Costs of digital adherence technologies for tuberculosis treatment support
Bibliographic record
Abstract
Abstract Digital adherence technologies (DATs) are increasingly used for tuberculosis (TB) adherence support, but information about their costs remains limited. We used microcosting to estimate health system costs (in 2022 US dollars) of 99DOTS pill sleeves and video-observed treatment (VOT), implemented in demonstration projects during 2018-21. Local cost estimates for standard directly observed treatment (DOT) were also obtained. The estimated per-person costs of 99DOTS for drug-sensitive (DS-) TB were $98, $106, and $174 in Bangladesh (n=719), the Philippines (n=396), and Tanzania (n=976) respectively. The estimated per-person costs of VOT were $1 154, $304, $457, and $661 in Haiti (n=87 DS-TB), Moldova (n=173 DS-TB), Moldova (n=135 drug-resistant [DR]-TB) and the Philippines (n=110 DR-TB) respectively. Health system costs of 99DOTS may be similar to or cheaper than standard DOT. VOT is considerably more expensive; labor cost offsets and/or economies of scale may yield savings relative to standard DOT in some settings. Summary In diverse settings, health system costs of 99DOTS pill sleeves may be similar to or cheaper than standard directly observed treatment for TB; video-observed treatment is considerably more expensive, but labor cost offsets and/or economies of scale may yield savings.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".