Combined impacts of environmental and socioeconomic covariates on HFMD risk in China: A spatiotemporal heterogeneous perspective

Author summaryHand-foot-and-mouth disease (HFMD) is one of the most common pediatric infectious diseases worldwide and it has affected up to two million children annually in China since 2008. This study used Bayesian spatiotemporal modeling to analyze provincial-level HFMD cases, environmental and socioeconomic data during 2009-2018. We found that both environmental (temperature, relative humidity and normalized difference vegetation index) and socioeconomic covariates (population density, birth rate, real GDP per capita and school vacation) are associated with HFMD occurrence under spatiotemporal scales. This model may help to (1) identify outbreaks of HFMD, including their timing, location and severity; (2) understand the transmission dynamics by mapping the spread of HFMD over time and space; (3) indicate areas of higher risk or the emergence of new outbreaks by detecting clusters of HFMD cases in specific geographic locations. Overall, this study provides valuable information for public health decision-makers and researchers, allowing them to better understand the spread of the disease and target their efforts more efficiently to control its transmission and prevent outbreaks. BackgroundUnderstanding geospatial impacts of multi-sourced influencing factors on the epidemic of hand-foot-and-mouth disease (HFMD) is of great significance for formulating disease control policies tailored to regional-specific needs, yet the knowledge is very limited. We aim to identify and further quantify the spatiotemporal heterogeneous effects of environmental and socioeconomic factors on HFMD dynamics. MethodsWe collected monthly province-level HFMD incidence and related environmental and socioeconomic data in China during 2009-2018. Hierarchical Bayesian models were constructed to investigate the spatiotemporal relationships between regional HFMD and various covariates: linear and nonlinear effects for environmental covariates, and linear effects for socioeconomic covariates. ResultsThe spatiotemporal distribution of HFMD cases was highly heterogeneous, indicated by the Lorenz curves and the corresponding Gini indices. The peak time (R-2 = 0.65, P = 0.009), annual amplitude (R-2 = 0.94, P<0.001), and semi-annual periodicity contribution (R-2 = 0.88, P<0.001) displayed marked latitudinal gradients in Central China region. The most likely cluster areas for HFMD were located in south China (Guangdong, Guangxi, Hunan, Hainan) from April 2013 to October 2017. The Bayesian models achieved the best predictive performance (R-2 = 0.87, P<0.001). We found significant nonlinear associations between monthly average temperature, relative humidity, normalized difference vegetation index and HFMD transmission. Besides, population density (RR = 1.261; 95%CI, 1.169-1.353), birth rate (RR = 1.058; 95%CI, 1.025-1.090), real GDP per capita (RR = 1.163; 95%CI, 1.033-1.310) and school vacation (RR = 0.507; 95%CI, 0.459-0.559) were identified to have positive or negative effects on HFMD respectively. Our model could successfully predict months with HFMD outbreaks versus non-outbreaks in provinces of China from Jan 2009 to Dec 2018. ConclusionsOur study highlights the importance of refined spatial and temporal data, as well as environmental and socioeconomic information, on HFMD transmission dynamics. The spatiotemporal analysis framework may provide insights into adjusting regional interventions to local conditions and temporal variations in broader natural and social sciences.