Advancing Spatial Evaluation Methods to Improve Healthcare Efficiency and Quality

描述(由申请人提供)：拟议的研究基于这样一种信念，即需要一种新的、空间上明确的分析方法来适当和有力地评估基于地点的卫生政策的有效性、成本和收益，这一信念得到了广泛的理论和经验证据的支持。这项工作将直接有助于实现AHRQ价值组合中概述的研究目标。我们将开发新的空间分析方法，专门解决目前可用于方案或政策分析的技术的不足。到目前为止，方案和政策评估中使用的统计和计量经济学方法的主要特点仍然是缺乏对空间溢出效应的考虑--一个社区发生的事情同时影响到它的邻居，导致结果变量不独立。虽然现有的方法可能会解释空间相关的解释变量和结果聚集在共同的地方(即空间自相关的外部来源)，但现有的方法忽略了空间溢出的同时(内生)动态。大量证据表明，忽视这种空间溢出效应可能会导致参数估计的偏差和不一致，对不确定性的误导性量化，以及模型预测的缺陷。这对基于地点的干预或政策影响的估计具有潜在的严重后果，导致夸大或低估计划效果估计，偏向于成本效益模拟实验和未来的政策决策。我们将通过以下目标解决关键的方法差距并传播新的方法：目标1：开发用于政策评价的新的空间分析方法，并在方便用户的开放源码软件中加以实施。我们将开发创新的空间分析方法，用于显式联合处理空间依赖、空间 在面板数据模型中实现异构性和选择性，并将其作为我们成熟的软件开发和传播工作的补充。目的2：进行空间显性评价分析，传播研究结果和应用方法。我们将评估2006年实施的特定联邦医疗保险医疗政策变化的影响，采用自然的实验(前)时空研究设计，以探索结直肠癌(CRC)筛查利用的差异随时间的变化以及CRC筛查技术的地理传播。选择偏见很普遍，因为老年人有选择地参加管理性保健计划，这一计划受到医疗保险改革的显著影响，而且是以一种地理上不同的方式。虽然这一政策应用很重要，但要开发的方法广泛适用于许多政策评估环境，其中空间溢出效应、其他形式的空间自相关、各种选择来源的BIA以及不适当或未建模的空间异质性的组合可能严重影响所测量的政策影响。

DESCRIPTION (provided by applicant): The proposed research is based on the conviction, supported by extensive theoretical and empirical evidence, that a new, spatially explicit analytical methodology is required to properly and robustly assess the effectiveness, costs, and benefits of place-based health policies. The work will contribute directly to attaining the researc objectives outlined in AHRQ's Value Portfolio. We will develop new spatial analytical methods designed specifically to address deficiencies in the techniques currently available for program or policy analysis. To date, the statistical and econometric methods employed in program and policy evaluation are still mostly characterized by a lack of accounting for spatial spillover effects-where what happens in one community simultaneously impacts its neighbors, leading to non-independence in the outcome variable. While existing methods may account for spatially correlated explanatory variables and outcomes clustered within common places (i.e. exogenous sources of spatial autocorrelation), existing methods ignore the simultaneous (endogenous) dynamics of spatial spillovers. Extensive evidence suggests that ignoring such spatial spillover effects can lead to biased and inconsistent parameter estimates, misleading quantification of uncertainty, and flawed model prediction. This has potentially serious consequences for the estimation of place-based intervention or policy impacts, leading to overstated or understated program effect estimates, biasing simulation experiments of cost-effectiveness and future policy decisions. We will address critical methodological gaps and disseminate new methods through the following Aims: Aim 1: To develop new spatial analytical methods for use in policy evaluations and implement them in user-friendly open source software. We will develop innovative spatial analytic methods for the explicit joint treatment of spatial dependence, spatial heterogeneity, and selectivity in panel data models and implement them as additions to our well-established software development and dissemination efforts. Aim 2: To conduct spatially explicit evaluation analysis and disseminate the findings and applied methods. We will assess the effects of particular Medicare health policy changes that were implemented in 2006 in a natural experimental (pre-post) space-time research design, to explore changes in disparities in the utilization of colorectal cancer (CRC) screening and the geographic diffusion of CRC screening technology over time. Selection bias is prevalent, as the elderly selectively enroll in managed care plans, which were significantly impacted by Medicare reforms, and in a geographically disparate fashion. While this policy application is important, the methods to be developed are broadly applicable to many policy evaluation contexts, where the combination of spatial spillover effects, other forms of spatial autocorrelation, various sources of selection bia, and inappropriately or un-modeled spatial heterogeneity may critically affect the measured policy impact.