1329 Predicting occupational injuries at the community level. (24th April 2018) - Forst, Linda; Chin, Brian; Madigan, Dana; Friedman, Lee
Abstract : Introduction: Low wage, minority, and immigrant workers suffer a high and inequitable rate of traumatic workplace injury. These workers are difficult to reach. We hypothesised that these workers cluster in communities that could be predicted on demographics. Methods: We assembled a database of severely injured workers by postal zipcode, as well as demographic variables for those zipcodes for Illinois from 2000–2009. We conducted a spatial cluster analysis, and multivariable regression of demographic features of a community that could be used to target high-risk populations for occupational health interventions. Results: Among the 23 200 occupational injuries, 80% of cases were located in 20% of zip codes and clustered in 10 locations. After component analysis, numbers and clusters of injuries multiple regression showed a positive association with 'immigrants' and a negative association with 'urban poverty.' Discussion: Traumatic occupational injuries were clustered spatially by home location of the affected workers and in a predictable way. This puts an inequitable burden on communities and provides evidence for the possible value of community-based interventions for prevention of occupational injuries. Work should be included in health disparities research. Stakeholders should determine whether and how to intervene at the community level to prevent occupational injuries.