An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk
An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk
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DOI：
10.1111/rssc.12155
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发表时间：
2017-01-01
影响因子：
1.6
通讯作者：
Sarran, Christophe
中科院分区：
文献类型：
--
作者：
Rushworth, Alastair;Lee, Duncan;Sarran, Christophe
统计模型用于估计疾病风险的时空格局，从面积单位数据代表风险表面为每个时间段与已知的协变量和一组空间平滑的随机效应。后者作为一个代理不可测的空间混杂，其空间结构的特点往往是一个空间平滑的演变之间的一些对相邻的面积单位，而其他对表现出大的步骤变化。这种空间异质性与现有的全球平滑模型不一致，在现有的全球平滑模型中，所有相邻的空间随机效应之间都存在部分相关性。因此，我们提出了一种新的时空疾病模型与自适应空间平滑规格，可以识别阶跃变化。该模型的动机是一项新的研究呼吸和循环系统疾病的风险在一组地方当局在英格兰和严格的模拟测试，以评估其有效性。英格兰的研究结果表明，这两种疾病的风险具有相似的空间模式，并在邻近地方当局之间的风险的不可测量部分中表现出一些共同的阶跃变化。
Statistical models used to estimate the spatiotemporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterized by a spatially smooth evolution between some pairs of adjacent areal units whereas other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of local authorities in England and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk and exhibit some common step changes in the unmeasured component of risk between neighbouring local authorities.