Epidemiological analysis of 54350 cases of foodborne diseases in Xinxiang City from 2019 to 2022
Objective By analyzing the epidemiological characteristics of foodborne diseases,so that to provide a scientific basis for accurate disease prevention.Methods Foodborne disease surveillance data were collected for Xinxiang City from 2019 to 2022.The 54 350 cases reported from sentinel hospital was described epidemiologically,and the Chi-square test was used for variance analysis of distribution,and Logistic regression models were used to analyze the factors influencing the severity and the clustering of disease.The test level was 0.05.Results The incidence rates in 2019-2022 were 1.75‰,2.54‰,2.27‰ and 2.47‰,respectively.There were 27 921 cases of women and 26429 cases of men.About 35 612 cases occurred from June to September,accounting for 65.52％,and 14216 cases in the main city,40 134 cases in the subordinate counties and cities,and a total of 31 542 cases of farmers,accounting for 58.03％.The distribution of age was dominated by young and middle-aged people aged 19-59 years(49.08％),but minors(OR=1.20,95％CI:1.11-1.30)and elderly people aged ≥60 years(OR=1.06,95％CI:1.02-1.11)were more severely affected,and the underage group(OR=1.47,95％CI:1.12-1.93)was prone to cluster disease.The exposed foods were mainly fruits and vegetables and their products(40.72％),but meat(OR=1.24,95％CI:1.16-1.33),beverages(OR=1.23,95％CI:1.10-1.37)and mixed foods,mushrooms and algae(OR=1.13,95％CI:1.05-1.22)foods caused more serious symptom.Although the home(85.37％)was the main eating place,compared to collective cafeteria(OR=2.00,95％CI:1.76-2.27)and roadside stalls(OR=1.88,95％CI:1.69-2.10)meals were more likely to have serious cases,and the clusters of foodborne diseases in collective cafeteria(OR=12.41,95％CI:9.69-15.90)was higher.Conclusions Minors and the elderly are the high-risk groups,meat products and beverages are high-risk foods,roadside stalls and dining in collective cafeteria are prone to serious illness,and collective cafeterias are the most likely to have aggregated morbidity.
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