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

Esra Ozdenerol, Bryan L Williams, Su Young Kang, Melina S. Magsumbol

Research output: Contribution to journalArticle

37 Citations (Scopus)

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 method 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.

Original languageEnglish (US)
Article number19
JournalInternational Journal of Health Geographics
Volume4
DOIs
StatePublished - Aug 2 2005

Fingerprint

Low Birth Weight Infant
Statistics
Parturition
Population Characteristics
Population
Economics
Mothers
Low birth weight
Spatial statistics

ASJC Scopus subject areas

  • Computer Science(all)
  • Business, Management and Accounting(all)
  • Public Health, Environmental and Occupational Health

Cite this

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters. / Ozdenerol, Esra; Williams, Bryan L; Kang, Su Young; Magsumbol, Melina S.

In: International Journal of Health Geographics, Vol. 4, 19, 02.08.2005.

Research output: Contribution to journalArticle

@article{a02dade0a9db4ca0890cf358a6b41fac,
title = "Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters",
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 method 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.",
author = "Esra Ozdenerol and Williams, {Bryan L} and Kang, {Su Young} and Magsumbol, {Melina S.}",
year = "2005",
month = "8",
day = "2",
doi = "10.1186/1476-072X-4-19",
language = "English (US)",
volume = "4",
journal = "International Journal of Health Geographics",
issn = "1476-072X",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters

AU - Ozdenerol, Esra

AU - Williams, Bryan L

AU - Kang, Su Young

AU - Magsumbol, Melina S.

PY - 2005/8/2

Y1 - 2005/8/2

N2 - 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 method 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.

AB - 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 method 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.

UR - http://www.scopus.com/inward/record.url?scp=27644473850&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27644473850&partnerID=8YFLogxK

U2 - 10.1186/1476-072X-4-19

DO - 10.1186/1476-072X-4-19

M3 - Article

AN - SCOPUS:27644473850

VL - 4

JO - International Journal of Health Geographics

JF - International Journal of Health Geographics

SN - 1476-072X

M1 - 19

ER -