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Zhang D, Kou W, Luo S, Chen J, An X, Fang S, Liang X. The effect of ambient temperature on lipid metabolism in children: From a prospective cohort study. ENVIRONMENTAL RESEARCH 2024; 261:119692. [PMID: 39068968 DOI: 10.1016/j.envres.2024.119692] [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: 06/05/2024] [Revised: 07/22/2024] [Accepted: 07/25/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Dyslipidemia is increasingly recognized as an essential risk factor for cardiovascular diseases. However, few studies illustrated the effects of ambient temperature exposure (TE) on lipid levels in children. The study aimed to examine the association between ambient TE and lipid levels in children. METHODS Based on a prospective cohort, a total of 2423 children (with 4466 lipids measure person-time) were collected from 2014 to 2019. The meteorological observation data and adjusted variables were collected. Mixed-effect models and generalized additive mixed model (GAMM) were applied to investigate the association between ambient TE and lipid levels. RESULTS A significant negative association was observed between TE and low-density lipoprotein cholesterol (LDL-C) or total cholesterol (TC) levels both in all children [LDL-C, β(95%CI) = -0.350(-0.434,-0.265), P < 0.001; TC, β(95%CI) = -0.274(-0.389,-0.160), P < 0.001] and by different sex group. However, no significant association was found in low-density lipoprotein cholesterol (HDL-C) or triglycerides (TG) levels. The estimated optimal ambient TEs for LDL-C were 18.273 °C and 18.024 °C for girls and boys, respectively. For TC, the optimal ambient TEs were 17.949 °C and 18.024 °C, respectively. With ambient TE decreased, the risk of dyslipidemia increased for both boys [OR = 0.032(0.006,0.179), P < 0.001] and girls [OR = 0.582(0.576,0.587), P < 0.001]. CONCLUSION This study provided a comprehensive illustration about the associations between ambient TE and lipid levels in different sex and ages from a prospective cohort study. The findings will provide evidence for the government to prevent dyslipidemia in vulnerable children through regulating TE.
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Affiliation(s)
- Di Zhang
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China; School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Wei Kou
- Department of Pediatric Otolaryngology Head and Neck Surgery, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Shunqing Luo
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China
| | - Jingyu Chen
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China
| | - Xizhou An
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China
| | - Shenying Fang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, China.
| | - Xiaohua Liang
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
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Li X, Ye Z, Lang H, Fang Y. Income inequality, trust, and depressive symptoms among Chinese adults (CFPS): A causal mediation analysis. J Affect Disord 2024:S0165-0327(24)01663-X. [PMID: 39368779 DOI: 10.1016/j.jad.2024.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 09/27/2024] [Accepted: 10/02/2024] [Indexed: 10/07/2024]
Abstract
OBJECTIVE Income inequality has been linked to depressive disorders, but the pathways behind this impact are insufficiently understood. Hence, we aimed to investigate the impact of income inequality on depressive disorders and evaluate the extent to which this impact is mediated by trust. METHODS Two waves (2012 and 2018) of the China Family Panel Studies (CFPS) were included. Depressive symptoms were assessed using the 8-item Center for Epidemiologic Studies Depression scale (CESD8) and income inequality was measured using the Gini index calculated with household income. Based on the counterfactual framework, causal mediation analysis was applied with the difference-in-difference (DID) method. The sequential ignorability assumption, an important assumption for mediation analysis, was examined by propensity score matching (PSM) and simulation-based sensitivity analysis. RESULTS Compared to the control group (Change of Gini index ≤0), CESD8 scores in the treatment group (Change of Gini index >0) increase by 0.233 (95 % CI: 0.039, 0.430), which 10.1 % (95 % CI: 3.1 %, 46.0 %) was mediated by reductions in trusts at the provincial level. At the county level, income inequality influences depressive symptoms through the indirect path (β=0.008, 95%CI: 0.001, 0.020) instead of the direct path (β= - 0.146, 95%CI: -0.287, 0.000). Dividing the trust, the significant indirect effect appeared in the trust in neighbors, foreigners, government, and doctors at the provincial level. LIMITATION This study didn't deal with the impact of post-treatment confounders of the mediator-outcome relationship. CONCLUSIONS Severe income inequality directly and indirectly exacerbated depressive symptoms. Government should carry out the implementation of decreasing income inequality and improving trust.
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Affiliation(s)
- Xueru Li
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Zirong Ye
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Haoxiang Lang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
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Cohen G, Rowland ST, Benavides J, Lindert J, Kioumourtzoglou MA, Parks RM. Daily temperature variability and mental health-related hospital visits in New York State. ENVIRONMENTAL RESEARCH 2024; 257:119238. [PMID: 38815717 DOI: 10.1016/j.envres.2024.119238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/11/2024] [Accepted: 05/25/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Despite plausible behavioral and physiological pathways, limited evidence exists on how daily temperature variability is associated with acute mental health-related episodes. OBJECTIVES We aimed to explore associations between daily temperature range (DTR) and mental health-related hospital visits using data of all hospital records in New York State during 1995-2014. We further examined factors that may modify these associations, including age, sex, hospital visit type and season. METHODS Using a case-crossover design with distributed lag non-linear DTR terms (0-6 days), we estimated associations between ZIP Code-level DTR and hospital visits for mood (4.6 million hospital visits), anxiety (2.4 million), adjustment (∼368,000), and schizophrenia disorders (∼211,000), controlling for daily mean temperature, via conditional logistic regression models. We assessed potential heterogeneity by age, sex, hospital visit type (in-patient vs. out-patient), and season (summer, winter, and transition seasons). RESULTS For all included outcomes, we observed positive associations from period minimum DTR (0.1 °C) until 25th percentile (5.2 °C) and between mean DTR (7.7 °C) and 90th percentile (12.2 °C), beyond which we observed negative associations. For mood disorders, an increase in DTR from 0.1 °C to 12.2 °C was associated with a cumulative 16.0% increase (95% confidence interval [CI]: 12.8, 19.2%) in hospital visit rates. This increase was highest during transition seasons (32.5%; 95%CI: 26.4, 39.0%) compared with summer (10.7%; 95%CI: 4.8, 16.8%) and winter (-1.6%; 95%CI: -7.6, 4.7%). For adjustment and schizophrenia disorders, the strongest associations were seen among the youngest group (0-24 years) with almost no association in the oldest group (65+ years). We observed no evidence for modification by sex and hospital visit type. DISCUSSION Daily temperature variability was positively associated with mental health-related hospital visits within specific DTR ranges in New York State, after controlling for daily mean temperature. Given uncertainty on how climate change modifies temperature variability, additional research is crucial to comprehend the implications of these findings, particularly under different scenarios of future temperature variability.
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Affiliation(s)
- Gali Cohen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA; Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Israel
| | - Sebastian T Rowland
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Jaime Benavides
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Jutta Lindert
- Department of Health and Social Work, University of Applied Sciences Emden, Emden, Germany
| | | | - Robbie M Parks
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
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Xu R, Luo L, Yuan T, Chen W, Wei J, Shi C, Wang S, Liang S, Li Y, Zhong Z, Liu L, Zheng Y, Deng X, Liu T, Fan Z, Liu Y, Zhang J. Association of short-term exposure to ambient fine particulate matter and ozone with outpatient visits for anxiety disorders: A hospital-based case-crossover study in South China. J Affect Disord 2024; 361:277-284. [PMID: 38844166 DOI: 10.1016/j.jad.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/25/2024] [Accepted: 06/02/2024] [Indexed: 06/17/2024]
Abstract
BACKGROUND The short-term adverse effects of ambient fine particulate matter (PM2.5) and ozone (O3) on anxiety disorders (ADs) remained inconclusive. METHODS We applied an individual-level time-stratified case-crossover study, which including 126,112 outpatient visits for ADs during 2019-2021 in Guangdong province, China, to investigate the association of short-term exposure to PM2.5 and O3 with outpatient visits for ADs, and estimate excess outpatient visits in South China. Daily residential air pollutant exposure assessments were performed by extracting grid data (spatial resolution: 1 km × 1 km) from validated datasets. We employed the conditional logistic regression model to quantify the associations and excess outpatient visits. RESULTS The results of the single-pollutant models showed that each 10 μg/m3 increase of PM2.5 and O3 exposures was significantly associated with a 3.14 % (95 % confidence interval: 2.47 %, 3.81 %) and 0.88 % (0.49 %, 1.26 %) increase in odds of outpatient visits for ADs, respectively. These associations remained robust in 2-pollutant models. The proportion of outpatient visits attributable to PM2.5 and O3 exposures was up to 7.20 % and 8.93 %, respectively. Older adults appeared to be more susceptible to PM2.5 exposure, especially in cool season, and subjects with recurrent outpatient visits were more susceptible to O3 exposure. LIMITATION As our study subjects were from one single hospital in China, it should be cautious when generalizing our findings to other regions. CONCLUSION Short-term exposure to ambient PM2.5 and O3 was significantly associated with a higher odds of outpatient visits for ADs, which can contribute to considerable excess outpatient visits.
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Affiliation(s)
- Ruijun Xu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Lu Luo
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ting Yuan
- Department of Psychosomatic Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wangni Chen
- Department of Psychosomatic Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA
| | - Chunxiang Shi
- Meteorological Data Laboratory, National Meteorological Information Center, Beijing, China
| | - Sirong Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Sihan Liang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yingxin Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zihua Zhong
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Likun Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Zheng
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyi Deng
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tingting Liu
- Health Department, The Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Zhaoyu Fan
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuewei Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Jie Zhang
- Department of Psychosomatic Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
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Du C, Liu W. Defending against environmental threats: Unveiling household adaptation strategies and population heterogeneity. ENVIRONMENT INTERNATIONAL 2024; 190:108858. [PMID: 38954925 DOI: 10.1016/j.envint.2024.108858] [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: 04/24/2024] [Revised: 06/04/2024] [Accepted: 06/27/2024] [Indexed: 07/04/2024]
Abstract
Humanity faces a variety of risks from pollution and environmental degradation. Societal advancement has equipped the public with numerous self-protection measures to mitigate these threats. However, the ways in which individuals deploy and balance self-defence mechanisms within this complex risk landscape and the resulting consequences remain largely unexplored. Drawing on a detailed survey of households' self-defence practices, this study rigorously analyses the heterogeneity and driving factors behind household-level self-defence strategies. Through exploratory latent class modelling, we identified four distinct defence patterns: inaction, water-sensitive, air-sensitive, and multifaceted. These patterns reveal varied defence capabilities among the population. By integrating frameworks from economics and social psychology, significant disparities were found in the driving factors behind these patterns. Practices aimed at combating air pollution are primarily driven by the actual severity of pollution and perceived coping capabilities, whereas measures to enhance water quality are influenced more by perceived threats. This disparity arises from variations in information availability and health awareness. The study also highlights a misalignment between the distribution of defence capabilities and the levels of pollution. Given that income restricts self-defence options, this mismatch indicates that economically disadvantaged groups are disproportionately affected by severe health inequalities.
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Affiliation(s)
- Chenyi Du
- School of Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Wenling Liu
- School of Economics, Beijing Institute of Technology, Beijing 100081, China; Centre for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China.
