Implementation, feasibility, and acceptability of 99DOTS-based supervision of treatment for drug-susceptible TB in Uganda
Bibliographic record
Abstract
99DOTS is a low-cost digital adherence technology that allows people with tuberculosis (TB) to self-report treatment adherence. There are limited data on its implementation, feasibility, and acceptability from sub-Saharan Africa. We conducted a longitudinal analysis and cross-sectional surveys nested within a stepped-wedge randomized trial at 18 health facilities in Uganda between December 2018 and January 2020. The longitudinal analysis assessed implementation of key components of a 99DOTS-based intervention, including self-reporting of TB medication adherence via toll-free phone calls, automated text message reminders and support actions by health workers monitoring adherence data. Cross-sectional surveys administered to a subset of people with TB and health workers assessed 99DOTS feasibility and acceptability. Composite scores for capability, opportunity, and motivation to use 99DOTS were estimated as mean Likert scale responses. Among 462 people with pulmonary TB enrolled on 99DOTS, median adherence was 58.4% (inter-quartile range [IQR] 38.7-75.6) as confirmed by self-reporting dosing via phone calls and 99.4% (IQR 96.4-100) when also including doses confirmed by health workers. Phone call-confirmed adherence declined over the treatment period and was lower among people with HIV (median 50.6% vs. 63.7%, p<0.001). People with TB received SMS dosing reminders on 90.5% of treatment days. Health worker support actions were documented for 261/409 (63.8%) people with TB who missed >3 consecutive doses. Surveys were completed by 83 people with TB and 22 health workers. Composite scores for capability, opportunity, and motivation were high; among people with TB, composite scores did not differ by gender or HIV status. Barriers to using 99DOTS included technical issues (phone access, charging, and network connection) and concerns regarding disclosure. 99DOTS was feasible to implement and highly acceptable to people with TB and their health workers. National TB Programs should offer 99DOTS as an option for TB treatment supervision.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".