D-ISN/Collaborative Research: Disrupting West Virginia's Opioid Crisis: a Multi-disciplinary Approach through Interdiction and Harm Reduction

D-ISN/合作研究：扰乱西弗吉尼亚州的阿片类药物危机：通过拦截和减少危害采取多学科方法

• 批准号: 2240362
• 负责人: Ankit Bansal
• 金额: $ 24.4万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240362&HistoricalAwards=false
• 关键词: ISN; Collaborative; Research; Disrupting; West

This Disrupting Operations of Illicit Supply Networks (D-ISN) project develops novel analytical methods to assess the effectiveness of interventions to disrupt opioid supply chains. At the local level, options to disrupt these networks can be broadly categorized into two groups: (i) supply-side interdiction strategies, which focus on disrupting the drug flow into the communities, and (ii) demand-side interdiction strategies, which concentrate on reducing drug demand and on mitigating the health impacts of drug use within communities. Supply- and demand-side interdiction approaches are often seen as exclusive and competing approaches for handling the opioid epidemic. They are, however, synergistic as supply-side interdiction reduces the preponderance of drugs in communities whereas harm-reduction mitigates their adverse effects. Finding balance between these interventions is particularly crucial for counties as they house many important governmental functions in emergency, social, and public safety services. Driven by empirical investigations of opioid network features at the local level, this project aims to develop decision support models that are powered by new advances in machine learning and utility theory to help county policy makers allocate their resources effectively to combat this epidemic. The models will be validated using data collected from the state of West Virginia, which has been uniquely negatively affected by opioids. The project will provide support for graduate students, who will be trained in multidisciplinary approaches to address complex societal problems.This project will link county expenditure data with available public health and public safety data on drug availability and use. In particular, the project (i) maps and compares West Virginia counties in terms of budgetary policy directions (supply-side, demand-side or both) and their relative success at disrupting the supply and demand for drugs in local communities and at reducing their negative health consequences and (ii) builds analytical models that tackle aspects of opioid network disruption at multiple state decision levels. These models will provide data-driven prescriptions for budget allocation taking into account three intrinsic challenges: (a) the forms of utilities of policymakers are not known exactly, (b) the health and safety consequences of budget decisions are complex nonlinear functions accessible through data, and (c) opioid networks adapt to policy decisions. Developed models and solution methodologies will build on recent developments in utility theory, machine learning, and optimization to provide local counties decision tools for policy prescriptions that can lead to optimal/improved outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这种破坏非法供应网络（D-ISN）项目的操作开发了新颖的分析方法，以评估破坏阿片类药物供应链的干预措施的有效性。 在地方一级，可以将破坏这些网络的选项大致分为两组：（i）供应方固定策略，重点是破坏药物流入社区的流动，以及（ii）需求侧的拦截策略，这些策略集中于减少药物需求和缓解社区中药物使用的健康影响。供需方固定方法通常被视为处理阿片类药物流行的独家和竞争方法。但是，随着供应侧拦截的协同作用会降低社区中药物的优势，而减少危害减轻了它们的不利影响。在这些干预措施之间找到平衡对于县尤为重要，因为它们在紧急，社会和公共安全服务中拥有许多重要的政府职能。在对地方一级阿片类网络特征的实证研究的驱动下，该项目旨在开发由机器学习和公用事业理论的新进展提供支持的决策支持模型，以帮助县政策制定者有效地分配其资源来打击这种流行病。这些模型将使用从西弗吉尼亚州收集的数据来验证，该数据已受阿片类药物的负面影响。该项目将为研究生提供支持，研究生将接受多学科方法的培训，以解决复杂的社会问题。该项目将将县支出数据与可用的公共卫生和公共安全数据联系起来，并提供有关药物可用性和使用的公共安全数据。特别是，该项目（i）地图并根据预算政策方向（供应方，需求方或两者兼而有之）及其在破坏当地社区中药物的供应和需求方面进行比较，并降低其负面健康后果，并降低其分析模型，以解决多个国家决策水平的阿片类网络方面的分析模型。这些模型将考虑到三个内在挑战的数据驱动处方：（a）政策制定者公用事业的形式尚不确切，（b）预算决策的健康和安全后果是可通过数据访问的复杂非线性功能，以及（c）阿片类药物网络适应政策决策。开发的模型和解决方案方法将基于实用性理论，机器学习和优化的最新发展，以为当地县的决策工具提供政策处方的决策工具，这可能导致最佳/改进的结果。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来提供支持的。