Location of Cleft Lip with or without Cleft Palate Prevalence Clusters using Kulldorff Scan Statistics
Więckowska Barbara 1, Materna-Kiryluk Anna 2, Kossowski Tomasz 3, Moczko Jerzy 1, Wiśniewska Katarzyna 4, Latos-Bieleńska Anna 2
1University of Medical Sciences in Poznań, Chair and Department of Computer Science and Statistics
Dąbrowskiego 79, 60-529 Poznań, Poland
e-mail: basia@ump.edu.pl
2University of Medical Sciences in Poznań,Department of Medical Genetics
Grunwaldzka 55/15, 60-352 Poznań, Poland
e-mail: akiryluk@ump.edu.pl
3Adam Mickiewicz University in Poznań, Institute of Socio-Economic Geography and Spatial Management
Dzięgielowa 27, 61-680 Poznań, Poland
e-mail: tkoss@amu.edu.pl
4University of Medical Sciences in Poznań, Department of Preventive Medicine
Smoluchowskiego 11, 60-179 Poznań, Poland
e-mail: kwisniewska@ump.edu.pl
Received:
(Received: 09 March 2011; revised: 20 March 2012; accepted: 31 March 2012; published online: 27 April 2012)
DOI: 10.12921/cmst.2012.18.01.53-62
OAI: oai:lib.psnc.pl:425
Abstract:
The prevalence of clusters with the increased morbidity rate is the area of interest among epidemiologists. Not only does the identification of clusters require collecting precise epidemiological data but it also requires the application of reliable spatial statistics techniques. The identification of atypical clusters in this article is performed using data from the Polish Registry of Congenital Malformations (PRWWR) on children with isolated cleft lip with or without cleft palate; the study was carried out in the Wielkopolska Region (Greater Poland). For this purpose, Kulldorff Scan Statistics and the LISA method were used. Since each technique used in the study focuses on a slightly different aspect of spatial structure, the obtained clusters do not always completely overlap. This study presents and compares the efficiency and accuracy of these two non-standard methods of geo-static analysis in children living in the Greater Poland counties. The study has identified 5 agglomerations with an increased prevalence rate of the examined malformation, no statistically significant cluster has been detected. On the basis of the agglomerations, it was possible to compare the applicability of two statistical methods used in the study. Despite the fact that the located clusters do not always completely overlap, the study has proved similarity in qualifying particular counties for given clusters and areas outside the clusters. Taking into account its applicability and monitoring the process of spatial scanning, the Kulldorff method has occurred more universal and accurate in examining the children with congenital malformations.
Key words:
cleft lip, cleft palate, clusters, Kulldorff Scan Statistics, LISA
References:
[1] A.S. Fotherigham, Trends in Quantitative Methods I: Stressing the Local, Progress in Human Geography 21, 88-96 (1997).
[2] A.S. Fotheringham, Context-dependent spatial analysis: A role for GIS? Journal of Geographical Systems 2, 71-76 (2000).
[3] S. Openshaw, M. Charlton, C. Wymer, A. Craft, A mark I geographical analysis machine for the automated analysis of point data sets, International Journal of Geographical Information System 1, 335-358 (1987).
[4] B.W. Turnbull, E.J. Iwano, W.S. Burnett, H.L. Howe, L.C. Clark, Monitoring for clusters of disease: application to leukemia incidence in upstate New York, American Journal of Epidemiology, Jul 132(1 Suppl), 136-143 (1990).
[5] J. Besag, J. Nowell, The detection of clusters in rare diseases, Journal of the Royal Statistical Society, Series A 154, 143-155 (1991).
[6] F.E. Alexander, J. Cuzick, Methods for the assessment of disease cluster, in: Elliott P., Cuzick J., English D., Stern R. (eds), Geographical end Environmental Epidemiology: Methods for Small – Area Studies, Oxford University Press, Oxford, p. 238-250 (1992).
[7] M. Kulldorff, N. Nagarwalla, Spatial disease clusters: Detection and inference, Statistics in Medicine 14, 799-819 (1995).
[8] M. Kulldorff, A spatial scan statistic, Communications in Statistics: Theory and Methods 2, 1481-1496 (1997)
[9] D.U. Pfeiffer, T.P. Robinson, M. Stevenson, K.B. Steven, D.J. Rogers, A.C.A. Clements, Spatial Analysis in Epidemiology, Oxford University Press, Oxford (2008).
