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Socioeconomic Determinants of Global Distribution of Multiple Sclerosis: An Ecological Investigation Based on Global Burden of Disease Data Publisher Pubmed



Moghaddam VK1 ; Dickerson AS2 ; Bazrafshan E3, 4 ; Seyedhasani SN4, 5 ; Najafi F6 ; Hadei M7 ; Momeni J8 ; Moradi G8 ; Sarmadi M3, 4, 9
Authors
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Authors Affiliations
  1. 1. Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran
  2. 2. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  3. 3. Department of Environmental Health Engineering, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
  4. 4. Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
  5. 5. Department of Health Information Technology, School of Paramedical Sciences, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
  6. 6. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  7. 7. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Science, Tehran, Iran
  8. 8. Student Research Committee, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
  9. 9. Neuroscience Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran

Source: BMC Neurology Published:2021


Abstract

Background: Socioeconomic factors may be involved in risk of multiple sclerosis (MS), either indirectly or as confounding factors. In this study two comprehensive indicators reflecting socioeconomic differences, including the Human Development Index (HDI) and Prosperity Index (PI), were used to assess the impact of these factors on the worldwide distribution of MS. Methods: The data for this global ecological study were obtained from three comprehensive databases including the Global Burden of Disease (as the source of MS indices), United Nations Development Programme (source for HDI) and the Legatum Institute Database for PI. MS indices (including prevalence, incidence, mortality, and disability-adjusted life years) were all analyzed in the form of age- and sex-standardized. Correlation and regression analyses were used to investigate the relationship between HDI and PI and their subsets with MS indices. Results: All MS indices were correlated with HDI and PI. It was also found that developed countries had significantly higher prevalence and incidence rates of MS than developing countries. Education and governance from the PI, and gross national income and expected years of schooling from the HDI were more associated with MS. Education was significantly related to MS indices (p < 0.01) in both developed and developing countries. Conclusion: In general, the difference in income and the socioeconomic development globally have created a landscape for MS that should be studied in more detail in future studies. © 2021, The Author(s).
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