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Tuberculosis diagnosis and treatment under uncertainty

Significance Tuberculosis (TB) remains a serious global health problem. A new, more accurate test for diagnosis was endorsed by the World Health Organization in 2010. However, trials showed that using the test did not yield reductions in TB-related deaths. To help understand why, we model how a clinician might decide whether to order tests for TB and whether to treat a patient for TB, with or without test results. We highlight the role of uncertainty about the prevalence of TB and the accuracy of different tests, for patients with different characteristics. We show that, given such uncertainty, a reasonable policy may be to diversify testing and treatment, randomly assigning patients with certain characteristics to different combinations of testing and treatment. In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test—Xpert MTB/RIF from Cepheid—was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy—the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to order Xpert or other tests for TB, and whether to treat a patient, with or without test results. Our first result characterizes the conditions under which it is optimal to perform empirical treatment; that is, treatment without diagnostic testing. We then examine the implications for decision making of partial knowledge of TB prevalence or test accuracy. This partial knowledge generates ambiguity, also known as deep uncertainty, about the best testing and treatment policy. In the presence of such ambiguity, we show the usefulness of diversification of testing and treatment.