Innovative methods for modeling longitudianl medical costs

建立纵向医疗成本模型的创新方法 预计人均保健费用将从2009年的8 160美元增加到2018年的13 100美元，以及 到2018年，医疗保健总成本将占国内生产总值的20%以上。统计分析 随着人们对遏制医疗成本上升的兴趣增强，医疗成本数据的重要性正变得越来越重要 医疗保健费用。 医疗费用数据通常收集在医院的账单记录和健康保险计划的索赔中 (例如，医疗保险、医疗补助或商业保险)。这些数据的广泛可获得性促使 发展和应用最先进的统计和计量方法。通过技术 在自动化数据收集和管理方面的进步，医疗成本现在经常定期收集 间隔(例如，每天或每月)，创建纵向数据模式。 这项研究的目的是开发和传播一些模型来分析纵向医学 成本数据。这笔赠款有五个目标。首先，我们将扩展目前可用的计量经济模型 医疗费用纵向数据，并比较这些模型的性能。第二，我们将探索 在纵向医疗费用数据建模中使用更灵活的协变量规范的函数形式。 第三，我们将扩展上述模型，以联合分析医疗成本和多种健康结果(例如， 生存，或生活质量)，并同时研究风险因素对它们的影响。第四，我们将申请 分层模型以解决在对不同级别的纵向医疗成本进行建模时的聚集效应， 例如，健康计划、家庭和成员。最后，我们将开发现成的软件，以促进 从拟议研究中开发的方法的实际应用。 除了在模拟研究中测试提出的方法的性能外，这些创新的方法 将应用于使用三个真实世界数据库的经验案例研究：临床数据存储库(CDR)，位于 弗吉尼亚大学(UVA)卫生系统、医疗支出小组调查(MEPS)和SEER- 医疗保险数据库。我们期望将拟议的方法应用到这些案例研究中 大大提高了我们对人口统计、医生执业模式、 疾病，以及关于医疗费用的卫生政策。

Innovative Methods for Modeling Longitudinal Medical Costs It is projected that health care costs per person would increase from $8,160 in 2009 to $13,100 in 2018, and that total health care costs will account for over 20% of the gross domestic product by 2018. Statistical analysis of medical cost data is becoming increasingly important with the heightened interests in containing the rising health care cost. Medical cost data are routinely collected in billing records of hospitals and claims of health insurance plans (e.g., Medicare, Medicaid, or commercial insurance). The wide availability of such data has motivated the development and application of the state-of-the-art statistical and econometric methods. With technological advances in automated data collection and management, medical costs are now often gathered at regular time intervals (e.g., daily or monthly), creating a longitudinal data pattern. The objective of this study is to develop and disseminate a number of models to analyze longitudinal medical costs data. There are five aims in this grant. First, we will expand the currently available econometric models of medical costs to longitudinal data and compare the performance of these models. Second, we will explore the use of more flexible functional forms of covariate specification in modeling longitudinal medical cost data. Third, we will extend the above models to jointly analyze medical costs and multiple health outcomes (e.g., survival, or quality of life), and study the effect of risk factors on them simultaneously. Fourth, we will apply hierarchical models to address the clustering effect in modeling longitudinal medical cost at different levels, e.g., health plans, families, and members. Finally, we will develop ready-to-use software to facilitate the practical application of methods developed from the proposed study. In addition to testing the performance of the proposed methods in simulation studies, these innovative methods will be applied to empirical case studies using three real-world databases: Clinical Data Repository (CDR) at the University of Virginia (UVA) Health System, Medical Expenditure Panel Survey (MEPS), and the SEER- Medicare databases. We expect the application of the proposed methods to these case studies will substantially advance our understanding of the influence of demographics, physician practice patterns, diseases, and health policies on the cost of medical care.