Factors influencing the spatial distribution of cystic echinococcosis in Tibet, China

Ma, Tian; Jiang, Dong; Quzhen, Gongsang; Xue, Cong; Han, Seung-Beom; Wu, Wei; Zheng, Canjun; Ding, Fangyu

doi:10.1016/j.scitotenv.2020.142229

Cited by 14 publications

(18 citation statements)

References 21 publications

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“…In France, the risk of AE is higher in areas with very cold winters and high annual Pre [ 7 ]. CE prevalence was found to be negatively correlated with land surface temperature in China [ 8 , 9 ], and nonlinearly related to annual mean Pre in the Tibet Autonomous Region (henceforth Tibet) [ 10 ]. Altitude, land use type, vegetation type, and landscape pattern are the main geographical landscape factors related to echinococcosis prevalence.…”

Section: Introductionmentioning

confidence: 99%

The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction

Yin

et al. 2022

Parasites Vectors

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Background Human echinococcosis is affected by natural environmental factors, and its prevalence shows a distinct geographical distribution. Western China has the highest endemicity of human echinococcosis worldwide, but the spatial pattern and environmental determinants of echinococcosis are still unclear. Methods Hot/cold spot analysis was used to investigate the spatial distribution of human echinococcosis prevalence. Geodetector was used to identify key natural factors, and a structured additive regression model was used to analyse the relationship between natural factors and human echinococcosis prevalence and spatially predict echinococcosis epidemics. Results Hot spots for human echinococcosis prevalence include western and southeastern parts of Tibet Autonomous Region (henceforth Tibet) and the border areas between Tibet and the provinces of Qinghai and Sichuan. Spatial effects are crucial when modelling epidemics, and relative humidity, altitude and grassland area ratio were found to have the most evident effects on echinococcosis epidemics. The relationship between these three factors and echinococcosis prevalence was non-linear, and echinococcosis risk was higher in areas with high relative humidity, high altitude, and a high ratio of grassland to other land use types. The prevalence that was predicted from the investigated environmental factors was generally higher than the actual prevalence, and more epidemic hot spots were predicted for the Qinghai-Tibet Plateau, Inner Mongolia Autonomous Region, and the provinces of Yunnan and Sichuan than the rest of western China. These results indicate that prevention and control measures may effectively reduce echinococcosis prevalence. Conclusions We suggest that the prevention and control of human echinococcosis should be prioritized in the hot spots identified here, through the rational allocation of limited medical resources to where they are most needed. Furthermore, the spatial epidemiological modelling methods used in this study can be employed in future studies on echinococcosis and similar diseases. Graphical abstract

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Section: Introductionmentioning

confidence: 99%

The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction

Yin

et al. 2022

Parasites Vectors

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“…Despite this situation, research on echinococcosis in China remains greatly inadequate compared to that in other countries [15]. Meanwhile, previous literatures have showed that the spatial distribution of echinococcosis was linked to geographical, meteorological, biological and socio-economic factors [16][17][18][19][20][21][22][23][24], while the main drive factors of echinococcosis has not been comprehensively and systematically analysed. Thus, in this study, we select the Tibet Autonomous as the study area (Fig 1), attempting to map the spatial distribution patterns of CE and AE and to explore the contribution of the potential environmental risk factors of the diseases, which is critical for decision-making that facilitates the design, implementation, and monitoring of spatially targeted interventions to reduce the burden of human echinococcosis in Tibet.…”

Section: Introductionmentioning

confidence: 99%

Geographical Detector-based influence factors analysis for Echinococcosis prevalence in Tibet, China

