Online Program

Examining birth outcomes hotspots in Maryland

Tuesday, November 3, 2015 : 10:30 a.m. - 10:45 a.m.

Andrew Williams, MPH, Family Science Department, University of Maryland School of Public Health, College Park, MD
Sandra Hofferth, PhD, Department of Family Science, University of Maryland, College Park, College Park, MD
Background: Research suggests spatial autocorrelation of birth outcomes exists at the Census tract level in Maryland, yet identification and analysis of birth outcomes hotspots has not occurred.

Methods: Tract-level LBW and PTB rates were drawn from 2010-2012 aggregate vital statistics data in Maryland, and demographic data were drawn from 2008-2012 American Community Survey. Global Moran’s I (GMI) is a global measure of spatial autocorrelation of LBW and PTB rates. Local Moran’s I (LMI) identified hotspots. T-tests examined demographic differences between “High” and “Low” hotspots.

Results: GMI suggests spatial autocorrelation for LBW (z-score: .291, p=.001) and PTB (z-score:.173, p=.001). LMI identified 127 LBW hotspots, with 77 percent (99 of 127) being “High LBW” (≥ 11 percent LBW). There are 88 PTB hotspots, with 68 percent (60 of 88) being “High PTB” (≥ 13 percent PTB). High LBW hotspots had higher mean poverty (23.39 percent) and mean concentration of Black non-Hispanics (81.75 percent) than Low LBW hotspots (p<.001). High PTB hotspots had higher mean poverty (24.74 percent) and mean concentration of Black non-Hispanics (85.69 percent) than Low PTB hotspots (p<.001).

Discussion: This study provides evidence that geographic concentration of birth outcomes does occur, and allows for the identification of areas with poor outcomes. Results suggest hotspots with poor outcomes have high rates of poverty, and are minority-majority areas. This can result in programs and policies to improve birth outcomes that are both geographically targeted, and culturally appropriate. Future research should include individual- and area-level factors to better understand clustering of birth outcomes.

Learning Areas:

Planning of health education strategies, interventions, and programs
Public health or related research

Learning Objectives:
Identify hotspots of poor birth outcomes by utilizing spatial statistics. Describe the demographic differences between High and Low birth outcomes hotspots. Discuss potential program and policy implications of this study.

Keyword(s): Geographic Information Systems (GIS), Birth Outcomes

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a doctoral candidate in the Maternal and Child Health program at the University of Maryland. I am responsible for planning and carrying out this research, as well as conducting all data analysis. My dissertation research focuses on Place and Health, and my previous work has been presented at other conferences.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.