Wakefield J, Elliott P
Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College School of Medicine, St Mary's Campus, Norfolk Place, London W2 1PG, U.K.
Stat Med. 1999;18(17-18):2377-99. doi: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2377::aid-sim263>3.0.co;2-g.
The availability of geographically indexed health and population data, with advances in computing, geographical information systems and statistical methodology, have opened the way for serious exploration of small area health statistics based on routine data. Such analyses may be used to address specific questions concerning health in relation to sources of pollution, to investigate clustering of disease or for hypothesis generation. We distinguish four types of analysis: disease mapping; geographic correlation studies; the assessment of risk in relation to a prespecified point or line source, and cluster detection and disease clustering. A general framework for the statistical analysis of small area studies will be considered. This framework assumes that populations at risk arise from inhomogeneous Poisson processes. Disease cases are then realizations of a thinned Poisson process where the risk of disease depends on the characteristics of the person, time and spatial location. Difficulties of analysis and interpretation due to data inaccuracies and aggregation will be addressed with particular reference to ecological bias and confounding. The use of errors-in-variables modelling in small area analyses will be discussed.
随着地理索引健康与人口数据的可得性，以及计算、地理信息系统和统计方法的进步，基于常规数据对小区域健康统计进行深入探索已成为可能。此类分析可用于解决与污染源相关的特定健康问题、调查疾病聚集情况或生成假设。我们区分了四种分析类型：疾病绘图；地理相关性研究；与预先指定的点源或线源相关的风险评估，以及聚集检测和疾病聚集。将考虑小区域研究统计分析的一般框架。该框架假设风险人群来自非齐次泊松过程。疾病病例则是稀疏泊松过程的实现，其中疾病风险取决于人、时间和空间位置的特征。将特别针对生态偏倚和混杂因素，探讨由于数据不准确和汇总导致的分析与解释困难。还将讨论小区域分析中变量误差建模的应用。
