Differential yield of universal versus selective drug susceptibility testing of patients with tuberculosis in high-burden countries: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Although universal drug susceptibility testing (DST) is a component of the End-TB Strategy, over 70% of drug-resistant tuberculosis (DR-TB) cases globally remain undetected. This detection gap reflects difficulties in DST scale-up and substantial heterogeneity in policies and implemented practices. We conducted a systematic review and meta-analysis to assess whether implementation of universal DST yields increased DR-TB detection compared with only selectively testing high-risk groups. METHODS: PubMed, Embase, Global Health, Cochrane Library and Web of Science Core Collection were searched for publications reporting on the differential yield of universal versus selective DST implementation on the proportion of DR-TB, from January 2007 to June 2019. Random-effects meta-analyses were used to calculate respective pooled proportions of DR-TB cases detected; Higgins test and prediction intervals were used to assess between-study heterogeneity. We adapted an existing risk-of-bias assessment tool for prevalence studies. RESULTS: Of 18 736 unique citations, 101 studies were included in the qualitative synthesis. All studies used WHO-endorsed DST methods, and most (87.1%) involved both high-risk groups and the general population. We found only cross-sectional, observational, non-randomised studies that compared universal with selective DST strategies. Only four studies directly compared the testing approaches in the same study population, with the proportion of DR-TB cases detected ranging from 2.2% (95% CI: 1.4% to 3.2%) to 12.8% (95% CI: 11.4% to 14.3%) with selective testing, versus 4.4% (95% CI: 3.3% to 5.8%) to 9.8% (95% CI: 8.9% to 10.7%) with universal testing. Broad population studies were very heterogeneous. The vast majority (88/101; 87.1%) reported on the results of universal testing. However, while 37 (36.6%)/101 included all presumptive TB cases, an equal number of studies applied sputum-smear as a preselection criterion. A meaningful meta-analysis was not possible. CONCLUSION: Given the absence of randomised studies and the paucity of studies comparing strategies head to head, and selection bias in many studies that applied universal testing, our findings have limited generalisability. The lack of evidence reinforces the need for better data to inform policies.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.059 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".