Trajectories of Nonmedical Prescription Drug Misuse

 描述（申请人提供）：对PA-13-080的答复：利用现有研究数据加快药物滥用研究的步伐，这个竞争性的更新“非医疗处方药滥用的轨迹”建立在我们的父R 01，并建议使用多个队列的国家代表性的纵向数据，以评估个人的模式和轨迹的医疗和非医疗使用的四类处方药物（即，阿片类药物、镇静剂、兴奋剂、安定剂），以及成年后出现物质使用障碍和其他长期不良后果。尽管在美国青少年和年轻人中处方药的医疗和非医疗使用有所增加，相对较少的前瞻性纵向研究调查了SUD的发展和成年后的其他不良后果。虽然在了解处方药的非医疗用途和短期后果方面取得了进展，但由于测量和研究设计的局限性，知识方面仍存在相当大的差距。来自我们的父母R 01的研究结果表明，在青春期和青年期（18-22岁），处方药的非医疗使用存在很大的异质性。监测未来（MTF）研究提供了一个宝贵的机会，以解决新的研究问题，这将导致更全面地了解处方药的医疗和非医疗使用。我们提出了几个二次分析，使用39个全国代表性的队列约585,000高中毕业生之间的1976年和2014年的MTF研究。MTF纵向面板样本具有23个独立的队列，约55，200名高中毕业生（模态年龄18岁），他们在3-4年（模态年龄21-22岁），7-8年（模态年龄25-26岁）和17年（模态年龄35岁）后被随访，每个队列产生四个纵向波的数据。MTF研究代表了唯一一项具有全国代表性的纵向研究，该研究具有足够的测量和样本量来测试潜在的性别，种族和社会经济地位差异，并满足我们研究的目的，该研究旨在：1）评估四种处方药类别（即，阿片类药物，镇静剂，兴奋剂，镇静剂）在从青春期（18岁）到成年期（35岁）的过渡期间使用横截面和纵向面板数据; 2）检查从青春期到成年期的四种药物类别的医疗和非医疗使用的个体模式和轨迹与SUD和其他不良后果（例如，健康问题、住院和法律的问题）; 3）调查个体模式的风险和保护因素 以及使用基于理论的发展模型和纵向面板数据，从青春期到成年期与SUD发展和成年期（35岁）其他后果相关的四种处方药类别的医疗和非医疗使用轨迹。

 DESCRIPTION (provided by applicant): In response to PA-13-080: Accelerating the Pace of Drug Abuse Research Using Existing Research Data, this competing renewal "Trajectories of Nonmedical Prescription Drug Misuse" builds on our parent R01 and proposes to use multiple cohorts of nationally representative longitudinal data to assess the individual patterns and trajectories of medical and nonmedical use of four classes of prescription medications (i.e., opioids, sedatives, stimulants, tranquilizers) from adolescence to adulthood and the development of substance use disorders (SUDs) and other long-term adverse consequences in adulthood. Despite increases in the medical and nonmedical use of prescription medications among adolescents and young adults in the U.S., relatively few prospective longitudinal studies have investigated the development of SUDs and other adverse consequences in adulthood. While there have been advances in understanding nonmedical use of prescription medications and short-term consequences, considerable gaps in knowledge remain due to limitations in measurement and study designs. Findings from our parent R01 suggest a great deal of heterogeneity in the nonmedical use of prescription medications during adolescence and young adulthood (ages 18-22). The Monitoring the Future (MTF) study provides a valuable opportunity to address new research questions that will lead to a more comprehensive understanding of medical and nonmedical use of prescription medications. We propose several secondary analyses using 39 nationally representative cohorts of approximately 585,000 high school seniors between 1976 and 2014 from the MTF study. The MTF longitudinal panel sample features 23 separate cohorts of approximately 55,200 high school seniors (modal age 18) who were followed 3-4 years (modal ages 21-22), 7-8 years (modal ages 25-26), and 17 years (modal age 35) later, resulting in four longitudinal waves of data for each cohort. The MTF study represents the only nationally representative longitudinal study that has sufficient measures and sample size to test for potential sex, racial, and socioeconomic status differences and to meet the objectives of our study, which aims to: 1) assess the individual patterns and trajectories of medical and nonmedical use of four prescription medication classes (i.e., opioids, sedatives, stimulants, tranquilizers) during the transition from adolescence (age 18) to adulthood (age 35) using cross-sectional and longitudinal panel data; 2) examine the associations between individual patterns and trajectories of medical and nonmedical use of four medication classes from adolescence to adulthood and development of SUDs and other adverse consequences (e.g., health problems, hospitalizations, and legal problems) during adulthood (age 35) using longitudinal panel data; and 3) investigate the risk and protective factors for individual patterns and trajectories of medical and nonmedical use of four prescription medication classes from adolescence to adulthood associated with development of SUDs and other consequences during adulthood (age 35) using a theory-based developmental model and longitudinal panel data.