Drones and digital adherence monitoring for community-based tuberculosis control in remote Madagascar: A cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Continuing tuberculosis control with current approaches is unlikely to reach the World Health Organization's objective to eliminate TB by 2035. Innovative interventions such as unmanned aerial vehicles (or drones) and digital adherence monitoring technologies have the potential to enhance patient-centric quality tuberculosis care and help challenged National Tuberculosis Programs leapfrog over the impediments of conventional Directly Observed Therapy (DOTS) implementation. A bundle of innovative interventions referred to for its delivery technology as the Drone Observed Therapy System (DrOTS) was implemented in remote Madagascar. Given the potentially increased cost these interventions represent for health systems, a cost-effectiveness analysis was indicated. METHODS: A decision analysis model was created to calculate the incremental cost-effectiveness of the DrOTS strategy compared to DOTS, the standard of care, in a study population of 200,000 inhabitants in rural Madagascar with tuberculosis disease prevalence of 250/100,000. A mixed top-down and bottom-up costing approach was used to identify costs associated with both models, and net costs were calculated accounting for resulting TB treatment costs. Net cost per disability-adjusted life years averted was calculated. Sensitivity analyses were performed for key input variables to identify main drivers of health and cost outcomes, and cost-effectiveness. FINDINGS: Net cost per TB patient identified within DOTS and DrOTS were, respectively, $282 and $1,172. The incremental cost per additional TB patient diagnosed in DrOTS was $2,631 and the incremental cost-effectiveness ratio of DrOTS compared to DOTS was $177 per DALY averted. Analyses suggest that integrating drones with interventions ensuring highly sensitive laboratory testing and high treatment adherence optimizes cost-effectiveness. CONCLUSION: Innovative technology packages including drones, digital adherence monitoring technologies, and molecular diagnostics for TB case finding and retention within the cascade of care can be cost effective. Their integration with other interventions within health systems may further lower costs and support access to universal health coverage.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".