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. - Elisa Ponzio; Katiuscia Di Biagio; Jacopo Dolcini; Donatella Sarti; Marco Pompili; Daniel Fiacchini; Chiara Cerioni; Andrea Ciavattini; Beatrice Gasperini; Emilia Prospero (Elsevier, 2023)
Background: 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. Methods: 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. Results: 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. Conclusions: 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.