ArticleHealth care management science2018
Optimal timing of drug sensitivity testing for patients on first-line tuberculosis treatment.
Article in Health care management science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Examining chronic kidney disease screening frequency among diabetics: a POMDP approach.Health care management science · 2024Article
- A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process.PharmacoEconomics · 2022Review
- Optimizing diabetes screening frequencies for at-risk groups.Health care management science · 2022Article
- Diagnostic Policies Optimization for Chronic Diseases Based on POMDP Model.Healthcare (Basel, Switzerland) · 2022Article
- Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases.Health care management science · 2021Article
- Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma.Production and operations management · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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