Goovaerts Pierre
BioMedware, Inc., Ann Arbor, MI, USA.
Int J Health Geogr. 2005 Dec 14;4:31. doi: 10.1186/1476-072X-4-31.
Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided.
The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothing techniques. Because of the small weight assigned to the rate observed over the entity being smoothed (kernel weight), the population-weighted average leads to risk maps that show little variability. Other techniques assign larger and similar kernel weights but they use a different piece of auxiliary information in the prediction: global or local means for global or local empirical Bayes smoothers, and spatial combination of surrounding rates for the geostatistical estimator. Simulation studies indicated that Poisson kriging outperforms other approaches for most scenarios, with a clear benefit when the risk values are spatially correlated. Global empirical Bayes smoothers provide more accurate predictions under the least frequent scenario of spatially random risk.
The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of mortality rates into the mapping of risk values and the quantification of the associated uncertainty, while being easier to implement than a full Bayesian model. The availability of a public-domain executable makes the geostatistical analysis of health data, and its comparison to traditional smoothers, more accessible to common users. In future papers this methodology will be generalized to the simulation of the spatial distribution of risk values and the propagation of the uncertainty attached to predicted risks in local cluster analysis.
公共卫生官员使用癌症死亡率地图来识别高风险区域，并指导监测和控制活动。因此，决策质量依赖于从观察到的发病率中准确量化风险，而当从人口稀少的地理单元计算发病率或为少数族裔人口记录发病率时，这些发病率可能非常不可靠。本文提出了一种地统计方法，该方法在处理癌症死亡率数据时考虑了空间变化的人口规模和空间模式。进行了模拟研究，以比较在疾病频率、人口规模和风险空间模式的不同情况下，泊松克里金法与一些简单平滑器（即人口加权估计器和经验贝叶斯平滑器）的性能。提供了一个带有示例数据集的公共领域可执行文件。
对乳腺癌和宫颈癌年龄调整死亡率的分析说明了常用平滑技术的一些关键特征。由于分配给在被平滑实体上观察到的发病率的权重较小（核权重），人口加权平均值导致风险地图显示出很小的变异性。其他技术分配更大且相似的核权重，但它们在预测中使用不同的辅助信息：全局或局部经验贝叶斯平滑器的全局或局部均值，以及地统计估计器的周围发病率的空间组合。模拟研究表明，在大多数情况下，泊松克里金法优于其他方法，当风险值在空间上相关时，有明显优势。全局经验贝叶斯平滑器在空间随机风险最不频繁的情况下提供更准确的预测。
本文提出的方法使研究人员能够将死亡率的空间依赖模式纳入风险值映射和相关不确定性的量化中，同时比完整的贝叶斯模型更易于实施。公共领域可执行文件的可用性使健康数据的地统计分析及其与传统平滑器的比较对普通用户更容易获得。在未来的论文中，该方法将推广到风险值空间分布的模拟以及局部聚类分析中预测风险所附带不确定性的传播。
