Increased prevalence of multiple sclerosis and clusters of different disease risk in Northern 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.

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley.

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe-Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified.

PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach.

Some environmental factors are associated with an increased risk of multiple sclerosis (MS). Air pollution could be a main one. This study was conducted to investigate the association of particulate matter 2.5 (PM2.5) concentrations with MS prevalence in the province of Pavia, Italy. The overall MS prevalence in the province of Pavia is 169.4 per 100,000 inhabitants. Spatial ground-level PM2.5 gridded data were analysed, by municipality, for the period 2010-2016. Municipalities were grouped by tertiles according to PM2.5 concentration. Ecological regression and Bayesian statistics were used to analyse the association between PM2.5 concentrations, degree of urbanization, deprivation index and MS risk. MS risk was higher among persons living in areas with an average winter PM2.5 concentration above the European annual limit value (25 µg/m3). The Bayesian map revealed sizeable MS high-risk clusters. The study found a relationship between low MS risk and lower PM2.5 levels, strengthening the suggestion that air pollution may be one of the environmental risk factors for MS.