Active screening for tuberculosis in high-incidence Inuit communities in Canada: a cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Active screening for tuberculosis (TB) involves systematic detection of previously undiagnosed TB disease or latent TB infection (LTBI). It may be an important step toward elimination of TB among Inuit in Canada. We aimed to evaluate the cost-effectiveness of community-wide active screening for TB infection and disease in 2 Inuit communities in Nunavik. METHODS: We incorporated screening data from the 2 communities into a decision analysis model. We predicted TB-related health outcomes over a 20-year time frame, beginning in 2019. We assessed the cost-effectiveness of active screening in the presence of varying outbreak frequency and intensity. We also considered scenarios involving variation in timing, impact and uptake of screening programs. RESULTS: Given a single large outbreak in 2019, we estimated that 1 round of active screening reduced TB disease by 13% (95% uncertainty range -3% to 27%) and was cost saving compared with no screening, over 20 years. In the presence of simulated large outbreaks every 3 years thereafter, a single round of active screening was cost saving, as was biennial active screening. Compared with a single round, we also determined that biennial active screening reduced TB disease by 59% (95% uncertainty range 52% to 63%) and was estimated to cost Can$6430 (95% uncertainty range -$29 131 to $13 658 in 2019 Can$) per additional active TB case prevented. With smaller outbreaks or improved rates of treatment initiation and completion for people with LTBI, we determined that biennial active screening remained reasonably cost-effective compared with no active screening. INTERPRETATION: Active screening is a potentially cost-saving approach to reducing disease burden in Inuit communities that have frequent TB outbreaks.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".