Innovative methods for modeling longitudianl medical costs

纵向医疗成本建模的创新方法

• 批准号: 8337204
• 负责人: Lei Liu
• 金额: $ 37.98万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8337204
• 关键词: Innovative; methods; modeling; longitudianl; medical

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.

建模纵向医疗费用的创新方法 预计，每人的医疗保健费用将从2009年的8,160美元增加到2018年的13,100美元，而 到2018年，该总医疗保健费用将占国内生产总值的20％以上。统计分析 随着包含上升的兴趣，医疗成本数据的越来越重要 医疗保健费用。 医疗费用数据通常在医院的计费记录和健康保险计划的索赔中收集 （例如，医疗保险，医疗补助或商业保险）。此类数据的广泛可用性促使 最先进的统计和计量经济学方法的开发和应用。使用技术 自动数据收集和管理的进步，医疗费用现在经常在常规时间收集 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许多模型以分析纵向医学 费用数据。这笔赠款有五个目标。首先，我们将扩展当前可用的计量经济学模型 纵向数据的医疗费用并比较这些模型的性能。第二，我们将探索 在建模纵向医疗成本数据中使用更灵活的协变量规范功能形式。 第三，我们将把上述模型扩展到共同分析医疗费用和多种健康成果（例如， 生存或生活质量），同时研究危险因素对它们的影响。第四，我们将申请 层次模型以解决在不同级别建模纵向医疗成本时的聚类效应， 例如，健康计划，家庭和会员。最后，我们将开发现成的软件，以促进 从建议的研究开发的方法的实际应用。 除了测试拟议方法在模拟研究中的性能外，这些创新方法还 将使用三个现实世界数据库应用于经验案例研究：临床数据存储库（CDR） 弗吉尼亚大学（UVA）卫生系统，医疗支出小组调查（MEPS）和先知 Medicare数据库。我们希望将拟议方法应用于这些案例研究 大大提高了我们对人口统计学，医师实践模式的影响的理解， 疾病和医疗费用的健康政策。