Methods for identifying 30 chronic conditions: application to administrative data

Marcello Tonelli; 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

doi:10.1186/s12911-015-0155-5

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 journal › Article › peer-review

278 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 language	English
Article number	31
Number of pages	11
Journal	BMC Medical Informatics and Decision Making
Volume	15
Issue number	31
DOIs	https://doi.org/10.1186/s12911-015-0155-5
Publication status	Published - 17 Apr 2015

Keywords

Administrative data
Multimorbidity

ASJC Scopus subject areas

Health Informatics
Health Policy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s12911-015-0155-5

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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), Article 31. https://doi.org/10.1186/s12911-015-0155-5

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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.",

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author = "Marcello Tonelli and Natasha Wiebe and Martin Fortin and Bruce Guthrie and Hemmelgarn, {Brenda R.} and James, {Matthew T.} and Klarenbach, {Scott W.} and Richard Lewanczuk and Manns, {Braden J.} and Paul Ronksley and Peter Sargious and Sharon Straus and Hude Quan and {for the Alberta Kidney Disease Network}",

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Tonelli, M, Wiebe, N, Fortin, M, Guthrie, B, Hemmelgarn, BR, James, MT, Klarenbach, SW, Lewanczuk, R, Manns, BJ, 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, vol. 15, no. 31, 31. https://doi.org/10.1186/s12911-015-0155-5

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

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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 -

Methods for identifying 30 chronic conditions: application to administrative data

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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