Brief Report: Differentiated Service Delivery Framework for People With Multidrug-Resistant Tuberculosis and HIV Coinfection
Bibliographic record
Abstract
INTRODUCTION: For people living with HIV/AIDS, care is commonly delivered through Differentiated Service Delivery (DSD). Although people with multidrug-resistant tuberculosis (MDR-TB) and HIV/AIDS experience severe treatment associated challenges, there is no DSD model to support their treatment. In this study, we defined patterns of medication adherence and characterized longitudinal barriers to inform development of an MDR-TB/HIV DSD framework. METHODS: Adults with MDR-TB and HIV initiating bedaquiline (BDQ) and receiving antiretroviral therapy (ART) in KwaZulu-Natal, South Africa, were enrolled and followed through the end of MDR-TB treatment. Electronic dose monitoring devices (EDM) measured BDQ and ART adherence. Longitudinal focus groups were conducted and transcripts analyzed thematically to describe discrete treatment stage-specific and cross-cutting treatment challenges. RESULTS: 283 participants were enrolled and followed through treatment completion (median 17.8 months [IQR 16.5-20.2]). Thirteen focus groups were conducted. Most participants (82.7%, 234/283) maintained high adherence (mean BDQ adherence 95.3%; mean ART adherence 85.5%), but an adherence-challenged subpopulation with <85% cumulative adherence (17.3%, 49/283) had significant declines in mean weekly BDQ adherence from 94.9% to 39.9% (p<0.0001) and mean weekly ART adherence from 83.9% to 26.6% (p<0.0001) over 6 months. Psychosocial, behavioral, and structural obstacles identified in qualitative data were associated with adherence deficits in discrete treatment stages, and identified potential stage specific interventions. CONCLUSION: A DSD framework for MDR-TB/HIV should intensify support for adherence-challenged subpopulations, provide multi-modal support for adherence across the treatment course and account for psychosocial, behavioral, and structural challenges linked to discrete treatment stages.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".