Development of Life Course Exposure Estimates Using Geospatial Data and Residence History

Life course exposure estimates developed using geospatial datasets must address issues of individual mobility, missing and incorrect data, and incompatible scaling of the datasets. We propose methods to assess and resolve these issues by developing individual exposure histories for an adult cohort of patients with amyotrophic lateral sclerosis (ALS) and matched controls using residence history and PM_2.5, black carbon, NO₂, and traffic intensity estimates. The completeness of the residence histories was substantially improved by adding both date and age questions to the survey and by accounting for the preceding and following residence. Information for the past five residences fully captured a 20-year exposure window for 95% of the cohort. A novel spatial multiple imputation approach dealt with missing or incomplete address data and avoided biases associated with centroid approaches. These steps boosted the time history completion to 99% and the geocoding success to 92%. PM_2.5 and NO₂, but not black carbon, had moderately high agreement with observed data; however, the 1 km resolution of the pollution datasets did not capture fine scale spatial heterogeneity and compressed the range of exposures. This appears to be the first study to examine the mobility of an older cohort for long exposure windows and to utilize spatial imputation methods to estimate exposure. The recommended methods are broadly applicable and can improve the completeness, reliability, and accuracy of life course exposure estimates.