Institute of Epidemiology and Social Medicine, Medical Faculty, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1 D3, D 48149, Münster, Germany.
Int J Health Geogr. 2013 Dec 7;12:54. doi: 10.1186/1476-072X-12-54.
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 source environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany.
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.
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 < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation.
High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage.
公众和政治对前瞻性癌症集群监测的需求日益增长。但是，在小样本量和异质样本大小以及不同空间尺度（如大多数现有人群癌症登记处的情况）下，既定的集群检测测试的性能几乎没有经验证据。因此，本模拟研究旨在评估不同的集群检测方法，这些方法在开源环境 R 中实现，以利用德国一个流行病学癌症登记处的真实生活数据识别肺癌集群。
使用两种不同的空间集群类型构建风险曲面，分别代表男性和女性的肺癌总体背景发病率的相对风险 RR = 2.0 或 RR = 4.0。使用非均匀泊松过程从该风险曲面中作为地理编码抽样肺癌病例。使用不均匀泊松过程从该风险曲面中作为地理编码抽样肺癌病例。在小空间（普查区，N = 1983）和大空间聚合尺度（社区，N = 78）内分析实际的癌症病例。随后，它们被提交给集群检测方法。集群位置的检测准确性是通过检测率（DR）、假阳性（FP）率和阳性预测值来确定的。使用 ROC 曲线评估贝叶斯平滑模型。
对于中度风险增加（RR = 2.0），局部集群测试显示出更好的 DR（两种空间聚合尺度均>0.90）和更低的 FP 率（均<0.05），优于贝叶斯平滑方法。当集群 RR 增加四倍时，局部集群测试仅在小空间尺度上显示出更好的 DR 和更低的 FP。在大空间尺度上，贝叶斯平滑方法，特别是那些实施空间邻域的方法，显示出比集群测试低得多的 FP 率。然而，在这个尺度上，风险增加大多被数据聚合所稀释。
高分辨率空间尺度似乎更适合作为癌症集群检测和监测的数据库，而不是常用的聚合尺度。我们建议开发一种两阶段方法，该方法将高检测率的方法结合起来作为第一阶段的筛选，然后将高预测能力的方法作为第二阶段的筛选。
