Determinants of Seasonal Influenza Vaccine Uptake Among the Elderly in the United States: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Despite the availability of a universal influenza vaccination program in the United States and Canada, seasonal influenza vaccine (SIV) uptake among the elderly remains suboptimal. Understanding the factors that determine SIV uptake in this important population subgroup is essential for designing effective interventions to improve seasonal influenza vaccination among the elderly. We evaluated the determinants of SIV uptake in the elderly in the United States and Canada. Methods: We systematically searched relevant bibliographic databases and websites from 2000 to 2017 for population-based clinical trials or observational studies conducted in community-based elderly individuals in the United States or Canada, irrespective of health status. Two reviewers independently screened the identified citations for eligibility using a two-stage sifting approach to review the title/abstract and full-text article. We gathered data on determinants of uptake (any vaccine receipt) and adherence (receipt of vaccine in more than one season) to seasonal influenza vaccination. Where possible, we pooled the data using inverse variance methods to minimize the variance of the weighted average. Results: Five cross-sectional studies on SIV uptake (none on adherence) from the United States met our eligibility criteria. Being older (pooled odds ratio [POR] = 1.44, 95% Confidence Interval [CI] = 1.11, 1.86); White (POR = 1.33, 95% CI = [1.10, 1.64]); and having higher income (POR = 1.06, 95% CI = [1.04, 1.09]); and health insurance (POR = 1.40, 95% CI = [1.25, 1.55]) were associated with increased SIV uptake. Conclusion: Older, ethnically White, higher income elderly individuals with access to health insurance coverage and a regular health care provider have higher SIV uptake in the United States. There was limited evidence for other socioeconomic and health-related determinants. Further studies are needed to provide an evidence base for planning more effective influenza vaccination programs in the United States.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".