Spatial statistical methods in environmental epidemiology: a critique.
Spatial statistical methods in environmental epidemiology: a critique.
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DOI：
10.1177/096228029500400204
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发表时间：
1995-06-01
影响因子：
2.3
通讯作者：
Shaddick, G
中科院分区：
文献类型：
--
作者：
Elliott, P;Martuzzi, M;Shaddick, G
尽管可用的地理分析统计方法最近取得了进展，但它们在环境流行病学中的应用仍有许多限制。这些问题包括数据的可得性和质量问题，特别是在大多数情况下缺乏环境暴露测量。对疾病“聚集性”调查、点源暴露、小区域疾病制图和生态相关性研究的方法进行了严格审查，重点是实际应用和流行病学解释。研究表明，除非处理罕见疾病、高特异性暴露和高相对风险，否则聚类调查不太可能取得成果，并且往往因此类研究的事后性质而复杂化。然而，人们认识到，在这种情况下，作为公共卫生应对措施的一部分，往往需要对现有数据进行适当评估。新出现的方法，特别是贝叶斯统计方法，为地理分析和疾病制图提供了适当的框架。同样，尽管它们确实给出了有价值的描述，但尚不确定它们是否会提供病因学方面的重要线索。也许最令人满意的方法是使用地理数据库检验先验假设，尽管解释问题仍然存在。
Despite recent advances in the available statistical methods for geographical analysis, there are many constraints to their application in environmental epidemiology. These include problems of data availability and quality, especially the lack in most situations of environmental exposure measurements. Methods for disease 'cluster' investigation, point source exposures, small-area disease mapping and ecological correlation studies are critically reviewed, with the emphasis on practical applications and epidemiological interpretation. It is shown that, unless dealing with rare diseases, high specificity exposures and high relative risks, cluster investigation is unlikely to be fruitful, and is often complicated by the post hoc nature of such studies. However, it is recognized that in these circumstances proper assessment of the available data is often required as part of the public health response. Newly available methods, particularly in Bayesian statistics, offer an appropriate framework for geographical analysis and disease mapping. Again, it is uncertain whether they will give important clues as to aetiology, although they do give valuable description. Perhaps the most satisfactory approach is to test a priori hypotheses using a geographical database, although problems of interpretation remain.