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Chen Y, Yuan Y. Examining the non-linear association between ambient temperature and mental health of elderly adults in the community: evidence from Guangzhou, China. BMC Public Health 2024; 24:2064. [PMID: 39085819 PMCID: PMC11293175 DOI: 10.1186/s12889-024-19511-9] [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] [Received: 04/30/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
The association between ambient temperature and mental health has been explored previously. However, research on the psychological effect of temperature in vulnerable groups and neighborhood scales have been scarce. Based on the survey and temperature data collected from 20 neighborhoods in Guangzhou, China, this study estimated the association between ambient temperature and community mental health among the elderly, adopting a fixed-effects methodology. According to this empirical analysis, compared to a comfortable temperature range of 20℃-25℃, measures of worse mental health among elderly were significant in high and low temperatures with increases in negative outcomes observable at both ends of the temperature range, leading to the U-shaped relationship. Second, the association between ambient temperature and worse mental health was found in the subcategories of gender, income, and symptom events. Specifically, from the hot temperature aspect, elderly males were more sensitive than elderly females. The effect on the low was far more than on the middle-high income group, and the probability of each symptom of the elderly's mental health significantly increased. From the cool temperature aspect, the temperature in the range of 5ºC-10ºC was significantly associated with the probability of some symptoms (feeling down, not calm, downheartedness, and unhappiness) and the middle-high income group. Our research enriches the empirical research on ambient temperature and mental health from a multidisciplinary perspective and suggests the need for healthy aging and age-friendly planning in Chinese settings.
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Affiliation(s)
- Yujie Chen
- Population Research Institute, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Yuan Yuan
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510006, China.
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, Guangzhou, 510006, China.
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Min Y, Wei X, Yang C, Duan Z, Yang J, Ju K, Peng X. Associations and attributable burdens in late-life exposure to PM 2.5 and its major components and depressive symptoms in middle-aged and older adults: A nationwide cohort study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 280:116531. [PMID: 38852465 DOI: 10.1016/j.ecoenv.2024.116531] [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: 12/06/2023] [Revised: 04/21/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Depression in late life has been associated with reduced quality of life and increased mortality. Whether the chronic fine particular matter (PM2.5) and its components exposure are contributed to the older depression symptoms remains unclear. METHOD Middle-aged and older adults (>45 years) were selected from the China Health and Retirement Longitudinal Study during the four waves of interviews. The concentrations of PM2.5 and its major constituents were calculated using near real-time data at a spatial resolution of 10 km during the study period. The depressive symptom was evaluated by the Depression Center for Epidemiologic Studies Depression (CES-D)-10 score. The fix-effect model was applied to evaluate the association between PM2.5 and its major constituents with depressive symptoms. Three three-step methods were used to explore the modification role of sleep duration against the depressive symptoms caused by PM2.5 exposure. RESULTS In our study, a total of 52,683 observations of 16,681 middle-aged and older adults were assessed. Each interquartile range (IQR) level of PM2.5 concentration exposure was longitudinally associated with a 2.6 % (95 % confidence interval [CI]: 1.3 %, 4.0 %) increase in the depression CES-D-10 score. Regarding the major components of PM2.5, OM, NO3-, and NH4+ showed the leading toxicity effects, which could increase the depression CES-D-10 score by 2.2 % (95 %CI: 1.0 %, 3.4 %), 2.2 % (0.6 %, 3.9 %), and 2.0 % (95 %CI: 0.6 %, 3.4 %) correspondingly. Besides, males were more susceptible to the worse depressive symptoms caused by PM2.5 and its major components exposure than female subpopulations. Shortened sleep duration might be the mediator of PM2.5-associated depressive symptoms. CONCLUSION Our results suggest that long-term exposure to PM2.5 and its major components were associated with an increased risk for depressive symptoms in middle-aged and older adults. Reducing the leading components of PM2.5 may cost-effectively alleviate the disease burden of depression and promote healthy longevity in heavy pollutant countries.
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Affiliation(s)
- Yu Min
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyuan Wei
- Department of Head and Neck Oncology, Department of Radiation Oncology, Cancer Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Chenyu Yang
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhongxin Duan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jingguo Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Ke Ju
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Xingchen Peng
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
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Li S, Liu Y, Li R, Xiao W, Ou J, Tao F, Wan Y. Association between green space and multiple ambient air pollutants with depressive and anxiety symptoms among Chinese adolescents: The role of physical activity. ENVIRONMENT INTERNATIONAL 2024; 189:108796. [PMID: 38838489 DOI: 10.1016/j.envint.2024.108796] [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: 02/21/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVE To explore the association between green space, multiple ambient air pollutants and depressive/anxiety symptoms and the mediating role of physical activity (PA) in Chinese adolescents. METHOD A school-based health survey was conducted in eight provinces in China in 2021. 22,868 students aged 14.64 (±1.77) years completed standard questionnaires to record details of depressive, anxiety symptoms and PA. We calculated the average normalized difference vegetation index (NDVI) in circular buffers of 200 m, 500 m and 1000 m and estimated the concentrations of PM10, PM2.5, CO, NO2, O3, SO2 around the adolescents' school addresses. RESULTS The exposure-response curves showed that the lower the NDVI value, the higher the risk of depressive and anxiety symptoms. CO, PM2.5 and SO2 and air pollution score were associated with increased risk of depressive and anxiety symptoms. NDVI in all circular buffers decreased the risk of depressive and anxiety symptoms at low levels of PA, but the associations were not significant at high levels of PA. In the subgroup analysis, PM10, PM2.5, CO, NO2, SO2, AQI and air pollution score increased the risk of depressive and anxiety symptoms at low PA levels, but the associations were not significant at high levels of PA. Mediation analysis indicated that the mediating effect of PA on the association between NDVI, NDVI-200 m NDVI-500 m, CO, PM10, PM2.5, SO2, AQI and depressive/anxiety symptoms was statistically significant(p < 0.05). CONCLUSION Middle-high level PA could reduce the strength of association between air pollution and depressive and anxiety symptoms. Meanwhile, the association between green space/air pollution and depressive/anxiety symptoms was partly mediated by PA.
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Affiliation(s)
- Shuqin Li
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China
| | - Yu Liu
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China
| | - Ruoyu Li
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China
| | - Wan Xiao
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China
| | - Jinping Ou
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China.
| | - Fangbiao Tao
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China.
| | - Yuhui Wan
- School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032, Anhui, China; Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei, 230032, Anhui, China; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, No 81 Meishan Road, Hefei 230032, Anhui, China.
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Niu YL, Lu F, Liu XJ, Wang J, Liu DL, Liu QY, Yang J. Global climate change: Effects of future temperatures on emergency department visits for mental disorders in Beijing, China. ENVIRONMENTAL RESEARCH 2024; 252:119044. [PMID: 38697599 DOI: 10.1016/j.envres.2024.119044] [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: 02/17/2024] [Revised: 04/08/2024] [Accepted: 04/27/2024] [Indexed: 05/05/2024]
Abstract
Rising temperatures can increase the risk of mental disorders. As climate change intensifies, the future disease burden due to mental disorders may be underestimated. Using data on the number of daily emergency department visits for mental disorders at 30 hospitals in Beijing, China during 2016-2018, the relationship between daily mean temperature and such visits was assessed using a quasi-Poisson model integrated with a distributed lag nonlinear model. Emergency department visits for mental disorders attributed to temperature changes were projected using 26 general circulation models under four climate change scenarios. Stratification analyses were then conducted by disease subtype, sex, and age. The results indicate that the temperature-related health burden from mental disorders was projected to increase consistently throughout the 21st century, mainly driven by high temperatures. The future temperature-related health burden was higher for patients with mental disorders due to the use of psychoactive substances and schizophrenia as well as for women and those aged <65 years. These findings enhance our knowledge of how climate change could affect mental well-being and can be used to advance and refine targeted approaches to mitigating and adapting to climate change with a view on addressing mental disorders.
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Affiliation(s)
- Yan-Lin Niu
- Institute for Nutrition and Food Hygiene, Beijing Center for Disease Prevention and Control, 100013 Beijing, China
| | - Feng Lu
- Beijing Municipal Health Big Data and Policy Research Center, 100034 Beijing, China
| | - Xue-Jiao Liu
- Department of Medical Record Management and Statistics, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Jun Wang
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - De Li Liu
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, NSW 2650, Australia; Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
| | - Qi-Yong Liu
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Jun Yang
- School of Public Health, Guangzhou Medical University, 511436 Guangzhou, China.
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10
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Xue T. Synergistic governance: China's roadmap to improved health through climate and clean air actions. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 20:100447. [PMID: 39105071 PMCID: PMC11298850 DOI: 10.1016/j.ese.2024.100447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/27/2024] [Accepted: 06/30/2024] [Indexed: 08/07/2024]
Abstract
•Health benefits from China's pollution-carbon co-control actions have already been seen.•Co-control of air pollution and greenhouse gases can avoid premature deaths.•More comparative evaluations of the health impacts of specific policies are needed.
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Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing, China
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11
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Bai L, Wang K, Liu D, Wu S. Potential Early Effect Biomarkers for Ambient Air Pollution Related Mental Disorders. TOXICS 2024; 12:454. [PMID: 39058106 PMCID: PMC11280925 DOI: 10.3390/toxics12070454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/18/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Air pollution is one of the greatest environmental risks to health, with 99% of the world's population living where the World Health Organization's air quality guidelines were not met. In addition to the respiratory and cardiovascular systems, the brain is another potential target of air pollution. Population- and experiment-based studies have shown that air pollution may affect mental health through direct or indirect biological pathways. The evidence for mental hazards associated with air pollution has been well documented. However, previous reviews mainly focused on epidemiological associations of air pollution with some specific mental disorders or possible biological mechanisms. A systematic review is absent for early effect biomarkers for characterizing mental health hazards associated with ambient air pollution, which can be used for early warning of related mental disorders and identifying susceptible populations at high risk. This review summarizes possible biomarkers involved in oxidative stress, inflammation, and epigenetic changes linking air pollution and mental disorders, as well as genetic susceptibility biomarkers. These biomarkers may provide a better understanding of air pollution's adverse effects on mental disorders and provide future research direction in this arena.
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Affiliation(s)
- Lijun Bai
- 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 710061, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi’an Jiaotong University), Ministry of Education, Xi’an 710061, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an 710061, China
- Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi’an 710061, China
| | - Kai Wang
- 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 710061, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi’an Jiaotong University), Ministry of Education, Xi’an 710061, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an 710061, China
- Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi’an 710061, China
| | - Dandan Liu
- 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 710061, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi’an Jiaotong University), Ministry of Education, Xi’an 710061, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an 710061, China
- Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi’an 710061, China
| | - 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 710061, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi’an Jiaotong University), Ministry of Education, Xi’an 710061, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an 710061, China
- Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi’an 710061, China
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12
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Chen Z, Wu F, Shi Y, Guo Y, Xu J, Liang S, Huang Z, He G, Hu J, Zhu Q, Yu S, Yang S, Wu C, Tang W, Dong X, Ma W, Liu T. Association of Residential Greenness Exposure with Depression Incidence in Adults 50 Years of Age and Older: Findings from the Cohort Study on Global AGEing and Adult Health (SAGE) in China. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:67004. [PMID: 38885140 PMCID: PMC11218708 DOI: 10.1289/ehp13947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/07/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Depression is a social and public health problem of great concern globally. Identifying and managing the factors influencing depression are crucial for preventing and decreasing the burden of depression. OBJECTIVES Our objectives are to explore the association between residential greenness and the incidence of depression in an older Chinese population and to calculate the disease burden of depression prevented by greenness exposure. METHODS This study was the Chinese part of the World Health Organization Study on Global AGEing and Adult Health (WHO SAGE). We collected the data of 8,481 residents ≥ 50 years of age in China for the period 2007-2018. Average follow-up duration was 7.00 (± 2.51 ) years. Each participant was matched to the yearly maximum normalized difference vegetation index (NDVI) at their residential address. Incidence of depression was assessed using the Composite International Diagnostic Interview (CIDI), self-reports of depression, and/or taking depression medication. Association between greenness and depression was examined using the time-dependent Cox regression model with stratified analysis by sex, age, urbanicity, annual family income, region, smoking, drinking, and household cooking fuels. Furthermore, the prevented fraction (PF) and attributable number (AN) of depression prevented by exposure to greenness were estimated. RESULTS Residential greenness was negatively associated with depression. Each interquartile range (IQR) increase in NDVI 500 -m buffer was associated with a 40% decrease [hazard ratio ( HR ) = 0.60 ; 95% confidence interval (CI): 0.37, 0.97] in the risk of depression incidence among the total participants. Subgroup analyses showed negative associations in urban residents (HR = 0.32 ; 95% CI: 0.12, 0.86) vs. rural residents, in high-income residents (HR = 0.28 ; 95% CI: 0.11, 0.71) vs. low-income residents, and in southern China (HR = 0.50 ; 95% CI: 0.26, 0.95) vs. northern China. Over 8.0% (PF = 8.69 % ; 95% CI: 1.38%, 15.40%) and 1,955,199 (95% CI: 310,492; 3,464,909) new cases of depression may be avoided by increasing greenness exposures annually across China. DISCUSSION The findings suggest protective effects of residential greenness exposure on depression incidence in the older population, particularly among urban residents, high-income residents, and participants living in southern China. The construction of residential greenness should be included in community planning. https://doi.org/10.1289/EHP13947.
