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Hua Q, Meng X, Gong J, Qiu X, Shang J, Xue T, Zhu T. Ozone exposure and cardiovascular disease: A narrative review of epidemiology evidence and underlying mechanisms. FUNDAMENTAL RESEARCH 2025; 5:249-263. [PMID: 40166088 PMCID: PMC11955045 DOI: 10.1016/j.fmre.2024.02.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 04/02/2025] Open
Abstract
Ozone (O3) poses a significant global public health concern as it exerts adverse effects on human cardiovascular health. Nevertheless, there remains a lack of comprehensive understanding regarding the relationships between O3 exposure and the risk of cardiovascular diseases (CVD), as well as the underlying biological mechanisms. To address this knowledge gap, this narrative review meticulously summarizes the existing epidemiological evidence, susceptibility, and potential underlying biological mechanisms linking O3 exposure with CVD. An increasing body of epidemiological studies has demonstrated that O3 exposure heightens the incidence and mortality of CVD, including specific subtypes such as ischemic heart disease, hypertension, and heart failure. Certain populations display heightened vulnerability to these effects, particularly children, the elderly, obese individuals, and those with pre-existing conditions. Proposed biological mechanisms suggest that O3 exposure engenders respiratory and systemic inflammation, oxidative stress, disruption of autonomic nervous and neuroendocrine systems, as well as impairment of coagulation function, glucose, and lipid metabolism. Ultimately, these processes contribute to vascular dysfunction and the development of CVD. However, some studies have reported the absence of associations between O3 and CVD, or even potentially protective effects of O3. Inconsistencies among the literature may be attributed to inaccurate assessment of personal O3 exposure levels in epidemiologic studies, as well as confounding effects stemming from co-pollutants and temperature. Consequently, our findings underscore the imperative for further research, including the development of reliable methodologies for assessing personal O3 exposure, exploration of O3 exposure's impact on cardiovascular health, and elucidation of its biological mechanisms. These endeavors will consolidate the causal relationship between O3 and cardiovascular diseases, subsequently aiding efforts to mitigate the risks associated with O3 exposure.
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Affiliation(s)
- Qiaoyi Hua
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing 100871, China
| | - Xin Meng
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing 100871, China
| | - Jicheng Gong
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing 100871, China
| | - Xinghua Qiu
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing 100871, China
| | - Jing Shang
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing 100871, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100871, China
| | - Tong Zhu
- SKL-ESPC & SEPKL-AERM, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing 100871, China
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Ji W, Wang Y, Liu XX, Li L, Yao H, Zhou Y, Yang BY. Exposure to ambient air pollution and chronic bronchitis: Findings from over 6.6 million adults in northwestern China. CHEMOSPHERE 2024; 350:140993. [PMID: 38141672 DOI: 10.1016/j.chemosphere.2023.140993] [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: 09/11/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Ambient air pollution increases the risk of respiratory mortality and morbidity, but evidence concerning effects of air pollution on chronic bronchitis (CB) is scarce. This study aimed to evaluate the associations of a set of air pollutants with the burden of CB, and to explore potential modifiers on the associations. METHODS In 2020, a total of 6,556,440 adults living in the Northwestern region of China were recruited. The Space-Time Extra-Trees model was employed to assess the annual average concentrations of six air pollutants for the three years (2017-2019) before 2020 , and subsequently allocated to the participants based on the latitude and longitude of their home addresses. We investigated the associations between the levels of various air pollutants and the odds of CB using generalized linear mixed models, and conducted multiple sensitivity analyses and subgroup analyses. RESULTS The odds of CB displays an approximately linear association with particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5), particulate matter with aerodynamic diameter ≤10 μm (PM10), while it shows a non-linear relationship with gaseous pollutants. In the adjusted model, the odds ratios and 95% confidence intervals for CB per 10 μg/m3 increase in PM2.5, PM10, and sulfur dioxide (SO2) were 1.297 (1.262-1.332), 1.072 (1.064-1.080), and 2.587 (2.186-3.063), respectively. Furthermore, several additional sensitivity analyses demonstrated the stability of these associations. Subgroup analyses found that the aforementioned associations were greater among participants aged below 50 years old and those who smoked and had no leisure time exercise. CONCLUSION Long-term exposure to ambient air pollutants may increase the odds of CB, especially among younger people and those with unhealthy lifestyles.
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Affiliation(s)
- Weidong Ji
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, Guangdong, 510080, China
| | - Yushan Wang
- Center of Health Management, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Xiao-Xuan Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Lin Li
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, Guangdong, 510080, China
| | - Hua Yao
- Center of Health Management, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Yi Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, Guangdong, 510080, China.
| | - Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
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Huang K, Yu D, Fang H, Ju L, Piao W, Guo Q, Xu X, Wei X, Yang Y, Zhao L. Association of fine particulate matter and its constituents with hypertension: the modifying effect of dietary patterns. Environ Health 2023; 22:55. [PMID: 37553681 PMCID: PMC10411005 DOI: 10.1186/s12940-023-01000-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/19/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Studies have shown that nutritional supplements could reduce the adverse effects induced by air pollution. However, whether dietary patterns can modify the association of long-term exposure to fine particulate matter (PM2.5) and its constituents with hypertension defined by the 2017 ACC/AHA guideline has not been evaluated. METHODS We included 47,501 Chinese adults from a nationwide cross-sectional study. PM2.5 and five constituents were estimated by satellite-based random forest models. Dietary approaches to stop hypertension (DASH) and alternative Mediterranean diet (AMED) scores were calculated for each participant. Interactions between dietary patterns and air pollution were examined by adding a multiplicative interaction term to logistic models. RESULTS Long-term exposure to PM2.5 and its constituents was associated with an increased risk of hypertension and stage 1-2 hypertension. The DASH and AMED scores significantly modified these associations, as individuals with higher scores had a significantly lower risk of air pollution-related hypertension and stage 1-2 hypertension (P-interaction < 0.05), except for interaction between PM2.5, sulfate, nitrate, ammonium, and AMED score on stage 1 hypertension. For each IQR increase in PM2.5, participants with the lowest DASH and AMED quintiles had hypertension risk with ORs (95%CI) of 1.20 (1.10, 1.30) and 1.19 (1.09, 1.29), whereas those with the highest DASH and AMED quintiles had lower risks with 0.98 (0.91, 1.05) and 1.04 (0.97, 1.11). The stratified analysis found modification effect was more prominent in the < 65 years age group. Consuming more fresh vegetables, fruits, whole grains, and dairy would reduce the risk of hypertension caused by PM2.5 and its constituents. CONCLUSIONS Dietary patterns rich in antioxidants can reduce long-term exposure to PM2.5 and its constituents-induced hypertension defined by the 2017 ACC/AHA guideline, especially in young and middle-aged individuals. Compared to the Mediterranean diet, the DASH diet offers superior dietary guidance to prevent stage 1 hypertension caused by air pollution.
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Affiliation(s)
- Kun Huang
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Dongmei Yu
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
- NHC Key Laboratory of Trace Element Nutrition, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Hongyun Fang
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
- NHC Key Laboratory of Trace Element Nutrition, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Lahong Ju
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Wei Piao
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
- NHC Key Laboratory of Trace Element Nutrition, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Qiya Guo
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Xiaoli Xu
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Xiaoqi Wei
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Yuxiang Yang
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Liyun Zhao
- National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing, 100050, China.
- NHC Key Laboratory of Trace Element Nutrition, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
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Kwak JH, Kim HJ. The Association between Air Pollutants Exposure with Pre- and Hypertension by Vitamin C Intakes in Korean Adults: A Cross-Sectional Study from the 2013-2016 Korea National Health and Nutrition Examination. J Nutr Health Aging 2023; 27:21-29. [PMID: 36651483 DOI: 10.1007/s12603-022-1872-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Oxidative stress and systemic inflammation are the main pathways by which air pollutants cause hypertension (HTN). Vitamin C intake may reduce the risk of HTN caused by air pollutants. This study aimed to investigate the association between air pollutants and pre-HTN and HTN in Korean adults and whether these associations were modified by vitamin C intake, using data from the 2013-2016 Korean National Health and Nutrition Examination Survey (KNHANES). DESIGN Cross-sectional study. SETTING This study used data from the KNHANES VI (2013-2015) and VII (2016) along with the data from the annual air pollution report of the Ministry of Environment. PARTICIPANTS We included 11,866 adults who had responded to a semi-food frequency questionnaire. MEASUREMENTS We used survey logistic regression models to evaluate the association of ambient PM10, SO2, NO2, CO, and O3 with pre-HTN and HTN according to vitamin C intake. RESULTS After adjusting for potential covariates, exposure to ambient PM10, SO2, NO2, and CO was significantly associated with a high prevalence of pre-HTN and HTN, whereas exposure to O3 was significantly associated with a low prevalence of pre-HTN and HTN. In particular, as the air pollutant scores increased (severe air pollution), the prevalence of pre-HTN and HTN increased in a dose-dependent manner (highest score vs. lowest score, OR=1.85, 95% CI=1.39-2.46, p for trend <.0001). However, these associations were found to be pronounced in adults with low vitamin C intake (highest score vs. lowest score, OR=2.30, 95% CI=1.50-3.54, p for trend <.0001), whereas the statistical significance disappeared for adults with high vitamin C intake (highest score vs. lowest score, OR=1.40, 95% CI=0.93-2.12, p for trend=0.007). CONCLUSION Exposure to air pollutants such as PM10, SO2, NO2, and CO may increase the prevalence of pre-HTN and HTN among Korean adults. In addition, a high intake of vitamin C may help prevent pre-HTN and HTN caused by air pollutants.
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Affiliation(s)
- Jung Hyun Kwak
- Hyun Ja Kim, Department of Food and Nutrition, Gangneung-Wonju National University, 7 Jukheon-gil, Gangneung-si, Gangwon-do 25457, Republic of Korea. Tel.: +82-33-640-2967, Fax: +82-33-640-2330, E-mail:
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Li B, Cao H, Liu K, Xia J, Sun Y, Peng W, Xie Y, Guo C, Liu X, Wen F, Zhang F, Shan G, Zhang L. Associations of long-term ambient air pollution and traffic-related pollution with blood pressure and hypertension defined by the different guidelines worldwide: the CHCN-BTH study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:63057-63070. [PMID: 35449329 DOI: 10.1007/s11356-022-20227-9] [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: 01/04/2022] [Accepted: 04/09/2022] [Indexed: 06/14/2023]
Abstract
The assessment of the generalization of the strict hypertension definition in the 2017 ACC/AHA Hypertension Guideline from environmental condition remains sparse. The aims of this study are to investigate and compare the associations of ambient air pollution and traffic-related pollution (TRP) with hypertension defined by the different criteria. A total of 32,135 participants were recruited from the baseline survey of the CHCN-BTH in 2017. We defined hypertension as SBP/DBP ≥ 140/90 mmHg according to the hypertension guidelines in China, Japan, Europe and ISH (traditional criteria) and defined as SBP/DBP ≥ 130/80 mmHg according to the 2017 ACC/AHA Hypertension Guideline (strict criteria). A two-level generalized linear mixed models were applied to investigate the associations of air pollutants (i.e. PM2.5, SO2, NO2) and TRP with blood pressure (BP) measures and hypertension. Stratified analyses and two-pollutant models were also performed. The stronger associations of air pollutants were found in the hypertension defined by the strict criteria than that defined by the traditional criteria. The ORs per an IQR increase in PM2.5 were 1.17 (95% CI: 1.09, 1.25) for the strict criteria and 1.14 (95% CI: 1.06, 1.23) for the traditional criteria. The similar conditions were also observed for TRP. The above results were robust in both stratified analyses and two-pollutant models. Our study assessed the significance of the hypertension defined by the strict criteria from environmental aspect and called attention to the more adverse effects of air pollution and TRP on the earlier stage of hypertension.
