Ambulatory Treatment for Multidrug-Resistant Tuberculosis in Rural Haiti: A Mixed-Methods Study of Social and Economic Factors Affecting Timely Diagnosis and Treatment Success
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patients with MDR-TB in remote areas of Haiti face barriers that delay their ability to initiate and complete treatment. These barriers must be addressed to ensure successful decentralization of MDR-TB treatment. We conducted a mixed-methods study at two MDR-TB facilities in rural Haiti to identify factors that impact MDR-TB treatment initiation and documented treatment response after discharge from hospital. METHODS: We enrolled patients who started MDR-TB treatment at two Zanmi Lasante MDR-TB facilities in Haiti between October 2015 and March 2016. We conducted interviews with patients at discharge to assess factors creating delay in treatment initiation; performed a longitudinal quantitative assessment of patient wellbeing at discharge and for four months after; and conducted a second interview with the same patients and family caregivers to identify obstacles to transition from hospital-based care to ambulatory treatment. Qualitative data were coded into six final conceptual categories representing patient experiences with TB treatment and care. We then explored how the patient experience during transition might affect the score obtained in the quantitative assessment. RESULTS: We enrolled 17 MDR-TB patients and 16 family members. Four patients were identified as having poor clinical response after discharge, characterized by low BMI, low hemoglobin, compromised activities of daily life, food insecurity, depression and other treatment side effects. Patients with poor response also experienced limited social support and difficult living conditions. Factors contributing to delayed treatment initiation were present at discharge and may compromise response. These include financial (lost wages, costly transportation) and logistical barriers to reaching highly centralized treatment facilities. The results highlight the barriers that MDR-TB patients in rural Haiti face to find and successfully complete lifesaving treatment. CONCLUSION: Properly implemented decentralized care will help overcome geographical barriers. Strong nutritional, social and financial support are essential to support appropriate treatment for the disease.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".