Perspectives on tuberculosis in migrants, refugees, and displaced populations in Europe
Bibliographic record
Abstract
• Finding and treating all tuberculosis (TB) types in migrants and refugees in Europe is challenging. • TB prevalence is higher in migrants from endemic areas and with social risk factors. • Community-based and integrated multi-disease approaches have enhanced TB programs. • Policy variation, limited resources, and barriers to services hinder migrant TB care. • Urgent need for more investment into TB services in Europe for refugees and migrants. Finding and treating all forms of tuberculosis (TB) (latent, drug-susceptible, drug-resistant, multidrug-resistant, and extensively drug-resistant tuberculosis) among migrants, displaced populations, and refugees are important challenges facing TB control programs in Europe. Many of these populations live in poor conditions, with limited access to healthcare and TB services. Ever-increasing armed conflicts in Europe and other parts of the world continue to exacerbate rates of migration to and within Europe, with considerable implications for health services. TB in Europe is more prevalent in migrants from high TB-endemic areas, as well as those with social risk factors, including poverty and poor housing or homelessness. We provide our perspectives on recent data on TB in Europe from the World Health Organization, the European Centre for Disease Prevention and Control, the United Kingdom Health Security Agency, and other 2023-2024 reports. Despite advancements in TB screening and prevention strategies, and treatment regimens including community-based and integrated multi-disease approaches, significant challenges remain. These include variations in national policies, resource limitations, and barriers to accessing healthcare. To help address these challenges, there is a need for clearer guidance through national policies, enhanced surveillance, and proactive community engagement There is also an urgent need for more investment into TB health services in Europe for refugees, migrants, and other displaced populations.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".