The Immunization Agenda 2030 Strategy to reach zero-dose children in low-income and middle-income countries: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Intensified efforts are needed to achieve the Immunization Agenda 2030 (IA2030) target to halve the number of children not reached by routine vaccination ('zero-dose' (ZD) children) by 2030. This scoping review maps and synthesises the scientific literature on ZD children in low-income and middle-income countries since the IA2030 launch. METHODS: Our protocol, developed per the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines, was registered on the Open Science Framework prior to execution, and our search strategies were designed by a research librarian. We searched MEDLINE (Ovid), CINAHL Complete (EBSCOhost), EBM Reviews (Ovid), EMBASE (Ovid), LILACS and Google Scholar for peer-reviewed studies with quantitative evidence on ZD children published between January 2020 and January 2024. Using Covidence, we screened citations in a two-stage process by two independent reviewers and conducted data charting using a pretested form. Disagreements were resolved by consensus or consultation with a senior reviewer. RESULTS: Of the 82 articles included, 73 provided evidence on prevalence, and/or distribution, 24 on barriers to vaccination services and 44 on deprivations faced by ZD children, their households and communities, with the risk of deprivations varying importantly across contexts. The barriers to vaccination most reported related to the intent to vaccinate (n=20), followed by community access (n=10). Deprivations mainly concerned access to health services, such as low utilisation of maternal health services (n=22), and other key development indicators, such as low socioeconomic status (n=29) and poor maternal education/literacy (n=28). We found no studies (n=0) on interventions focused on reaching ZD children with routine immunisation services. CONCLUSION: Our findings highlight the imperative to generate evidence on interventions delivering routine immunisation to ZD children and missed communities. Results also underscore the need for transformative approaches that address multiple deprivations using carefully selected, integrated services, tailored to context-specific needs.
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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 | 0.059 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".