2018~2021年中国食源性疾病地理分布及影响因素分析
Analysis on the Geographical Distribution and Influencing Factors of Foodborne Diseases in China, 2018~2021

目的：分析2018~2021年中国内地食源性疾病的时空分布特征，为预防和控制提供相应的理论依据。方法：基于2018~2021年《中国卫生健康统计年鉴》，采用空间自相关分析、时空扫描等方法，研究中国内地居民食源性疾病的空间分布格局及其变化。结果：国内内地报告的食源性疾病在2.71~3.24/10万。全局空间自相关分析结果显示仅2020年食源性疾病发病率存在空间自相关。时空扫描结果显示在2018~2021期间有三个时空聚集区。公共卫生间建设数量、气温对食源性疾病呈正相关；居民个人食品支出、健康支出对食源性疾病呈现负相关。地理加权回归(Geographic Weighted Regression, GWR)模型能够更好地解释模型效果。结论：对食源性疾病采用空间聚集性分析能够较好地探测出食源性疾病的聚集点。对开展有关防控措施提供依据。
Objective: To analyze the spatial-temporal distribution characteristics of foodborne diseases in mainland of China from 2018 to 2021, and provide a theoretical basis for the prevention and control of foodborne diseases. Methods: Based on the China Health and Wellness Statistical Yearbook from 2018 to 2021, spatial autocorrelation analysis and space-time scanning were used to study the spatial distribution pattern of foodborne illnesses and their changes in mainland China residents. Results: The reported foodborne diseases in China ranged from 2.71 per 100,000 to 3.24 per 100,000. The results of global spatial autocorrelation analysis showed that only the incidence of foodborne diseases in 2020 had spatial autocorrelation. The space-time scanning results showed that there were three spatial-temporal clusters from 2018 to 2021. The temperature has an impact on the increase in foodborne diseases. The number of public toilets and temperature were positively correlated with foodborne diseases. Personal food expenditure and health expenditure were negatively correlated with foodborne diseases. The geographic weighted regression (GWR) model could better explain the model effect. Conclusion: Spatial clustering analysis could better detect the clustering points of foodborne diseases, which can provide evidence for prevention and control measures.