[10] M. Kulldorff, W.F. Athas, E.J. Feuer, B.A. Miller, C.R. Key, Evaluating cluster alarms. A space-time scan statistic and brain cancer in Los Alamos, New Mexico, American Journal of Public Health 88, 1377-1380 (1998a).
[11] M. Kulldorff, Prospective time periodic geographical disease surveillance using a scan statistic, Journal of the Royal Statistical Society, Series A 164, 61-72 (2001).
[12] M. Kulldorff, R. Heffernan, J. Hartman, R. Assuncao, F. Mostashari, A space-time permutation scan statistic for disease outbreak detection, PLoS Medicine 2, e59 (2005).
[13] L. Anselin, Local indicators of spatial association – LISA, Geographical Analysis 27, 93-115 (1995).
[14] G.M. Jacquez, D.A. Greiling, Local clustering in breast, lung and colorectal cancer in Long Island, New York, International Journal of Health Geographics 2: 3, (2003)
[15] C.E. Hanson, W.F. Wieczorek, Alcohol mortality: a comparison of spatial clustering methods, Social Science and Medicine 55, 791-802 (2002).
[16] L. Fang, L. Yan, S. Liang, S.J. de Vlas, D. Feng, X. Han, W. Zhao, B. Xu, L. Bian, H. Yang, P. Gong, J.H. Richardus, W. Cao, Spatial analysis of hemorrhagic fever with renal syndrome in China, BMC Infectious Diseases, 6, 77 (2006).
[17] B.F. Hwang, Jouni J.K. Jaakkola, Ozone and Other Pollutans and the risk of Oral Cleft, Environ Health Perspectives 116(10), 1411-1415 (2008).
[18] L.C. Messer, T.J. Luben, P. Mendola, S.E. Carozza, S.A. Horel, P.H. Langlois, Urban-Rural Residence the Occurrence of Cleft Lip and Cleft Palate in Texas, 1999-2000, Annals of Epidemiology 20, 32-39 (2010).
[19] E.W. Harville, A.J. Wilcox, R.T. Lie, H. Vindenes, F. Åbyholm, Cleft Lip and Palate versus Cleft Lip Only: Are They Distinct Deffects? American Journal of Epidemiology 162 (5), 448-453 (2005).
[20] S.Y. Gebreab, Spatial Epidemiology of Birth Defects in the United States and the State of Utah Using Geographic Information Systems and Spatial Statistics. All Graduate Theses and Dissertations. Paper 852 (2010).
[21] L. Anselin, Y.W. Kim, I. Syabri, Web-based analytical tools for the exploration of spatial data, Journal of Geographical Systems 6, 197-218 (2004b).
[22] F.P. Boscoe, C. McLaughlin, M.J. Schymura, C.L. Kielb, Visualization of the spatial scan statistic using nested circles, Health and Place 9, 273-277 (2003).
The prevalence of clusters with the increased morbidity rate is the area of interest among epidemiologists. Not only does the identification of clusters require collecting precise epidemiological data but it also requires the application of reliable spatial statistics techniques. The identification of atypical clusters in this article is performed using data from the Polish Registry of Congenital Malformations (PRWWR) on children with isolated cleft lip with or without cleft palate; the study was carried out in the Wielkopolska Region (Greater Poland). For this purpose, Kulldorff Scan Statistics and the LISA method were used. Since each technique used in the study focuses on a slightly different aspect of spatial structure, the obtained clusters do not always completely overlap. This study presents and compares the efficiency and accuracy of these two non-standard methods of geo-static analysis in children living in the Greater Poland counties. The study has identified 5 agglomerations with an increased prevalence rate of the examined malformation, no statistically significant cluster has been detected. On the basis of the agglomerations, it was possible to compare the applicability of two statistical methods used in the study. Despite the fact that the located clusters do not always completely overlap, the study has proved similarity in qualifying particular counties for given clusters and areas outside the clusters. Taking into account its applicability and monitoring the process of spatial scanning, the Kulldorff method has occurred more universal and accurate in examining the children with congenital malformations.
Key words:
cleft lip, cleft palate, clusters, Kulldorff Scan Statistics, LISA
References:
[1] A.S. Fotherigham, Trends in Quantitative Methods I: Stressing the Local, Progress in Human Geography 21, 88-96 (1997).
