Addressing the drug resistant tuberculosis challenge through implementing a mixed model of care in Uganda, 2012- 2017
Bibliographic record
Abstract
Abstract Background Worldwide, Drug resistant Tuberculosis (DR-TB) remains a big problem; the diagnostic capacity has superseded the DR-TB clinical management capacity thereby causing ethical challenges. In Sub-Saharan Africa, treatment is either inadequate or lacking and some diagnosed patients are on treatment waiting lists. In Uganda, various health system challenges impeded scale up of DR-TB care in 2012; only three treatment initiation facilities existed, with only 41 of the estimated 1010 cases enrolled on treatment yet 300 were on the waiting list and there was no DR-TB treatment scale up plan. To scale up care, National TB/Leprosy Program (NTLP) with partners rolled out a DR-TB mixed model of care. In this paper, we share achievements and outcomes resulting from the implementation of this mixed Model of DR-TB care. Methods Routine NTLP DR-TB program data from 2013 to 2017 cohorts was collected from all the 15 DR-TB treatment initiation sites and analyzed using STATA version 14.2. We presented outcomes as the number of patient backlog cleared, DR-TB initiation sites, cumulative patients enrolled, percentage of co-infected patients on co-trimoxazole preventive therapy (CPT) and antiretroviral therapy (ART) as well as the six, twelve interim and 24 months treatment outcomes as per the Uganda NTLP 2016 Programmatic Management of drug Resistant Tuberculosis (PMDT) guidelines. Results Over the period 2013-2017, DR-TB treatment initiation sites increased from three to 15, cumulative patient enrollment rose from 41 to 1,311 and the 300-patient backlog was cleared. Treatment success rate (TSR) of 73% was achieved above the global TSR average rate of 50%. Conclusions The Uganda DR-TB mixed model of care coupled with early application of continuous improvement approaches, enhanced cohort reviews and use of multi-disciplinary teams allowed for rapid DR-TB program expansion, rapid clearance of patient backlog, attainment of high cumulative enrollment and high treatment success rates. Sustainability of these achievements is needed to further reduce the DR-TB burden in the country. We highly recommend this mixed model of care in settings with similar challenges.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".