Jiang

Hao

et al. 2021

PLoS Negl Trop Dis

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Echinococcosis, caused by genus Echinococcus, is the most pathogenic zoonotic parasitic disease in the world. In Tibet of the People’s Republic of China, echinococcosis refers principally to two types of severe zoonosis, cystic echinococcosis (CE) and alveolar echinococcosis (AE), which place a serious burden on public health and economy in the local community. However, research on the spatial epidemiology of echinococcosis remains inadequate in Tibet, China. Based on the recorded human echinococcosis data, maps of the spatial distribution of human CE and AE prevalence in Tibet were produced at city level and county level respectively, which show that the prevalence of echinococcosis in northern and western Tibet was much higher than that in other regions. We employ a geographical detector to explore the influencing factors for causing CE and AE while sorting information on the maps of disease prevalence and environment factors (e.g. terrain, population, and yak population). The results of our analysis showed that biological factors have the most impact on the prevalence of echinococcosis, of which the yak population contributes the most for CE, while the dog population contributes the most for AE. In addition, the interaction between various factors, as we found out, might further explain the disease prevalence, which indicated that the echinococcosis prevalence is not simply affected by one single factor, but by multiple factors that are correlated with each other complicatedly. Our results will provide an important reference for the evaluation of the echinococcosis risk, control projects, and prevention programs in Tibet.

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“…A further explanation of the high prevalence observed in the Qinghai-Tibet Plateau is that most of its residents belong to an ethnic minority which adhere to traditional ways of production and life and religious practices. In Tibetan areas particularly, where cattle and sheep are mainly slaughtered in families, stray dogs are present in high number and are fed with Echinococcus- infected remains [ 19 , 20 ]. Dogs are definitive hosts for E .…”

Section: Discussionmentioning

confidence: 99%

“…and sheep are mainly slaughtered in families, stray dogs are present in high number and are fed with Echinococcus-infected remains [19,20]. Dogs are definitive hosts for E. granulosus senso lato and E. multilocularis in China [8,21].…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Prevalence and spatial distribution characteristics of human echinococcosis in China

et al. 2021

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Background Echinococcosis is a zoonotic parasitic disease caused by larval stages of cestodes belonging to the genus Echinococcus. The infection affects people’s health and safety as well as agropastoral sector. In China, human echinococcosis is a major public health burden, especially in western China. Echinococcosis affects people health as well as agricultural and pastoral economy. Therefore, it is important to understand the prevalence status and spatial distribution of human echinococcosis in order to advance our knowledge of basic information for prevention and control measures reinforcement. Methods Report data on echinococcosis were collected in 370 counties in China in 2018 and were used to assess prevalence and spatial distribution. SPSS 21.0 was used to obtain the prevalence rate for CE and AE. For statistical analyses and mapping, all data were processed using SPSS 21.0 and ArcGIS 10.4, respectively. Chi-square test and Exact probability method were used to assess spatial autocorrelation and spatial clustering. Results A total of 47,278 cases of echinococcosis were recorded in 2018 in 370 endemic counties in China. The prevalence rate of human echinococcosis was 10.57 per 10,000. Analysis of the disease prevalence showed obvious spatial positive autocorrelation in globle spatial autocorrelation with two aggregation modes in local spatial autocorrelation, namely high-high and low-high aggregation areas. The high-high gathering areas were mainly concentrated in northern Tibet, western Qinghai, and Ganzi in the Tibetan Autonomous Region and in Sichuan. The low-high clusters were concentrated in Gamba, Kangma and Yadong counties of Tibet. In addition, spatial scanning analysis revealed two spatial clusters. One type of spatial clusters included 71 counties in Tibet Autonomous Region, 22 counties in Qinghai, 11 counties in Sichuan, three counties in Xinjiang Uygur Autonomous Region, two counties in Yunnan, and one county in Gansu. In the second category, six types of spatial clusters were observed in the counties of Xinjiang Uygur Autonomous Region, and the Qinghai, Gansu, and Sichuan Provinces. Conclusion This study showed a serious prevalence of human echinococcosis with obvious spatial aggregation of the disease prevalence in China. The Qinghai-Tibet Plateau is the "hot spot" area of human echinococcosis in China. Findings from this study indicate that there is an urgent need of joint strategies to strengthen efforts for the prevention and control of echinococcosis in China, especially in the Qinghai-Tibet Plateau.

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Factors influencing the spatial distribution of cystic echinococcosis in Tibet, China

Cited by 14 publications

References 21 publications

The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction

The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction

Geographical Detector-based influence factors analysis for Echinococcosis prevalence in Tibet, China

Prevalence and spatial distribution characteristics of human echinococcosis in China

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