Sabel Clive Eric, Wilson Jeff Gaines, Kingham Simon, Tisch Catherine, Epton Mike
Department of Geography, University of Canterbury, Christchurch, New Zealand.
Soc Sci Med. 2007 Jul;65(1):43-59. doi: 10.1016/j.socscimed.2007.02.040. Epub 2007 Apr 30.
Epidemiological studies that examine the relationship between environmental exposures and health often address other determinants of health that may influence the relationship being studied by adjusting for these factors as covariates. While disease surveillance methods routinely control for covariates such as deprivation, there has been limited investigative work on the spatial movement of risk at the intraurban scale due to the adjustment. It is important that the nature of any spatial relocation be well understood as a relocation to areas of increased risk may also introduce additional localised factors that influence the exposure-response relationship. This paper examines the spatial patterns of relative risk and clusters of hospitalisations based on an illustrative small-area example from Christchurch, New Zealand. A four-stage test of the spatial relocation effects of covariate adjustment was performed. First, relative risks for respiratory hospitalisations from 1999 to 2004 at the census area unit level were adjusted for age and sex. In three subsequent tests, admissions were adjusted for annual exposure to particulate matter less than 10 microm in diameter (PM10), then for a deprivation index, and finally for both PM10 and deprivation. Spatial patterns of risk, disease clusters and cold and hot spots were generated using a spatial scan statistic and a Getis-Ord Gi* statistic. In all disease groups tested (except the control disease), adjustment for chronic PM10 exposure and deprivation modified the position of clusters substantially, as well as notably shifting patterns and hot/cold spots of relative risk. Adjusting for PM10 and/or for deprivation shifted clusters in a similar spatial fashion. In Christchurch, the resulting shift relocated the cluster from a purely residential area to a mixed residential/industrial area, possibly introducing new environmental exposures. Researchers should be aware of the potential spatial effects inherent in adjusting for covariates when considering study design and interpreting results.
研究环境暴露与健康之间关系的流行病学研究，通常会通过将其他健康决定因素作为协变量进行调整，来处理那些可能影响所研究关系的因素。虽然疾病监测方法通常会对诸如贫困等协变量进行控制，但由于这种调整，关于城市内部尺度风险空间移动的调查工作有限。重要的是，要充分了解任何空间重新定位的性质，因为重新定位到风险增加的地区可能还会引入影响暴露 - 反应关系的其他局部因素。本文基于新西兰克赖斯特彻奇一个具有代表性的小区域示例，研究了相对风险的空间模式和住院病例群。对协变量调整的空间重新定位效应进行了四阶段测试。首先，对1999年至2004年人口普查区单位层面的呼吸道住院相对风险进行年龄和性别调整。在随后的三次测试中，入院病例先按每年接触直径小于10微米的颗粒物（PM10）进行调整，然后按贫困指数进行调整，最后同时按PM10和贫困指数进行调整。使用空间扫描统计量和Getis - Ord Gi*统计量生成风险、疾病群以及冷热区的空间模式。在所有测试的疾病组（对照疾病除外）中，对慢性PM10暴露和贫困进行调整后，群的位置发生了显著变化，相对风险的模式和冷热区也明显移动。对PM10和/或贫困进行调整使群在空间上以类似方式移动。在克赖斯特彻奇，这种移动将群从一个纯粹的居民区转移到了一个混合的居住/工业区，可能引入了新的环境暴露。研究人员在考虑研究设计和解释结果时，应意识到协变量调整中固有的潜在空间效应。