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Affiliation(s)
- Bingxiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Han Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
- Department of Biostatistics, Peking University First Hospital, Beijing, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yanyan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Wenjuan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yunyi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Chunyue Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiaohui Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fuyuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fengxu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Guangliang Shan
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences; School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Fengtai District, Beijing, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
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Yan L, Pang Y, Wang Z, Luo H, Han Y, Ma S, Li L, Yuan J, Niu Y, Zhang R. Abnormal fasting blood glucose enhances the risk of long-term exposure to air pollution on dyslipidemia: A cross-sectional study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 237:113537. [PMID: 35468441 DOI: 10.1016/j.ecoenv.2022.113537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/16/2022] [Indexed: 06/14/2023]
Abstract
Both long-term exposure to air pollution and abnormal fasting blood glucose (FBG) are linked to dyslipidemia prevalence. However, the joint role of air pollution and FBG on dyslipidemia remains unknown clearly. In this study, we aimed to test whether abnormal FBG could enhance the risks of long-term exposure to air pollutants on dyslipidemia in general Chinese adult population. The present study recruited 8917 participants from 4 cities in Hebei province, China. Participants' individual exposure to air pollutants was evaluated by the Empirical Bayesian Kriging statistical model in ArcGIS10.2 geographic information system. Dyslipidemia was defined according to Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults. Subjects were grouped into normal, prediabetes, diabetes according to FBG level. Generalized linear models were applied to analyze the interaction of air pollutants and FBG on dyslipidemia prevalence. The prevalence of dyslipidemia was 43.83% in our investigation. After adjusting all covariates, we found the risk of four air pollutants (PM2.5, PM10, NO2, SO2) on dyslipidemia prevalence was stronger as higher FBG level, and the adjusted odd ratio of interaction (ORinter (95% CI)) between PM2.5, PM10, NO2, SO2 and FBG levels on dyslipidemia was 1.171 (1.162, 1.189), 1.119 (1.111, 1.127), 1.124 (1.115, 1.130), 1.107 (1.098, 1.115), respectively. Stratified analyses indicated the modifying effects of FBG on the association of air pollution with dyslipidemia were stronger among male, less than 65 years old, overweight/obesity (all Pinter<0.1). Our study concluded that high FBG levels strengthened the risk of long-term exposure to air pollution on dyslipidemia, especially more noticeable in male, less than 65 years old, overweight.
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Affiliation(s)
- Lina Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China
| | - Yaxian Pang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China
| | - Zhikun Wang
- Office of Academic Affairs, The First Affiliated Hospital of Hebei College of Traditional Chinese Medicine, Shijiazhuang 050017, PR China
| | - Haixia Luo
- Department of Cardiology, Shijiazhuang No.1 Hospital, Shijiazhuang 050011, PR China
| | - Yuquan Han
- Emergency Department, People's Hospital of Qingdao West Coast New Area, Shandong 266400, PR China
| | - Shitao Ma
- Department of Hospital Infection Control, The People's Hospital of Luanzhou, Luanzhou 063700, PR China
| | - Lipeng Li
- Department of Reproductive Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, PR China
| | - Jing Yuan
- Department of Biostatistics,Clinical Development Division of CSPC, Shijiazhuang 050035, PR China
| | - Yujie Niu
- Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China; Department occupational Health and Environmental Health, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China.
| | - Rong Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China.
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Lin LZ, Su F, Fang QL, Ho HC, Zhou Y, Ma HM, Chen DH, Hu LW, Chen G, Yu HY, Yang BY, Zeng XW, Xiang MD, Feng WR, Dong GH. The association between anthropogenic heat and adult hypertension in Northeast China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 815:152926. [PMID: 34998766 DOI: 10.1016/j.scitotenv.2022.152926] [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: 05/30/2021] [Revised: 12/21/2021] [Accepted: 01/01/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Although the potential serious threat of anthropogenic heat on human health was receiving considerable attention worldwide, its long-term health effect on blood pressure (BP) remained unknown. We aimed to evaluate the associations of long-term anthropogenic heat exposure with different components of BP and hypertension. METHODS In this cross-sectional study (Liaoning province, China) conducted in 2009, we included a total of 24,845 Chinese adults (18-74 years). We estimated the anthropogenic heat exposure in 2008 using multisource remote sensing images and ancillary data. We measured systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP), and defined hypertension. We used generalized linear mixed model to examine the associations. RESULTS In the adjusted model, the estimates indicated that the difference in SBP, MAP and PP for those in highest quartiles of total anthropogenic heat exposure was greater compared with the lowest quartile (highest quartile: β = 1.11 [95% CI: 0.28-1.94], 0.60 [95% CI: 0.04-1.17], 0.76 [95% CI: 0.17-1.35]). Compared with the lowest quartile, the odds of hypertension were higher among those in higher quartiles (second quartile: OR = 1.17 [95% CI: 1.05-1.30]; third quartile:1.10 [95% CI: 1.1.01-1.21]; highest quartile: 1.17 [95% CI: 1.06-1.28]). These associations were stronger in female participants. CONCLUSION Our study showed that long-term exposure to anthropogenic heat was associated with elevated BP and higher odds of hypertension. These findings suggest that mitigation strategies to reduce anthropogenic heat should be considered.
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Affiliation(s)
- Li-Zi Lin
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Fan Su
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Qiu-Ling Fang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Hung Chak Ho
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
| | - Yang Zhou
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Hui-Min Ma
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Duo-Hong Chen
- Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou 510308, China
| | - Li-Wen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Hong-Yao Yu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Bo-Yi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Ming-Deng Xiang
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China.
| | - Wen-Ru Feng
- Department of Environmental Health, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China.
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China.
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Xu H, Jia Y, Sun Z, Su J, Liu QS, Zhou Q, Jiang G. Environmental pollution, a hidden culprit for health issues. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:31-45. [PMID: 38078200 PMCID: PMC10702928 DOI: 10.1016/j.eehl.2022.04.003] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/26/2022] [Accepted: 04/23/2022] [Indexed: 12/12/2023]
Abstract
The environmental and health impacts from the massive discharge of chemicals and subsequent pollution have been gaining increasing public concern. The unintended exposure to different pollutants, such as heavy metals, air pollutants and organic chemicals, may cause diverse deleterious effects on human bodies, resulting in the incidence and progression of different diseases. The article reviewed the outbreak of environmental pollution-related public health emergencies, the epidemiological evidence on certain pollution-correlated health effects, and the pathological studies on specific pollutant exposure. By recalling the notable historical life-threatening disasters incurred by local chemical pollution, the damning evidence was presented to criminate certain pollutants as the main culprit for the given health issues. The epidemiological data on the prevalence of some common diseases revealed a variety of environmental pollutants to blame, such as endocrine-disrupting chemicals (EDCs), fine particulate matters (PMs) and heavy metals. The retrospection of toxicological studies provided illustrative clues for evaluating ambient pollutant-induced health risks. Overall, environmental pollution, as the hidden culprit, should answer for the increasing public health burden, and more efforts are highly encouraged to strive to explore the cause-and-effect relationships through extensive epidemiological and pathological studies.
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Affiliation(s)
- Hanqing Xu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, and Zhejiang Provincial Key Lab for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou, 325035, China
| | - Yang Jia
- National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, and Zhejiang Provincial Key Lab for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou, 325035, China
| | - Zhendong Sun
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310000, China
| | - Jiahui Su
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qian S. Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
| | - Qunfang Zhou
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310000, China
- Institute of Environment and Health, Jianghan University, Wuhan, 430056, China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310000, China
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Wang J, Wu S, Cui J, Ding Z, Meng Q, Sun H, Li B, Teng J, Dong Y, Aschner M, Wu S, Li X, Chen R. The influences of ambient fine particulate matter constituents on plasma hormones, circulating TMAO levels and blood pressure: A panel study in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 296:118746. [PMID: 34968616 DOI: 10.1016/j.envpol.2021.118746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Considerable investigations have been carried out to address the relationship between ambient fine particulate matter (PM2.5) and blood pressure (BP) in patients with hypertension. However, few studies have explored the influence of PM2.5 and its constituents on Trimethylamine N-oxide (TMAO), an established risk factor for hypertension and cardiovascular disease (CVD), particularly in severely air-polluted areas. To explore the potential impact of PM2.5 constituents on BP, plasma hormones, and TMAO, a panel study was conducted to investigate changes in BP, plasma hormones, and TMAO in response to ambient air pollution exposure in stage 1 hypertensive young adults. Linear mixed effect models were used to estimate the cumulative effects of fine particulate matters (PM2.5) and its constituents on BP, plasma hormones and TMAO. We found that one interquartile range (IQR) (35 μg/m3) increase in 0-1 day moving-average PM2.5 concentrations was statistically significantly associated with elevated systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with estimated values of 0.13 (95% confidence interval (CI): 0.03 to 0.23) mmHg, 0.18 (95% CI: 0.08 to 0.28) mmHg, and 0.17 (95% CI: 0.09 to 0.26) mmHg, respectively. Hormone disturbance in the renin-angiotensin-aldosterone system was also associated with PM2.5 exposure. Elevated TMAO levels with an IQR increase for 0-4, 0-5, 0-6 moving-average concentrations of PM2.5 were found, and the increased values ranged from 26.28 (95% CI: 2.92 to 49.64) to 60.78 (31.95-89.61) ng/ml. More importantly, the PM2.5-bound metal constituents, such as manganese (Mn), titanium (Ti), and selenium (Se) showed robust associations with elevated BP and plasma TMAO levels. This study demonstrates associations between PM2.5 metal constituents and increased BP, changes in plasma hormones and TMAO, in stage 1 hypertensive young adults. Source control, aiming to reduce the emission of PM2.5-bound metals should be implemented to reduce the risk of hypertension and CVD.
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Affiliation(s)
- Jiajia Wang
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China; Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, PR China
| | - Shenshen Wu
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China; Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, PR China
| | - Jian Cui
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, PR China
| | - Zhen Ding
- Department of Environmental Health and Endemic Disease Control, Jiangsu Provincial Center for Disease Prevention and Control, Nanjing, 210009, PR China
| | - Qingtao Meng
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China; Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China
| | - Hao Sun
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, PR China
| | - Bin Li
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, PR China
| | - Jun Teng
- Nanjing Xiaozhuang University, Nanjing, 211171, PR China
| | - Yanping Dong
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Nanjing, 210029, PR China
| | - Michael Aschner
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Shaowei Wu
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Yanta District, Xi'an, Shaanxi, 710061, China
| | - Xiaobo Li
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China; Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, PR China
| | - Rui Chen
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China; Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou, 511436, PR China.
