Treatment outcomes of drug susceptible Tuberculosis in private health facilities in Lagos, South-West Nigeria
Bibliographic record
Abstract
BACKGROUND: The Lagos State Tuberculosis, Buruli Ulcer, and Leprosy Control Program (LSTBLCP) started engaging private hospitals under the Public-Private Mix (PPM) Program in 2008. The study aimed to evaluate the trend and predictors of successful Tuberculosis (TB) treatment outcomes of patients managed across these private health facilities between 2010-2016 in Lagos, Nigeria. METHODS: Retrospective review of TB treatment register and treatment cards of patients commenced on TB treatment between January 2010 and December 2016 in 36 private health facilities engaged by the LSTBLCP. Between December 2016 and February 2017, data were collected and entered into Microsoft Excel by trained data entry clerks. The analysis was done using SPSS software. Independent predictors of successful treatment outcomes were determined using multivariate analysis at the statistical significance of p<0.05 and 95% confidence interval. RESULTS: A total of 1660 records of TB patients were reviewed. 1535 (92.47%) commenced treatment, while 1337 (87.10%) of all records had documented treatment outcomes. Of the 1337 patients with outcomes, 1044 (78.09%) had a successful treatment outcome, and 293 (21.91%) had an unsuccessful outcome. Majority were male, 980 (59.04%), Human Immunodeficiency Virus (HIV) negative status, 1295 (80.24%), diagnosed with smear, 1141 (73.14%), treated in private not-for-profit (PNFP) hospital, 1097 (66.08%), treated for TB between 2014-2016 (18.96%-19.52%). In multivariate analysis, age>20years (aOR = 0.26, p = 0.001), receiving TB treatment in 2013 (aOR = 0.39, p = 0.001), having genexpert for TB diagnosis (aOR = 0.26, p = 0.031) and being HIV positive (aOR = 0.37, p = 0.001) significantly reduced likelihood of successful treatment outcome. The site of TB, being on ART or CPT, were confounding determinants of successful treatment outcomes as they became non-significant at the multivariate analysis level. CONCLUSION: Treatment outcome among Lagos private hospitals was low compared with NTBLCP and World Health Organization (WHO) target. We urge the government and TB stakeholders to strengthen the PPM interventions to improve adherence, particularly among People Living with HIV (PLHIV) and older TB patients. Hence, promotion of early care-seeking, improving diagnostic and case holding efficiencies of health facilities, and TB/HIV collaborative interventions can reduce the risk of an unsuccessful outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".