Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data

Abstract Background Quantifying the burden of multimorbidity for healthcare research using administrative data has been constrained. Existing measures incompletely capture chronic conditions of relevance and are narrowly focused on risk-adjustment for mortality, healthcare cost or utilization. Moreo...

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Main Authors: Melissa Y. Wei, Jamie E. Luster, Chiao-Li Chan, Lillian Min
Format: Article
Language:English
Published: BMC 2020-06-01
Series:BMC Health Services Research
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12913-020-05207-4
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spelling doaj-2a06f9282cf24558ba3c8919225cd33b2020-11-25T03:20:38ZengBMCBMC Health Services Research1472-69632020-06-0120111110.1186/s12913-020-05207-4Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative dataMelissa Y. Wei0Jamie E. Luster1Chiao-Li Chan2Lillian Min3Division of General Medicine, Department of Internal Medicine, University of MichiganDivision of General Medicine, Department of Internal Medicine, University of MichiganDivision of Geriatric and Palliative Medicine, Department of Internal Medicine, University of MichiganInstitute for Healthcare Policy and Innovation, University of MichiganAbstract Background Quantifying the burden of multimorbidity for healthcare research using administrative data has been constrained. Existing measures incompletely capture chronic conditions of relevance and are narrowly focused on risk-adjustment for mortality, healthcare cost or utilization. Moreover, the measures have not undergone a rigorous review for how accurately the components, specifically the International Classification of Diseases, Ninth Revision (ICD-9) codes, represent the chronic conditions that comprise the measures. We performed a comprehensive, structured literature review of research studies on the accuracy of ICD-9 codes validated using external sources across an inventory of 81 chronic conditions. The conditions as a weighted measure set have previously been demonstrated to impact not only mortality but also physical and mental health-related quality of life. Methods For each of 81 conditions we performed a structured literature search with the goal to identify 1) studies that externally validate ICD-9 codes mapped to each chronic condition against an external source of data, and 2) the accuracy of ICD-9 codes reported in the identified validation studies. The primary measure of accuracy was the positive predictive value (PPV). We also reported negative predictive value (NPV), sensitivity, specificity, and kappa statistics when available. We searched PubMed and Google Scholar for studies published before June 2019. Results We identified studies with validation statistics of ICD-9 codes for 51 (64%) of 81 conditions. Most of the studies (47/51 or 92%) used medical chart review as the external reference standard. Of the validated using medical chart review, the median (range) of mean PPVs was 85% (39–100%) and NPVs was 91% (41–100%). Most conditions had at least one validation study reporting PPV ≥70%. Conclusions To help facilitate the use of patient-centered measures of multimorbidity in administrative data, this review provides the accuracy of ICD-9 codes for chronic conditions that impact a universally valued patient-centered outcome: health-related quality of life. These findings will assist health services studies that measure chronic disease burden and risk-adjust for comorbidity and multimorbidity using patient-centered outcomes in administrative data.http://link.springer.com/article/10.1186/s12913-020-05207-4MultimorbidityICD-9ValidationLiterature review
collection DOAJ
language English
format Article
sources DOAJ
author Melissa Y. Wei
Jamie E. Luster
Chiao-Li Chan
Lillian Min
spellingShingle Melissa Y. Wei
Jamie E. Luster
Chiao-Li Chan
Lillian Min
Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data
BMC Health Services Research
Multimorbidity
ICD-9
Validation
Literature review
author_facet Melissa Y. Wei
Jamie E. Luster
Chiao-Li Chan
Lillian Min
author_sort Melissa Y. Wei
title Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data
title_short Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data
title_full Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data
title_fullStr Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data
title_full_unstemmed Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data
title_sort comprehensive review of icd-9 code accuracies to measure multimorbidity in administrative data
publisher BMC
series BMC Health Services Research
issn 1472-6963
publishDate 2020-06-01
description Abstract Background Quantifying the burden of multimorbidity for healthcare research using administrative data has been constrained. Existing measures incompletely capture chronic conditions of relevance and are narrowly focused on risk-adjustment for mortality, healthcare cost or utilization. Moreover, the measures have not undergone a rigorous review for how accurately the components, specifically the International Classification of Diseases, Ninth Revision (ICD-9) codes, represent the chronic conditions that comprise the measures. We performed a comprehensive, structured literature review of research studies on the accuracy of ICD-9 codes validated using external sources across an inventory of 81 chronic conditions. The conditions as a weighted measure set have previously been demonstrated to impact not only mortality but also physical and mental health-related quality of life. Methods For each of 81 conditions we performed a structured literature search with the goal to identify 1) studies that externally validate ICD-9 codes mapped to each chronic condition against an external source of data, and 2) the accuracy of ICD-9 codes reported in the identified validation studies. The primary measure of accuracy was the positive predictive value (PPV). We also reported negative predictive value (NPV), sensitivity, specificity, and kappa statistics when available. We searched PubMed and Google Scholar for studies published before June 2019. Results We identified studies with validation statistics of ICD-9 codes for 51 (64%) of 81 conditions. Most of the studies (47/51 or 92%) used medical chart review as the external reference standard. Of the validated using medical chart review, the median (range) of mean PPVs was 85% (39–100%) and NPVs was 91% (41–100%). Most conditions had at least one validation study reporting PPV ≥70%. Conclusions To help facilitate the use of patient-centered measures of multimorbidity in administrative data, this review provides the accuracy of ICD-9 codes for chronic conditions that impact a universally valued patient-centered outcome: health-related quality of life. These findings will assist health services studies that measure chronic disease burden and risk-adjust for comorbidity and multimorbidity using patient-centered outcomes in administrative data.
topic Multimorbidity
ICD-9
Validation
Literature review
url http://link.springer.com/article/10.1186/s12913-020-05207-4
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