Cakmak Sabit, Burnett Richard T, Jerrett Michael, Goldberg Mark S, Pope C Arden, Ma Renjun, Gultekin Timur, Thun Michael, Krewski Daniel
Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada.
J Toxicol Environ Health A. 2003;66(16-19):1811-23. doi: 10.1080/15287390306444.
Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component.
队列研究设计常用于评估基于社区的环境空气污染浓度与健康结果之间的关联，如死亡率、疾病的发生与流行以及肺功能。通常，在少数几个社区中，每个社区都招募大量受试者。使用固定站点监测器来确定长期暴露于环境污染的情况。在控制个体层面测量的健康结果的风险因素（即吸烟）之后，确定社区平均污染水平与健康之间的关联。我们提出了一种新的空间回归模型，将环境空气污染的空间变化与健康联系起来。健康结果可以用连续变量（肺功能）、二元变量（疾病流行率）或事件发生时间数据（疾病的生存或发生）来衡量。该模型纳入了个体层面测量的风险因素，如吸烟，以及社区层面的风险因素，如空气污染。我们证明，社区健康结果中的空间自相关（这表明尚未充分表征空气污染与健康关联中潜在的混杂风险因素）可以通过在评估空气污染对健康影响的模型的确定性部分纳入位置信息，或者通过模型随机部分中的距离衰减空间自相关函数，或者两者兼用来加以解释。我们提出了一种可用于非常大型队列研究的统计方法。我们通过对美国癌症协会队列的分析来说明我们的方法，以确定心脏病的流行率是否与硫酸盐颗粒浓度有关。从统计学角度来看，似乎模型确定性部分中的位置表面比模型随机部分中的距离衰减自相关结构更可取。
