Effectiveness of Catch-Up Vaccination Interventions Versus Standard or Usual Care Procedures in Increasing Adherence to Recommended Vaccinations Among Different Age Groups: Systematic Review and Meta-Analysis of Randomized Controlled Trials and Before-After Studies
Bibliographic record
Abstract
BACKGROUND: To address the global challenge of vaccine hesitancy, the Strategic Advisory Group of Experts on Immunization strongly promotes vaccination reminder and recall interventions. Coupled with the new opportunities presented by scientific advancements, these measures are crucial for successfully immunizing target population groups. OBJECTIVE: This systematic review and meta-analysis aims to assess the effectiveness of various interventions in increasing vaccination coverage compared with standard or usual care. The review will cover all vaccinations recommended for different age groups. METHODS: In February 2022, 2 databases were consulted, retrieving 1850 studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 79 manuscripts were included after the assessment phase. These comprised 46 trials/randomized controlled trials (RCTs) and 33 before-after studies. A meta-analysis using a random-effects model was performed with STATA software (version 14.1.2). The selected outcome was the risk ratio (RR) of vaccination coverage improvement effectiveness. Additionally, meta-regression analyses were conducted for the included manuscripts. RESULTS: The analyses showed an overall efficacy of RR 1.22 (95% CI 1.19-1.26) for RCTs and RR 1.70 (95% CI 1.54-1.87) for before-after studies when considering all interventions cumulatively. Subgroup analyses identified multicomponent interventions (RR 1.58, 95% CI 1.36-1.85) and recall clinical interventions (RR 1.24, 95% CI 1.17-1.32) as the most effective in increasing vaccination coverage for RCTs. By contrast, educational interventions (RR 2.13, 95% CI 1.60-2.83) and multicomponent interventions (RR 1.61, 95% CI 1.43-1.82) achieved the highest increases for before-after studies. Meta-regression analyses indicated that the middle-aged adult population was associated with a higher increase in vaccination coverage (RCT: coefficient 0.54, 95% CI 0.12-0.95; before-after: coefficient 1.27, 95% CI 0.70-1.84). CONCLUSIONS: Community, family, and health care-based multidimensional interventions, as well as education-based catch-up strategies, effectively improve vaccination coverage. Therefore, their systematic implementation is highly relevant for targeting undervaccinated population groups. This approach aligns with national vaccination schedules and aims to eliminate or eradicate vaccine-preventable diseases.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.032 | 0.065 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".