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Affiliation(s)
- Zhiqing Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
- Key Laboratory of Viral Pathogenesis & Infection Prevention and Control (Jinan University), Ministry of Education, Guangzhou, China
| | - Fan Wu
- Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Shi
- Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, China
| | - Yanfei Guo
- Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, China
| | - Jiahong Xu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Shuru Liang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Zhongguo Huang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Jianxiong Hu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Qijiong Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Siwen Yu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Shangfeng Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Cuiling Wu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Weiling Tang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
| | - Xiaomei Dong
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
- Key Laboratory of Viral Pathogenesis & Infection Prevention and Control (Jinan University), Ministry of Education, Guangzhou, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou, China
- Key Laboratory of Viral Pathogenesis & Infection Prevention and Control (Jinan University), Ministry of Education, Guangzhou, China
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13
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Zhang H, Huang G, Lin P, Chen X, Lin W. Visual effect of air pollution on the need for arousal and variety-seeking behavior. Front Psychol 2024; 15:1342267. [PMID: 38845776 PMCID: PMC11154011 DOI: 10.3389/fpsyg.2024.1342267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/06/2024] [Indexed: 06/09/2024] Open
Abstract
Research on air pollution, one of the most common environmental factors, has primarily focused on its effects on physical, mental, and cognitive health. However, air pollution-induced achromatic color of an environment, which is a prominent feature of air pollution, has received little attention. This study explored the visual effects of air pollution on the variety-seeking purchase behavior of consumers through two scenario-based experiments and primed manipulation (Study 1 and Study 2) and one natural experiment using data from a local fruit chain store (Study 3). Study 1 tested the main effect of air pollution on the variety-seeking behavior and found that primed air pollution increased variety-seeking when consumers purchased beverages. Study 2 broadened the category and tested the mechanism, and the results indicated that primed air pollution increased the variety of purchased chocolates and demonstrated the mediating effect of the need for arousal. Study 3 tested the boundary condition and extended the external validity with actual purchases. The results revealed that severe air pollution increased the purchased SKUs by 22.9% and visibility reduced the moderation effect. This research extended the literature on the visual effect of air pollution by providing evidence of the effects of air pollution on variety-seeking behavior through the need for arousal. And, product managers could leverage the results by offering a greater variety of goods on days with air pollution to increase sales.
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Affiliation(s)
- Han Zhang
- Newhuadu Business School, Minjiang University, Fuzhou, Fujian, China
| | - Guanling Huang
- Newhuadu Business School, Minjiang University, Fuzhou, Fujian, China
| | - Ping Lin
- Newhuadu Business School, Minjiang University, Fuzhou, Fujian, China
| | - Xiuqi Chen
- Newhuadu Business School, Minjiang University, Fuzhou, Fujian, China
| | - Wenhe Lin
- Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
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14
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Kotz M, Levermann A, Wenz L. The economic commitment of climate change. Nature 2024; 628:551-557. [PMID: 38632481 PMCID: PMC11023931 DOI: 10.1038/s41586-024-07219-0] [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] [Received: 01/25/2023] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons1-6. Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes7,8. Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11-29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.
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Affiliation(s)
- Maximilian Kotz
- Research Domain IV, Research Domain IV, Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Physics, Potsdam University, Potsdam, Germany
| | - Anders Levermann
- Research Domain IV, Research Domain IV, Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Physics, Potsdam University, Potsdam, Germany
| | - Leonie Wenz
- Research Domain IV, Research Domain IV, Potsdam Institute for Climate Impact Research, Potsdam, Germany.
- Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany.
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15
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van Hagen CCE, Huiberts AJ, Mutubuki EN, de Melker HE, Vos ERA, van de Wijgert JHHM, van den Hof S, Knol MJ, van Hoek AJ. Health-related quality of life during the COVID-19 pandemic: The impact of restrictive measures using data from two Dutch population-based cohort studies. PLoS One 2024; 19:e0300324. [PMID: 38498510 PMCID: PMC10947685 DOI: 10.1371/journal.pone.0300324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES We describe health-related quality of life during the COVID-19 pandemic in the general Dutch population and correlations with restrictive measures. METHODS Data were obtained from 18-85 year-old participants of two population-based cohort studies (February 2021-July 2022): PIENTER Corona (n = 8,019) and VASCO (n = 45,413). Per cohort, mean scores of mental and physical health and health utility from the SF-12 were calculated by age group, sex and presence of a medical risk condition. Spearman correlations with stringency of measures were calculated. RESULTS Both cohorts showed comparable results. Participants <30 years had lowest health utility and mental health score, and highest physical health score. Health utility and mental health score increased with age (up to 79 years), while physical health score decreased with age. Women and participants with a medical risk condition scored lower than their counterparts. Fluctuations were small over time but most pronounced among participants <60 years, and correlated weakly, but mostly positively with measure stringency. CONCLUSIONS During the Dutch COVID-19 epidemic, health utility and mental health scores were lower and fluctuated strongest among young adults compared to older adults. In our study population, age, sex and presence of a medical risk condition seemed to have more impact on health scores than stringency of COVID-19 non-pharmaceutical interventions.
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Affiliation(s)
- Cheyenne C. E. van Hagen
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Anne J. Huiberts
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Elizabeth N. Mutubuki
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Hester E. de Melker
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Eric R. A. Vos
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Janneke H. H. M. van de Wijgert
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), Utrecht, the Netherlands
| | - Susan van den Hof
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Mirjam J. Knol
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Albert Jan van Hoek
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
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16
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Song W, Kwan MP, Huang J. Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure. PLoS One 2024; 19:e0294605. [PMID: 38412153 PMCID: PMC10898763 DOI: 10.1371/journal.pone.0294605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 11/03/2023] [Indexed: 02/29/2024] Open
Abstract
Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536μg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608μg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.
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Affiliation(s)
- Wanying Song
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Mei-Po Kwan
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Jianwei Huang
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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17
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Wang JX, Liu XQ. Climate change, ambient air pollution, and students' mental health. World J Psychiatry 2024; 14:204-209. [PMID: 38464763 PMCID: PMC10921291 DOI: 10.5498/wjp.v14.i2.204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/29/2023] [Accepted: 01/23/2024] [Indexed: 02/06/2024] Open
Abstract
The impact of global climate change and air pollution on mental health has become a crucial public health issue. Increased public awareness of health, advancements in medical diagnosis and treatment, the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution. In the context of climate change, extreme weather events seriously disrupt people's living environments, and unstable educational environments lead to an increase in mental health issues for students. Air pollution affects students' mental health by increasing the incidence of diseases while decreasing contact with nature, leading to problems such as anxiety, depression, and decreased cognitive function. We call for joint efforts to reduce pollutant emissions at the source, improve energy structures, strengthen environmental monitoring and gover-nance, increase attention to the mental health issues of students, and help student groups build resilience; by establishing public policies, enhancing social support and adjusting lifestyles and habits, we can help students cope with the constantly changing environment and maintain a good level of mental health. Through these comprehensive measures, we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.
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Affiliation(s)
- Jing-Xuan Wang
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
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18
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Zhong S, Zhou Z, Jing H. The impact of foreign direct investment on green innovation efficiency: Evidence from Chinese provinces. PLoS One 2024; 19:e0298455. [PMID: 38354170 PMCID: PMC10866484 DOI: 10.1371/journal.pone.0298455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
Improving green innovation efficiency (GIE) is the key to achieve high-quality economic development in China, and the introduction of foreign direct investment (FDI) has become an important path choice to promote the GIE. Based on the data of 30 provinces in China, this paper explores the linear and nonlinear effects of FDI on GIE from both quantity and quality perspectives, and further analyzes the mediating role of environmental regulation level. The results show that: (1) From 2011 to 2020, the GIE of all provinces in China generally shows an upward trend. (2) The quantity and quality of FDI have a significant positive impact on the improvement of GIE in China's provinces, and this impact has regional heterogeneity. (3) The quantity and quality of FDI can promote the improvement of GIE in China through the level of environmental regulation (ER). (4) With the level of knowledge accumulation and GIE as the threshold variables, the quantity and quality of FDI have a single threshold effect on the GIE of China's provinces. The conclusions of this study provide some policy implications for local governments to make full use of FDI to perform green innovation activities.
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Affiliation(s)
- Shen Zhong
- School of Finance, Harbin University of Commerce, Harbin, China
| | - Zhicheng Zhou
- School of Finance, Harbin University of Commerce, Harbin, China
| | - Hongjun Jing
- School of Public Finance and Administration, Harbin University of Commerce, Harbin, China
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19
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Xue T, Wang R, Wang M, Wang Y, Tong D, Meng X, Huang C, Ai S, Li F, Cao J, Tong M, Ni X, Liu H, Deng J, Lu H, Wan W, Gong J, Zhang S, Zhu T. Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality. Natl Sci Rev 2024; 11:nwad263. [PMID: 38213522 PMCID: PMC10776362 DOI: 10.1093/nsr/nwad263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/13/2023] [Accepted: 10/08/2023] [Indexed: 01/13/2024] Open
Abstract
Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.
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Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
- Advanced Institute of Information Technology, Peking University, Hangzhou311215, China
| | - Ruohan Wang
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY14214, USA
| | - Yanying Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
| | - Dan Tong
- Department of Earth System Science, Tsinghua University, Beijing100084, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai200433, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
- National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China
| | - Siqi Ai
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Fangzhou Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Jingyuan Cao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Mingkun Tong
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Xueqiu Ni
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hengyi Liu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Jianyu Deng
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hong Lu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Wei Wan
- Clean Air Asia, Beijing100600, China
| | - Jicheng Gong
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Shiqiu Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Tong Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
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20
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Zhou W, Wang Q, Li R, Zhang Z, Kadier A, Wang W, Zhou F, Ling L. Heatwave exposure in relation to decreased sleep duration in older adults. ENVIRONMENT INTERNATIONAL 2024; 183:108348. [PMID: 38064924 DOI: 10.1016/j.envint.2023.108348] [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: 03/19/2023] [Revised: 10/31/2023] [Accepted: 11/21/2023] [Indexed: 01/25/2024]
Abstract
Few studies have delved into the effects of heatwaves on sleep duration loss among older adults. Our study examined correlations between heatwave exposure and sleep duration reductions in this demographic. Utilizing data of 7,240 older adults drawn from the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2018, we assessed sleep duration differences between the baseline year (2015) and follow-up year (2018). Absolute reductions in sleep duration were defined as differences of ≥ 1, 1.5, or 2 h. Changes in sleep duration were categorized based on cut-offs of 5 and 8 h, including excessive decrease, moderate to short and persistent short sleep duration types. 12 heatwave definitions combining four thresholds (90th, 92.5th, 95th, and 97.5th percentiles of daily minimum temperature) and three durations (≥2, ≥3 and ≥ 4 days) were used. Heatwave exposure was determined by the difference in the number of 12 preceding months' heatwave days or events in 2015 and the number of 12 preceding months' heatwave days or events in 2018. The results showed that increased heatwave events (defined as ≥ P90th percentile & lasting three days) were associated with a higher likelihood of ≥ 1-hour sleep reduction and persistent short sleep duration. An increase in heatwave event (defined as ≥ P95th percentile & lasting three days) was linked to shifts from moderate to short sleep duration. For the association between an absolute reduction in sleep duration and heatwave exposure, while higher thresholds signified greater sleep reduction risks, the effect estimates of longer durations were not uniformly consistent. We observed that air pollution and green space modified the relationship between heatwaves and sleep duration. Females, urban residents, and individuals with chronic diseases were identified as vulnerable populations. This study found that increased heatwave exposure was associated with a higher risk of sleep duration loss in older adults.
