Methods for identifying 30 chronic conditions: application to administrative data

BMC Med Inform Decis Mak. 2015 Apr 17:15:31. doi: 10.1186/s12911-015-0155-5.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alberta / epidemiology
  • Algorithms
  • Chronic Disease*
  • Comorbidity*
  • Databases, Factual / statistics & numerical data*
  • Epidemiologic Research Design*
  • Humans
  • International Classification of Diseases
  • National Health Programs / statistics & numerical data*
  • Reproducibility of Results
  • Sensitivity and Specificity