Twelve-month Treatment Retention and Associated Factors: A Comparison of 2 Medically Assisted Therapy Clinics in Dar es Salaam, Tanzania
Bibliographic record
Abstract
OBJECTIVES: Retention in methadone maintenance treatment is instrumental in achieving better treatment outcomes. In this study, we compared 2 medication-assisted treatment (MAT) clinics in Dar es Salaam, Tanzania with respect to patient characteristics, outcomes, and factors that predict 12-month treatment retention. METHODS: This retrospective registry-based cohort study utilized data collected for routine clinical and program monitoring at 2 sites, Mwananyamala and Muhimbili MAT clinics. Cumulative retention in treatment was calculated using life tables. The analysis of treatment retention predictor variables used both Kaplan-Meier and Cox proportional hazard analyses. RESULTS: We examined the socio-demographic and program-related characteristics of 362 (181 from each clinic) patients. Twelve-month treatment retention was higher at Mwananyamala (73%) than Muhimbili (64%) MAT clinic, but the difference was not significant. In both clinics, a higher methadone dose (>60mg) significantly predicted treatment retention ( P < 0.05). Being employed and traveling an average short distance (<5 km) from home to clinic significantly increased the likelihood of remaining in treatment in Muhimbili MAT clinic (P< 0.05) only. CONCLUSIONS: A methadone dose of 60 mg and above was associated with longer retention in treatment. At 1 clinic in a denser and more central location, employment and a short travel distance from home to clinic were associated with longer tenure in treatment. These findings have potential implications for clinical practice, research, and scaling up MAT services in Tanzania.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".