Evidence of localized clustering of gastroschisis births in North Carolina, 1999-2004

Examining the geographic distribution of birth defects can be useful in exploratory etiologic research. Identification of clusters of certain defects may uncover possible environmental or socio-economic risk factors and assist with the generation of hypotheses about underlying causes of these conditions. In North Carolina, the prevalence of gastroschisis, a serious abdominal wall defect, has increased over the past decade and anecdotal evidence from clinicians suggests the possibility of clustering of this condition. This study uses a spatial scan statistic to identify the location and extent of clusters of gastroschisis births in North Carolina between 1999 and 2004. Data on cases of gastroschisis were obtained from the North Carolina Birth Defect Monitoring Program (NCBDMP) and control births were chosen from all resident live births without birth defects contained in the North Carolina composite linked birth files. The clusters were controlled for five major risk factors (maternal age, race, parity, Medicaid status, maternal smoking) to ensure that the clusters were not artifacts of unequal population distribution. Results indicate a localized cluster of gastroschisis in the rural southern Piedmont of North Carolina which persists even after controlling for all major risk factors. Adjusting for these risk factors shifted the location of the cluster substantially, demonstrating the importance of adjusting for underlying population distribution. Since clusters persisted after adjusting for individual-level risk factors, environmental contaminants may explain the excess of gastroschisis cases. This study is among the first to assess spatial clustering of gastroschisis using GIS methods. This study also demonstrates the importance of controlling for covariates in spatial analysis and illustrates the usefulness of the spatial scan statistic in exploratory etiologic research.Birth defects Gastroschisis Spatial scan statistic Spatial cluster Covariates USA SaTScan