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Updated: Jan 1, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Increased prevalence of multiple sclerosis and clusters of different disease risk in Northern Italy
Roberto Bergamaschi1, Maria Cristina Monti2, Leonardo Trivelli2
1Multiple Sclerosis Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
Background:
The increasing multiple sclerosis (MS) prevalence is varying across the macroscopic regional areas. Only few studies have explored the microscopic geographic variation of MS prevalence, which could highlight MS spatial clusters.
Objective:
In this ecological study, we aimed to estimate 2016 MS prevalence in the province of Pavia (Northern Italy) and to describe MS risk geographical variation across small area units, compared to the year 2000.
Methods:
Bayesian models were fit to estimate area-specific MS relative risks. The mean of the posterior marginal distribution of relative risks differences for each area were used to describe the risk variation.
Results:
The 2016 overall prevalence was 169.4 per 100,000 inhabitants (95% CI 158.8-180.6). The Bayesian mapping of MS showed some clusters of higher and lower disease prevalence. Furthermore, several municipalities located in the north part of the province were more at risk with respect to the year 2000.
Conclusions:
The current MS prevalence sets the province of Pavia among high-risk areas and, compared with the previous prevalence estimate (86 per 100,000 in year 2000), indicates an increased MS risk. The Bayesian mapping highlighted area with a significantly higher/lower MS risk where to investigate etiologic hypotheses based on environmental and genetic exposures.
Insights
Multiple sclerosis (MS) prevalence in Pavia, Italy, increased significantly from 2000 to 2016, with higher risk clusters identified in northern municipalities. This highlights geographic variations in MS risk for further investigation.
Area of Science:
- Epidemiology
- Neurology
- Spatial Analysis
Background:
- Multiple sclerosis (MS) prevalence varies geographically.
- Microscopic geographic variations and spatial clusters of MS are underexplored.
- Understanding spatial MS prevalence is crucial for etiological research.
Purpose of the Study:
- Estimate 2016 MS prevalence in Pavia, Northern Italy.
- Describe geographical variations in MS risk across small areas.
- Compare 2016 MS risk with the year 2000.
Main Methods:
- Ecological study design.
- Bayesian models used for area-specific MS relative risk estimation.
- Posterior marginal distribution of relative risk differences analyzed for risk variation.
Main Results:
- Overall 2016 MS prevalence was 169.4 per 100,000 inhabitants.
- Bayesian mapping revealed spatial clusters of high and low MS prevalence.
- Northern municipalities showed increased MS risk compared to the year 2000.
Conclusions:
- Pavia province is identified as a high-risk area for MS.
- MS risk has increased in Pavia province since 2000.
- Highlighted areas warrant further investigation into environmental and genetic MS risk factors.
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