Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013–2016

BACKGROUND Job burnout is an occupational illness with high prevalence among nurses in China. The job burnout status among hemodialysis nurses should be given more attention because of they handle sophisticated machinery, and there is a high risk of infection in hemodialysis nursing. OBJECTIVE The level and influencing factors of job burnout among hemodialysis nurses in mainland China were investigated. METHODS This was a cross-sectional study conducted in all 31 provinces of mainland China in 2019. Data from nurses responsible for direct care in 2738 hemodialysis units were analyzed. An effective response rate of 99.00% (10570/10677) was achieved. Hemodialysis nurse burnout was measured by the Maslach Burnout Inventory. Working atmosphere and interpersonal relationships with colleagues were each measured by a single question. Multiple linear regression analysis was used to explore the factors related to nurse burnout. Structural equation modeling was used to explore the effect of the working environment, individual factors, and specialist nurse training on the HD nurse burnout and the intention to leave. RESULTS The total burnout score was 38.69 (SD17.47), indicating that the level of job burnout among hemodialysis nurses in mainland China was relatively low. Hemodialysis nurses experienced low-level burnout on the emotional exhaustion and depersonalization subscale and high-level burnout on the personal accomplishment subscale. Statistically significant differences in burnout levels were associated with working atmosphere, interpersonal relationships with colleagues, specialist nurse training, number of children, night shift, and marital status. CONCLUSIONS The burnout level of HD nurses in mainland China was relatively low. Working atmosphere, interpersonal relationships with colleagues, and training of specialist nurses are the most important influencing factors regarding job burnout in hemodialysis nurses. Therefore, it is suggested that improving the working atmosphere and interpersonal relationship processing ability and providing more training opportunities for nurses can alleviate job burnout in nurses.

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Spatial distribution of soil cadmium and its influencing factors in peri-urban farmland: a case study in the Jingyang District, Sichuan, China

Environmental Monitoring and Assessment ◽

10.1007/s10661-016-5744-y ◽

2016 ◽

Vol 189 (1) ◽

Cited By ~ 10

Author(s):

Bing Li ◽

Rui Xiao ◽

Changquan Wang ◽

Linhai Cao ◽

Yi Zhang ◽

...

Keyword(s):

Spatial Distribution ◽

Influencing Factors ◽

Soil Cadmium

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Spatial Distribution and Influencing Factors of Crop Loss Caused by Wildlife

Journal of Forest Economics ◽

10.1561/112.00000527 ◽

2021 ◽

Vol 36 (1-2) ◽

pp. 103-123

Author(s):

Wenhui Chen ◽

Sha Li ◽

Xi Chenqi

Keyword(s):

Spatial Distribution ◽

Influencing Factors ◽

Crop Loss

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Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010067 ◽

2022 ◽

Vol 11 (1) ◽

pp. 67

Author(s):

Meijie Chen ◽

Yumin Chen ◽

John P. Wilson ◽

Huangyuan Tan ◽

Tianyou Chu

Keyword(s):

Risk Factors ◽

Health Risk ◽

Spatial Heterogeneity ◽

Influencing Factors ◽

Mainland China ◽

Spatial Filtering ◽

Varying Coefficient Model ◽

Health Risk Factors ◽

Eigenvector Spatial Filtering ◽

Spatially Varying

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

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