Outcome assessment of decentralization of antiretroviral therapy provision in a rural district of Malawi using an integrated primary care model
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of decentralization (DC) of antiretroviral therapy (ART) provision in a rural district of Malawi using an integrated primary care model. METHODS: Between October 2004 and December 2008, 8093 patients (63% women) were registered for ART. Of these, 3440 (43%) were decentralized to health centres for follow-up ART care. We applied multivariate regression analysis that adjusted for sex, age, clinical stage at initiation, type of regimen, presence of side effects because of ART, and duration of treatment and follow-up at site of analysis. RESULTS: Patients managed at health centres had lower mortality [adjusted OR 0.19 (95% C.I. 0.15-0.25)] and lower loss to follow-up (defaulted from treatment) [adjusted OR 0.48 (95% C.I. 0.40-0.58)]. During the first 10 months of follow-up, those decentralized to health centres were approximately 60% less likely to default than those not decentralized; and after 10 months of follow-up, 40% less likely to default. DC was significantly associated with a reduced risk of death from 0 to 25 months of follow-up. The lower mortality may be explained by the selection of stable patients for DC, and the mentorship and supportive supervision of lower cadre health workers to identify and refer complicated cases. CONCLUSION: Decentralization of follow-up ART care to rural health facilities, using an integrated primary care model, appears a safe and effective way to rapidly scale-up ART and improves both geographical equity in access to HIV-related services and adherence to ART.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".