A population-based, province-wide, record-linkage interrupted time series analysis of impact of the universal seasonal influenza vaccination policy on seasonal influenza vaccine uptake among 5–64-year-olds in the province of Manitoba, Canada
Bibliographic record
Abstract
INTRODUCTION: Universal seasonal influenza vaccination policy (USIVP) was introduced in Manitoba, Canada in 2010. Its impact on seasonal influenza vaccine (SIV) uptake remains underexplored. METHODS: We used population-wide data from Manitoba to assess the impact of the USIVP on SIV uptake. The study covered twenty influenza seasons (2000/01-2019/20). We summarized SIV uptake for influenza seasons before and after the USIVP. Utilizing a single-group interrupted time series analysis and appropriately accounting for autocorrelation, we estimated absolute change and annual trend in SIV uptake percentages among 5-17-, 18-44-, and 45-64-year-olds across strata of certain population socioeconomic and health-related characteristics following the USIVP. RESULTS: Average SIV uptake percentage in all age groups was significantly higher after compared with before the USIVP. Following the USIVP, there was no significant absolute change in SIV uptake percentage among 18-44- and 45-64-year-olds overall; however, a significant decrease was observed among 18-44-year-old males in the higher income quintiles, across healthcare utilization, and in some regions of residence. A significant increase was observed among 5-17-year-olds in the lowest income quintiles, in Northern Manitoba, and among those with less healthcare utilization, and no chronic disease. Overall, there was mostly no significant annual trend in SIV uptake percentage among 18-44-year-olds, and while a significant upward and downward trend was observed among 5-17-year-olds and 45-64-year-olds, respectively, a significant downward trend was observed across all strata of population characteristics within all age groups in Northern Manitoba. CONCLUSIONS: The USIVP in Manitoba was followed by an absolute increase in SIV uptake percentage only in some socioeconomically disadvantaged subpopulations among 5-17-year-olds. While there was mostly an upward annual trend in SIV uptake percentage among 5-17-year-olds, a downward trend was observed among 45-64-year-olds and across all age groups and subpopulations in socioeconomically disadvantaged Northern Manitoba. These findings are novel for Manitoba and require investigation and public health attention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".