Advancing Spatial Evaluation Methods to Improve Healthcare Efficiency and Quality

描述（由申请人提供）：拟议的研究是基于信念，广泛的理论和经验证据的支持下，需要一个新的，空间明确的分析方法，以适当和稳健地评估的有效性，成本和效益的地方为基础的卫生政策。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，专门用于解决目前可用于计划或政策分析的技术缺陷。到目前为止，统计和计量经济学方法在项目和政策评估中仍然主要是缺乏对空间溢出效应的解释，即一个社区发生的事情同时影响其邻居，导致结果变量的非独立性。虽然现有的方法可能占空间相关的解释变量和结果聚集在共同的地方（即空间自相关的外源），现有的方法忽略了空间溢出的同时（内源）动态。大量的证据表明，忽视这种空间溢出效应可能导致有偏见和不一致的参数估计，误导性的不确定性量化，以及有缺陷的模型预测。这可能会对基于地点的干预或政策影响的估计产生严重后果，导致高估或低估计划效果估计，使成本效益和未来政策决策的模拟实验产生偏差。我们将通过以下目标解决关键的方法差距并传播新方法：目标1：开发新的空间分析方法用于政策评估，并在用户友好的开放源码软件中实施。我们将开发创新的空间分析方法，用于明确联合处理空间依赖，空间 异质性和选择性的面板数据模型，并实施它们作为我们完善的软件开发和传播工作的补充。 目标2：进行空间上明确的评价分析，并传播研究结果和应用方法。我们将评估2006年在自然实验（前后）时空研究设计中实施的特定医疗保险健康政策变化的影响，以探索结直肠癌（CRC）筛查利用率和CRC筛查技术地理扩散随时间的变化。选择偏见是普遍存在的，因为老年人选择性地参加管理式医疗计划，这是医疗保险改革的重大影响，并在地理上不同的方式。虽然这一政策的应用是重要的，要开发的方法是广泛适用于许多政策评估的情况下，空间溢出效应，其他形式的空间自相关，各种来源的选择偏差，以及不适当的或未建模的空间异质性的组合可能会严重影响测量的政策影响。

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.