We develop new methodology to improve our understanding of the causal effects of multivariate air pollution exposures on public health. Typically, exposure to air pollution for an individual is measured at their home geographic region, though people travel to different regions with potentially different levels of air pollution. To account for this, we incorporate estimates of the mobility of individuals from cell phone mobility data to get an improved estimate of their exposure to air pollution. We treat this as an interference problem, where individuals in one geographic region can be affected by exposures in other regions due to mobility into those areas. We propose policy-relevant estimands and derive expressions showing the extent of bias one would obtain by ignoring this mobility. We additionally highlight the benefits of the proposed interference framework relative to a measurement error framework for accounting for mobility. We develop novel estimation strategies to estimate causal effects that account for this spatial spillover utilizing flexible Bayesian methodology. Lastly, we use the proposed methodology to study the health effects of ambient air pollution on mortality among Medicare enrollees in the United States.

翻译：我们开发了一种新方法，以增进对多元空气污染暴露对公共健康因果效应的理解。通常，个体的空气污染暴露是在其居住地理区域测量的，但人们会前往具有不同污染水平的其他区域。为考虑这一点，我们整合了来自手机移动数据的个体流动性估计，以改进对其空气污染暴露的评估。我们将此视为一个干扰问题，即由于人员流动，一个地理区域的个体可能受到其他区域暴露的影响。我们提出了与政策相关的估计量，并推导了表达式以展示忽略这种流动性将导致的偏倚程度。此外，我们强调了所提出的干扰框架相对于测量误差框架在考虑流动性方面的优势。我们利用灵活的贝叶斯方法，开发了新颖的估计策略来估计因果效应，以解释这种空间溢出。最后，我们应用所提出的方法研究了环境空气污染对美国医疗保险参保者死亡率的健康影响。