Samet J, Zeger S, Kelsall J, Xu J, Kalkstein L
Johns Hopkins University School of Public Health, Baltimore, Maryland 21205, USA.
Environ Res. 1998 Apr;77(1):9-19. doi: 10.1006/enrs.1997.3821.
Because weather has the potential to confound or modify the pollution-mortality relationship, researchers have developed several approaches for controlling it in estimating the independent effect of air pollution on mortality. This report considers the consequences of using alternative approaches to controlling for weather and explores modification of air pollution effects by weather, as weather patterns could plausibly alter air pollution's effect on health. We analyzed 1973-1980 total mortality data for Philadelphia using four weather models and compared estimates of the effects of TSP and SO2 on mortality using a Poisson regression model. Two synoptic categories developed by Kalkstein were selected--the Temporal Synoptic Index (TSI) and the Spatial Synoptic Classification (SSC)--and compared with (1) descriptive models developed by Schwartz and Dockery (S-D); and (2) LOESS, a non-parametric function of the previous day's temperature and dew point. We considered model fit using Akaike's Information Criterion (AIC) and changes in the estimated effects of TSP and SO2. In the full-year analysis, S-D is better than LOESS at predicting mortality, and S-D and LOESS are better than TSI, as measured by AIC. When TSP or SO2 was fit alone, the results were qualitatively similar, regardless of how weather was controlled; when TSP and SO2 were fit simultaneously, the S-D and LOESS models give qualitatively different results than TSI, which attributes more of the pollution effect to SO2 than to TSP. Model fit is substantially poorer with TSI. This pattern was repeated in analyses of summer and winter months, which included SSC. In summary, using synoptic weather categories in regression models does not meaningfully change the association between mortality and air pollution indexes. We also found little evidence that weather conditions modified the effect of pollution, regardless of the approach used to represent weather.
由于天气有可能混淆或改变污染与死亡率之间的关系,研究人员已开发出几种在估计空气污染对死亡率的独立影响时对其进行控制的方法。本报告考虑了使用替代方法控制天气的后果,并探讨天气对空气污染影响的修正作用,因为天气模式可能会合理地改变空气污染对健康的影响。我们使用四种天气模型分析了费城1973 - 1980年的总死亡率数据,并使用泊松回归模型比较了总悬浮颗粒物(TSP)和二氧化硫(SO₂)对死亡率影响的估计值。选择了卡尔施泰因开发的两种天气类型——时间天气指数(TSI)和空间天气分类(SSC),并与(1)施瓦茨和多克里(S - D)开发的描述性模型;以及(2)局部加权回归散点平滑法(LOESS)进行比较,LOESS是前一天温度和露点的非参数函数。我们使用赤池信息准则(AIC)来考虑模型拟合情况以及TSP和SO₂估计效应的变化。在全年分析中,以AIC衡量,S - D在预测死亡率方面优于LOESS,且S - D和LOESS优于TSI。当单独拟合TSP或SO₂时,无论天气如何控制,结果在质量上是相似的;当同时拟合TSP和SO₂时,S - D和LOESS模型给出的结果在质量上与TSI不同,TSI将更多的污染效应归因于SO₂而非TSP。TSI的模型拟合明显较差。在包括SSC的夏季和冬季月份分析中也重复了这种模式。总之,在回归模型中使用天气类型并不能显著改变死亡率与空气污染指数之间的关联。我们还发现几乎没有证据表明天气条件会改变污染的影响,无论用于表示天气的方法如何。