Suppr超能文献

美国近期的双重流行趋势:一项关于阿片类药物和兴奋剂联合处方的10年纵向队列研究。

The recent trend of twin epidemic in the United States: a 10-year longitudinal cohort study of co-prescriptions of opioids and stimulants.

作者信息

Lee Seungyeon, Song Wenyu, Bates David W, Urman Richard D, Zhang Ping

机构信息

Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.

出版信息

Lancet Reg Health Am. 2025 Feb 17;44:101030. doi: 10.1016/j.lana.2025.101030. eCollection 2025 Apr.

Abstract

BACKGROUND

In recent years, the use of central nervous system stimulant medications has increased among the population already using opioids, referred to as a "twin epidemic." There is an increasing concern about its harmful outcomes in large populations. However, very few studies examined the co-prescription pattern of these two drug categories over a long period, and there is currently no clear restriction on stimulant prescriptions among patients under opioid treatment in the United States. The objectives of our study were to identify opioid prescription dosage time-dependent patterns and patient subgroups representing distinct trajectories on a national level in the recent 10 years, and to further investigate longitudinal associations between stimulant and opioid prescriptions and the impact of stimulant prescriptions on opioid dosage patterns.

METHODS

We obtained patient records from MarketScan, one of the largest clinical databases of health insurance in the United States. 10 years (2012-2021) of prescription records and related patient profiles, who received at least two independent opioid prescriptions, were utilized for developing a group-based opioid dose trajectory model.

FINDINGS

From an initial cohort including 22 million patients with 96 million opioid prescriptions, we developed a study cohort of 2,895,960 patients with a mean age of 43.9 years (standard deviation [SD] 13.0), of whom 1,244,077 (43%) were male. Significant geographical variations in opioid prescription frequency and dosage among four U.S. regions were observed. The trajectory model identified five distinct opioid dose groups. Stimulant prescription before the initial opioid prescription was positively associated with escalating opioid doses (odds ratio [OR]: 7.58; 95% confidence intervals [CI] 6.14-9.35, opioid dose increasing group compared to the decreasing group). Stimulant co-prescriptions were also associated with increasing opioid doses (OR: 1.73; 95% CI 1.40-2.14) and were identified in patients with a higher prevalence of opioid use disorder.

INTERPRETATION

During the recent 10 years, stimulant prescription is positively associated with escalating opioid prescription activities in U.S. healthcare systems, suggesting co-prescriptions of these two types of drugs are an important contributing factor for a national-level twin epidemic. Healthcare leaders and policymakers should pay more attention to this issue and its potential harms.

FUNDING

National Institute of General Medical Sciences, National Institute on Drug Abuse, and National Science Foundation.

摘要

背景

近年来,在已经使用阿片类药物的人群中,中枢神经系统兴奋剂药物的使用有所增加,这被称为“双重流行”。人们越来越担心其在大量人群中的有害后果。然而,很少有研究长期考察这两类药物的联合处方模式,而且目前美国对于接受阿片类药物治疗的患者,在兴奋剂处方方面没有明确的限制。我们研究的目的是在国家层面识别近10年阿片类药物处方剂量的时间依赖性模式以及代表不同轨迹的患者亚组,并进一步研究兴奋剂与阿片类药物处方之间的纵向关联以及兴奋剂处方对阿片类药物剂量模式的影响。

方法

我们从美国最大的医疗保险临床数据库之一MarketScan获取患者记录。利用10年(2012 - 2021年)的处方记录以及至少接受过两次独立阿片类药物处方的相关患者资料,来建立基于群组的阿片类药物剂量轨迹模型。

研究结果

从最初包含2200万患者和9600万阿片类药物处方的队列中,我们建立了一个包含2895960名患者的研究队列,这些患者的平均年龄为43.9岁(标准差[SD] 13.0),其中1244077名(43%)为男性。在美国四个地区观察到阿片类药物处方频率和剂量存在显著的地理差异。轨迹模型识别出五个不同的阿片类药物剂量组。初始阿片类药物处方之前开具兴奋剂处方与阿片类药物剂量增加呈正相关(优势比[OR]:7.58;95%置信区间[CI] 6.14 - 9.35,与剂量减少组相比,阿片类药物剂量增加组)。兴奋剂联合处方也与阿片类药物剂量增加相关(OR:1.73;95% CI 1.40 - 2.14),并且在阿片类药物使用障碍患病率较高的患者中被识别出来。

解读

在最近10年中,在美国医疗系统中,兴奋剂处方与阿片类药物处方活动的增加呈正相关,这表明这两类药物的联合处方是全国性双重流行的一个重要促成因素。医疗保健领导者和政策制定者应更加关注这个问题及其潜在危害。

资金来源

美国国立综合医学科学研究所、美国国立药物滥用研究所和美国国家科学基金会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3414/11876894/f33fc27916d6/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验