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Multiple sclerosis projection in Tehran, Iran using Bayesian structural time series.

Payam Amini1, Amir Almasi-Hashiani2, Mohammad Ali Sahraian3

  • 1Department of Biostatistics and Epidemiology, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

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Multiple Sclerosis (MS) prevalence is rising in Tehran, Iran, with projections indicating continued increases over the next decade. Females show a higher risk for both familial and sporadic MS.

Keywords:
Multiple SclerosisPrevalenceTehranTime series

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Area of Science:

  • Neurology
  • Epidemiology
  • Public Health

Background:

  • Multiple Sclerosis (MS) prevalence is increasing globally, with Iran reporting the highest rates in Asia.
  • This study addresses the rising MS occurrence in Tehran over three decades and forecasts future trends.

Purpose of the Study:

  • To estimate the increase in Multiple Sclerosis (MS) occurrence in Tehran from 1999 to 2019.
  • To forecast MS prevalence for the next ten years using time series analysis.
  • To analyze the prevalence of familial and sporadic MS, and associated risk factors.

Main Methods:

  • A cross-sectional study utilizing data from the Iranian MS Society registry (1999-2019).
  • Prevalence estimation based on data from the Statistical Centre of Iran.
  • Bayesian Structural Time Series (BSTS) model applied for future prevalence prediction.

Main Results:

  • The study analyzed 22,421 MS cases: 75.1% female, 24.9% male (3:1 female to male ratio).
  • Familial MS accounted for 13.3% of cases. Female gender showed a lower association with familial MS (beta=0.020) compared to sporadic MS (beta=0.034).
  • BSTS forecasting predicts a continued rise in total, familial, and sporadic MS prevalence through 2029.

Conclusions:

  • MS prevalence has increased over three decades in Tehran and is projected to rise further.
  • Tehran exhibits one of Asia's highest MS prevalence rates.
  • Females are at a significantly higher risk for MS than males, irrespective of familial or sporadic classification.