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Affiliation(s)
- Wensu Zhou
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Qiong Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Rui Li
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zhirong Zhang
- School of Mathematics, Sun Yat-Sen University, Sun Yat-sen University, Guangzhou, China
| | - Aimulaguli Kadier
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wenjuan Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Fenfen Zhou
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Li Ling
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China; Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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21
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Ye Z, Li X, Lang H, Fang Y. Income inequality and depressive symptoms among Chinese adults: a quasi-experimental study. Public Health 2024; 226:58-65. [PMID: 38007842 DOI: 10.1016/j.puhe.2023.10.042] [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] [Received: 07/06/2023] [Revised: 10/08/2023] [Accepted: 10/25/2023] [Indexed: 11/28/2023]
Abstract
OBJECTIVE There is a lack of causal evidence on the impact of income inequality on depressive symptoms. The impact of China's Targeted Poverty Alleviation (TPA) policy on depressive symptoms is also unclear. Using a quasi-experimental design, this study aims to investigate the causal effects of TPA and income inequality on depressive symptoms among Chinese adults. STUDY DESIGN This is a population-based study. METHODS Three waves (2012, 2016, and 2018) of the China Family Panel Studies (CFPS), a nationally representative sample of China, were included in this study. We performed difference-in-difference (DID) models to assess the effect of TPA and income inequality on depressive symptoms. We further conducted the mixed effect models to examine the impact of income inequality on depressive symptoms. The study considered a range of spatial factors and spatial splines to address spatial autocorrelations. RESULTS This study included valid measures of depressive symptoms (Center for Epidemiologic Studies Depression Scale [CES-D-8] score) from 14,442 adults of CFPS. The DID results indicated that at the provincial level, the CES-D-8 score of the TPA treatment group was on average 0.570 (95% confidence interval [CI]: 0.358-0.783) less than the control group. Furthermore, a 0.1 increase in Gini index would lead to a 0.256 (95% CI: 0.064-0.448) increase in CES-D-8 score. The mixed effect model showed that income inequality was a risk factor for depressive symptoms at the provincial level (excess risk = 5.602% [95% CI: 3.047%-8.219%]). CONCLUSIONS Our findings suggest that income inequality adversely affects mental health, but China's Targeted Poverty Alleviation improves the mental health of the Chinese population.
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Affiliation(s)
- Z Ye
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - X Li
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - H Lang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Y Fang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
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22
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Zhou Y, Li C, Wang W, Ding L. Large-scale data reveal disparate associations between leisure time physical activity patterns and mental health. COMMUNICATIONS MEDICINE 2023; 3:175. [PMID: 38129660 PMCID: PMC10739930 DOI: 10.1038/s43856-023-00399-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/03/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Leisure time physical activity (LTPA) is known to be associated with a lower risk for mental health burden, while whether the underlying mechanisms vary across populations is unknown. We aimed to explore the disparate associations between LTPA and mental health based on large-scale data. METHODS In this study, we analyzed data including 711,759 individuals aged 15 years or above from the latest four rounds (2003, 2008, 2013, and 2018) of the National Health Service Survey (NHSS) in China. We used multiple logistic regression models adjusted for potential confounders to investigate associations between LTPA and mental health in the total population and subgroups by measuring a diverse set of activity frequencies, intensities, and types. To examine the dose-response associations between total activity volume and mental health, we conducted restricted cubic splines to investigate possible nonlinearity. RESULTS LTPA was associated with remarkably lower self-reported mental health burden (OR 0.56, 95% CI 0.54-0.58). The dose-response relationship between total activity volume and mental health was highly nonlinear (p < 0.001), presenting L-shaped with first 1200 metabolic equivalents of task (METs)-min/week for significant risk reduction (OR 0.58, 95% CI 0.56-0.60). Notably, merely exercising 3-5 times per week with moderate swimming was significantly associated with lower mental health burden among younger people, while the association was strongly large in older adults aged 60 years or above doing 55-min moderate apparatus exercise at least six times a week. CONCLUSIONS In a large Chinese sample, LTPA was meaningfully and disparately associated with mental health burden across different people. Policy targeted at prompting activity may be effective for reducing mental health burden, but importantly, tailored strategies are needed based on population contexts.
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Affiliation(s)
- Ying Zhou
- Center for Smart and Healthy Buildings, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
| | - Chenshuang Li
- Center for Smart and Healthy Buildings, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
| | - Wei Wang
- Center for Smart and Healthy Buildings, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China.
- Department of Neurology, Tongji Hospital, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China.
| | - Lieyun Ding
- Center for Smart and Healthy Buildings, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China.
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23
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Ye Z, Li X, Lang H, Fang Y. Long-Term PM2.5 Exposure, Lung Function, and Cognitive Function Among Middle-Aged and Older Adults in China. J Gerontol A Biol Sci Med Sci 2023; 78:2333-2341. [PMID: 37493944 DOI: 10.1093/gerona/glad180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Long-term exposure to PM2.5 is related to poor lung function and cognitive impairment, but less is known about the pathway involved in this association. We aimed to explore whether the effect of PM2.5 on cognitive function was mediated by lung function. METHODS A total of 7 915 adults older than 45 years old were derived from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2011 and 2015. PM2.5 exposure was estimated using a geographically weighted regression model. Lung function was measured by peak expiratory flow (PEF). Cognitive function was evaluated through a structured questionnaire with 4 dimensions: episodic memory, attention, orientation, and visuoconstruction. Under the counterfactual framework, causal mediation analysis was applied to examine direct and indirect associations. RESULTS An interquartile range (IQR) increase in PM2.5 change was significantly related to an 8.480 (95% confidence interval [CI]: 3.116, 13.845) decrease in PEF change and a 0.301 (95% CI: 0.100, 0.575) decrease in global cognitive score change. The direct and indirect effects of PM2.5 exposure on global cognitive performance were -0.279 (95% CI: -0.551, -0.060) and -0.023 (95% CI: -0.041, -0.010), respectively. The proportion of the indirect effect was 7.48% (p = .010). The same significant association appeared in only 2 dimensions, episodic memory and attention, which were both mediated by PEF. CONCLUSIONS Lung function played a partially mediating role in the association between long-term PM2.5 exposure and cognition. More clean air actions should be undertaken to improve lung function and cognitive function in older adults.
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Affiliation(s)
- Zirong Ye
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Xueru Li
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Haoxiang Lang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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24
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Xue K, Gao B, Chen F, Wang M, Cheng J, Zhang B, Zhu W, Qiu S, Geng Z, Zhang X, Cui G, Yu Y, Zhang Q, Liao W, Zhang H, Xu X, Han T, Qin W, Liu F, Liang M, Guo L, Xu Q, Xu J, Fu J, Zhang P, Li W, Shi D, Wang C, Lui S, Yan Z, Zhang J, Li J, Wang D, Xian J, Xu K, Zuo XN, Zhang L, Ye Z, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Poustka L, Hohmann S, Holz N, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Shen W, Miao Y, Yu C. Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits. Mol Psychiatry 2023; 28:4853-4866. [PMID: 37737484 DOI: 10.1038/s41380-023-02261-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 09/05/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023]
Abstract
Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.
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Affiliation(s)
- Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Bo Gao
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, 264000, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, 450003, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shijun Qiu
- Department of Medical Imaging, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, China
| | - Zuojun Geng
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, 710038, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, 300162, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Molecular Imaging Research Center of Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Hui Zhang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, 310009, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300350, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300203, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Dapeng Shi
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, 450003, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Su Lui
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Jiance Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, China
| | - Xi-Nian Zuo
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
- AP-HP. Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076, Bordeaux, France
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China.
| | - Yanwei Miao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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Feng J, Cai M, Qian ZM, Zhang S, Yang Y, McMillin SE, Chen G, Hua J, Tabet M, Wang C, Wang X, Lin H. The effects of long-term exposure to air pollution on incident mental disorders among patients with prediabetes and diabetes: Findings from a large prospective cohort. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 897:165235. [PMID: 37414192 PMCID: PMC10522921 DOI: 10.1016/j.scitotenv.2023.165235] [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: 05/10/2023] [Revised: 06/16/2023] [Accepted: 06/28/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND The association between air pollution and mental disorders has been widely documented in the general population. However, the evidence among susceptible populations, such as individuals with prediabetes or diabetes, is still insufficient. METHODS We analyzed data from 48,515 participants with prediabetes and 24,393 participants with diabetes from the UK Biobank. Annual pollution data were collected for fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), and nitrogen dioxides (NOx) during 2006-2021. The exposure to air pollution and temperature for each participant were estimated by the bilinear interpolation approach and time-weighted method based on their geocoded home addresses and time spent at each address. We employed the generalized propensity score model based on the generalized estimating equation and the time-varying covariates Cox model to assess the effects of air pollution. RESULTS We observed causal links between air pollutants and mental disorders among both prediabetic and diabetic participants, with stronger effects among those with diabetes than prediabetes. The hazard ratios were 1.18 (1.12, 1.24), 1.15 (1.10, 1.20), 1.18 (1.13, 1.23), and 1.15 (1.11, 1.19) in patients with prediabetes, and 1.21 (1.13, 1.29), 1.17 (1.11, 1.24), 1.19 (1.13, 1.25), and 1.17 (1.12, 1.23) in patients with diabetes per interquartile range elevation in PM2.5, PM10, NO2, and NOx. Furthermore, the effects were more pronounced among people who were older, alcohol drinkers, and living in urban areas. CONCLUSIONS Our study indicates the potential causal links between long-term exposure to air pollution and incident mental disorders among those with prediabetes and diabetes. Reducing air pollution levels would significantly benefit this vulnerable population by reducing the incidence of mental disorders.
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Affiliation(s)
- Jin Feng
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO 63104, USA
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Yin Yang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Stephen Edward McMillin
- School of Social Work, Saint Louis University, Tegeler Hall, 3550 Lindell Boulevard, Saint Louis, MO 63103, USA
| | - Ge Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Junjie Hua
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Maya Tabet
- College of Global Population Health, University of Health Sciences and Pharmacy in St. Louis, 1 Pharmacy Place, Saint Louis, MO 63110, USA
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Xiaojie Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.
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Zhang Q, Yin Z, Lu X, Gong J, Lei Y, Cai B, Cai C, Chai Q, Chen H, Dai H, Dong Z, Geng G, Guan D, Hu J, Huang C, Kang J, Li T, Li W, Lin Y, Liu J, Liu X, Liu Z, Ma J, Shen G, Tong D, Wang X, Wang X, Wang Z, Xie Y, Xu H, Xue T, Zhang B, Zhang D, Zhang S, Zhang S, Zhang X, Zheng B, Zheng Y, Zhu T, Wang J, He K. Synergetic roadmap of carbon neutrality and clean air for China. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2023; 16:100280. [PMID: 37273886 PMCID: PMC10236195 DOI: 10.1016/j.ese.2023.100280] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 06/06/2023]
Abstract
It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.
