title: An examination of longitudinal CAUTI, SSI, and CDI rates from key HHS data systems.
abstract: BACKGROUND
  In response to the growing concern about healthcare-associated infections (HAIs), US Department of Health and Human Services (HHS) developed the National Action Plan to Prevent Healthcare-associated Infections. A key focus of the Action Plan is the setting of HAI metrics and targets and the enhancement and development of data systems to support HAI surveillance.
  
  
  OBJECTIVE
  To identify and assess the strengths and weaknesses of HHS data systems available for surveillance of catheter-associated urinary tract infections, surgical site infections, and Clostridium difficile infections. To present national data from each of the data systems and assess concordance in trends over time.
  
  
  METHODS
  Literature review on data system characteristics and HAI measurement. Graphical and descriptive analyses of longitudinal HAI rates from HHS data systems.
  
  
  METHODS
  HAI rate information expressed as prevalence rates or standardized infection ratios.
  
  
  RESULTS
  We identified four HHS data systems--Medicare claims data, Healthcare Cost and Utilization Project, Medicare Patient Safety Monitoring System, and National Healthcare Safety Network--capable of surveillance of at least one of the HAIs under study. Surgical site infection and Clostridium difficile infection rates display concordance in trends, although there is no evidence of concordance in catheter-associated urinary tract infections rates. We have identified a number of desirable HAI data system characteristics: clinically valid; provide information on a broad range of HAIs; have large sample size to support statistical inference; be representative of the United States; and display consistency in cohort, surveillance protocols, and data collection methodology.
  
  
  CONCLUSIONS
  Although the data systems included in this study vary along the desirable data system dimensions we identified, trends in HAI rates are generally concordant across the data systems. This increases confidence in observed trends.
authors.affiliation: *IMPAQ International, LLC, Columbia, MD †RAND Health, Santa Monica, CA ‡Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at University of California, Los Angeles.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Weinberg
authors.forename: Katherine L
authors.initials: KL
authors.lastname: Kahn
mesh_terms: D055499:Catheter-Related Infections / Q000453:epidemiology / Q000517:prevention & control; D016360:Clostridioides difficile; D003428:Cross Infection / Q000453:epidemiology / Q000517:prevention & control*; D016208:Databases, Factual; D004761:Enterocolitis, Pseudomembranous / Q000453:epidemiology / Q000517:prevention & control; D006291:Health Policy; D006801:Humans; D008137:Longitudinal Studies; D011159:Population Surveillance / Q000379:methods; D013530:Surgical Wound Infection / Q000453:epidemiology / Q000517:prevention & control; D014481:United States / Q000453:epidemiology; D014483:United States Dept. of Health and Human Services / Q000458:organization & administration; D014546:Urinary Catheterization / Q000009:adverse effects