Abstract:Objective To analyze the prevalence of foodborne diseases in Hunan Province, to reveal their distribution regularity and explore the high-risk areas and their risk factors, so as to provide a theoretical basis for developing effective prevention and control measures. Methods The data regarding foodborne diseases in Hunan Province in 2015 were collected. The descriptive analysis method was used to analyze the temporal, regional and population distribution of foodborne diseases, suspicious exposure food, pathogenic microbial species and other indicators. The spatial clustering regularity of foodborne diseases was analyzed and described by using global spatial autocorrelation analysis and spatiotemporal scanning statistics. The spatial regression model was used to analyze the factors affecting the incidence of foodborne diseases. Results The incidence rate of foodborne diseases in Hunan Province in 2015 was 3.25/10,000. It varied among different cities and prefectures (χ2=2,441.46, P<0.05), with the highest incidence rate in Zhuzhou City(5.77/10,000). The main suspected foods were grain and its products. The main eating place related to the incidence was at home. The detection rate of pathogenic bacteria was found to be the highest in Salmonella, and it differed in various areas (χ2=155.30, P<0.05), with the highest in Changsha City (30.35%). The high incidence areas of foodborne diseases were mainly concentrated in southeastern areas, western mountainous areas and northern plains in Hunan Province. High-risk clustering areas were Shigu District, Zhengxiang District,Yanfeng District, Tianyuan District and Lusong District. The incidence rates of foodborne diseases were positively correlated with monthly mean relative humidity and monthly mean temperature, but negatively correlated with regional gross domestic product (GDP) and GDPper capita, and the regression coefficients showed statistically significant differences (P<0.05). Conclusions There was spatial clustering in the sporadic cases of foodborne diseases in Hunan Province in 2015, which was influenced by monthly mean relative humidity, monthly mean temperature, regional GDP and GDP per capita.