title: Epidemiology of multiple sclerosis in south-western Sardinia.
abstract: BACKGROUND
  Sardinia is a known high-risk area for multiple sclerosis (MS), but no data for south-western Sardinia (SWS) are available. SWS has a genetically homogeneous population, apart from St Peter Island, and represents a peculiar environment related to the industrial, mineralogical and military economy.
  
  
  OBJECTIVE
  To estimate prevalence and incidence and to evaluate temporal trends and geographical distribution of MS in SWS.
  
  
  METHODS
  MS prevalence was evaluated on 31 December 2007 and crude mean annual incidence rate was defined between 2003 and 2007. Temporal trend in MS incidence was assessed using the Armitage test. To identify MS clusters, Standard Morbidity Ratio (SMR) was calculated for each village and geographical distribution prevalence by means of a Bayesian hierarchical model.
  
  
  RESULTS
  Total crude prevalence rate was 210.4 (95% CI 186.3-234.5): 280.3 (95% CI 241.4-319.3) for females, 138 (95% CI 110.1-165.8) for males. The crude mean annual incidence rate was 9.7/100,000 (95% CI 3.4-13.2): 4.7/100,000 (95% CI 2.4-17.0) and 14.6/100,000 (95% CI 11.8-34.8) for males and females respectively. MS incidence has increased over the last 50 years. Cluster analysis showed an SMR of 0.2 (95% CI 0.05-0.68, p = 0.002) on the island of San Pietro, and 2.0 (95% CI 1.35-2.95, p = 0.001) in Domusnovas. Spatial distribution of MS was confirmed by Bayesian geographical analysis.
  
  
  CONCLUSIONS
  Our data confirm Sardinia as a high-risk area for MS and support the relevance of genetic factors in MS, as evidenced in St Peter Island. However, we found an unexpectedly high MS prevalence in one village, in particular in males, suggesting an environmental influence on MS occurrence.
authors.affiliation: Department of Cardiovascular and Neurological Science, University of Cagliari, Italy. ecocco@unica.it
authors.forename: Eleonora
authors.initials: E
authors.lastname: Cocco
authors.forename: Claudia
authors.initials: C
authors.lastname: Sardu
authors.forename: Rita
authors.initials: R
authors.lastname: Massa
authors.forename: Elena
authors.initials: E
authors.lastname: Mamusa
authors.forename: Luigina
authors.initials: L
authors.lastname: Musu
authors.forename: Paola
authors.initials: P
authors.lastname: Ferrigno
authors.forename: Maurizio
authors.initials: M
authors.lastname: Melis
authors.forename: Cristina
authors.initials: C
authors.lastname: Montomoli
authors.forename: Virginia
authors.initials: V
authors.lastname: Ferretti
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Coghe
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Fenu
authors.forename: Jessica
authors.initials: J
authors.lastname: Frau
authors.forename: Lorena
authors.initials: L
authors.lastname: Lorefice
authors.forename: Nicola
authors.initials: N
authors.lastname: Carboni
authors.forename: Paolo
authors.initials: P
authors.lastname: Contu
authors.forename: Maria G
authors.initials: MG
authors.lastname: Marrosu
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004777:Environment; D005260:Female; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D007558:Italy / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000453:epidemiology* / Q000235:genetics; D015995:Prevalence; D012111:Residence Characteristics; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013997:Time Factors; D055815:Young Adult