Modeling Health Utilization of Medicaid Children

描述（由申请人提供）：保险公司，公共卫生官员，提供者和健康管理专业人员有兴趣预测谁使用医疗保健和为什么。预测谁将使用服务的能力对于若干实际应用是重要的，例如保健筹资和有针对性的健康促进举措。最近的研究表明，更好的预测估计个人水平的医疗保健利用率现在是可能的。 这项研究旨在建立一个更好的模型来预测卫生服务的使用。具体而言，它试图预测1999年格鲁吉亚医疗补助儿童的全部人口的医疗保健费用。本论文的研究结合了两个领先的成本预测模型的优势，以解决三个主要目标：第一，建立一个更好的预测模型的卫生服务利用率;第二，以解决一个特定的卫生政策领域，儿童有特殊医疗保健需求（CSHCN），通过提供信息，以帮助他们的定义和分类。 第三，利用新的方法学方法获得的知识，在以前的卫生服务利用理论的基础上，如卫生服务利用的行为模型和委托代理模型。 为了实现这些目标，将开发一个两部分模型，模型的每个部分都被视为至少两个亚群的有限混合物。 通过使用估计/最大化（EM）算法，使用最大似然估计来估计该模型。 新模型与已发布的模型进行比较，以确定预测能力的改进，使用BIC和GOF标准。 新模型中的信息可用于为医疗补助计划中的儿童设计医疗保健融资策略;识别不同的CSHCN群体;并扩展有关谁使用医疗服务以及为什么使用医疗服务的理论。用于建立这种预测模型的方法直接适用于卫生服务研究的其他几个领域。

DESCRIPTION (provided by applicant): Insurers, public health officials, providers, and health management professionals are interested in predicting who uses health care and why. The ability to predict who will use services is important for several practical applications, such as health care financing and targeting health promotion initiatives. Recent research suggests that better predictive estimates of individual level health care utilization are now possible. This research seeks to build a better model to predict the use of health services. Specifically, it seeks to predict the health care expenses for the full population of Georgia Medicaid children in 1999. This dissertation research combines the strengths of two leading cost forecasting models in order to address three primary goals: First, to build a better predictive model of health service utilization; Second, to address a particular health policy area, that of children with special health care needs (CSHCN), by providing information to help in their definition and categorization. Third, to build upon previous theory in health service utilization, such as the behavioral model of health service utilization and the principal agent model, using knowledge gained from new methodological approaches. To achieve these goals, a two-part model will be developed, with each portion of the model being treated as a finite mixture of at least two sub-populations. The model is estimated using maximum likelihood estimation, through use of the estimation/maximization (EM) algorithm. The new model is compared to published models to determine improvements in predictive power, using the BIC and the GOF criteria. Information from the new model can be used to design health care financing strategies for children in Medicaid; to identify different groups of CSHCN; and to expand the theory about who uses health services and why. The methodologies used to build this predictive model are directly applicable to several other areas of health services research.