title: Identifying risk factors for medical injury.
abstract: OBJECTIVE
  To examine multiple risk factors for medical injury using administrative data.
  
  
  METHODS
  This cross-sectional study used logistic regression models to examine associations among patient characteristics such as age, sex, and insurance payer status and hospital characteristics such as ownership, teaching status and trauma level, and comorbidities and presence of a medical injury diagnosis. Data were from the Bureau of Health Information: Wisconsin hospital in-patient discharge records and hospital characteristics for the year 2001.
  
  
  METHODS
  All Wisconsin non-federal, acute-care hospitals.
  
  
  METHODS
  A total of 556 899 patients discharged from 132 Wisconsin hospitals, excluding newborns, participated.
  
  
  METHODS
  None. Main outcome measure. Medical injury, defined as untoward harm to a patient as a result of a medical intervention, was determined using discharge diagnosis criteria.
  
  
  RESULTS
  Medical injuries were found in 13.3% of 556 899 hospital discharge records. Covariates associated with increased risk of a recorded medical injury code included age 45-84 years, female sex, comorbidities, non-profit religious order ownership, proportion of cardiac intensive care in-patient days to total in-patient days, percent of board-certified medical staff, and community trauma center or regional trauma resource.
  
  
  CONCLUSIONS
  This article describes an innovative analysis of risk factors for medical injury that controlled for numerous potential confounding factors, including hospital coding characteristics. The associations we found, such as increased risk of medical injury in women, can be used to generate hypotheses for further testing through other methods and suggest intervention points for patient safety efforts.
authors.affiliation: Medical College of Wisconsin, Family & Community Medicine, Milwaukee, WI 53226, USA. cguse@mcw.edu
authors.forename: Clare E
authors.initials: CE
authors.lastname: Guse
authors.forename: Hongyan
authors.initials: H
authors.lastname: Yang
authors.forename: Peter M
authors.initials: PM
authors.lastname: Layde
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008485:Medical Audit; D019300:Medical Errors; D008875:Middle Aged; D012307:Risk Factors; D017751:Safety Management; D014922:Wisconsin / Q000453:epidemiology; D014947:Wounds and Injuries / Q000453:epidemiology*