The effectiveness and cost-effectiveness of screening for active tuberculosis among migrants in the EU/EEA: a systematic review
Bibliographic record
Abstract
Background : The foreign-born population make up an increasing and large proportion of tuberculosis (TB) cases in European Union/European Economic Area (EU/EEA) low-incidence countries and challenge TB elimination efforts. Methods : We conducted a systematic review to determine effectiveness (yield and performance of chest radiography (CXR) to detect active TB, treatment outcomes and acceptance of screening) and a second systematic review on cost-effectiveness of screening for active TB among migrants living in the EU/EEA. Results : We identified six systematic reviews, one report and three individual studies that addressed our aims. CXR was highly sensitive (98%) but only moderately specific (75%). The yield of detecting active TB with CXR screening among migrants was 350 per 100,000 population overall but ranged widely by host country (110–2,340), migrant type (170–1,192), TB incidence in source country (19–336) and screening setting (220–1,720). The CXR yield was lower (19.6 vs 336/100,000) and the numbers needed to screen were higher (5,076 vs 298) among migrants from source countries with lower TB incidence (≤ 50 compared with ≥ 350/100,000). Cost-effectiveness was highest among migrants originating from high (> 120/100,000) TB incidence countries. The foreign-born had similar or better TB treatment outcomes than those born in the EU/EEA. Acceptance of CXR screening was high (85%) among migrants. Discussion: Screening programmes for active TB are most efficient when targeting migrants from higher TB incidence countries. The limited number of studies identified and the heterogeneous evidence highlight the need for further data to inform screening programmes for migrants in the EU/EEA.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".