title: Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.
abstract: OBJECTIVE
  Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.
  
  
  METHODS
  Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995-2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.
  
  
  RESULTS
  57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.
  
  
  CONCLUSIONS
  Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.
authors: Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors: Maaike S M
authors: MS
authors: van Mourik
authors: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors: Pleun Joppe
authors: PJ
authors: van Duijn
authors: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors: Karel G M
authors: KG
authors: Moons
authors: Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors: Marc J M
authors: MJ
authors: Bonten
authors: Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA Division of Infectious Diseases, Boston Children's Hospital, Boston, Massachusetts, USA.
authors: Grace M
authors: GM
authors: Lee
mesh_terms: D000465:Algorithms; D059019:Clinical Coding; D003428:Cross Infection / Q000145:classification* / Q000453:epidemiology*; D000068598:Data Accuracy; D003695:Delivery of Health Care / Q000592:standards*; D006801:Humans; D011785:Quality Assurance, Health Care / Q000592:standards*
keywords: EPIDEMIOLOGY