1474, 2021 11.
Article in English | MEDLINE | ID: covidwho-1386926
16.
Glob Public Health ; : 1-20, 2021 Aug 17.
Article in English | MEDLINE | ID: covidwho-1360275

ABSTRACT

We examine the typologies of workplaces for sex workers in Dnipro, Ukraine as part of the larger Dynamics Study, which explores the influence of conflict on sex work. We conducted a cross-sectional survey with 560 women from September 2017 to October 2018. The results of our study demonstrate a diverse sex work environment with heterogeneity across workplace typologies in terms of remuneration, workload, and safety. Women working in higher prestige typologies earned a higher hourly wage, however client volume also varied which resulted in comparable monthly earnings from sex work across almost all workplace types. While sex workers in Dnipro earn a higher monthly wage than the city mean, they also report experiencing high rates of violence and a lack of personal safety at work. Sex workers in all workplaces, with the exception of those working in art clubs, experienced physical and sexual violence perpetrated by law enforcement officers and sex partners. By understanding more about sex work workplaces, programmes may be better tailored to meet the needs of sex workers and respond to changing work environments due to ongoing conflict and COVID-19 pandemic.

17.
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
18.
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
19.
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
20.
J Acquir Immune Defic Syndr ; 87(3): 899-911, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1169727

ABSTRACT

BACKGROUND: The COVID-19 pandemic indirectly impacts HIV epidemiology in Central/West Africa. We estimated the potential impact of COVID-19-related disruptions to HIV prevention/treatment services and sexual partnerships on HIV incidence and HIV-related deaths among key populations including female sex workers (FSW), their clients, men who have sex with men, and overall. SETTING: Yaoundé (Cameroon) and Cotonou (Benin). METHODS: We used mathematical models of HIV calibrated to city population-specific and risk population-specific demographic/behavioral/epidemic data. We estimated the relative change in 1-year HIV incidence and HIV-related deaths for various disruption scenarios of HIV prevention/treatment services and decreased casual/commercial partnerships, compared with a scenario without COVID-19. RESULTS: A 50% reduction in condom use in all partnerships over 6 months would increase 1-year HIV incidence by 39%, 42%, 31%, and 23% among men who have sex with men, FSW, clients, and overall in Yaoundé, respectively, and 69%, 49%, and 23% among FSW, clients, and overall, respectively, in Cotonou. Combining a 6-month interruption of ART initiation and 50% reduction in HIV prevention/treatment use would increase HIV incidence by 50% and HIV-related deaths by 20%. This increase in HIV infections would be halved by a simultaneous 50% reduction in casual and commercial partnerships. CONCLUSIONS: Reductions in condom use after COVID-19 would increase infections among key populations disproportionately, particularly FSW in Cotonou, who need uninterrupted condom provision. Disruptions in HIV prevention/treatment services have the biggest impacts on HIV infections and deaths overall, only partially mitigated by equal reductions in casual/commercial sexual partnerships. Maintaining ART provision must be prioritized to minimize short-term excess HIV-related deaths.


Subject(s)
COVID-19/complications , COVID-19/epidemiology , HIV Infections/complications , HIV Infections/epidemiology , HIV-1 , SARS-CoV-2 , Benin/epidemiology , Cameroon/epidemiology , Condoms , Female , Humans , Male , Models, Biological , Risk Factors , Safe Sex , Sex Workers , Urban Population
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