Modeling the impact of case finding for tuberculosis: The role of infection dynamics.

Population-level case-finding is globally recommended as a strategy to reduce the burden of tuberculosis in high-burden settings, but mathematical models overwhelmingly project that case-finding will yield only modest reductions in incidence. We evaluated whether the common assumption of lifelong risk of progression after infection leads models to undervalue the impact of case-finding. Specifically, clearance of latent infection reduces the rate of long-term "reactivation", which could result in more pronounced effects from interventions (such as case-finding) that reduce transmission. To investigate this possibility, we constructed two models - one assuming lifelong progression risk after infection ("Conventional"), and one assuming that some individuals clear their infections ("Clearance"). We calibrated these models to empirical data on tuberculosis burden in India and progression after infection. We used the calibrated models to project the impact of community-wide active case-finding campaigns covering 75% of the population every two years, with or without mass provision of tuberculosis preventive treatment (TPT). Relative to the Conventional model, the posterior distributions of Clearance model outputs were more similar to empirical estimates of disease incidence, infection prevalence, progression over time, and incidence trends, demonstrating better fit. The Clearance model also projected a greater impact of case-finding on incidence: 26% [95% uncertainty interval: 15-41%] vs. 11% [7-24%] reduction after 10 years. Given that clearance of some infections is biologically likely and results in better model fit, we conclude that the epidemiological impact of population-level case-finding for tuberculosis may be greater than that projected by models that exclude this effect.