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Liu Q, Yang S, Chen H. Global trends and hotspots in the study of the effects of PM2.5 on ischemic stroke. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2024; 43:133. [PMID: 39198927 PMCID: PMC11360839 DOI: 10.1186/s41043-024-00622-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Accepted: 08/14/2024] [Indexed: 09/01/2024]
Abstract
AIM The objective of this study was to visually analyse global research trends and hotspots regarding the role of PM2.5 in ischemic stroke. METHODS The Web of Science core collection database was used to search the literature on PM2.5 and ischemic stroke from 2006 to 2024. Visualization analysis was conducted using CiteSpace, VOSviewer, and an online bibliometric platform. RESULTS The analysis comprises 190 articles published between 2006 and 2024 by 1229 authors from 435 institutions in 39 countries, across 78 journals. Wellenius GA has the highest number of published and cited papers. China has the highest number of papers, while Canada has the highest citation frequency. Capital Medical University published the highest number of papers, and Harvard University had the highest citation frequency for a single paper. The study investigated the impact of PM2.5 on ischemic stroke in three phases. The first phase analysed hospitalisation rates for correlations. The second phase utilised large-scale multi-cohort data from around the world. The third phase involved studying global exposure risk through machine learning and model construction. Currently, there is limited research on the mechanisms involved, and further in-depth investigation is required. CONCLUSION This paper presents a bibliometric analysis of the research framework and hotspots concerning the effect of PM2.5 on ischemic stroke. The analysis aims to provide a comprehensive understanding of this field for researchers. It is expected that research on the effect of PM2.5 on ischemic stroke will remain an important research topic in the future.
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Affiliation(s)
- Qian Liu
- Cerebrovascular Disease Department, Gansu Provincial Hospital, No.204 West Donggang Road, Lanzhou, 730000, Gansu Province, China
- Key Laboratory of Cerebrovascular Disease of Gansu Province, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Shijie Yang
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
| | - HeCheng Chen
- Cerebrovascular Disease Department, Gansu Provincial Hospital, No.204 West Donggang Road, Lanzhou, 730000, Gansu Province, China.
- Key Laboratory of Cerebrovascular Disease of Gansu Province, Gansu Provincial Hospital, Lanzhou, Gansu, China.
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Zhang T, Liu W, Yang T, Zhai Y, Gu X, Xu L, Li F, Wu M, Lin J. Association between ambient fine particular matter components and subsequent cognitive impairment in community-dwelling older people: a prospective cohort study from eastern China. Aging Clin Exp Res 2024; 36:150. [PMID: 39060791 PMCID: PMC11282123 DOI: 10.1007/s40520-024-02793-9] [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: 09/19/2023] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND Fine particular matter (PM2.5) has been associated with dementia, but limited information is available regarding the association between PM2.5 components and dementia. AIMS We aimed to identify the major components of PM2.5 that affect cognitive function to further investigate its mechanism of action, and develop a prevention strategy for dementia. METHODS In this study, we included 7804 participants aged ≥ 60 years recruited from seven counties in Zhejiang province, eastern China. The participants completed the baseline survey between 2014 and 2015, and were followed up until the end of 2020. We adopted single-component robust Poisson regression models for analyses, and estimated relative risks and 95% confidence intervals describing associations between the chemical constituents of PM2.5 exposure and incident cognitive impairment in those who were free from cognitive impairment at baseline. RESULTS Significantly positive associations were observed between sulfate, nitrate, ammonium, and organic matter in PM2.5 and incident cognitive impairment across different exposure periods; the relative risks of 10-year exposure before enrollment ranged from 1.01 to 1.02. However, we did not find a significant association between black carbon and cognitive impairment. The point estimates of the relative risk values did not change substantially after performing the sensitivity analyses. CONCLUSIONS Our findings strengthen the idea that long-term exposure to PM2.5 mass and its chemical components is associated with an elevated risk of incident cognitive impairment among older adults.
