Draper G J
Childhood Cancer Research Group, University of Oxford, UK.
Cent Eur J Public Health. 1997 Jun;5(2):90-2.
Disease registries will often contain the addresses of cases included in the registry. If the registry includes information on all cases, or deaths, occurring in a defined geographical area and time period and if there is a postcode/zip code or map reference for each case it is possible to carry out a variety of different types of geographical analysis that may give clues to the aetiology of the disease. For such analyses it will usually also be necessary to have population data for the region covered by the registry and for separate sub-regions within it. In this paper we review types of analysis that may be applied to such data and give references to examples of applications and the statistical methods used. These include, first, methods of presenting incidence rates, and particularly the use of maps; of particular concern is the development of methods for presenting data that take into account the problems of rates calculated for small populations and which may therefore happen to be high or low simply by chance. Secondly, we consider, the analysis of "clustering" and "clusters" of cases of disease. These problems have been the subject of considerable methodological development in recent years. Analyses of clustering address the question of whether there is a general tendency for there to be aggregations of cases or areas of high incidence the analysis of clusters is concerned with problems of detecting specific locations where there are unusual aggregations of cases. The third type of problem considered here is whether there are, within the registry region, aetiological factors that vary geographically with consequent variations in disease incidence in different sub-regions. Where there is geographical variation it may be possible to use regression analysis to relate such variation to factors such as socio-economic status or levels of some environmental hazard. Finally we consider the problem of determining whether disease rates in certain areas may be related to distance from the source of some potential causative agent.
疾病登记册通常会包含登记在册病例的地址。如果登记册涵盖了特定地理区域和时间段内发生的所有病例或死亡信息，并且每个病例都有邮政编码或地图参考，那么就有可能进行各种不同类型的地理分析，这些分析可能会为疾病的病因提供线索。对于此类分析，通常还需要有登记册所覆盖区域以及其中各个子区域的人口数据。在本文中，我们回顾了可应用于此类数据的分析类型，并给出了应用示例及所使用统计方法的参考文献。这些分析类型包括：首先，呈现发病率的方法，尤其是地图的使用；特别值得关注的是开发考虑到小群体发病率计算问题的呈现数据的方法，因为小群体发病率可能仅仅由于偶然因素而偏高或偏低。其次，我们考虑疾病病例的“聚类”和“聚集区”分析。近年来，这些问题一直是大量方法学发展的主题。聚类分析关注病例是否存在聚集或高发病率区域的总体趋势问题，而聚集区分析则涉及检测病例异常聚集的特定位置的问题。这里考虑的第三类问题是，在登记册区域内，是否存在病因因素在地理上有所不同，从而导致不同子区域疾病发病率的差异。如果存在地理差异，可能可以使用回归分析将这种差异与社会经济地位或某些环境危害水平等因素联系起来。最后，我们考虑确定某些地区的疾病发病率是否可能与距某些潜在致病源的距离有关的问题。
