A Causal Inference Approach to Understand the Link between Air Pollution Exposure and the Occurrence of Multiple Sclerosis Relapses

摘要

When answering environmental health related questions, most often, only non-randomized observational data can be collected for ethical or practical reasons. The lack of randomized exposure prevents the identification of the causal effects on health outcomes. This barrier led the environmental epidemiology field to mainly consider associational models to examine relationships between environmental exposures and health outcomes. However, we believe that epidemiological researches should focus on the estimation of causal effects of plausible hypothetical interventions (e.g., reducing PM10) and suggest preventive environmental actions. Some statistical matching techniques exist to reconstruct plausible randomized experiments, which are the "gold standard" to establish causality. Based on an environmental epidemiology example, we will present, 1) how the conceptual and design stages can be performed prior to statistical estimation of causal effects, and 2) why this conceptual and computational work is relevant for making policy recommendations. A case-cross over study by Jeanjean et al. 2017 reported significant associations between multiple sclerosis (MS) relapse incidence and exposures to NO2, PM10, and O3. With the same data, we will show how constructing hypothetical experiments, thereby creating comparable groups of MS patients can provide results that are relevant for policy makers. Several epidemiological studies have reported significant associations between air pollution and multiple sclerosis (Oikonen et al. 2003, Gregory et al. 2008, Heydarpour et al. 2014, Angelici et al. 2016, Jeanjean et al. 2017), whereas other studies did not find any (Palacios et al. 2017). These conflicted findings demonstrate the gap of knowledge in understanding the causal air pollution-MS relationship. Our objective is to show that carefully avoiding confounding of the exposure assignment prior to any analysis can help to examine whether observed associations are truly causal.