Advancing Spatial Evaluation Methods to Improve Healthcare Efficiency and Quality

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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.