Cost-effectiveness analysis of 3 months of weekly rifapentine and isoniazid compared to isoniazid monotherapy in a Canadian arctic setting
Bibliographic record
Abstract
OBJECTIVE: To assess the cost effectiveness of once weekly rifapentine and isoniazid for 12 weeks (3HP) to the current standard care for latent tuberculosis (TB) infection (LTBI) in Iqaluit, Nunavut. DESIGN: A cost-effectiveness analysis using a Markov model reflecting local practices for LTBI treatment. SETTING: A remote Canadian arctic community with a high incidence of TB. PARTICIPANTS: Hypothetical patients with LTBI. INTERVENTIONS: The cost effectiveness of 3HP was compared with the existing standard of care in the study region which consists of 9 months of twice weekly isoniazid (9H) given by directly observed therapy. OUTCOME MEASURES: Effectiveness was measured in quality-adjusted life years (QALYs) with model parameters were derived from historical programmatic data, a local implementation study of 3HP and published literature. Costs from the perspective of the Nunavut healthcare system were measured in 2019 US dollars and were obtained primarily from local, empirically collected data. Secondary health outcomes included estimated TB cases and TB deaths averted using 3HP versus 9H. One way and probabilistic sensitivity analyses were performed. RESULTS: The 3HP regimen was dominant over 9H: costs were lower (US$628 vs US$924/person) and health outcomes slightly improved (20.14 vs 20.13 QALYs/person). In comparison to 9H, 3HP treatment resulted in fewer TB cases (27.89 vs 30.16/1000 persons) and TB deaths (2.29 vs 2.48/1000 persons). 3HP completion, initiation and risk of fatal adverse events were the primary drivers of cost effectiveness. CONCLUSION: In a remote Canadian arctic setting, using 3HP instead of 9H for LTBI treatment may result in cost savings and similar or improved health outcomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".