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Affiliation(s)
- Tao Zhang
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wenfeng Liu
- Office, Changshan Center for Disease Control and Prevention, Quzhou, Zhejiang, China
| | - Tao Yang
- Office, Yuhang Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yujia Zhai
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Xue Gu
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Le Xu
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Fudong Li
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Mengna Wu
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Junfen Lin
- Department of Public Health Surveillance and Advisory, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
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Wang Y, Hu J, Wu Y, Kota SH, Zhang H, Gong K, Xie X, Yue X, Liao H, Huang L. Continued Rise in Health Burden from Ambient PM 2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8685-8695. [PMID: 38709795 DOI: 10.1021/acs.est.4c02264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.
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Affiliation(s)
- Yiyi Wang
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jianlin Hu
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
| | - Yangyang Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Sri Harsha Kota
- Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Hongliang Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Kangjia Gong
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
| | - Xiaodong Xie
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
| | - Xu Yue
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
| | - Hong Liao
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
| | - Lei Huang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
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Fang B, Wei J, Chen L, Jin S, Li Q, Cai R, Qian N, Gu Z, Chen L, Santon R, Wang C, Song W. Short-term association of particulate matter and cardiovascular disease mortality in Shanghai, China between 2003 and 2020. Front Public Health 2024; 12:1388069. [PMID: 38651122 PMCID: PMC11034551 DOI: 10.3389/fpubh.2024.1388069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 03/18/2024] [Indexed: 04/25/2024] Open
Abstract
Objective Evidence regarding the effects of particulate matter (PM) pollutants on cardiovascular disease (CVD) mortality remains limited in Shanghai, China. Our objective was to thoroughly evaluate associations between PM pollutants and CVD mortality. Methods Daily data on CVD mortality, PM (PM10 and PM2.5) pollutants, and meteorological variables in Shanghai, China were gathered from 2003 to 2020. We utilized a time-series design with the generalized additive model to assess associations between PM pollutants and CVD mortality. Additionally, we conducted stratified analyses based on sex, age, education, and seasons using the same model. Results We found that PM pollutants had a significant association with CVD mortality during the study period. Specifically, there was a 0.29% (95%CI: 0.14, 0.44) increase in CVD mortality for every 10 μg/m3 rise in a 2-day average (lag01) concentration of PM10. A 0.28% (95% CI: 0.07, 0.49) increase in CVD mortality was associated with every 10 μg/m3 rise in PM2.5 concentration at lag01. Overall, the estimated effects of PM10 and PM2.5 were larger in the warm period compared with the cold period. Furthermore, males and the older adult exhibited greater susceptibility to PM10 and PM2.5 exposure, and individuals with lower education levels experienced more significant effects from PM10 and PM2.5 than those with higher education levels. Conclusion Our findings suggested that PM pollutants have a substantial impact on increasing CVD mortality in Shanghai, China. Moreover, the impacts of air pollution on health may be altered by factors such as season, sex, age, and educational levels.
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Affiliation(s)
- Bo Fang
- School of Public Health, Fudan University, Shanghai, China
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States
| | - Lei Chen
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Shan Jin
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Qi Li
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Renzhi Cai
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Naisi Qian
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Zhen Gu
- Vital Strategies, Shanghai, China
| | - Lei Chen
- Vital Strategies, Shanghai, China
| | | | - Chunfang Wang
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Weimin Song
- School of Public Health, Fudan University, Shanghai, China
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Zhang S, Chen L, Qian ZM, Li D, Cai M, Wang C, Zhang Z, Vaughn MG, Keith AE, Li H, Lin H. Associations between air pollution and the risk of first admission and multiple readmissions for cardiovascular diseases. Heart 2024; 110:337-345. [PMID: 37673655 DOI: 10.1136/heartjnl-2023-322682] [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: 03/29/2023] [Accepted: 08/19/2023] [Indexed: 09/08/2023] Open
Abstract
OBJECTIVES We aimed to investigate the associations between air pollutants and the risk of admission and multiple readmission events for cardiovascular disease (CVD). METHODS A total of 285 009 participants free of CVD at baseline from the UK Biobank were included in this analysis. Four major cardiovascular admission events were identified during the follow-up: chronic ischaemic heart disease (CIHD), cerebrovascular disease, atrial fibrillation and heart failure. We used Prentice, Williams and Peterson-Total Time model to examine the association between ambient air pollution and first admission, as well as multiple readmissions for these CVDs. RESULTS During a median follow-up of 12 years, 17 176 (6.03%) participants were hospitalised with CVDs, and 6203 (36.11%) patients with CVD had subsequent readmission events for CVDs. We observed significant associations between air pollution and both first admission and readmission for CVDs, with generally stronger associations on readmission for cardiovascular events. For example, the adjusted HRs for the first admission and subsequent readmission for cerebrovascular disease were 1.130 (95% CI 1.070 to 1.194) and 1.270 (95% CI 1.137 to 1.418) for each IQR increase of particulate matter with a diameter ≤2.5 µm. The corresponding HRs for CIHD were 1.060 (95% CI 1.008 to 1.114) and 1.120 (95% CI 1.070 to 1.171). Sex stratified analyses showed that the associations were generally more pronounced among females than males. CONCLUSION This study provides evidence that ambient air pollutants might play an important role in both first admission and readmission for cardiovascular events. In addition, patients with pre-existing CVDs may be more vulnerable to air pollution compared with healthy population.
