Characterizing influenza vaccine coverage and factors associated with missed vaccination among adults from 2018 to 2021: an analysis of the Canadian Longitudinal Study on Aging (CLSA) follow-up 2
Bibliographic record
Abstract
BACKGROUND: Influenza vaccination remains one of the best tools available to prevent severe disease in individuals at high risk of influenza complications. Yet, influenza vaccination among older adults and those at high risk of severe outcomes has remained low in Canada and other countries even when the vaccine is routinely recommended. Assessing the prevalence of influenza vaccination coverage over time and factors associated with missed vaccination can provide evidence to inform efforts to improve coverage. Among adults aged ≥ 65 years and adults aged 49-64 years with one or more chronic medical condition (CMC), we aimed to (1) estimate the prevalence of missed influenza vaccination and (2) evaluate factors associated with missed vaccination using recent data from a large national survey of Canadian adults. METHODS: We analyzed data collected by the Canadian Longitudinal Study on Aging during follow-up 2 from 2018 to 2021. Participants were asked to self-report whether they received an influenza vaccine in the year prior to completing the survey. We estimated the prevalence of missed vaccination overall and by participant characteristics. We assessed factors associated with missed vaccination using logistic regression and report adjusted odds ratios among adults aged ≥ 65 years and adults aged 49-64 years with ≥ 1 CMC. RESULTS: Among the 18,894 participants surveyed, 27.0% (95% CI: 26.1, 27.8%) of those aged 65 years and older and 45.2% (95% CI: 43.7, 46.8%) of those aged 49-64 years with ≥ 1 CMC reported not receiving influenza vaccination within the prior year. For both groups, reporting receiving influenza vaccination in the previous CLSA wave of data collection (2015-2017) and contact with a family doctor within the prior year were strongly associated with lower odds of missed influenza vaccination. CONCLUSIONS: Our analysis suggests that a large proportion of eligible Canadian adults at higher risk of severe complications due to influenza are not receiving a seasonal influenza vaccine, despite recommendations. These estimates and this detailed analysis provide important insights into trends in influenza vaccination coverage among older adults and can serve as a baseline assessment for tracking changes in influenza vaccination coverage over time and in the years following the SARS-CoV-2 pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".