Influenza vaccine coverage and factors associated with non-vaccination among caregiving and care-receiving adults in the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
BACKGROUND: Influenza vaccination is recommended for those at increased risk of influenza complications and their household contacts to help reduce influenza exposure. Adults who require care often experience health issues that could increase the risk of severe influenza and have close contact with caregivers. Assessing influenza vaccination prevalence in caregivers and care recipients can provide important information about uptake. OBJECTIVES: We aimed to (1) estimate influenza non-vaccination prevalence and (2) assess factors associated with non-vaccination among caregivers aged ≥ 45 years and among care recipients aged ≥ 65 years. METHODS: We conducted an analysis of cross-sectional data from the Canadian Longitudinal Study on Aging collected 2015-2018. We estimated non-vaccination prevalence and reported adjusted odds ratios with 95% confidence intervals from logistic regression models to identify factors associated with non-vaccination among caregivers and care recipients. RESULTS: Of the 23,500 CLSA participants who reported providing care, 41.4% (95% CI: 40.8%, 42.0%) reported not receiving influenza vaccine in the previous 12 months. Among the 5,559 participants who reported receiving professional or non-professional care, 24.8% (95% CI: 23.7%, 26.0%) reported not receiving influenza vaccine during the same period. For both groups, the odds of non-vaccination were higher for those who had not visited a family doctor in the past year, were daily smokers, and those who identified as non-white. DISCUSSION: Identifying groups at high risk of severe influenza and their close contacts can inform public health efforts to reduce the risk of influenza. Our results suggest sub-optimal influenza vaccination uptake among caregivers and care recipients. Efforts are needed to increase influenza vaccination and highlight the direct and indirect benefits for caregiver-care recipient pairs. CONCLUSION: The proportions of both caregivers and care recipients who had not been vaccinated for influenza was high, despite the benefits of vaccination. Influenza vaccination campaigns could target undervaccinated, high-risk groups to increase coverage.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".