title: Departmental consumption of antibiotic drugs and subsequent resistance: a quantitative link.
abstract: OBJECTIVE
  To look for a quantitative model linking departmental consumption of antibiotic drugs to the subsequent isolation of resistant hospital-acquired coliform pathogens.
  
  
  METHODS
  Included in the study were all patients with hospital-acquired bloodstream infections caused by a coliform pathogen, detected in six departments of internal medicine of one university hospital during the period 1991-1996, who had not been hospitalized in the month before the infection (n = 394). Departmental consumption of antibiotics in the year before the infection [expressed as defined daily dosages (DDD)/100 patient days], antibiotic treatment given to the individual patient before the infection, the day of hospital stay on which the infection occurred, and the department and the calendar year were all included in a logistic model to predict the isolation of a resistant pathogen. We looked at five drugs: gentamicin, amikacin, cefuroxime, ceftazidime and ciprofloxacin.
  
  
  RESULTS
  Five logistic models were fitted for the resistance to each of the antibiotic drugs. The multivariable-adjusted odds ratios for a pathogen resistant to the specific antibiotic were 1.03 [95% confidence interval (CI) 0.70-1.50] for gentamicin, 1.80 (95% CI 1.00-3.24) for amikacin, 1.12 (95% CI 1.02-1.23) for cefuroxime, 1.45 (95% CI 1.19-1.76) for ceftazidime and 1.06 (95% CI 0.57-1.97) for ciprofloxacin, per 1 DDD/100 patient days.
  
  
  CONCLUSIONS
  The departmental consumption of cephalosporin drugs and amikacin in six autonomous departments of medicine in the same hospital was associated with a measurable and statistically significant increase in the probability of infection caused by a resistant pathogen.
authors.affiliation: Department of Medicine, Rabin Medical Center, Beilinson Campus, Petah-Tiqva, Israel. leibovic@post.tau.ac.il
authors.forename: L
authors.initials: L
authors.lastname: Leibovici
authors.forename: R
authors.initials: R
authors.lastname: Berger
authors.forename: T
authors.initials: T
authors.lastname: Gruenewald
authors.forename: J
authors.initials: J
authors.lastname: Yahav
authors.forename: Y
authors.initials: Y
authors.lastname: Yehezkelli
authors.forename: G
authors.initials: G
authors.lastname: Milo
authors.forename: M
authors.initials: M
authors.lastname: Paul
authors.forename: Z
authors.initials: Z
authors.lastname: Samra
authors.forename: S D
authors.initials: SD
authors.lastname: Pitlik
mesh_terms: D000583:Amikacin / Q000494:pharmacology / Q000627:therapeutic use*; D000900:Anti-Bacterial Agents / Q000494:pharmacology / Q000627:therapeutic use*; D016470:Bacteremia / Q000188:drug therapy / Q000453:epidemiology / Q000382:microbiology; D002511:Cephalosporins / Q000494:pharmacology / Q000627:therapeutic use*; D003428:Cross Infection / Q000188:drug therapy / Q000453:epidemiology / Q000382:microbiology; D024881:Drug Resistance, Bacterial; D004755:Enterobacteriaceae / Q000187:drug effects*; D004756:Enterobacteriaceae Infections / Q000188:drug therapy / Q000453:epidemiology / Q000382:microbiology*; D006785:Hospitals, University; D006801:Humans; D007388:Internal Medicine; D016015:Logistic Models
chemical_list: D000900:Anti-Bacterial Agents; D002511:Cephalosporins; D000583:Amikacin