Gebäude: Bonifazius-Turm A
18. Etage, Raum 16
Mail: distasor@uni-mainz.de
rossana.distaso2@unibo.it
Geostatistical methodologies for cluster identification applied to georeferenced data of incident cases of childhood leukaemia in Germany between 1990 and 2019
Background
Leukemia is the most common pediatric cancer, followed by central nervous system (CNS) tumor and lymphoma in Europe in 2022 [1]. To the best of our knowledge, since the EUROCLUS study (1999) [2], no further studies have been conducted to assess the incidence of leukemia in Germany with up-to-dated geo-referenced data. Alexander et al. (1999) analyzed incidence data for 13,551 cases of childhood leukaemia diagnosed between 1980 and 1989 in 17 countries and found that a higher risk of leukaemia was highest in more densely populated areas. Results related to Germany did not show a statistically significant excess of observed cases of childhood leukaemia to space-time expected cases, computed combining country- age- and sex-specific rates to the person-years at risk [2]. In 2010, Schmiedel et al. published a study on leukemia incidence in Germany [3]. They, investigated the presence of clusters and clustering at municipality level, i.e. using aggregated data at municipal level, using data of children up to the age of 14 years with primary leukemia diagnosed in the period 1987–2007 and registered in the German Childhood Cancer Registry. Schmiedel et al. did not find any evidence of a tendency to clustering; however, as discussed by the authors, using aggregated data by municipalities might have diluted small localized clusters and is considered a limitation.
Aim of the study
Aim of the present study is to determine whether cases childhood leukemia in Germany between 1990 and 2022 show a tendency towards spatial clustering.
Methods
For this purpose, geo-referenced data from the German Childhood Cancer Registry (DKKR) will be used for tumors in patients aged 0-14, with place of residence at diagnosis . Specifically, primary tumors diagnosed in Germany between 1990 and 2022 will be included. The outcome of interest is the incidence of leukemia. Incident cases will be classified according to the International Classification of Childhood Cancer, 3rd edition (ICCC-3).
For the cluster analysis, geospatial distribution of incident cases will be compared with the distribution of negative controls. Negative controls will be defined as a simulated population obtained using a raster from WorlPop project [4] of population counts in 100 m squared as probability function and the real population available from the DKKR at municipality level as constraint size.
The using of negative controls will allow us to test the null hypothesis of complete spatial randomness in the distribution of incident cases.
Expected results
With this study, it will be possible to identify geographical areas where cases tend to cluster using recently geo-referenced German data that have not yet been analyzed. Aim of the study is also to develop a method to select controls that simulate the population at risk in order to apply classical geo-statistical methodologies for cluster identification. This could then be extended to different regions, years and diseases in order to support epidemiological research and disease surveillance.
References
[1] gco.iarc.who.int/today/en/dataviz/pie
[2] Alexander FE, Boyle P, Carli PM, Coebergh JW, Ekbom A, Levi F, McKinney PA, McWhirter W, Michaelis J, Peris-Bonet R, Petridou E, Pompe-Kirn V, Plĕsko I, Pukkala E, Rahu M, Stiller CA, Storm H, Terracini B, Vatten L, Wray N. Population density and childhood leukaemia: results of the EUROCLUS Study. Eur J Cancer. 1999 Mar;35(3):439-44. doi: 10.1016/s0959-8049(98)00385-2. PMID: 10448296.
[3] Schmiedel S, Blettner M, Kaatsch P, Schüz J. Spatial clustering and space-time clusters of leukemia among children in Germany, 1987-2007. Eur J Epidemiol. 2010 Sep;25(9):627-33. doi: 10.1007/s10654-010-9488-7. Epub 2010 Jul 11. PMID: 20623321.
[4] Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 170004 (2017).