The impacts of meteorological factors on the incidence of hand, foot, and mouth disease in China: An interactive perspective

Hand, foot, and mouth disease (HFMD) is a contagious illness and a matter of public health concern globally. It has a widespread impact in China, where it poses a significant risk to the health of young children. Previous research has discovered that the occurrence and spread of HFMD are closely linked to meteorological factors, but the specifics of these intricate nonlinear relationships and how they interact with the incidence of HFMD are still uncertain. In the present study, we filled this gap in knowledge by first obtaining data on hand, foot, and mouth disease from the National Health Commission of China alongside real-time surveillance data from meteorological stations. We then used Spearman's correlation analysis to identify the variables significantly related to HFMD, investigating the existence of co-curvilinearity among the contributing factors by using the independent effects model in the generalized additive model to analyze both the contribution of individual factors and their combined effects on the incidence of HFMD. The interactions between meteorological factors were then modeled to investigate their influence on HFMD. The results showed that, in descending order of effect size, air temperature (AT), sunshine duration (SD), wind speed (WS), and barometric pressure (BP) had a significant nonlinear impact on the incidence of HFMD, which displayed a fluctuating increase in line with rising temperatures. The interactions of (1) high temperature and low air pressure and (2) low wind speed and poor sunshine had the largest influence on HFMD. In descending order, the strengths of the interaction effects between factors were as follows: AT similar to SD > AT similar to WS > AT similar to BP > WS similar to BP > WS similar to SD > BP similar to SD. Our findings will inform policy initiatives contributing to the Healthy China strategy and assist in the prevention and control of HFMD by enabling the prediction and early warning of disease outbreaks.