期刊
ENVIRONMENTAL HEALTH PERSPECTIVES
卷 116, 期 8, 页码 1111-1119出版社
US DEPT HEALTH HUMAN SCIENCES PUBLIC HEALTH SCIENCE
DOI: 10.1289/ehp.10814
关键词
Bayesian hierarchical models; congenital anomalies; disease mapping; mixture models; space-time interactions; stable disease patterns
资金
- Department of Health Funding Source: Medline
- Economic and Social Research Council [RES-576-25-5003, ES/F032196/1] Funding Source: researchfish
- ESRC [ES/F032196/1] Funding Source: UKRI
BACKGROUND: 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. OBJECTIVE: We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. METHODS: 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. RESULTS: 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据