Estimating Life Course Patterns and Years of Life Lived Taking Prescription Drugs

项目摘要 处方药的使用在美国达到了历史新高，近一半的美国人 超过80%的60岁及以上人口报告在过去30天内服用处方药， 2015-2016.处方药已成为医疗的支柱，为不断扩大的阵列， 条件不仅有更多的美国人在吸毒，而且他们同时也在吸毒 （多药疗法）和更长的持续时间。虽然处方药有助于改善健康状况 和死亡率，处方药使用的这些趋势也引发了与过度处方有关的担忧， 医疗保健成本上升、药物不良事件和生活质量影响。这些问题特别突出 老年人是处方药的最大人均消费者。越来越多的注意力被 支付处方药使用的后果，特别是鉴于当代美国阿片类药物 这一流行病最初是由处方阿片类止痛药的过度使用造成的。但没有 处方药使用的生命历程模式的现有措施，包括他们的生活中有多少美国人 现在可以预期花费服用处方药或年龄在开始持续使用处方药。这 该项目将利用医疗支出小组调查（MEPS）的数据制定这些措施， 长期的、具有全国代表性的调查，收集关于处方药使用的高质量数据， 结合人类死亡率数据库（HMD）的生命表。我们将估计周期生命表， 使用沙利文方法的新应用来评估处方药的使用，该方法用于评估健康状况 期望我们将估计美国人可以预期服用（和不服用）的年数：（1） 任何处方药;（2）特定类型的处方药（即，治疗药物类别）;以及（3）数量 1996年至2000年期间，按年龄和性别分列的处方药（0、1、2、3、4、5+）的使用情况， 和2017我们还对改变出生队列中处方药使用模式感兴趣。的 大多数用于治疗慢性病的药物是在1950年之后开发的;因此，今天的老年人，不像他们的 他们是第一批经历了数十年持续处方的老年人的出生队列 药物使用在整个生命过程中。我们实施了一种方法创新，使用沙利文的方法， 计算临时预期寿命（即，在特定的时间段内， 年龄范围）。这种创新，可以扩展到许多其他研究问题， 队列的多状态方法，将使我们能够产生类似的估计，上述的 1927年至1996年出生的同龄人服用任何处方药的年数， 药物类别和处方药的具体数量。这些估计对以下方面具有重要意义： 当代围绕处方药使用的争论，包括过度开药、花费、多种药物， 以及发生药物不良反应的风险。

PROJECT SUMMARY Prescription drug use has reached historic highs in the United States, with nearly half of all Americans and over 80% of the population aged 60 and older reporting taking prescription drugs in the past 30 days in 2015-2016. Prescription drugs have become a mainstay of medical treatment for an ever expanding array of conditions. Not only are more Americans taking drugs, but they are taking more of them concurrently (polypharmacy) and for longer durations. While prescription drugs have contributed to improvements in health and mortality, these trends in prescription drug use have also prompted concerns related to overprescribing, rising health care costs, adverse drug events, and quality of life impacts. These issues are particularly salient for older adults, who are the largest per capita consumers of prescription drugs. Increasing attention is being paid to the consequences of prescription drug use, particularly in light of the contemporary American opioid epidemic, which was initially driven by the overuse of prescription opioid painkillers. However, there are no existing measures of life course patterns of prescription drug use, including how much of their lives Americans can now expect to spend taking prescription drugs or ages at initiation of sustained prescription drug use. This project will produce those measures leveraging data from the Medical Expenditure Panel Survey (MEPS), a long running, nationally representative survey that collects high quality data on prescription drug use, combined with life tables from the Human Mortality Database (HMD). We will estimate period life tables for prescription drug use using a novel application of Sullivan's method, which is used to estimate health expectancies. We will estimate the number of years Americans can expect to spend taking (and not taking): (1) any prescription drugs; (2) specific types of prescription drugs (i.e., therapeutic drug classes); and (3) numbers of prescription drugs (0, 1, 2, 3, 4, 5+) to capture polypharmacy by age and sex for each year between 1996 and 2017. We are also interested in changing patterns of prescription drug use across birth cohorts. The majority of drugs used to treat chronic disease were developed after 1950; thus, older adults today, unlike their predecessors, are the first birth cohorts of older adults to have experienced decades of sustained prescription drug use over the life course. We implement a methodological innovation that uses Sullivan's method to calculate temporary life expectancy (i.e., the years of life lived spent taking prescription drugs within a specific age range) for birth cohorts. This innovation, which can be extended to many other research questions using multistate methods for cohorts, will allow us to produce similar estimates to those described above of the number of years birth cohorts born between 1927 and 1996 have spent taking any prescription drugs, specific classes of drugs, and specific numbers of prescription drugs. These estimates have important implications for contemporary debates surrounding prescription drug use including overprescribing, spending, polypharmacy, and the risk of experiencing adverse drug events.