Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

•PM2.5 distributions differed marginally between imputed and non-imputed datasets.•Specificity of dichotomized exposures using geo-imputation was high (90.54–99.8%).•Sensitivity of dichotomized exposures was variable (62.4–87.7%).•Geo-imputation had minimal impact on PM2.5-respiratory hospitalization associations. Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM2.5) and respiratory hospitalizations in New York State (2000–2005). We observed non-significantly higher PM2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (−0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.