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.

纵向医疗费用建模的创新方法 预计人均医疗费用将从 2009 年的 8,160 美元增加到 2018 年的 13,100 美元， 到2018年，医疗卫生总费用将占国内生产总值的20%以上。 统计分析 随着人们对遏制不断上升的医疗费用的兴趣日益浓厚，医疗费用数据的收集变得越来越重要。 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）。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。凭借科技 自动化数据收集和管理的进步，现在经常定期收集医疗费用 间隔（例如每天或每月），创建纵向数据模式。 本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，我们将扩展当前可用的计量经济学模型 医疗成本纵向数据并比较这些模型的性能。其次，我们将探讨 在纵向医疗成本数据建模中使用更灵活的协变量规范函数形式。 第三，我们将扩展上述模型来共同分析医疗成本和多种健康结果（例如， 生存率或生活质量），并同时研究危险因素对他们的影响。四、我们要申请 分层模型解决不同级别纵向医疗成本建模中的聚类效应， 例如，健康计划、家庭和成员。最后，我们将开发即用型软件，以方便 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所提出方法的性能之外，这些创新方法 将应用于使用三个真实世界数据库的实证案例研究：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 SEER- 医疗保险数据库。我们期望所提出的方法在这些案例研究中的应用将 大大提高我们对人口统计、医生执业模式的影响的理解， 疾病以及有关医疗费用的卫生政策。