Characteristics and determinants of seasonal influenza vaccination in Manitoba, Canada: A population-wide record-linkage study
Bibliographic record
Abstract
Background: Seasonal influenza vaccine (SIV) uptake (receipt of vaccine) in Manitoba, Canada is consistently low notwithstanding vaccine availability and free-of-charge vaccination. Despite, there is a lack of published evidence on the determinants of uptake of the vaccine. We sought to assess the association between SIV uptake and certain population and primary care physician (PCP) characteristics in Manitoba. Methods: We conducted a longitudinal study utilizing Manitoba administrative health databases. We summarized SIV uptake from 2000/01-2019/20 influenza seasons across subpopulations defined by socioeconomic, health-related and PCP characteristics. Utilizing multivariable generalized estimating equation logistic regression models, we assessed the association between SIV uptake and the socioeconomic, health-related and PCP characteristics, stratified by age group (<5-, 5-17-, 18-44-, 45-64-, ≥65-year-olds) and sex. Results are adjusted odds ratios with associated 95 % confidence intervals. Results: SIV uptake percentage increased over time with 4.4 %, 13.1 %, 17.5 % and 21.7 % of < 5-year-olds, 2 %, 4.9 %, 9.7 % and 13.1 % of 5-17-year-olds, 5.4 %, 8.8 %, 10.7 % and 13.5 % of 18-44-year-olds, 16.8 %, 21.3 %, 23.6 % and 24.6 % of 45-64-year-olds receiving the SIV in 2000-2004, 2005-2009, 2010-2014 and 2015-2019, respectively. There was a decline among ≥ 65-year-olds from 58.5 % to 53.5 %. We observed a similar pattern across subpopulations. There were significantly increased odds of SIV uptake among females within the age groups ≥ 18 years, in higher income quintiles, mostly with increased contact with a PCP/hospitalization within age groups ≥ 18 years, among those who had older or female PCPs (the opposite observation among ≥ 65-year-olds) and whose PCP administered at least one SIV in prior influenza season. These observations were largely consistent irrespective of sex. Conclusion: SIV uptake in Manitoba appears to increase with age, and many socioeconomic, health-related and PCP characteristics appear to be associated with it. These findings may inform targeted vaccination programs to optimize influenza vaccination in Manitoba and similar Canadian jurisdictions.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".