Cost and cost-effectiveness of digital technologies for support of tuberculosis treatment adherence: a systematic review
Bibliographic record
Abstract
BACKGROUND: Digital adherence technologies (DATs) may provide a patient-centred approach to supporting tuberculosis (TB) medication adherence and improving treatment outcomes. We synthesised evidence addressing costs and cost-effectiveness of DATs to support TB treatment. METHODS: A systematic review (PROSPERO-CRD42022313531) identified relevant literature from January 2000 to April 2023 in MEDLINE, Embase, CENTRAL, CINAHL, Web of Science along with preprints from medRxiv, Europe PMC and ClinicalTrials.gov. Studies with observational, experimental or quasi-experimental designs (minimum 20 participants) and modelling studies reporting quantitative data on the cost or cost-effectiveness of DATs for TB infection or disease treatment were included. Study characteristics, cost and cost-effectiveness outcomes were extracted. RESULTS: Of 3619 titles identified by our systematic search, 29 studies met inclusion criteria, of which 9 addressed cost-effectiveness. DATs included short message service (SMS) reminders, phone-based technologies, digital pillboxes, ingestible sensors and video-observed therapy (VOT). VOT was the most extensively studied (16 studies) and was generally cost saving when compared with healthcare provider directly observed therapy (DOT), particularly when costs to patients were included-though findings were largely from high-income countries. Cost-effectiveness findings were highly variable, ranging from no clinical effect in one study (SMS), to greater effectiveness with concurrent cost savings (VOT) in others. Only eight studies adequately reported at least 80% of the elements required by Consolidated Health Economic Evaluation Reporting Standards, a standard reporting checklist for health economic evaluations. CONCLUSION: DATs may be cost saving or cost-effective compared with healthcare provider DOT, particularly in high-income settings. However, more data of higher quality are needed, notably in lower-income and middle-income countries which have the greatest TB burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".