A Novel Casual Difference-in-differences Method to Study the Medical Home Effects

描述（由申请人提供）：评估医疗之家对儿童医疗保健费用影响的一种新的因果差异方法PI：Bing Han，co-PI：Hao Yu其他关键人员：Bonnie Ghosh-Dastidar，Mark Friedberg在过去的十年中，医疗之家的概念作为确保所有儿童和慢性病成人优质医疗保健的机制受到了国家的关注。许多研究报告了医疗之家对儿童卫生保健的获得，利用和质量的积极影响。然而，关于其对医疗保健费用的影响，报告的结果喜忧参半。医疗家庭对医疗保健费用的影响的评估通常是基于差异中的差异（DID）方法。经典的DID方法采用两个核心假设：平行轨迹和离散治疗假设，这两个假设在医疗家庭研究中都可能被违反。许多医学家庭研究是基于观察数据或准实验，这不能安全地保证平行轨迹假设。医疗居家状态（在文献中称为医疗居家（MH））是一个连续的剂量，它必须被离散化，以满足离散的治疗假设。这些困难导致损失的准确性和精度的估计。因此，目前还不清楚医疗保健成本的混合结果是否反映了医疗之家的真实影响或效率低下的分析方法的结果。为了解决这个问题，我们提出了一个创新的因果推理方法的具体目标1下的DID设置。我们提出了一套新的概念，包括潜在的轨迹，（动态）治疗方案，和纵向广义倾向评分（LGPS）。这些新概念是现有广义倾向评分和潜在结果的自然扩展（即，反事实），但它可以同时放松经典DID方法中的两个核心假设。我们还建议开发有效的非参数方法来估计LGPS和适合LGPS调整估计的各种治疗效果。在具体目标2下，我们将使用新方法研究MH对医疗保健成本的详细影响，目的是探索准确和精确的估计，以填补现有文献的差距。

DESCRIPTION (provided by applicant): A Novel Causal Difference-in-differences Approach to Estimate the Effect of Medical Homes on Health Care Costs Among Children PI: Bing Han, co-PI: Hao Yu Other key personnel: Bonnie Ghosh-Dastidar, Mark Friedberg During the past decade, the medical home concept has received national attention as a mechanism for ensuring quality health care for all children and for adults with chronic conditions. Numerous studies have reported on the positive effects a medical home has on children health care access, utilization, and quality. However, mixed results have been reported about its effect on health care costs. Evaluations of the medical home effect on health care costs are often based on the difference-in-differences (DID) method. The classic DID method employs two core assumptions: the parallel trajectory and discrete treatment assumptions, both of which are likely violated in the medical home studies. Many medical home studies are based on observational data or quasi-experiments, which cannot safely guarantee the parallel trajectory assumption. Medical home statuses (known as medical homeness (MH) in the literature) are a continuous dosage, which has to be discretized to satisfy the discrete treatment assumption. These difficulties result in th loss of both accuracy and precision in the estimates. Thus, it is unclear whether the mixed results on health care cost reflect the true impact of medical home or the results of inefficient analytic methods. To address this issue, we propose an innovative causal inference method for the DID setting under specific aim 1. We propose a set of novel concepts-including the potential trajectory, the (dynamic) treatment scheme, and the longitudinal generalized propensity score (LGPS). These new concepts are natural extensions of the existing generalized propensity score and potential outcome (i.e., counterfactual) for dichotomous treatment statuses, but which can simultaneously relax both core assumptions in the classic DID method. We also propose to develop efficient nonparametric methods to estimate the LGPS and to fit the LGPS-adjusted estimate for various treatment effects. Under specific aim 2, we will examine the detailed impact of MH on health care costs using the new method, with the goal of exploring an accurate and precise estimate to fill in the gap of the existing literature.