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Optimal timing of drug sensitivity testing for patients on first-line tuberculosis treatment

Author

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  • Sze-chuan Suen

    (University of Southern California)

  • Margaret L. Brandeau

    (Stanford University)

  • Jeremy D. Goldhaber-Fiebert

    (Stanford University)

Abstract

Effective treatment for tuberculosis (TB) patients on first-line treatment involves triaging those with drug-resistant (DR) TB to appropriate treatment alternatives. Patients likely to have DR TB are identified using results from repeated inexpensive sputum-smear (SS) tests and expensive but definitive drug sensitivity tests (DST). Early DST may lead to high costs and unnecessary testing; late DST may lead to poor health outcomes and disease transmission. We use a partially observable Markov decision process (POMDP) framework to determine optimal DST timing. We develop policy-relevant structural properties of the POMDP model. We apply our model to TB in India to identify the patterns of SS test results that should prompt DST if transmission costs remain at status-quo levels. Unlike previous analyses of personalized treatment policies, we take a societal perspective and consider the effects of disease transmission. The inclusion of such effects can significantly alter the optimal policy. We find that an optimal DST policy could save India approximately $1.9 billion annually.

Suggested Citation

  • Sze-chuan Suen & Margaret L. Brandeau & Jeremy D. Goldhaber-Fiebert, 2018. "Optimal timing of drug sensitivity testing for patients on first-line tuberculosis treatment," Health Care Management Science, Springer, vol. 21(4), pages 632-646, December.
    • Handle: RePEc:kap:hcarem:v:21:y:2018:i:4:d:10.1007_s10729-017-9416-4
    DOI: 10.1007/s10729-017-9416-4
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    References listed on IDEAS

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    Cited by:

    1. Chou-Chun Wu & Yiwen Cao & Sze-chuan Suen & Eugene Lin, 2024. "Examining chronic kidney disease screening frequency among diabetics: a POMDP approach," Health Care Management Science, Springer, vol. 27(3), pages 391-414, September.
    2. Xuecheng Yin & İ. E. Büyüktahtakın, 2021. "A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations," Health Care Management Science, Springer, vol. 24(3), pages 597-622, September.
    3. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.

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