Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

Background: There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the c...

Authors: Lemke, Dorothea
Mattauch, Volkmar Robert
Heidinger, Oliver
Pebesma, Edzer J.
Hense, Hans-Werner
Division/Institute:FB 05: Medizinische Fakultät
FB 14: Geowissenschaften
Document types:Article
Media types:Text
Publication date:2013
Date of publication on miami:26.02.2014
Modification date:24.01.2020
Edition statement:[Electronic ed.]
Source:International Journal of Health Geographics 12 (2013) 54
Subjects:Spatial cancer cluster; Local cluster tests, R; DCluster; Bayesian smoothing methods; Simulation design; Epidemiological cancer registry
DDC Subject:610: Medizin und Gesundheit
License:CC BY 2.0
Language:English
Notes:Finanziert durch den Open-Access-Publikationsfonds 2013/2014 der Deutschen Forschungsgemeinschaft (DFG) und der Westfälischen Wilhelms-Universität Münster (WWU Münster).
Format:PDF document
URN:urn:nbn:de:hbz:6-74309620837
Permalink:http://nbn-resolving.de/urn:nbn:de:hbz:6-74309620837
Other Identifiers:DOI: 10.1186/1476-072X-12-54
Digital documents:1476-072X-12-54.pdf

Background: There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods: Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results: With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both