Country-specific approaches to latent tuberculosis screening targeting migrants in EU/EEA* countries: A survey of national experts, September 2019 to February 2020
Bibliographic record
Abstract
BackgroundMigrants in low tuberculosis (TB) incidence countries in the European Union (EU)/European Economic Area (EEA) are an at-risk group for latent tuberculosis infection (LTBI) and are increasingly included in LTBI screening programmes.AimTo investigate current approaches and implement LTBI screening in recently arrived migrants in the EU/EEA and Switzerland.MethodsAt least one TB expert working at a national level from the EU/EEA and one TB expert from Switzerland completed an electronic questionnaire. We used descriptive analyses to calculate percentages, and framework analysis to synthesise free-text responses.ResultsExperts from 32 countries were invited to participate (30 countries responded): 15 experts reported an LTBI screening programme targeting migrants in their country; five reported plans to implement one in the near future; and 10 reported having no programme. LTBI screening was predominantly for asylum seekers (n = 12) and refugees (n = 11). Twelve countries use 'country of origin' as the main eligibility criteria. The countries took similar approaches to diagnosis and treatment but different approaches to follow-up. Six experts reported that drop-out rates in migrants were higher compared with non-migrant groups. Most of the experts (n = 22) called for a renewed focus on expanding efforts to screen for LTBI in migrants arriving in low-incidence countries.ConclusionWe found a range of approaches to LTBI screening of migrants in the EU/EEA and Switzerland. Findings suggest a renewed focus is needed to expand and strengthen efforts to meaningfully include migrants in these programmes, in order to meet regional and global elimination targets for TB.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".