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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».