Tuberculosis screening for long-term care: a cost-effectiveness analysis
Bibliographic record
Abstract
SETTING: Long-term care facilities in Canada, a low tuberculosis (TB) incidence country. OBJECTIVE: To compare the impact and cost-effectiveness of three screening strategies for TB on entry to long-term care: no screening, screening for latent tuberculous infection (LTBI) using the tuberculin skin test (TST) or screening for active disease with a chest X-ray. DESIGN: Cost effectiveness analysis. RESULTS: With the LTBI screening strategy, the number needed to screen to prevent one active case was 1410 and the cost per case averted was Canadian $109 913. The number needed to screen to prevent one case using the active screening strategy was 1266, and the cost per case averted was $672 298. CONCLUSIONS: Our findings suggest that TB screening strategies on entry to long-term care are costly, with large numbers needed to screen. Screening with TST was more cost-effective than chest X-ray screening. Higher risk of reactivation of LTBI is associated with improved cost-effectiveness of screening. Short time horizons and test performance characteristics place limitations on screening programmes in this setting. Future considerations include the changing demographics of the institutionalised elderly.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".