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Affiliation(s)
- Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Zhanfeng Dong
- Institute of Environmental Policy Management, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Jianing Kang
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, 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
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yongsheng Lin
- School of Economics and Resource Management, Beijing Normal University, Beijing, 100875, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Zhili Wang
- State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Honglei Xu
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People's Republic of China, Beijing, 100028, 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, Beijing, 100080, China
| | - Bing Zhang
- State Key Laboratory of Pollution Control & Resource Reuse School of Environment, Nanjing University, Nanjing, 210008, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Institute of Environmental Policy Management, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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Cheng Y, Yu Z, Xu C, Manoli G, Ren X, Zhang J, Liu Y, Yin R, Zhao B, Vejre H. Climatic and Economic Background Determine the Disparities in Urbanites' Expressed Happiness during the Summer Heat. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:10951-10961. [PMID: 37458710 DOI: 10.1021/acs.est.3c01765] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Climate-change-induced extreme weather events increase heat-related mortality and health risks for urbanites, which may also affect urbanites' expressed happiness (EH) and well-being. However, the links among EH, climate, and socioeconomic factors remain unclear. Here we collected ∼6 million geotagged tweets from 44 Chinese prefecture-level cities based on Sina Weibo and performed a quadratic regression model to explore the relationships between summer heat and EH. A three-stage analysis was developed to examine spatiotemporal heterogeneity and identify factors contributing to disparities in urbanites' EH. Results show that all cities exhibited a similar hump-shaped relationship, with an overall optimal temperature (OT) of 22.8 °C. The estimated OT varied geographically, with 25.3, 23.8, and 20.0 °C from north to south. Moreover, a 1 standard deviation increase in heatwave intensity was associated with a 0.813 (95% CI: 0.177, 1.449) standard deviation decrease in EH. Notably, within the geographic scope of this study, it was observed that urbanites in northern China and economically underdeveloped cities faced significantly lower heat risks during the summer heat. This research provides insight for future studies and practical applications concerning extreme weather events, urbanites' mental health, and sustainable urban development goal.
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Affiliation(s)
- Yingyi Cheng
- Department of Environmental Science and Engineering, Fudan University, Shanghai 2005, People's Republic of China
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, People's Republic of China
| | - Zhaowu Yu
- Department of Environmental Science and Engineering, Fudan University, Shanghai 2005, People's Republic of China
| | - Chi Xu
- School of Life Sciences, Nanjing University, Nanjing 210023, People's Republic of China
| | - Gabriele Manoli
- Laboratory of Urban and Environmental Systems, School of Architecture, Civil & Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Xiaopeng Ren
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, People's Republic of China
| | - Jinguang Zhang
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, People's Republic of China
| | - Yawen Liu
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, People's Republic of China
| | - Rui Yin
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, People's Republic of China
| | - Bing Zhao
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, People's Republic of China
| | - Henrik Vejre
- Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Copenhagen 1958, Denmark
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28
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Wang L, Gao X, Wang R, Song M, Liu X, Wang X, An C. Ecological correlation between short term exposure to particulate matter and hospitalization for mental disorders in Shijiazhuang, China. Sci Rep 2023; 13:11412. [PMID: 37452053 PMCID: PMC10349047 DOI: 10.1038/s41598-023-37279-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023] Open
Abstract
The associations between particulate matter (PM) and overall and specific mental disorders (MDs) are investigated using data from two general hospitals in Shijiazhuang, China, from January 2014 to December 2019. A longitudinal time series study, as one type of ecological study, is conducted using a generalized additive model to examine the relationship between short-term exposure to PM2.5, PM10, and daily hospital admissions for MDs, and further stratification by subtypes, age, and gender. A total of 10,709 cases of hospital admissions for MDs have been identified. The significant short-time effects of PM2.5 on overall MDs at lag01 and PM10 at lag05 are observed, respectively. For specific mental disorders, there are substantial associations of PM pollution with mood disorders and organic mental disorders. PM2.5 has the greatest cumulative effect on daily admissions of mood disorders and organic mental disorders in lag01, and PM 10 has the greatest cumulative effect in lag05. Moreover, the effect modification by sex or age is statistically significant, with males and the elderly (≥ 45 years) having a stronger effect. Short-term exposure to PM2.5 and PM10can be associated with an increased risk of daily hospital admissions for MDs.
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Affiliation(s)
- Lan Wang
- Mental Health Center, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050031, China
- Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, China
- Hebei technical Innovation Center for Mental Health assessment and Intervention, Shijiazhuang, China
| | - Xian Gao
- Department of Gastrointestinal Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ran Wang
- Mental Health Center, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050031, China
- Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, China
- Hebei technical Innovation Center for Mental Health assessment and Intervention, Shijiazhuang, China
| | - Mei Song
- Mental Health Center, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050031, China
- Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, China
- Hebei technical Innovation Center for Mental Health assessment and Intervention, Shijiazhuang, China
| | - Xiaoli Liu
- The third Hospital of Shijiazhuang, Shijiazhuang, China
| | - Xueyi Wang
- Mental Health Center, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050031, China.
- Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, China.
- Hebei technical Innovation Center for Mental Health assessment and Intervention, Shijiazhuang, China.
| | - Cuixia An
- Mental Health Center, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050031, China.
- Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, China.
- Hebei technical Innovation Center for Mental Health assessment and Intervention, Shijiazhuang, China.
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29
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Lin J, Huang B, Kwan MP, Chen M, Wang Q. COVID-19 infection rate but not severity is associated with availability of greenness in the United States. LANDSCAPE AND URBAN PLANNING 2023; 233:104704. [PMID: 36718417 PMCID: PMC9870763 DOI: 10.1016/j.landurbplan.2023.104704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 01/14/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.
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Affiliation(s)
- Jian Lin
- Sierra Nevada Research Institute, University of California, Merced, Merced, CA, 95340, USA
| | - Bo Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Mei-Po Kwan
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
| | - Qiang Wang
- State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 350007, China
- School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
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30
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Wang S, Cai W, Tao Y, Sun QC, Wong PPY, Huang X, Liu Y. Unpacking the inter- and intra-urban differences of the association between health and exposure to heat and air quality in Australia using global and local machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:162005. [PMID: 36758700 DOI: 10.1016/j.scitotenv.2023.162005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Environmental stressors including high temperature and air pollution cause health problems. However, understanding how the combined exposure to heat and air pollution affects both physical and mental health remains insufficient due to the complexity of such effects mingling with human society, urban and natural environments. Our study roots in the Social Ecological Theory and employs a tri-environmental conceptual framework (i.e., across social, built and natural environment) to examine how the combined exposure to heat and air pollution affect self-reported physical and mental health via, for the first time, the fine-grained nationwide investigation in Australia and highlight how such effects vary across inter- and intra-urban areas. We conducted an ecological study to explore the importance of heat and air quality to physical and mental health by considering 48 tri-environmental confounders through the global and local random forest regression models, as advanced machine learning methods with the advantage of revealing the spatial heterogeneity of variables. Our key findings are threefold. First, the social and built environmental factors are important to physical and mental health in both urban and rural areas, and even more important than exposure to heat and air pollution. Second, the relationship between temperature and air quality and health follows a V-shape, reflecting people's different adaptation and tolerance to temperature and air quality. Third, the important roles that heat and air pollution play in physical and mental health are most obvious in the inner-city and near inner-city areas of the major capital cities, as well as in the industrial zones in peri-urban regions and in Darwin city with a low-latitude. We draw several policy implications to minimise the inter- and intra-urban differences in healthcare access and service distribution to populations with different sensitivity to heat and air quality across urban and rural areas. Our conceptual framework can also be applied to examine the relationship between other environmental problems and health outcomes in the era of a warming climate.
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Affiliation(s)
- Siqin Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia; Graduate School of Interdisciplinary Information Studies, University of Tokyo, Tokyo, Japan.
| | - Wenhui Cai
- Centre for Social Policy & Social Change, Lingnan University, Hong Kong.
| | - Yaguang Tao
- School of Science, RMIT University, Melbourne, Victoria, Australia.
| | - Qian Chayn Sun
- School of Science, RMIT University, Melbourne, Victoria, Australia.
| | | | - Xiao Huang
- Department of Geosciences, University of Arkansas, AR, USA.
| | - Yan Liu
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia.
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31
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Hajek A, König HH. Do Individuals with High Climate Anxiety Believe That They Will Die Earlier? First Evidence from Germany. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5064. [PMID: 36981973 PMCID: PMC10048977 DOI: 10.3390/ijerph20065064] [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: 01/04/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES To examine the association between climate anxiety and perceived longevity in the general adult German population (also stratified by age group). STUDY DESIGN Nationally representative survey. METHODS Data were used of the general adult German population, with n = 3015 individuals (18 to 74 years; data collection: March 2022). Climate anxiety was assessed using the validated Climate Anxiety Scale. It was adjusted for a wide array of covariates in linear-log regression analysis. RESULTS Even after adjusting for various covariates, there was an association between higher (log) climate anxiety and a lower perceived longevity in the total sample (β = -1.41, p < 0.01). Stratified by age group, a significant association was only present among individuals aged 18 to 29 years (β = -3.58, p = 0.01), whereas it was not present in the other age groups (i.e., individuals aged 30 to 49 years, individuals aged 50 to 64 years, and individuals aged 65 years and over). CONCLUSIONS This study showed an association between higher climate anxiety and lower perceived longevity, particularly among younger individuals. More clearly, younger individuals with a higher climate anxiety think they will die earlier. This is the first study on this topic and could serve as a foundation for upcoming research. For example, longitudinal studies are needed to confirm our findings.
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32
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Wang M, Han Y, Wang CJ, Xue T, Gu HQ, Yang KX, Liu HY, Cao M, Meng X, Jiang Y, Yang X, Zhang J, Xiong YY, Zhao XQ, Liu LP, Wang YL, Guan TJ, Li ZX, Wang YJ. Short-term effect of PM2.5 on stroke in susceptible populations: A case-crossover study. Int J Stroke 2023; 18:312-321. [PMID: 35722790 DOI: 10.1177/17474930221110024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Fine particulate matter (PM2.5) is a risk factor for stroke, and patients with pre-existing diseases appear to be particularly susceptible. We conducted a case-crossover study to examine the association between short-term exposure to fine particulate matter (PM2.5) and hospital admission for stroke in individuals with atrial fibrillation (AF), hypertension, diabetes, or hyperlipidemia. METHODS Patients diagnosed with acute ischemic stroke (AIS) were recruited from 2015 to 2017 in Chinese Stroke Center Alliances. We estimated daily PM2.5 average exposures with a spatial resolution of 0.1° using a data assimilation approach combining satellite measurements, air model simulations, and monitoring values. Conditional logistic regression was used to assess PM2.5-related stroke risk in patients with pre-existing medical co-morbidities. RESULTS A total of 155,616 patients diagnosed with AIS were admitted. Patients with a history of AF (n = 15,430), hypertension (n = 138,220), diabetes (n = 43,737), or hyperlipidemia (n = 16,855) were assessed separately. A 10 µg/m3 increase in daily PM2.5 was associated with a significant increase in AIS for individuals with AF at lag 4 (odds ratio (OR), 1.008; 95% confidence interval (CI), 1.002-1.014), and with hypertension (OR, 1.008; 95% CI, 1.006-1.010), diabetes (OR, 1.006; 95% CI, 1.003-1.010), and hyperlipidemia (OR, 1.007; 95% CI, 1.001-1.012) at lags 0-7. Elderly (⩾ 65 years old) and female patients with AF had significantly higher associations at lag 5 (OR, 1.009; 95% CI, 1.002-1.015) and lag 5 (OR, 1.010; 95% CI, 1.002-1.018), respectively. CONCLUSION Short-term exposure to PM2.5 is significantly associated with hospital admission for stroke in individuals with pre-existing medical histories, especially in older or female patients with AF. Preventive measures to reduce PM2.5 concentrations are particularly important in individuals with other medical co-morbidities.
