[Statistical methods for research on regional health-care services].
Accurate modeling of spatial dependencies between observations is a significant challenge in research on regional health-care services. This article provides insight into current methods of modeling relationships in regional health-care service research, with consideration of spatial dependencies. Spatial dependencies may be triggered by spillover effects between neighboring regions and spatially distributed differences in - e.g., morbidity - which are not observable. If not considered in the model, the results of the analyses may be biased. Spatial dependencies can be added to the regression model as a spatial lag or a spatial error term. Using an example study, we illustrate that failing to consider spatial autocorrelation may lead to biased coefficients and/or standard errors. Research on regional health-care services should, therefore, if possible, test for spatial autocorrelation in the data and adjust the model accordingly.
Fachbereich Health Services Management, Ludwig-Maximilians-Universität, Schackstr. 4, 80539, München, Deutschland, sundmacher@bwl.lmu.de.
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D003153:Community Health Services / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005858:Germany; D006291:Health Policy; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods*; D015233:Models, Statistical; D019035:Rural Health Services / Q000706:statistics & numerical data*; D017062:Small-Area Analysis