Innovative methods for modeling longitudianl medical costs

描述(申请人提供)：纵向医疗成本建模的创新方法预计，人均医疗成本将从2009年的8,160美元增加到2018年的13,100美元，到2018年，总医疗成本将占国内生产总值的20%以上。随着人们对控制不断上涨的医疗成本的兴趣日益高涨，对医疗成本数据的统计分析变得越来越重要。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(例如，联邦医疗保险、医疗补助或商业保险)的索赔中。这些数据的广泛获得推动了最先进的统计和计量经济学方法的发展和应用。随着自动化数据收集和管理方面的技术进步，医疗成本现在通常以固定的时间间隔(例如，每天或每月)收集，从而创建了纵向数据模式。这项研究的目的是开发和传播一些模型来分析纵向医疗费用数据。这笔赠款有五个目标。首先，我们将把目前可用的医疗费用计量模型扩展到纵向数据，并比较这些模型的性能。其次，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(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，我们将应用分层模型来解决在不同水平(例如，健康计划、家庭和成员)建模纵向医疗费用时的聚集效应。最后，我们将开发现成的软件，以促进从拟议研究开发的方法的实际应用。除了在模拟研究中测试建议方法的性能外，这些创新方法还将应用于使用三个真实世界数据库的实证案例研究：弗吉尼亚大学(UVA)卫生系统的临床数据存储库(CDR)、医疗支出小组调查(MEPS)和SEER-Medicare数据库。我们期望将建议的方法应用到这些案例研究中，将大大提高我们对人口统计学、医生执业模式、疾病和健康政策对医疗成本的影响的理解。 公共卫生相关性：不断上涨的医疗保健成本是卫生政策制定者的主要关切。为了更好地理解与医疗成本增长相关的因素，研究医疗成本数据的纵向历史是重要的。我们建议开发更好的方法来分析纵向医疗成本数据。为了证明我们建议的方法在临床或政策决策中的优势，我们将这些方法应用于一些临床或政策相关的案例研究。我们还将向其他对医疗成本研究感兴趣的研究人员提供这些方法的编程代码。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Rising health care cost is a major concern for health policy makers. To better understand the factors associated with the growth in medical cost, it is important to study the longitudinal history of medical cost data. We propose to develop better methods to analyze longitudinal medical care costs data. To demonstrate the advantages of our proposed methods in clinical or policy decision making, we will apply these methods to a number of clinical- or policy-relevant case studies. We will also make programming codes of these methods available to other researchers who are interested in medical cost studies.