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Beate Sander Person1 #679757 Canada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative). | Current Position and Professional Functions - Canada Research Chair in Economics of Infectious Diseases
- Director, Health Modeling & Health Economics and Population Health Economics Research (PHER), THETA
- Scientist, Toronto General Hospital Research Institute
- Associate Professor and Faculty Lead HTA program, Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto
- Adjunct Scientist, Public Health Ontario
- Adjunct Scientist, Institute for Clinical Evaluative Sciences
- Adjunct Faculty, Department of Mathematics and Statistics, York University
- Faculty Associate, Canadian Centre for Health Economics (CCHE)
- Editorial Board member, Medical Decision Making
Affiliations - Toronto General Hospital Research Institute (TGHRI), University Health Network
- Institute of Health Policy, Management and Evaluation (IHPME), U of T
- ICES UofT, Primary Care & Population Health Research Program
- Public Health Ontario (PHO)
Education and Training - Beate completed her degree in Nursing (RN) in Germany and received postgraduate degrees in Business Administration (MBA) from Germany, Economics of Development (MEcDev) from the Australian National University, a Doctorate in Health Services Research (PhD) from the University of Toronto, and postdoctoral training in Public Health Policy.
Research Interests and Expertise - Dr. Sander’s areas of expertise include health economics, decision analysis and simulation, infectious disease epidemiology, and population health decision-making. Beate’s current research focuses on economic evaluation, ranging from methods development to applied research on infectious diseases. She is leading large multidisciplinary international teams evaluating Zika and West Nile virus (WNv) mitigation strategies using data-driven simulation models, and estimating the burden of infectious diseases (C.difficile, S.pneumoniae, hepatitis, WNv, Lyme disease) using linked population-based data. She has spearheaded the linkage of laboratory and reportable disease data with administrative data, enabling novel approaches to study the burden of infectious diseases. Beate has received several awards recognizing research excellence.
- Beate provides scientific advice to decision makers and serves on scientific working groups and advisory bodies, including Canada’s National Advisory Committee on Immunization (NACI), Ontario’s Universal Influenza Immunization Program (UIIP) Review, and the National West Nile virus task force.
- Dr. Sander is the Faculty Lead for the Health Technology Assessment (HTA) program at IHPME, and enjoys teaching a popular graduate course on clinical decision making and cost-effectiveness at IHPME.
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CitationsAdd new citationList by: CiterankMap Link[2] Modelling the impact of extending dose intervals for COVID-19 vaccines in Canada
Author: Austin Nam, Raphael Ximenes, Man Wah Yeung, Sharmistha Mishra, Jianhong Wu, Matthew Tunis, Beate Sander Publication date: 10 April 2021 Publication info: medRxiv 2021.04.07.21255094 Cited by: David Price 10:38 AM 21 October 2022 GMT Citerank: (3) 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 690189Man Wah YeungSenior Health Economist at the Public Health Agency of Canada.10019D3ABAB URL: DOI: https://doi.org/10.1101/2021.04.07.21255094
| Excerpt / Summary Background: Dual dose SARS-CoV-2 vaccines demonstrate high efficacy and will be critical in public health efforts to mitigate the COVID-19 pandemic and its health consequences; however, many jurisdictions face very constrained vaccine supply. We examined the impacts of extending the interval between two doses of mRNA vaccines in Canada in order to inform deliberations of Canada’s National Advisory Committee on Immunization.
Methods: We developed an age-stratified, deterministic, compartmental model of SARS-CoV-2 transmission and disease to reproduce the epidemiologic features of the epidemic in Canada. Simulated vaccination comprised mRNA vaccines with explicit examination of effectiveness against disease (67% [first dose], 94% [second dose]), hospitalization (80% [first dose], 96% [second dose]), and death (85% [first dose], 96% [second dose]) in adults aged 20 years and older. Effectiveness against infection was assumed to be 90% relative to the effectiveness against disease. We used a 6-week mRNA dose interval as our base case (consistent with early program rollout across Canadian and international jurisdictions) and compared extended intervals of 12 weeks, 16 weeks, and 24 weeks. We began vaccinations on January 1, 2021 and simulated a third wave beginning on April 1, 2021.
