Advancing Spatial Evaluation Methods to Improve Healthcare Efficiency and Quality

项目摘要/摘要 说明：请参阅说明。它必须包含适合向公众传播的拟议活动的摘要(否 专有/机密信息)。它应该是对项目的独立描述，并包含对目标和方法的声明 受雇的。它应该为在相同或相关领域工作的其他人提供信息。不要超过所提供的空间。 建议的研究是基于信念，并有广泛的理论和实证支持。 证据表明，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 以地点为基础的卫生政策的有效性、成本和效益。这项工作将直接有助于实现 在AHRQ的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 专门设计用于解决当前可用于计划或政策的技术中的缺陷 分析。到目前为止，方案和政策评估中使用的统计和计量方法有 仍然主要的特点是缺乏对空间溢出效应的解释-在那里发生在一个 社区同时影响其邻居，导致结果变量的非独立性。 虽然现有方法可以解释空间相关的解释变量和聚集的结果 在共同的地方(即空间自相关的外源)，现有方法忽略了 空间溢出的同步(内生)动态。广泛的证据表明，忽视这些 空间溢出效应可能会导致参数估计有偏差和不一致，从而误导量化 不确定性，以及有缺陷的模型预测。这可能会对估计 以地点为基础的干预或政策影响，导致夸大或低估计划效果估计， 偏向于成本效益和未来政策决策的模拟实验。 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，用于政策评估和执行 它们在用户友好的开源软件中。我们将创新空间分析方法，为 面板数据中空间相关性、空间异质性和选择性的显式联合处理 模型，并将其作为对我们成熟的软件开发和 传播努力。 目标2：进行空间显式评估分析，并传播结果和应用 方法：研究方法。我们将评估实施的特定医疗保险健康政策变化的影响 2006年在一项自然实验(前后)的时空研究设计中，探索差异的变化 在结直肠癌筛查中的应用和结直肠癌筛查的地理扩散 随着时间的推移，技术的进步。选择偏见很普遍，因为老年人有选择地参加管理性护理计划， 它们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 评价环境，其中空间溢出效应、其他形式的空间自相关、 选择偏差的各种来源，以及不适当或未建模的空间异质性可能会非常严重 影响衡量的政策影响。

PROJECT SUMMARY/ABSTRACT DESCRIPTION: See instructions. This must contain a summary of the proposed activity suitable for dissemination to the public (no proprietary/confidential information). It should be a self-contained description of the project and contain a statement of objectives and methods to be employed. It should be informative to other persons working in the same or related fields. DO NOT EXCEED THE SPACE PROVIDED. 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 research 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 bias, and inappropriately or un-modeled spatial heterogeneity may critically affect the measured policy impact.