Impact of Community Masking on SARS-CoV-2 Transmission in Ontario after Adjustment for Differential Testing by Age and Sex
Notice bibliographique
Résumé
Abstract Background Use of masks and respirators for prevention of respiratory infectious disease transmission is not new, but has proven controversial, and even politically polarizing during the SARS-CoV-2 pandemic. In the Canadian province of Ontario, mask mandates were introduced by the 34 regional health authorities in an irregular fashion from June to September 2020, creating a quasi-experiment that can be used to evaluate impact of community mask mandates. Ontario SARS-CoV-2 case counts were strongly biased by testing focussed on long-term care facilities and healthcare workers. We developed a simple regression-based test-adjustment method that allowed us to adjust cases for undertesting by age and gender. We used this test- adjusted time series to evaluate mask mandate effectiveness. Methods We evaluated the effect of masking using count-based regression models that allowed adjustment for age, sex, public health region and time trends with either reported (unadjusted) cases, or testing-adjusted case counts, as dependent variables. Mask mandates were assumed to take effect in the week after their introduction. Model based estimates of effectiveness were used to estimate the fraction of SARS- CoV-2 cases, severe outcomes, and costs, averted by mask mandates. Results Models that used unadjusted cases as dependent variable identified protective effects of masking (effectiveness 15-42%), though effectiveness was variably statistically significant, depending on model choice. Mask effectiveness in models predicting test-adjusted case counts was substantially higher, ranging from 49% (44- 53%) to 73% (48-86%) depending on model choice. Effectiveness was greater in women than men (P = 0.016), and in urban health units as compared to rural units (P < 0.001). The prevented fraction associated with mask mandates was 46% (41-51%), averting approximately 290,000 clinical cases, averting 3008 deaths and loss of 29,038 QALY. Costs averted represented $CDN 610 million in economic wealth. Conclusions Lack of adjustment for SARS-CoV-2 undertesting in younger individuals and males generated biased estimates of infection risk and obscures the impact of public health preventive measures. After adjustment for under-testing, the effectiveness of mask mandates emerges as substantial, and robust regardless of model choice. Mask mandates saved substantial numbers of lives, and prevented economic costs, during the SARS-CoV-2 pandemic in Ontario, Canada.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».