To describe the risk of work injury by socioeconomic status (SES) in hospital workers, and to assess whether SES gradient in injury risk is explained by differences in psychosocial, ergonomic or organisational factors at work.
Workforce rosters and Occupational Safety and Health Administration injury logs for a 5‐year period were obtained from two hospitals in Massachusetts. Job titles were classified into five SES strata on the basis of educational requirements and responsibilities: administrators, professionals, semiprofessionals, skilled and semiskilled workers. 13 selected psychosocial, ergonomic and organisational exposures were assigned to the hospital jobs through the national O*NET database. Rates of injury were analysed as frequency records using the Poisson regression, with job title as the unit of analysis. The risk of injury was modelled using SES alone, each exposure variable alone and then each exposure variable in combination with SES.
An overall annual injury rate of 7.2 per 100 full‐time workers was estimated for the two hospitals combined. All SES strata except professionals showed a significant excess risk of injury compared with the highest SES category (administrators); the risk was highest among semiskilled workers (RR 5.3, p<0.001), followed by nurses (RR 3.7, p<0.001), semiprofessionals (RR 2.9, p = 0.006) and skilled workers (RR 2.6, p = 0.01). The risk of injury was significantly associated with each exposure considered except pause frequency. When workplace exposures were introduced in the regression model together with SES, four remained significant predictors of the risk of injury (decision latitude, supervisor support, force exertion and temperature extremes), whereas the RR related to SES was strongly reduced in all strata, except professionals.
A strong gradient in the risk of injury by SES was reported in a sample population of hospital workers, which was greatly attenuated by adjusting for psychosocial and ergonomic workplace exposures, indicating that a large proportion of that gradient can be explained by differences in working conditions.