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10
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Xiao X, Wang R, Knibbs LD, Jalaludin B, Heinrich J, Markevych I, Gao M, Xu SL, Wu QZ, Zeng XW, Chen GB, Hu LW, Yang BY, Yu Y, Dong GH. Street view greenness is associated with lower risk of obesity in adults: Findings from the 33 Chinese community health study. ENVIRONMENTAL RESEARCH 2021; 200:111434. [PMID: 34087194 DOI: 10.1016/j.envres.2021.111434] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Urban greenness may protect against obesity, but very few studies have assessed 'street view' (SV) greenness metrics, which may better capture people's actual exposure to greenness compared to commonly-used satellite-derived metrics. We aimed to investigate these associations further in a Chinese adult study. METHODS Our analysis included 24,845 adults in the 33 Chinese Community Health Study in 2009. SV images from Tencent Map, segmented by machine learning algorithms, were used to determine the average proportion of green vegetation in SV images at community level in 800m road network buffer. Sensitivity analyses were performed with an alternative buffer size. Overall greenness was assessed as normalized difference vegetation index (NDVI) in 800 m buffer. We used predicted PM2.5 and monitored NO2 as proxies of air pollution. Body mass index (BMI), waist circumference (WC) and hip circumference (HC) were regressed on SV greenness by generalized linear mixed models, with adjustment for covariates. Mediation analyses were performed to assess the mediation effects of air pollution. RESULTS Each interquartile range (IQR = 3.6%) increase in street view greenness was associated with a 0.15 kg/m2 (95% CI: -0.22, -0.09) decrease in BMI and 0.23 cm (95% CI: -0.35, -0.11) reduction in HC, and was associated with 7% lower odds of overweight (OR = 0.93, 95% CI:0.90, 0.96) and 18% lower odds of obesity (OR = 0.82, 95% CI:0.76, 0.89). Similar effect estimation was observed compared with commonly-used NDVI measures. PM2.5 and NO2 mediated 15.5% and 6.1% of the effects of SV greenness with BMI, respectively. CONCLUSIONS Our findings suggest beneficial associations between community-level SV greenness and lower body weight in Chinese adults. The effects were observed in women but not in men. Air pollution may partially mediate the association. These findings may have implications to support efforts to promote greening in urban areas.
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Affiliation(s)
- Xiang Xiao
- Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China; Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Ruoyu Wang
- School of GeoSciences, University of Edinburgh, Edinburgh, UK
| | - Luke D Knibbs
- School of Public Health, The University of Queensland, Herston, Queensland, 4006, Australia; Centre for Air Pollution, Energy and Health Research, Glebe, NSW, 2037, Australia
| | - Bin Jalaludin
- Centre for Air Pollution, Energy and Health Research, Glebe, NSW, 2037, Australia; IIngham Institute for Applied Medial Research, University of New South Wales, Sydney, Australia
| | - Joachim Heinrich
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, Ludwig Maximilian University, Munich, 80336, Germany
| | - Iana Markevych
- Institute of Psychology, Jagiellonian University, Krakow, 30060, Poland
| | - Meng Gao
- Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China
| | - Shu-Li Xu
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Qi-Zhen Wu
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Gong-Bo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Li-Wen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Bo-Yi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, 510655, China.
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
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11
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Cao H, Li B, Liu K, Pan L, Cui Z, Zhao W, Zhang H, Niu K, Tang N, Sun J, Han X, Wang Z, Xia J, He H, Cao Y, Xu Z, Meng G, Shan A, Guo C, Sun Y, Peng W, Liu X, Xie Y, Wen F, Zhang F, Shan G, Zhang L. Association of long-term exposure to ambient particulate pollution with stage 1 hypertension defined by the 2017 ACC/AHA Hypertension Guideline and cardiovascular disease: The CHCN-BTH cohort study. ENVIRONMENTAL RESEARCH 2021; 199:111356. [PMID: 34048743 DOI: 10.1016/j.envres.2021.111356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/13/2021] [Accepted: 05/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Evidence regarding the effects of ambient air pollution on new stage 1 hypertension defined by the 2017 ACC/AHA Hypertension Guideline remains sparse. OBJECTIVES To investigate the association of long-term exposure to ambient PM2.5 with stage 1 hypertension and to explore the mediating and modifying effects of PM2.5 on cardiovascular disease (CVD). METHODS A total of 32,135 participants aged 18-80 years were recruited in 2017. The three-year (2014-2016) average PM2.5 concentrations were assessed by a spatial statistical model. Blood pressure (BP) was divided into four categories according to the 2017 ACC/AHA Hypertension Guideline: normal BP (SBP<120 mmHg and DBP<80 mmHg), elevated BP (SBP 120-129 mmHg and DBP<80 mmHg), stage 1 hypertension (SBP 130-139 mmHg or DBP 80-89 mmHg), and stage 2 hypertension (SBP≥140 mmHg or DBP≥90 mmHg or taking antihypertensive medications). The associations of PM2.5 with BP categories were estimated by two-level generalized linear mixed models. Analyses stratified by age, mediation and interaction analyses of PM2.5 and stage 1 hypertension with CVD were performed. RESULTS We detected a positive significant association between long-term exposure to PM2.5 and stage 1 hypertension. Compared to normal BP, the OR was 1.05 (95% CI: 1.02, 1.08) per 10 μg/m3 increase in PM2.5. The association was stronger than that of elevated BP but weaker than that of stage 2 hypertension. Stage 1 hypertension only partially mediated the association between PM2.5 and CVD, and the mediation proportions ranged from 1.55% to 11.00%. However, it modified the association between PM2.5 and CVD, which was greater in participants with stage 1 hypertension (OR: 1.66; 95% CI: 1.43, 1.93) than in participants with normal BP (OR: 1.32; 95% CI: 1.11, 1.57), with Pinteraction<0.001. In the analysis stratified by age, the above associations were age-specific, and significant associations were only observed in the young and middle-aged (<60 years) groups. CONCLUSIONS Long-term exposure to ambient PM2.5 was significantly associated with stage 1 hypertension. This earlier stage of hypertension may be a trigger BP range for adverse effects of air pollution in the development of hypertension and CVD, especially in young and middle-aged individuals.
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Affiliation(s)
- Han Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Bingxiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Li Pan
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, And School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Ze Cui
- Department of Chronic and Noncommunicable Disease Prevention and Control, Hebei Provincial Center for Disease Prevention and Control, Shijiazhuang, Hebei, China
| | - Wei Zhao
- Department of Chronic and Noncommunicable Disease Prevention and Control, Chaoyang District Center for Disease Prevention and Control, Beijing, China
| | - Han Zhang
- Health Management Center, Beijing Aerospace General Hospital, Beijing, China
| | - Kaijun Niu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Naijun Tang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Jixin Sun
- Department of Chronic and Noncommunicable Disease Prevention and Control, Hebei Provincial Center for Disease Prevention and Control, Shijiazhuang, Hebei, China
| | - Xiaoyan Han
- Department of Chronic and Noncommunicable Disease Prevention and Control, Chaoyang District Center for Disease Prevention and Control, Beijing, China
| | - Zhengfang Wang
- Health Management Center, Beijing Aerospace General Hospital, Beijing, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Huijing He
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, And School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Yajing Cao
- Department of Chronic and Noncommunicable Disease Prevention and Control, Hebei Provincial Center for Disease Prevention and Control, Shijiazhuang, Hebei, China
| | - Zhiyuan Xu
- Department of Chronic and Noncommunicable Disease Prevention and Control, Chaoyang District Center for Disease Prevention and Control, Beijing, China
| | - Ge Meng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Anqi Shan
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Chunyue Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yanyan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Wenjuan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiaohui Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yunyi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fuyuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fengxu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Guangliang Shan
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, And School of Basic Medicine, Peking Union Medical College, Beijing, China.
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
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Ma R, Ban J, Wang Q, Zhang Y, Yang Y, He MZ, Li S, Shi W, Li T. Random forest model based fine scale spatiotemporal O 3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 276:116635. [PMID: 33639490 DOI: 10.1016/j.envpol.2021.116635] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/12/2021] [Accepted: 01/29/2021] [Indexed: 06/12/2023]
Abstract
Ambient ozone (O3) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01 ° × 0.01 ° resolution, and estimated daily maximum 8h average O3 (O3-8hmax) concentration, daily average O3 (O3-mean) concentration, and daily maximum 1h O3 (O3-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R2 of sample-based O3-8hmax, O3-mean and O3-1hmax models were all greater than 0.80, while the R2 of site-based and date-based model were 0.68-0.87. From 2010 to 2017, O3-8hmax, O3-mean, and O3-1hmax concentrations in the BTH region increased by 4.18 μg/m3, 0.11 μg/m3, and 4.71 μg/m3, especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude.
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Affiliation(s)
- Runmei Ma
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Jie Ban
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Qing Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yayi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China; Jiangsu Ocean University, Jiangsu, 222000, China
| | - Yang Yang
- Institute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China
| | - Mike Z He
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York NY, 10029, USA
| | - Shenshen Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing, 100101, China
| | - Wenjiao Shi
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
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13
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Weaver AM, Wang Y, Wellenius GA, Bidulescu A, Sims M, Vaidyanathan A, Hickson DA, Shimbo D, Abdalla M, Diaz KM, Seals SR. Long-Term Air Pollution and Blood Pressure in an African American Cohort: the Jackson Heart Study. Am J Prev Med 2021; 60:397-405. [PMID: 33478866 PMCID: PMC10388406 DOI: 10.1016/j.amepre.2020.10.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 09/21/2020] [Accepted: 10/30/2020] [Indexed: 11/23/2022]
Abstract
INTRODUCTION African Americans are disproportionately affected by high blood pressure, which may be associated with exposure to air pollutants, such as fine particulate matter and ozone. METHODS Among African American Jackson Heart Study participants, this study examined associations between 1-year and 3-year mean fine particulate matter and ozone concentrations with prevalent and incident hypertension at Visits 1 (2000-2004, n=5,191) and 2 (2005-2008, n=4,105) using log binomial regression. Investigators examined associations with systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure using linear regression and hierarchical linear models, adjusting for sociodemographic, behavioral, and clinical characteristics. Analyses were conducted in 2017-2019. RESULTS No associations were observed between fine particulate matter or ozone concentration and prevalent or incident hypertension. In linear models, an IQR increase in 1-year ozone concentration was associated with 0.67 mmHg higher systolic blood pressure (95% CI=0.27, 1.06), 0.42 mmHg higher diastolic blood pressure (95% CI=0.20, 0.63), and 0.50 mmHg higher mean arterial pressure (95% CI=0.26, 0.74). In hierarchical models, fine particulate matter was inversely associated with systolic blood pressure (-0.72, 95% CI= -1.31, -0.13), diastolic blood pressure (-0.69, 95% CI= -1.02, -0.36), and mean arterial pressure (-0.71, 95% CI= -1.08, -0.33). Attenuated associations were observed with 1-year concentrations and at Visit 1. CONCLUSIONS Positive associations were observed between ozone and systolic blood pressure, diastolic blood pressure, and mean arterial pressure, and inverse associations between fine particulate matter and systolic blood pressure, diastolic blood pressure, and mean arterial pressure in an African American population with high (56%) prevalence of hypertension. Effect sizes were small and may not be clinically relevant.
