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

Health Care Manag Sci. 2018 Dec;21(4):632-646. doi: 10.1007/s10729-017-9416-4. Epub 2017 Aug 31.

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.

Keywords: Drug resistance; Optimal testing; POMDP; Tuberculosis.

MeSH terms

  • Age Factors
  • Algorithms
  • Antitubercular Agents / administration & dosage
  • Antitubercular Agents / therapeutic use*
  • Communicable Disease Control / organization & administration
  • Cost of Illness
  • Health Policy
  • Humans
  • India
  • Markov Chains
  • Sex Factors
  • Sputum / microbiology
  • Time Factors
  • Tuberculosis / diagnosis
  • Tuberculosis / drug therapy
  • Tuberculosis, Multidrug-Resistant / diagnosis*
  • Tuberculosis, Multidrug-Resistant / drug therapy*

Substances

  • Antitubercular Agents