Journal
STATISTICS IN MEDICINE
Volume 40, Issue 6, Pages 1321-1335Publisher
WILEY
DOI: 10.1002/sim.8843
Keywords
causal inference; environmental epidemiology; matching; multiple sclerosis; observational data
Categories
Funding
- John Harvard Distinguished Science Fellows Program
- NIH Office of the Director [DP5OD021412]
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Environmental epidemiology mainly estimates associations between environmental exposures and health outcomes, and a causal inference pipeline has been developed to help researchers estimate the effects of plausible hypothetical interventions.
When addressing environmental health-related questions, most often, only observational data are collected for ethical or practical reasons. However, the lack of randomized exposure often prevents the comparison of similar groups of exposed and unexposed units. This design barrier leads the environmental epidemiology field to mainly estimate associations between environmental exposures and health outcomes. A recently developed causal inference pipeline was developed to guide researchers interested in estimating the effects of plausible hypothetical interventions for policy recommendations. This article illustrates how this multistaged pipeline can help environmental epidemiologists reconstruct and analyze hypothetical randomized experiments by investigating whether an air pollution reduction intervention decreases the risk of multiple sclerosis relapses in Alsace region, France. The epidemiology literature reports conflicted findings on the relationship between air pollution and multiple sclerosis. Some studies found significant associations, whereas others did not. Two case-crossover studies reported significant associations between the risk of multiple sclerosis relapses and the exposure to air pollutants in the Alsace region. We use the same study population as these epidemiological studies to illustrate how appealing this causal inference approach is to estimate the effects of hypothetical, but plausible, environmental interventions.
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