The Impact of Mask Mandates on Face Mask Use During the COVID-19 Pandemic: Longitudinal Survey Study
Bibliographic record
Abstract
BACKGROUND: Face mask use has been associated with declines in COVID-19 incidence rates worldwide. A handful of studies have examined the factors associated with face mask use in North America during the COVID-19 pandemic; however, much less is known about the patterns of face mask use and the impact of mask mandates during this time. This information could have important policy implications, now and in the event of future pandemics. OBJECTIVE: To address existing knowledge gaps, we assessed face mask usage patterns among British Columbia COVID-19 Population Mixing Patterns (BC-Mix) survey respondents and evaluated the impact of the provincial mask mandate on these usage patterns. METHODS: Between September 2020 and July 2022, adult British Columbia residents completed the web-based BC-Mix survey, answering questions on the circumstances surrounding face mask use or lack thereof, movement patterns, and COVID-19-related beliefs. Trends in face mask use over time were assessed, and associated factors were evaluated using multivariable logistic regression. A stratified analysis was done to examine effect modification by the provincial mask mandate. RESULTS: Of the 44,301 respondents, 81.9% reported wearing face masks during the 23-month period. In-store and public transit mask mandates supported monthly face mask usage rates of approximately 80%, which was further bolstered up to 92% with the introduction of the provincial mask mandate. Face mask users mostly visited retail locations (51.8%) and travelled alone by car (49.6%), whereas nonusers mostly traveled by car with others (35.2%) to their destinations-most commonly parks (45.7%). Nonusers of face masks were much more likely to be male than female, especially in retail locations and restaurants, bars, and cafés. In a multivariable logistic regression model adjusted for possible confounders, factors associated with face mask use included age, ethnicity, health region, mode of travel, destination, and time period. The odds of face mask use were 3.68 times greater when the provincial mask mandate was in effect than when it was not (adjusted odds ratio [aOR] 3.68, 95% CI 3.33-4.05). The impact of the mask mandate was greatest in restaurants, bars, or cafés (mandate: aOR 7.35, 95% CI 4.23-12.78 vs no mandate: aOR 2.81, 95% CI 1.50-5.26) and in retail locations (mandate: aOR 19.94, 95% CI 14.86-26.77 vs no mandate: aOR 7.71, 95% CI 5.68-10.46). CONCLUSIONS: Study findings provide added insight into the dynamics of face mask use during the COVID-19 pandemic. Mask mandates supported increased and sustained high face mask usage rates during the first 2 years of the pandemic, having the greatest impact in indoor public locations with limited opportunity for physical distancing targeted by these mandates. These findings highlight the utility of mask mandates in supporting high face mask usage rates during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".