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Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study

BACKGROUND: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanisms and clinical impacts on young people. The prevalence of this disease in Iran is reported to be 5.30 to 74.28 per 100,000-person. Because of high prevalence of this disease in Fars pr...

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Autores principales: Asmarian, Naeimehossadat, Sharafi, Zahra, Mousavi, Amin, Jacques, Reis, Tamayo, Ibon, Bind, Marie-Abèle, Abutorabi-zarchi, Marzie, Moradian, Mohammad Javad, Izadi, Sadegh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8353854/
https://www.ncbi.nlm.nih.gov/pubmed/34376167
http://dx.doi.org/10.1186/s12883-021-02342-1
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author Asmarian, Naeimehossadat
Sharafi, Zahra
Mousavi, Amin
Jacques, Reis
Tamayo, Ibon
Bind, Marie-Abèle
Abutorabi-zarchi, Marzie
Moradian, Mohammad Javad
Izadi, Sadegh
author_facet Asmarian, Naeimehossadat
Sharafi, Zahra
Mousavi, Amin
Jacques, Reis
Tamayo, Ibon
Bind, Marie-Abèle
Abutorabi-zarchi, Marzie
Moradian, Mohammad Javad
Izadi, Sadegh
author_sort Asmarian, Naeimehossadat
collection PubMed
description BACKGROUND: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanisms and clinical impacts on young people. The prevalence of this disease in Iran is reported to be 5.30 to 74.28 per 100,000-person. Because of high prevalence of this disease in Fars province, the purpose of this study was to assess the spatial pattern of MS incidence rate by modeling both the associations s of spatial dependence between neighboring regions and risk factors in a Bayesian Poisson model, which can lead to the improvement of health resource allocation decisions. METHOD: Data from 5468 patients diagnosed with MS were collected, according to the McDonald’s criteria. New cases of MS were reported by the MS Society of Fars province from 1991 until 2016. The association between the percentage of people with low vitamin D intake, smoking, abnormal BMI and alcohol consumption in addition to spatial structure in a Bayesian spatio-temporal hierarchical model were used to determine the relative risk and trend of MS incidence rate in 29 counties of Fars province. RESULTS: County-level crude incidence rates ranged from 0.22 to 11.31 cases per 100,000-person population. The highest relative risk was estimated at 1.80 in the county of Shiraz, the capital of Fars province, while the lowest relative risk was estimated at 0.11 in Zarindasht county in southern of Fars. The percentages of vitamin D supplementation intake and smoking were significantly associated with the incidence rate of MS. The results showed that 1% increase in vitamin D supplementation intake is associated with 2% decrease in the risk of MS and 1% increase in smoking is associated with 16% increase in the risk of MS. CONCLUSION: Bayesian spatio-temporal analysis of MS incidence rate revealed that the trend in the south and south east of Fars province is less steep than the mean trend of this disease. The lower incidence rate was associated with a higher percentage of vitamin D supplementation intake and a lower percentage of smoking. Previous studies have also shown that smoking and low vitamin D, among all covariates or risk factors, might be associated with high incidence of MS.
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spelling pubmed-83538542021-08-11 Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study Asmarian, Naeimehossadat Sharafi, Zahra Mousavi, Amin Jacques, Reis Tamayo, Ibon Bind, Marie-Abèle Abutorabi-zarchi, Marzie Moradian, Mohammad Javad Izadi, Sadegh BMC Neurol Research Article BACKGROUND: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanisms and clinical impacts on young people. The prevalence of this disease in Iran is reported to be 5.30 to 74.28 per 100,000-person. Because of high prevalence of this disease in Fars province, the purpose of this study was to assess the spatial pattern of MS incidence rate by modeling both the associations s of spatial dependence between neighboring regions and risk factors in a Bayesian Poisson model, which can lead to the improvement of health resource allocation decisions. METHOD: Data from 5468 patients diagnosed with MS were collected, according to the McDonald’s criteria. New cases of MS were reported by the MS Society of Fars province from 1991 until 2016. The association between the percentage of people with low vitamin D intake, smoking, abnormal BMI and alcohol consumption in addition to spatial structure in a Bayesian spatio-temporal hierarchical model were used to determine the relative risk and trend of MS incidence rate in 29 counties of Fars province. RESULTS: County-level crude incidence rates ranged from 0.22 to 11.31 cases per 100,000-person population. The highest relative risk was estimated at 1.80 in the county of Shiraz, the capital of Fars province, while the lowest relative risk was estimated at 0.11 in Zarindasht county in southern of Fars. The percentages of vitamin D supplementation intake and smoking were significantly associated with the incidence rate of MS. The results showed that 1% increase in vitamin D supplementation intake is associated with 2% decrease in the risk of MS and 1% increase in smoking is associated with 16% increase in the risk of MS. CONCLUSION: Bayesian spatio-temporal analysis of MS incidence rate revealed that the trend in the south and south east of Fars province is less steep than the mean trend of this disease. The lower incidence rate was associated with a higher percentage of vitamin D supplementation intake and a lower percentage of smoking. Previous studies have also shown that smoking and low vitamin D, among all covariates or risk factors, might be associated with high incidence of MS. BioMed Central 2021-08-10 /pmc/articles/PMC8353854/ /pubmed/34376167 http://dx.doi.org/10.1186/s12883-021-02342-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Asmarian, Naeimehossadat
Sharafi, Zahra
Mousavi, Amin
Jacques, Reis
Tamayo, Ibon
Bind, Marie-Abèle
Abutorabi-zarchi, Marzie
Moradian, Mohammad Javad
Izadi, Sadegh
Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study
title Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study
title_full Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study
title_fullStr Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study
title_full_unstemmed Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study
title_short Multiple sclerosis incidence rate in southern Iran: a Bayesian epidemiological study
title_sort multiple sclerosis incidence rate in southern iran: a bayesian epidemiological study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8353854/
https://www.ncbi.nlm.nih.gov/pubmed/34376167
http://dx.doi.org/10.1186/s12883-021-02342-1
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