Impact of Roadside Mobile Monitoring Design on Epidemiologic Inference – A Case Study of Ultrafine Particles and Cognitive Function

Background: Mobile monitoring campaigns are frequently used to develop air pollution exposure models to be used in health studies. Monitoring designs vary substantially, however, and it is unclear how design features impact exposure assessment models or health inferences. Methods: We conducted a case study of the impact of mobile monitoring study design on ultrafine particle (UFP) exposure assessment and the estimated association between UFP and late-life cognitive function. We leveraged UFP measures from an extensive mobile monitoring campaign consisting of 309 temporary roadside stationary sites, each with ~29 temporally balanced visits over a year. We subsampled the data following common field designs: fewer visits per site (4-12); shorter campaign durations (1-4 seasons); business or rush hours (unadjusted and temporally adjusted); and an unbalanced number of visits where high variability sites received more or less visits than low variability sites. We developed annual average UFP exposure models with the resulting data and ran health analyses to estimate the adjusted association between five-year UFP exposure and baseline cognitive function (Cognitive Abilities Screening Instrument – Item Response Theory [CASI-IRT]) in the Adult Changes in Thought (ACT) cohort (N=5,409). Results: The reference UFP all-data exposure model (R2=0.65) estimated that the adjusted mean CASI-IRT was lower by 0.020 (95% CI: -0.036, -0.004) per each 1,900 pt/cm3. More restricted designs generally produced poorer performing exposure models (median R2: 0.40-0.61), with business hours (R2: 0.40-0.45), one-season (R2: 0.43), and unbalanced visits (R2: 0.48) performing worst. Health inferences were broadly consistent with those from the all-data exposure model with just fewer visits per location, but they had more bias and/or were inconsistent across campaigns with fewer seasons, business or rush hours, or unbalanced visits. Business and rush hour designs had the most biased and attenuated health estimates. Conclusions: Thoughtful monitoring design can improve exposure models and subsequent health inferences.