title: Geographical differences in cancer incidence in the Belgian province of Limburg.
abstract: Correctly addressing the questions of worried citizens with respect to possible clusters of cancer occurrence requires a risk communication strategy that is informed by a previously established analytical procedure. The aim of this study was to analyse cancer registration data in order to identify municipalities or clusters of municipalities with an increased incidence of one or more cancer types, adjusted for background characteristics at the same level. Ideally, the approach is proactive, straightforward, and easy for untrained citizens to follow and imprecision effects are taken into account. For all municipalities and most cancers, all relevant calculations were performed proactively and all methods and decision thresholds were defined beforehand. For each municipality, standardised incidence ratios (SIRs) were calculated and smoothed using a Poisson-gamma (PG) and a conditional autoregressive (CAR) model. Clusters were confirmed using the Spatial scan statistic of Kulldorff. Identified clusters were tested for possible confounders using all information that was available for each municipality. The Limburg Cancer Registry, serving the population of the Belgian province of Limburg (n=781 759) was used. We identified a possible cluster of increased prostate cancer incidence (smoothed SIRs around 1.2) and a cluster of increased bladder cancer incidence in males that included seven municipalities with CAR-smoothed SIRs between 1.5 and 2.1. SIRs followed a more or less circular decrease around the centre that was situated in Alken and Hasselt, the provincial capital. Bladder cancer incidence was positively related to an index of socio-economic status (SES) per municipality. No relationship was found with the other indexes that were available. 82% of all bladder cancers were transitional cell carcinomas (TCC). A repeated analysis based on TCCs only resulted in similar results with CAR-smoothed relative risks that tended to be even higher in the cluster zone. A pre-emptive analysis of possible cancer incidence clustering on the municipality level proved to be feasible.
authors.affiliation: Limburg Cancer Registry (LIKAR), Limburgse Kankerstichting, Stadsomvaart 9, 3500, Hasselt, Belgium. frank.buntinx@med.kuleuven.ac.be
authors.forename: F
authors.initials: F
authors.lastname: Buntinx
authors.forename: H
authors.initials: H
authors.lastname: Geys
authors.forename: D
authors.initials: D
authors.lastname: Lousbergh
authors.forename: G
authors.initials: G
authors.lastname: Broeders
authors.forename: E
authors.initials: E
authors.lastname: Cloes
authors.forename: D
authors.initials: D
authors.lastname: Dhollander
authors.forename: L
authors.initials: L
authors.lastname: Op De Beeck
authors.forename: J
authors.initials: J
authors.lastname: Vanden Brande
authors.forename: A
authors.initials: A
authors.lastname: Van Waes
authors.forename: G
authors.initials: G
authors.lastname: Molenberghs
mesh_terms: D001530:Belgium / Q000453:epidemiology; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D009369:Neoplasms / Q000453:epidemiology*; D016012:Poisson Distribution; D012042:Registries; D012111:Residence Characteristics; D012307:Risk Factors; D001749:Urinary Bladder Neoplasms / Q000453:epidemiology