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Affiliation(s)
- Meng Wang
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, China
| | - Ying Han
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chun-Juan Wang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, 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, Beijing, China
| | - Hong-Qiu Gu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, China
| | - Kai-Xuan Yang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, China
| | - Heng-Yi Liu
- 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, Beijing, China
| | - Man Cao
- Department of Health Policy and Management, School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xia Meng
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin Yang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, China
| | - Jing Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, China
| | - Yun-Yun Xiong
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing-Quan Zhao
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Li-Ping Liu
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yi-Long Wang
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tian-Jia Guan
- Department of Health Policy and Management, School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zi-Xiao Li
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Healthcare Quality Management in Neurological Diseases, Beijing, China.,Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China.,Chinese Institute for Brain Research, Beijing, China
| | - Yong-Jun Wang
- Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China.,Chinese Institute for Brain Research, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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Zhou W, Wang Q, Kadier A, Wang W, Zhou F, Li R, Ling L. The role of residential greenness levels, green land cover types and diversity in overweight/obesity among older adults: A cohort study. ENVIRONMENTAL RESEARCH 2023; 217:114854. [PMID: 36403655 DOI: 10.1016/j.envres.2022.114854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/27/2022] [Accepted: 11/17/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Few studies have investigated the effects of greenness exposure, green land cover types and diversity and their interaction with particulate matter (PM) to adiposity. METHOD Cohort data were collected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Baseline data on greenness levels, green land cover types and diversity were assessed by the Normalized Difference Vegetation Index (NDVI), three greenery types (trees, shrublands and grassland) and Shannon's diversity index, respectively. Body mass index (BMI) and waist circumference (WC) were separately used as dependent variables and represented for peripheral overweight/obesity and central obesity, respectively. The mixed Cox model with random intercept was used to estimate the effects of greenness levels, types and diversity on overweight/obesity using single and multiple exposure models. We also examined the interaction of PM and the aforementioned indicators on overweight/obesity on both additive and multiplicative scales. RESULTS Single exposure models showed that higher levels of residential greenness, tree coverage and ratio of trees to shrublands/grassland were inversely associated with peripheral overweight/obesity and central obesity. An increase in shrublands, grassland and diversity of green was related to lower odds of peripheral overweight/obesity. Multiple exposure models confirmed the association between greenness levels and peripheral overweight/obesity. Males, educated participants and elderly who lived in southern regions and areas with cleaner air environments acquired more benefits from greenspace exposure. Single and multiple exposure models indicated that an antagonistic effect of increasing PM and decreasing greenness levels on peripheral overweight/obesity and central obesity. Single exposure models showed the potential interaction of tree coverage, ratio of trees to grassland and PM2.5 exposures on the risk of peripheral overweight/obesity. CONCLUSION Increasing residential greenness and diversity of green were associated with healthy weight status. The relationship between greenery and overweight/obesity varied, and the effects of greenspace exposure on overweight/obesity were associated with air pollution.
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Affiliation(s)
- Wensu Zhou
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Qiong Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Aimulaguli Kadier
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wenjuan Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Fenfen Zhou
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Rui Li
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Li Ling
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China; Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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34
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Wen B, Su BB, Xue J, Xie J, Wu Y, Chen L, Dong Y, Wu X, Wang M, Song Y, Ma J, Zheng X. Temperature variability and common diseases of the elderly in China: a national cross-sectional study. Environ Health 2023; 22:4. [PMID: 36609287 PMCID: PMC9824998 DOI: 10.1186/s12940-023-00959-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND In the context of climate change, it has been well observed that short-term temperature variability (TV) could increase the overall and cause-specific mortality and morbidity. However, the association between long-term TV and a broader spectrum of diseases is not yet well understood, especially in the elderly. METHODS Our study used data from the fourth Urban and Rural Elderly Population (UREP) study. Long-term TV was calculated from the standard deviation (SD) of daily minimum and maximum temperatures within the study periods (2010-2014, 2011-2014, 2012-2014, 2013-2014, and 2014). Ten self-reported diseases and conditions were collected by questionnaire, including cataract, hypertension, diabetes, cardio-cerebrovascular diseases, stomach diseases, arthritis, chronic lung disease, asthma, cancer, and reproductive diseases. The province-stratified logistic regression model was used to quantify the association between long-term TV and the prevalence of each disease. RESULTS A total of 184,047 participants were included in our study. In general, there were significant associations between TV and the prevalence of most diseases at the national level. Cardio-cerebrovascular disease (OR: 1.16, 95% CI: 1.13, 1.20) generated the highest estimates, followed by stomach diseases (OR: 1.15, 95% CI: 1.10, 1.19), asthma (OR: 1.14, 95% CI: 1.06, 1.22), chronic lung diseases (OR: 1.08, 95% CI: 1.03, 1.13), arthritis (OR: 1.08, 95% CI: 1.05, 1.11), and cataract (OR: 1.06, 95% CI: 1.02, 1.10). Moreover, the associations varied by geographical regions and across subgroups stratified by sex, household income, physical activity, and education. CONCLUSIONS Our study showed that long-term exposure to TV was associated with the prevalence of main diseases in the elderly. More attention should be paid to the elderly and targeted strategies should be implemented, such as an early warning system.
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Affiliation(s)
- Bo Wen
- Institute of Child and Adolescent Health, School of Public Health, Peking University Health Science Center, No 38 Xue Yuan Road, Haidian District, Beijing, 100191, China
- Climate, Air Quality Research (CARE) Unit, School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Bin Bin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No.31, Beijige-3, Dongcheng District, Beijing, 100730, China
| | - Jiahui Xue
- First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan City, 030001, Shanxi Province, China
| | - Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Yao Wu
- Climate, Air Quality Research (CARE) Unit, School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Li Chen
- Institute of Child and Adolescent Health, School of Public Health, Peking University Health Science Center, No 38 Xue Yuan Road, Haidian District, Beijing, 100191, China
| | - Yanhui Dong
- Institute of Child and Adolescent Health, School of Public Health, Peking University Health Science Center, No 38 Xue Yuan Road, Haidian District, Beijing, 100191, China.
| | - Xiaolan Wu
- China Research Center on Ageing, 48 Guang 'anmen South Street, Xicheng District, Beijing, 100054, China
| | - Mengfan Wang
- University of Toronto, St.Geogre, 27 King's College Cir, Toronto, ON, M5S, Canada
| | - Yi Song
- Institute of Child and Adolescent Health, School of Public Health, Peking University Health Science Center, No 38 Xue Yuan Road, Haidian District, Beijing, 100191, China
| | - Jun Ma
- Institute of Child and Adolescent Health, School of Public Health, Peking University Health Science Center, No 38 Xue Yuan Road, Haidian District, Beijing, 100191, China
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No.31, Beijige-3, Dongcheng District, Beijing, 100730, China.
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35
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Liang M, Min M, Ye P, Duan L, Sun Y. Are there joint effects of different air pollutants and meteorological factors on mental disorders? A machine learning approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:6818-6827. [PMID: 36008583 DOI: 10.1007/s11356-022-22662-0] [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: 06/09/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
Exposure to air pollutants is considered to be associated with mental disorders (MD). Few studies have addressed joint effect of multiple air pollutants and meteorological factors on admissions of MD. We examined the association between multiple air pollutants (PM2.5, PM10, O3, SO2, and NO2), meteorological factors (temperature, precipitation, relative humidity, and sunshine time), and MD risk in Yancheng, China. Associations were estimated by a generalized linear regression model (GLM) adjusting for time trend, day of the week, and patients' average age. Empirical weights of environmental exposures were judged by a weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel machine regression (BKMR), was used to assess the overall effect of mixed exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for each exposure. According to the effect of temperature on MD, we divided the exposure of all factors into different temperature groups. In the high temperature group, GLM found that for every 10 μg/m3 increase in O3, PM2.5 and PM10 exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. Temperature, relative humidity, and sunshine time also reported significant results. The WQS identified O3 and temperature (above the threshold) had the highest weights among air pollutants and meteorological factors. BKMR found a significant positive association between mixed exposure and MD risks. In the low temperature group, only O3 and temperature (below the threshold) showed significant results. These findings provide policymakers and practitioners with important scientific evidence for possible interventions. The association between different exposures and MD risk warrants further study.
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Affiliation(s)
- Mingming Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Min Min
- Anhui Institute of Medical Information (Anhui Medical Association), Hefei, 230061, Anhui, China
| | - Pengpeng Ye
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Leilei Duan
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Yehuan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, China.
- Chaohu Hospital, Anhui Medical University, Hefei, 238000, Anhui, China.
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36
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Muniz NO, Gabut S, Maton M, Odou P, Vialette M, Pinon A, Neut C, Tabary N, Blanchemain N, Martel B. Electrospun Filtering Membrane Designed as Component of Self-Decontaminating Protective Masks. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 13:9. [PMID: 36615926 PMCID: PMC9823851 DOI: 10.3390/nano13010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
The 2019 coronavirus outbreak and worsening air pollution have triggered the search for manufacturing effective protective masks preventing both particulate matter and biohazard absorption through the respiratory tract. Therefore, the design of advanced filtering textiles combining efficient physical barrier properties with antimicrobial properties is more newsworthy than ever. The objective of this work was to produce a filtering electrospun membrane incorporating a biocidal agent that would offer both optimal filtration efficiency and fast deactivation of entrapped viruses and bacteria. After the eco-friendly electrospinning process, polyvinyl alcohol (PVA) nanofibers were stabilized by crosslinking with 1,2,3,4-butanetetracarboxylic acid (BTCA). To compensate their low mechanical properties, nanofiber membranes with variable grammages were directly electrospun on a meltblown polypropylene (PP) support of 30 g/m2. The results demonstrated that nanofibers supported on PP with a grammage of around only 2 g/m2 presented the best compromise between filtration efficiencies of PM0.3, PM0.5, and PM3.0 and the pressure drop. The filtering electrospun membranes loaded with benzalkonium chloride (ADBAC) as a biocidal agent were successfully tested against E. coli and S. aureus and against human coronavirus strain HCoV-229E. This new biocidal filter based on electrospun nanofibers supported on PP nonwoven fabric could be a promising solution for personal and collective protection in a pandemic context.
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Affiliation(s)
- Nathália Oderich Muniz
- UMET—Unité Matériaux et Transformations, University of Lille, CNRS, INRAE, Centrale Lille, UMR 8207, 59650 Villeneuve d’Ascq, France
| | - Sarah Gabut
- UMET—Unité Matériaux et Transformations, University of Lille, CNRS, INRAE, Centrale Lille, UMR 8207, 59650 Villeneuve d’Ascq, France
| | - Mickael Maton
- University of Lille, INSERM, CHU Lille, U1008—Advanced Drug Delivery Systems, 59000 Lille, France
| | - Pascal Odou
- ULR 7365—GRITA—Groupe de Recherche sur les Formes Injectables et les Technologies Associées, University of Lille, CHU Lille F-59000, 59006 Lille, France
| | - Michèle Vialette
- Institut Pasteur de Lille, Unité de Sécurité Microbiologique, 59000 Lille, France
| | - Anthony Pinon
- Institut Pasteur de Lille, Unité de Sécurité Microbiologique, 59000 Lille, France
| | - Christel Neut
- Institute for Translational Research in Inflammation, University of Lille, INSERM, CHU Lille, U1286, 59045 Lille, France
| | - Nicolas Tabary
- UMET—Unité Matériaux et Transformations, University of Lille, CNRS, INRAE, Centrale Lille, UMR 8207, 59650 Villeneuve d’Ascq, France
| | - Nicolas Blanchemain
- University of Lille, INSERM, CHU Lille, U1008—Advanced Drug Delivery Systems, 59000 Lille, France
| | - Bernard Martel
- UMET—Unité Matériaux et Transformations, University of Lille, CNRS, INRAE, Centrale Lille, UMR 8207, 59650 Villeneuve d’Ascq, France
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Yang T, Zhou K, Ding T. Air pollution impacts on public health: Evidence from 110 cities in Yangtze River Economic Belt of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158125. [PMID: 35988618 DOI: 10.1016/j.scitotenv.2022.158125] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/04/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
This study intends to further reveal the relationship between air pollution and public health on a city scale in China and explore the spillover effect among cities. On the basis of collecting the panel data of 110 cities in the Yangtze River Economic Belt from 2010 to 2018, we establish a spatial econometric model to analyze the impacts of air pollution, economic development, and other factors on public health. According to the results, a significant spatial correlation exists between the public health and air pollution levels in all of the cities in the Yangtze River Economic Belt. Air pollution also shows a spillover effect among these cities; the relationships between the industrial fume (dust) emissions, industrial sulfur dioxide emissions, and particulate matter (PM 2.5) concentration and the public health level are not simple linear relationships, but instead U-shaped curvilinear relationships. The economic development in recent years has contributed to the improvement of the public health level of the cities in the Yangtze River Economic Belt. The economic development of their neighboring cities, however, has adversely affected the public health levels of these cities.
