Clinical Risk, Sociodemographic Factors, and SARS-CoV-2 Infection Over Time in Ontario, Canada
Notice bibliographique
Résumé
Abstract Background Sociodemographic and clinical factors are emerging as important predictors for developing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objective To determine whether public health interventions that culminated in a stay-at-home lockdown instituted during the first wave of the pandemic in March/April 2020 were effective at mitigating the association of any of these factors with the risk of infection. Design Population-based cohort study Setting Ontario, Canada Patients All adults that underwent testing for SARS-CoV-2 between January 1 and June 12, 2020. Measurements The outcome of interest was SARS-CoV-2 infection, determined by reverse transcription polymerase chain reaction testing. Adjusted odds ratios (ORs) were determined for sociodemographic and clinical risk factors before and after the peak of the pandemic to assess for changes in effect sizes. Results Among 578,263 community-dwelling individuals, 20,524 (3.5%) people tested positive. The association between age and SARS-CoV-2 infection risk among tested community-dwelling individuals varied over time (P-interaction <0.0001). Prior to the first-wave peak of the pandemic, the likelihood of SARS-CoV-2 infection increased progressively with age compared with individuals aged 18-45 years (P<0.0001). This association subsequently reversed, with all age groups younger than 85 years at progressively higher risk of infection (P<0.0001) after the peak. Otherwise, risk factors that persisted throughout included male sex, residing in lower income neighborhoods, residing in more racially/ethnically diverse communities, immigration to Canada, and history of hypertension and diabetes. While there was a reduction in infection rates across Ontario after mid-April, there was less impact in regions with higher degrees of racial/ethnic diversity. When considered in an additive risk model, following the initial peak of the pandemic, individuals living in the most racially/ethnically diverse communities with 2, 3, or ≥4 risk factors had ORs of 1.89, 3.07, and 4.73-fold higher for SARS-CoV-2 infection compared to lower risk individuals in their community (all P<0.0001). In contrast, in the least racially/ethnically diverse communities, there was little to no gradient in infection rates across risk strata. Conclusion After public health interventions in March/April 2020, people with multiple risk factors residing in the most racially diverse communities of Ontario continued to have the highest likelihood of SARS-CoV-2 infection while risk was mitigated for people with multiple risk factors residing in less racially/ethnically diverse communities. Further efforts are necessary to reduce the risk of SARS-CoV-2 infection among the highest risk individuals residing in these communities. Primary Funding Source Canadian Institutes of Health Research and the Ted Rogers Centre for Heart Research.
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 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 | 0,002 |
| Études des sciences et des technologies | 0,003 | 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 ».