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Open Access Methodology

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters

Esra Ozdenerol1*, Bryan L Williams2, Su Young Kang1 and Melina S Magsumbol2

Author Affiliations

1 Department of Earth Sciences, 236 Johnson Hall, University of Memphis, Tennessee, 38152, USA

2 Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA

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International Journal of Health Geographics 2005, 4:19  doi:10.1186/1476-072X-4-19

Published: 2 August 2005

Abstract

Background

The purpose of this study is to examine the spatial and population (e.g., socio-economic) characteristics of low birthweight using two different cluster estimation techniques. We compared the results of Kulldorff's Spatial Scan Statistic with the results of Rushton's Spatial filtering technique across increasing sizes of spatial filters (circle). We were able to demonstrate that varying approaches exist to explore spatial variation in patterns of low birth weight.

Results

Spatial filtering results did not show any particular area that was not statistically significant based on SaTScan. The high rates, which remain as the filter size increases to 0.4, 0.5 to 0.6 miles, respectively, indicate that these differences are less likely due to chance. The maternal characteristics of births within clusters differed considerably between the two methods. Progressively larger Spatial filters removed local spatial variability, which eventually produced an approximate uniform pattern of low birth weight.

Conclusion

SaTScan and Spatial filtering cluster estimation methods produced noticeably different results from the same individual level birth data. SaTScan clusters are likely to differ from Spatial filtering clusters in terms of population characteristics and geographic area within clusters. Using the two methods in conjunction could provide more detail about the population and spatial features contained with each type of cluster.