The effect of vaccine mandate announcements on vaccine uptake in Canada: An interrupted time series analysis
Bibliographic record
Abstract
INTRODUCTION: In 2021, the ten provinces in Canada enacted COVID-19 vaccine mandates that restricted access to non-essential businesses and services to those that could provide proof of full vaccination to decrease the risk of transmission and provide an incentive for vaccination. This analysis aims to examine the effects of vaccine mandate announcements on vaccine uptake over time by age group and province. METHODS: Aggregated data from the Canadian COVID-19 Vaccination Coverage Surveillance System (CCVCSS) were used to measure vaccine uptake (defined as the weekly proportion of individuals who received at least one dose) among those 12 years and older following the announcement of vaccination requirements. We performed an interrupted time series analysis using a quasi-binomial autoregressive model adjusted for the weekly number of new COVID-19 cases, hospitalizations, and deaths to model the effect of mandate announcements on vaccine uptake. Additionally, counterfactuals were produced for each province and age group to estimate vaccine uptake without mandate implementation. RESULTS: The times series models demonstrated significant increases in vaccine uptake following mandate announcement in BC, AB, SK, MB, NS, and NL. No trends in the effect of mandate announcements were observed by age group. In AB and SK, counterfactual analysis showed that announcement were followed by 8 % and 7 % (310,890 and 71,711 people, respectively) increases in vaccination coverage over the following 10 weeks. In MB, NS, and NL, there was at least a 5 % (63,936, 44,054, and 29,814 people, respectively) increase in coverage. Lastly, BC announcements were followed by a 4 % (203,300 people) increase in coverage. CONCLUSION: Vaccine mandate announcements could have increased vaccine uptake. However, it is difficult to interpret this effect within the larger epidemiological context. Effectiveness of the mandates can be affected by pre-existing levels of uptake, hesitancy, timing of announcements and local COVID-19 activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".