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University of Cincinnati Academic Health Center
Publish Date: 05/01/10
Media Contact: Nick Miller, 513-803-6035
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Data Analysis Enables Identification of Neighborhood-Specific Risk Factors for Preterm Birth

Through computer analysis of geographical information, researchers believe they can provide optimal and targeted strategies for preventing preterm birth – and they have data from a study of Hamilton County, Ohio, to support their contention.

"By identifying risk factors for each area that are potentially modifiable, we are able to begin to also identify the right intervention for that specific population of mothers,” says Andrew South, MD, a neonatologist at Cincinnati Children’s and senior author of the study. "The list of risk factors that we used in this study is consistent with what is in published literature, suggesting that preterm birth in Hamilton County is similar in terms of risk factors to preterm births seen elsewhere in the United States, and thus our methods should be applicable in any given geographical area.” South is also an Assistant Professor of Pediatric at the University of Cincinnati College of Medicine.

South will present his study at 1:45 p.m. Eastern time Saturday, May 1, at the annual meeting of the Pediatric Academic Societies in Vancouver, Canada.

The Cincinnati Children’s Hospital Medical Center study included over 41,000 singleton (one baby) births, all of them infants born between 2003 and 2006 whose mothers resided in Hamilton County. Infant demographics, gestational age, complications of pregnancy and latitude and longitude coordinates of mother’s address were determined from State of Ohio Vital Statistics.

Five distinct areas were identified for further analysis. All births in the five areas were further analyzed to determine differences in demographics and potentially modifiable risk factors for preterm birth, including previous preterm birth, chronic or gestational hypertension, education level, diabetes, short inter-pregnancy interval, smoking, advanced maternal age and low pre-pregnancy weight.

The researchers used Geographic Information System (GIS) techniques to determine the proportion of preterm births for geographical points throughout the county. In doing this, they removed artificial geopolitical boundaries in favor of allowing the natural disease pattern to identify areas for further evaluation, according to South.

"While use of political boundaries, such as counties, may be useful in defining the scope of a problem, it is less useful from a public health standpoint because it does not allow for precise identification of specific populations at risk for a poor outcome,” he says.

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