[2] A.S. Fotheringham, Context-dependent spatial analysis: A role for GIS? Journal of Geographical Systems 2, 71-76 (2000).
[3] S. Openshaw, M. Charlton, C. Wymer, A. Craft, A mark I geographical analysis machine for the automated analysis of point data sets, International Journal of Geographical Information System 1, 335-358 (1987).
[4] B.W. Turnbull, E.J. Iwano, W.S. Burnett, H.L. Howe, L.C. Clark, Monitoring for clusters of disease: application to leukemia incidence in upstate New York, American Journal of Epidemiology, Jul 132(1 Suppl), 136-143 (1990).
[5] J. Besag, J. Nowell, The detection of clusters in rare diseases, Journal of the Royal Statistical Society, Series A 154, 143-155 (1991).
[6] F.E. Alexander, J. Cuzick, Methods for the assessment of disease cluster, in: Elliott P., Cuzick J., English D., Stern R. (eds), Geographical end Environmental Epidemiology: Methods for Small – Area Studies, Oxford University Press, Oxford, p. 238-250 (1992).
[7] M. Kulldorff, N. Nagarwalla, Spatial disease clusters: Detection and inference, Statistics in Medicine 14, 799-819 (1995).
[8] M. Kulldorff, A spatial scan statistic, Communications in Statistics: Theory and Methods 2, 1481-1496 (1997)
[9] D.U. Pfeiffer, T.P. Robinson, M. Stevenson, K.B. Steven, D.J. Rogers, A.C.A. Clements, Spatial Analysis in Epidemiology, Oxford University Press, Oxford (2008).
[10] M. Kulldorff, W.F. Athas, E.J. Feuer, B.A. Miller, C.R. Key, Evaluating cluster alarms. A space-time scan statistic and brain cancer in Los Alamos, New Mexico, American Journal of Public Health 88, 1377-1380 (1998a).
[11] M. Kulldorff, Prospective time periodic geographical disease surveillance using a scan statistic, Journal of the Royal Statistical Society, Series A 164, 61-72 (2001).
[12] M. Kulldorff, R. Heffernan, J. Hartman, R. Assuncao, F. Mostashari, A space-time permutation scan statistic for disease outbreak detection, PLoS Medicine 2, e59 (2005).
[13] L. Anselin, Local indicators of spatial association – LISA, Geographical Analysis 27, 93-115 (1995).
[14] G.M. Jacquez, D.A. Greiling, Local clustering in breast, lung and colorectal cancer in Long Island, New York, International Journal of Health Geographics 2: 3, (2003)
[15] C.E. Hanson, W.F. Wieczorek, Alcohol mortality: a comparison of spatial clustering methods, Social Science and Medicine 55, 791-802 (2002).
[16] L. Fang, L. Yan, S. Liang, S.J. de Vlas, D. Feng, X. Han, W. Zhao, B. Xu, L. Bian, H. Yang, P. Gong, J.H. Richardus, W. Cao, Spatial analysis of hemorrhagic fever with renal syndrome in China, BMC Infectious Diseases, 6, 77 (2006).
[17] B.F. Hwang, Jouni J.K. Jaakkola, Ozone and Other Pollutans and the risk of Oral Cleft, Environ Health Perspectives 116(10), 1411-1415 (2008).
[18] L.C. Messer, T.J. Luben, P. Mendola, S.E. Carozza, S.A. Horel, P.H. Langlois, Urban-Rural Residence the Occurrence of Cleft Lip and Cleft Palate in Texas, 1999-2000, Annals of Epidemiology 20, 32-39 (2010).
[19] E.W. Harville, A.J. Wilcox, R.T. Lie, H. Vindenes, F. Åbyholm, Cleft Lip and Palate versus Cleft Lip Only: Are They Distinct Deffects? American Journal of Epidemiology 162 (5), 448-453 (2005).
[20] S.Y. Gebreab, Spatial Epidemiology of Birth Defects in the United States and the State of Utah Using Geographic Information Systems and Spatial Statistics. All Graduate Theses and Dissertations. Paper 852 (2010).
[21] L. Anselin, Y.W. Kim, I. Syabri, Web-based analytical tools for the exploration of spatial data, Journal of Geographical Systems 6, 197-218 (2004b).
[22] F.P. Boscoe, C. McLaughlin, M.J. Schymura, C.L. Kielb, Visualization of the spatial scan statistic using nested circles, Health and Place 9, 273-277 (2003).
