Community screening for tuberculosis offers an opportunity to interrupt transmission and accelerate progress towards end TB targets. However, disease burden is inequitably distributed across population groups (by, e.g., age, sex, location), and heterogeneity in the coverage of community screening approaches across these groups has not been explored. This project will use systematic data synthesis and mathematical modelling to quantify how heterogeneity in screening coverage across specific population groups – in particular, underrepresentation of groups with higher rates of disease – affects the epidemiological impact of screening approaches. Translating these insights across settings, where the balance between heterogeneity in coverage and inequities in disease burden may differ, will directly inform local policy decisions on effective, equitable, and locally relevant screening approaches.