Grant: Project Grant

Tuberculosis (TB) is the world's leading infectious disease killer with an estimated 9.6 million new cases of TB in 2014. Though public health efforts typically focus on pulmonary TB, extra pulmonary TB (EPTB) accounts for an unknown 15%-40% of the burden and is associated with important morbidity and mortality. A key challenge to combating EPTB is the lack of a perfect diagnostic test, presenting a hurdle for disease diagnosis in individual patients, and also for estimation of disease burden and evaluation of new tests at the population level. To date most studies estimating disease burden or diagnostic accuracy have relied on naïve methods resulting in biased estimates. Our objective is to address this by developing a Bayesian latent class model, which is a statistical model that acknowledges the imperfect nature of all tests and possible dependence between them. We will focus on three common forms of EPTB (pleural, lymphatic, meningitis). Our methods will be applied to large cohorts of adults with suspected EPTB from India and South Africa, two countries with a heavy disease burden. We will also use data from an ongoing Cochrane review of the accuracy of Xpert for EPTB. The results of these different latent class analyses will allow us to estimate the predictive value of composite decision rules for EPTB, allowing us to estimate for the first time the extent of overtreatment and underdiagnosis. These predictive values will be used to carry out cost-effectiveness studies. The methods we describe will contribute to addressing the major public health problem of reducing EPTB burden. This project brings together an international group whose complementary methodological and clinical expertise in latent class analysis, systematic reviews and tuberculosis, is necessary to execute it. The project will support training of graduate students. The methods and software described in this proposal will be relevevant in other disease areas as well.