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(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

BACKGROUND: Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when infer...

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Autores principales: Sheriffdeen, A., Millar, J. L., Martin, C., Evans, M., Tikellis, G., Evans, S. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488579/
https://www.ncbi.nlm.nih.gov/pubmed/32917193
http://dx.doi.org/10.1186/s12913-020-05713-5
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author Sheriffdeen, A.
Millar, J. L.
Martin, C.
Evans, M.
Tikellis, G.
Evans, S. M.
author_facet Sheriffdeen, A.
Millar, J. L.
Martin, C.
Evans, M.
Tikellis, G.
Evans, S. M.
author_sort Sheriffdeen, A.
collection PubMed
description BACKGROUND: Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. METHODS: We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager’s in each hospital, provided each patient’s associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. RESULTS: The percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. CONCLUSION: Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities.
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spelling pubmed-74885792020-09-16 (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports Sheriffdeen, A. Millar, J. L. Martin, C. Evans, M. Tikellis, G. Evans, S. M. BMC Health Serv Res Research Article BACKGROUND: Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. METHODS: We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager’s in each hospital, provided each patient’s associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. RESULTS: The percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. CONCLUSION: Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities. BioMed Central 2020-09-11 /pmc/articles/PMC7488579/ /pubmed/32917193 http://dx.doi.org/10.1186/s12913-020-05713-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Sheriffdeen, A.
Millar, J. L.
Martin, C.
Evans, M.
Tikellis, G.
Evans, S. M.
(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
title (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
title_full (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
title_fullStr (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
title_full_unstemmed (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
title_short (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
title_sort (dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488579/
https://www.ncbi.nlm.nih.gov/pubmed/32917193
http://dx.doi.org/10.1186/s12913-020-05713-5
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