title: Methods for identifying 30 chronic conditions: application to administrative data.
abstract: BACKGROUND
  Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity.
  
  
  METHODS
  We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as "high validity"; those with positive predictive value ≥70% and sensitivity <70% were graded as "moderate validity". To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year.
  
  
  RESULTS
  Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities.
  
  
  CONCLUSIONS
  We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions.
authors.affiliation: Department of Medicine, University of Calgary, Calgary, Canada. tonelli.admin@ucalgary.ca.
authors.forename: Marcello
authors.initials: M
authors.lastname: Tonelli
authors.affiliation: Department of Medicine, University of Alberta, Edmonton, Canada.
authors.forename: Natasha
authors.initials: N
authors.lastname: Wiebe
authors.affiliation: Department of Family Medicine, Université de Sherbrooke, Sherbrooke, Canada.
authors.forename: Martin
authors.initials: M
authors.lastname: Fortin
authors.affiliation: Population Health Sciences Division, Medical Research Institute, University of Dundee, Dundee, UK.
authors.forename: Bruce
authors.initials: B
authors.lastname: Guthrie
authors.affiliation: Department of Medicine, University of Calgary, Calgary, Canada.
authors.forename: Brenda R
authors.initials: BR
authors.lastname: Hemmelgarn
authors.affiliation: Department of Medicine, University of Calgary, Calgary, Canada.
authors.forename: Matthew T
authors.initials: MT
authors.lastname: James
authors.affiliation: Department of Medicine, University of Alberta, Edmonton, Canada.
authors.forename: Scott W
authors.initials: SW
authors.lastname: Klarenbach
authors.affiliation: Alberta Health Services, Edmonton, Canada.
authors.forename: Richard
authors.initials: R
authors.lastname: Lewanczuk
authors.affiliation: Department of Medicine, University of Calgary, Calgary, Canada.
authors.forename: Braden J
authors.initials: BJ
authors.lastname: Manns
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Canada.
authors.forename: Paul
authors.initials: P
authors.lastname: Ronksley
authors.affiliation: Alberta Health Services, Edmonton, Canada.
authors.forename: Peter
authors.initials: P
authors.lastname: Sargious
authors.affiliation: Department of Medicine, University of Toronto, Toronto, Canada.
authors.forename: Sharon
authors.initials: S
authors.lastname: Straus
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Canada.
authors.forename: Hude
authors.initials: H
authors.lastname: Quan
mesh_terms: D000416:Alberta / Q000453:epidemiology; D000465:Algorithms; D002908:Chronic Disease; D015897:Comorbidity; D016208:Databases, Factual / Q000706:statistics & numerical data*; D015340:Epidemiologic Research Design; D006801:Humans; D038801:International Classification of Diseases; D009313:National Health Programs / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity