Understanding implementation barriers in the national scale-up of differentiated ART delivery in Uganda
Bibliographic record
Abstract
BACKGROUND: Although Differentiated Service Delivery (DSD) for anti-retroviral therapy (ART) has been rolled-out nationally in several countries since World Health Organization (WHO)'s landmark 2016 guidelines, there is little research evaluating post-implementation outcomes. The objective of this study was to explore patients' and HIV service managers' perspectives on barriers to implementation of Differentiated ART service delivery in Uganda. METHODS: We employed a qualitative descriptive design involving 124 participants. Between April and June 2019 we conducted 76 qualitative interviews with national-level HIV program managers (n = 18), District Health Team leaders (n = 24), representatives of PEPFAR implementing organizations (11), ART clinic in-charges (23) in six purposively selected Uganda districts with a high HIV burden (Kampala, Luwero, Wakiso, Mbale, Budadiri, Bulambuli). Six focus group discussions (48 participants) were held with patients enrolled in DSD models in case-study districts. Data were analyzed by thematic approach as guided by a multi-level analytical framework: Individual-level factors; Health-system factors; Community factors; and Context. RESULTS: Our data shows that multiple barriers have been encountered in DSD implementation. Individual-level: Individualized stigma and a fear of detachment from health facilities by stable patients enrolled in community-based models were reported as bottlenecks. Socio-economic status was reported to have an influence on patient selection of DSD models. Health-system: Insufficient training of health workers in DSD delivery and supply chain barriers to multi-month ART dispensing were identified as constraints. Patients perceived current selection of DSD models to be provider-intensive and not sufficiently patient-centred. Community: Community-level stigma and insufficient funding to providers to fully operationalize community drug pick-up points were identified as limitations. CONTEXT: Frequent changes in physical addresses among urban clients were reported to impede the running of patient groups of rotating ART refill pick-ups. CONCLUSION: This is one of the first multi-stakeholder evaluations of national DSD implementation in Uganda since initial roll-out in 2017. Multi-level interventions are needed to accelerate further DSD implementation in Uganda from demand-side (addressing HIV-related stigma, community engagement) and supply-side dimensions (strengthening ART supply chain capacities, increasing funding for community models and further DSD program design to improve patient-centeredness).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".