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Affiliation(s)
- Ting Yang
- School of Health Services Management, Anhui Medical University, Hefei 230032, China
| | - Kaile Zhou
- School of Management, Hefei University of Technology, Hefei 230009, China.
| | - Tao Ding
- School of Management, Hefei University of Technology, Hefei 230009, China
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38
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Guo J, Xie X, Wu J, Yang L, Ruan Q, Xu X, Wei D, Wen Y, Wang T, Hu Y, Lin Y, Chen M, Wu J, Lin S, Li H, Wu S. Association between fine particulate matter and coronary heart disease: A miRNA microarray analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 313:120163. [PMID: 36122657 DOI: 10.1016/j.envpol.2022.120163] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 09/01/2022] [Accepted: 09/08/2022] [Indexed: 06/15/2023]
Abstract
Several studies have reported an association between residential surrounding particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and coronary heart disease (CHD). However, the underlying biological mechanism remains unclear. To fill this research gap, this study enrolled a residentially stable sample of 942 patients with CHD and 1723 controls. PM2.5 concentration was obtained from satellite-based annual global PM2.5 estimates for the period 1998-2019. MicroRNA microarray and pathway analysis of target genes was performed to elucidate the potential biological mechanism by which PM2.5 increases CHD risk. The results showed that individuals exposed to high PM2.5 concentrations had higher risks of CHD than those exposed to low PM2.5 concentrations (odds ratio = 1.22, 95% confidence interval: 1.00, 1.47 per 10 μg/m3 increase in PM2.5). Systolic blood pressure mediated 6.6% of the association between PM2.5 and CHD. PM2.5 and miR-4726-5p had an interaction effect on CHD development. Bioinformatic analysis demonstrated that miR-4726-5p may affect the occurrence of CHD by regulating the function of RhoA. Therefore, individuals in areas with high PM2.5 exposure and relative miR-4726-5p expression have a higher risk of CHD than their counterparts because of the interaction effect of PM2.5 and miR-4726-5p on blood pressure.
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Affiliation(s)
- Jianhui Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Xiaoxu Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Jieyu Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Le Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Qishuang Ruan
- Department of Orthopedics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xingyan Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Donghong Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Yeying Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Tinggui Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Yuduan Hu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Yawen Lin
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Mingjun Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Jiadong Wu
- School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Shaowei Lin
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Huangyuan Li
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou, 350122, China
| | - Siying Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
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39
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Wang L, Zhang J, Wei J, Zong J, Lu C, Du Y, Wang Q. Association of ambient air pollution exposure and its variability with subjective sleep quality in China: A multilevel modeling analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 312:120020. [PMID: 36028077 DOI: 10.1016/j.envpol.2022.120020] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM2.5) and ≤10 μm (PM10) led to 0.4% (95% confidence interval (CI): 1.002-1.006) and 0.3% (95% CI: 1.001-1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM2.5 and PM10 were 2.2% (95% CI: 1.012-1.032) and 2.2% (95% CI: 1.012-1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.
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Affiliation(s)
- Lingli Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jingxuan Zhang
- Shandong Provincial Mental Health Center, Jinan City, Shandong, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, USA
| | - Jingru Zong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunyu Lu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yajie Du
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qing Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
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40
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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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Association between built environments and quality of life among community residents: mediation analysis of air pollution. Public Health 2022; 211:75-80. [PMID: 36030597 DOI: 10.1016/j.puhe.2022.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/25/2022] [Accepted: 07/18/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVES The aim of this study was to analyze the relationship between built environments and quality of life (QoL), and the mediating role of air pollution in that relationship. STUDY DESIGN This was a cross-sectional population-based study. METHODS Data of 5196 adults residing in 148 communities in three cities in Liaoning Province, China, were analyzed. Objective measures of traffic design included street connectivity, road network density, bus station density, and parking lot density; residential greenness was controlled as a confounder. QoL was evaluated using the 12-Item Short Form Health Survey. The average concentrations of PM2.5 and SO2 one month before QoL collection for each community were calculated. RESULTS Road network density and parking lot density were negatively associated with the Physical Component Summary (PCS), but street connectivity was positively associated with PCS for the participants. Bus station density, street connectivity, and parking lot density were negatively associated with the Mental Component Summary (MCS), and PM2.5 and SO2 mediated this association. In addition, gender and road network density and parking lot density had an interactive effect on the MCS of the participants. CONCLUSIONS Dense traffic affects people's health not only directly but also indirectly through air pollution. The effects of built environments and air pollution should be considered when building healthy, supportive communities, and healthy cities.
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Ab Kader NI, Yusof UK, Khalid MNA, Nik Husain NR. Recent Techniques in Determining the Effects of Climate Change on Depressive Patients: A Systematic Review. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:1803401. [PMID: 35978588 PMCID: PMC9377838 DOI: 10.1155/2022/1803401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 04/03/2022] [Accepted: 07/11/2022] [Indexed: 11/18/2022]
Abstract
Climate change is amongst the most serious issues nowadays. Climate change has become a concern for the scientific community as it could affect human health. Researchers have found that climate change potentially impacts human mental health, especially among depressive patients. However, the relationship is still unclear and needs further investigation. The purpose of this systematic review is to systematically evaluate the evidence of the association between climate change effects on depressive patients, investigate the effects of environmental exposure related to climate change on mental health outcomes for depressive patients, analyze the current technique used to determine the relationship, and provide the guidance for future research. Articles were identified by searching specified keywords in six electronic databases (Google Scholar, PubMed, Scopus, Springer, ScienceDirect, and IEEE Digital Library) from 2012 until 2021. Initially, 1823 articles were assessed based on inclusion criteria. After being analyzed, only 15 studies fit the eligibility criteria. The result from included studies showed that there appears to be strong evidence of the association of environmental exposure related to climate change in depressive patients. Temperature and air pollution are consistently associated with increased hospital admission of depressive patients; age and gender became the most frequently considered vulnerability factors. However, the current evidence is limited, and the output finding between each study is still varied and does not achieve a reasonable and mature conclusion regarding the relationship between the variables. Therefore, more evidence is needed in this domain study. Some variables might have complex patterns, and hard to identify the relationship. Thus, technique used to analyze the relationship should be strengthened to identify the relevant relationship.
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Affiliation(s)
- Nur Izzati Ab Kader
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
| | - Umi Kalsom Yusof
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
| | - Mohd Nor Akmal Khalid
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
- School of Information Science, Japan Advance Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan
| | - Nik Rosmawati Nik Husain
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu 16150, Kelantan, Malaysia
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Wensu Z, Wenjuan W, Fenfen Z, Wen C, Li L. The effects of greenness exposure on hypertension incidence among Chinese oldest-old: a prospective cohort study. Environ Health 2022; 21:66. [PMID: 35820901 PMCID: PMC9277785 DOI: 10.1186/s12940-022-00876-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 06/27/2022] [Indexed: 06/13/2023]
Abstract
BACKGROUND Although the oldest-old (those aged over 80 years) are vulnerable to environmental factors and have the highest prevalence of hypertension, studies focusing on greenness exposure and the development of hypertension among them are insufficient. The aim of this study was to explore the association between residential greenness and hypertension in the oldest-old population. METHODS This cohort study included data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The oldest-old were free of hypertension at baseline (2008), and hypertension events were assessed by follow-up surveys in 2011, 2014, and 2018. The one-year averages of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at 500-m buffer before the interview year of incident hypertension or last censoring interview were collected at the level of 652 residential units (district or county). The linear or nonlinear association between greenness and hypertension incidence was analyzed using the Cox proportional hazards model with penalized splines. The linear links between greenness and hypertension incidence were determined using the Cox proportional hazards model included a random effect term. RESULTS Among 5253 participants, the incidence rate of hypertension was 7.25 (95% confidence interval [CI]: 6.83-7.67) per 100 person-years. We found a nonlinear association between greenness exposure and hypertension risk, and the exposure-response curve showed that 1 change point existed. We examined the linear effect of greenness on hypertension by categorizing the NDVI/EVI into low and high-level exposure areas according to the change point. We found more notable protective effects of each 0.1-unit increase in greenness on hypertension incidence for participants living in the high-level greenness areas (hazard ratio (HR) = 0.60; 95% CI: 0.53-0.70 for NDVI; HR = 0.46; 95% CI: 0.37-0.57 for EVI). In contrast, no significant influence of greenness exposure on hypertension risk was found for participants living in the low-level greenness areas (HR = 0.77; 95% CI: 0.38-1.55 for NDVI; HR = 0.73; 95% CI: 0.33-1.63 for EVI). CONCLUSIONS Greenness exposure is nonlinearly associated with hypertension risk among the oldest-old, presenting its relationship in an inverse "U-shaped" curve. Greenness is a protective factor that decreases the risk of hypertension.
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Affiliation(s)
- Zhou Wensu
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wang Wenjuan
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zhou Fenfen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Chen Wen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
| | - Ling Li
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
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Environmentally vulnerable or sensitive groups exhibiting varying concerns toward air pollution can drive government response to improve air quality. iScience 2022; 25:104460. [PMID: 35707724 PMCID: PMC9189109 DOI: 10.1016/j.isci.2022.104460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/11/2022] [Accepted: 05/16/2022] [Indexed: 11/20/2022] Open
Abstract
Air pollution seriously threatens human health, and its consequences are particularly prevalent among environmentally vulnerable or sensitive groups. However, whether the concerns among these groups are different and how they affect air pollution governance remain unclear. Here, we extract 3.8 million haze-related posts from China’s Sina Weibo and analyze the concerns raised by these groups by constructing an air pollution notability index. The results show that protection is the key theme for women aged 20–35 years, while elderly individuals are easily influenced by haze-related product ads yet lack awareness of scientific-based protection. Concerns shared by young individuals are more effective in pressuring the government in cities that experience higher levels of pollution. Concerns shared by women are more effective in cities that experience lower levels of pollution. This study evidences the influence of the public concerns conveyed via social media on air pollution governance in China. Online haze concerns from environmentally vulnerable groups can drive air clean Government response to their concern plays the key role in improving air quality When PM2.5 exceeds 200 μg/m3, their concern shifts from self-protection to governance Elders are easily drawn by haze-related ads yet lack aware of scientific protection
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The Impact of Sustained Exposure to Air Pollutant on the Mental Health: Evidence from China. SUSTAINABILITY 2022. [DOI: 10.3390/su14116693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Emerging evidence suggests that poor mental health is particularly pronounced among Chinese residents, who are exposed to nearly the worst air pollution worldwide. However, the correlations between air pollutant concentration and mental health have not been consistently reported in previous studies. Methodologically speaking, a sufficiently rigorous design is required to demonstrate the causal relationship between the two factors. In this study, we aimed to infer the causal relationship between air pollutant concentration and mental health. In this panel research, the data were compiled through a combination of statistics from the China Family Panel Study, China Environmental Statistics Yearbook, World Meteorological Association, and China National Bureau of Statistics. Ultimately, this study enrolled 65,326 individuals whose mental health, air pollutant concentration, and other demographic information was available and robust. The RD design of this study utilizes the discontinuous variation in air pollutant concentrations and mental health as one crosses the Huai River boundary, which is an arbitrary heating policy that caused the difference in air pollutant concentrations between the north and south of China. In this study, we found that a 10 μg/m3 increase in air pollutant concentrations (air particulate matter smaller than 10 μm (PM10)) leads to a 4.9-unit decrease in the mental health of the Chinese residents(coeff = 0.49, SD = 0.07, p < 0.05), equivalent to 36% of the average of Chinese residents. In the heterogeneity model, the impairment of mental health by air pollutant concentrations was more pronounced in male residents (coeff = 1.37, SD = 0.10, p < 0.05) compared to female residents (coeff = 0.42, SD = 0.04, p < 0.05) and smokers (compared to non-smokers). The robustness of the results is ensured by changing the RD bandwidth and polynomial order, and by two unique sensitivity analyses. The results indicate that air pollutant concentrations significantly impair the mental health of Chinese residents, which provides empirical evidence supporting the Chinese government’s decision to invest more in combating air pollution and ensuring the mental health of Chinese residents.
