Abstract：
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.
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.
摘要：
背景：沿食物链的不同单核细胞增生李斯特菌菌株的污染和传播对公众健康和食品安全构成严重威胁。了解疾病在时间和时空上的分布是流行病学研究和预防医学计划的基础。这项研究的目的是估计10年期间的李斯特菌病发病率，并对马尔凯地区的李斯特菌病病例进行时空聚类分析，意大利。
方法：每年观察到的李斯特菌病病例数来自法定报告疾病监测和出院表的区域数据。采用捕获和再捕获法(C-R法)估计马尔切地区李斯特菌病病例的实际发病率，并进行时空扫描统计分析，检测李斯特菌病病例的时空聚类，提高常规流行病学分析的准确性。
结果：在10年期间（2010-2019年），李斯特菌病病例的C-R方法估计为119，平均31.93%的未观察病例（丢失病例）。李斯特菌病的估计年平均发病率为每100,000居民0.77（95CI0.65-0.92），占每年额外李斯特菌病病例的6.07%。使用扫描统计信息，确定了两个最有可能的集群，其中一项具有统计学意义(p<0.05)。除了李斯特菌病的发病率变异性外，诊断不足和报告不足还表明，应改善马尔凯地区的监测系统。
结论：本研究提供了时空聚类分析的能力的证据，以补充传统的食源性疾病监测，并通过及时实施有针对性的干预措施了解当地的危险因素。
方法：每年观察到的李斯特菌病病例数来自法定报告疾病监测和出院表的区域数据。采用捕获和再捕获法(C-R法)估计马尔切地区李斯特菌病病例的实际发病率，并进行时空扫描统计分析，检测李斯特菌病病例的时空聚类，提高常规流行病学分析的准确性。
结果：在10年期间（2010-2019年），李斯特菌病病例的C-R方法估计为119，平均31.93%的未观察病例（丢失病例）。李斯特菌病的估计年平均发病率为每100,000居民0.77（95CI0.65-0.92），占每年额外李斯特菌病病例的6.07%。使用扫描统计信息，确定了两个最有可能的集群，其中一项具有统计学意义(p<0.05)。除了李斯特菌病的发病率变异性外，诊断不足和报告不足还表明，应改善马尔凯地区的监测系统。
结论：本研究提供了时空聚类分析的能力的证据，以补充传统的食源性疾病监测，并通过及时实施有针对性的干预措施了解当地的危险因素。
