Interventions designed to improve vaccination uptake: Scoping review of systematic reviews and meta-analyses - (version 1)
Bibliographic record
Abstract
ABSTRACT Background Resources to ensure high vaccination uptake differ widely across countries, but the best use of these precious resources is unclear. To better meet immunization programmes’ a pressing need to understand what works, particularly in low-resource settings, the World Health Organization commissioned a scoping view. Methods We conducted a scoping review of interventions designed to increase vaccination uptake, including systematic reviews and meta-analyses of interventional studies. We searched the following electronic databases: MEDLINE, Cochrane Database of Systematic Reviews, EMBASE, Epistemonikos, Google Scholar, LILACs and TRIP database until 01 July 2021 and hand-searched the reference lists of included articles. We included systematic reviews if they summarized studies with quantitative data on the impact on vaccine uptake for any age group. To assess review quality, we used a modified AMSTAR score. To evaluate the quality of the evidence in included reviews, we used the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results The final analysis set included 107 full-text reviews. Publication of reviews increased markedly over time, from seven reviews in 2010 to 38 reviews filtered in 2021. We conducted quality assessments for 72 reviews (132 outcomes). Based on the AMSTAR criteria, 40 included reviews (56%) received a quality rating of good, while the remaining 32 (44%) were of moderate quality. Only 13 reviews summarized data primarily for low- and middle-income countries (LMIC). The interventions were commonly multi-component, educational or reminder interventions; the description of intervention components was suboptimal and heterogeneous across most reviews. Effect estimates were available for 73 outcomes; in 52 (71%) of these, interventions led to statistically significant higher vaccine uptake compared with controls. Conclusions The literature has a large number of relevant systematic reviews on interventions to increase vaccine uptake, with an increased publication rate over time. However, problems with the definitions and the current reporting of vaccine uptake evidence make it difficult to determine what works best in low-resource settings.
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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 | 0.076 | 0.218 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".