title: Effect of healthcare-acquired infection on length of hospital stay and cost.
abstract: OBJECTIVE
  To estimate the independent effect of a single lower respiratory tract infection, urinary tract infection, or other healthcare-acquired infection on length-of-stay and variable costs and to demonstrate the bias from omitted variables that is present in previous estimates.
  
  
  METHODS
  Prospective cohort study.Setting. A tertiary care referral hospital and regional district hospital in southeast Queensland, Australia.
  
  
  METHODS
  Adults aged 18 years or older with a minimum inpatient stay of 1 night who were admitted to selected clinical specialities.
  
  
  RESULTS
  Urinary tract infection was not associated with an increase in length of hospital stay or variable costs. Lower respiratory tract infection was associated with an increase of 2.58 days in the hospital and variable costs of AU $24, whereas other types of infection were associated with an increased length of stay of 2.61 days but not with variable costs. Many other factors were found to be associated with increased length of stay and variable costs alongside healthcare-acquired infection. The exclusion of these variables caused a positive bias in the estimates of the costs of healthcare-acquired infection.
  
  
  CONCLUSIONS
  The existing literature may overstate the costs of healthcare-acquired infection because of bias, and the existing estimates of excess costs may not make intuitive sense to clinicians and policy makers. Accurate estimates of the costs of healthcare-acquired infection should be made and used in appropriately designed decision-analytic economic models (ie, cost-effectiveness models) that will make valid and believable predictions of the economic value of increased infection control.
authors.affiliation: Centre for Healthcare Related Infection Surveillance and Prevention, Princess Alexandra Hospital, and School of Public Health, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia. n.graves@qut.edu.au
authors.forename: Nicholas
authors.initials: N
authors.lastname: Graves
authors.forename: Diana
authors.initials: D
authors.lastname: Weinhold
authors.forename: Edward
authors.initials: E
authors.lastname: Tong
authors.forename: Frances
authors.initials: F
authors.lastname: Birrell
authors.forename: Shane
authors.initials: S
authors.lastname: Doidge
authors.forename: Prabha
authors.initials: P
authors.lastname: Ramritu
authors.forename: Kate
authors.initials: K
authors.lastname: Halton
authors.forename: David
authors.initials: D
authors.lastname: Lairson
authors.forename: Michael
authors.initials: M
authors.lastname: Whitby
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D003365:Costs and Cost Analysis; D003428:Cross Infection / Q000191:economics* / Q000453:epidemiology; D005260:Female; D006785:Hospitals, University; D006801:Humans; D007902:Length of Stay / Q000191:economics / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D011793:Queensland / Q000453:epidemiology; D012044:Regression Analysis; D012141:Respiratory Tract Infections / Q000191:economics; D014552:Urinary Tract Infections / Q000191:economics