Infectious and clinical tuberculosis trajectories: Bayesian modeling with case finding implications

Abstract

The importance of finding people with undiagnosed tuberculosis (TB) hinges on their future disease trajectories. Assays for systematic screening should be optimized to find those whose TB will contribute most to future transmission or morbidity. In this study, we constructed a mathematical model that tracks the future trajectories of individuals with TB at a cross-sectional timepoint (“baseline”), classifying them by bacterial burden (smear positive/negative) and symptom status (symptomatic/subclinical). We used Bayesian methods to calibrate this model to targets derived from historical survival data and notification, mortality, and prevalence data from five countries. We combined resulting disease trajectories with evidence on infectiousness to estimate each baseline TB state’s contribution to future transmission. For a person with smear-negative subclinical TB at baseline, the expected future duration of disease was short (mean 4.8 [95% uncertainty interval 3.3 to 8.4] mo); nearly all disease courses ended in spontaneous resolution, not treatment. In contrast, people with baseline smear-positive subclinical TB had longer undiagnosed disease durations (15.9 [11.1 to 23.5] mo); nearly all eventually developed symptoms and ended in treatment or death. Despite accounting for only 11 to 19% of prevalent disease, smear-positive subclinical TB accounted for 35 to 51% of future transmission—a greater contribution than symptomatic or smear-negative TB. Subclinical TB with a high bacterial burden accounts for a disproportionate share of future transmission. Priority should be given to developing inexpensive, easy-to-use assays for screening both symptomatic and asymptomatic individuals at scale—akin to rapid antigen tests for other diseases—even if these assays lack the sensitivity to detect paucibacillary disease.