title: An approach to the study of potentially preventable nosocomial infections.
abstract: OBJECTIVE
  To analyze a method that identifies potentially preventable nosocomial infections, as a tool to evaluate the performance of infection control programs through quantification of their potential for reducing nosocomial infections.
  
  
  METHODS
  The database of the Study of the Prevalence of Nosocomial Infections in Spain (EPINE) was reanalyzed. The method was based on the use of false negatives of the classification table obtained from application of a fixed multiple logistic regression model, as an estimator of the number of potentially preventable nosocomial infections.
  
  
  RESULTS
  The calculated number of patients with preventable infections was 7,493, which constituted 21.6% of the infected patients. Among hospital areas, intensive care had the lowest preventability rate (4.6%), whereas gynecology and obstetrics had the highest (40.6%). There was a significant inverse exposure-effect relationship between the proportion of preventable infections and the National Nosocomial Infections Surveillance (NNIS) System risk index. No correlation was observed between the prevalence of patients with nosocomial infection and the percentage of preventable infections.
  
  
  CONCLUSIONS
  This analysis suggests that fewer nosocomial infections may be preventable in Spanish hospitals than previously assumed.
authors.affiliation: Servicio de Medicina Preventiva y Epidemiología, Hospital Universitario Vall d'Hebron, Universidad Autónoma de Barcelona, Barcelona, Spain.
authors.forename: José
authors.initials: J
authors.lastname: Rosselló-Urgell
authors.forename: Josep
authors.initials: J
authors.lastname: Vaqué-Rafart
authors.forename: Eduardo
authors.initials: E
authors.lastname: Hermosilla-Pérez
authors.forename: Alejandro
authors.initials: A
authors.lastname: Allepuz-Palau
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003428:Cross Infection / Q000453:epidemiology / Q000517:prevention & control*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D015999:Multivariate Analysis; D011159:Population Surveillance / Q000379:methods*; D013030:Spain / Q000453:epidemiology