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.
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.
Authors: Marcello Moccia; Vincenzo Brescia Morra; Roberta Lanzillo; Ilaria Loperto; Roberta Giordana; Maria Grazia Fumo; Martina Petruzzo; Nicola Capasso; Maria Triassi; Maria Pia Sormani; Raffaele Palladino Journal: Int J Environ Res Public Health Date: 2020-05-13 Impact factor: 3.390