Abstract
BACKGROUND: Despite being the leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource-intensive and often inaccurate. Back-calculation was developed to estimate HIV incidence by considering reported cases to be a convolution of the disease duration and the incidence of new cases. New estimates of TB natural history parameters allow us to develop Bayesian back-calculation methods for TB to assign case notification data to the time point of onset of disease. METHODS: Recorded counts of TB cases are underestimates of the true burden of disease, so we include a multiplier derived from prevalence to notification ratios to account for underreporting. We assume a Poisson distribution for notifications and incidence and use a penalized-likelihood before smooth estimates. We estimate sex-stratified TB incidence for Vietnam, Cambodia, and the Philippines via Markov chain Monte Carlo. RESULTS: Annual estimated TB incidence was, on average 19% greater than recorded notifications. TB incidence among males was on average 3.8% higher than females in Vietnam, 1.3% in Cambodia, and 2.5% higher in the Philippines. CONCLUSIONS: These estimates account for the delay between bacteriologically positive subclinical disease and notification and, as such, may be more temporally accurate than existing methods.