Fig. 1
From: Addressing health disparities using multiply imputed injury surveillance data
Trends in missing proportions for variables with missingnessa in NEISS-AIPb data from 2014 to 2018. Patient race/ethnicity (RACE) and location of injury (LOC) show the highest proportions of missing data across years (> 30%). aLOC: location where the injury occurred. RACE: race and ethnicity of patient. CAUSE: external cause of injury. BDYPT: primary body part affected. TYPE: work-relatedness. AGE: patient age in year. DISP: disposition at emergency department discharge. SEX: gender of patient. bNEISS-AIP: National Electronic Injury Surveillance System-All Injury Program