49 Policy and practice in the delivery of catch-up and life-course vaccination in low- and middle-income countries (LMICs) for adolescents and adults: a systematic review and policy analysis
Bibliographic record
Abstract
Abstract PTH 6: Health Policy and Health Services 1, B307 (FCSH), September 4, 2025, 16:30 - 17:30 Aims Catch-up vaccination is vital in low- and middle-income countries (LMICs) to improve immunisation coverage across the lifespan for migrants, and marginalized populations who may have missed routine vaccines as children. The WHO’s big catch-up initiative aims to recover immunisation rates post-COVID-19, prioritising zero-dose children, however, evidence on catch-up strategies in LMICs, particularly for older groups, remains limited. This study evaluates interventions, policies, and best practices in catch-up vaccination in LMICs. Methods A systematic review of studies from 2000–2024 examined catch-up vaccination policies, strategies, and interventions for adolescents (12–18 years) and adults (19–65 years) across 124 LMICs following PRISMA guidelines. Research was sourced from five academic databases (Embase, MEDLINE, PsycINFO, Global Health, Web of Science) and grey literature (including WHO, Ministry of Health websites) in any language. Primary outcomes assessed policy implementation, intervention strategies, and barriers, while secondary outcomes identified facilitators and best practices for reaching underserved populations. Results From 7,820 studies screened, 310 were included, covering successful catch-up interventions for measles, polio, cholera, HPV, and COVID-19 vaccination in Nigeria, Brazil, Morocco, India, Uganda, and Ethiopia. Strategies used to support catch-up included mobile clinics, school-based vaccination, SMS reminders, and culturally tailored outreach. Barriers include low awareness, financial constraints, and health system limitations. Of the 124 LMICs included, 84 had a national catch-up vaccination policy, but only 16 explicitly included migrants and refugees. Within these the focus was on children. In the MENA region there is a need for more inclusive policies at the national level to remove financial and legal obstacles to vaccination for migrant populations. Conclusions Expanding catch-up vaccination programmes for measles, polio, and other preventable diseases is essential to closing immunity gaps, aligning with key global priorities to ensure equitable access for refugees and migrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".