Factors influencing diagnosis and treatment initiation for multidrug-resistant/rifampicin-resistant tuberculosis in six sub-Saharan African countries: a mixed-methods systematic review
Bibliographic record
Abstract
BACKGROUND: Drug-resistant tuberculosis burdens fragile health systems in sub-Saharan Africa (SSA), complicated by high prevalence of HIV. Several African countries reported large gaps between estimated incidence and diagnosed or treated cases. Our review aimed to identify barriers and facilitators influencing diagnosis and treatment for drug-resistant tuberculosis (DR-TB) in SSA, which is necessary to develop effective strategies to find the missing incident cases and improve quality of care. METHODS: Using an integrative design, we reviewed and narratively synthesised qualitative, quantitative and mixed-methods studies from nine electronic databases: Medline, Global Health, CINAHL, EMBASE, Scopus, Web of Science, International Journal of Tuberculosis and Lung Disease, PubMed and Google Scholar (January 2006 to June 2019). RESULTS: Of 3181 original studies identified, 55 full texts were screened, and 29 retained. The studies included were from 6 countries, mostly South Africa. Barriers and facilitators to DR-TB care were identified at the health system and patient levels. Predominant health system barriers were laboratory operational issues, provider knowledge and attitudes and information management. Facilitators included GeneXpert MTB/RIF (Xpert) diagnosis and decentralisation of services. At the patient level, predominant barriers were patients being lost to follow-up or dying due to lengthy diagnostic and treatment delays, negative public sector care perceptions, family, work or school commitments and using private sector care. Some patient-level facilitators were HIV positivity and having more symptoms. CONCLUSION: Case detection and treatment for DR -TB in SSA currently relies on individual patients presenting voluntarily to the hospital for care. Specific interventions targeting identified barriers may improve rates and timeliness of detection and treatment.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".