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Geographical Heterogeneity of Multiple Sclerosis Prevalence in France

INTRODUCTION: Geographical variation in the prevalence of multiple sclerosis (MS) is controversial. Heterogeneity is important to acknowledge to adapt the provision of care within the healthcare system. We aimed to investigate differences in prevalence of MS in departments in the French territory. M...

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Autores principales: Pivot, Diane, Debouverie, Marc, Grzebyk, Michel, Brassat, David, Clanet, Michel, Clavelou, Pierre, Confavreux, Christian, Edan, Gilles, Leray, Emmanuelle, Moreau, Thibault, Vukusic, Sandra, Hédelin, Guy, Guillemin, Francis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147922/
https://www.ncbi.nlm.nih.gov/pubmed/27936086
http://dx.doi.org/10.1371/journal.pone.0167556
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author Pivot, Diane
Debouverie, Marc
Grzebyk, Michel
Brassat, David
Clanet, Michel
Clavelou, Pierre
Confavreux, Christian
Edan, Gilles
Leray, Emmanuelle
Moreau, Thibault
Vukusic, Sandra
Hédelin, Guy
Guillemin, Francis
author_facet Pivot, Diane
Debouverie, Marc
Grzebyk, Michel
Brassat, David
Clanet, Michel
Clavelou, Pierre
Confavreux, Christian
Edan, Gilles
Leray, Emmanuelle
Moreau, Thibault
Vukusic, Sandra
Hédelin, Guy
Guillemin, Francis
author_sort Pivot, Diane
collection PubMed
description INTRODUCTION: Geographical variation in the prevalence of multiple sclerosis (MS) is controversial. Heterogeneity is important to acknowledge to adapt the provision of care within the healthcare system. We aimed to investigate differences in prevalence of MS in departments in the French territory. METHODS: We estimated MS prevalence on October 31, 2004 in 21 administrative departments in France (22% of the metropolitan departments) by using multiple data sources: the main French health insurance systems, neurologist networks devoted to MS and the Technical Information Agency of Hospitalization. We used a spatial Bayesian approach based on estimating the number of MS cases from 2005 and 2008 capture–recapture studies to analyze differences in prevalence. RESULTS: The age- and sex-standardized prevalence of MS per 100,000 inhabitants ranged from 68.1 (95% credible interval 54.6, 84.4) in Hautes-Pyrénées (southwest France) to 296.5 (258.8, 338.9) in Moselle (northeast France). The greatest prevalence was in the northeast departments, and the other departments showed great variability. DISCUSSION: By combining multiple data sources into a spatial Bayesian model, we found heterogeneity in MS prevalence among the 21 departments of France, some with higher prevalence than anticipated from previous publications. No clear explanation related to health insurance coverage and hospital facilities can be advanced. Population migration, socioeconomic status of the population studied and environmental effects are suspected.
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spelling pubmed-51479222016-12-28 Geographical Heterogeneity of Multiple Sclerosis Prevalence in France Pivot, Diane Debouverie, Marc Grzebyk, Michel Brassat, David Clanet, Michel Clavelou, Pierre Confavreux, Christian Edan, Gilles Leray, Emmanuelle Moreau, Thibault Vukusic, Sandra Hédelin, Guy Guillemin, Francis PLoS One Research Article INTRODUCTION: Geographical variation in the prevalence of multiple sclerosis (MS) is controversial. Heterogeneity is important to acknowledge to adapt the provision of care within the healthcare system. We aimed to investigate differences in prevalence of MS in departments in the French territory. METHODS: We estimated MS prevalence on October 31, 2004 in 21 administrative departments in France (22% of the metropolitan departments) by using multiple data sources: the main French health insurance systems, neurologist networks devoted to MS and the Technical Information Agency of Hospitalization. We used a spatial Bayesian approach based on estimating the number of MS cases from 2005 and 2008 capture–recapture studies to analyze differences in prevalence. RESULTS: The age- and sex-standardized prevalence of MS per 100,000 inhabitants ranged from 68.1 (95% credible interval 54.6, 84.4) in Hautes-Pyrénées (southwest France) to 296.5 (258.8, 338.9) in Moselle (northeast France). The greatest prevalence was in the northeast departments, and the other departments showed great variability. DISCUSSION: By combining multiple data sources into a spatial Bayesian model, we found heterogeneity in MS prevalence among the 21 departments of France, some with higher prevalence than anticipated from previous publications. No clear explanation related to health insurance coverage and hospital facilities can be advanced. Population migration, socioeconomic status of the population studied and environmental effects are suspected. Public Library of Science 2016-12-09 /pmc/articles/PMC5147922/ /pubmed/27936086 http://dx.doi.org/10.1371/journal.pone.0167556 Text en © 2016 Pivot et al http://creativecommons.org/licenses/by/4.0/ 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 author and source are credited.
spellingShingle Research Article
Pivot, Diane
Debouverie, Marc
Grzebyk, Michel
Brassat, David
Clanet, Michel
Clavelou, Pierre
Confavreux, Christian
Edan, Gilles
Leray, Emmanuelle
Moreau, Thibault
Vukusic, Sandra
Hédelin, Guy
Guillemin, Francis
Geographical Heterogeneity of Multiple Sclerosis Prevalence in France
title Geographical Heterogeneity of Multiple Sclerosis Prevalence in France
title_full Geographical Heterogeneity of Multiple Sclerosis Prevalence in France
title_fullStr Geographical Heterogeneity of Multiple Sclerosis Prevalence in France
title_full_unstemmed Geographical Heterogeneity of Multiple Sclerosis Prevalence in France
title_short Geographical Heterogeneity of Multiple Sclerosis Prevalence in France
title_sort geographical heterogeneity of multiple sclerosis prevalence in france
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147922/
https://www.ncbi.nlm.nih.gov/pubmed/27936086
http://dx.doi.org/10.1371/journal.pone.0167556
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