ArticleBMC infectious diseases2016
Different effects of meteorological factors on hand, foot and mouth disease in various climates: a spatial panel data model analysis.
Article in BMC infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 26 citations in OpenAlex.
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- Spatiotemporal distribution and sociodemographic and socioeconomic factors associated with primary and secondary syphilis in Guangdong, China, 2005-2017.PLoS neglected tropical diseases · 2021Article
- Early warning of hand, foot, and mouth disease transmission: A modeling study in mainland, China.PLoS neglected tropical diseases · 2021Article
- Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model.Infectious Disease Modelling · 2021Article
- The epidemic characteristics and spatial autocorrelation analysis of hand, foot and mouth disease from 2010 to 2015 in Shantou, Guangdong, China.BMC public health · 2019Article
- Health impact of climate change in cities of middle-income countries: the case of China.British medical bulletin · 2019Review
- Space-time heterogeneity of hand, foot and mouth disease in children and its potential driving factors in Henan, China.BMC infectious diseases · 2018Article
- The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison.Journal of biomedical informatics · 2018Article
- Spatio-temporal analysis of the relationship between meteorological factors and hand-foot-mouth disease in Beijing, China.BMC infectious diseases · 2018Article
- Meteorological factors and the incidence of mumps in Fujian Province, China, 2005-2013: Non-linear effects.The Science of the total environment · 2018Article
- Geographical variations and influential factors in prevalence of cardiometabolic diseases in South Korea.PloS one · 2018Article
- Spatiotemporal risk mapping of hand, foot and mouth disease and its association with meteorological variables in children under 5 years.Epidemiology and infection · 2017Article
- Phylogeography of Coxsackievirus A16 Reveals Global Transmission Pathways and Recent Emergence and Spread of a Recombinant Genogroup.Journal of virology · 2017Article
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor outbreaks of hand, foot and mouth disease (HFMD) have been reported in China since 2008, posing a great threat to the health of children. Although many studies have examined the effect of meteorological variables on the incidence of HFMD, the results have been inconsistent. This study aimed to quantify the relationship between meteorological factors and HFMD occurrence in different climates of mainland China using spatial panel data models.
methodsAll statistical analyses were carried out according to different climate types. We firstly conducted a descriptive analysis to summarize the epidemic characteristics of HFMD from May 2008 to November 2012 and then detected the spatial autocorrelation of HFMD using a global autocorrelation statistic (Moran's I) in each month. Finally, the association between HFMD incidence and meteorological factors was explored by spatial panel data models.
resultsThe 353 regions were divided into 4 groups according to climate (G1: subtropical monsoon climate; G2: temperate monsoon climate; G3: temperate continental climate; G4: plateau mountain climate). The Moran's I values were significant with high correlations in most months of group G1 and G2 and some months of group G3 and G4. This suggested the existence of a high spatial autocorrelation with HFMD. Spatial panel data models were more appropriate to describe the data than fixed effect models. The results showed that HFMD incidences were significantly associated with average atmospheric pressure (AAP), average temperature (AT), average vapor pressure (AVP), average relative humidity (ARH), monthly precipitation (MP), average wind speed (AWS), monthly total sunshine hours (MSH), mean temperature difference (MTD), rain day (RD) and average temperature distance (ATD), but the effect of meteorological factors might differ in various climate types.
conclusionsSpatial panel data models are useful and effective when longitudinal data are available and spatial autocorrelation exists. Our findings showed that meteorological factors were related to the occurrence of HFMD, which were also affected by climate type.
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