IDEAS home Printed from https://ideas.repec.org/a/inm/ordeca/v21y2024i3p165-193.html
   My bibliography  Save this article

Curbing the Opioid Crisis: Optimal Dynamic Policies for Preventive and Mitigating Interventions

Author

Listed:
  • Sina Ansari

    (Driehaus College of Business, DePaul University, Chicago, Illinois 60604)

  • Shakiba Enayati

    (Supply Chain and Analytics Department, College of Business Administration, University of Missouri, St. Louis, Missouri 63101)

  • Raha Akhavan-Tabatabaei

    (Sabanci Business School, Sabanci University, 34956 Istanbul, Turkey)

  • Julie M. Kapp

    (Public Health, College of Health Sciences, University of Missouri, University of Missouri, Columbia, Missouri 65201)

Abstract

Problem statement : This paper addresses the challenge of effectively responding to the opioid epidemic stemming from prescription pills through a public health lens. It centers on the strategic distribution of resources across diverse interventions aimed at preventing and mitigating the consequences of opioid use disorder (OUD) and overdose occurrences. Methodology : This paper proposes a decision aid tool built on the expected utility theory that leverages a Susceptible-Infected-Removed compartmental model to simulate the dynamics of the epidemic in a population. This model then feeds into a Markov Decision Process (MDP) model to generate optimal policies upon the current state of the epidemic. The optimal policies allocate the intervention budget to primary preventive and mitigating interventions in each decision period by minimizing the cost of fatal overdoses relative to the population’s number of individuals with OUD, considering the impact magnitude of each intervention, based on the current state of the epidemic. A 10-year simulation of the epidemic’s progression is conducted to assess the dynamic efficacy of the proposed decision tool. Results : The findings reveal an average reduction of 29% in total costs compared to the scenario without interventions and a decrease of 12% in total costs on average compared to the scenario with a 50-50 allocation. The extensive sensitivity analysis of key parameters validates the decision aid tool. We observe that it is optimal to allocate a significant portion of the budget to prevention when the rate of opioid pill acquisition rises. Even with a heightened rate of fatal overdoses, it remains optimal to mostly invest in preventive interventions, as long as fatal overdose rates are lower than opioid access rates. Practical implications : This study provides practitioners with a tool to effectively address the opioid epidemic and enhance public health by deciding how to allocate their budget to various levels of intervention.

Suggested Citation

  • Sina Ansari & Shakiba Enayati & Raha Akhavan-Tabatabaei & Julie M. Kapp, 2024. "Curbing the Opioid Crisis: Optimal Dynamic Policies for Preventive and Mitigating Interventions," Decision Analysis, INFORMS, vol. 21(3), pages 165-193, September.
    • Handle: RePEc:inm:ordeca:v:21:y:2024:i:3:p:165-193
    DOI: 10.1287/deca.2023.0084
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/deca.2023.0084
    Download Restriction: no
    File URL: https://libkey.io/10.1287/deca.2023.0084?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jagpreet Chhatwal & Oguzhan Alagoz & Elizabeth S. Burnside, 2010. "Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors," Operations Research, INFORMS, vol. 58(6), pages 1577-1591, December.
    2. Franklin, G. & Sabel, J. & Jones, C.M. & Mai, J. & Baumgartner, C. & Banta-Green, C.J. & Neven, D. & Tauben, D.J., 2015. "A comprehensive approach to address the prescription opioid epidemic in Washington state: Milestones and lessons learned," American Journal of Public Health, American Public Health Association, vol. 105(3), pages 463-469.
    3. Erik Rosenstrom & Sareh Meshkinfam & Julie Simmons Ivy & Shadi Hassani Goodarzi & Muge Capan & Jeanne Huddleston & Santiago Romero-Brufau, 2022. "Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model," Decision Analysis, INFORMS, vol. 19(4), pages 265-296, December.
    4. Oguzhan Alagoz & Jagpreet Chhatwal & Elizabeth S. Burnside, 2013. "Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis," Decision Analysis, INFORMS, vol. 10(3), pages 200-224, September.
    5. Stewart, H. & Malinowski, A. & Ochs, L. & Jaramillo, J. & McCall, K., III & Sullivan, M., 2015. "Inside Maine's medicine cabinet: Findings from the Drug Enforcement Administration's medication take-back events," American Journal of Public Health, American Public Health Association, vol. 105(1), pages 65-71.
    6. Edward J. Sondik, 1978. "The Optimal Control of Partially Observable Markov Processes over the Infinite Horizon: Discounted Costs," Operations Research, INFORMS, vol. 26(2), pages 282-304, April.
    7. C. Peter Rydell & Jonathan P. Caulkins & Susan S. Everingham, 1996. "Enforcement or Treatment? Modeling the Relative Efficacy of Alternatives for Controlling Cocaine," Operations Research, INFORMS, vol. 44(5), pages 687-695, October.
    8. Abdullah Gökçınar & Metin Çakanyıldırım & Theodore Price & Meredith C. B. Adams, 2022. "Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity," Decision Analysis, INFORMS, vol. 19(4), pages 297-318, December.
    9. Molani, Sevda & Madadi, Mahboubeh & Wilkes, Wesley, 2019. "A partially observable Markov chain framework to estimate overdiagnosis risk in breast cancer screening: Incorporating uncertainty in patients adherence behaviors," Omega, Elsevier, vol. 89(C), pages 40-53.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robert Kraig Helmeczi & Can Kavaklioglu & Mucahit Cevik & Davood Pirayesh Neghab, 2023. "A multi-objective constrained partially observable Markov decision process model for breast cancer screening," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    2. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    3. Eike Nohdurft & Elisa Long & Stefan Spinler, 2017. "Was Angelina Jolie Right? Optimizing Cancer Prevention Strategies Among BRCA Mutation Carriers," Decision Analysis, INFORMS, vol. 14(3), pages 139-169, September.
    4. Mehmet A. Ergun & Ali Hajjar & Oguzhan Alagoz & Murtuza Rampurwala, 2022. "Optimal breast cancer risk reduction policies tailored to personal risk level," Health Care Management Science, Springer, vol. 25(3), pages 363-388, September.
    5. Jagpreet Chhatwal & Suren Jayasuriya & Elamin H. Elbasha, 2016. "Changing Cycle Lengths in State-Transition Models," Medical Decision Making, , vol. 36(8), pages 952-964, November.
    6. Sait Tunç & Oguzhan Alagoz & Elizabeth S. Burnside, 2022. "A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2361-2378, May.
    7. Zeiler, I. & Caulkins, J.P. & Tragler, G., 2011. "Optimal control of interacting systems with DNSS property: The case of illicit drug use," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1), pages 60-73.
    8. Williams, Byron K., 2009. "Markov decision processes in natural resources management: Observability and uncertainty," Ecological Modelling, Elsevier, vol. 220(6), pages 830-840.
    9. Michael T. French & Kerry Anne McGeary, 1997. "Letter: Estimating the economic cost of substance abuse treatment," Health Economics, John Wiley & Sons, Ltd., vol. 6(5), pages 539-544, September.
    10. Gernot Tragler & Jonathan P. Caulkins & Gustav Feichtinger, 2001. "Optimal Dynamic Allocation of Treatment and Enforcement in Illicit Drug Control," Operations Research, INFORMS, vol. 49(3), pages 352-362, June.
    11. Yanling Chang & Alan Erera & Chelsea White, 2015. "Value of information for a leader–follower partially observed Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 129-153, December.
    12. Jing Li & Ming Dong & Yijiong Ren & Kaiqi Yin, 2015. "How patient compliance impacts the recommendations for colorectal cancer screening," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 920-937, November.
    13. Caulkins, Jonathan P. & Reuter, Peter, 2006. "Illicit drug markets and economic irregularities," Socio-Economic Planning Sciences, Elsevier, vol. 40(1), pages 1-14, March.
    14. Seites-Rundlett, William & Bashar, Mohammad Z. & Torres-Machi, Cristina & Corotis, Ross B., 2022. "Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    15. Elliot Lee & Mariel Lavieri & Michael Volk & Yongcai Xu, 2015. "Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity," Health Care Management Science, Springer, vol. 18(3), pages 363-375, September.
    16. Monica Hubbard & Luke Fowler, 2021. "Institutional Collective Action on Drugs: Functional and Vertical Dilemmas of Unused Pharmaceuticals," Review of Policy Research, Policy Studies Organization, vol. 38(1), pages 76-96, January.
    17. Baveja, Alok & Jamil, Mamnoon & Kushary, Debashis, 2004. "A sequential model for cracking down on street markets for illicit drugs," Socio-Economic Planning Sciences, Elsevier, vol. 38(1), pages 7-41, March.
    18. Baruch Keren & Joseph Pliskin, 2011. "Optimal timing of joint replacement using mathematical programming and stochastic programming models," Health Care Management Science, Springer, vol. 14(4), pages 361-369, November.
    19. Oguzhan Alagoz & Jagpreet Chhatwal & Elizabeth S. Burnside, 2013. "Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis," Decision Analysis, INFORMS, vol. 10(3), pages 200-224, September.
    20. MacCoun Robert & Cook Philip J. & Muschkin Clara & Vigdor Jacob L, 2008. "Distinguishing Spurious and Real Peer Effects: Evidence from Artificial Societies, Small-Group Experiments, and Real Schoolyards," Review of Law & Economics, De Gruyter, vol. 4(3), pages 695-714, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ordeca:v:21:y:2024:i:3:p:165-193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.