Social and behavioral risk reduction strategies for tuberculosis prevention in Canadian Inuit communities: a cost-effectiveness analysis
Bibliographic record
Abstract
Abstract Background Tuberculosis (TB) is an important public health problem in Inuit communities across Canada, with an annual incidence rate in 2017 that was nearly 300 times higher than in Canadian-born non-Indigenous individuals. Social and behavioral factors that are prevalent in the North, such as commercial tobacco use, excessive alcohol use, food insecurity and overcrowded housing put individuals at higher risk for TB morbidity and mortality. We examined the potential impact of mitigation strategies for these risk factors, in reducing TB burden in this setting. Methods We created a transmission model to simulate the epidemiology of TB in Nunavut, Canada. We then used a decision analysis model to assess the potential impact of several evidence-based strategies targeting tobacco use, excessive alcohol use, food insecurity and overcrowded housing. We predicted TB incidence, TB-related deaths, quality adjusted life years (QALYs), and associated costs and cost-effectiveness over 20 years. All costs were expressed in 2018 Canadian dollars. Results Compared to a status quo scenario with no new interventions for these risk factors, the reduction strategy for tobacco use was most effective and cost-effective, reducing TB incidence by 5.5% (95% uncertainty range: 2.7–11%) over 20 years, with an estimated cost of $95,835 per TB case prevented and $49,671 per QALY gained. The addition of the food insecurity reduction strategy reduced incidence by a further 2% (0.5–3%) compared to the tobacco cessation strategy alone, but at significant cost. Conclusions Strategies that aim to reduce commercial tobacco use and improve food security will likely lead to modest reductions in TB morbidity and mortality. Although important for the communities, strategies that address excess alcohol use and overcrowding will likely have a more limited impact on TB-related outcomes at current scale, and are associated with much higher cost. Their benefits will be more substantial with scale up, which will also likely have important downstream impacts such as improved mental health, educational attainment and food security.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".