Supporting data-driven decision-making to support substance use service expansion policies and to prevent overdoses

项目摘要 跨部门决策者-如社区服务提供者、公共卫生、司法、倡导者和 支付者呼吁以可持续的、有用的方式收集和共享可操作的数据。俄勒冈州排名最后 在美国获得物质使用服务，首先是阿片类药物和甲基苯丙胺的使用。最近的一个国家- 广泛的分析估计，服务差距甚至可能比以前估计的更大。拟定研究 旨在以易于使用的格式提供与决策者相关的数据，以便他们能够 及时做出有证据依据的决策，以减少药物使用服务缺口和过量， 增进健康。我们将利用美国第一次推出的同类政策--选票措施110 （M110）。M110正在带来前所未有的资金水平，以扩大与过量用药支柱相一致的服务 它使拥有个人数量的物质合法化。评论家和 M110的倡导者呼吁提供更好的数据，以全面了解物质使用服务和服务- 接受者的影响，并为即将做出的决定提供信息，例如如何分配阿片类药物结算资金。满足这一 需要，符合NIH帮助结束成瘾长期数据行动计划的目标， 创新项目与跨部门的合作伙伴关系，我们建议制定，完善和测试政策 实施战略-发现和设计会议（DDS）-让跨部门决策者参与， 讨论哪些数据是他们的优先事项，并制定可行的协议， 通过他们共同设计的产品传播数据（例如，报告、模拟、仪表板）。在R61 在第一阶段，我们将加强和扩大与五类决策者的现有伙伴关系，包括国家 负责监测和/或实施M110以及维护全州数据存储库的机构。 DDS、DDS生成的数据产品以及共同开发的数据共享和研究生成数据协议 收集工作将进一步完善，以便进行可行性研究和测试。在R33阶段，各县将被随机分组， 参与DDS并接收完全定制的数据产品（N = 18）或稍后仅接收产品（N = 18）， 阶梯形楔形设计。我们将：（目标1）确定DDS是否是一种有效的，可推广的优化策略 根据各州数据收集系统生成的数据产品的相对可用性执行政策; (Aim 2）测试DDS对物质使用服务差距和服务接受者结果的影响，以及交叉- （目标3）研究DDS产生的数据产品是否与具体的 动作（例如，加强以证据为基础的、对文化敏感的 物质使用服务。根据研究结果和合作伙伴的投入，我们将为国家决策者提供 支持维持研究基础设施和产出的建议和方案，包括：可行的 优先数据收集和数据产品传播的方法，以及研究产生的数据的转移 到全州范围的数据基础设施。拟议的研究具有很大的潜力，立即，现实世界的影响。

PROJECT SUMMARY Cross-sector decision makers—such as community service providers, public health, justice, advocates, and payers—are calling for actionable data to be collected and shared in sustainable, useful ways. Oregon ranks last in the U.S. for access to substance use services and first in opioid and methamphetamine use. A recent state- wide analysis estimated that service gaps may even be larger than previously estimated. The proposed study aims to make data that are relevant to decision makers available to them in easy-to-use formats so that they can make timely, evidence-informed decisions to reduce substance use service gaps and overdoses, and ultimately improve health. We will leverage the roll-out of a first-of-its-kind policy in the United States—Ballot Measure 110 (M110). M110 is bringing unprecedented levels of funding to expand services aligned with the pillars of overdose prevention statewide, and it decriminalized the possession of personal amounts of substances. Both critics and advocates of M110 have called for better data to provide a holistic picture of substance use service and service- recipient impacts, and to inform looming decisions such as how to allocate opioid settlement funds. To meet this need, consistent with goals of the NIH Helping to End Addiction Long-term Data2Action Program call for Innovation Projects with cross-sector partnerships, we propose to develop, refine, and test a policy implementation strategy—Discovery and Design Sessions (DDS)—to engage cross-sector decision makers in conversations about what data are of priority to them and to develop feasible protocols for linking and disseminating data through products that they co-design (e.g., reports, simulations, dashboards). In the R61 phase, we will strengthen and expand existing partnerships with five types of decision makers, including state agencies responsible for monitoring and/or implementing M110 and for maintaining a statewide data repository. DDS, DDS-generated data products, and co-developed protocols for data sharing and for study-generated data collection will be refined for feasibility and testing. In the R33 phase, counties will be cluster randomized to participate in DDS and receive fully tailored data products (N = 18) or to later receive products only (N = 18) in a stepped-wedge design. We will: (Aim 1) identify whether DDS is an efficient, generalizable strategy to optimize policy implementation based on the comparative usability of DDS-generated data products between counties; (Aim 2) test the impact of DDS on substance use service gaps and service-recipient outcomes, as well as cross- sector collaboration; and (Aim 3) examine whether DDS-generated data products are associated with concrete actions (e.g., funding) to strengthen the availability and quality of evidence-based, culturally-responsive substance use services. Based on study results and partners’ input, we will provide state decision makers with recommendations and protocols for supporting sustainment of study infrastructure and output, including: feasible methods for prioritized data collection and data product dissemination, and the transfer of study-generated data to state-wide data infrastructures. The proposed study holds strong potential for immediate, real-world impact.