Tuberculosis screening of long-term visitors from low incidence to high incidence countries : a cost-effectiveness study
Bibliographic record
Abstract
This study compared the cost-effectiveness of four tuberculosis (TB) control strategies for detecting and treating latent tuberculosis infection (LTBI) among long-term travellers to countries where TB incidence is elevated. The decision analysis considered hypothetical cohorts of travellers from Canada and the United States (U.S.) to Mexico, Haiti, and the Dominican Republic. Strategy I consisted of screening for incident infection via two-step tuberculin skin testing prior to travel and skin testing upon return from abroad, followed by standard LTBI treatment with isoniazid for skin test converters. Strategy II carried the additional recommendation of isoniazid treatment for individuals who screen positive for LTBI before travel. Strategy III consisted of post-travel tuberculin skin testing alone, and isoniazid for all reactors. Strategy IV consisted of post-travel chest radiographic screening alone, and LTBI treatment for travellers with inactive TB. All strategies were compared with the status quo strategy of passive case detection. Costs were assessed from the viewpoint of the health system, in year 2003 Canadian dollars, and effectiveness was measured as cases of active TB prevented. In the base analysis, travel duration was 3 months. Strategy III (the single post-trip tuberculin test) was most effective in reducing future incidence of active TB, with the lowest incremental cost per TB case prevented. The best candidates for TB screening were travellers born in Haiti who visited Haiti, for whom Strategy III cost an estimated $37,613 per TB case prevented. Even for this group, however, no net cost-savings resulted from any screening strategy.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".