Results: Extending mRNA dose intervals were projected to result in 12.1-18.9% fewer symptomatic cases, 9.5-13.5% fewer hospitalizations, and 7.5-9.7% fewer deaths in the population over a 12-month time horizon. The largest reductions in hospitalizations and deaths were observed in the longest interval of 24 weeks, though benefits were diminishing as intervals extended. Benefits of extended intervals stemmed largely from the ability to accelerate coverage in individuals aged 20-74 years as older individuals were already prioritized for early vaccination. Conditions under which mRNA dose extensions led to worse outcomes included: first-dose effectiveness < 65% against death; or protection following first dose waning to 0% by month three before the scheduled 2nd dose at 24-weeks. Probabilistic simulations from a range of likely vaccine effectiveness values did not result in worse outcomes with extended intervals.
Conclusion: Under real-world effectiveness conditions, our results support a strategy of extending mRNA dose intervals across all age groups to minimize symptomatic cases, hospitalizations, and deaths while vaccine supply is constrained. |
Link[3] Clinical Severity of Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variant Relative to Delta in British Columbia, Canada: A Retrospective Analysis of Whole-Genome Sequenced Cases
Author: Sean P Harrigan, James Wilton, Mei Chong, Younathan Abdia, Hector Velasquez Garcia, Caren Rose, Marsha Taylor, Sharmistha Mishra, Beate Sander, Linda Hoang, John Tyson, Mel Krajden, Natalie Prystajecky, Naveed Z Janjua, Hind Sbihi Publication date: 30 August 2022 Publication info: Clinical Infectious Diseases, Volume 76, Issue 3, 1 February 2023, Pages e18–e25 Cited by: David Price 11:06 PM 25 November 2023 GMT Citerank: (6) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 704045Covid-19859FDEF6, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac705
| Excerpt / Summary [Clinical Infectious Diseases, 1 February 2023]
Background: In late 2021, the Omicron severe acute respiratory syndrome coronavirus 2 variant emerged and rapidly replaced Delta as the dominant variant. The increased transmissibility of Omicron led to surges in case rates and hospitalizations; however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta.
Methods: This retrospective cohort study was conducted with data from the British Columbia COVID-19 Cohort, a large provincial surveillance platform with linkage to administrative datasets. To capture the time of cocirculation with Omicron and Delta, December 2021 was chosen as the study period. Whole-genome sequencing was used to determine Omicron and Delta variants. To assess the severity (hospitalization, intensive care unit [ICU] admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW).
Results: The cohort was composed of 13 128 individuals (7729 Omicron and 5399 Delta). There were 419 coronavirus disease 2019 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared with Delta (adjusted hazard ratio [aHR] = 0.50, 95% confidence interval [CI] = 0.43 to 0.59), a 73% lower risk of ICU admission (aHR = 0.27, 95% CI = 0.19 to 0.38), and a 5-day shorter hospital stay (aß = −5.03, 95% CI = −8.01 to −2.05).
Conclusions: Our analysis supports findings from other studies that have demonstrated lower risk of severe outcomes in Omicron-infected individuals relative to Delta. |
Link[4] Efficacy of a “stay-at-home” policy on SARS-CoV-2 transmission in Toronto, Canada: a mathematical modelling study
Author: Pei Yuan, Juan Li, Elena Aruffo, Evgenia Gatov, Qi Li, Tingting Zheng, Nicholas H. Ogden, Beate Sander, Jane Heffernan, Sarah Collier, Yi Tan, Jun Li, Julien Arino, Jacques Bélair, James Watmough, Jude Dzevela Kong, Iain Moyles, Huaiping Zhu Publication date: 19 April 2022 Publication info: cmaj OPEN, April 19, 2022 10 (2) E367-E378 Cited by: David Price 4:16 PM 4 December 2023 GMT
Citerank: (10) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RÉUNIS). 10019D3ABAB, 679799Iain MoylesAssistant Professor in the Department of Mathematics and Statistics at York University. 10019D3ABAB, 679805James WatmoughProfessor in the Department of Mathematics and Statistics at the University of New Brunswick.10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 679817Julien ArinoProfessor and Faculty of Science Research Chair in Fundamental Science with the Department of Mathematics at the University of Manitoba.10019D3ABAB, 701222OMNI – Publications144B5ACA0, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada – April 2023 [1] 2794CAE1, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB URL: DOI: https://doi.org/10.9778/cmajo.20200242
| Excerpt / Summary Background: Globally, nonpharmaceutical interventions for COVID-19, including stay-at-home policies, limitations on gatherings and closure of public spaces, are being lifted. We explored the effect of lifting a stay-at-home policy on virus resurgence under different conditions.
