Accuracy of Administrative Code Data for the Surveillance of Healthcare-Associated Infections: A Systematic Review and Meta-Analysis

• Published in: Clinical infectious diseases Vol. 58; no. 5; pp. 688 - 696
• Main Authors: Goto, Michihiko, Ohl, Michael E., Schweizer, Marin L., Perencevich, Eli N.
• Format: Journal Article
• Language: English
• Published: Oxford OXFORD UNIVERSITY PRESS 01.03.2014; Oxford University Press
• Subjects: Accuracy; Algorithms; Biological and medical sciences; Clinical coding; Clostridium difficile; Cross Infection - epidemiology; Epidemiologic Methods; Epidemiology; General aspects; Health care; Health care industry; Health surveillance; HEALTHCARE EPIDEMIOLOGY; Human infectious diseases. Experimental studies and models; Humans; Infections; Infectious diseases; International Classification of Diseases - statistics & numerical data; International Statistical Classification of Diseases; Medical instruments; Medical sciences; Meta-analysis; Methicillin resistant staphylococcus aureus; Nosocomial infections; Predictive Value of Tests; Sensitivity and Specificity; Surveillance; Systematic review; Urethral catheterization; Nosocomial infection; Accuracy; Surveillance; Bibliographic review; surveillance; systematic review; healthcare-associated infections; international classification of diseases
• ISSN: 1058-4838; 1537-6591; 1537-6591
• DOI: 10.1093/cid/cit737

Administrative code data (ACD), such as International Classifications of Diseases, Ninth Revision, Clinical Modification codes, are widely used in surveillance and public reporting programs that seek to identify healthcare-associated infections (HAIs); however, little is known about their accuracy. This systematic review summarizes evidence for the accuracy of ACD for the detection of selected HAIs, including catheter-associated urinary tract infection, Clostridium difficile infection (CDI), central line–associated bloodstream infection, ventilator-associated pneumonia/events, postprocedure pneumonia, methicillin-resistant Staphylococcus aureus, and surgical site infections (SSIs). We conducted meta-analysis for SSIs and CDIs, where acceptable numbers of primary studies were available. For these 2 conditions, ACD have moderate sensitivity and high specificity, but evidence for detection of other HAIs is limited. With current low prevalence of HAIs, the positive predictive value of ACD algorithms would be low. ACD may be inaccurate for detection of many HAIs and should be used cautiously for surveillance and reporting purposes.