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Enregistrement W4220809340 · doi:10.1101/2022.03.14.22272368

Area-level social and structural inequalities determine mortality related to COVID-19 diagnosis in Ontario, Canada: a population-based explanatory modeling study of 11.8 million people

2022· preprint· en· W4220809340 sur OpenAlexafffundabout
Linwei Wang, Andrew Calzavara, Stefan Baral, Janet Smylie, Adrienne K. Chan, Beate Sander, Peter C. Austin, Jeffrey Kwong, Sharmistha Mishra

Notice bibliographique

RevuemedRxiv · 2022
Typepreprint
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensHealth Sciences CentreSunnybrook Health Science CentrePublic Health OntarioUniversity Health NetworkUniversity of TorontoSt. Michael's Hospital
Organismes subventionnairesCanadian Institutes of Health ResearchDepartment of Family and Community Medicine, University of TorontoUniversity of TorontoSt. Michael's Hospital Foundation
Mots-clésDemographyHazard ratioPopulationMedicineGerontologyConfidence intervalSociology

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Importance Social determinants of health (SDOH) play an important role in COVID-19 outcomes. More research is needed to quantify this relationship and understand the underlying mechanisms. Objectives To examine differential patterns in COVID-19-related mortality by area-level SDOH accounting for confounders; and to compare these patterns to those for non-COVID-19 mortality, and COVID-19 case fatality (COVID-19-related death among those diagnosed). Design, setting, and participants Population-based retrospective cohort study including all community living individuals aged 20 years or older residing in Ontario, Canada, as of March 1, 2020 who were followed through to March 2, 2021. Exposure SDOH variables derived from the 2016 Canada Census at the dissemination area-level including: median household income; educational attainment; proportion of essential workers, racialized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. Main outcomes and measures COVID-19-related death was defined as death within 30 days following, or 7 days prior to a positive SARS-CoV-2 test. Cause-specific hazard models were employed to examine the associations between SDOH and COVID-19-related mortality, treating non-COVID-19 mortality as a competing risk. Results Of 11,810,255 individuals included, 3,880 (0.03%) died related to COVID-19 and 88,107 (0.75%) died without a positive test. After accounting for demographics, baseline health, and other SDOH, the following SDOH were associated with increased hazard of COVID-19-related death (hazard ratios [95% confidence intervals]) comparing the most to least vulnerable group): lower income (1.30[1.09-1.54]), lower educational attainment (1.27[1.10-1.47]), higher proportion essential workers (1.28[1.10-1.50]), higher proportion racialized groups (1.42[1.16-1.73]), higher proportion apartment buildings (1.25[1.11-1.41]), and larger vs. medium household size (1.30[1.13-1.48]). In comparison, areas with higher proportion racialized groups were associated with a lower hazard of non-COVID-19 mortality (0.88[0.85-0.92]). With the exception of income, SDOH were not independently associated with COVID-19 case fatality. Conclusions and relevance Area-level social and structural inequalities determine COVID-19-related mortality after accounting for individual demographic and clinical factors. COVID-19 has reversed the pattern of lower non-COVID-19 mortality by racialized groups. Pandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin disproportionate acquisition and transmission risks and shape barriers to the reach of, and access to prevention interventions. Key points Question Are area-level social determinants of health factors independently associated with coronavirus disease 2019 (COVID-19)-related mortality after accounting for demographics and clinical factors? Findings In this population-based cohort study including 11.8 million adults residing in Ontario, Canada and 3,880 COVID-19-related death occurred between Mar 1, 2020 and Mar 2, 2021, we found that areas characterized by lower SES (including lower income, lower educational attainment, and higher proportion essential workers), greater ethnic diversity, more apartment buildings, and larger vs. medium household size were associated with increased hazard of COVID-19-related mortality compared to their counterparts, even after accounting for individual-level demographics, baseline health, and other area-level SDOH. Meaning Pandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin inequalities in acquisition and transmission risks, and in the reach of, and access to prevention interventions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,163

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0030,001
Communication savante0,0010,000
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,211
Tête enseignante GPT0,399
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2022
Routes d'admission3
Résumé présentoui

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