Methods: Using confirmed case data from Toronto, Canada, between Feb. 24 and June 24, 2020, we ran a compartmental model with household structure to simulate the impact of the stay-at-home policy considering different levels of compliance. We estimated threshold values for the maximum number of contacts, probability of transmission and testing rates required for the safe reopening of the community.
Results: After the implementation of the stay-at-home policy, the contact rate outside the household fell by 39% (from 11.58 daily contacts to 7.11). The effective reproductive number decreased from 3.56 (95% confidence interval [CI] 3.02–4.14) on Mar. 12 to 0.84 (95% CI 0.79–0.89) on May 6. Strong adherence to stay-at-home policies appeared to prevent SARS-CoV-2 resurgence, but extending the duration of stay-at-home policies beyond 2 months had little added effect on cumulative cases (25 958 for 65 days of a stay-at-home policy and 23 461 for 95 days, by July 2, 2020) and deaths (1404 for 65 days and 1353 for 95 days). To avoid a resurgence, the average number of contacts per person per day should be kept below 9, with strict nonpharmaceutical interventions in place.
Interpretation: Our study demonstrates that the stay-at-home policy implemented in Toronto in March 2020 had a substantial impact on mitigating the spread of SARS-CoV-2. In the context of the early pandemic, before the emergence of variants of concern, reopening schools and workplaces was possible only with other nonpharmaceutical interventions in place.
Nonpharmaceutical interventions for COVID-19, including stay-at-home policies, isolation of cases and contact tracing, as well as physical distancing, handwashing and use of protective equipment such as face masks, are effective mitigation strategies for preventing virus spread.1–4 Many studies investigating SARS-CoV-2 transmission and nonpharmaceutical interventions point to the importance of within- and between-household transmission. 5–8 Although stay-at-home policies can help curb spread of SARS-CoV-2 in the community by reducing contacts outside the household,8 they can increase contacts among family members, leading to higher risk within the household, 9 with secondary infection rates in households shown to be as high as 30%–52.7%.5,10 Furthermore, prolonged periods of stay-at-home policies may not be practical because of the essential operations of society, and may directly or indirectly harm the economy and the physical and mental health of individuals.11,12 Therefore, it is important to assess the optimal length of policy implementation for preventing virus resurgence.
During the epidemic, stay-at-home policies have been used to mitigate virus spread. The proportion of people staying at home is a paramount factor for evaluating the effectiveness of this policy implementation. For example, symptomatic individuals, those who tested positive for SARS-CoV-2 infection, and traced contacts are more likely to remain in the home through self-isolation or quarantine than uninfected or asymptomatic individuals. 13 Hence, rates of testing, diagnosis, isolation of cases, contact tracing and quarantine of contacts, as well as public compliance with stay-at-home policies, are essential factors for determining virus transmission and the likelihood of epidemic resurgence after the lifting of restrictive closures.1 To allow for this level of complexity, we developed a household-based transmission model to capture differences in policy uptake behaviour using confirmed case data from Toronto, Canada.
