TY - JOUR
T1 - What has geography got to do with it? Using GWR to explore place-specific associations with prenatal care utilization
AU - Shoff, Carla
AU - Yang, Tse Chuan
AU - Matthews, Stephen A.
N1 - Funding Information:
Acknowledgments Color reproduction costs were supported by funds from the Department of Agricultural Economics and Rural Sociology, College of Liberal Arts, Gordon DeJong, and the Social Science Research Institute at the Pennsylvania State University. This paper benefited from research begun at the Geographically Weighted Regression (GWR) workshop led by A. Stewart Fotheringham, Chris Brunsdon and Martin Charlton held at the University of California-Santa Barbara, July 2010; a workshop supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R25-HD057002-03) on Advanced Spatial Analysis Training Program for Population Scientists (PI: Stephen Matthews, Penn State). This work was partially supported by internal funds from the Social Science Research Institute at Penn State. Additional support has been provided by the Geographic Information Analysis Core at Penn State’s Population Research Institute, which receives core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24-HD41025).
PY - 2012/6
Y1 - 2012/6
N2 - 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.
AB - 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.
KW - Geographically weighted regression
KW - Local modeling
KW - Prenatal care
KW - Spatial non-stationarity
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U2 - 10.1007/s10708-010-9405-3
DO - 10.1007/s10708-010-9405-3
M3 - Article
AN - SCOPUS:84858163935
SN - 0343-2521
VL - 77
SP - 331
EP - 341
JO - Geo Journal
JF - Geo Journal
IS - 3
ER -