Buntinx F, Geys H, Lousbergh D, Broeders G, Cloes E, Dhollander D, Op De Beeck L, Vanden Brande J, Van Waes A, Molenberghs G
Limburg Cancer Registry (LIKAR), Limburgse Kankerstichting, Stadsomvaart 9, 3500, Hasselt, Belgium.
Eur J Cancer. 2003 Sep;39(14):2058-72. doi: 10.1016/s0959-8049(02)00734-7.
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.
要正确解答忧心忡忡的市民关于可能出现的癌症聚集问题，需要一种基于先前确立的分析程序的风险沟通策略。本研究的目的是分析癌症登记数据，以识别一个或多个癌症类型发病率增加的市或市集群，并根据相同层面的背景特征进行调整。理想情况下，该方法应积极主动、直截了当，便于未经培训的市民理解，同时考虑到不精确性影响。对于所有市和大多数癌症，所有相关计算均提前进行，所有方法和决策阈值均预先确定。对于每个市，计算标准化发病率比（SIR），并使用泊松 - 伽马（PG）模型和条件自回归（CAR）模型进行平滑处理。使用Kulldorff的空间扫描统计量确认集群。利用每个市可得的所有信息，对识别出的集群进行可能的混杂因素检验。使用了为比利时林堡省人口（n = 781759）服务的林堡癌症登记处的数据。我们识别出一个前列腺癌发病率可能增加的集群（平滑后的SIR约为1.2），以及一个男性膀胱癌发病率增加的集群，该集群包括七个市，其CAR平滑后的SIR在1.5至2.1之间。SIR在位于阿尔肯和省会哈瑟尔特的中心周围呈或多或少的圆形下降。膀胱癌发病率与每个市的社会经济地位（SES）指数呈正相关。未发现与其他可用指数存在关联。所有膀胱癌中有82%为移行细胞癌（TCC）。仅基于TCC进行的重复分析得出了类似结果，集群区域内CAR平滑后的相对风险甚至更高。事实证明，在市一级对可能的癌症发病率聚集进行预先分析是可行的。
