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Multiple sclerosis in the Republic of San Marino, Italian peninsula: an incidence and prevalence study from a high-risk area.

Marta Caniglia-TenagliaSusanna GuttmannChiara MonaldiniDario ManzaroliMirco VolpiniMaurizio StumpoElisabetta GroppoIlaria CasettaVittorio GovoniMattia FondericoMaura PugliattiEnrico Granieri
Published in: Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology (2018)
Epidemiological studies on multiple sclerosis (MS) carried out in Southern Europe in the last years have shown a significant increase in the disease frequency. Previous surveys conducted in the Republic of San Marino, Northern Italian peninsula, identified that the population is at high risk for MS, with a prevalence of 51.6 per 100,000 population in 1982 and of 166.7 in 2005 and with a mean annual incidence of 7.9 per 100,000 for the period 1990-2005. The present work is a community-based intensive prevalence and incidence survey, by a complete enumeration approach, to update the prevalence and incidence of MS in the Republic of San Marino. The mean annual incidence for the period 2005-14 was 7.7 (95% CI 4.9-11.4) per 100,000, 3.3 (95% CI 1.1-7.6) for men and 11.9 (95% CI 7.2-18.6) for women. On 31 December 2014, 67 patients (19 men and 48 women), suffering from definite or probable MS and living in the Republic of San Marino, yielded a crude prevalence of 204.3 (95% CI 158.4-259.5) per 100,000, 117.8 (95% CI 70.9-183.7) for men and 288.2 (95% CI 212.4-383.3) for women. Our study has confirmed San Marino is an area at high risk for MS, in line with epidemiological data from continental Italy. The marked increase in MS prevalence over time in this population can be ascribable to increased survival and improved ascertainment, in the presence of a substantially stable, yet high, incidence rate.
Keyphrases
  • risk factors
  • multiple sclerosis
  • mass spectrometry
  • ms ms
  • white matter
  • polycystic ovary syndrome
  • ejection fraction
  • cross sectional
  • metabolic syndrome
  • machine learning
  • pregnancy outcomes