Spatiotemporal drivers of foodborne disease seasonality pattern in a coastal city of China.

backgroundThe seasonal patterns of foodborne diseases (FBD) result from the complex interplay of environmental, biological, and social factors, yet their spatiotemporal heterogeneity remains inadequately explored, posing challenges for developing targeted prevention strategies.

methodsThis study, focusing on a representative coastal city in China, employed a multi-stage analytical approach integrating multi-source heterogeneous data (case monitoring reports, meteorological monitoring, geographic information and socioeconomic statistics) to investigate FBD seasonal patterns and their drivers. Initially, adaptive STL decomposition was applied to extract trend, seasonal, and residual components from the incidence time series. Subsequently, spatiotemporal K-means clustering identified distinct seasonal evolution patterns. Finally, logistic regression models quantified the modulating effects of environmental, geographical, and socioeconomic variables on seasonal pattern differentiation.

resultsTwo distinct seasonal patterns were identified: a relatively balanced seasonality type and a summer-dominant type. The contributions of factors to pattern differentiation varied: (1) Population size emerged as the primary driver of the summer-dominant pattern (OR = 18.32, p = 0.032), exceeding the effect strength of traditional meteorological variables. (2) Green space coverage demonstrated a significant association with reduced summer FBD risk. (3) Accommodation and catering industry GDP and east-west wind speed exhibited marginally significant associations, highlighting the potential synergistic risks posed by economic factors and extreme weather under climate change. ​

conclusionThis study uncovers the asymmetric drivers of seasonal FBD patterns in a coastal city, providing crucial scientific evidence for refining seasonal prevention and control strategies against FBD in coastal city settings amidst global climate change.