Impact of Medical and Recreational Marijuana Laws On Cannabis, Opioids And Psychiatric Medications: National Study of VA Patients, 2000 - 2024

现在有30个州有医用大麻法(MML)，9个州有娱乐用大麻法(RML)，还有许多州 更多的州正在考虑这样的法律。大麻法律对健康的影响是有争议的；理解 它们是一项重要的公共卫生和NIDA优先事项(PA-17-135)。到目前为止，只有3项成人研究(其中2项是我们的； Hasin等人，2017，Martins等人，2016)使用多水平建模来检查MML对大麻结果的影响 使用单独的数据。这些研究表明，MML后大麻使用增加和大麻使用障碍 (CUD)。然而，他们留下了许多问题没有得到回答，包括MML是否在这些人中有更强的效果 具有关键的脆弱性因素(慢性疼痛、精神障碍)。此外，阿片类药物处方率飙升 过量导致呼吁将MML作为美国阿片类药物危机解决方案的一部分，但大多数MML-阿片类药物 研究是生态的(研究个人行为的薄弱设计)，来自个人层面的研究结果离开 证据还不清楚。生态学研究还表明，通过大麻替代，MML减少 常见精神疾病(如创伤后应激障碍、抑郁症)的药物处方，但没有个人层面的 对此已经进行了研究。重要的是，RML效果几乎是完全未知的，这是 知识。在退伍军人管理局(VA)患者中，CUD患病率自2002年以来翻了一番，在那些 年龄≥，35岁，是一般人群的2-6倍。退伍军人管理局患者也有很高的阿片类药物使用率 处方、过量服药以及慢性疼痛和精神障碍，这些都可能增加他们患上 MML和RML的不良影响。因此，他们是一个庞大的、易受MML和RML影响的人群 未知。我们将利用来自退伍军人管理局的个人数据这一主要资源来调查MML和RML效应 电子病历，自2000年起可从退伍军人管理局每年服务的约500万名患者中获得 医疗保健系统。我们将创建年度电子病历数据集，并将其与国家死亡指数数据、联邦医疗保险合并 我们将创建的数据(对于那些年龄为≥65岁的人)以及州年份的mml和rml变量。使用多层次模型 和差值测试，我们将检查MML和RML对三个主要结果的影响：大麻 阿片类药物(处方、致命性和非致命性过量、阿片类药物使用障碍)和精神药物 药物处方(抗抑郁药、抗焦虑药、镇静剂/催眠药)。重要的是，我们将确定是否 疼痛、精神障碍或人口统计(性别、年龄、种族/民族)会影响MML/RML效应。我们还将 检查具体的MML/RML条款、时间滞后和反映联邦政策变化的趋势中的断点。 分析将纳入个人和州一级的混杂因素，例如国家规范、经济因素。我们会 也要探索酒精和烟草的后果。研究小组包括物质流行病学/政策专家。 退伍军人成瘾和内科专家。研究结果将传播给临床医生和政策制定者。 确定VA患者的MML/RML效应将对有限的成人文献做出重大贡献 MML/RML，有助于提供知识，为政策和对有脆弱性因素的个人的护理提供信息。

Thirty states now have medical marijuana laws (MML), 9 have recreational marijuana laws (RML), and many more states are considering such laws. The health effects of cannabis laws are controversial; understanding them is a major public health and NIDA priority (PA-17-135). Thus far, only 3 studies of adults (2 of them ours; Hasin et al., 2017, Martins et al., 2016) used multi-level modeling to examine MML effects on cannabis outcomes with individual data. These studies suggested post-MML increases in cannabis use and Cannabis Use Disorder (CUD). However, they left many questions unanswered, including whether MML have stronger effects in those with key vulnerability factors (chronic pain, psychiatric disorders). In addition, soaring rates of opioid prescriptions and overdoses have led to calls for MML as part of the solution to the US opioid crisis, but most MML-opioid studies are ecological (a weak design to study individual behavior), and results from individual-level studies leave the evidence unclear. Ecological studies also suggest that through cannabis substitution, MML reduce medication prescriptions for common psychiatric disorders (e.g., PTSD, depression), but no individual-level studies of this have been conducted. Importantly, RML effects are almost entirely unknown, a major gap in knowledge. In Veterans Administration (VA) patients, CUD prevalence has doubled since 2002, and in those age ≥35, is 2-6 times higher than in the general population. VA patients also have high rates of opioid prescriptions, overdoses, and of chronic pain and psychiatric disorders that may increase their vulnerability to adverse MML and RML effects. They thus are a large, vulnerable population in whom MML and RML effects are unknown. We will investigate MML and RML effects utilizing a major resource, the individual data from the VA Electronic Medical Record, available since 2000 from the ~5,000,000 patients served each year by the VA healthcare system. We will create yearly EMR datasets, and merge this with National Death Index data, Medicare data (for those age ≥65) and state-year MML and RML variables that we will create. Using multi-level models and difference-in-difference tests, we will examine MML and RML effects on three main outcomes: cannabis (use, CUD), opioids (prescriptions, fatal and non-fatal overdoses, opioid use disorders), and psychotropic medication prescriptions (antidepressants, anxiolytics, sedatives/hypnotics). Importantly, we will determine if pain, psychiatric disorders or demographics (sex, age, race/ethnicity) modify MML/RML effects. We will also examine specific MML/RML provisions, time lags, and breakpoints in trends reflecting federal policy changes. Analyses will incorporate individual- and state-level confounders, e.g., state norms, economic factors. We will also explore alcohol and tobacco outcomes. The research team includes substance epidemiology/policy experts and VA addiction and internal medicine experts. Findings will be disseminated to clinicians and policy-makers. Determining MML/RML effects in VA patients will make a major contribution to the limited adult literature on MML/RML, contributing to knowledge that will inform policy and the care of individuals with vulnerability factors.