Influenza vaccination uptake among Canadian adults before and during the COVID-19 pandemic: An analysis of the Canadian Longitudinal study on Aging (CLSA)
Bibliographic record
Abstract
INTRODUCTION: Understanding how influenza vaccine uptake changed during the 2020/2021 influenza season compared to previous pre-pandemic seasons is a key priority, as is identifying the relationship between prior influenza vaccination and COVID-19 vaccine willingness. METHODS: We analyzed data from a large, nationally representative cohort of Canadian residents aged 50 and older to assess influenza vaccination status three times between 2015 and 2020. We investigated: 1) changes in self-reported influenza vaccine uptake, 2) predictors of influenza vaccine uptake in 2020/2021, and 3) the association between influenza vaccination history and self-reported COVID-19 vaccine willingness using logistic regression models. RESULTS: Among 23,385 participants analyzed for aims 1-2, influenza vaccination increased over time: 14,114 (60.4%) in 2015-2018, 15,692 (67.1%) in 2019/2020, and 19,186 (82.0%; combining those already vaccinated and those planning to get a vaccine) in 2020/2021. After controlling for socio-demographics, history of influenza vaccination was most strongly associated with influenza vaccination in 2020/2021 (adjusted odds ratio [aOR] 147.9 [95% CI: 120.9-180.9]); this association remained after accounting for multiple health and pandemic-related factors (aOR 140.3 [95% CI: 114.5-171.8]). To a lesser degree, those more concerned about COVID-19 were also more likely to report influenza vaccination in fall 2020, whereas those reporting a very negative impact of the pandemic were less likely to get vaccinated. Among 23,819 participants with information on COVID-19 vaccine willingness during the last quarter of 2020 (aim 3), prior influenza vaccination was most strongly associated with willingness to get a COVID-19 vaccine (aOR 15.1 [95% CI: 13.5-16.8] for those who had received influenza vaccine at all previous timepoints versus none). CONCLUSIONS: Our analysis highlights the importance of previous vaccination in driving vaccination uptake and willingness. Efforts to increase vaccination coverage for influenza and COVID-19 should target individuals who do not routinely engage with immunization services regardless of demographic factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".