P-88 Risk of SARS-CoV-2 infection in a large cohort of Ontario workers
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Résumé
<h3>Introduction</h3> Work is a key determinant of COVID-19 outcomes, however occupational surveillance is a critical information gap in many countries, including Canada. Understanding the risk of SARS-CoV-2 by occupation can identify high risk groups that can be targeted for prevention strategies. <h3>Materials and Methods</h3> The cohort includes 1,205,847 former workers compensation (non-COVID-19) claimants (aged 15–65) linked to health databases in Ontario, Canada. Incident cases were defined as either having a confirmed positive polymerase chain reaction (PCR) test in the Ontario Laboratory Information System (OLIS), or an International Classification of Diseases (ICD-10-CA) diagnostic code of U07.1 in hospitalization or emergency department records (February 2020-December 2021). Workers were followed until diagnosis, death, emigration, age 65 or end of follow-up. Sex- and age-adjusted Cox proportional hazards models were used to estimate hazards ratios (HR) and 95% confidence intervals (CI) by occupation, compared to all other cohort members. Analyses were also conducted to examine occupational trends in testing and diagnosis during waves of infection. <h3>Results</h3> Overall, 80,740 COVID-19 cases were diagnosed among workers during follow-up, of those, 80% were diagnosed with a positive PCR test. Associations were identified between COVID-19 diagnosis and employment in nursing (HR=1.44, CI95%=1.40–1.49), air transport operating (HR=1.61, CI95%=1.47–1.77), textile/fur/leather products fabricating, assembling, and repairing (HR=1.38, CI95%=1.25–1.54), apparel and furnishing services (HR=1.38, CI95%=1.19–1.60), and janitor and cleaning services (HR=1.11, CI95%=1.06–1.16). Restricted analyses where health care workers were omitted from the comparison group strengthened some associations for other high-risk workers. Test positivity ranged between 4–16% across major occupation groups. Risks varied over time and with changes in protective measures in workplaces and in broader communities. <h3>Conclusions</h3> Elevated risk of SARS-CoV-2 infection in health care, manufacturing, transportation, and service workers were identified, underscoring the importance of including occupational data in COVID-19 surveillance. Occupational trends in severe outcomes and vaccination are also being explored.
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle