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Planning for HIV Screening, Testing, and Care at the Veterans Health Administration

Author

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  • Sarang Deo

    (Indian School of Business, Gachibowli, Hyderabad, India 500032)

  • Kumar Rajaram

    (UCLA Anderson School of Management, Los Angeles, California 90095)

  • Sandeep Rath

    (UCLA Anderson School of Management, Los Angeles, California 90095)

  • Uday S. Karmarkar

    (UCLA Anderson School of Management, Los Angeles, California 90095)

  • Matthew B. Goetz

    (Veteran’s Health Administration, Greater Los Angeles Station, Los Angeles, CA 90073)

Abstract

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station.

Suggested Citation

  • Sarang Deo & Kumar Rajaram & Sandeep Rath & Uday S. Karmarkar & Matthew B. Goetz, 2015. "Planning for HIV Screening, Testing, and Care at the Veterans Health Administration," Operations Research, INFORMS, vol. 63(2), pages 287-304, April.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:2:p:287-304
    DOI: 10.1287/opre.2015.1353
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    References listed on IDEAS

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    1. James G. Kahn & Margaret L. Brandeau & John Dunn-Mortimer, 1998. "OR Modeling and AIDS Policy: From Theory to Practice," Interfaces, INFORMS, vol. 28(3), pages 3-22, June.
    2. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    3. Sarang Deo & Seyed Iravani & Tingting Jiang & Karen Smilowitz & Stephen Samuelson, 2013. "Improving Health Outcomes Through Better Capacity Allocation in a Community-Based Chronic Care Model," Operations Research, INFORMS, vol. 61(6), pages 1277-1294, December.
    4. Owens, D.K. & Sundaram, V. & Lazzeroni, L.C. & Douglass, L.R. & Sanders, G.D. & Taylor, K. & VanGroningen, R. & Shadle, V.M. & McWhorter, V.C. & Agoncillo, T. & Haren, N. & Nyland, J. & Tempio, P. & K, 2007. "Prevalence of HIV infection among inpatients and outpatients in department of veterans affairs health care systems: Implications for screening programs for HIV," American Journal of Public Health, American Public Health Association, vol. 97(12), pages 2173-2178.
    5. Brandeau, Margaret L. & Zaric, Gregory S. & Richter, Anke, 2003. "Resource allocation for control of infectious diseases in multiple independent populations: beyond cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 22(4), pages 575-598, July.
    6. Gregory S. Zaric & Margaret L. Brandeau & Paul G. Barnett, 2000. "Methadone Maintenance and HIV Prevention: A Cost-Effectiveness Analysis," Management Science, INFORMS, vol. 46(8), pages 1013-1031, August.
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