Social inequalities in COVID-19 death by area-level income in 11.2 million people in Ontario, Canada: patterns over time and the mediating role of vaccination
Bibliographic record
Abstract
Knowledge of patterns in COVID-19 deaths by area-level income over time and the mediating role of vaccination in inequality patterns remains limited. We used data from a population-based retrospective cohort of 11 248 572 adults in Ontario, Canada. Cause-specific hazard models were used examine the relationship between income (2016 Census at the dissemination area level) and COVID-19 deaths between March 1, 2020 and January 30, 2022, stratified by wave. We used regression-based causal mediation analyses to examine the mediating role of vaccination in the relationship between income and COVID-19 deaths during waves 4 and 5. After accounting for demographics, baseline health, and other social determinants of health, inequalities in COVID-19 deaths by income persisted over time (HR [95% CI] comparing lowest income vs highest income quintiles were 1.37 [0.98-1.92] for wave 1, 1.21 [0.99-1.48] for wave 2, 1.55 [1.22-1.96] for wave 3, and 1.57 [1.15-2.15] for waves 4 and 5). By the start of wave 4, 7 534 259 (67.7%) of those alive were vaccinated, with lower odds of vaccination in the lowest income vs highest income quintiles (0.71 [0.70-0.71]). This inequality in vaccination accounted for 56.9% [22.5%-91.3%] of inequalities in COVID-19 deaths between individuals in the lowest income vs highest income quintiles. Efforts are needed to address vaccination gaps and residual heightened risks associated with lower income to 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".