An ecological study on the association between characteristics of hospital units and the risk of occupational injuries and adverse events on the example of an Italian teaching hospital

Int J Occup Med Environ Health. 2016;29(1):149-59. doi: 10.13075/ijomeh.1896.00580.

Abstract

Objectives: We explored the association of workplace characteristics with occupational injuries and adverse events in an Italian teaching hospital.

Material and methods: This ecological study was conducted using data routinely collected in the University Hospital of Udine, Northeastern Italy. Poisson regression models were used to investigate, at the hospital unit level, the association between 5 outcomes, including: occupational injuries, patient falls, medication errors, other adverse events and near-misses, and various characteristics of the units.

Results: The proportion of female workers in a unit, the average number of sick-leave days and of overtime hours, the number of medical examinations requested by employees, and being a surgical unit were significantly associated with some of the outcomes.

Conclusions: Despite ecological nature of the study, which does not allow for inferences to be drawn at the individual level, the results of our study provide useful clues to support strategies and interventions directed towards healthier work environments and better patient care in hospitals.

Keywords: accidental falls; ecological study; hospital incident reporting; medication errors; occupational injuries; teaching hospital.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidental Falls / statistics & numerical data
  • Appointments and Schedules
  • Female
  • Hospital Departments / statistics & numerical data*
  • Hospitals, Teaching / statistics & numerical data
  • Humans
  • Italy / epidemiology
  • Male
  • Medical Errors / statistics & numerical data
  • Medication Errors / statistics & numerical data
  • Occupational Injuries / epidemiology*
  • Occupational Injuries / etiology
  • Risk Factors
  • Sick Leave / statistics & numerical data