Dataset Information

Spatiotemporal Variations in Gastric Cancer Mortality and Their Relations to Influencing Factors in S County, China.

ABSTRACT: Increasingly stricter and wider official efforts have been made by multilevel Chinese governments for seeking the improvements of the environment and public health status. However, the contributions of these efforts to environmental changes and spatiotemporal variations in some environmental diseases have been seldom explored and evaluated. Gastric cancer mortality (GCM) data in two periods (I: 2004?2006 and II: 2012?2015) was collected for the analysis of its spatiotemporal variations on the grid scale across S County in Central China. Some environmental and socioeconomic factors, including river, farmlands, topographic condition, population density, and gross domestic products (GDP) were obtained for the exploration of their changes and their relationships with GCM's spatiotemporal variations through a powerful tool (GeoDetector, GD). During 2004?2015, S County achieved environmental improvement and socioeconomic development, as well as a clear decline of the age-standardized mortality rate of gastric cancer from 35.66/10? to 23.44/10?. Moreover, the GCM spatial patterns changed on the grid scale, which was spatially associated with the selected influencing factors. Due to the improvement of rivers' water quality, the distance from rivers posed relatively larger but reversed impacts on the gridded GCM. In addition, higher population density and higher economic level (GDP) acted as important protective factors, whereas the percentage of farmlands tended to have adverse effects on the gridded GCM in period II. It can be concluded that the decline of GCM in S County was spatiotemporally associated with increasingly strengthened environmental managements and socioeconomic developments over the past decade. Additionally, we suggest that more attentions should be paid to the potential pollution caused by excessive pesticides and fertilizers on the farmlands in S County. This study provided a useful clue for local authorities adopting more targeted measures to improve environment and public health in the regions similar to S County.

SUBMITTER: Cui C

PROVIDER: S-EPMC6427783 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatiotemporal Variations in Gastric Cancer Mortality and Their Relations to Influencing Factors in S County, China.

Cui Cheng C Wang Baohua B Ren Hongyan H Wang Zhen Z

International journal of environmental research and public health 20190304 5

Increasingly stricter and wider official efforts have been made by multilevel Chinese governments for seeking the improvements of the environment and public health status. However, the contributions of these efforts to environmental changes and spatiotemporal variations in some environmental diseases have been seldom explored and evaluated. Gastric cancer mortality (GCM) data in two periods (I: 2004⁻2006 and II: 2012⁻2015) was collected for the analysis of its spatiotemporal variations on the gr ...[more]

PMID: 30836673

Similar Datasets

Project description:BackgroundDengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics.MethodsA generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed.ResultsDF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics.ConclusionsThe spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.

Project description:Spatiotemporal behaviors of particulate matter (PM2.5 and PM10) and trace gases (SO2, NO2, CO, and O3) in Hefei during the period from December 2013 to November 2015 are investigated. The mean annual PM2.5 (PM10) concentrations are 89.1 ± 59.4 µg/m3 (118.9 ± 66.8 µg/m3) and 61.6 ± 32.2 µg/m3 (91.3 ± 40.9 µg/m3) during 2014 and 2015, respectively, remarkably exceeding the Chinese Ambient Air Quality Standards (CAAQS) grade II. All trace gases basically meet the requirements though NO2 and O3 have a certain upward trend. Old districts have the highest pollution levels, followed by urban periphery sites and new districts. Severe haze pollution occurs in Hefei, with frequent exceedances in particulate matter with 178 (91) days in 2014 (2015). The abnormal PM2.5 concentrations in June 2014 attributed to agricultural biomass burning from moderate resolution imaging spectroradiometry (MODIS) wildfire maps and aerosol optical depth (AOD) analysis. PM2.5 is recognized as the major pollutant, and a longer interspecies relationship is found between PM2.5 and other criteria pollutants for episode days as compared to non-episode days. The air pollution in Hefei tends to be influenced by local primary emissions, secondary formation, and regional transport from adjacent cities and remote regions. Most areas of Anhui, southern Jiangsu, northern Zhejiang, and western Shandong are identified as the common high-potential source regions of PM2.5. Approximately 9.44 and 8.53 thousand premature mortalities are attributed to PM2.5 exposure in 2014 and 2015. The mortality benefits will be 32% (24%), 47% (41%), 70% (67%), and 85% (83%) of the total premature mortalities in 2014 (2015) when PM2.5 concentrations meet the CAAQS grade II, the World Health Organization (WHO) IT-2, IT-3, and Air Quality Guideline, respectively. Hence, joint pollution prevention and control measures need to be strengthened due to pollutant regional diffusion, and much higher health benefits could be achieved as the Hefei government adopts more stringent WHO guidelines for PM2.5.

Project description:Carbon emissions from civil buildings refer to the carbon emissions generated during the operation of civil buildings. With the continuous development of the urban economy and the improvement of people’s living standards, this part of carbon emission puts tremendous pressure on China to achieve the goal of carbon peaking and carbon neutral. In the context of rapid urbanization, studying the spatiotemporal characteristics and influencing factors of the carbon emissions from civil buildings have strong practical significance for China to achieve the "dual carbon" goal. Based on the emission data from 104 prefecture-level cities in China, we examine the spatiotemporal characteristics of the civil building carbon emissions from the perspectives of temporal evolution trend, spatial distribution and its dynamic evolution, spatial difference and its decomposition, and spatial autocorrelation characteristics. Finally, we reveal the influencing factors of the carbon emissions from civil buildings using static panel data models and spatial dynamic panel data models. The results of the study show that: (1) During the sample period, the carbon emissions from civil buildings have increased year by year. The civil building carbon emissions have become an important source of China’s overall carbon emissions. Realizing energy saving and emission reduction in the operational stage of civil buildings is crucial to realizing China’s "dual carbon" goal. (2) According to the estimated results, there is a significant inverted U-shaped non-linear relationship between urbanization and civil building carbon emissions. Most Chinese cities are located in the upward part of the inverted U-shaped curve at present. Thus, the traditional economic growth model characterized by high energy consumption and high emission during rapid urbanization should be abandoned to reduce the carbon emissions from civil buildings. (3) Technological progress and fixed asset investment can effectively reduce the carbon emissions from civil buildings. At the same time, the level of marketization and social consumption expenditure positively affect the carbon emissions from civil buildings. It is necessary to improve the relevant market mechanisms, policy subsidies, and other means to encourage the application of green energy-saving technologies in civil buildings. Also, it is needed to guide the urban residents’ consumption structure and lifestyle in a low-carbon direction, to reduce the energy consumption and carbon emissions during the operation of civil buildings.

Dataset Information

Spatiotemporal Variations in Gastric Cancer Mortality and Their Relations to Influencing Factors in S County, China.

Publications

Spatiotemporal Variations in Gastric Cancer Mortality and Their Relations to Influencing Factors in S County, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets