Spatial correlation analysis and influencing factors of toxic mushroom poisoning in Hunan Province from 2016 to 2022
Objective To understand the occurrence pattern and epidemiological characteristics of poisonous mushroom poisoning in Hunan Province from 2016 to 2022,and explore its spatial distribution characteristics,so as to provide scientific basis for preventing mushroom poisoning incidents in Hunan Province.Methods Data of mushroom poisoning incidents in all counties of Hunan Province from 2016 to 2022 were collected.SPSS 19.0 was used for correlation analysis and ArcGIS 10.2 was used for global spatial autocorrelation analysis and local spatial autocorrelation analysis.Results There was a spatial clustering of mushroom poisoning cases in Hunan Province from 2016 to 2022(Moran's Ⅰ=0.231 522,P＜0.05).The results of local spatial autocorrelation showed that the hot spots of poisoning mainly concentrated in central and southern Hunan,and the cold spots mainly concentrated in northern Hunan.From 2016 to 2022,a total of 1 685 cases of mushroom poisoning have been reported,with a cumulative number of cases of 5 464 people and 79 deaths.The period from May to October was not only the high incidence period of toxic mushroom poisoning incidents in Hunan Province,accounting for 92.23％of the number of toxic mushroom poisoning incidents,but also the peak of the number of cases,accounting for 91.23％of the number of toxic mushroom poisoning cases.Conclusion The poisonous mushroom poisoning incidents in Hunan Province from 2016 to 2022 have spatial clustering,and there are key occurrence areas.Temperature and precipitation affect the growth of poisonous mushrooms to a certain extent,and there is a positive correlation with poisonous mushroom poisoning.Meteorological early warning prevention and control should be carried out in specific regions during the high incidence of poisoning,and publicity and warning education should be strengthened to reduce the occurrence of poisoning incidents.
Toxic mushroomEpidemiological characteristicsSpatial autocorrelationEarly warning and prevention
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