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A typology of prescription drug monitoring programs: a latent transition analysis of the evolution of programs from 1999 to 2016

机译:处方药监测计划的类型学:1999年至2016年计划演变的潜在过渡分析

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Background and aims Prescription drug monitoring programs (PDMP), defined as state-level databases used in the United States that collect prescribing information when controlled substances are dispensed, have varied substantially between states and over time. Little is known about the combinations of PDMP features that, collectively, may produce the greatest impact on prescribing and overdose. We aimed to (1) identify the types of PDMP models that have developed from 1999 to 2016, (2) estimate whether states have transitioned across PDMP models over time and (3) examine whether states have adopted different types of PDMP models in response to the burden of opioid overdose. Methods A latent transition analysis of PDMP models based on an adaptation of nine PDMP characteristics classified by prescription opioid policy experts as potentially important determinants of prescribing practices and prescription opioid overdose events. Results We divided the time-period into three intervals (1999-2004, 2005-09, 2010-16), and found three distinct PDMP classes in each interval. The classes in the first and second interval can be characterized as 'no/weak', 'proactive' and 'reactive' types of PDMPs, and in the third interval as 'weak', 'cooperative' and 'proactive'. The meaning of these classes changed over time: until 2009, states in the 'no/weak' class had no active PDMP, whereas states in the 'proactive' class were more likely to proactively provide unsolicited information to PDMP users, provide open access to law enforcement, and require more frequent data reporting than states in the 'reactive' class. In 2010-16, the 'weak' class resembled the 'reactive' class in previous intervals. States in the 'cooperative' class in 2010-16 were less likely than states in the 'proactive' class to provide unsolicited reports proactively or to provide open access to law enforcement; however, they were more likely than those in the 'proactive' class to share PDMP data with other states and to report more federal drug schedules. Conclusions Since 1999, US states have tended to transition to more robust classes of prescription drug monitoring programs. Opioid overdose deaths in prior years predicted the state's prescription drug monitoring program class but did not predict transitions between prescription drug monitoring program classes over time.
机译:背景和AIMS处方药监测计划(PDMP),定义为美国使用的状态级数据库,当征收受控物质时收集规定信息,在状态之间和随着时间的推移基本上变化。关于PDMP特征的组合众所周知,集体可能产生对规定和过量的最大影响。我们的目标是(1)确定从1999年到2016年开发的PDMP模型的类型,(2)估计各国是否随着时间的推移,(3)审查各国是否采用了不同类型的PDMP模型阿片类药物过量的负担。方法对PDMP模型的基于九个PDMP特征的PDMP模型的潜在转换分析,分类为九个PDMP特征,作为处方实践和处方阿片类药物过量事件的潜在重要决定因素。结果我们将时间周期分为三个间隔(1999-2004,2005-09,2010-16),在每个间隔中发现了三个不同的PDMP类。第一和第二间隔中的类可以被称为“无/弱”,“主动”和“反应性”类型的PDMP,以及第三个间隔为“弱”,“合作”和“主动”。这些类的含义随着时间的推移而变化:直到2009年,“无/弱”课程中的各州没有活跃的PDMP,而“主动”课程中的状态更有可能主动向PDMP用户提供未经请求的信息,提供开放访问执法部门,并需要比“反应性”课程更频繁的数据报告。在2010年至16日,“弱”课程以前的间隔以“反应性”课程相似。 2010-16的“合作社”课程中的国家比“主动”课程中的国家不太可能主动提供未经请求的报告,或者提供对执法人员的开放机会;但是,它们比“主动”课程中的人更有可能与其他国家分享PDMP数据,并报告更多联邦药物时间表。结论自1999年以来,美国各国倾向于过渡到更强大的处方药监测计划。阿片类药物在前几年内过量死亡预测了该州的处方药监测计划阶级,但在随着时间的推移中没有预测处方药监测计划课程之间的转型。

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