Assessment of Shared and Unshared Exposure Measurement Error in Ensemble Learning Estimates of Nitrogen Oxides and Its Implications on Epidemiological Findings in Air Pollution Studies

To improve exposure spatial and temporal resolution, researchers are using machine learning spatiotemporal air pollution models for large cohort studies. We aim to (1) measure shared, unshared, multiplicative, and additive (SUMA) measurement error in a three-stage (mixed effect ensemble learning with constrained optimization) spatiotemporal nitrogen oxides (NOx) model and (2) assess the impact of shared error and advanced exposure algorithms on epidemiological results. By treating NOx ensembles as realizations from an external dosimetry system, we quantified SUMA measurement error by extracting variance and covariance elements across realizations. To identify geographic locations with significantly elevated error, we used generalized additive models with a smooth term for location. We iteratively analyzed the risk of recent wheeze and NOx exposure among children using predictions from each stage of the NOx model to assess incremental influences of modeling stages on epidemiological conclusions and adjuste...