Methods for identifying 30 chronic conditions: application to administrative data

TY - JOUR

T1 - Methods for identifying 30 chronic conditions

T2 - application to administrative data

AU - Tonelli, Marcello

AU - Wiebe, Natasha

AU - Fortin, Martin

AU - Guthrie, Bruce

AU - Hemmelgarn, Brenda R.

AU - James, Matthew T.

AU - Klarenbach, Scott W.

AU - Lewanczuk, Richard

AU - Manns, Braden J.

AU - Ronksley, Paul

AU - Sargious, Peter

AU - Straus, Sharon

AU - Quan, Hude

AU - for the Alberta Kidney Disease Network

PY - 2015/4/17

Y1 - 2015/4/17

N2 - 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.

AB - 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.

KW - Administrative data

KW - Multimorbidity

UR - https://www.scopus.com/pages/publications/84928565751

U2 - 10.1186/s12911-015-0155-5

DO - 10.1186/s12911-015-0155-5

M3 - Article

C2 - 25886580

SN - 1472-6947

VL - 15

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

IS - 31

M1 - 31

ER -