Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China

Zeng, Li; Fu, Jingying; Jiang, Dong; Lin, Gang; Dong, Donglin; Yan, Xiaoxi

doi:10.3390/ijerph14111428

Cited by 3 publications

(1 citation statement)

References 35 publications

(49 reference statements)

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“…The geographically weighted regression (GWR) model is commonly used to detect the spatial relationships between environmental and socioeconomic factors (or other ones) [ 24 , 30 ]. The GWR model is developed to explore the spatial heterogeneity, producing a set of local estimates of the parameters which demonstrate the spatial inhomogeneity of the relationship.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Yao

Huang

Jing

et al. 2018

IJERPH

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Analyzing the association between fine particulate matter (PM2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m3 in mainland China, and the highest population exposure to PM2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM2.5 was positively related to GDP. An apparent upward trend of population exposure to PM2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM2.5 increases by 1.05 person/km2 at the 80th percentile, and 1.88 person/km2 at the 95th percentile, respectively.

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Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Yao

Huang

Jing

et al. 2018

IJERPH

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Spatial Relationships of Water Resources with Energy Consumption at Coal Mining Operations in China

Lin

Jiang

et al. 2020

Mine Water Environ

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Exploring spatiotemporal trends and impacts of health resources and services on under-5 mortality in West African countries, 2010–2019: a spatial data analysis

Zeng,

Niu

2023

Front. Public Health

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BackgroundWest African countries experienced the highest under-5 mortality rate (U5MR), the lowest life expectancy, and the poorest economic development. This study aimed to explore the spatiotemporal trend of U5MR and spatial spillover effects of health resources and services to provide a basis for establishing health policies and international cooperative mechanisms in West Africa.MethodsWe retrieved data from the World Health Organization’s Global Health Observatory, the United Nations Human Development Report, and the Global Burden of Disease Study 2019. Joinpoint regression analysis and Moran’s I method were used to examine the temporal trend and spatial dependence of U5MR, respectively. Spatial regression analysis was used to examine the spatial spillover effects.ResultsThe long-term downward trends in U5MR were divided into multiple segments by two or three change points in 2013, 2014, or 2015, and the annual percentage change after 2015 was higher than before 2015. Global Moran’s I was positive, significantly indicating positive spatial autocorrelation, which increased from 0.2850 (p = 0.0210) to 0.3597 (p = 0.0080). Based on spatial regression analysis, human development index (HDI), physicians density, nurses and midwives density, health center density, percentage of infants lacking immunization for diphtheria and measles, and coverage rate of at least one antenatal visit had negative spatial spillover effects on U5MR. HDI had the strongest negative correlation (β = −0.0187 to −0.1054, p < 0.0001). Current health expenditure (CHE) per capita had positive spatial spillover effects on U5MR.ConclusionThis study revealed the spatiotemporal trend of U5MR in West African countries and spatial spillover effects of health resources and services. Promoting economic development, increasing health human resources, health expenditure, vaccination rate, antenatal care coverage, and the proportion of health professionals attending births not only reduced the local U5MR but also exerted spatial spillover effects on adjacent countries. The West African Health Organization may consider regional spillover mechanisms to develop regional health policy and intervention cooperation mechanisms, which will contribute to achieving the sustainable development goal on U5MR, Africa Agenda 2063, and universal health coverage.

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Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China

Cited by 3 publications

References 35 publications

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Spatial Relationships of Water Resources with Energy Consumption at Coal Mining Operations in China

Exploring spatiotemporal trends and impacts of health resources and services on under-5 mortality in West African countries, 2010–2019: a spatial data analysis

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