Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization. (February 2020)

• Record Type: Journal Article
• Authors: Jones, Rena R.; Boscoe, Francis P.; Medgyesi, Danielle N.; Fitzgerald, Edward F.; Hwang, Syni-An; Lin, Shao
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 32(2020)
• Publication Date: 2020-02
• Subjects: Geographic imputation -- Exposure misclassification -- Spatial error -- Environmental exposure assessment -- Administrative data; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2019.100322
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: 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. Abstract: 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.