ArticlePLoS neglected tropical diseases2023
Combined impacts of environmental and socioeconomic covariates on HFMD risk in China: A spatiotemporal heterogeneous perspective.
Article in PLoS neglected tropical diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Estimating the nationwide incidence of coxsackievirus A6-associated hand, foot and mouth disease in China, 2008-2022.Infectious diseases of poverty · 2026Article
- Spatial heterogeneity in the temperature-hand, foot, and mouth disease association among children: A multicounty time-series study in western China.PLoS neglected tropical diseases · 2026Article
- Study on the spatial-temporal evolution and influencing factors of AIDS incidence in China from 2006 to 2022.BMC infectious diseases · 2025Article
- Spatiotemporal Distribution of Hand, Foot, and Mouth Disease and the Influence of Air Pollutants and Socioeconomic Factors on Incidence in Fujian, China.Tropical medicine and infectious disease · 2025Article
- Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.JMIR public health and surveillance · 2024Article
- Assessing the modification impact of vaccination on the relationship of the Discomfort Index with hand, foot, and mouth disease in Guizhou: A multicounty study.PLoS neglected tropical diseases · 2024Article
- Impact of the COVID-19 outbreak and interventions on hand, foot and mouth disease in Zhengzhou, China, 2014-2022: a retrospective study.BMC infectious diseases · 2024Article
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7 authors.
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Abstract
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 (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.
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.
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