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：项目摘要/摘要 虽然存在有效的治疗方法，而且已经向各州和领地分发了大量新的资金，以 在扩大准入的同时，美国继续受到阿片类药物和兴奋剂相关死亡的困扰。非致命性过量用药， 急诊室和住院使用率也持续上升。研究项目1建议 开发创新模式驱动的决策支持系统，帮助县域优先和优化使用 解决阿片类药物和兴奋剂死亡问题的现有资源。这种新的实施策略不同于 并扩展了数据驱动的决策制定，而数据驱动的决策本身并不会产生有关预测影响的信息。在……里面 具体地说，该系统检查适合一个县的实施战略的多种不同选择 需求和能力，并确定对不断变化的环境具有强大生命力的最佳解决方案。这个模型驱动的 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，这是至关重要的，如果决策者认为预测是与当地相关的， 值得信赖；2)综合管理、访谈和调查数据，这些数据需要说明 本地唯一的上下文因素；以及3)集成数据的模块化的基于代理的模型(ABM)，表示 可供选择的实施策略，并模拟了他们在不同情况下的过量死亡行为 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 不同战略对过量死亡和差异的影响的预测。响应面 采用方法论和中介建模的方法对研究结果进行总结。前端工具的开发是沿着 经过培训，该系统可以被其他县使用。第二个目标是考察的概括性 这种方法在三个不同的县：佛罗里达州皮内拉斯县、伊利诺伊州朱红色县和加利福尼亚州圣克拉拉县。 我们与所有三个社区都有积极的伙伴关系。如果这3个县的死亡率没有变化， 在接下来的5年里，预计将有3000多人死于服药过量。第三个目标集中在 在这些社区内，通过监测和反馈推动改善护理的方法 以及使用协作学习的方式。这一目标还使用以下阶段正式评估MDDS 实施完成度(SIC)以及衡量社区的可接受性和程度 使用这种方法与仅基于数据的方法不同。在实施的准备阶段 在此过程中，研究项目1与C-DIAS双向集成，并与 其他C-DIAS研究项目。为了实现C-DIAS的具体目标2，与研究核心协调一致，这 项目将作为测试和推进作业成本管理和成本建模决策支持的工具，为决策者提供支持 并在全国范围内实施。项目结果将因C-DIAS而产生广泛影响 传播平台，包括对ABM工具的开放科学评估，产生的影响大于 否则，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.