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Spatial patterns of adverse birth outcomes among black and white women in Massachusetts – the role of population-level and individual-level factors

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This study explores spatial distribution of adverse birth outcomes (ABO), defined as low birth weight (<=2500 g) and preterm deliveries (gestational age <37 weeks), in black and white mothers in the state of Massachusetts, USA. It uses 817877 individual birth records from 2000-2014 aggregated to census tracts (census enumeration unit with population of approximately 4500 people). To account for small numbers of births in some tracts, an Empirical Bayes smoother algorithm is used to calculate ABO rates. The study applies ordinary least squares (OLS) and spatial regression to examine the relationship between ABO rates, seven individual-level factors from birth certificates and nine population-level factors (income level, education level, race) from census data. Explanatory power of these factors varies between the two races. In models based only on individual-level factors, all seven factors were significant (p<0.05) in the black mothers’ model while only three were significant in the white mothers’ model. Models based only on population-level variables produced better results for the white mothers than for black mothers. Models that included both individual and population-level variables explained 40% and 29% of ABO variance for black and white women respectively. The findings from this study give health-care providers and health-care policy-makers important information regarding ABO rates and the contributing factors at a local level, thus enabling them to isolate specific areas with the highest need for targeted interventions.

About the Authors

Yelena A. Ogneva-Himmelberger
Clark University
United States
950 Main St., Worcester, MA 01610, USA

Madeline Haynes
Clark University
United States
950 Main St., Worcester, MA 01610, USA


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For citation:

Ogneva-Himmelberger Y.A., Haynes M. Spatial patterns of adverse birth outcomes among black and white women in Massachusetts – the role of population-level and individual-level factors. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2020;13(1):99-106.

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ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)