C-DIAS RP 1: A community-driven modeling approach for identifying and implementing evidence-based interventions and implementation strategies to reduce overdose deaths.

C-DIAS研究项目1：项目总结/摘要 虽然存在有效的治疗方法，而且已经向各州和地区分配了大量新的资金， 尽管扩大了获取途径，但美国仍然受到阿片类药物和兴奋剂相关死亡的困扰。非致命的过量， 急诊室和住院的使用率也继续上升。研究项目1建议 开发一个创新的模型驱动的决策支持系统，以帮助县优先考虑和优化使用 现有资源用于解决阿片类药物和兴奋剂死亡问题。这一新的实施战略不同于 并扩展了数据驱动的决策，而数据驱动的决策本身并不产生关于预期影响的信息。在 特别是，该系统检查了许多不同的选择，以实施战略，适合一个县的 这是一项关于需求和能力的研究，并确定了适应不断变化的环境的最佳解决方案。模型驱动 决策支持（MDDS）实施战略包括三个组成部分：1）持续的社区- 研究伙伴关系，这是必不可少的，如果决策者认为预测是当地相关的， 可信; 2）整合的行政，访谈和调查数据，需要考虑到 本地独特的上下文因素;和3）一个模块化的基于代理的模型（ABM），集成数据，代表 替代的实施策略，并模拟他们的行为对过量死亡的不同条件下， 条件第一个目标是扩展现有的ABM，该ABM将本地数据作为输入并生成 不同策略对过量死亡和差异的影响的预测。响应面 方法和中介建模用于总结调查结果。前端工具是沿着 这样系统就可以被其他国家使用了第二个目的是检验 这种方法在三个不同的县：皮内拉斯县佛罗里达州，朱红县IL和圣克拉拉县CA。 我们与所有三个社区都建立了积极的伙伴关系。如果这三个县的死亡率没有变化， 预计未来5年将有3，000多人死于过量。第三个目标是 在这些社区内，通过监测和反馈， and across横过using运用a learning学习collaborative协作.这一目标也正式评估MDDS使用的阶段， 执行完成情况（SIC）以及衡量可接受性和社区 使用的方法与仅基于数据的方法不同。在执行的可持续发展阶段 研究项目1与C-DIAS双向集成，并使用与 其他C-DIAS研究项目。为了实现C-DIAS的具体目标2，与研究核心一致， 该项目将作为测试和推进ABM和决策者成本模型决策支持的工具 并在全国范围内实施。由于C-DIAS，项目结果将具有广泛的影响力 传播平台，包括对反弹道导弹工具的开放科学评估，产生比 如果独立进行，也是可能的。

C-DIAS RESEARCH PROJECT 1: PROJECT SUMMARY/ABSTRACT While effective treatments exist, and substantial new funding has been distributed to states and territories to expand access, the US continues to be plagued with opioid and stimulant related deaths. Nonlethal overdoses, emergency room and hospitalization utilization also continue to escalate. Research Project 1 proposes to develop an innovative model-driven decision support system to aid counties to prioritize and optimize the use of available resources to address opioid and stimulant deaths. This new implementation strategy differs from and extends data-driven decision making, which by itself does not produce information on projected impact. In particular, this system examines a multitude of different options for implementation strategies that fit a county’s needs and capacities, and identifies optimal solutions that are robust to changing contexts. This model-driven decision support (MDDS) implementation strategy integrates three components: 1) an ongoing community- research partnership, which is essential if decision makers are to feel the predictions are locally relevant and trustworthy; 2) integrated administrative, interview, and survey data, which are required to account for the locally unique contextual factors; and 3) a modular agent-based model (ABM) that integrates data, represents alternative implementation strategies, and simulates their behavior on overdose deaths under varying conditions. The first aim focuses on extending an existing ABM that takes as input local data and produces projections across diverse strategies’ impact on overdose deaths and disparities. Response surface methodology and mediation modeling are used to summarize findings. Front-end tools are developed along with training so the system can be used by other counties. The second aim examines the generalizability of this approach in three diverse counties: Pinellas County FL, Vermilion County IL, and Santa Clara County CA. We have active partnerships with all three communities. If there is no change in mortality in these 3 counties, more than 3,000 individuals are expected to die from overdose in the coming 5 years. The third aim focuses on advancing an approach for improving care through monitoring and feedback, both within these communities and across using a learning collaborative. This aim also formally evaluates the MDDS using the Stages of Implementation Completion (SIC) as well as measuring acceptability and the degree to which the communities are using this approach differently from one based on data only. At the PREPARE phase of the implementation process, Research Project 1 is bi-directionally integrated with C-DIAS, and uses common measures with the other C-DIAS Research Projects. In pursuit of C-DIAS specific aim 2, in concert with the Research Core, this project will serve as a vehicle to test and advance ABM and cost modeling decision supports for policymakers and implementation endeavors nationwide. Project findings will be widely influential because of C-DIAS dissemination platforms, including open science assess to ABM tools, resulting in greater impact than otherwise possible if conducted independently.