Driving uptake of missed routine vaccines in adolescent and adult migrants: a prospective observational mixed-methods pilot study of catch-up vaccination in UK general practice
Bibliographic record
Abstract
Abstract Background Migrants in Europe may be vulnerable to vaccine preventable diseases (VPDs) because of missed routine vaccines in childhood in their country of origin and marginalisation from health and vaccine systems. To align with European schedules, migrants should be offered catch-up vaccinations, considering MMR, Td/IPV, and age-appropriate MenACWY and HPV. However, awareness and implementation of catch-up guidelines by primary care staff in the UK is considered to be poor, and there is a lack of research on effective approaches to strengthen the primary-care pathway. Methods We conducted a prospective observational mixed-methods pilot study ‘Vacc on Track’ (May 2021-September 2022) to better understand and define new care pathways to increase catch-up vaccination for adolescent and adult migrants presenting to primary care (≥16 years, born outside Western Europe, North America, Australia, or New Zealand) in two London boroughs. We designed a standardised data collection tool to assess rates of under-vaccination in migrant populations and previous VPDs, which then prompted a referral to practice nurses to deliver catch-up vaccination for those with uncertain or incomplete immunisation status, following UK guidelines. We explored views of practice staff on delivering catch-up vaccination to migrant populations through focus group discussions and engaged migrants in in-depth interviews around approaches to catch-up vaccination. Data were analysed in STATA12 and Microsoft Excel. Results We recruited 57 migrant participants (mean age 41 [SD 7.2] years; 62% female; mean 11.3 [SD 9.1] years in UK) from 18 countries, with minimum 6 months’ follow-up. We did 3 focus groups with 30 practice staff and 39 qualitative in-depth interviews with migrants. Nearly all migrant participants required catch-up vaccination for MMR (86%) and Td/IPV (88%) and most reported not having been previously engaged in UK primary care around catch-up vaccination. 12 (55%) of 22 participants in Site 1 reported a past VPD, including measles and rubella. 53 (93%) of participants were referred for catch-up vaccination. However, although 43 (81%) had at least one dose (at follow-up) of a required vaccine, only 6 (12%) referred for Td/IPV and 33 (64%) of those referred for MMR had completed their required course and vaccination pathway at follow-up, suggesting there were a range of personal and environmental obstacles to migrants accessing vaccinations and all multiple doses of vaccines that need to be better considered. Staff identified seven barriers to delivering catch-up vaccines to migrants, including limited time for appointments and follow-up, language and literacy barriers when taking histories and to encourage vaccination, lack of staff knowledge of current guidelines, inadequate engagement routes, and the absence of primary care targets or incentives. Conclusions Our findings suggest adolescent and adult migrants are an under-vaccinated group and would benefit from being offered catch-up vaccination on arrival to the UK. Primary care is an important setting to deliver catch-up vaccination, but effective pathways are currently lacking, and improving vaccine coverage for key routine vaccines across a broader range of migrant groups will require designated staff champions, training, awareness-raising and financial incentives. Novel ways to deliver vaccinations in community settings should be explored, along with co-designing community-based interventions to raise awareness among these populations of the benefits of life-course immunisation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".