Nonlinear effects of meteorological factors on foodborne disease outbreaks in yantai from 2014 to 2024.

Meteorological factors are of crucial importance in foodborne disease (FBD) outbreaks, and more quantitative research is required to elucidate the underlying mechanisms. This study aimed to evaluate the impact of meteorological factors on FBD outbreaks in Yantai from 2014 to 2024, using data reported by the Yantai Foodborne Disease Surveillance Network. The distributed lag non-linear model (DLNM) was employed to model the exposure-response relationships between temperature, precipitation, wind speed, and FBD outbreaks. Prior to model fitting, multicollinearity among variables was addressed via the variance inflation factor (VIF). Results demonstrated non-linear relationships and complex correlations between these variables. Higher temperatures, greater precipitation, and slower wind speeds were associated with an increased risk of FBD outbreaks. In the lagged time dimension, temperature had an immediate effect that decayed after 2-3 weeks; the maximum lagged risk for precipitation occurred within 1-3 weeks; and wind speed showed subdued fluctuations. The lag patterns of high-value and low-value effects of different elements exhibited symmetry. These findings underline the necessity of integrating meteorological monitoring into FBD surveillance systems in accordance with their respective impact patterns.