Throughout the pandemic, Canadian provinces and territories have implemented restrictive closures of businesses, schools, workplaces and public spaces to reduce the number of contacts in the population and prevent further virus spread, with these restrictions lifted and reinstituted at various times.14 On Mar. 17, 2020, Ontario declared a state of emergency, with directives including stay-at-home policies.15
We aimed to evaluate the effect of the stay-at-home policy issued in March 2020 on the transmission of SARS-CoV-2 in Toronto, accounting for average household size, the degree of adherence to the stay-at-home policy, and the length of policy implementation. Additionally, on the basis of the average family size and local epidemic data, we estimated the basic reproduction number (R0) and effective reproduction number (Rt) and investigated potential thresholds for the number of contacts, testing rates and use of nonpharmaceutical interventions that would be optimal for mitigating the epidemic. Hence, we conducted simulations of dynamic population behaviour under different reopening and adherence scenarios, to compare different public health strategies in hopes of adding those evaluations to the scientific literature. |
Link[5] COVID-19 Hospitalizations, ICU Admissions and Deaths Associated with the New Variants of Concern
Author: Ashleigh R. Tuite, David N. Fisman, Ayodele Odutayo, et al., on behalf of the Ontario COVID-19 Science Advisory Table - Pavlos Bobos, Vanessa Allen, Isaac I. Bogoch, Adalsteinn D. Brown, Gerald A. Evans, Anna Greenberg, Jessica Hopkins, Antonina Maltsev, Douglas G. Manuel, Allison McGeer, Andrew M. Morris, Samira Mubareka, Laveena Munshi, V. Kumar Murty, Samir N. Patel, Fahad Razak, Robert J. Reid, Beate Sander, Michael Schull, Brian Schwartz, Arthur S. Slutsky, Nathan M. Stall, Peter Jüni Publication date: 29 March 2021 Publication info: [Science Briefs of the Ontario COVID-19 Science Advisory Table, 2021;1(18) Cited by: David Price 6:19 PM 4 December 2023 GMT
Citerank: (11) 679746Steini BrownProfessor and Dean of the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 679802Isaac BogochClinician Investigator, Toronto General Hospital Research Institute (TGHRI)10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 685230Doug ManuelDr. Manuel is a Medical Doctor with a Masters in Epidemiology and Royal College specialization in Public Health and Preventive Medicine. He is a Senior Scientist in the Clinical Epidemiology Program at Ottawa Hospital Research Institute, and a Professor in the Departments of Family Medicine and Epidemiology and Community Medicine.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH – Publications144B5ACA0, 704045Covid-19859FDEF6, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.47326/ocsat.2021.02.18.1.0
| Excerpt / Summary [Science Briefs of the Ontario COVID-19 Science Advisory Table, 29 March 2021]
Background: As of March 28, 2021 new variants of concern (VOCs) account for 67% of all Ontario SARS-CoV-2 infections. The B.1.1.7 variant originally detected in Kent, United Kingdom accounts for more than 90% of all VOCs in Ontario, with emerging evidence that it is both more transmissible and virulent.
Questions: What are the risks of COVID-19 hospitalization, ICU admission and death caused by VOCs as compared with the early variants of SARS-CoV-2?
What is the early impact of new VOCs on Ontario’s healthcare system?
Findings: A retrospective cohort study of 26,314 people in Ontario testing positive for SARS-CoV-2 between February 7 and March 11, 2021, showed that 9,395 people (35.7%) infected with VOCs had a 62% relative increase in COVID-19 hospitalizations (odds ratio [OR] 1.62, 95% confidence interval [CI] 1.41 to 1.87), a 114% relative increase in ICU admissions (OR 2.14, 95% CI 1.52 to 3.02), and a 40% relative increase in COVID-19 deaths (OR 1.40, 95% CI 1.01 to 1.94), after adjusting for age, sex and comorbidities.
A meta-analysis including the Ontario cohort study and additional cohort studies in the United Kingdom and Denmark showed that people infected with VOCs had a 63% higher risk of hospitalization (RR 1.63, 95% CI 1.44 to 1.83), a doubling of the risk of ICU admission (RR 2.03, 95% CI 1.69 to 2.45), and a 56% higher risk of all-cause death (RR 1.56, 95% CI 1.30 to 1.87). Estimates observed in different studies and regions were completely consistent, and the B.1.1.7 variant was dominant in all three jurisdictions over the study periods.
The number of people hospitalized with COVID-19 on March 28, 2021, is 21% higher than at the start of the province-wide lockdown during the second wave on December 26, 2020, while ICU occupancy is 28% higher.
Between December 14 to 20, 2020, there were 149 new admissions to ICU; people aged 59 years and younger accounted for 30% of admissions. Between March 15, 2021 and March 21, 2021, there were 157 new admissions to ICU; people aged 59 years and younger accounted for 46% of admissions.
Interpretation: The new VOCs will result in a considerably higher burden to Ontario’s health care system during the third wave compared to the impact of early SARS-CoV-2 variants during Ontario’s second wave.
