Medications and the Risk of Motor Vehicle Crashes in Older Adults

项目总结 尽管人们普遍认为，使用处方药是导致机动车撞车的主要原因，但数据显示 关于药物对65岁≥老年人撞车的影响，很少见，而且仍然存在争议。 与车祸的其他决定因素(例如，医疗条件)不同，药物是为数不多的可修改的潜力之一 每年发生的6800起与车祸有关的死亡和191,000起与车祸有关的非致命性伤害的决定因素 在老年人中。特别是，精神活性药物通常在老年司机中使用，但可能会干扰 安全驾驶。随着越来越多的成年人继续驾车进入老年，迫切需要了解 药物的影响，并将其与同时代与年龄相关的疾病的影响区分开来， 损伤和生理变化。这项建议的总体目标是检查因果关系。 药物对老年司机撞车的影响，以及这些药物对撞车的影响程度 各亚组[例如阿尔茨海默病和相关痴呆症(ADRD)、多药联用]的撞车风险 服药依从性状态。中心假设是镇静精神活性药物(阿片类药物， 非苯二氮类安眠药、抗抑郁药和抗精神病药物)会增加撞车风险，而 神经系统(CNS)激活药物(胆碱酯酶抑制剂、中枢神经系统类似物)和其他(非类固醇抗精神病药物) 炎症性药物)将降低风险，这些影响将在ADRD患者中最大 以及那些坚持服药的人。这一假设将通过追求三个具体目标来检验： 1)评估启动镇静、精神活性、中枢神经系统激活和其他药物的效果，包括剂量， 对老年人撞车的影响；2)量化开始镇静、精神活性、中枢神经系统激活等的效果 对老年人重要亚群的撞车用药，包括ADRD；多药联用； 多发病；以及睡眠、精神、神经和肌肉骨骼疾病；以及3)评估 不坚持镇静剂、精神活性药物、中枢神经系统激活药物和其他药物，每种药物分别与 坚持，承担坠机风险。为了实现这三个目标，我们的团队将开发一个独特的数据库，它结合了 关于老年司机执照和撞车历史的数据；医疗保险健康保险和药物索赔的数据；以及 药物使用和撞车事故的重要决定因素(例如，能否获得替代交通工具)。这 方法是创新的，因为它是第一个汇编所有三个领域的高质量美国数据的公司，这三个领域是 研究药物对车祸的影响--1)医疗条件(协变量)；2)用药(暴露)； 以及3)崩溃(结果)--在一个足够大的数据集中，可以使用因果推理精确地估计影响 方法，同时考虑司机之间的不同驾驶频率。这项拟议的研究具有重要意义 因为：1)它将提供经验证据，帮助指导药物管理，以最大限度地提高老年 成人保持安全移动的能力；以及2)它将建立一个独特的大数据资源，可用于 对老年司机进行重要的未来用药相关研究。本提案是对PA-17-088的响应。

PROJECT SUMMARY Despite the common belief that prescription drug use is a leading cause of motor vehicle crashes, data are scarce and controversy remains about the effects of medications on crashes in older adults aged ≥65 years. Unlike other determinants of crashes (e.g., medical conditions), medications are one of few modifiable potential determinants of the 6,800 crash-related deaths and 191,000 crash-related non-fatal injuries that occur annually among older adults. In particular, psychoactive drugs are commonly used among older drivers, but may interfere with safe driving. As more adults continue to drive into older age, there is an urgent need to understand the effects of medications and distinguish them from the effects of contemporaneous age-related medical conditions, impairments, and physiological changes. The overall objective of this proposal is to examine the causal effects of medications on crashes in older drivers, and the extent to which these medications disproportionately affect crash risk across subgroups [e.g., Alzheimer's disease and related dementias (ADRD), polypharmacy] and by medication adherence status. The central hypothesis is that sedating psychoactive medications (opioids, nonbenzodiazepine hypnotics, antidepressants, and antipsychotics) will increase crash risk while central nervous system (CNS)-activating drugs (cholinesterase inhibitors, CNS simulants) and others (non-steroidal anti- inflammatory drugs) will decrease the risk, and that these effects will be greatest among individuals with ADRD and those who are adherent to their medications. This hypothesis will be tested by pursuing three specific aims: 1) Estimate the effect of initiating sedating psychoactive, CNS-activating, and other medications, including dose, on crashes in older adults; 2) Quantify the effect of initiating sedating psychoactive, CNS-activating, and other medications on crashes across important subgroups of older adults, including those with ADRD; polypharmacy; multimorbidity; and sleep, psychiatric, neurological, and musculoskeletal disorders; and 3) Evaluate the effect of non-adherence to sedating psychoactive, CNS-activating, and other medications, each separately compared to adherence, on crash risk. To accomplish the three aims, our team will develop a unique database that combines data on older drivers' licensing and crash histories; Medicare health insurance and drug claims; and data on important determinants of medication use and crashes (e.g., access to transportation alternatives). This approach is innovative because it is the first to compile high-quality U.S. data on all three domains necessary to study the effect of medications on crashes—1) medical conditions (covariates); 2) medication use (exposure); and 3) crashes (outcome)—in a dataset that is large enough to precisely estimate effects using causal inference methods while accounting for differential driving frequency between drivers. The proposed research is significant because: 1) it will provide empirical evidence to help guide the management of medications to maximize older adults' ability to maintain safe mobility; and 2) it will establish a unique large data resource that can be used to conduct important future medication-related studies of older drivers. This proposal is responsive to PA-17-088.