Interventions to Improve Influenza and Pneumococcal Vaccination Rates Among Community-Dwelling Adults: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Influenza and pneumococcal vaccination rates remain below national targets. We systematically reviewed the effectiveness of quality improvement interventions for increasing the rates of influenza and pneumococcal vaccinations among community-dwelling adults. METHODS: We included randomized and nonrandomized studies with a concurrent control group. We estimated pooled odds ratios using random effects models, and used the Downs and Black tool to assess the quality of included studies. RESULTS: Most studies involved elderly primary care patients. Interventions were associated with improvements in the rates of any vaccination (111 comparisons in 77 studies, pooled odds ratio [OR] = 1.61, 95% CI, 1.49-1.75), and influenza (93 comparisons, 65 studies, OR = 1.46, 95% CI, 1.35-1.57) and pneumococcal (58 comparisons, 35 studies, OR = 2.01, 95% CI, 1.72-2.3) vaccinations. Interventions that appeared effective were patient financial incentives (influenza only), audit and feedback (influenza only), clinician reminders, clinician financial incentives (influenza only), team change, patient outreach, delivery site changes (influenza only), clinician education (pneumococcus only), and case management (pneumococcus only). Patient outreach was more effective if personal contact was involved. Team changes were more effective where nurses administered influenza vaccinations independently. Heterogeneity in some pooled odds ratios was high, however, and funnel plots showed signs of potential publication bias. Study quality varied but was not associated with outcomes. CONCLUSIONS: Quality improvement interventions, especially those that assign vaccination responsibilities to nonphysician personnel or that activate patients through personal contact, can modestly improve vaccination rates in community-dwelling adults. To meet national policy targets, more-potent interventions should be developed and evaluated.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".