Social inequalities in COVID-19 deaths by area-level income: patterns over time and the mediating role of vaccination in a population of 11.2 million people in Ontario, Canada

Importance Social inequalities in COVID-19 deaths were evident early in the pandemic. Less is known about how vaccination may have influenced inequalities in COVID-19 deaths. Objectives To examine patterns in COVID-19 deaths by area-level income over time and to examine the impact of vaccination on inequality patterns in COVID-19 deaths. Design, setting, and participants Population-based retrospective cohort study including community-living individuals aged ≥18 years residing in Ontario, Canada, as of March 1, 2020 who were followed through to January 30, 2022 (five pandemic waves). Exposure Area-level income derived from the 2016 Census at the level of dissemination area categorized into quintiles. Vaccination defined as receiving ≥ 1 dose of Johnson-Johnson vaccine or ≥ 2 doses of other vaccines. Main outcome measures COVID-19 death defined as death within 30 days following, or 7 days prior to a positive SARS-CoV-2 PCR test. Cause-specific hazard models were used to examine the relationship between income and COVID-19 deaths in each wave. We used regression-based causal mediation analyses to examine the impact of vaccination in the relationship between income and COVID-19 deaths during waves four and five. Results Of 11,248,572 adults, 7044 (0.063%) experienced a COVID-19 death. After accounting for demographics, baseline health, and area-level social determinants of health, inequalities in COVID-19 deaths by income persisted over time (adjusted hazard ratios (aHR) [95% confidence intervals] comparing lowest-income vs. highest-income quintiles were 1.37[0.98-1.92] for wave one, 1.21[0.99-1.48] for wave two, 1.55[1.22-1.96] for wave three, and 1.57[1.15-2.15] for waves four and five). Of 11,122,816 adults alive by the start of wave four, 7,534,259(67.7%) were vaccinated, with lower odds of vaccination in the lowest-income compared to highest-income quintiles (0.71[0.70-0.71]). This inequality in vaccination accounted for 57.9%[21.9%-94.0%] of inequalities in COVID-19 deaths between individuals in the lowest-income vs. highest-income quintiles. Conclusions Inequalities by income persisted in COVID-19 deaths over time. Efforts are needed to address both vaccination gaps and residual heightened risks associated with lower income to improve health equity in COVID-19 outcomes. Section 1: What is already known on this topic Section 2: What this study adds ### Competing Interest Statement SDB participates in Health Canada related programming including vaccination, and COVID-19-related clinical work; and serves on the National Institutes of Health-funded data and safety monitoring boards (unpaid). All other co-authors declared no conflict of interest. ### Funding Statement This work was supported by the Canadian Institutes of Health Research (grant no. VR5-172683; VS1-175536; GA1-177697) and the Ontario Ministry of Health (MOH) funding for COVID-19 and Health Equity Projects. This study was also supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). This study was also supported by the Ontario Health Data Platform (OHDP), a Province of Ontario initiative to support Ontario ongoing response to COVID-19 and its related impacts. The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by the OHDP, its partners, or the Province of Ontario is intended or should be inferred. Sharmistha Mishra is supported by a Tier 2 Canada Research Chair in Mathematical Modelling and Program Science (CRC-950-232643). Beate Sander is supported by a Tier 2 Canada Research Chair in Economics of Infectious Diseases held by (CRC-950-232429). Janet Smylie is supported by a Tier 1 Canada Research Chair in Advancing Generative Health Services for Indigenous Populations in Canada. Jeffrey Kwong is supported by a Clinician-Scientist Award from the University of Toronto Department of Family and Community Medicine. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Data use is authorized under section 45 of Ontario Personal Health Information Protection Act and does not require review by a Research Ethics Board. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at [www.ices.on.ca/DAS][1] (email: das{at}ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification. [1]: http://www.ices.on.ca/DAS