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The use of administrative health care databases to identify patients with rheumatoid arthritis

OBJECTIVE: To validate and compare the decision rules to identify rheumatoid arthritis (RA) in administrative databases. METHODS: A study was performed using administrative health care data from a population of 1 million people who had access to universal health care. Information was available on ho...

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Autores principales: Hanly, John G, Thompson, Kara, Skedgel, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045118/
https://www.ncbi.nlm.nih.gov/pubmed/27790047
http://dx.doi.org/10.2147/OARRR.S92630
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author Hanly, John G
Thompson, Kara
Skedgel, Chris
author_facet Hanly, John G
Thompson, Kara
Skedgel, Chris
author_sort Hanly, John G
collection PubMed
description OBJECTIVE: To validate and compare the decision rules to identify rheumatoid arthritis (RA) in administrative databases. METHODS: A study was performed using administrative health care data from a population of 1 million people who had access to universal health care. Information was available on hospital discharge abstracts and physician billings. RA cases in health administrative databases were matched 1:4 by age and sex to randomly selected controls without inflammatory arthritis. Seven case definitions were applied to identify RA cases in the health administrative data, and their performance was compared with the diagnosis by a rheumatologist. The validation study was conducted on a sample of individuals with administrative data who received a rheumatologist consultation at the Arthritis Center of Nova Scotia. RESULTS: We identified 535 RA cases and 2,140 non-RA, noninflammatory arthritis controls. Using the rheumatologist’s diagnosis as the gold standard, the overall accuracy of the case definitions for RA cases varied between 68.9% and 82.9% with a kappa statistic between 0.26 and 0.53. The sensitivity and specificity varied from 20.7% to 94.8% and 62.5% to 98.5%, respectively. In a reference population of 1 million, the estimated annual number of incident cases of RA was between 176 and 1,610 and the annual number of prevalent cases was between 1,384 and 5,722. CONCLUSION: The accuracy of case definitions for the identification of RA cases from rheumatology clinics using administrative health care databases is variable when compared to a rheumatologist’s assessment. This should be considered when comparing results across studies. This variability may also be used as an advantage in different study designs, depending on the relative importance of sensitivity and specificity for identifying the population of interest to the research question.
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spelling pubmed-50451182016-10-27 The use of administrative health care databases to identify patients with rheumatoid arthritis Hanly, John G Thompson, Kara Skedgel, Chris Open Access Rheumatol Original Research OBJECTIVE: To validate and compare the decision rules to identify rheumatoid arthritis (RA) in administrative databases. METHODS: A study was performed using administrative health care data from a population of 1 million people who had access to universal health care. Information was available on hospital discharge abstracts and physician billings. RA cases in health administrative databases were matched 1:4 by age and sex to randomly selected controls without inflammatory arthritis. Seven case definitions were applied to identify RA cases in the health administrative data, and their performance was compared with the diagnosis by a rheumatologist. The validation study was conducted on a sample of individuals with administrative data who received a rheumatologist consultation at the Arthritis Center of Nova Scotia. RESULTS: We identified 535 RA cases and 2,140 non-RA, noninflammatory arthritis controls. Using the rheumatologist’s diagnosis as the gold standard, the overall accuracy of the case definitions for RA cases varied between 68.9% and 82.9% with a kappa statistic between 0.26 and 0.53. The sensitivity and specificity varied from 20.7% to 94.8% and 62.5% to 98.5%, respectively. In a reference population of 1 million, the estimated annual number of incident cases of RA was between 176 and 1,610 and the annual number of prevalent cases was between 1,384 and 5,722. CONCLUSION: The accuracy of case definitions for the identification of RA cases from rheumatology clinics using administrative health care databases is variable when compared to a rheumatologist’s assessment. This should be considered when comparing results across studies. This variability may also be used as an advantage in different study designs, depending on the relative importance of sensitivity and specificity for identifying the population of interest to the research question. Dove Medical Press 2015-11-06 /pmc/articles/PMC5045118/ /pubmed/27790047 http://dx.doi.org/10.2147/OARRR.S92630 Text en © 2015 Hanly et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Hanly, John G
Thompson, Kara
Skedgel, Chris
The use of administrative health care databases to identify patients with rheumatoid arthritis
title The use of administrative health care databases to identify patients with rheumatoid arthritis
title_full The use of administrative health care databases to identify patients with rheumatoid arthritis
title_fullStr The use of administrative health care databases to identify patients with rheumatoid arthritis
title_full_unstemmed The use of administrative health care databases to identify patients with rheumatoid arthritis
title_short The use of administrative health care databases to identify patients with rheumatoid arthritis
title_sort use of administrative health care databases to identify patients with rheumatoid arthritis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045118/
https://www.ncbi.nlm.nih.gov/pubmed/27790047
http://dx.doi.org/10.2147/OARRR.S92630
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