Evaluating the potential costs and impact of digital health technologies for tuberculosis treatment support
Bibliographic record
Abstract
Ensuring adherence and support during treatment of tuberculosis (TB) is a major public health challenge. Digital health technologies could help improve treatment outcomes. We considered their potential cost and impact on treatment for active or latent TB in Brazil.Decision analysis models simulated two adult cohorts with 1) drug-susceptible active TB, and 2) multidrug-resistant TB, and two cohorts treated with isoniazid for latent TB infection (LTBI): 1) close contacts of persons with active TB, and 2) others newly diagnosed with LTBI. We evaluated four digital support strategies: two different medication monitors, synchronous video-observed therapy (VOT), and two-way short message service (SMS). Comparators were standard directly observed treatment for active TB and self-administered treatment for LTBI. Projected outcomes included costs (2016 US dollars), plus active TB cases and disability-adjusted life years averted among persons with LTBI.For individuals with active TB, medication monitors and VOT are projected to lead to substantial (up to 58%) cost savings, in addition to alleviating inconvenience and cost to patients of supervised treatment visits. For LTBI treatment, SMS and medication monitors are projected to be the most cost-effective interventions. However, all projections are limited by the scarcity of published estimates of clinical effect for the digital technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".