What has geography got to do with it? Using GWR to explore place-specific associations with prenatal care utilization

Abstract

We use a geographically weighted regression (GWR) approach to examine how the relationships between a set of predictors and prenatal care vary across the continental US. At its most fundamental, GWR is an exploratory technique that can facilitate the identification of areas with low prenatal care utilization and help better understand which predictors are associated with prenatal care at specific locations. Our work complements existing prenatal care research in providing an ecological, place-sensitive analysis. We found that the percent of the population who was uninsured was positively associated with the percent of women receiving late or no prenatal care in the global model. The GWR map not only confirmed, but also demonstrated the spatial varying association. Additionally, we found that the number of Ob-Gyn doctors per 100,000 females of childbearing age in a county was associated with the percentage of women receiving late or no prenatal care, and that a higher value of female disadvantage is associated with higher percentages of late or no prenatal care. GWR offers a more nuanced examination of prenatal care and provides empirical evidence in support of locally tailored health policy formation and program implementation, which may improve program effectiveness.

Keywords: Geographically weighted regression; Local modeling; Prenatal care; Spatial non-stationarity.

Fig. 1

Map of the Percentages of Women Receiving Late or No Prenatal Care by County (1999–2001)

Fig. 2

Map of Local R-Square Estimates and GWR Estimates for Percent Uninsured, Female Disadvantage Composite, and Percent Foreign Born Note: Significant Areas at +/− 1.96 level

Fig. 3

Map of GWR Estimates for Percent Black Females Ages 15–44, Percent American Indian/Alaskan Native Females Ages 15–44, Percent Asian Females Ages 15–44, and Percent Hispanic Females Ages 15–44 Note: Significant Areas at +/− 1.96 level