Interventions to Improve Immunization Coverage Among Children and Adolescents: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Vaccinations are recognized as a feasible, cost-effective public health intervention for reducing the burden and associated mortality of many infectious diseases. The purpose of this study was to evaluate the effectiveness of potential interventions to improve the uptake of vaccines among children and adolescents. METHODS: We performed a literature search until December 2020. Eligible studies were identified using Cochrane Central Register of Controlled Trials, MEDLINE, PubMed, and other sources. We included studies conducted on children and adolescents aged 5 to 19 years. Studies comprised of hospitalized children and those with comorbid conditions were excluded. Two authors independently performed the meta-analysis. RESULTS: Findings from 120 studies (123 articles), of which 95 were meta-analyzed, reveal that vaccination education may increase overall vaccination coverage by 19% (risk ratio [RR], 1.19; 95% confidence interval [CI], 1.12-1.26), reminders by 15% (RR, 1.15; 95% CI, 1.11-1.18), interventions for providers by 13% (RR, 1.13; 95% CI, 1.07-1.19), financial incentives by 67% (RR, 1.67; 95% CI, 1.40-1.99), and multilevel interventions by 25% (RR, 1.25; 95% CI, 1.10-1.41). The impact of school-based clinics and policy and legislation on overall vaccination coverage is still uncertain, and no impact of a multicomponent intervention on overall vaccination coverage was found. CONCLUSIONS: Educational interventions, reminders, provider-directed interventions, financial incentives, and multilevel interventions may improve vaccination coverage among school-aged children and adolescents.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.067 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".