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Affiliation(s)
- Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, Saint Louis University College for Public Health and Social Justice, Saint Louis, Missouri, USA
| | - Dan Li
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Michael G Vaughn
- School of Social Work, College for Public Health and Social Justice, Saint Louis University, Saint Louis, Missouri, USA
| | - Amy E Keith
- Department of Epidemiology and Biostatistics, Saint Louis University College for Public Health and Social Justice, Saint Louis, Missouri, USA
| | - Haitao Li
- Department of Social Medicine and Health Service Management, Health Science Center, Shenzhen University, Shenzhen, Guangdong, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China
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Lai Z, Zhang J, Ran S, Zheng D, Feng J, Wu G, Cai M, Lin H. Ambient fine particulate matter chemical composition associated with in-hospital case fatality, hospital expenses, and length of hospital stay among patients with heart failure in China. J Glob Health 2024; 14:04032. [PMID: 38299774 PMCID: PMC10832573 DOI: 10.7189/jogh.14.04032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Abstract
*Joint senior authorship. BACKGROUND Previous studies have observed the adverse effects of ambient fine particulate matter pollution (PM2.5) on heart failure (HF). However, evidence regarding the impacts of specific PM2.5 components remains scarce. METHODS We included 58 129 patients hospitalised for HF between 2013 and 2017 in 11 cities of Shanxi, China from inpatient discharge database. We evaluated exposure to PM2.5 and its components ((sulphate (SO42-), nitrate (NO3-), ammonium (NH4+), organic matter (OM) and black carbon (BC)), along with meteorological factors using bilinear interpolation at each patients' residential address. We used multivariable logistic and linear regression models to assess the associations of these components with in-hospital case fatality, hospital expenses, and length of hospital stay. RESULTS Increase equivalents to the interquartile range (IQR) in OM (odds ratio (OR) = 1.13; 95% confidence interval (CI) = 1.02, 1.26) and BC (OR = 1.14; 95% CI = 1.02, 1.26) were linked to in-hospital case fatality. Per IQR increments in PM2.5, SO42-, NO3-, OM, and BC were associated with cost increases of 420.62 (95% CI = 285.75, 555.49), 221.83 (95% CI = 96.95, 346.71), 214.93 (95% CI = 68.66, 361.21), 300.06 (95% CI = 176.96, 423.16), and 303.09 (95% CI = 180.76, 425.42) CNY. Increases of 1 IQR in PM2.5, SO42-, OM, and BC were associated with increases in length of hospital stay of 0.10 (95% CI = 0.02, 0.19), 0.09 (95% CI = 0.02, 0.17), 0.10 (95% CI = 0.03, 0.17), and 0.16 (95% CI = 0.08, 0.23) days. CONCLUSIONS Our findings suggest that ambient SO42-, OM, and BC might be significant risk factors for HF, emphasising the importance of formulating customised guidelines for the chemical constituents of PM and controlling the emissions of the most dangerous components.
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Kong D, Tao Y, Xiao H, Xiong H, Wei W, Cai M. Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data. Front Pediatr 2024; 12:1330420. [PMID: 38362001 PMCID: PMC10867966 DOI: 10.3389/fped.2024.1330420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024] Open
Abstract
Background To develop and compare different AutoML frameworks and machine learning models to predict premature birth. Methods The study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample (N = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuffling. Results The H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia. Conclusions Our study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes.
