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

• Main Authors: Kang Su Young, Williams Bryan L, Ozdenerol Esra, Magsumbol Melina S
• Format: Article
• Language: English
• Published: BMC 2005-08-01
• Series: International Journal of Health Geographics
• Online Access: http://www.ij-healthgeographics.com/content/4/1/19

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>