Cost-effectiveness of tuberculosis control strategies among immigrants and refugees
Bibliographic record
Abstract
Today, in Western Europe, Canada and the USA, more than half of all new active tuberculosis (TB) cases occur among foreign-born migrants. This article examines the impact of migration from high TB-incidence to low TB-incidence countries, and compares the cost-effectiveness of different TB control strategies. A Medline search was conducted to identify relevant English language publications prior to December 2003. Additional articles were identified from the reference lists from these publications. Despite the high proportion of active cases in low-incidence countries attributable to foreign-born residents, the public health impact is relatively low. Current chest radiograph screening programmes have little impact and are not cost-effective. Screening with sputum culture would improve cost-effectiveness marginally. Treatment of latent infection detected through screening with tuberculin skin testing or chest radiographs may require coercive measures to maximise impact and cost-effectiveness. In contrast, contact tracing, particularly within ethnic communities, appears to be more cost-efficient and less intrusive. In low-incidence countries, screening of migrants at entry has little overall impact and is not a very cost-effective tuberculosis control strategy. More effective alternatives include contact tracing delivered through primary care and increased investment in global tuberculosis control.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".