Strengthening screening for infectious diseases and vaccination among migrants in Europe: What is needed to close the implementation gaps?
Bibliographic record
Abstract
Migration to the European Union (EU)/European Economic Area (EEA) affects the epidemiology of infectious diseases, including tuberculosis (TB), HIV, hepatitis B/C, and parasitic diseases. Some sub-populations of migrants are also considered to be an under-immunised group and thus at risk of vaccine-preventable diseases. Providing high-risk migrants access to timely and efficacious screening and vaccination, and understanding how best to implement more integrated screening and vaccination programmes into European health systems ensuring linkage to care and treatment, is key to improving the health of migrants and their communities, alongside meeting national and regional targets for infection surveillance, control, and elimination. The European Centre for Disease Prevention and Control (ECDC) has responded to calls to action to improve migrant health and strengthen universal health coverage by developing evidence-based guidance for policy makers, public health experts, and front-line healthcare professionals on how to approach screening and vaccination in newly arrived migrants within the EU/EEA. In this Commentary, we provide a perspective towards developing efficacious screening and vaccination of newly arrived migrants, with a focus on defining implementation challenges and evidence gaps in high-migrant receiving EU/EEA countries. There is a need now to leverage the increasing momentum around migrant health to both strengthen the evidence-base and to advocate for universal access to health care for all migrants in the EU/EEA, including undocumented migrants. This should include voluntary, confidential, and non-stigmatising screening and vaccination that should be free of charge and facilitate linkage to appropriate care and treatment.
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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 | 0.057 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.021 | 0.016 |
| 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".