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Performance of administrative databases for identifying individuals with multiple sclerosis

Administrative databases are an alternative to disease registries as a research tool to study multiple sclerosis. However, they are not initially designed to fulfill research purposes. Therefore, an evaluation of their performance is necessary. Our objective was to assess the performance of the Fren...

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Autores principales: Ducatel, Pauline, Debouverie, Marc, Soudant, Marc, Guillemin, Francis, Mathey, Guillaume, Epstein, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600163/
https://www.ncbi.nlm.nih.gov/pubmed/37880330
http://dx.doi.org/10.1038/s41598-023-45384-w
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author Ducatel, Pauline
Debouverie, Marc
Soudant, Marc
Guillemin, Francis
Mathey, Guillaume
Epstein, Jonathan
author_facet Ducatel, Pauline
Debouverie, Marc
Soudant, Marc
Guillemin, Francis
Mathey, Guillaume
Epstein, Jonathan
author_sort Ducatel, Pauline
collection PubMed
description Administrative databases are an alternative to disease registries as a research tool to study multiple sclerosis. However, they are not initially designed to fulfill research purposes. Therefore, an evaluation of their performance is necessary. Our objective was to assess the performance of the French administrative database comprising hospital discharge records and national health insurance databases in identifying individuals with multiple sclerosis, in comparison with a registry that exhaustively compiles resident multiple sclerosis cases in Lorraine, northeastern France, as reference. We recorded all individuals residing in the Lorraine region who were identified by the administrative database or the registry as having multiple sclerosis from 2011 to 2016. We calculated the Matthews correlation coefficient and other concordance indicators. For identifying individuals with multiple sclerosis, the Matthews correlation coefficient by the administrative database was 0.79 (95% CI 0.78–0.80), reflecting moderate performance. The mean time to identification was 5.5 years earlier with the registry than the administrative database. Administrative databases, although useful to study multiple sclerosis, should be used with caution because results of studies based on them may be biased. Our study highlights the value of regional registries that allow for a more exhaustive and rapid identification of cases.
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spelling pubmed-106001632023-10-27 Performance of administrative databases for identifying individuals with multiple sclerosis Ducatel, Pauline Debouverie, Marc Soudant, Marc Guillemin, Francis Mathey, Guillaume Epstein, Jonathan Sci Rep Article Administrative databases are an alternative to disease registries as a research tool to study multiple sclerosis. However, they are not initially designed to fulfill research purposes. Therefore, an evaluation of their performance is necessary. Our objective was to assess the performance of the French administrative database comprising hospital discharge records and national health insurance databases in identifying individuals with multiple sclerosis, in comparison with a registry that exhaustively compiles resident multiple sclerosis cases in Lorraine, northeastern France, as reference. We recorded all individuals residing in the Lorraine region who were identified by the administrative database or the registry as having multiple sclerosis from 2011 to 2016. We calculated the Matthews correlation coefficient and other concordance indicators. For identifying individuals with multiple sclerosis, the Matthews correlation coefficient by the administrative database was 0.79 (95% CI 0.78–0.80), reflecting moderate performance. The mean time to identification was 5.5 years earlier with the registry than the administrative database. Administrative databases, although useful to study multiple sclerosis, should be used with caution because results of studies based on them may be biased. Our study highlights the value of regional registries that allow for a more exhaustive and rapid identification of cases. Nature Publishing Group UK 2023-10-25 /pmc/articles/PMC10600163/ /pubmed/37880330 http://dx.doi.org/10.1038/s41598-023-45384-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ducatel, Pauline
Debouverie, Marc
Soudant, Marc
Guillemin, Francis
Mathey, Guillaume
Epstein, Jonathan
Performance of administrative databases for identifying individuals with multiple sclerosis
title Performance of administrative databases for identifying individuals with multiple sclerosis
title_full Performance of administrative databases for identifying individuals with multiple sclerosis
title_fullStr Performance of administrative databases for identifying individuals with multiple sclerosis
title_full_unstemmed Performance of administrative databases for identifying individuals with multiple sclerosis
title_short Performance of administrative databases for identifying individuals with multiple sclerosis
title_sort performance of administrative databases for identifying individuals with multiple sclerosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600163/
https://www.ncbi.nlm.nih.gov/pubmed/37880330
http://dx.doi.org/10.1038/s41598-023-45384-w
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