%0 Journal Article
%T Epidemiology of listeriosis in a region in central Italy from 2010 to 2019: Estimating the real incidence and space-time analysis for detecting cluster of cases.
%A Ponzio E
%A Di Biagio K
%A Dolcini J
%A Sarti D
%A Pompili M
%A Fiacchini D
%A Cerioni C
%A Ciavattini A
%A Gasperini B
%A Prospero E
%J J Infect Public Health
%V 16
%N 12
%D 2023 Dec 20
%M 37866268
%F 7.537
%R 10.1016/j.jiph.2023.10.008
%X <B>BACKGROUND: </B>Contamination and transmission of different Listeria monocytogenes strains along food chain are a serious threat to public health and food safety. Understanding the distribution of diseases in time and space-time is fundamental in the epidemiological study and in preventive medicine programs. The aim of this study is to estimate listeriosis incidence along 10-years period and to perform space-time cluster analysis of listeriosis cases in Marche Region, Italy.<BR><B>METHODS: </B>The number of observed listeriosis cases/year was derived from regional data of surveillance of notifiable diseases and hospital discharge form. The capture and recapture method (C-R method) was applied to estimate the real incidence of listeriosis cases in Marche Region and the space-time scan statistics analysis was performed to detect clusters of space-time of listeriosis cases and add precision to the conventional epidemiological analysis.<BR><B>RESULTS: </B>The C-R method estimation of listeriosis cases was 119 in the 10- year period (2010-2019), with an average of 31.93 % of unobserved cases (lost cases). The estimated mean annual incidence of listeriosis was 0.77 per 100,000 inhabitants (95 %CI 0.65-0.92), accounting for 6.07 % of additional listeriosis cases per year than observed cases. Using the scan statistic, the two most likely clusters were identified, one of these was statistically significant (p < 0.05). The underdiagnosis and under-reporting in addition to listeriosis incidence variability suggested that the surveillance system of Marche Region should be improved.<BR><B>CONCLUSIONS: </B>This study provides evidence of the ability of space-time cluster analysis to complement traditional surveillance of food-borne diseases and to understand the local risk factors by implementing timely targeted interventions.