Methods for identifying 30 chronic conditions: application to administrative data

Marcello Tonelli (Lead / Corresponding author), Natasha Wiebe, Martin Fortin, Bruce Guthrie, Brenda R. Hemmelgarn, Matthew T. James, Scott W. Klarenbach, Richard Lewanczuk, Braden J. Manns, Paul Ronksley, Peter Sargious, Sharon Straus, Hude Quan, for the Alberta Kidney Disease Network

    Research output: Contribution to journalArticle

    85 Citations (Scopus)

    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.

    Original languageEnglish
    Article number31
    Number of pages11
    JournalBMC Medical Informatics and Decision Making
    Volume15
    Issue number31
    DOIs
    Publication statusPublished - 17 Apr 2015

    Keywords

    • Administrative data
    • Multimorbidity

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  • Research Output

    • 85 Citations
    • 1 Article

    Correction to: Methods for identifying 30 chronic conditions: application to administrative data

    Tonelli, M., Wiebe, N., Fortin, M., Guthrie, B., Hemmelgarn, B. R., James, M. T., Klarenbach, S. W., Lewanczuk, R., Manns, B. J., Ronksley, P., Sargious, P., Straus, S. & Quan, H., 4 Sep 2019, In : BMC Medical Informatics and Decision Making. 19, p. 1-4 4 p., 177.

    Research output: Contribution to journalArticle

  • Cite this

    Tonelli, M., Wiebe, N., Fortin, M., Guthrie, B., Hemmelgarn, B. R., James, M. T., Klarenbach, S. W., Lewanczuk, R., Manns, B. J., Ronksley, P., Sargious, P., Straus, S., Quan, H., & for the Alberta Kidney Disease Network (2015). Methods for identifying 30 chronic conditions: application to administrative data. BMC Medical Informatics and Decision Making, 15(31), [31]. https://doi.org/10.1186/s12911-015-0155-5