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Affiliation(s)
- Deming Kong
- Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ye Tao
- Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Haiyan Xiao
- Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huini Xiong
- Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Weizhong Wei
- Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
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Song C, Wang X, Ge E, Shi X, Pan J. Editorial: Applications of geospatial information technologies and spatial statistics in health services research. Front Public Health 2024; 11:1349985. [PMID: 38239794 PMCID: PMC10794292 DOI: 10.3389/fpubh.2023.1349985] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024] Open
Affiliation(s)
- Chao Song
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, China
| | - Xiuli Wang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, China
| | - Erjia Ge
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Xun Shi
- Department of Geography, Dartmouth College, Hanover, NH, United States
| | - Jay Pan
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- China Center for South Asian Studies, Sichuan University, Chengdu, China
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Zhang J, Chen L, Zhang S, Cai M, Zou H, Vaughn MG, Tabet M, Qian Z(M, Lin H. Associations of Sleep Patterns With Dynamic Trajectory of Cardiovascular Multimorbidity and Mortality: A Multistate Analysis of a Large Cohort. J Am Heart Assoc 2023; 12:e029463. [PMID: 37776189 PMCID: PMC10727256 DOI: 10.1161/jaha.123.029463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/11/2023] [Indexed: 10/02/2023]
Abstract
Background The purpose of this study was to explore the association of sleep patterns with the development of first cardiovascular diseases (FCVD), progression to cardiovascular multimorbidity (CVM), and subsequently to mortality. Methods and Results This prospective study included 381 179 participants without coronary heart disease, stroke, atrial fibrillation, or heart failure at baseline, and they were followed up until March 31, 2021. We generated sleep patterns by summing the scores for 5 sleep behaviors, whereby <7 or >8 hours/d of sleep, evening chronotype, frequent insomnia, snoring, and daytime dozing were defined as high-risk groups. We used a multistate model to estimate the impacts of sleep patterns on the dynamic progression of cardiovascular diseases. Over a median follow-up of 12.1 years, 41 910 participants developed FCVD, 7302 further developed CVM, and 20 707 died. We found that adverse sleep patterns were significantly associated with the transition from health to FCVD, from FCVD to CVM, and from health to death, with hazard ratio associated with 1-factor increase in sleep scores being 1.08 (95% CI, 1.07-1.09), 1.04 (95% CI, 1.02-1.06), and 1.04 (95% CI, 1.02-1.05), respectively. When further dividing FCVD into coronary heart disease, stroke, atrial fibrillation, and heart failure, adverse sleep patterns showed a significant and persistent effect on the transition from health to each cardiovascular disease, and from heart failure or atrial fibrillation to CVM. Conclusions Our study provides evidence that adverse sleep patterns might increase the risk for the progression from health to cardiovascular diseases and further to CVM. Our findings suggest that improving sleep behaviors might be helpful for the primary and secondary prevention of cardiovascular diseases.
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Affiliation(s)
- Jingyi Zhang
- Department of Epidemiology, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Lan Chen
- Department of Epidemiology, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Shiyu Zhang
- Department of Epidemiology, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Miao Cai
- Department of Epidemiology, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Hongtao Zou
- Department of Epidemiology, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Michael G. Vaughn
- School of Social WorkCollege for Public Health & Social Justice, Saint Louis UniversitySaint LouisMO
| | - Maya Tabet
- College of Global Population Health, University of Health Sciences and Pharmacy in St. LouisSaint LouisMO
| | - Zhengmin (Min) Qian
- Department of Epidemiology and BiostatisticsCollege for Public Health & Social Justice, Saint Louis UniversitySaint LouisMO
| | - Hualiang Lin
- Department of Epidemiology, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
