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Multiple sclerosis spatial cluster in Tuscany.

Daiana Bezzini1,2, Pasquale Pepe3, Francesco Profili3

  • 1Department of Life Sciences, University of Siena, via A Moro 2, 53100, Siena, Italy. daiana.bezzini@unisi.it.

Neurological Sciences : Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
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Summary

Tuscany remains a high-risk area for multiple sclerosis (MS). Updated 2013 prevalence data revealed spatial clusters, particularly in northern and eastern Tuscany, using a Bayesian model to refine analysis.

Keywords:
Administrative dataBayesian mappingItalyMultiple sclerosisPrevalenceSpatial cluster

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Area of Science:

  • Epidemiology
  • Neurology
  • Spatial Analysis

Background:

  • Tuscany, Central Italy, is recognized as a high-risk region for multiple sclerosis (MS).
  • Previous studies indicated a prevalence of 188 cases per 100,000 in 2011, with a heterogeneous geographic distribution.
  • The need for updated prevalence data and spatial cluster evaluation was identified.

Purpose of the Study:

  • To update the prevalence of multiple sclerosis (MS) in Tuscany as of December 31, 2013.
  • To evaluate the presence and geographic distribution of MS spatial clusters within the region.
  • To compare the effectiveness of traditional spatial analysis with a hierarchical Bayesian model.

Main Methods:

  • A validated case-finding algorithm utilizing administrative data was employed to identify MS patients.
  • Standardized morbidity ratios (SMRs) were calculated for each municipality to assess spatial variations.
  • A hierarchical Bayesian model was applied to smooth prevalence estimates and identify significant clusters, mitigating issues with small population sizes.

Main Results:

  • A total of 7330 MS patients (2251 males, 5079 females) were identified, resulting in an overall prevalence of 195.4 per 100,000 population in 2013.
  • The traditional SMR approach yielded a highly variable and non-homogeneous map of MS distribution.
  • The Bayesian model produced a smoother map, revealing significant prevalence clusters in specific areas, including provinces of Massa-Carrara, Pistoia, Arezzo, and municipalities of Siena, Florence, and Barberino Val d'Elsa.

Conclusions:

  • The updated prevalence data confirm Tuscany as a high-risk area for MS, with an observed increasing trend over time.
  • The hierarchical Bayesian model proved superior to the classical SMR approach for estimating area-specific prevalence and identifying spatial clusters by reducing random variability.
  • Identified clusters highlight specific geographic areas requiring further investigation into potential environmental or genetic risk factors for MS in Tuscany.