Air pollution is a major public health concern, and large numbers of epidemiological studies have been conducted to quantify its impacts. One study design used to quantify these impacts is a spatial areal unit design, which estimates a population-level association using data on air pollution concentrations and disease incidence that have been spatially aggregated to a set of nonoverlapping areal units. A major criticism of this study design is that the specification of these areal units is arbitrary, and if one changed their boundaries then the aggregated data would change despite the locations of the disease cases and the air pollution surface remaining the same. This is known as the modifiable areal unit problem, and this is the first article to quantify its likely effects in air pollution and health studies. In addition, we derive an aggregate model for these data directly from an idealized individual-level risk model and show that it provides better estimation than the commonly used ecological model. Our work is motivated by a new study of air pollution and health in Scotland, and we find consistent significant associations between air pollution and respiratory disease but not for circulatory disease.

中文翻译：

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在估计空气污染的健康影响时量化可修改面积单位问题的影响

空气污染是一个主要的公共卫生问题，已经进行了大量流行病学研究以量化其影响。用于量化这些影响的一项研究设计是空间区域单位设计，它使用空气污染浓度和疾病发病率的数据来估计人口水平的关联，这些数据已在空间上聚合到一组不重叠的区域单位。对这项研究设计的一个主要批评是，这些区域单位的规范是任意的，如果改变它们的边界，那么尽管疾病病例的位置和空气污染表面保持不变，汇总数据也会发生变化。这被称为可修改的面积单位问题，这是第一篇量化其在空气污染和健康研究中可能产生的影响的文章。此外，我们直接从理想化的个人级别风险模型推导出这些数据的汇总模型，并表明它比常用的生态模型提供了更好的估计。我们的工作受到苏格兰空气污染与健康的一项新研究的启发，我们发现空气污染与呼吸系统疾病之间存在一致的显着关联，但与循环系统疾病无关。