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
Bibliographic record
Abstract
ABSTRACT 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. Summary box Section 1: What is already known on this topic Emerging data suggest social inequalities in COVID-19 deaths might have persisted over time, but existing studies were limited by their ecological design and/or inability to account for potential confounders. Vaccination has contributed to reducing COVID-19 deaths but there were social inequalities in vaccination coverage. The impact of inequalities in vaccination on inequalities in COVID-19 deaths has not yet been well-studied. Section 2: What this study adds Across five pandemic waves (2020-2021) in Ontario, Canada, COVID-19 deaths remained higher in individuals living in lower-income neighbourhoods, even after accounting for individual-level demographics and baseline health, and other area-level social determinants of health. During later waves (following the vaccination roll-out), over half (57.9%) of the inequalities in COVID-19 deaths between individuals living in the lowest and highest income neighbourhoods could be attributed to differential vaccination coverage by income. This means that if vaccine equality was achieved, inequalities in deaths would persist but be reduced. Addressing vaccination gaps, as well as addressing the residual heightened risks of COVID-19 associated with lower income could improve health equity in COVID-19 outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".