Am J Epidemiol. 2020 Sep 1;189(9):885-893. doi: 10.1093/aje/kwaa015.
In 2011, Florida established a prescription drug monitoring program and adopted new regulations for independent pain-management clinics. We examined the association of those reforms with drug overdose deaths and other injury fatalities. Florida's postreform monthly mortality rates-for drug-involved deaths, motor vehicle crashes, and suicide by means other than poisoning-were compared with a counterfactual estimate of what those rates would have been absent reform. The counterfactual was estimated using a Bayesian structural time-series model based on mortality trends in similar states. By December 2013, drug overdose deaths were down 17% (95% credible interval: -21, -12), motor vehicle crash deaths were down 9% (95% credible interval: -14, -4), and suicide deaths were unchanged compared with what would be expected in the absence of reform. Florida's opioid prescribing reform substantially reduced drug overdose deaths. Reforms may also have reduced motor vehicle crash deaths but were not associated with a change in suicides. More research is needed to understand these patterns. Bayesian structural time-series modeling is a promising new approach to interrupted time-series studies.
2011 年，佛罗里达州建立了一个处方药物监测计划，并对独立的疼痛管理诊所采用了新的规定。我们研究了这些改革与药物过量死亡和其他伤害致死事件之间的关联。将佛罗里达州改革后的每月死亡率（与药物有关的死亡、机动车事故和非中毒自杀）与如果没有改革预计会出现的死亡率进行了比较。这个反事实估计是使用基于类似州死亡率趋势的贝叶斯结构时间序列模型来估计的。截至 2013 年 12 月，药物过量死亡人数下降了 17%（95%可信区间：-21，-12），机动车事故死亡人数下降了 9%（95%可信区间：-14，-4），与没有改革时的预期相比，自杀死亡人数没有变化。佛罗里达州的阿片类药物处方改革大大减少了药物过量死亡人数。改革可能还减少了机动车事故死亡人数，但与自杀人数的变化无关。需要进一步的研究来了解这些模式。贝叶斯结构时间序列模型是一种有前途的中断时间序列研究的新方法。
