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The Price of Nonabandonment: HIV in Resource-Limited Settings

Author

Listed:
  • Amin Khademi

    (Clemson University, Clemson, South Carolina 29634)

  • Denis R. Saure

    (University of Chile, Santiago, Chile)

  • Andrew J. Schaefer

    (University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

  • Ronald S. Braithwaite

    (New York University, New York, New York 10003)

  • Mark S. Roberts

    (University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

Abstract

The global fight against HIV/AIDS is hindered by a lack of drugs in the developing world. When patients in these countries initiate treatment, they typically remain on it until death; thus, policy makers and physicians follow nonabandonment policies. However, treated patients develop resistance to treatment, so in many cases untreated patients might benefit more from the drugs. In this paper we quantify the opportunity cost associated with restricting attention to nonabandonment policies. For this, we use an approximate dynamic programming framework to bound the benefit from allowing premature treatment termination. Our results indicate that in sub-Saharan Africa, the price associated with restricting attention to nonabandonment policies lies between 4.4% and 8.1% of the total treatment benefit. We also derive superior treatment allocation policies, which shed light on the role behavior and health progression play in prioritizing treatment initiation and termination.

Suggested Citation

  • Amin Khademi & Denis R. Saure & Andrew J. Schaefer & Ronald S. Braithwaite & Mark S. Roberts, 2015. "The Price of Nonabandonment: HIV in Resource-Limited Settings," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 554-570, October.
    • Handle: RePEc:inm:ormsom:v:17:y:2015:i:4:p:554-570
    DOI: 10.1287/msom.2015.0545
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    References listed on IDEAS

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    Cited by:

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    3. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    4. Ting-Yu Ho & Shan Liu & Zelda B. Zabinsky, 2019. "A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management," Health Care Management Science, Springer, vol. 22(4), pages 727-755, December.
    5. Amin Khademi & Burak Eksioglu, 2018. "Spare Parts Inventory Management with Substitution-Dependent Reliability," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 507-521, August.
    6. Turgay Ayer & Can Zhang & Anthony Bonifonte & Anne C. Spaulding & Jagpreet Chhatwal, 2019. "Prioritizing Hepatitis C Treatment in U.S. Prisons," Operations Research, INFORMS, vol. 67(3), pages 853-873, May.
    7. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    8. Farhad Hasankhani & Amin Khademi, 2021. "Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2653-2671, August.
    9. Elliot Lee & Mariel S. Lavieri & Michael Volk, 2019. "Optimal Screening for Hepatocellular Carcinoma: A Restless Bandit Model," Service Science, INFORMS, vol. 21(1), pages 198-212, January.
    10. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.
    11. Pinar Keskinocak & Nicos Savva, 2020. "A Review of the Healthcare-Management (Modeling) Literature Published in Manufacturing & Service Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 59-72, January.

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