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Li M, Edgell RC, Wei J, Li H, Qian ZM, Feng J, Tian F, Wang X, Xin Q, Cai M, Lin H. Air pollution and stroke hospitalization in the Beibu Gulf Region of China: A case-crossover analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114814. [PMID: 36965278 PMCID: PMC10107400 DOI: 10.1016/j.ecoenv.2023.114814] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The relationship between air pollution and stroke has been extensively studied, however, the evidence regarding the association between air pollution and hospitalization due to stroke and its subtypes in coastal areas of China is limited. OBJECTIVE To estimate the associations between air pollution and hospitalizations of stroke and its subtypes in the Beibu Gulf Region of China. METHODS We conducted a time-stratified case-crossover study in 15 cities in Beibu Gulf Region in China from 2013 to 2016. Exposures to PM1, PM2.5, PM10, SO2, NO2, O3, and CO on the case and control days were assessed at residential addresses using bilinear interpolation. Conditional logistic regressions were constructed to estimate city-specific associations adjusting for meteorological factors and public holidays. Meta-analysis was further conducted to pool all city-level estimates. RESULTS There were 271,394 case days and 922,305 control days. The odds ratios (ORs) for stroke hospitalizations associated with each interquartile range (IQR) increase in 2-day averages of SO2 (IQR: 10.8 µg/m3), NO2 (IQR: 11.2 µg/m3), and PM10 (IQR: 37 µg/m3) were 1.047 (95 % CI [confidence interval]: 1.015-1.080), 1.040 (95 % CI: 1.027-1.053), and 1.018 (95 % CI: 1.004-1.033), respectively. The associations with hospitalizations of ischemic stroke were significant for all seven pollutants, while the association with hemorrhagic stroke was significant only for CO. The associations of SO2, NO2, and O3 with stroke hospitalization were significantly stronger in the cool season. CONCLUSIONS Short-term increase in SO2, NO2, and PM10 might be important triggers of stroke hospitalization. All seven air pollutants were associated with ischemic stroke hospitalization, while only CO was associated with hemorrhagic stroke hospitalization. These results should be considered in public health policy.
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Affiliation(s)
- Meijun Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Randall C Edgell
- Department of Neurology, School of Medicine, Saint Louis University, 1008 South Spring, Saint Louis, MO 63110, USA
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park 20740, USA
| | - Haopeng Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO 63104, USA
| | - Jin Feng
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaojie Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Qinghua Xin
- Shandong Academy of Occupational Health and Occupational Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 271016, China.
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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11
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Su B, Liu C, Chen L, Wu Y, Li J, Zheng X. Long-term exposure to PM 2.5 and O 3 with cardiometabolic multimorbidity: Evidence among Chinese elderly population from 462 cities. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114790. [PMID: 36948004 DOI: 10.1016/j.ecoenv.2023.114790] [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: 01/14/2023] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Cardiometabolic multimorbidity (CMM) refers to the presence of multiple cardiovascular and metabolic diseases (CMDs), such as hypertension, diabetes, and cardio-cerebrovascular diseases (CCVD), in the same individual, and has emerge as a significant global health concern due to population aging. Although previous research has demonstrated the association between cardiovascular and metabolic diseases and air pollutants, evidence on the link between CMM and air pollution exposure among Chinese older adults is limited. To address this research gap, we conducted a national representative survey of 222,179 adults aged 60 and older to investigate the epidemiology of CMM and its association with long-term exposure to PM2.5 and O3 in China's elderly population. We found that the prevalence of CMM among Chinese older adults was 16.9%, and hypertension and CCVD were the most common CMM cluster (10.8%). After adjusting for confounding variables, we observed a significant positive association between PM2.5 exposure and the prevalence of hypertension, diabetes, and CCVD, with a respective excess risk increase of 3.2%, 3.6%, and 5.5% for every 10-unit increase. Moreover, every 10-unit increase in PM2.5 was linked to a higher risk of hypertension and diabetes (2.2%), hypertension and CCVD (5.4%), diabetes and CCVD (5.6%), and hypertension, diabetes, and CCVD combined (7.6%). We also found a U-shaped curve relationship between O3 exposure and the occurrence of hypertension, diabetes, and CCVD, as well as different subtypes of CMM, with the lowest risk of O3 exposure was observed near 75-80 μg/m3. Furthermore, we identified that female and rural residents are more vulnerable to the health risks of air pollution than male and urban residents. Given the increasing aging of the population and rising prevalence of multimorbidity, policymakers should focus more attention on the female and rural elderly population to prevent and control CMM. This study provides compelling evidence that reducing air pollution levels can be an effective strategy to prevent and manage CMM among older adults.
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Affiliation(s)
- Binbin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, People's Republic of China
| | - