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Record W4281739236 · doi:10.1016/s2468-2667(22)00089-5

Immunisation status of UK-bound refugees between January, 2018, and October, 2019: a retrospective, population-based cross-sectional study

2022· article· en· W4281739236 on OpenAlexaff
Anna Deal, S E Hayward, Alison F Crawshaw, Lucy Goldsmith, Charles Hui, Warren Dalal, Fatima Wurie, Mary-Ann Bautista, May Antonnette Lebanan, Sweetmavourneen Agan, Farah Amin Hassan, Kolitha Wickramage, Inês Campos-Matos, Sally Hargreaves

Bibliographic record

VenueThe Lancet Public Health · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Ottawa
FundersMedical Research CouncilNational Institute for Health and Care ResearchAcademy of Medical SciencesWorld Health Organization
KeywordsRefugeeMeaslesMedicineVaccinationMeasles vaccineLogistic regressionPopulationDemographyCross-sectional studyPediatricsFamily medicineEnvironmental healthImmunologyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: WHO's new Immunization Agenda 2030 places a focus on ensuring migrants and other marginalised groups are offered catch-up vaccinations across the life-course. Yet, it is not known to what extent specific groups, such as refugees, are immunised according to host country schedules, and the implications for policy and practice. We aimed to assess the immunisation coverage of UK-bound refugees undergoing International Organization for Migration (IOM) health assessments through UK resettlement schemes, and calculate risk factors for under-immunisation. METHODS: We undertook a retrospective cross-sectional study of all refugees (children <10 years, adolescents aged 10-19 years, and adults >19 years) in the UK resettlement programme who had at least one migration health assessment conducted by IOM between Jan 1, 2018 and Oct 31, 2019, across 18 countries. Individuals' recorded vaccine coverage was calculated and compared with the UK immunisation schedule and the UK Refugee Technical Instructions. We carried out multivariate logistic regression analyses to assess factors associated with varying immunisation coverage. FINDINGS: Our study included 12 526 refugees of 36 nationalities (median age 17 years [IQR 7-33]; 6147 [49·1%] female; 7955 [63·5%] Syrian nationals). 26 118 vaccine doses were administered by the IOM (most commonly measles, mumps, and rubella [8741 doses]). During the study, 6870 refugees departed for the UK, of whom 5556 (80·9%) had at least one recorded dose of measles-containing vaccine and 5798 (84·4%) had at least one dose of polio vaccine, as per the UK Refugee Technical Instructions, and 1315 (19·1%) had at least one recorded dose of diphtheria-containing vaccine or tetanus-containing vaccine. 764 (11·1%) of refugees were fully aligned with the UK schedule for polio, compared with 2338 (34·0%) for measles and 380 (5·5%) for diphtheria and tetanus. Adults were significantly less likely than children to be in line with the UK immunisation schedule for polio (odds ratio 0·0013, 95% CI 0·0001-0·0052) and measles (0·29, 0·25-0·32). INTERPRETATION: On arrival to the UK, refugees' recorded vaccination coverage is suboptimal and varies by age, nationality, country of health assessment, and by disease, with particularly low coverage reported for diphtheria and tetanus, and among adult refugees. These findings have important implications for the delivery of refugee pre-entry health assessments and catch-up vaccination policy and delivery targeting child, adolescent, and adults migrants in the UK, and other refugee-receiving countries. This research highlights the need for improved data sharing and clearer definition of where responsibilities lie between host countries and health assessment providers. FUNDING: UK National Institute for Health Research (NIHR300072) and Medical Research Council (MR/N013638/1).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.404
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2022
Admission routes1
Has abstractyes

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