Sundmacher L, Götz N, Vogt V
Fachbereich Health Services Management, Ludwig-Maximilians-Universität, Schackstr. 4, 80539, München, Deutschland,
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2014 Feb;57(2):174-9. doi: 10.1007/s00103-013-1887-y.
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.
在区域医疗服务研究中，准确模拟观测值之间的空间依赖性是一项重大挑战。本文深入探讨了区域医疗服务研究中当前用于模拟关系的方法，并考虑了空间依赖性。空间依赖性可能由相邻区域之间的溢出效应以及空间分布差异（例如发病率）引发，而这些差异是不可观测的。如果在模型中不加以考虑，分析结果可能会产生偏差。空间依赖性可以作为空间滞后项或空间误差项添加到回归模型中。通过一个实例研究，我们表明未考虑空间自相关性可能会导致系数和/或标准误差出现偏差。因此，区域医疗服务研究应尽可能对数据中的空间自相关性进行检验，并相应地调整模型。
