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.
Cluster detection
Epidemiology
Food safety
Listeriosis
Public health
Journal
Journal of infection and public health
ISSN: 1876-035X
Titre abrégé: J Infect Public Health
Pays: England
ID NLM: 101487384
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
24
07
2023
revised:
25
09
2023
accepted:
03
10
2023
medline:
20
11
2023
pubmed:
23
10
2023
entrez:
22
10
2023
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 37866268
pii: S1876-0341(23)00335-0
doi: 10.1016/j.jiph.2023.10.008
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1904-1910Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.