title: [Factors determining antibiotic prescription in primary care].
abstract: BACKGROUND
  To determine patient and physician-related factors associated with variability in antibiotic prescription.
  
  
  METHODS
  Observational study of the prevalence of antibacterial medication prescription >14 years old.
  
  
  METHODS
  official prescriptions, clinical histories and individual health cards. Patient-related variables were: age, sex, number of medical visits-year, comorbidity, antibacterials dispensed with prescription. Physician-related variables were age, sex, number of patients assigned, place of work and rurality. Variables associated with prescription were studied by estimating the odds ratio (OR) from the fit of the multilevel logistic regression models.
  
  
  RESULTS
  The rate of antibiotic prescription-year in the population was 31.4%. Factors associated with prescription were high rate of visits (users with more than 5 annual visits multiply the probability of receiving antibiotics, compared to those who madeno visits: OR=10.8), age (non-linearly, with a greater likelihood in the young and the elderly) and sex, with a higher rate in women (OR=1.5). No association was found between prescription and age and sex of the physician, but an association was found with workload: the higher the physician's workload, the higher the likelihood of antibiotic prescription.
  
  
  CONCLUSIONS
  The most important factor associated with the increase in prescription rate was the frequency of visits. In addition, women, the young and the elderly receive more antibiotics. A multi-factor intervention focusing on demand, patients, and physicians should be carried out to reduce prescription rates.
authors.affiliation: Universidad de Lleida-IRB Lleida, Lleida, España.
authors.forename: M Catalina
authors.initials: MC
authors.lastname: Serna
authors.forename: Jordi
authors.initials: J
authors.lastname: Real
authors.forename: Esther
authors.initials: E
authors.lastname: Ribes
authors.forename: Josep R
authors.initials: JR
authors.lastname: Marsal
authors.forename: Pere
authors.initials: P
authors.lastname: Godoy
authors.forename: Leonardo
authors.initials: L
authors.lastname: Galván
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D015897:Comorbidity; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D004363:Drug Utilization; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009819:Office Visits / Q000706:statistics & numerical data; D058007:Physicians, Primary Care / Q000706:statistics & numerical data; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012424:Rural Population; D012494:Sampling Studies; D017678:Sex Distribution; D013030:Spain; D014505:Urban Population; D016526:Workload; D055815:Young Adult
chemical_list: D000900:Anti-Bacterial Agents