title: When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization.
abstract: Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilisation in the United States of America (USA) and found that most of the relationships between late or no prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study was to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employed an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and that the results change after considering the SL effect of prenatal care utilisation. The GWR-SL approach allowed us to gain a placespecific understanding of prenatal care utilisation in USA counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., ordinary least squares and spatial lag models) and found that GWR-SL is the preferred modelling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilisation across space, and determine whether and how the level of prenatal care utilisation in neighbouring counties matters.
authors.forename: Carla
authors.initials: C
authors.lastname: Shoff
authors.forename: Vivian Yi-Ju
authors.initials: VY
authors.lastname: Chen
authors.forename: Tse-Chuan
authors.initials: TC
authors.lastname: Yang
mesh_terms: D062306:Geography, Medical / Q000379:methods / Q000706:statistics & numerical data*; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D006801:Humans; D011295:Prenatal Care / Q000706:statistics & numerical data*; D012044:Regression Analysis; D062206:Spatial Analysis; D014481:United States