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Fei X, Lou Z, Sheng M, Lv X, Ren Z, Xiao R. Different "nongrain" activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands. ENVIRONMENTAL RESEARCH 2024; 251:118642. [PMID: 38485078 DOI: 10.1016/j.envres.2024.118642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
"Nongrain" production on cultivated land is one of the primary environmental issues in China. Different "nongrain" activities may introduce different pollution sources to the local environment, leading to variations in heavy metal contents in soil, which can profoundly impact national food security. In this study, three typical "nongrain" regions (Nanxun (NX), Xiaoshan (XS) and Lin'an (LA)) with intensive aquaculture, tea planting and flower (seedling) growth on cultivated land around the Hangzhou metropolitan area were selected to address the spatial heterogeneity of accumulation levels, sources and source-oriented health risks of heavy metals in soil. The results showed that Hg was the main pollutant in NX and XS, while Cd and As were the major contaminants in LA. Aquiculture and sericultural industries (37.43%), natural sources (23.59%) and industrial activities (38.99%) were the major sources in NX; atmospheric deposition (37.73%), flower and seedling planting (23.49%) and metal-related industries (35.16%) were the major sources in XS; and atmospheric deposition (28.06%), excessive application of fertilizers and pesticides during tea planting (43.47%) and natural sources (28.47%) were the major sources in LA. The major risk population, area, exposure route and hazardous elements were children, LA, ingestion and As and Cr, respectively. From the perspective of source-based health risk assessment, in addition to natural sources that are difficult to intervene in, industrial activities, especially leather and wood process industries, metal-related industries and excessive fertilizer and pesticide application during tea planting contributed the most to the total health risk, which explained 67%, 41% and 42%, respectively, of the total risk in NX, XS and LA. High health risks are present in sources with heavy loadings of hazardous heavy metals (As and Cr); thus, to protect human health, the corresponding high-risk anthropogenic pollution sources in different "nongrain" areas need to be controlled.
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Affiliation(s)
- Xufeng Fei
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China.
| | - Zhaohan Lou
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Meiling Sheng
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Xiaonan Lv
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Zhouqiao Ren
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Rui Xiao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
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Bai X, Zhang X, Shi H, Geng G, Wu B, Lai Y, Xiang W, Wang Y, Cao Y, Shi B, Li Y. Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors. Front Public Health 2022; 10:954247. [PMID: 36268002 PMCID: PMC9578696 DOI: 10.3389/fpubh.2022.954247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/12/2022] [Indexed: 01/24/2023] Open
Abstract
Background Currently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions. Methods A total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors. Results The results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east. Conclusion The fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC.
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Affiliation(s)
- Xiaodan Bai
- Department of Economics, School of Economics, Minzu University of China, Beijing, China
| | - Xiyu Zhang
- Research Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China
| | - Hongping Shi
- Department of Oncology, Heze Municipal Hospital, Heze, China
| | - Guihong Geng
- Department of Economics, School of Economics, Minzu University of China, Beijing, China
| | - Bing Wu
- Research Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China
| | - Yongqiang Lai
- Research Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China
| | - Wenjing Xiang
- Department of Economics, School of Economics, Minzu University of China, Beijing, China
| | - Yanjie Wang
- Department of Economics, School of Economics, Minzu University of China, Beijing, China
| | - Yu Cao
- Department of Economics, School of Economics, Minzu University of China, Beijing, China
| | - Baoguo Shi
- Department of Economics, School of Economics, Minzu University of China, Beijing, China,*Correspondence: Baoguo Shi
| | - Ye Li
- Research Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China,Ye Li
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Xu J, Li J. A model for the early identification of sentinel lymph node metastasis in patients with breast cancer based on contrast‑enhanced ultrasound and clinical features. Oncol Lett 2022; 24:378. [PMID: 36238843 PMCID: PMC9494614 DOI: 10.3892/ol.2022.13498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/25/2022] [Indexed: 11/06/2022] Open
Abstract
The present study was designed to establish a model for the early identification of sentinel lymph node (SLN) metastasis in patients with breast cancer (BC). The SLN metastasis predictive model was established with a retrospective training set of 365 patients with BC and was re-evaluated using a prospective validation set of 402 patients with BC. The multivariable analysis indicated that the tumor diameter [odds ratio (OR), 1.189; 95% confidence interval (CI), 1.124-1.257; P<0.001], menopause (OR, 1.011; 95% CI, 0.603-1.436; P<0.001), estrogen receptor (ER) expression (OR, 3.199; 95% CI, 1.077-6.567; P=0.043) and contrast-enhanced ultrasonography (CEUS) type (OR, 10.563; 95% CI, 6.890-28.372; P<0.001) were independent predictors of SLN status in patients with BC. The SLN metastasis predictive model was as follows: (0.173 × tumor diameter)-(4.490 × menopause) + (2.322 × ER) + (5.445 × CEUS type)-1.9521. In the training set, the model was highly sensitive (83.6%) and specific (94.3%) for the early identification of SLN metastasis. Similarly, in the validation set, the model was highly sensitive (70.4%) and specific (89.5%) for the early identification of SLN metastasis in patients with BC. Overall, in the present study, a model was successfully established to predict SLN metastasis in patients with BC that includes tumor diameter, menopausal status, ER expression and CEUS detection.