Since the start of the third wave on March 1, 2021, the number of new cases of SARS-CoV-2 infection, and the COVID-19 hospital and ICU occupancies have surpassed prior thresholds at the start of the province-wide lockdown on December 26, 2020. |
Link[6] Protocol for a longitudinal cohort study of Lyme disease with physical, mental and immunological assessment
Author: Mark Loeb, Robert Brison, Jonathan Bramson, Todd Hatchette, Beate Sander, Elizabeth Stringer Publication date: 2 November 2023 Publication info: BMJ Open 2023;13:e076833. doi: 10.1136/bmjopen-2023-076833 Cited by: David Price 5:15 PM 9 December 2023 GMT Citerank: (3) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 703972Lyme disease859FDEF6 URL: DOI: http://dx.doi.org/10.1136/bmjopen-2023-076833
| Excerpt / Summary [BMJ Open, 2 November 2023]
Introduction: There are limited data on the longitudinal impact of Lyme disease. Predictors of recovery have not been fully established using validated data collection instruments. There are sparse data on the immunological response to infection over time.
Methods and analysis: This study is a longitudinal cohort study that will recruit 120 participants with Lyme disease in Ontario and Nova Scotia, Canada, with follow-up for up to 24 months. Data will be collected using the Short-Form 36 physical and mental component summaries, Depression and Anxiety Severity Scale Questionnaire, Fatigue Severity Scale and a battery of neuropsychological tests. Mononuclear cells, gene expression and cytokine profiling from blood samples will be used to assess immunological response. Analyses will include the use of non-linear mixed-effects modelling and proportional hazards models.
Ethics and dissemination: Ethics approval has been obtained from ethics boards at McMaster University (Hamilton Integrated Research Ethics Board) (7564), Queens University (EMD 315-20) and Nova Scotia Health Research Ethics Board (1027173), and the study is enrolling participants. Written informed consent is obtained from all participants. The results will be disseminated by publication in a peer-reviewed journal and presented at a relevant conference. A brief report will be provided to decision-makers and patient groups. |
Link[7] Differential Patterns by Area-Level Social Determinants of Health in Coronavirus Disease 2019 (COVID-19)–Related Mortality and Non–COVID-19 Mortality: A Population-Based Study of 11.8 Million People in Ontario, Canada
Author: Linwei Wang, Andrew Calzavara, Stefan Baral, Janet Smylie, Adrienne K Chan, Beate Sander, Peter C Austin, Jeffrey C Kwong, Sharmistha Mishra Publication date: 28 October 2022 Publication info: Clinical Infectious Diseases, Volume 76, Issue 6, 15 March 2023, Pages 1110–1120, Cited by: David Price 7:06 PM 11 December 2023 GMT Citerank: (5) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 703966Social determinants859FDEF6, 704045Covid-19859FDEF6, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac850
| Excerpt / Summary [Clinical Infectious Diseases, 28 October 2022]
Background: Social determinants of health (SDOH) have been associated with coronavirus disease 2019 (COVID-19) outcomes. We examined patterns in COVID-19–related mortality by SDOH and compared these patterns to those for non–COVID-19 mortality.
Methods: Residents of Ontario, Canada, aged ≥20 years were followed from 1 March 2020 to 2 March 2021. COVID-19–related death was defined as death within 30 days following or 7 days prior to a positive COVID-19 test. Area-level SDOH from the 2016 census included median household income; proportion with diploma or higher educational attainment; proportion essential workers, racially minoritized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. We examined associations between SDOH and COVID-19–related mortality, and non-COVID-19 mortality using cause-specific hazard models.
Results: Of 11 810 255 individuals, we observed 3880 COVID-19–related deaths and 88 107 non–COVID-19 deaths. After accounting for demographics, baseline health, and other area-level SDOH, the following were associated with increased hazards of COVID-19–related death (hazard ratio [95% confidence interval]: lower income (1.30 [1.04–1.62]), lower educational attainment (1.27 [1.07–1.52]), higher proportions essential workers (1.28 [1.05–1.57]), racially minoritized groups (1.42 [1.08–1.87]), apartment buildings (1.25 [1.07–1.46]), and large vs medium household size (1.30 [1.12–1.50]). Areas with higher proportion racially minoritized groups were associated with a lower hazard of non–COVID-19 mortality (0.88 [0.84–0.92]).
Conclusions: Area-level SDOH are associated with COVID-19–related mortality after accounting for demographic and clinical factors. COVID-19 has reversed patterns of lower non–COVID-19 mortality among racially minoritized groups. Pandemic responses should include strategies to address disproportionate risks and inequitable coverage of preventive interventions associated with SDOH. |
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