Evaluation of TB elimination strategies in Canadian Inuit populations: Nunavut as a case study
Bibliographic record
Abstract
Tuberculosis (TB) continues to disproportionately affect Inuit populations in Canada with some communities having over 300 times higher rate of active TB than Canadian-born, non-Indigenous people. Inuit Tuberculosis Elimination Framework has set the goal of reducing active TB incidence by at least 50% by 2025, aiming to eliminate it by 2030. Whether these goals are achievable with available resources and treatment regimens currently in practice has not been evaluated. We developed an agent-based model of TB transmission to evaluate timelines and milestones attainable in Nunavut, Canada by including case findings, contact-tracing and testing, treatment of latent TB infection (LTBI), and the government investment on housing infrastructure to reduce the average household size. The model was calibrated to ten years of TB incidence data, and simulated for 20 years to project program outcomes. We found that, under a range of plausible scenarios with tracing and testing of 25%-100% of frequent contacts of detected active cases, the goal of 50% reduction in annual incidence by 2025 is not achievable. If active TB cases are identified rapidly within one week of becoming symptomatic, then the annual incidence would reduce below 100 per 100,000 population, with 50% reduction being met between 2025 and 2030. Eliminating TB from Inuit populations would require high rates of contact-tracing and would extend beyond 2030. The findings indicate that time-to-identification of active TB is a critical factor determining program effectiveness, suggesting that investment in resources for rapid case detection is fundamental to controlling TB.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".