Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an exposure-sensitivity-adaptability vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases.

Automated summary

This study developed an exposure-sensitivity-adaptability vulnerability assessment framework to analyze the risk of foodborne disease outbreaks in different climatic and socioeconomic environments in Zhejiang Province, China. The results showed that temperature, precipitation, road density, construction area proportions, and GDP were positively correlated with foodborne diseases. The strength and significance of these relationships varied locally, and the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas than rural areas. Distance from the coastline was negatively correlated with predicted regional risks.