Use of space-time models to investigate the stability of patterns of disease.
Use of space-time models to investigate the stability of patterns of disease.
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DOI：
10.1289/ehp.10814
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发表时间：
2008-08
影响因子：
10.4
通讯作者：
Best, Nicky
中科院分区：
文献类型：
--
作者：
Abellan, Juan Jose;Richardson, Sylvia;Best, Nicky
关键词：
贝叶斯分层空间模型的使用已经在疾病地图和健康-环境关联的生态研究中得到广泛应用。在这种类型的研究中，数据通常是在很长的一段时间内汇总的，因此忽略了时间维度。因此，纯空间疾病地图研究的输出是所分析时期内风险的平均空间模式，但结果不能告知例如，高平均风险是随着时间的推移而持续的还是随时间的变化而变化的。我们调查了在疾病映射模型中包括时间维度如何加强对总体风险模式的流行病学解释。我们讨论了一类贝叶斯分层模型，它同时刻画和估计了稳定的空间和时间模式以及偏离这些稳定分量的情况。我们展示了如何基于时空相互作用的后验分布来构建用于将区域分类为稳定的有用规则。我们进行了一项模拟研究，以考察我们提出的决策规则的敏感性和特异性，并在英国先天畸形的案例研究中说明了我们的方法。我们的结果证实，将分层疾病映射模型扩展到同时考虑空间和时间的模型，在解释和检测局部过度方面会带来许多好处。
The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health–environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time. We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. We discuss a class of Bayesian hierarchical models that simultaneously characterize and estimate the stable spatial and temporal patterns as well as departures from these stable components. We show how useful rules for classifying areas as stable can be constructed based on the posterior distribution of the space–time interactions. We carry out a simulation study to investigate the sensitivity and specificity of the decision rules we propose, and we illustrate our approach in a case study of congenital anomalies in England. Our results confirm that extending hierarchical disease-mapping models to models that simultaneously consider space and time leads to a number of benefits in terms of interpretation and potential for detection of localized excesses.
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DOI：
10.1111/1467-9868.00095
发表时间：
1997-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
Richardson, S;Green, PJ
通讯作者：
Green, PJ
影响因子：
6.4
作者：
Jarup, L;Best, N;Elliott, P
通讯作者：
Elliott, P
影响因子：
1
作者：
BESAG, J;YORK, J;MOLLIE, A
通讯作者：
MOLLIE, A
影响因子：
105.7
作者：
Boyd, PA;Armstrong, B;Wellesley, D
通讯作者：
Wellesley, D
影响因子：
6.3
作者：
Prieto, Rebeca Ramis;Garcia-Perez, Javier;Lopez-Abente, Gonzalo
通讯作者：
Lopez-Abente, Gonzalo