Impact of COVID-19 Vaccine Mandates on COVID-19 Incidence and Deaths in Saskatchewan, Canada: Findings From the First Year Following Vaccine Rollout
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic significantly impacted Saskatchewan, resulting in high per capita case counts and COVID-19-related deaths. While vaccination mandates have been a key strategy to control the pandemic, their impact in Saskatchewan remains poorly documented. This study assessed the effect of COVID-19 vaccine mandates on the incidence of COVID-19 cases and deaths in Saskatchewan during the first year following vaccine rollout. METHODS: A single-group interrupted time series analysis with multiple intervention points was conducted using aggregated daily COVID-19 incidence and mortality rates as outcome variables. The models accounted for confounding effects of daily total vaccine doses administered and public health countermeasures, including the stringency index and economic support index, from April 1, 2020 to January 20, 2022. Average daily COVID-19 incidence and mortality rates were estimated for the pre-vaccine rollout period (April 1 to December 14, 2020), and the post-rollout period (December 15, 2020 to January 20, 2022). In addition, nine supplementary initiatives were introduced during the implementation phase. All estimated effects reflected cumulative changes in trend relative to the pre-vaccination period. RESULTS: Cumulatively, COVID-19 incidence increased faster than the pre-vaccination trend, likely driven by successive variant surges from wild-type to Omicron, while COVID-19-related deaths remained stable across the same period. The implementation of vaccine rollout, prioritization of vaccines for high-risk populations, and proof-of-vaccination policy were effective in reducing daily COVID-19 incidence and deaths in Saskatchewan. Economic support and an increased number of daily vaccine doses administered were also associated with an improved provincial COVID-19 response. Conversely, surges in COVID-19 incidence and deaths occurred following the introduction of the centralized virtual booking system and booster doses. These surges may reflect accessibility challenges, increased testing, emergence of immune-escape variants, relaxation of public health measures before achieving herd immunity, and waning immunity over time. CONCLUSIONS: Economic support, policy measures, and vaccination efforts played important roles in managing public health crises, hence the need for an integrated approach to managing public health crises. However, temporary surges following certain interventions underscore the need for accessible, adaptable strategies that account for variant emergence, immunity waning and public adherence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".