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DESCRIPTION (provided by applicant): Antiretroviral therapy (ART) increases both the clinical effectiveness, and cost of HIV/AIDS treatment. As a result, U.S. health care programs ensuring access to ART do not have sufficient resources to provide therapy to all who might benefit. U.S. AIDS Drug Assistance Programs (ADAPs) provide ART to those with no other access to medications. ADAPs serve primarily low-income, uninsured, minorities, providing access to care to historically underserved populations. As survival with HIV increases, ADAPs are in crisis. Programs face difficult decisions about prioritizing ART resources. There is significant, preventable morbidity among patients on ADAP waitlists. Further, as HCV treatment becomes more effective, pressure will build on ADAPs to provide HCV therapy to HIV/HCV co-infected patients, threatening the stability of ART access for many. This situation highlights questions about the impact of the HIV-care system on outcomes. In this K01 mentored career award application, I propose using discrete event simulation, an innovative mathematical modeling method, to estimate the impact of resource prioritization strategies on HIV and HIV/HCV morbidity, mortality, and access to care. The three specific aims are: 1. To build and validate a discrete event simulation model of HIV infection in the context of U.S. AIDS Drug Assistance Programs. 2. To use the validated model to estimate outcomes associated with several AIDS Drug Assistance Program resource prioritization strategies including altering eligibility rules, restructuring drug formularies, and Improving cost projections. 3. To expand the model to include HIV/HCV co-infection and use it to compare outcomes under spectrum of potential strategies for allocating HCV resources. To complement these research efforts, I will pursue course-work in advanced biostatistics, operations research, and the impact of institutional structures on health equity. This research and didactic experience will lay the foundation for R01 funding to investigate the implications of large-scale, programmatic resource allocation decisions on clinical outcomes in HIV and HCV. This K01 award will lead to a novel, independent trajectory of research, and will inform HIV and HCV care and policy in the United States.
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HEAL Data2Action Modeling and Economic Resource Center
HEAL Data2Action Modeling and Economic Resource Center
Researching Effective Strategies to Prevent Opioid Death (RESPOND)
  • 批准号:
    10804924
  • 项目类别:
  • 资助金额:
    $89.58万
  • 财政年份:
    2018
  • 负责人:
    Benjamin P. Linas
  • 依托单位:
Researching Effective Strategies to Prevent Opioid Death (RESPOND)
  • 批准号:
    10369647
  • 项目类别:
  • 资助金额:
    $68.65万
  • 财政年份:
    2018
  • 负责人:
    Benjamin P. Linas
  • 依托单位:
海外基金