389 Defining drivers of human papillomavirus (HPV) vaccine uptake in migrant populations globally and strategies and interventions to improve coverage
Bibliographic record
Abstract
Abstract OP 10: Determinants of Health Disease and Interventions 2, B207 (FCSH), September 3, 2025, 15:45 - 16:45 Aims WHO’s Cervical Cancer Elimination Initiative has set a target of 90% human papillomavirus (HPV) vaccination coverage among girls by age 15 by 2030. However, progress has been slow, with only 27% global coverage in 2023. Migrants are considered an under-immunised group globally for many vaccine-preventable diseases, with data showing that they may experience a high burden of HPV infection and widespread HPV under-immunisation. We aimed to better understand the factors influencing the ability of these communities to get vaccinated for HPV. Methods We searched seven databases (e.g., Medline, Global Health) and websites (WHO, IOM, Google Scholar) for literature on drivers of HPV vaccination uptake among migrants globally, to December 2024 in any language. We conducted a hybrid thematic analysis using the WHO BeSD model (PROSPERO protocol: CRD42023401694). Results We identified 1,806 database records and included 117 studies involving 5,638,836 migrants across 16 countries. Factors negatively influencing vaccine uptake included concerns about vaccine safety, cultural beliefs, uncertainty about HPV vaccines/infection, low knowledge of HPV/HPV vaccine, inter-generational and family dynamics, exposure to negative information, and lack of recommendations from healthcare providers. Practical barriers included limited information on services, language issues combined with a lack of skilled interpreters, logistical challenges, and the high cost of the vaccine. Findings highlighted that free-of-charge and school-based schemes were effective in increasing uptake. Deploying trusted mediators (e.g., peer school health promoters, religious champions, community health workers) and implementing practical solutions to address missed opportunities (e.g., bundling HPV vaccination with other services) and for mobile migrants (e.g., eHealth) were also emphasised. Conclusions Migrants worldwide face complex barriers to HPV vaccination, resulting in missed opportunities for protection. In many low- and middle-income countries (LMICs), the vaccine is either unavailable or has to be paid for. Making progress toward cervical cancer elimination, requires addressing these barriers through multi-pronged strategies.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".