Authors: Jun Tu*, Kennesaw State University, Wei Tu, Georgia Southern University
Topics: Medical and Health Geography, Spatial Analysis & Modeling, Human-Environment Geography
Keywords: Preterm Birth, Risk Factors, Geographically Weighted Logistic Regression, Geographic Information System
Session Type: Paper
Start / End Time: 5:35 PM / 6:50 PM
Room: Capitol Ballroom 5, Hyatt Regency, Fourth Floor
Presentation File: No File Uploaded
Preterm birth (PTB) is a major cause of infant mortality and morbidity. The relationships between PTB and risk factors have been examined by many previous studies worldwide, but the results vary among different studies, and no general conclusion could be drawn about the relationships. We analyzed 116,112 live and singleton births in year 2000 in Georgia, USA. A spatial statistical method, Geographically Weighted Logistic Regression (GWLR), was employed to model the relationships between PTB and nine individual-level birth and maternal demographic, socioeconomic, and behavioral factors, and five community-level socioeconomic and environmental factors. Different from the results calculated from global logistic regression, the results obtained from the GWLR model show that the relationships between PTB and risk factors vary over space. Positively significant, negatively significant, and non-significant relationships between PTB and factors are all discovered in different regions of Georgia, and the varying relationships are strongly related to the varying SES (Socioeconomic Status) and urbanization level of the communities of the births. These findings suggest that in order to more successfully reduce PTB risk, it is necessary to consider the varying relationships between PTB and risk factors across the communities with different levels of urbanization and SES for making and implementation of local public health policies.