Hierarchical Spatio-Temporal Mapping of Disease Rates
Hierarchical Spatio-Temporal Mapping of Disease Rates
复制标题
DOI：
10.1080/01621459.1997.10474012
复制
发表时间：
1997-06
影响因子：
3.7
通讯作者：
L. Waller;B. Carlin;Hong Xia;A. Gelfand
中科院分区：
文献类型：
--
作者：
L. Waller;B. Carlin;Hong Xia;A. Gelfand
摘要区域发病率和死亡率地图是确定疾病空间格局的有用工具。结合社会人口普查信息，还可以评估环境正义，即某些亚群体是否不成比例地遭受某些疾病或有害环境暴露的其他不利影响。贝叶斯和经验贝叶斯方法已被证明在平滑疾病风险的粗略地图方面很有用，消除了人口较少地区估计数的不稳定性，同时保持了地理分辨率。在这篇文章中，我们扩展了现有的层次空间模型，占时间的影响和时空的相互作用。拟合得到的高度参数化的模型需要仔细执行马尔可夫链蒙特卡罗（MCMC）方法，以及新的技术模型评估和选择。我们使用1968 - 1988年期间俄亥俄州特定县肺癌发病率的数据集来说明我们的方法。
Abstract Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with sociodemographic census information, they also permit assessment of environmental justice; that is, whether certain subgroups suffer disproportionately from certain diseases or other adverse effects of harmful environmental exposures. Bayes and empirical Bayes methods have proven useful in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining geographic resolution. In this article we extend existing hierarchical spatial models to account for temporal effects and spatio-temporal interactions. Fitting the resulting highly parameterized models requires careful implementation of Markov chain Monte Carlo (MCMC) methods, as well as novel techniques for model evaluation and selection. We illustrate our approach using a dataset of county-specific lung cancer rates in the state of Ohio during the period 1968–1988.