Drug Abuse Policy Surveillance System

美国一直依赖州立法作为解决阿片类药物健康问题的主要工具 误用干预措施包括处方药监测系统、第三方纳洛酮管理法 和好撒玛利亚人911条款是全面应对的关键要素。评估这些 采取干预措施，看看哪些法律和法律的具体内容会产生影响，这一点至关重要，但也很困难。 法律的科学有限责任公司（LS）在支持更好地跟踪和评估法律方面发挥着独特的作用。的 公司建立在政策监督的原则上-系统，科学地跟踪法律， 公共卫生意义。2016年3月，LS推出了MonQcle，这是一个SaaS软件平台， 1)建立经验政策数据与科学法典和协议; 2）这些数据存储在一个 冗余、可扩展的数据库; 3）通过集成的CMS发布地图、表格和数据 可视化工具。对于第二阶段，我们建议大幅扩展MonQcle，以包括健康成果 政策评估实验室。处方药滥用政策系统的权力 （PDAPS）和药物滥用政策系统（DAPS），2017年初推出。我们提出一项新政策 分析范式，以简化和加快对政策影响的评估。我们将以人为本 设计原则来设计仪表板。使用应用程序编程接口（API），我们将包括 药物相关结果数据的存储和检索，包括导入、导出、计算和可视化 设计和显示功能。我们将整合一套医学上可用的纵向结果数据， 全国药物使用和健康调查和CDC的WONDER与现有的药物政策数据，包括 pdmp的，纳洛酮，好撒玛利亚人，美沙酮辅助治疗，吸毒驾驶和医疗和 休闲大麻法与巴特尔纪念研究所（Battelle）的研究人员合作，LS将 扩展第一阶段行动计划，使其包括一个通用的希望体系结构，并随后部署一种独特的物质 使用特定的政策实验室称为DOPE -药物结果政策评估。DOPE将是一个友好的研究人员， 扩大行动计划，促进将卫生结果数据覆盖在国家政策数据上， R中的计算功能，对各州的政策或政策特征进行排名，并衡量其对 人口健康。巴特尔高级分析和健康研究（AAHR）团队的合作者将 评估关键的国家法律的干预措施对减少阿片剂流行的影响。使用DOPE，团队将 检查一组研究问题，包括但不限于：1）当政策纳洛酮访问，好 撒玛利亚人的保护，药物治疗，药物辅助治疗美沙酮和/或吸毒驾驶是在 它是否降低了与毒品有关的死亡率（所有毒品）？2)与合法化相关的政策 大麻（用于娱乐或医疗目的）显示大麻使用和风险感知的变化最后，3） 哪一套政策能最大程度地降低与毒品有关的死亡率？最后，一起 小组将解决一系列关于DOPE的功能、效率和可行性的研究问题。

The United States has relied on state legislation as a primary tool to address the rising health toll of opioid misuse. Interventions including prescription drug monitoring systems, third-party naloxone administration laws and Good Samaritan 911 provisions are a key elements of a comprehensive response. Evaluating these interventions to see what laws, and specific elements of laws, make a difference, is crucial – but difficult. Legal Science, LLC (LS) plays a unique role in supporting better tracking and evaluation of law. The company is founded on the principles of Policy Surveillance – the systematic, scientific tracking of laws of public health significance. In March of 2016, LS launched the MonQcle, a SaaS software platform that allows 1) building empirical policy data with scientific codebooks and protocols; 2) storing of these data in a redundant, scalable database; and 3) publishing via an integrated CMS with maps, tables and data visualization tools. For Phase II, we propose to significantly expand the MonQcle to include a Health Outcome Policy Evaluation (HOPE) laboratory. The MonQcle powers the Prescription Drug Abuse Policy System (PDAPS) and the Drug Abuse Policy System (DAPS), launching early, 2017. We propose a new policy analysis paradigm to streamline and accelerate the assessment of policy impact. We will use human-centered design principles to design the dashboard. Using the application programming interface (API) we will include the storage and retrieval of drug related outcome data, including import, export, computational and visual design and display features. We will integrate a suite of publically available longitudinal outcomes data from the National Survey on Drug Use and Health and CDC’s WONDER with the existing drug policy data including pdmp’s, naloxone, Good Samaritan, methadone-assisted treatment, drugged driving and medical and recreational marijuana laws. In partnership with researchers from Battelle Memorial Institute’s (Battelle) LS will extend the Phase I Daps to include a generic HOPE architecture and subsequently deploy a unique substance use specific policy lab called DOPE – Drug Outcome Policy Evaluation. DOPE will be a researcher friendly, extension of Daps that facilitates overlay of health outcome data on state policy data, as well as integrated computational functionality in R to rank policies or policy features across states and measure their effects on population health. Collaborators from Battelle’s Advanced Analytics and Health Research (AAHR) team will assess the impact of key state legal interventions on reducing the opiate epidemic. Using DOPE, the team will examine a set of research questions including but not limited to: 1) When policies for Naloxone access, Good Samaritan protections, PDMP, medication assisted treatment with methadone and/or drugged driving are in place, does it decrease the rate of drug-related deaths (all drugs)? 2) Do policies related to legalizing marijuana (for recreation or medical purposes) show a change in marijuana use and risk perception Finally, 3) what suite of policies yields the greatest reduction in the rate of drug-related deaths? Lastly, together the teams will address a suite of research questions about the functionality, efficiency, feasibility of DOPE.