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Hou F, Han X, Wang Q, Zhou S, Zhang J, Shen G, Zhang Y. Cross-Sectional Associations between Living and Built Environments and Depression Symptoms among Chinese Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105819. [PMID: 35627355 PMCID: PMC9140945 DOI: 10.3390/ijerph19105819] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/07/2022] [Indexed: 02/04/2023]
Abstract
In this study, we explored the cross-sectional associations between living and built environments and depression among older Chinese adults. Data from 5822 participants were obtained. Depression symptoms were evaluated through the use of the Patient Health Questionnaire (PHQ-9), with a score higher than 4 categorized as having depression symptoms. The living environment was assessed by asking about dust in the environment and barrier-free facilities. We considered the presence of amenities within a 10 min walking distance and the proportion of green space within an 800 m distance from participants’ dwellings to reflect the built environment. Data were analyzed by multilevel logistic regression. Participants living in a non-dusty environment with proximity to green space had a lower risk of depression (non-dusty environment: OR = 0.784, 95% CI = 0.642, 0.956; green space: OR = 0.834, 95% CI = 0.697, 0.998). However, having no access to barrier-free facilities and hospital proximity increased the depression risk (barrier-free facilities: OR = 1.253, 95% CI = 1.078, 1.457; hospital: OR = 1.318, 95% CI = 1.104, 1.574). Dusty environments, access to barrier-free facilities and proximity to hospitals and green spaces were associated with depression symptoms among older Chinese adults.
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Affiliation(s)
- Fangfang Hou
- School of Health Service Management, Anhui Medical University, Hefei 230032, China; (F.H.); (X.H.); (Q.W.); (S.Z.); (J.Z.)
| | - Xiao Han
- School of Health Service Management, Anhui Medical University, Hefei 230032, China; (F.H.); (X.H.); (Q.W.); (S.Z.); (J.Z.)
| | - Qiong Wang
- School of Health Service Management, Anhui Medical University, Hefei 230032, China; (F.H.); (X.H.); (Q.W.); (S.Z.); (J.Z.)
| | - Shuai Zhou
- School of Health Service Management, Anhui Medical University, Hefei 230032, China; (F.H.); (X.H.); (Q.W.); (S.Z.); (J.Z.)
| | - Jingya Zhang
- School of Health Service Management, Anhui Medical University, Hefei 230032, China; (F.H.); (X.H.); (Q.W.); (S.Z.); (J.Z.)
| | - Guodong Shen
- Department of Geriatrics, The First Affiliated Hospital of University of Science and Technology of China, Gerontology Institute of Anhui Province, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
- Anhui Provincial Key Laboratory of Tumor Immunotherapy and Nutrition Therapy, Hefei 230001, China
- Correspondence: (G.S.); (Y.Z.); Tel.: +86-551-62282371 (G.S.); +86-551-65161220 (Y.Z.)
| | - Yan Zhang
- School of Health Service Management, Anhui Medical University, Hefei 230032, China; (F.H.); (X.H.); (Q.W.); (S.Z.); (J.Z.)
- Correspondence: (G.S.); (Y.Z.); Tel.: +86-551-62282371 (G.S.); +86-551-65161220 (Y.Z.)
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A Novel Composite Index to Measure Environmental Benefits in Urban Land Use Optimization Problems. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11040220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of methodologies. According to previous studies, there is no standard approach for calculating environmental benefits in urban land use allocation problems. Against this background, this study aims to (a) identify indicators of environmental benefits and (b) propose a novel composite index to measure environmental benefits in urban land use optimization problems. This study identified four indicators as a measure of environmental benefits based on a literature assessment and expert opinion. These are spatial compactness, land surface temperature, carbon storage, and ecosystem service value. In this work, we proposed a novel composite environmental benefits index (EBI) to quantify environmental benefits in urban land use allocation problems using an ordered weighted averaging (OWA) method. The study results showed that land surface temperature (LST) is the most influential indicator of environmental benefit while carbon storage is the least important factor. Finally, the proposed method was applied in Rajshahi city in Bangladesh. This study identified that, in an average-risk decision, most of the land (64.55%) of the study area falls within the low-environmental-benefit zone due to a lack of vegetated land cover. The result suggests the potential of using EBI in the land use allocation problem to ensure environmental benefits.
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Abed Al Ahad M, Demšar U, Sullivan F, Kulu H. Air pollution and individuals' mental well-being in the adult population in United Kingdom: A spatial-temporal longitudinal study and the moderating effect of ethnicity. PLoS One 2022; 17:e0264394. [PMID: 35263348 PMCID: PMC8906596 DOI: 10.1371/journal.pone.0264394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/09/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Recent studies suggest an association between ambient air pollution and mental well-being, though evidence is mostly fragmented and inconclusive. Research also suffers from methodological limitations related to study design and moderating effect of key demographics (e.g., ethnicity). This study examines the effect of air pollution on reported mental well-being in United Kingdom (UK) using spatial-temporal (between-within) longitudinal design and assesses the moderating effect of ethnicity. METHODS Data for 60,146 adult individuals (age:16+) with 349,748 repeated responses across 10-data collection waves (2009-2019) from "Understanding-Society: The-UK-Household-Longitudinal-Study" were linked to annual concentrations of NO2, SO2, PM10, and PM2.5 pollutants using the individuals' place of residence, given at the local-authority and at the finer Lower-Super-Output-Areas (LSOAs) levels; allowing for analysis at two geographical scales across time. The association between air pollution and mental well-being (assessed through general-health-questionnaire-GHQ12) and its modification by ethnicity and being non-UK born was assessed using multilevel mixed-effect logit models. RESULTS Higher odds of poor mental well-being was observed with every 10μg/m3 increase in NO2, SO2, PM10 and PM2.5 pollutants at both LSOAs and local-authority levels. Decomposing air pollution into spatial-temporal (between-within) effects showed significant between, but not within effects; thus, residing in more polluted local-authorities/LSOAs have higher impact on poor mental well-being than the air pollution variation across time within each geographical area. Analysis by ethnicity revealed higher odds of poor mental well-being with increasing concentrations of SO2, PM10, and PM2.5 only for Pakistani/Bangladeshi, other-ethnicities and non-UK born individuals compared to British-white and natives, but not for other ethnic groups. CONCLUSION Using longitudinal individual-level and contextual-linked data, this study highlights the negative effect of air pollution on individuals' mental well-being. Environmental policies to reduce air pollution emissions can eventually improve the mental well-being of people in UK. However, there is inconclusive evidence on the moderating effect of ethnicity.
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Affiliation(s)
- Mary Abed Al Ahad
- School of Geography and Sustainable Development, University of St. Andrews, Scotland, United Kingdom
| | - Urška Demšar
- School of Geography and Sustainable Development, University of St. Andrews, Scotland, United Kingdom
| | - Frank Sullivan
- School of Medicine, University of St. Andrews, Scotland, United Kingdom
| | - Hill Kulu
- School of Geography and Sustainable Development, University of St. Andrews, Scotland, United Kingdom
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Lee KS, Kim G, Ham BJ. ORIGINAL ARTICLE: Associations of antidepressant medication with its various predictors including particulate matter: Machine learning analysis using national health insurance data. J Psychiatr Res 2022; 147:67-78. [PMID: 35026595 DOI: 10.1016/j.jpsychires.2022.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/23/2021] [Accepted: 01/03/2022] [Indexed: 10/19/2022]
Abstract
This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 μm (PM2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15-79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002-2012. The dependent variable was antidepressant-free months during 2013-2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013-2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM2.5 (0.0005) and November 2012 PM2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis. In conclusion, antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University College of Medicine, Seoul, South Korea
| | - Geunyeong Kim
- Korea University Graduate School of Policy Studies, Seoul, South Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University College of Medicine, Seoul, South Korea.
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Tamoor M, Samak NA, Yang M, Xing J. The Cradle-to-Cradle Life Cycle Assessment of Polyethylene terephthalate: Environmental Perspective. Molecules 2022; 27:molecules27051599. [PMID: 35268703 PMCID: PMC8911646 DOI: 10.3390/molecules27051599] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 12/21/2022] Open
Abstract
Over the last several years, the number of concepts and technologies enabling the production of environmentally friendly products (including materials, consumables, and services) has expanded. One of these ways is cradle-to-cradle (C2C) certifiedTM. Life cycle assessment (LCA) technique is used to highlight the advantages of C2C and recycling as a method for reducing plastic pollution and fossil depletion by indicating the research limitations and gaps from an environmental perspective. Also, it estimates the resources requirements and focuses on sound products and processes. The C2C life cycle measurements for petroleum-based poly (ethylene terephthalate) (PET) bottles, with an emphasis on different end-of-life options for recycling, were taken for mainland China, in brief. It is considered that the product is manufactured through the extraction of crude oil into ethylene glycol and terephthalic acid. The CML analysis method was used in the LCIA for the selected midpoint impact categories. LCA of the product has shown a drastic aftermath in terms of environmental impacts and energy use. But the estimation of these consequences is always dependent on the system and boundary conditions that were evaluated throughout the study. The impacts that burden the environment are with the extraction of raw material, resin, and final product production. Minor influences occurred due to the waste recycling process. This suggests that waste degradation is the key process to reduce the environmental impacts of the production systems. Lowering a product’s environmental impact can be accomplished in a number of ways, including reducing the amount of materials used or choosing materials with a minimal environmental impact during manufacture processes.
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Affiliation(s)
- Muhammad Tamoor
- CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
- College of Chemical Engineering, University of Chinese Academy of Sciences, 19 A Yuquan Road, Beijing 100049, China
| | - Nadia A. Samak
- CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
- Correspondence: (N.A.S.); (M.Y.); (J.X.); Tel.: +86-10-6255-0913 (J.X)
| | - Maohua Yang
- CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
- Correspondence: (N.A.S.); (M.Y.); (J.X.); Tel.: +86-10-6255-0913 (J.X)
| | - Jianmin Xing
- CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
- College of Chemical Engineering, University of Chinese Academy of Sciences, 19 A Yuquan Road, Beijing 100049, China
- Chemistry and Chemical Engineering Guangdong Laboratory, Shantou 515031, China
- Correspondence: (N.A.S.); (M.Y.); (J.X.); Tel.: +86-10-6255-0913 (J.X)
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