Disease risk near point sources: Statistical issues for analyses using individual or spatially aggregated data
Disease risk near point sources: Statistical issues for analyses using individual or spatially aggregated data
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DOI：
10.1136/jech.49.suppl_2.s20
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发表时间：
1995-12-01
影响因子：
6.3
通讯作者：
Elliott, P
中科院分区：
文献类型：
--
作者：
Diggle, P;Elliott, P
研究目标——审查在个人和群体（地区）两级都有数据的环境污染源点源附近的疾病风险分析所涉及的统计问题。探讨这些问题，参考可能的社会经济混淆。设计-统计审查。环境污染源的设定点。主要结果——除非在实践中不太可能成立的非常特殊的情况下，将数据汇总到地区一级将导致对疾病风险的估计存在偏差。结论：当一些协变量（例如病例的年龄和性别）在个体水平上已知，而其他协变量（例如人口、年龄-性别分布、小面积剥夺指数）仅在区域（生态）水平上已知时，空间数据分析没有简单的解决方案。为了更好地理解现有方法的局限性，以及为解释结果提供信息，需要明确认识分析这些数据所固有的基本假设。理想情况下，数据应该尽可能地分类，以最大限度地利用可获得的信息，并最大限度地减少偏见的可能性。
Study objective - To examine the statistical issues involved in the analysis of disease risk near point sources of environmental pollution, where data are held at both the individual and group (areal) level. To explore these issues with reference to possible socioeconomic confounding.Design - Statistical review.Setting - Point sources of environmental pollution.Main results - Except in very specific circumstances unlikely to hold in practice, aggregation of data to the areal level will lead to bias in the estimation of disease risk.Conclusions - There is no easy solution to the analysis of spatial data when some covariates (for example, age and sex of cases) are known at individual level, whereas others (for example, populations, age-sex distributions, small area deprivation indices) are known only at the areal (ecological) level. The underlying assumptions inherent in the analysis of these data need to be explicitly recognised in order to understand better the limitations of the available methodology as well as to inform interpretation of results. Ideally, the data should be kept as disaggregated as possible, to maximise the information available and minimise potential for bias.