Department of Statistical Science, Duke University, Durham, NC 27708, USA.
Biostatistics. 2011 Oct;12(4):637-52. doi: 10.1093/biostatistics/kxr002. Epub 2011 Feb 5.
In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.
在空气污染流行病学中，人们越来越关注估计空气动力学直径在 2.5 至 10 微米之间的粗颗粒物 (PM) 的健康影响。粗颗粒物浓度可能表现出相当大的空间异质性，因为这些颗粒的传输距离较短，并且不会在大气中长时间悬浮。在本文中，我们开发了一种建模方法，用于在研究区域内环境浓度在空间上变化的时间序列分析中估计空气污染的短期影响。具体来说，我们的方法通过在任何给定的一天量化监测站之间测量的环境浓度的差异来量化暴露变量的误差。这是通过将监测器水平的测量值视为对未观察到的人群平均暴露的易错重复测量来实现的。推断是在贝叶斯框架中进行的，以充分考虑模型参数估计中的不确定性。最后，通过使用不同的暴露指标，我们根据最近的全国多地点时间序列分析，研究了粗颗粒物与每日住院人数之间的关联的敏感性。在 1999 年至 2005 年期间来自 59 个美国县的医疗保险参保者中，我们发现粗颗粒物与心血管疾病当天住院之间存在一致的正相关关系。
