Developing Methodological Tools to Strengthen Concurrent State Opioid Policy Evaluation

项目摘要 阿片类药物危机导致阿片类药物过量死亡人数稳步增加，目前为 一天将近100个。各国制定了一套不同的政策来遏制这场危机。随着各州继续 努力解决如何分配资源以减少危机，高质量的评估研究对于 确定最有效的政策。这项研究试图为阿片类药物研究人员提供明确的统计数据 指导和新的统计方法，以进行复杂、高质量的政策评估。 本项目将解决当前评价中面临的几个关键挑战。一个是关于 如何最好地选择适当的控制组-这对于有效估计政策的影响至关重要，因为 实施某一特定政策的国家可能与不实施该政策的国家有根本的不同。第二把钥匙 挑战是政策格局的动态性质，因为国家在 在滚动的基础上解决不断演变的危机。虽然各州通常会同时实施一系列改革，但这些 策略通常会单独评估。然而，未能解释同时发生的另一项政策 目标相同的结果可能会产生对给定政策的有偏见的估计。此外，潜在的添加剂或 只有通过考察两种政策的相互作用，才能确定共生政策的协同效应 这反过来又需要足够的样本量，以便能够估计所需的影响。识别 最优统计方法与次优统计方法(例如，次优回归)一样关键 规范、缺乏测试假设、混淆的最小调整、较短的评估窗口)可以 做出不准确的推断，错误地描述真正的政策效果的大小甚至方向。 在这个项目中，我们将对阿片类药物的统计科学状况进行全面总结 政策空间。我们还将创建一系列模拟工具，以告知和改进阿片类药物的方法 策略研究人员和策略研究人员更广泛地利用来确定哪些策略在以下方面最有效 帮助决策者处理阿片类药物(以及未来的危机)。这些工具将为 后续研究，其中可以利用模拟结果和基础设施来更好地评估 哪些类型的政策或政策组合在减少阿片类药物相关危害方面最有效。最后，我们 将开发新的统计方法来解决识别稳健控制状态和 评估同时发生的政策。该项目的方法开发工作将提供阿片类药物(以及更多 更广泛地说，成瘾)研究人员拥有更合适的方法来获得对政策的公正估计 来自纵向观测数据的有效性。

Project Summary The opioid crisis has led to a steady increase in the number of opioid overdose deaths, which currently number nearly 100 a day. States have enacted heterogeneous sets of policies to curtail the crisis. As states continue to grapple with how to allocate resources to reduce the crisis, high-quality evaluation studies are crucial to identifying the most effective policies. This study seeks to provide opioid researchers with clear statistical guidance and novel statistical methods to conduct complex, high-quality policy evaluations. There are several key challenges faced in current evaluations that this project will address. One is the issue of how best to select an appropriate control group – this is crucial for valid estimates of the impact of policies, as states implementing a given policy may be fundamentally different than states that do not. A second key challenge is the dynamic nature of the policy landscape, as states enact and modify various policies on a rolling basis to address the evolving crisis. While states often enact a co-occurring set of reforms, these policies are often evaluated individually. Yet, failure to account for the co-occurrence of another policy that targets the same outcome may yield a biased estimate of a given policy. Furthermore, potential additive or synergistic effects of co-occurring policies can only be identified by examining the interaction of the two policies jointly which in turn will require sufficient sample sizes to allow for estimation of the needed effects. Identifying optimal statistical methods is critical as suboptimal statistical methods (e.g., suboptimal regression specification, lack of testing assumptions, minimal adjustment for confounding, short evaluation windows) may produce inaccurate inferences that misrepresent the magnitude or even direction of true policy effects. In this project, we will provide a comprehensive summary of the state of the statistical science in the opioid policy space. We will also create a series of simulation tools that will inform and improve the methods opioid policy researchers and policy researchers more broadly utilize to determine which policies are most effective at helping decision makers deal with the opioid (and future crises). These tools will provide the groundwork for subsequent research, in which the simulation results and infrastructure can be leveraged to better assess which types of policies or policy combinations are most effective in reducing opioid-related harms. Finally, we will develop novel statistical methods to address the complexities of identifying robust control states and evaluating co-occurring policies. This project’s methods development work will provide opioid (and more broadly, addiction) researchers with more appropriate methods for obtaining unbiased estimates of policy effectiveness from longitudinal observational data.