Methods for identifying 30 chronic conditions: application to administrative data

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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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-015-0155-5) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 23

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The Kidney Disease: Improving Global Outcomes (KDIGO) organization developed clinical practice guidelines in 2012 to provide guidance on the evaluation, management, and treatment of chronic kidney disease (CKD) in adults and children who are not receiving renal replacement therapy. The KDIGO CKD Guideline Development Work Group defined the scope of the guideline, gathered evidence, determined topics for systematic review, and graded the quality of evidence that had been summarized by an evidence review team. Searches of the English-language literature were conducted through November 2012. Final modification of the guidelines was informed by the KDIGO Board of Directors and a public review process involving registered stakeholders. The full guideline included 110 recommendations. This synopsis focuses on 10 key recommendations pertinent to definition, classification, monitoring, and management of CKD in adults.

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Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database.

Hude Quan, Bing Li, L. Duncan Saunders … (2008)

The goal of this study was to assess the validity of the International Classification of Disease, 10th Version (ICD-10) administrative hospital discharge data and to determine whether there were improvements in the validity of coding for clinical conditions compared with ICD-9 Clinical Modification (ICD-9-CM) data. We reviewed 4,008 randomly selected charts for patients admitted from January 1 to June 30, 2003 at four teaching hospitals in Alberta, Canada to determine the presence or absence of 32 clinical conditions and to assess the agreement between ICD-10 data and chart data. We then re-coded the same charts using ICD-9-CM and determined the agreement between the ICD-9-CM data and chart data for recording those same conditions. The accuracy of ICD-10 data relative to chart data was compared with the accuracy of ICD-9-CM data relative to chart data. Sensitivity values ranged from 9.3 to 83.1 percent for ICD-9-CM and from 12.7 to 80.8 percent for ICD-10 data. Positive predictive values ranged from 23.1 to 100 percent for ICD-9-CM and from 32.0 to 100 percent for ICD-10 data. Specificity and negative predictive values were consistently high for both ICD-9-CM and ICD-10 databases. Of the 32 conditions assessed, ICD-10 data had significantly higher sensitivity for one condition and lower sensitivity for seven conditions relative to ICD-9-CM data. The two databases had similar sensitivity values for the remaining 24 conditions. The validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions was generally similar though validity differed between coding versions for some conditions. The implementation of ICD-10 coding has not significantly improved the quality of administrative data relative to ICD-9-CM. Future assessments like this one are needed because the validity of ICD-10 data may get better as coders gain experience with the new coding system.

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Author and article information

Contributors

Marcello Tonelli: tonelli.admin@ucalgary.ca

Natasha Wiebe: nwiebe@ualberta.ca

Martin Fortin: Martin.Fortin@usherbrooke.ca

Bruce Guthrie: b.guthrie@dundee.ac.uk

Brenda R Hemmelgarn: Brenda.Hemmelgarn@albertahealthservices.ca

Matthew T James: mjames@ucalgary.ca

Scott W Klarenbach: swk@ualberta.ca

Richard Lewanczuk: richard.lewanczuk@albertahealthservices.ca

Braden J Manns: Braden.Manns@albertahealthservices.ca

Paul Ronksley: peronksl@ucalgary.ca

Peter Sargious: peter.sargious@albertahealthservices.ca

Sharon Straus: sharon.straus@utoronto.ca

Hude Quan: hquan@ucalgary.ca

Journal

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6947

Publication date (Electronic): 17 April 2015

Publication date PMC-release: 17 April 2015

Publication date Collection: 2015

Volume: 15

Electronic Location Identifier: 31

Affiliations

[ ]Department of Medicine, University of Calgary, Calgary, Canada

[ ]Department of Medicine, University of Alberta, Edmonton, Canada

[ ]Department of Family Medicine, Université de Sherbrooke, Sherbrooke, Canada

[ ]Population Health Sciences Division, Medical Research Institute, University of Dundee, Dundee, UK

[ ]Alberta Health Services, Edmonton, Canada

[ ]Department of Community Health Sciences, University of Calgary, Calgary, Canada

[ ]Department of Medicine, University of Toronto, Toronto, Canada

Article

Publisher ID: 155

DOI: 10.1186/s12911-015-0155-5

PMC ID: 4415341

PubMed ID: 25886580

SO-VID: d8e8bf3c-1f15-4f27-89da-008faf116014

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.