EP24-331-23 Yield and coverage of active case finding interventions for tuberculosis control in high-burden countries: a systematic review and meta-analysis

Background: Active case finding (ACF) has had low priority in countries with a high burden of tuberculosis (TB), but is a key strategy to reduce diagnostic delays, expedite treatment, and prevent TB transmission. WHO estimated a global incidence of 10.0 million TB cases in 2018, of which only ≈70% were reported. Design/Methods: We conducted a systematic review evaluating yield (i.e., proportion of those screened who had active TB) and coverage (i.e., proportion of those targeted who were screened) of different ACF approaches among individuals and communities in high-burden countries, identifying peer-reviewed studies published from 1980-2016 that reported ACF outcomes. We conducted meta-analyses and meta-regression with random effects models to identify populations, settings, WHO regions, and screening/diagnostic approaches for which yield and coverage were higher.
\nResults: Of 3,972 abstracts screened, 224 papers met criteria after full text review. The pooled yield of ac- tive TB was 3.2% (95% confidence interval [CI] 2.9%–3.4%) and pooled coverage was 93.3% (95% CI 91.9%– 94.5%). In meta-regression, the use of laboratory tests (microscopy, culture, or GeneXpert) for initial screening had significantly higher yield compared to studies using symptom screening (beta 3.5%, 95% CI 0.6%–6.4%). In addition, studies that used laboratory screening (usu- ally microscopy) followed by diagnosis using culture or GeneXpert had higher yield than those using symptom screening followed by microscopy for diagnosis (beta 4.4%, 95% CI 0.9%–7.9%). In a model comparing approaches with and without GeneXpert, PLWH had higher yield versus general population (beta 5.1 95% CI 1.1%–9.2%). In all models, studies targeting children only had higher yield (p<0.01).
\nConclusions: ACF yield was higher when implemented in health care settings and among high-risk populations such as PLWH and children. Scaling up screening algo- rithms that use laboratory tests for both screening and diagnosis increases yield compared to approaches using symptom screening and other methods.