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Affiliation(s)
- Anne M Weaver
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Chapel Hill, North Carolina; Department of Environmental Health, Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana
| | - Yi Wang
- Department of Environmental Health, Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana.
| | - Gregory A Wellenius
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts
| | - Aurelian Bidulescu
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, Indiana
| | - Mario Sims
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Ambarish Vaidyanathan
- School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - DeMarc A Hickson
- Department of Epidemiology and Biostatistics, School of Public Health, Jackson State University, Jackson, Mississippi
| | - Daichi Shimbo
- Division of Cardiology, Columbia University Medical Center, New York, New York
| | - Marwah Abdalla
- Division of Cardiology, Columbia University Medical Center, New York, New York
| | - Keith M Diaz
- Division of Cardiology, Columbia University Medical Center, New York, New York
| | - Samantha R Seals
- Department of Mathematics and Statistics, University of West Florida, Pensacola, Florida
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14
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Yang BY, Cao K, Luo YN, He ZZ, Guo PY, Ma HM, Yang M, Zhou Y, Hu LW, Chen GB, Zeng XW, Yu HY, Yu Y, Dong GH. Associations of ambient particulate matter with homocysteine metabolism markers and effect modification by B vitamins and MTHFR C677T gene polymorphism. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116211. [PMID: 33348139 DOI: 10.1016/j.envpol.2020.116211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 06/12/2023]
Abstract
Evidence concerning effects of ambient air pollution on homocysteine (HCY) metabolism is scarce. We aimed to explore the associations between ambient particulate matter (PM) exposure and the HCY metabolism markers and to evaluate effect modifications by folate, vitamin B12, and methylenetetrahyfrofolate reductase (MTHFR) C677T gene polymorphism. Between December 1, 2017 and January 5, 2018, we conducted a panel study in 88 young college students in Guangzhou, China, and received 5 rounds of health examinations. Real-time concentrations of PMs with aerodynamic diameter ≤2.5 (PM2.5), ≤1.0 (PM1.0), and ≤0.1 (PM0.1) were monitored, and the serum HCY metabolism markers (i.e., HCY, S-Adenosylhomocysteine [SAH], and S-Adenosylmethionine [SAM]) were repeatedly measured. We applied linear mixed effect models combined with a distributed lag model to evaluate the associations of PMs with the HCY metabolism markers. We also explored effect modifications of folate, vitamin B12, and the MTHFR C677T polymorphism on the associations. We observed that higher concentrations of PM2.5 and PM1.0 were associated with higher serum levels of HCY, SAH, SAM, and SAM/SAH ratio (e.g., a 10 μg/m3 increase in PM2.5 during lag 0 day and lag 5 day was significantly associated with 1.3-19.4%, 1.3-28.2%, 6.2-64.4%, and 4.8-28.2% increase in HCY, SAH, SAM, and SAM/SAH ratio, respectively). In addition, we observed that the associations of PM2.5 with the HCY metabolism markers were stronger in participants with lower B vitamins levels. This study demonstrated that short-term exposure to PM2.5 and PM1.0 was deleteriously associated with the HCY metabolism markers, especially in people with lower B vitamins levels.
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Affiliation(s)
- Bo-Yi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Ke Cao
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Ya-Na Luo
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Zhi-Zhou He
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Peng-Yue Guo
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Hui-Min Ma
- State Key Laboratory of Organic Geochemistry, Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guanghou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
| | - Mo Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yang Zhou
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Li-Wen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Gong-Bo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Hong-Yao Yu
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
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Xu J, Zhang Y, Yao M, Wu G, Duan Z, Zhao X, Zhang J. Long-term effects of ambient PM2.5 on hypertension in multi-ethnic population from Sichuan province, China: a study based on 2013 and 2018 health service surveys. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 28:5991-6004. [PMID: 32978739 DOI: 10.1007/s11356-020-10893-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/16/2020] [Indexed: 02/08/2023]
Abstract
Hypertension, a major risk factor of many severe chronic diseases and leading cause of global disease burden, is reported to be associated with long-term exposure to PM2.5. China's high PM2.5 pollution level has become a major public health issue. However, existing studies from China have got inconsistent results with very limited investigation into the multi-ethnic peoples. This study adds multi-ethnic evidence from Sichuan Province, southwestern China, and assesses ethnic differences of PM2.5 exposure effect on hypertension. We pooled large cross-sectional data from two surveys conducted in 2013 and 2018 to examine the association of long-term exposure to PM2.5 on prevalence of hypertension in adults aged 30 years old and above. Community-specified annual PM2.5 concentration was estimated using satellite data. Thirty-one thousand four hundred sixty-two participants with average exposure concentration of 32.8 μg/m3 were included. The proportions of the Han, the Tibetan, the Yi, and other ethnic people were 89.2%, 7.3%, 3.2%, and 0.3%, respectively. The adjusted odds ratio (OR) was 1.08 (95% CI, 1.04-1.12) for a 10 μg/m3 PM2.5 concentration increment. The adjusted ORs for the Han, the Tibetan, and the Yi were 1.08 (95% CI, 1.04-1.12), 0.03 (95% CI, 0.00-0.27), and 1.75 (95% CI, 1.28-2.38) for a 10 μg/m3 PM2.5 concentration increment, respectively. Stratification analysis found stronger associations in participants with chronic diseases and Yi minority population. The results showed that long-term exposure to PM2.5 may increase the risk of hypertension prevalence in Chinese multi-ethnic adults. The associations were different among ethnicities.
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Affiliation(s)
- Jiayue Xu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yuqin Zhang
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Minghong Yao
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Gonghua Wu
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Zhanqi Duan
- Big Data Center of Sichuan Province, Chengdu, 610041, Sichuan, China
| | - Xing Zhao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Juying Zhang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
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16
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Li Y, Lin TY, Chiu YH. Dynamic linkages among economic development, environmental pollution and human health in Chinese. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2020; 18:32. [PMID: 32944004 PMCID: PMC7487810 DOI: 10.1186/s12962-020-00228-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 08/27/2020] [Indexed: 01/12/2023] Open
Abstract
Background Research on the relationships between economic development, energy consumption, environmental pollution, and human health has tended to focus on the relationships between economic growth and air pollution, energy and air pollution, or the impact of air pollution on human health. However, there has been little past research focused on all the above associations. Methods The few studies that have examined the interconnections between the economy, energy consumption, environmental pollution and health have tended to employ regression analyses, DEA (Data Envelopment Analysis), or DEA efficiency analyses; however, as these are static analysis tools, the analyses did not fully reveal the sustainable economic, energy, environmental or health developments over time, did not consider the regional differences, and most often ignored community health factors. To go some way to filling this gap, this paper developed a modified two stage Undesirable Meta Dynamic Network model to jointly analyze energy consumption, economic growth, air pollution and health treatment data in 31 Chinese high-income and upper-middle income cities from 2013-2016, for which the overall efficiency, production efficiency, healthcare resource utilization efficiency and technology gap ratio (TGR) for all input and output variables were calculated. Results It was found that: (1) the annual average overall efficiency in China's eastern region was the highest; (2) the production stage efficiencies were higher than the healthcare resource utilization stage efficiencies in most cities; (3) the high-income cities had lower TGRs than the upper-middle income cities; (4) the high-income cities had higher average energy consumption efficiencies than the upper-middle income cities; (5) the health expenditure efficiencies were the lowest of all inputs; (6) the high-income cities' respiratory disease and mortality rate efficiencies were higher than in the upper-middle income cities, which had improving mortality rate efficiencies; and (7) there were significant regional differences in the annual average input and output indicator efficiencies. Conclusions First, the high-income cities had higher average efficiencies than the upper-middle income cities. Of the ten eastern region high-income cities, Guangzhou and Shanghai had average efficiencies of 1, with the least efficient being Shijiazhuang. In the other regions, the upper-middle income cities required greater technology and health treatment investments. Second, Guangzhou, Lhasa, Nanning, and Shanghai had production efficiencies of 1, and Guangzhou, Lhasa, Nanning, Shanghai and Fuzhou had healthcare resource utilization efficiencies of 1. As the average production stage efficiencies in most cities were higher than the healthcare resource utilization stage efficiencies, greater efforts are needed to improve the healthcare resource utilization. Third, the technology gap ratios (TGRs) in the high-income cities were slightly higher than in the upper-middle income cities. Therefore, the upper-middle income cities need to learn from the high-income cities to improve their general health treatment TGRs. Fourth, while the high-income cities had higher energy consumption efficiencies than the upper-middle income cities, these were decreasing in most cities. There were few respiratory disease efficiency differences between the high-income and upper-middle income cities, the high-income cities had falling mortality rate efficiencies, and the upper-middle income cities had increasing mortality rate efficiencies. Overall, therefore, most cities needed to strengthen their health governance to balance economic growth and urban expansion. Fifth, the average AQI efficiencies in both the high-income and upper-middle income cities were higher than the average CO2 efficiencies. However, the high-income cities had lower average CO2 emissions and AQI efficiencies than the upper-middle income cities, with the AQI efficiency differences between the two city groups expanding. As most cities were focusing more on air pollution controls than carbon dioxide emissions, greater efforts were needed in coordinating the air pollution and carbon dioxide emissions treatments. Therefore, the following suggestions are given. (1) The government should reform the hospital and medical systems. (2) Local governments need to strengthen their air pollution and disease education. (3) High-income cities need to improve their healthcare governance to reduce the incidence of respiratory diseases and the associated mortality. (4) Healthcare governance efficiency needs to be prioritized in 17 upper-middle income cities, such as Hangzhou, Changchun, Harbin, Chengdu, Guiyang, Kunming and Xi'an, by establishing sound medical management systems and emergency environmental pollution treatments, and by increasing capital asset medical investments. (5) Upper-middle income cities need to adapt their treatment controls to local conditions and design medium to long-term development strategies. (6) Upper-middle income cities need to actively learn from the technological and governance experiences in the more efficient higher-income cities.
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Affiliation(s)
- Ying Li
- Business School, Sichuan University, Wangjiang Road No. 29, Chengdu, 610064 People's Republic of China
| | - Tai-Yu Lin
- Department of Business Administration, National Cheng Kung University, No. 1, University Road, Tainan, 701 Taiwan R.O.C
| | - Yung-Ho Chiu
- Department of Economics, Soochow University, No. 56, Kueiyang St., Sec. 1, Taipei, 100 Taiwan R.O.C
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17
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Feng Y, Yu X, Chiu YH, Lin TY. Energy Efficiency and Health Efficiency of Old and New EU Member States. Front Public Health 2020; 8:168. [PMID: 32582601 PMCID: PMC7297082 DOI: 10.3389/fpubh.2020.00168] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 04/20/2020] [Indexed: 12/22/2022] Open
Abstract
Environmental protection and health issues have always been of great concern. This study employed modified Meta-Frontier Dynamic Network Data Envelopment Analysis to explore the environmental pollution effects from energy consumption on the mortality of children and adults, tuberculosis rate, survival rate, and health expenditure efficiencies in 15 old EU states and 13 new EU states from 2010 to 2014. We calculated the overall efficiency scores and technology gap ratios for each old EU and new EU states as well as the efficiencies of non-renewable energy, renewable energy, PM2.5, CO2, labor, GDP, tuberculosis, child mortality, adult mortality, health expenditure efficiency, and survival efficiency at the health stage. The average annual overall efficiencies of the old EU states are higher than that of the new EU states. Whether in terms of energy efficiencies or health efficiencies, the inputs and outputs of the old EU states are always higher than that of the new EU states. Overall, developing countries in Eastern Europe are lagging behind in terms of energy and health efficiencies. At the same time, the efficiency of child mortality is lower than that of adult mortality, and the efficiency of PM2.5 is higher than that of CO2 in both old and new EU states.
