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

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Chun-Hu Li, Jun-Jie Mao, You-Jia Wu, Bin Zhang, Xun Zhuang, Gang Qin, Hong-Mei Liu
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2023
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0011286; https://doaj.org/article/4c4991ca912d4b3f8200a2238a48afb8

Background Understanding 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. Methods We 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. Results The spatiotemporal distribution of HFMD cases was highly heterogeneous, indicated by the Lorenz curves and the corresponding Gini indices. The peak time (R2 = 0.65, P = 0.009), annual amplitude (R2 = 0.94, P<0.001), and semi-annual periodicity contribution (R2 = 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 (R2 = 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. Conclusions Our study highlights the importance of refined spatial and temporal data, ...