Coquet Jean, Zammit Alban, Hajouji Oualid El, Humphreys Keith, Asch Steven M, Osborne Thomas F, Curtin Catherine M, Hernandez-Boussard Tina
Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Computational & Mathematical Engineering, Stanford University, Stanford, CA, United States.
Front Digit Health. 2022 Dec 6;4:995497. doi: 10.3389/fdgth.2022.995497. eCollection 2022.
The opioid crisis brought scrutiny to opioid prescribing. Understanding how opioid prescribing patterns and corresponding patient outcomes changed during the epidemic is essential for future targeted policies. Many studies attempt to model trends in opioid prescriptions therefore understanding the temporal shift in opioid prescribing patterns across populations is necessary. This study characterized postoperative opioid prescribing patterns across different populations, 2010-2020.
Administrative data from Veteran Health Administration (VHA), six Medicaid state programs and an Academic Medical Center (AMC).
Surgeries were identified using the Clinical Classifications Software.
Trends in average daily discharge Morphine Milligram Equivalent (MME), postoperative pain and subsequent opioid prescription were compared using regression and likelihood ratio test statistics.
The cohorts included 595,106 patients, with populations that varied considerably in demographics. Over the study period, MME decreased significantly at VHA (37.5-30.1; = 0.002) and Medicaid (41.6-31.3; = 0.019), and increased at AMC (36.9-41.7; < 0.001). Persistent opioid users decreased after 2015 in VHA ( < 0.001) and Medicaid ( = 0.002) and increase at the AMC ( = 0.003), although a low rate was maintained. Average postoperative pain scores remained constant over the study period.
VHA and Medicaid programs decreased opioid prescribing over the past decade, with differing response times and rates. In 2020, these systems achieved comparable opioid prescribing patterns and outcomes despite having very different populations. Acknowledging and incorporating these temporal distribution shifts into data learning models is essential for robust and generalizable models.
阿片类药物危机引发了对阿片类药物处方的审查。了解在疫情期间阿片类药物处方模式及相应的患者结局如何变化,对于未来有针对性的政策至关重要。许多研究试图对阿片类药物处方趋势进行建模,因此有必要了解不同人群中阿片类药物处方模式的时间变化。本研究描述了2010 - 2020年不同人群术后阿片类药物的处方模式。
退伍军人健康管理局(VHA)、六个医疗补助州项目和一个学术医疗中心(AMC)的管理数据。
使用临床分类软件识别手术。
使用回归和似然比检验统计量比较平均每日出院吗啡毫克当量(MME)、术后疼痛及随后的阿片类药物处方趋势。
队列包括595,106名患者,其人群在人口统计学上差异很大。在研究期间,VHA(从37.5降至30.1;P = 0.002)和医疗补助项目(从41.6降至31.3;P = 0.019)的MME显著下降,而AMC(从36.9升至41.7;P < 0.001)的MME上升。2015年后,VHA(P < 0.001)和医疗补助项目(P = 0.002)中持续使用阿片类药物的患者减少,而AMC中此类患者增加(P = 0.003),不过维持在低水平。在研究期间,平均术后疼痛评分保持不变。
在过去十年中,VHA和医疗补助项目减少了阿片类药物处方,反应时间和速率有所不同。2020年,尽管这些系统的人群差异很大,但它们实现了可比的阿片类药物处方模式和结局。认识到并将这些时间分布变化纳入数据学习模型对于强大且可推广的模型至关重要。