Modeling spatial variation in disease risk: A geostatistical approach
Modeling spatial variation in disease risk: A geostatistical approach
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DOI：
10.1198/016214502388618438
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发表时间：
2002-09-01
影响因子：
3.7
通讯作者：
Wakefield, J
中科院分区：
文献类型：
--
作者：
Kelsall, J;Wakefield, J
一个有价值的公共卫生实践是检查跨地理区域的疾病发病率和死亡率。可用于构建疾病地图的数据通常是不相交集合内的聚集计数的形式。政治上界定的地区，以及泊松变化固有的这些计数可能会导致极端的原始率在小地区。相邻地区的相对风险往往是相似的，一种常见的方法是使用随机效应模型，该模型允许估计一个地区的相对风险，以从相邻地区“借势”。从而产生更稳定的估计。通常假设马尔可夫随机场结构来模拟由于不可测量的风险因素的空间依赖性。这种模型考虑了一个区域的相对风险分布，但以其邻居为条件，尽管邻居方案通常只定义得非常简单。比如如果两个区域共享公共边界，则它们可以被视为相邻区域，在这种情况下，不考虑区域的相对位置、大小和形状。在这篇文章中，我们描述了一种新的方法，其中的相关性结构是通过考虑一个潜在的连续的风险表面，具体来说，我们建模的日志相对风险作为一个高斯随机场，建模方法，已被广泛使用的地质统计学文献。我们近似的分布区域水平的相对风险提供一个易于分析的形式。这导致相邻地区之间更现实的相关性结构，并允许估计不仅是个别地区的相对风险。而且是连续的潜在相对风险函数。我们首先探索和说明我们的方法与模拟数据。然后，我们分析了一组关于英国结直肠癌的数据。伯明翰区。分析的目的是调查空间变异的程度，并调查这种变异在多大程度上与地区一级的社会经济地位衡量标准有关。
A valuable public health practice is to examine disease incidence and mortality rates across geographic regions. The data available for the construction of disease maps are typically in the form of aggregate counts within sets of disjoint. politically defined areas, and the Poisson variation inherent in these counts can lead to extreme raw rates in small areas. Relative risks tend to be similar in neighboring areas, and a common approach is to use random-effects models that allow estimation of relative risk in an area to "borrow strength" from neighboring areas. thus producing more stable estimation. Often a Markov random field structure is assumed to model the spatial dependence due to unmeasured risk factors. Such models consider the distribution of the relative risk of an area conditional on its neighbors, although the neighborhood schemes are typically defined only very simplistically. For example. two areas may be viewed as neighbors if they share a common boundary, in which case the relative positions, sizes, and shapes of the areas are not taken into account. In this article we describe a new method in which the correlation structure is derived through consideration of an underlying continuous risk surface, Specifically, we model the log relative risk as a Gaussian random field, a modeling approach that has seen extensive use in the geostatistics literature. We approximate the distribution of the area-level relative risks to provide an analytically tractable form. This leads to more realistic correlation structures between neighboring areas, and allows estimation not only of individual area-level relative risks. but also of the continuous underlying relative risk function. We first explore and illustrate our methods with simulated data. We then analyze a set of data on colorectal cancer in the U.K. district of Birmingham. The aims of the analysis were to investigate the extent of spatial variability and to investigate the extent to which this variability was associated with an area-level measure of socioeconomic status.