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Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence

Author

Listed:
  • Jennifer E. Mason
  • Darin A. England
  • Brian T. Denton
  • Steven A. Smith
  • Murat Kurt
  • Nilay D. Shah

Abstract

Background . Statins are an important part of the treatment plan for patients with type 2 diabetes. However, patients who are prescribed statins often take less than the prescribed amount or stop taking the drug altogether. This suboptimal adherence may decrease the benefit of statin initiation. Objective . To estimate the influence of adherence on the optimal timing of statin initiation for patients with type 2 diabetes. Method . The authors use a Markov decision process (MDP) model to optimize the treatment decision for patients with type 2 diabetes. Their model incorporates a Markov model linking adherence to treatment effectiveness and long-term health outcomes. They determine the optimal time of statin initiation that minimizes expected costs and maximizes expected quality-adjusted life years (QALYs). Results . In the long run, approximately 25% of patients remain highly adherent to statins. Based on the MDP model, generic statins lower costs in men and result in a small increase in costs in women relative to no treatment. Patients are able to noticeably increase their expected QALYs by 0.5 to 2 years depending on the level of adherence. Conclusions . Adherence-improving interventions can increase expected QALYs by as much as 1.5 years. Given suboptimal adherence to statins, it is optimal to delay the start time for statins; however, changing the start time alone does not lead to significant changes in costs or QALYs.

Suggested Citation

  • Jennifer E. Mason & Darin A. England & Brian T. Denton & Steven A. Smith & Murat Kurt & Nilay D. Shah, 2012. "Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence," Medical Decision Making, , vol. 32(1), pages 154-166, January.
    • Handle: RePEc:sae:medema:v:32:y:2012:i:1:p:154-166
    DOI: 10.1177/0272989X11404076
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    References listed on IDEAS

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    Cited by:

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    4. Boloori, Alireza & Saghafian, Soroush & Chakkera, Harini A. A. & Cook, Curtiss B., 2017. "Data-Driven Management of Post-transplant Medications: An APOMDP Approach," Working Paper Series rwp17-036, Harvard University, John F. Kennedy School of Government.
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    6. Zlatana Nenova & Jennifer Shang, 2022. "Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 583-606, February.

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