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Computational discovery of effective hepatitis C intervention strategies

Computational discovery of effective hepatitis C intervention strategies
有效丙型肝炎干预策略的计算发现
批准号:
9383459
负责人:
Basmattee Boodram
金额:
$42.33万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-08-15 至 2022-07-31

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英文摘要
7. PROJECT SUMMARY/ABSTRACT Hepatitis C (HCV) is a leading cause of chronic liver disease and mortality worldwide. The World Health Organization (WHO) has recently recognized the need to prevent and control HCV infection, and proposed that HCV elimination is feasible by 2030 by reducing new chronic infections by 90% and HCV-related mortality by 65%. In the U.S., elimination strategies are urgently needed that focus on persons who inject drugs (PWID), the group at most risk for acquiring and transmitting HCV infection. Despite the long-term availability of harm reduction strategies such as syringe exchange programs (SEP), opioid substitution therapies (OSTs), and behavioral counseling, HCV incidence in the U.S. is on the rise among PWID. The recent availability of all oral direct-acting antivirals (DAAs) with high reported cure rates (e.g., >90%) that can prevent liver disease progression and HCV transmission, combined with prevention and harm reduction strategies, make HCV elimination an attainable goal. However, given considerable barriers (e.g., cost of DAAs, poor linkage to care and adherence, possible reinfection, PWID lifestyle), it is essential for policy development and strategic planning to understand the factors that would most effectively promote HCV elimination among PWID. Understanding the dynamic and complex interplay of factors at the individual (e.g., risk behaviors), social (e.g., injection networks), structural (e.g., access to syringe exchange programs and opioid substitution therapies), and geographic (e.g., non-urban residence) levels is essential to improve understanding and development of HCV elimination strategies. Current models cannot account for such dynamic and complex interactions. As such we propose to develop a comprehensive, data-driven agent-based model for Hepatitis C Elimination in PWID (HepCEP) using the Chicago PWID population as a template and proof of concept that would enable policy makers to identify the most effective intervention strategies for elimination of HCV by 2030 based on the aforementioned WHO's proposed reduction estimates. The long term significance of these efforts would be to adapt the HepCEP framework to (i) model HCV transmission in the general population of Chicago and in Illinois prisons, (ii) forecast the spread of HCV in other U.S. urban and non-urban PWID populations (e.g., Albuquerque, NM), (iii) perform cost-effectiveness analyses, and (iv) assist vaccine-trial sponsors in designing and evaluating clinical trials.
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Computational modeling for HCV vaccine trial design and optimal vaccine-based combination interventions
  • 批准号:
    10514625
  • 项目类别:
  • 资助金额:
    $70.81万
  • 财政年份:
    2021
  • 负责人:
    Basmattee Boodram
  • 依托单位:
Computational modeling for HCV vaccine trial design and optimal vaccine-based combination interventions
  • 批准号:
    10367717
  • 项目类别:
  • 资助金额:
    $75.97万
  • 财政年份:
    2021
  • 负责人:
    Basmattee Boodram
  • 依托单位:
Contextual risk factors for hepatitis C among young persons who inject drugs
  • 批准号:
    10179349
  • 项目类别:
  • 资助金额:
    $52.6万
  • 财政年份:
    2017
  • 负责人:
    Basmattee Boodram
  • 依托单位:
Contextual risk factors for hepatitis C among young persons who inject drugs
  • 批准号:
    9926034
  • 项目类别:
  • 资助金额:
    $0.79万
  • 财政年份:
    2017
  • 负责人:
    Basmattee Boodram
  • 依托单位:
海外基金