Burden of non-adherence to latent tuberculosis infection drug therapy and the potential cost-effectiveness of adherence interventions in Canada: a simulation study
Bibliographic record
Abstract
OBJECTIVE: Pharmaceutical treatment of latent tuberculosis infection (LTBI) reduces the risk of progression to active tuberculosis (TB); however, poor adherence tempers the protective effect. We aimed to estimate the health burden of non-adherence, the maximum allowable cost of hypothetical new adherence interventions to be cost-effective and the potential value of existing adherence interventions for patients with low-risk LTBI in Canada. DESIGN: A microsimulation model of LTBI progression over 25 years. SETTING: General practice in Canada. PARTICIPANTS: Individuals with LTBI who are initiating drug therapy. INTERVENTIONS: A hypothetical intervention with a range of effectiveness was evaluated. Existing drug adherence interventions including peer support, two-way text messaging support, enhanced adherence counselling and adherence incentives were also evaluated. PRIMARY AND SECONDARY OUTCOME MEASURES: Simulation outcomes included healthcare costs, TB incidence, TB deaths and quality-adjusted life years (QALYs). Base case results were interpreted against a willingness-to-pay threshold of $C50 000/QALY. RESULTS: Compared with current adherence levels, full adherence to LTBI drug therapy could reduce new TB cases from 90.3 cases per 100 000 person-years to 35.9 cases per 100 000 person-years and reduce TB-related deaths from 7.9 deaths per 100 000 person-years to 3.1 deaths per 100 000 person-years. An intervention that increases relative adherence by 40% would bring the population near full adherence to drug therapy and could have a maximum allowable annual cost of approximately $C450 per person to be cost-effective. Based on estimates of effect sizes and costs of existing adherence interventions, we found that they yielded between 900 and 2400 additional QALYs per million people, reduced TB deaths by 5%-25% and were likely to be cost-effective over 25 years. CONCLUSION: Full adherence could reduce the number of future TB cases by nearly 60%, offsetting TB-related costs and health burden. Several existing interventions are could be cost-effective to help achieve this goal.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".