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Tuberculosis diagnosis and treatment under uncertainty

Author

Listed:
  • Rachel Cassidy

    (Institute for Fiscal Studies, London, WC1E 7AE, United Kingdom)

  • Charles F. Manski

    (Department of Economics, Northwestern University, Evanston, IL 60208; Institute for Policy Research, Northwestern University, Evanston, IL 60208)

Abstract

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.

Suggested Citation

  • Rachel Cassidy & Charles F. Manski, 2019. "Tuberculosis diagnosis and treatment under uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(46), pages 22990-22997, November.
    • Handle: RePEc:nas:journl:v:116:y:2019:p:22990-22997
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    Cited by:

    1. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
    2. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," CESifo Working Paper Series 8702, CESifo.
    3. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2022. "Treatment Choice with Nonlinear Regret," Papers 2205.08586, arXiv.org, revised Feb 2024.

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