Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy
Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy
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DOI：
10.1111/1467-985x.00170
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发表时间：
2000-01-01
影响因子：
2
通讯作者：
Zeger, SL
中科院分区：
文献类型：
--
作者：
Dominici, F;Samet, JM;Zeger, SL
在过去的十年中，室外空气中颗粒水平与每日死亡率计数之间关联的报道引起了人们的关注，即即使在当前的监管限制内，空气污染也会缩短寿命。这些报告的批评集中在用于估算城市之间污染 - 历史悠久关系关系的统计技术和发现的不一致之上。我们已经开发了解决这些问题的分析方法，并结合了来自多个位置的证据，以获得对数据的统一分析。本文介绍了来自美国最大20个城市的每日时间序列数据的对数线性回归分析，并引入了分层回归模型，以结合整个城市污染性关系的估计。我们通过关注PM10的死亡率效应（颗粒物在空气直径中小于10 MUM）以及通过PM10和臭氧（O-3）水平进行单变量和双变量分析来说明这种方法。在分层模型的第一阶段，我们通过使用半参数对数线性模型来估计20个城市中每个城市中每个城市中的PM10的相对死亡率。该模型的第二阶段描述了真正的相对速率之间的城市之间变化是所选城市特异性协变量的函数。我们还符合空间模型的两个变体，目的是探索城市之间污染物特异性系数的空间相关性。最后，为了探索共同考虑两种污染物的结果，我们拟合并比较单变量和双变量模型。通过使用马尔可夫链蒙特卡洛技术来估计第二阶段的后验分布。在使用并发日污染值的单变量分析以预测死亡率时，我们发现美国平均在PM10中增加了10杯M（-3）与死亡率增加0.48％有关（95％间隔：0.05，0.92） 。通过调整O-3级别，PM10-Coeffity略高。结果在很大程度上对模糊但正确的先前分布的特定选择不敏感。模型和估计方法是一般的，可用于任何数量的位置和污染物测量值，并可能应用于其他环境代理。
Reports over the last decade of association between levels of particles in outdoor air and daily mortality counts have raised concern that air pollution shortens life, even at concentrations within current regulatory limits. Criticisms of these reports have focused on the statistical techniques that are used to estimate the pollution-mortality relationship and the inconsistency in findings between cities. We have developed analytical methods that address these concerns and combine evidence from multiple locations to gain a unified analysis of the data. The paper presents log-linear regression analyses of daily time series data from the largest 20 US cities and introduces hierarchical regression models for combining estimates of the pollution-mortality relationship across cities. We illustrate this method by focusing on mortality effects of PM10 (particulate matter less than 10 mum in aerodynamic diameter) and by performing univariate and bivariate analyses with PM10 and ozone (O-3) level. In the first stage of the hierarchical model, we estimate the relative mortality rate associated with PM10 for each of the 20 cities by using semiparametric log-linear models. The second stage of the model describes between-city Variation in the true relative rates as a function of selected city-specific covariates. We also fit two Variations of a spatial model with the goal of exploring the spatial correlation of the pollutant-specific coefficients among cities. Finally, to explore the results of considering the two pollutants jointly, we fit and compare univariate and bivariate models. Ail posterior distributions from the second stage are estimated by using Markov chain Monte Carlo techniques. In univariate analyses using concurrent day pollution Values to predict mortality, we find that an increase of 10 mug m(-3) in PM10 on average in the USA is associated with a 0.48% increase in mortality (95% interval: 0.05, 0.92). With adjustment for the O-3 level the PM10-coefficient is slightly higher. The results are largely insensitive to the specific choice of vague but proper prior distribution. The models and estimation methods are general and can be used for any number of locations and pollutant measurements and have potential applications to other environmental agents.