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.
All Science Journal Classification (ASJC) codes
- Health Policy
- Public Health, Environmental and Occupational Health