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Affiliation(s)
- Yongqi Feng
- School of Economics, Jilin University, Changchun, China
| | - Xinye Yu
- School of Economics, Jilin University, Changchun, China
| | - Yung-Ho Chiu
- Department of Economics, Soochow University, Taipei, Taiwan
| | - Tai-Yu Lin
- Department of Business Administration, National Cheng Kung University, Tainan City, Taiwan
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18
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The Energy Efficiency and the Impact of Air Pollution on Health in China. Healthcare (Basel) 2020; 8:healthcare8010029. [PMID: 32028563 PMCID: PMC7151220 DOI: 10.3390/healthcare8010029] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 01/06/2020] [Accepted: 01/20/2020] [Indexed: 11/17/2022] Open
Abstract
The rapid growth of China's economy in recent years has greatly improved its citizens' living standards, but economic growth consumes many various energy sources as well as produces harmful air pollution. Nitrogen oxides, SO2 (sulfur dioxide), and other polluting gases are damaging the environment and people's health, with a particular spike in incidences of many air pollution-related diseases in recent years. While there have been many documents discussing China's energy and environmental issues in the past, few of them analyze economic development, air pollution, and residents' health together. Therefore, this study uses the modified undesirable dynamic two-stage DEA (data envelopment analysis) model to explore the economic, environmental, and health efficiencies of 30 provinces in China. The empirical results show the following: (1) Most provinces have lower efficiency values in the health stage than in the production stage. (2) Among the provinces with annual efficiency values below 1, their energy consumption, CO2 (carbon dioxide), and NOx (nitrogen oxide) efficiency values have mostly declined from 2013 to 2016, while their SO2 efficiency values have increased (less SO2 emissions). (3) The growth rate of SO2 efficiency in 2016 for 10 provinces is much higher than in previous years. (4) The health expenditure efficiencies of most provinces are at a lower level and show room for improvement. (5) In most provinces, the mortality rate is higher, but on a decreasing trend. (6) Finally, as representative for a typical respiratory infection, most provinces have a high level of tuberculosis efficiency, indicating that most areas of China are highly effective at respiratory disease governance.
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19
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Huang WZ, Yang BY, Yu HY, Bloom MS, Markevych I, Heinrich J, Knibbs LD, Leskinen A, Dharmage SC, Jalaludin B, Morawska L, Jalava P, Guo Y, Lin S, Zhou Y, Liu RQ, Feng D, Hu LW, Zeng XW, Hu Q, Yu Y, Dong GH. Association between community greenness and obesity in urban-dwelling Chinese adults. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 702:135040. [PMID: 31726339 DOI: 10.1016/j.scitotenv.2019.135040] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 10/14/2019] [Accepted: 10/16/2019] [Indexed: 05/23/2023]
Abstract
Living in greener places may protect against obesity, but epidemiological evidence is inconsistent and mainly comes from developed nations. We aimed to investigate the association between greenness and obesity in Chinese adults and to assess air pollution and physical activity as mediators of the association. We recruited 24,845 adults from the 33 Communities Chinese Health Study in 2009. Central and peripheral obesity were defined by waist circumference (WC) and body mass index (BMI), respectively, based on international obesity standards. The Normalized Difference Vegetation Index (NDVI) was used to quantify community greenness. Two-level logistic and generalized linear mixed regression models were used to evaluate the association between NDVI and obesity, and a conditional mediation analysis was used also performed. In the adjusted models, an interquartile range increase in NDVI500-m was significantly associated with lower odds of peripheral 0.80 (95% confidence interval [CI]: 0.74-0.87) and central obesity 0.88 (95% CI: 0.83-0.93). Higher NDVI values were also significantly associated with lower BMI. Age, gender, and household income significantly modified associations between greenness and obesity, with stronger associations among women, older participants, and participants with lower household incomes. Air pollution mediated 2.1-20.8% of the greenness-obesity associations, but no mediating effects were observed for physical activity. In summary, higher community greenness level was associated with lower odds of central and peripheral obesity, especially among women, older participants, and those with lower household incomes. These associations were partially mediated by air pollutants. Future well-designed longitudinal studies are needed to confirm our findings.
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Affiliation(s)
- Wen-Zhong Huang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Bo-Yi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Hong-Yao Yu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Michael S Bloom
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Department of Environmental Health Sciences and Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY 12144, USA
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336 Munich, Germany
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336 Munich, Germany
| | - Luke D Knibbs
- School of Public Health, The University of Queensland, Herston, Queensland 4006, Australia
| | - Ari Leskinen
- Finnish Meteorological Institute, Kuopio 70211, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio 70211, Finland
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Queensland 4001, Australia
| | - Pasi Jalava
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, FI 70211, Finland
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shao Lin
- Department of Environmental Health Sciences and Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY 12144, USA; Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336 Munich, Germany
| | - Yang Zhou
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ru-Qing Liu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Dan Feng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Wen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Qiang Hu
- Department of Pediatric Surgery, Weifang People's Hospital, Weifang 261041, China.
| | - Yunjing Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China.
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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How Birth Season Affects Vulnerability to the Effect of Ambient Ozone Exposure on the Disease Burden of Hypertension in the Elderly Population in a Coastal City in South China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17030824. [PMID: 32012989 PMCID: PMC7036818 DOI: 10.3390/ijerph17030824] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/18/2020] [Accepted: 01/22/2020] [Indexed: 01/13/2023]
Abstract
Birth season is an important factor that reflects prenatal nutritional conditions during early development, and which might have lifelong impacts on health. Moreover, ambient ozone pollution has been considered an important environmental risk factor for hypertension. However, whether birth season affects vulnerability to the effect of ambient ozone exposure on late-life hypertension is still unknown. A flexible case–crossover design was used to explore the effect of ambient ozone exposure on the disease burden of hypertension using years of life lost (YLL) in the elderly population in a coastal city in South China from 2013 to 2016. The influence of birth season was also explored. Ozone exposure was significantly associated with increased YLL from hypertension. The association was higher in the elderly individuals who were born in autumn than in those born in other seasons. Specifically, every 10 μg/m3 increase in ozone was associated with 0.68 (95% CI: 0.27, 1.10) YLL from hypertension in the elderly population born in autumn, while nonsignificant associations were found for those born in other seasons. The birth season, which affects the nutritional condition during early development, could affect vulnerability to the effect of ambient ozone exposure on the disease burden of hypertension in late life. The findings highlighted the importance of taking birth season into consideration when exploring the hypertensive effects of ozone exposure.
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21
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Ma R, Ban J, Wang Q, Li T. Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 701:134463. [PMID: 31704405 DOI: 10.1016/j.scitotenv.2019.134463] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 08/28/2019] [Accepted: 09/13/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Studies have discovered the adverse health impacts of ambient ozone. Most epidemiological studies explore the relationship between ambient ozone and health effects based on fixed site monitoring data. Fine modeling of ground-level ozone exposure conducted by statistical models has great advantages for improving exposure accuracy and reducing exposure bias. However, there is no review summarizing such studies. OBJECTIVES A review is presented to summarize the basic process of model development and to provide some suggestions for researchers. METHODS A search of PubMed, Web of Science and the Wanfang Database was performed for dates through July 1, 2019 to obtain relevant studies worldwide. We also examined the references of the articles of interest to ensure that as many articles as possible were included. RESULTS The land use regression model (LUR model), random forest model and artificial neural network model have been used in this field. We summarized these studies in terms of model selection, data preparation, simulation scale selection, and model establishment and validation. Multiparameters are a major feature of models. Parameters that influence the formation of ground-level ozone concentrations and parameters that have been extremely important in previous articles should be considered first. The process of model establishment and validation is essentially a process of continuously optimizing the model performance, but there are certain differences in the specific models. CONCLUSION This review summarized the basic process of the statistical model for ambient ozone exposure. We gave the applicable conditions and application scope of different models and summarized the advantages and disadvantages of various models in ozone modeling research. In the future, research is still needed to explore this area based on its own research purposes and capabilities.
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Affiliation(s)
- Runmei Ma
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Jie Ban
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Qing Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Tiantian Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
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Zhang S, Liu D, Gesang DZ, Lv M. Characteristics of Cerebral Stroke in the Tibet Autonomous Region of China. Med Sci Monit 2020; 26:e919221. [PMID: 31917778 PMCID: PMC6977622 DOI: 10.12659/msm.919221] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 10/03/2019] [Indexed: 11/09/2022] Open
Abstract
It is well known that cerebrovascular disease has become an important cause of adult death and disability. Strikingly, the Tibet Autonomous Region (TAR) ranks on the top in China for the incidence of stroke. To help explain this phenomenon, we have searched for and analyzed stroke-related literature for the TAR in the past 2 decades and have referenced reports from other regions at similar altitudes. This article focuses on epidemiology features, risk factors, and pathogenesis of stroke in the TAR in an effort to generate a better understanding of the characteristics of stroke in this region. The special plateau-related factors such as its high elevation, limited oxygen, the high incidence of hypertension, smoking, and the unique dietary habits of the region are correlated with the high incidence of stroke. In addition to these factors, the pathogenesis of stroke in this high-altitude area is also unique. However, there is no established explanation for the unique occurrence and high incidence of stroke in the TAR. Our study provides an important rationale not only for the clinic to prevent and treat this disease, but also for the government to develop appropriate health policies for the prevention of stroke in the TAR.
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Affiliation(s)
- Shuai Zhang
- Department of Neurosurgery, Beijing Aerospace General Hospital, Beijing, P.R. China
| | - Dong Liu
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, P.R. China
| | - Dun Zhu Gesang
- Department of Neurosurgery, Second People’s Hospital of Tibet Autonomous Region, Lhasa, Tibet Autonomous Region, P.R. China
| | - Ming Lv
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
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Dynamic Linkages among Economic Development, Energy Consumption, Environment and Health Sustainable in EU and Non-EU Countries. Healthcare (Basel) 2019; 7:healthcare7040138. [PMID: 31698803 PMCID: PMC6955713 DOI: 10.3390/healthcare7040138] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/04/2019] [Accepted: 11/04/2019] [Indexed: 11/17/2022] Open
Abstract
There is a close and important relationship between environmental pollution and public health, and environmental pollution has an important impact on the public health. This study employed the two-stage meta-frontier dynamic network data envelopment analysis (TMDN-DEA) model to explore the environment pollution effects from energy consumption on the mortality of children and adult, tuberculosis rate, survival rate and health expenditure efficiencies in 28 EU countries and 53 non-EU countries from 2010 to 2014. We calculated the overall efficiency scores and the technology gap ratios of each EU and non-EU countries and the efficiencies of input and output variables in the production and health stage. The average overall efficiencies each year in EU countries are higher than in the non-EU countries. But EU countries have higher energy efficiency than non-EU countries, and non-EU countries have higher health efficiency than EU countries. The health expenditure efficiencies in the EU countries are obviously lower than those in non-EU countries. The renewable energy efficiencies are obviously higher than the non-renewable energy efficiencies; PM2.5 efficiencies are obviously higher than the CO2 efficiencies and the children’s mortality rate efficiencies are higher than the adult’s mortality rate efficiencies for EU countries and non-EU countries. The government management in the EU and non-EU countries should be strengthened to reduce the air pollutant and carbon dioxide emissions and raise energy transformation to the clean energy in renewable energy and improve health efficiencies in medical and health care field.
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Energy and Health Efficiencies in China with the Inclusion of Technological Innovation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16214225. [PMID: 31683540 PMCID: PMC6862312 DOI: 10.3390/ijerph16214225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 10/26/2019] [Accepted: 10/27/2019] [Indexed: 01/01/2023]
Abstract
The price people pay for low energy efficiency includes not only high manufacturing costs, but also public health. With technological innovation as the driving factor for improving energy efficiency, this study uses two-stage dynamic undesirable data envelopment analysis (TDU-DEA) under variable return to scale to evaluate energy and health efficiencies with inclusion of technological innovation in 30 provinces of China over the period 2013–2016. The results show that the mean overall efficiencies and ranks in the eastern region are significantly higher than those in the non-eastern region, with or without the inclusion of technological innovations, and that energy efficiency in most provinces is higher than health efficiency. The average technological innovation efficiencies for energy conservation are higher than those for respiratory medical treatment. The former gap between the eastern region and non-east region is also smaller than the latter. Lastly, regions with the best technological innovation efficiencies are Beijing, Shanghai, Guangdong, Fujian, Hainan, Hebei, Inner Mongolia, Ningxia, Qinghai, Shandong, Shanxi, Tianjin, Xinjiang, and Yunnan.
