Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series
Abstract Background The prevalence of Multiple Sclerosis (MS) has been increasing worldwide and the highest prevalence ratio among Asian countries was reported in Iran. This study aims to estimate the increase in MS occurrence during more than three decades in Tehran and forecast the future conditio...
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doaj-c25a93e30be042a9998244cc2d83cb2d2021-06-27T11:23:49ZengBMCBMC Neurology1471-23772021-06-012111610.1186/s12883-021-02281-xMultiple sclerosis projection in Tehran, Iran using Bayesian structural time seriesPayam Amini0Amir Almasi-Hashiani1Mohammad Ali Sahraian2Masood Najafi3Sharareh Eskandarieh4Department of Biostatistics and Epidemiology, School of Public Health, Ahvaz Jundishapur University of Medical SciencesDepartment of Epidemiology, School of Health, Arak University of Medical SciencesMultiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical SciencesMultiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical SciencesMultiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical SciencesAbstract Background The prevalence of Multiple Sclerosis (MS) has been increasing worldwide and the highest prevalence ratio among Asian countries was reported in Iran. This study aims to estimate the increase in MS occurrence during more than three decades in Tehran and forecast the future condition of the disease using time series approaches for the next ten years. Methods The cross-sectional study was conducted from 1999 to 2019 based on records of MS cases from Iranian MS Society (IMSS) registry system. The prevalence was estimated using population data presented by the Statistical Centre of Iran. Through Bayesian Structural Time Series (BSTS) model, we want to predict the prevalence of familial and sporadic MS in the next ten years. . Results Among 22,421 cases with MS, 16,831 (75.1 %) were female and 5589 (24.9 %) were male. Female to male ratio was 3.0:1 and the number of familial MS cases were 2982 (13.3 %) of subjects. Female gender was less responsible for higher rate of MS in familial definition (beta = 0.020) in comparison to sporadic cases (beta = 0.034). Forecasting by BSTS revealed an increase in MS prevalence for the next ten years so that the prevalence rate for total, familial and sporadic MS respectively begins with 189.50 (183.94-195.14), 25.69 (24.97–26.45) and 163.74(159.06-168.57) in 2020 and ends with 220.84 (171.48-266.92), 30.79 (24.16–37.15), and 189.33(146.97-230.19) in 2029. Conclusions According to the findings, MS prevalence increased during three decades and it will increase over the next ten years. Tehran province is one of the regions with highest MS prevalence in Asia. The results of present study indicated that females are at higher risk for MS than males in both sporadic and familial MS.https://doi.org/10.1186/s12883-021-02281-xMultiple SclerosisPrevalenceTime seriesTehran |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Payam Amini Amir Almasi-Hashiani Mohammad Ali Sahraian Masood Najafi Sharareh Eskandarieh |
spellingShingle |
Payam Amini Amir Almasi-Hashiani Mohammad Ali Sahraian Masood Najafi Sharareh Eskandarieh Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series BMC Neurology Multiple Sclerosis Prevalence Time series Tehran |
author_facet |
Payam Amini Amir Almasi-Hashiani Mohammad Ali Sahraian Masood Najafi Sharareh Eskandarieh |
author_sort |
Payam Amini |
title |
Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series |
title_short |
Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series |
title_full |
Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series |
title_fullStr |
Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series |
title_full_unstemmed |
Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series |
title_sort |
multiple sclerosis projection in tehran, iran using bayesian structural time series |
publisher |
BMC |
series |
BMC Neurology |
issn |
1471-2377 |
publishDate |
2021-06-01 |
description |
Abstract Background The prevalence of Multiple Sclerosis (MS) has been increasing worldwide and the highest prevalence ratio among Asian countries was reported in Iran. This study aims to estimate the increase in MS occurrence during more than three decades in Tehran and forecast the future condition of the disease using time series approaches for the next ten years. Methods The cross-sectional study was conducted from 1999 to 2019 based on records of MS cases from Iranian MS Society (IMSS) registry system. The prevalence was estimated using population data presented by the Statistical Centre of Iran. Through Bayesian Structural Time Series (BSTS) model, we want to predict the prevalence of familial and sporadic MS in the next ten years. . Results Among 22,421 cases with MS, 16,831 (75.1 %) were female and 5589 (24.9 %) were male. Female to male ratio was 3.0:1 and the number of familial MS cases were 2982 (13.3 %) of subjects. Female gender was less responsible for higher rate of MS in familial definition (beta = 0.020) in comparison to sporadic cases (beta = 0.034). Forecasting by BSTS revealed an increase in MS prevalence for the next ten years so that the prevalence rate for total, familial and sporadic MS respectively begins with 189.50 (183.94-195.14), 25.69 (24.97–26.45) and 163.74(159.06-168.57) in 2020 and ends with 220.84 (171.48-266.92), 30.79 (24.16–37.15), and 189.33(146.97-230.19) in 2029. Conclusions According to the findings, MS prevalence increased during three decades and it will increase over the next ten years. Tehran province is one of the regions with highest MS prevalence in Asia. The results of present study indicated that females are at higher risk for MS than males in both sporadic and familial MS. |
topic |
Multiple Sclerosis Prevalence Time series Tehran |
url |
https://doi.org/10.1186/s12883-021-02281-x |
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