Socio-ecological factors and hand, foot and mouth disease in dry climate regions: a Bayesian spatial approach in Gansu, China

Gou, Faxiang; Liu, Xinfeng; Ren, Xiaowei; Liu, Dongpeng; Liu, Haixia; Wei, Kongfu; Yang, Xiaoting; Cheng, Yao; Zheng, Yan; Jiang, Xiaojuan; Li, Juansheng; Meng, Lu

doi:10.1007/s00484-016-1197-9

Cited by 21 publications

(17 citation statements)

References 40 publications

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“…Hence, mapping the variation of spatial correlation residuals may explain other factors which may associated with geographical difference of JE incidence. The Bayesian CAR model has been widely used in examining the impact of social and climate variables on public health, including infectious disease [ 23 ], non-communicable disease [ 24 , 25 ] and injury [ 26 ]. We used the Bayesian CAR model with Poisson distribution to analyze the association of socio-environmental variables with JE incidence at the county level.…”

Section: Methodsmentioning

confidence: 99%

How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China—A Bayesian Spatial Analysis

Zhang

Xin

et al. 2018

IJERPH

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Evidence indicated that socio-environmental factors were associated with occurrence of Japanese encephalitis (JE). This study explored the association of climate and socioeconomic factors with JE (2006–2014) in Shaanxi, China. JE data at the county level in Shaanxi were supplied by Shaanxi Center for Disease Control and Prevention. Population and socioeconomic data were obtained from the China Population Census in 2010 and statistical yearbooks. Meteorological data were acquired from the China Meteorological Administration. A Bayesian conditional autoregressive model was used to examine the association of meteorological and socioeconomic factors with JE. A total of 1197 JE cases were included in this study. Urbanization rate was inversely associated with JE incidence during the whole study period. Meteorological variables were significantly associated with JE incidence between 2012 and 2014. The excessive precipitation at lag of 1–2 months in the north of Shaanxi in June 2013 had an impact on the increase of local JE incidence. The spatial residual variations indicated that the whole study area had more stable risk (0.80–1.19 across all the counties) between 2012 and 2014 than earlier years. Public health interventions need to be implemented to reduce JE incidence, especially in rural areas and after extreme weather.

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Section: Methodsmentioning

confidence: 99%

How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China—A Bayesian Spatial Analysis

Zhang

Xin

et al. 2018

IJERPH

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“…These all increase the difficulty of characterizing the particular spatio-temporal transmission dynamics of a host-pathogen system. Previous studies mostly focus on the static spatio-temporal pattern of HFMD [15,16,[18][19][20][21], while its transmission mechanism over time and across space have not been discussed and is still unclear. Characterization of the particular spatio-temporal dynamics of a host-pathogen system can be used to illuminate the mechanisms most important in the dynamic system [22].…”

Section: Introductionmentioning

confidence: 99%

Transmission center and driving factors of hand, foot, and mouth disease in China: A combined analysis

Xu²,

Pān

et al. 2020

PLoS Negl Trop Dis

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Hand, foot, and mouth disease (HFMD) has become a major public health issue in China. The disease incidence varies substantially over time and across space. To understand the heterogeneity of HFMD transmission, we compare the spatiotemporal dynamics of HFMD in Qinghai and Shanghai by conducting combined analysis of epidemiological, wavelet time series, and mathematical methods to county-level data from 2009 to 2016. We observe hierarchical epidemic waves in Qinghai, emanating from Huangzhong and in Shanghai from Fengxian. Besides population, we also find that the traveling waves are significantly associated with socioeconomic and geographical factors. The population mobility also varies between the two regions: long-distance movement in Qinghai and between-neighbor commuting in Shanghai. Our findings provide important evidence for characterizing the heterogeneity of HFMD transmission and for the design and implementation of interventions, such as deploying optimal vaccine and changing local driving factors in the transmission center, to prevent or limit disease spread in these areas.

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“…Several studies have used GIS techniques to understand the spatial variations and trends in disease risk [ 1 , 6 – 9 ] or to explore the connections between spatial patterns in diseases and the corresponding risk factors on various geographic scales [ 2 – 5 , 10 – 12 ]. Those studies focused mainly on uncovering the spatial pattern of disease prevalence or incidence using spatial statistics and map visualization [ 1 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…All of them suggested that spatial patterns of diseases could be utilized as supporting evidence for further research on disease outbreak mechanisms. Further, many of those studies endeavored to explain the causes and risk factors of diseases with information derived from their spatial patterns [ 2 – 5 , 10 – 12 ]. Various analytic solutions and statistical methods, moreover, were utilized in exploring potential explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Geographical variations and influential factors in prevalence of cardiometabolic diseases in South Korea

Yoon

Noh

et al. 2018

PLoS ONE

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Geographical variations and influential factors of disease prevalence are crucial information enabling optimal allocation of limited medical resources and prioritization of appropriate treatments for each regional unit. The purpose of this study was to explore the geographical variations and influential factors of cardiometabolic disease prevalence with respect to 230 administrative districts in South Korea. Global Moran’s I was calculated to determine whether the standardized prevalences of cardiometabolic diseases (hypertension, stroke, and diabetes mellitus) were spatially clustered. The CART algorithm was then applied to generate decision tree models that could extract the diseases’ regional influential factors from among 101 demographic, economic, and public health data variables. Finally, the accuracies of the resulting model–hypertension (67.4%), stroke (62.2%), and diabetes mellitus (56.5%)–were assessed by ten-fold cross-validation. Marriage rate was the main determinant of geographic variation in hypertension and stroke prevalence, which has the possibility that married life could have positive effects in lowering disease risks. Additionally, stress-related variables were extracted as factors positively associated with hypertension and stroke. In the opposite way, the wealth status of a region was found to have an influence on the prevalences of stroke and diabetes mellitus. This study suggested a framework for provision of novel insights into the regional characteristics of diseases and the corresponding influential factors. The results of the study are anticipated to provide valuable information for public health practitioners’ cost-effective disease management and to facilitate primary intervention and mitigation efforts in response to regional disease outbreaks.

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Socio-ecological factors and hand, foot and mouth disease in dry climate regions: a Bayesian spatial approach in Gansu, China

Cited by 21 publications

References 40 publications

How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China—A Bayesian Spatial Analysis

How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China—A Bayesian Spatial Analysis

Transmission center and driving factors of hand, foot, and mouth disease in China: A combined analysis

Geographical variations and influential factors in prevalence of cardiometabolic diseases in South Korea

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