Case fatality and recurrent tuberculosis among patients managed in the private sector: A cohort study in Patna, India
Bibliographic record
Abstract
BACKGROUND: A key component of the WHO End TB Strategy is quality of care, for which case fatality is a critical marker. Half of India's nearly 3 million TB patients are treated in the highly unregulated private sector, yet little is known about the outcomes of these patients. Using a retrospective cohort design, we estimated the case fatality ratio (CFR) and rate of recurrent TB among patients managed in the private healthcare sector in Patna, India. METHODS: World Health Partners' Private Provider Interface Agencies (PPIA) pilot project in Patna has treated 89,906 private sector TB patients since 2013. A random sample of 4,000 patients treated from 2014 to 2016 were surveyed in 2018 for case fatality and recurrent TB. CFR is defined as the proportion of patients who die during the period of interest. Treatment CFRs, post-treatment CFRs and rates of recurrent TB were estimated. Predictors for fatality and recurrence were identified using Cox proportional hazards modelling. Survey non-response was adjusted for using inverse probability selection weighting. RESULTS: The survey response rate was 56.0%. The weighted average follow-up times were 8.7 months in the treatment phase and 26.4 months in the post-treatment phase. Unobserved patients were more likely to have less than one month of treatment adherence (32.0% vs. 13.5%) and were more likely to live in rural Patna (21.9% vs. 15.0%). The adjusted treatment phase CFR was 7.27% (5.97%, 8.49%) and at 24 months post-treatment was 3.32% (2.36%, 4.42%). The adjusted 24 month post-treatment phase recurrent TB rate was 3.56% (2.54%, 4.79%). CONCLUSIONS: Our cohort study provides critical estimates of TB patient outcomes in the Indian private sector, and accounts for selection bias. Patients in the private sector in Patna experienced a moderate treatment CFR but rates of recurrent TB and post-treatment fatality were low.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".