Comparison of Cost-Effectiveness of Tuberculosis Screening of Close Contacts and Foreign-Born Populations
Bibliographic record
Abstract
Although tuberculosis (TB) screening of immigrants has been conducted for over 50 yr in many industrialized countries, its cost- effectiveness has never been evaluated. We prospectively compared the yield and cost-effectiveness of two immigrant TB screening programs, using close-contact investigation and passive case detection. Study subjects included all immigration applicants undergoing radiographic screening, already arrived immigrants requiring surveillance for inactive TB, and close contacts of active cases resident in Montreal, Quebec, Canada, who were referred from June 1996 to June 1997 to the Montreal Chest Institute (MCI), a referral center specializing in respiratory diseases. For all subjects seen, demographic data, investigations, diagnoses, and therapy were abstracted from administrative data bases and medical charts. Estimated costs of detecting and treating each prevalent active case and preventing future active cases, based on federal and provincial health reimbursement schedules, were compared with the costs for passively diagnosed cases of active TB. Over a period of 1 yr, the three programs detected 27 cases of prevalent active TB and prevented 14 future cases. As compared with passive case detection, close-contact investigation resulted in net savings of $815 for each prevalent active case detected and treated and of $2,186 for each future active case prevented. The incremental cost to treat each case of prevalent active TB was $39,409 for applicant screening and $24,225 for surveillance, and the cost of preventing each case was $33,275 for applicants and $65,126 for surveillance. Close-contact investigation was highly cost effective and resulted in net savings. Immigrant applicant screening and surveillance programs had a significant impact but were much less cost effective, in large part because of substantial operational problems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".