Impact of community tracer teams on treatment outcomes among tuberculosis patients in South Africa
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) indicators in South Africa currently remain well below global targets. In 2008, the National Tuberculosis Program (NTP) implemented a community mobilization program in all nine provinces to trace TB patients that had missed a treatment or clinic visit. Implementation sites were selected by TB program managers and teams liaised with health facilities to identify patients for tracing activities. The objective of this analysis was to assess the impact of the TB Tracer Project on treatment outcomes among TB patients. METHODS: The study population included all smear positive TB patients registered in the Electronic TB Registry from Quarter 1 2007-Quarter 1 2009 in South Africa. Subdistricts were used as the unit of analysis, with each designated as either tracer (standard TB program plus tracer project) or non-tracer (standard TB program only). Mixed linear regression models were utilized to calculate the percent quarterly change in treatment outcomes and to compare changes in treatment outcomes from Quarter 1 2007 to Quarter 1 2009 between tracer and non-tracer subdistricts. RESULTS: For all provinces combined, the percent quarterly change decreased significantly for default treatment outcomes among tracer subdistricts (-0.031%; p < 0.001) and increased significantly for successful treatment outcomes among tracer subdistricts (0.003%; p = 0.03). A significant decrease in the proportion of patient default was observed for all provinces combined over the time period comparing tracer and non-tracer subdistricts (p = 0.02). Examination in stratified models revealed the results were not consistent across all provinces; significant differences were observed between tracer and non-tracer subdistricts over time in five of nine provinces for treatment default. CONCLUSIONS: Community mobilization of teams to trace TB patients that missed a clinic appointment or treatment dose may be an effective strategy to mitigate default rates and improve treatment outcomes. Additional information is necessary to identify best practices and elucidate discrepancies across provinces; these findings will help guide the NTP in optimizing the adoption of tracing activities for TB control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".