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Affiliation(s)
- Juan Xu
- Department of Interventional Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
| | - Junzhi Li
- Department of Interventional Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
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Wang Y, Wan Z. Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province. GEOSPATIAL HEALTH 2022; 17. [PMID: 35579243 DOI: 10.4081/gh.2022.1078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/29/2022] [Indexed: 06/15/2023]
Abstract
Study of socioeconomic factors can play an important role in the spatial distribution of breast cancer by leading to a better understanding of its spatial pattern and assist breast cancer screening and early diagnosis. Taking Nanchang, a major city in central China, as an example, spatial autocorrelation and stratified heterogeneity were applied using a 10 10 km grid division to analyse breast cancer risk and socioeconomic factors. The research results showed that the median incidence rate of female breast cancer in Nanchang from 2016 to 2018 was 6.6/100,000 with a standard deviation of 12.3/100,000. Areas with higher incidence rates were mainly located in the central urban area and the major county towns. Spatial regression analysis showed that there was a statistically significant correlation between the spatial patterns of breast cancer incidence on the one hand, and on the other socioeconomic factors, such as total gross domestic product (GDP), per capita GDP and density of places of social and economic activities, i.e. points of interest. In addition, the normalized difference vegetation index also played a part in this respect. This research could serve as a reference for regional public health policy formulation and breast cancer screening.
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Affiliation(s)
- Yaqi Wang
- Comprehensive Tumour Internal Department, Jiangxi Provincial Cancer Hospital, Nanchang.
| | - Zhiwei Wan
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou.
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Liao Y, Li C, Xia C, Zheng R, Xu B, Zeng H, Zhang S, Wang J, Chen W. Spatial distribution of esophageal cancer mortality in China: a machine learning approach. Int Health 2021; 13:70-79. [PMID: 32478387 PMCID: PMC7807241 DOI: 10.1093/inthealth/ihaa022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 03/26/2020] [Accepted: 04/08/2020] [Indexed: 12/29/2022] Open
Abstract
Background Esophageal cancer (EC) is one of the most common cancers, causing many people to die every year worldwide. Accurate estimations of the spatial distribution of EC are essential for effective cancer prevention. Methods EC mortality surveillance data covering 964 surveyed counties in China in 2014 and three classes of auxiliary data, including physical condition, living habits and living environment data, were collected. Genetic programming (GP), a hierarchical Bayesian model and sandwich estimation were used to estimate the spatial distribution of female EC mortality. Finally, we evaluated the accuracy of the three mapping methods. Results The results show that compared with the root square mean error (RMSE) of the hierarchical Bayesian model at 6.546 and the sandwich estimation at 7.611, the RMSE of GP is the lowest at 5.894. According to the distribution estimated by GP, the mortality of female EC was low in some regions of Northeast China, Northwest China and southern China; in some regions downstream of the Yellow River Basin, north of the Yangtze River in the Yangtze River Basin and in Southwest China, the mortality rate was relatively high. Conclusions This paper provides an accurate map of female EC mortality in China. A series of targeted preventive measures can be proposed based on the spatial disparities displayed on the map.
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Affiliation(s)
- Yilan Liao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China
| | - Chunlin Li
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Changfa Xia
- National Office for Cancer Prevention and Control, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rongshou Zheng
- National Office for Cancer Prevention and Control, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bing Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Hongmei Zeng
- National Office for Cancer Prevention and Control, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Siwei Zhang
- National Office for Cancer Prevention and Control, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China
| | - Wanqing Chen
- National Office for Cancer Prevention and Control, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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