Tuberculosis case fatality in India: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: The WHO End TB Strategy calls for a global reduction in the case fatality ratio (CFR) below 5%. India accounts for a third of global tuberculosis (TB) deaths. This systematic review estimated CFRs among Indian patients with TB both during and after treatment. Methods: We systematically searched Medline, Embase and Global Health for eligible studies published between 1 January 2006 and 8 January 2019, including both cohort studies and intervention study control arms that followed Indian patients with TB for fatality either during treatment or post-treatment. From relevant studies we extracted CFRs in addition to study demographics. Study quality was assessed using modified Scottish Intercollegiate Guidelines Network cohort criteria. Sufficiently homogenous studies were pooled using a random effect generalised linear mixed model. A meta-regression was performed to associate study characteristics with resulting CFRs. Results: 218 relevant studies were identified, of which 211 provided treatment phase CFRs. Most patients (92.4%) were treated in the public sector. Quality concerns were identified in 74% of papers. We estimated a pooled treatment phase CFR of 5.16% (95% CI 4.20% to 6.34%) which fell to 3.78% (2.77% to 5.16%) when restricted to 52 high-quality studies. Treatment phase CFRs were higher for paediatric (n=27, 6.50% (2.65% to 10.36%)), drug-resistant (n=43, 14.06% (10.15% to 19.49%)) and HIV-infected (n=35, 10.91% (7.68% to 15.50%)) patients. Nineteen post-treatment CFR studies were too heterogeneous to pool except when restricting to three high-quality studies (2.69% (-0.79% to 6.18%)). Poor study quality (OR=2.27 (2.01 to 2.57)) and tertiary centres patients (OR=1.15 (1.03 to 1.28)) were significantly associated with increased treatment phase case fatality. Conclusions: Case fatality is a critical measure of the quality of TB care. While India's treatment CFRs are in line with WHO targets, several key patient groups remain understudied and most studies suffer from methodological issues. Increased high-quality reporting on patient outcomes will help improve the evidence base on this topic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.027 | 0.004 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".