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Santos UP, Ferreira Braga AL, Bueno Garcia ML, Amador Pereira LA, Lin CA, Chiarelli PS, Saldiva de André CD, Afonso de André P, Singer JM, Nascimento Saldiva PH. Exposure to fine particles increases blood pressure of hypertensive outdoor workers: A panel study. ENVIRONMENTAL RESEARCH 2019; 174:88-94. [PMID: 31054526 DOI: 10.1016/j.envres.2019.04.021] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 04/19/2019] [Accepted: 04/20/2019] [Indexed: 05/27/2023]
Abstract
BACKGROUND Hypertension and air pollution are two important risk factors for cardiovascular morbidity and mortality. Although several studies suggest that air pollution has a significant impact on blood pressure, studies on long-term effects are sparse and still controversial. OBJECTIVE To evaluate the effects of exposure of outdoor workers to different levels of traffic-generated PM2.5 on blood pressure. DESIGN This is an observational panel study. PARTICIPANTS 88 non-smoking workers exposed to different concentrations of air pollution were evaluated weekly along four successive weeks. MEASUREMENTS In each week, personal monitoring of 24-h PM2.5 concentration and 24-h ambulatory blood pressure were measured. The association between blood pressure variables and PM2.5, adjusted for age, body mass index, time in job, daily work hours, diabetes, hypertension and cholesterol was assessed by means of multiple linear regression models fitted by least squares. RESULTS Exposure to PM2.5 (ranging from 8.5 to 89.7 μg/m3) is significantly and consistently associated with an increase in average blood pressure. An elevation of 10 μg/m3 in the concentration of PM2.5 is associated with increments of 3.9 mm Hg (CI 95% = [1.5; 6.3]) in average systolic 24-h blood pressure for hypertensive and/or diabetic workers. CONCLUSION Exposure to fine particles, predominantly from vehicular traffic, is associated with elevated blood pressure in hypertensive and/or diabetic workers.
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Affiliation(s)
- Ubiratan Paula Santos
- Divisao de Pneumologia do Instituto do Coraçao (InCor) Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Enéas Carvalho de Aguiar, 44, 8 Andar, Jardim Paulista, CEP 05403-000, Sao Paulo, SP, Brazil.
| | - Alfésio Luís Ferreira Braga
- Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution, Departamento de Patologia, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Arnaldo, 455, 1 Andar sala 1304, Cerqueira César, CEP 01246-903, Sao Paulo, SP, Brazil; Environmental Exposure and Risk Assessment Group, Collective Health Post-Graduation Program, Catholic University of Santos. Avenida Conselheiro Nébias, 300, Vila Mathias, CEP 11015-002, Santos, SP, Brazil
| | - Maria Lúcia Bueno Garcia
- Departamento de Medicina Interna, Faculdade de Medicina, Universidade de Sao Paulo. Avenida Dr. Arnaldo, 455, 1 Andar, Cerqueira César, CEP 01246-903, São Paulo, SP, Brazil
| | - Luiz Alberto Amador Pereira
- Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution, Departamento de Patologia, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Arnaldo, 455, 1 Andar sala 1304, Cerqueira César, CEP 01246-903, Sao Paulo, SP, Brazil; Environmental Exposure and Risk Assessment Group, Collective Health Post-Graduation Program, Catholic University of Santos. Avenida Conselheiro Nébias, 300, Vila Mathias, CEP 11015-002, Santos, SP, Brazil
| | - Chin An Lin
- Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution, Departamento de Patologia, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Arnaldo, 455, 1 Andar sala 1304, Cerqueira César, CEP 01246-903, Sao Paulo, SP, Brazil; Departamento de Medicina Interna, Faculdade de Medicina, Universidade de Sao Paulo. Avenida Dr. Arnaldo, 455, 1 Andar, Cerqueira César, CEP 01246-903, São Paulo, SP, Brazil
| | - Paulo S Chiarelli
- Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution, Departamento de Patologia, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Arnaldo, 455, 1 Andar sala 1304, Cerqueira César, CEP 01246-903, Sao Paulo, SP, Brazil
| | - Carmen Diva Saldiva de André
- Instituto de Matemática e Estatística, Universidade de São Paulo. Rua do Matão, 1010, Butantã, CEP 05508-090, São Paulo, SP, Brazil
| | - Paulo Afonso de André
- Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution, Departamento de Patologia, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Arnaldo, 455, 1 Andar sala 1304, Cerqueira César, CEP 01246-903, Sao Paulo, SP, Brazil
| | - Julio M Singer
- Instituto de Matemática e Estatística, Universidade de São Paulo. Rua do Matão, 1010, Butantã, CEP 05508-090, São Paulo, SP, Brazil
| | - Paulo Hilário Nascimento Saldiva
- Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution, Departamento de Patologia, Faculdade de Medicina, Universidade de São Paulo. Avenida Dr. Arnaldo, 455, 1 Andar sala 1304, Cerqueira César, CEP 01246-903, Sao Paulo, SP, Brazil; Instituto de Estudos Avançados da Universidade de São Paulo. Rua do Anfiteatro, 513, Butantã, CEP 05508-060, São Paulo, SP, Brazil
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26
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Yang BY, Markevych I, Bloom MS, Heinrich J, Guo Y, Morawska L, Dharmage SC, Knibbs LD, Jalaludin B, Jalava P, Zeng XW, Hu LW, Liu KK, Dong GH. Community greenness, blood pressure, and hypertension in urban dwellers: The 33 Communities Chinese Health Study. ENVIRONMENT INTERNATIONAL 2019; 126:727-734. [PMID: 30878868 DOI: 10.1016/j.envint.2019.02.068] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 02/10/2019] [Accepted: 02/25/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Living in greener areas has many health benefits, but evidence concerning the effects on blood pressure remains mixed. We sought to assess associations between community greenness and both blood pressure and hypertension in Chinese urban dwellers, and whether the associations were mediated by air pollution, body mass index, and physical activity. METHODS We analyzed data from 24,845 adults participating in the 33 Communities Chinese Health Study, which was conducted in Northeastern China during 2009. We measured each participant's blood pressure according to a standardized protocol. We assessed community greenness using two satellite-derived vegetation indexes - the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). Particulate matter ≤2.5 μm and nitrogen dioxide were used as proxies of ambient air pollution. We applied generalized linear mixed models to investigate the association between greenness and blood pressure. We also performed mediation analyses. RESULTS Living in greener areas was associated with lower blood pressure and hypertension prevalence; an interquartile range increase in both NDVI500-m and SAVI500-m were significantly associated with reductions in systolic blood pressure of 0.82 mm Hg (95% CI: -1.13, -0.51) and 0.89 mm Hg (95% CI: -1.21, -0.57), respectively. The same increases in greenness were also significantly associated with a 5% (95% CI: 1%, 8%) and 5% (95% CI: 1%, 9%) lower odds of having hypertension, respectively. These associations remained consistent in sensitivity analyses. The associations were stronger among women than men. Air pollutants and body mass index partly mediated the associations, but there was no evidence of mediation effects for physical activity. CONCLUSIONS Our findings indicate beneficial associations between community greenness and blood pressure in Chinese adults, especially for women. Air pollution and body mass index only partly mediated the associations.
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Affiliation(s)
- Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336 Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Munich, Ludwig Maximilian University of Munich, Munich, Germany
| | - Michael S Bloom
- Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY 12144, USA
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336 Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstraße 1, 80336 Munich, Germany
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, Queensland 4001, Australia
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Murdoch Children Research Institute, Melbourne, VIC 3010, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Queensland, Herston, Queensland 4006, Australia
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia; Population Health, South Western Sydney Local Health District, Liverpool, NSW 2170, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia; School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW 2052, Australia
| | - Pasi Jalava
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio FI 70211, Finland
| | - Xiao-Wen Zeng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Kang-Kang Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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27
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Song J, Lu M, Lu J, Chao L, An Z, Liu Y, Xu D, Wu W. Acute effect of ambient air pollution on hospitalization in patients with hypertension: A time-series study in Shijiazhuang, China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2019; 170:286-292. [PMID: 30530180 DOI: 10.1016/j.ecoenv.2018.11.125] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 11/20/2018] [Accepted: 11/28/2018] [Indexed: 06/09/2023]
Abstract
Although numerous studies have investigated the association between air pollution and hospitalization, few studies have focused on the health effect of air pollution on populations with hypertension. In this study, we conducted a time-series study to investigate the acute adverse effect of six criteria ambient air pollutants (fine particulate matter [PM2.5], inhalable particulate matter [PM10], nitrogen dioxide [NO2], sulfur dioxide [SO2], ozone [O3], and carbon monoxide [CO]) on hospitalization of patients for hypertension in Shijiazhuang, China, from 2013 to 2016. An over-dispersed Poisson generalized addictive model adjusting for weather conditions, day of the week, and long-term and seasonal trends was used. In addition, we evaluated the effect of modification by season, sex, and age. A total of 650,550 hospitalization records were retrieved during the study period. A 10 μg/m3 increase of PM2.5 (lag06), PM10 (lag06), NO2 (lag03), O3 (lag6), and CO (lag04) corresponded to 0.56% (95% confidence interval [CI]: 0.28-0.83%), 0.31% (95% CI: 0.12-0.50%), 1.18% (95% CI: 0.49-1.87%), 0.40% (95% CI: 0.09-0.71%), and 0.03% (95% CI: 0.01-0.05%) increments in hospitalization of patients for hypertension, respectively. We observed statistically significant associations with PM2.5, PM10, NO2, O3, and CO, while positive but insignificant associations with SO2. The effects of PM2.5, PM10, NO2, O3, and CO were robust when adjusted for co-pollutants. We found stronger associations in the cool season than in the warm season. Moreover, there were non-significant differences in the associations between air pollution and sex or age group. This study suggests that patients with hypertension had an increased risk of hospital admission when exposed to air pollution.
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Affiliation(s)
- Jie Song
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang 453003, China.
| | - Mengxue Lu
- Xinxiang Medical University, Xinxiang 453003, China
| | - Jianguo Lu
- The First Affiliated Hospital of Xinxiang Medical University, Xinxiang 453100, China
| | - Ling Chao
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China
| | - Zhen An
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang 453003, China
| | - Yue Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Dongqun Xu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Weidong Wu
- School of Public Health, Xinxiang Medical University, Xinxiang 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang 453003, China
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28
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Yang BY, Guo Y, Bloom MS, Xiao X, Qian ZM, Liu E, Howard SW, Zhao T, Wang SQ, Li S, Chen DH, Ma H, Yim SHL, Liu KK, Zeng XW, Hu LW, Liu RQ, Feng D, Yang M, Xu SL, Dong GH. Ambient PM 1 air pollution, blood pressure, and hypertension: Insights from the 33 Communities Chinese Health Study. ENVIRONMENTAL RESEARCH 2019; 170:252-259. [PMID: 30597289 DOI: 10.1016/j.envres.2018.12.047] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 12/18/2018] [Accepted: 12/20/2018] [Indexed: 05/27/2023]
Abstract
No evidence exists concerning the association between blood pressure and ambient particles with aerodynamic diameter ≤ 1.0 µm (PM1), a major component of PM2.5 (≤ 2.5 µm) particles, and potentially causing more hazardous health effects than PM2.5. We aimed to examine the associations of blood pressure in adults with both PM1 and PM2.5 in China. In 2009, we randomly selected 24,845 participants aged 18-74 years from 33 communities in China. Using a standardized mercuric-column sphygmomanometer, we measured blood pressure. Long-term exposure (2006-08) to PM1 and PM2.5 were estimated using a spatial statistical model. Generalized linear mixed models were used to evaluate the associations between air pollutants and blood pressure and hypertension prevalence, controlling for multiple covariates. A 10-μg/m3 increase in PM1 was significantly associated with an increase of 0.57 (95% CI 0.31-0.83) mmHg in systolic blood pressure (SBP), 0.19 (95% CI 0.03-0.35) mmHg increase in diastolic blood pressure (DBP), and a 5% (OR=1.05; 95% CI 1.01-1.10) increase in odds for hypertension. Similar associations were detected for PM2.5. Furthermore, PM1-2.5 showed no association with blood pressure or hypertension. In summary, both PM1 and PM2.5 exposures were associated with elevated blood pressure levels and hypertension prevalence in Chinese adults. In addition, most of the pro-hypertensive effects of PM2.5 may come from PM1. Further longitudinal designed studies are warranted to validate our findings.
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Affiliation(s)
- Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Michael S Bloom
- Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, United States
| | - Xiang Xiao
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, United States
| | - Echu Liu
- Department of Health Management and Policy, College for Public Health and Social Justice Saint Louis University, Saint Louis, MO 63104, United States
| | - Steven W Howard
- Department of Health Management and Policy, College for Public Health and Social Justice Saint Louis University, Saint Louis, MO 63104, United States
| | - Tianyu Zhao
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, Ludwig Maximilian University of Munich, Comprehensive Pneumology Center (CPC) Munich, Member DZL, German Center for Lung Research, 80336 Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Si-Quan Wang
- Department of Biostatistics, Havard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Duo-Hong Chen
- Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou 510308, China
| | - Huimin Ma
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Steve Hung-Lam Yim
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Stanley Ho Big Data Decision Analytics Research Centre, Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
| | - Kang-Kang Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiao-Wen Zeng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ru-Qing Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Dan Feng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Mo Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shu-Li Xu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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29
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Yang BY, Guo Y, Morawska L, Bloom MS, Markevych I, Heinrich J, Dharmage SC, Knibbs LD, Lin S, Yim SHL, Chen G, Li S, Zeng XW, Liu KK, Hu LW, Dong GH. Ambient PM 1 air pollution and cardiovascular disease prevalence: Insights from the 33 Communities Chinese Health Study. ENVIRONMENT INTERNATIONAL 2019; 123:310-317. [PMID: 30557810 DOI: 10.1016/j.envint.2018.12.012] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 11/16/2018] [Accepted: 12/05/2018] [Indexed: 05/13/2023]
Abstract
BACKGROUNDS Evidence on the association between long-term exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) and cardiovascular disease (CVD) is scarce in developing countries. Moreover, few studies assessed the role of the PM1 (≤1.0 μm) size fraction and CVD. We investigated the associations between PM1 and PM2.5 and CVD prevalence in Chinese adults. METHODS In 2009, we randomly recruited 24,845 adults at the age of 18-74 years from 33 communities in Northeastern China. CVD status was determined by self-report of doctor-diagnosed CVD. Three-year (2006-08) average concentrations of PM1 and PM2.5 were assigned using a satellite-based exposure. We used spatial Generalized Linear Mixed Models to evaluate the associations between air pollutants and CVD prevalence, adjusting for multiple covariates. Stratified and interaction analyses and sensitivity analyses were also performed. RESULTS A 10 μg/m3 increase in long-term exposure to ambient PM1 levels was associated a 12% higher odds for having CVD (OR = 1.12; 95% CI = 1.05-1.20). Compared to PM1, association between PM2.5 and CVD was lower (OR = 1.06; 95% CI = 1.01-1.11). No significant association was observed for PM1-2.5 (1-2.5 μm) size fraction (OR = 0.98; 95% CI = 0.85-1.13). Stratified analyses showed greater effect estimates in men and the elder. CONCLUSIONS Long-term PM1 exposure was positively related to CVD, especially in men and the elder. In addition, PM1 may play a greater role than PM2.5 in associations with CVD. Further longitudinal studies are warranted to confirm our findings.
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Affiliation(s)
- Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Lidia Morawska
- Queensland University of Technology, International Laboratory for Air Quality & Health, Brisbane, QLD, Australia; Queensland University of Technology, Science and Engineering Faculty, Brisbane, QLD, Australia
| | - Michael S Bloom
- Department of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Germany; Institute of Epidemiology, Helmholtz ZentrumMünchen-German Research Center for Environmental Health, Neuherberg, Germany; Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Munich, Ludwig Maximilian University of Munich, Munich, Germany
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstrasse 1, 80336 Muenchen, Germany
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia; Murdoch Childrens Research Institute, Melbourne, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Queensland, Herston, Queensland 4006, Australia
| | - Shao Lin
- Department of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Steve Hung-Lam Yim
- Department of Geography and Resource Management, The Chinese University of Hong Kong; Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
| | - Gongbo Chen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Xiao-Wen Zeng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Kang-Kang Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Associations of greenness with diabetes mellitus and glucose-homeostasis markers: The 33 Communities Chinese Health Study. Int J Hyg Environ Health 2018; 222:283-290. [PMID: 30545606 DOI: 10.1016/j.ijheh.2018.12.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 11/24/2018] [Accepted: 12/02/2018] [Indexed: 01/19/2023]
Abstract
BACKGROUND Residing in greener places may be protective against diabetes mellitus (DM) but evidence is scarce and comes mainly from developed countries. OBJECTIVES To investigate associations of residential greenness with DM prevalence and glucose-homeostasis markers in Chinese adults and whether these associations were mediated by air pollution, physical activity, and body mass index. METHODS In 2009, a total of 15,477 adults from the cross-sectional 33 Communities Chinese Health Study provided blood samples and completed a questionnaire. We considered fasting and 2-h glucose and insulin concentrations, as well as the homoeostasis model assessment of insulin resistance and β-cell function, as glucose-homeostasis markers. DM was defined according to the American Diabetes Association's recommendations. Residential greenness was estimated by two satellite-derived vegetation indexes - Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). Nitrogen dioxide and particulate matter ≤2.5 μm were used as air pollution proxies. Associations were assessed by two-level adjusted logistic and linear regression models. RESULTS A 0.1-unit increase in NDVI500 m and SAVI500 m was significantly associated with lower odds of DM by factors of 0.88 (95% Confidence Interval 0.82-0.94) and 0.80 (0.72-0.90), respectively. Higher greenness was also significantly associated with lower fasting and 2-h glucose levels, 2-h insulin level, as well as lower insulin resistance and higher β-cell function. Air pollution and body mass index significantly mediated 6.9-51.1% and 8.6-78.7% these associations, respectively, while no mediation role was observed for physical activity. CONCLUSIONS Higher residential greenness appears to be associated with a lower prevalence of DM. This association might be due to glucose and insulin metabolism and pancreatic β-cell function. Lower levels of air pollution and body mass index can be pathways linking greenspace to diabetes.
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Afsar B, Elsurer Afsar R, Kanbay A, Covic A, Ortiz A, Kanbay M. Air pollution and kidney disease: review of current evidence. Clin Kidney J 2018; 12:19-32. [PMID: 30746128 PMCID: PMC6366136 DOI: 10.1093/ckj/sfy111] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/02/2018] [Indexed: 12/22/2022] Open
Abstract
Along with amazing technological advances, the industrial revolution of the mid-19th century introduced new sources of pollution. By the mid-20th century, the effects of these changes were beginning to be felt around the world. Among these changes, health problems due to environmental air pollution are increasingly recognized. At the beginning, respiratory and cardiovascular diseases were emphasized. However, accumulated data indicate that every organ system in the body may be involved, and the kidney is no exception. Although research on air pollution and kidney damage is recent, there is now scientific evidence that air pollution harms the kidney. In this holistic review, we have summarized the epidemiology, disease states and mechanisms of air pollution and kidney damage.
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Affiliation(s)
- Baris Afsar
- Department of Nephrology, Suleyman Demirel University School of Medicine, Isparta, Turkey
| | - Rengin Elsurer Afsar
- Department of Nephrology, Suleyman Demirel University School of Medicine, Isparta, Turkey
| | - Asiye Kanbay
- Department of Pulmonary Medicine, Istanbul Medeniyet University School of Medicine, Istanbul, Turkey
| | - Adrian Covic
- Nephrology Department, Dialysis and Renal Transplant Center, "Dr. C.I. Parhon" University Hospital, "Grigore T. Popa" University of Medicine and Pharmacy, Iasi, Romania
| | - Alberto Ortiz
- Dialysis Unit, School of Medicine, IIS-Fundacion Jimenez Diaz, Universidad Autónoma de Madrid, Madrid, Spain
| | - Mehmet Kanbay
- Division of Nephrology, Department of Medicine, Koc University School of Medicine, Istanbul, Turkey
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32
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Yang BY, Bloom MS, Markevych I, Qian ZM, Vaughn MG, Cummings-Vaughn LA, Li S, Chen G, Bowatte G, Perret JL, Dharmage SC, Heinrich J, Yim SHL, Lin S, Tian L, Yang M, Liu KK, Zeng XW, Hu LW, Guo Y, Dong GH. Exposure to ambient air pollution and blood lipids in adults: The 33 Communities Chinese Health Study. ENVIRONMENT INTERNATIONAL 2018; 119:485-492. [PMID: 30048882 DOI: 10.1016/j.envint.2018.07.016] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 07/08/2018] [Accepted: 07/09/2018] [Indexed: 05/05/2023]
Abstract
BACKGROUND Little information exists on the lipidemic effects of air pollution, particularly in developing countries. We aimed to investigate the associations of long-term exposure to ambient air pollutants with lipid levels and dyslipidemias in China. METHODS In 2009, a total of 15,477 participants aged 18-74 years were recruited from the 33 Communities Chinese Health Study conducted in three Northeastern China cities. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured in participants' blood specimens. Three year (2006-08) average air pollution concentrations were assessed using data from 33 communities (particles with diameters ≤1.0 μm (PM1) and ≤2.5 μm (PM2.5) were predicted using a spatial statistical model) or 11 air monitoring stations (particles with diameters ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)). Associations were evaluated by two-level logistic and generalized linear regression models. RESULTS We detected many significant associations between exposure to air pollutants (especially for PM1 and PM2.5) and blood lipid levels. Most of the associations suggested deleterious effects on blood lipid markers (e.g., a 10 μg/m3 increase in PM1 was associated with 1.6% (95% confidence interval (CI): 1.1, 2.0), 2.9% (95% CI: -3.3, 9.3), and 3.2% (95% CI: 2.6, 3.9) higher levels of TC, TG, and LDL-C, respectively, but 1.4% (95% CI: -1.8, -0.9) lower HDL-C levels), although beneficial associations were found for O3. In analysis with dyslipidemias, all the observed associations suggested deleterious lipidemic effects of air pollutants, and no significant beneficial association was observed for O3. Stratified analyses showed that the associations were stronger in overweight or obese participants; sex and age modified the associations, but the pattern of effects was mixed. CONCLUSIONS Long-term ambient air pollution was associated with both altered lipid profiles and dyslipidemias, especially among overweight or obese participants.
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Affiliation(s)
- Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Michael S Bloom
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Zhengmin Min Qian
- Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA
| | - Michael G Vaughn
- School of Social Work, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA
| | - Lenise A Cummings-Vaughn
- Division of Geriatrics and Nutritional Science, School of Medicine, Washington University-St. Louis, 4921 Parkview Place, St.Louis, MO 63110, USA
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia
| | - Gongbo Chen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia
| | - Gayan Bowatte
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia; National Institute of Fundamental Studies, Kandy, Sri Lanka
| | - Jennifer L Perret
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia; Murdoch Childrens Research Institute, Melbourne, Australia
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstrasse 1, 80336 Muenchen, Germany
| | - Steve Hung-Lam Yim
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China; Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China; Institute of Environment, Energy and Sustainability, The Chinese Uni-versity of Hong Kong, Shatin, NT, Hong Kong, China
| | - Shao Lin
- Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Linwei Tian
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mo Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Kang-Kang Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiao-Wen Zeng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia.
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Abstract
Traffic-related particulate matter (PM) is a major source of outdoor air pollution worldwide. It has been recently hypothesized to cause cardiometabolic syndrome, including cardiovascular dysfunction, obesity, and diabetes. The environmental and toxicological factors involved in the processes, and the detailed mechanisms remain to be explored. The objective of this study is to assess the current scientific evidence of traffic-related PM-induced cardiometabolic syndrome. We conducted a literature review by searching the keywords of “traffic related air pollution”, “particulate matter”, “human health”, and “metabolic syndrome” from 1980 to 2018. This resulted in 25 independent research studies for the final review. Both epidemiological and toxicological findings reveal consistent correlations between traffic-related PM exposure and the measured cardiometabolic health endpoints. Smaller sizes of PM, particularly ultrafine particles, are shown to be more harmful due to their greater concentrations, reactive compositions, longer lung retention, and bioavailability. The active components in traffic-related PM could be attributed to metals, black carbon, elemental carbon, polyaromatic hydrocarbons, and diesel exhaust particles. Existing evidence points out that the development of cardiometabolic symptoms can occur through chronic systemic inflammation and increased oxidative stress. The elderly (especially for women), children, genetically susceptible individuals, and people with pre-existing conditions are identified as vulnerable groups. To advance the characterization of the potential health risks of traffic-related PM, additional research is needed to investigate the detailed chemical compositions of PM constituents, atmospheric transformations, and the mode of action to induce adverse health effects. Furthermore, we recommend that future studies could explore the roles of genetic and epigenetic factors in influencing cardiometabolic health outcomes by integrating multi-omics approaches (e.g., genomics, epigenomics, and transcriptomics) to provide a comprehensive assessment of biological perturbations caused by traffic-related PM.
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Lawrence WR, Yang M, Lin S, Wang SQ, Liu Y, Ma H, Chen DH, Yang BY, Zeng XW, Hu LW, Dong GH. Pet exposure in utero and postnatal decreases the effects of air pollutants on hypertension in children: A large population based cohort study. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 238:177-185. [PMID: 29554565 PMCID: PMC11917086 DOI: 10.1016/j.envpol.2018.03.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/06/2018] [Accepted: 03/11/2018] [Indexed: 05/20/2023]
Abstract
The effect of ambient air pollution exposure on childhood hypertension has emerged as a concern in China, and previous studies suggested pet ownership is associated with lower blood pressure (BP). However, limited information exists on the interactive effects pet ownership and air pollution exposure has on hypertension. We investigated the interactions between exposure to pet ownership and air pollutants on hypertension in Chinese children. 9354 students in twenty-four elementary and middle schools (aged 5-17 years) in Northeastern China were evaluated during 2012-2013. Four-year average concentrations of particulate matter with aerodynamic diameter of ≤10 μm (PM10), SO2, NO2, and O3, were collected in the 24 districts from 2009 to 2012. Hypertension was defined as average diastolic or systolic BP (three time measurements) in the 95th percentile or higher based on height, age, and sex. To examine effects, two-level regression analysis was used, controlling covariates. Consistent interactions between exposure to pet and air pollutants were observed. Compared to children exposed to pet, those not exposed exhibited consistently stronger effects of air pollution. The highest odds ratios (ORs) per 30.6 μg/m3 increase in PM10 were 1.79 (95%confidence interval [95%CI]: 1.29-2.50) in children without current pet exposure compared to 1.24 (95%CI: 0.85-1.82) in children with current pet exposure. As for BP, only O3 had an interaction for all exposure to pet ownership types, and showed lower BP in children exposed to pet. The increases in mean diastolic BP per 46.3 μg/m3 increase in O3 were 0.60 mmHg (95%CI: 0.21, 0.48) in children without pet exposure in utero compared with 0.34 mmHg (95%CI: 0.21, 0.48) in their counterparts. When stratified by age, pet exposure was more protective among younger children. In conclusion, in this large population-based cohort, pet ownership is associated with smaller associations between air pollution and hypertension in children, suggesting pet ownership reduces susceptibility to the health effects of pollutants.
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Affiliation(s)
- Wayne R Lawrence
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, China; School of Public Health, State University of New York at Albany, One University Place, Rensselaer, NY, USA
| | - Mo Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Shao Lin
- School of Public Health, State University of New York at Albany, One University Place, Rensselaer, NY, USA
| | - Si-Quan Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Yimin Liu
- Guangzhou Prevention and Treatment Center for Occupational Diseases, Guangzhou No.12 Hospital, Guangzhou, China
| | - Huimin Ma
- State Key Laboratory of Organic Geochemistry, Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, 511 Tianhua Street, Tianhe District, Guangzhou, China
| | - Duo-Hong Chen
- Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, No. 28 Modiesha Street Xingang Rd. E, Guangzhou, China
| | - Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Xiao-Wen Zeng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, China.
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Yang BY, Qian ZM, Li S, Fan S, Chen G, Syberg KM, Xian H, Wang SQ, Ma H, Chen DH, Yang M, Liu KK, Zeng XW, Hu LW, Guo Y, Dong GH. Long-term exposure to ambient air pollution (including PM 1) and metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS). ENVIRONMENTAL RESEARCH 2018; 164:204-211. [PMID: 29501830 DOI: 10.1016/j.envres.2018.02.029] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 02/09/2018] [Accepted: 02/20/2018] [Indexed: 05/25/2023]
Abstract
Little evidence exists about the effects of long-term exposure to ambient air pollution on metabolic syndrome (MetS). This study aimed to determine the association between long-term ambient air pollution and MetS in China. A total of 15,477 adults who participated in the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. MetS was defined based on the recommendation by the Joint Interim Societies. Exposure to air pollutants was assessed using data from monitoring stations and a spatial statistical model (including particles with diameters ≤ 1.0 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)). Two-level logistic regression analyses were utilized to assess the associations between air pollutants and MetS. The prevalence of MetS was 30.37%. The adjusted odds ratio of MetS per 10 µg/m3 increase in PM1, PM2.5, PM10, SO2, NO2, and O3 were 1.12 (95% CI = 1.00-1.24), 1.09 (95% CI = 1.00-1.18), 1.13 (95% CI = 1.08-1.19), 1.10 (95% CI = 1.02-1.18), 1.33 (95% CI = 1.12-1.57), and 1.10 (95% CI = 1.01-1.18), respectively. Stratified analyses indicated that the above associations were stronger in participants with the demographic variables of males, < 50 years of age, and higher income, as well as with the behavioral characteristics of smoking, drinking, and consuming sugar-sweetened soft drinks frequently. This study indicates that long-term exposure to ambient air pollutants may increase the risk of MetS, especially among males, the young to middle aged, those of low income, and those with unhealthy lifestyles.
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Affiliation(s)
- Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia
| | - Shujun Fan
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
| | - Gongbo Chen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia
| | - Kevin M Syberg
- Department of Health Management and Policy, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA
| | - Hong Xian
- Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA
| | - Si-Quan Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Huimin Ma
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Duo-Hong Chen
- Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou 510308, China
| | - Mo Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Kang-Kang Liu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiao-Wen Zeng
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia.
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Salameh P, Chahine M, Hallit S, Farah R, Zeidan RK, Asmar R, Hosseiny H. Hypertension prevalence and living conditions related to air pollution: results of a national epidemiological study in Lebanon. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:11716-11728. [PMID: 29442307 DOI: 10.1007/s11356-018-1411-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 01/29/2018] [Indexed: 06/08/2023]
Abstract
Hypertension is a risk factor of several diseases, linked to high mortality and morbidity, particularly in developing countries. Some studies have linked indoor and outdoor pollution exposure items to hypertension, but results were inconsistent. Our objective was to assess the association of living conditions related to air pollution to hypertension in Lebanon, a Middle Eastern country. A national cross-sectional study was conducted all over Lebanon. Blood pressure and its related medications were assessed to be able to classify participants as hypertensive or not. Moreover, in addition to living conditions related to air pollution exposure, we assessed potential predictors of hypertension, including sociodemographic characteristics, self-reported health information and biological measurements. Furthermore, we assessed dose-effect relationship of air pollution items in relation with hypertension. Living conditions related to indoor and outdoor air pollution exposures were associated with hypertension, with or without taking biological values into account. Moreover, we found a dose-effect relationship of exposure with risk of disease (15% increase in risk of disease for every additional pollution exposure item), after adjustment for sociodemographics and biological characteristics (Ora = 1.15 [1.03-1.28]). Although additional studies would be necessary to confirm these findings, interventions should start to sensitize the population about the effect of air pollution on chronic diseases. The work on reducing pollution and improving air quality should be implemented to decrease the disease burden on the population and health system.
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Affiliation(s)
- Pascale Salameh
- Faculty of Medical Sciences, Lebanese University, Hadath, Lebanon
- Faculty of Pharmacy, Lebanese University, Hadath, Lebanon
- Institut National de Sante Publique, Epidemiologie Clinique et Toxicologie, Faculty of Public Health, Lebanese University, Beirut, Lebanon
| | - Mirna Chahine
- Faculty of Medical Sciences, Lebanese University, Hadath, Lebanon
- Foundation-Medical Research Institutes, F-MRI, Beirut, Lebanon
| | - Souheil Hallit
- Faculty of Pharmacy, Lebanese University, Hadath, Lebanon.
- Institut National de Sante Publique, Epidemiologie Clinique et Toxicologie, Faculty of Public Health, Lebanese University, Beirut, Lebanon.
- Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Kaslik, Lebanon.
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon.
- Occupational Health Environment Research Team, U1219 BPH Bordeaux Population Health Research Center Inserm - Université de Bordeaux, Bordeaux, France.
- Faculty of Pharmacy, Saint Joseph University, Beirut, Lebanon.
| | - Rita Farah
- Faculty of Pharmacy, Lebanese University, Hadath, Lebanon
- Institut National de Sante Publique, Epidemiologie Clinique et Toxicologie, Faculty of Public Health, Lebanese University, Beirut, Lebanon
- Faculty of Public Health 2, Lebanese University, Fanar, Lebanon
| | - Rouba Karen Zeidan
- Institut National de Sante Publique, Epidemiologie Clinique et Toxicologie, Faculty of Public Health, Lebanese University, Beirut, Lebanon
- Faculty of Public Health 2, Lebanese University, Fanar, Lebanon
| | - Roland Asmar
- Faculty of Medical Sciences, Lebanese University, Hadath, Lebanon
- Foundation-Medical Research Institutes, F-MRI, Beirut, Lebanon
| | - Hassan Hosseiny
- Department of Neurology, Henri Mondor Hospital AP-HP, Creteil, France
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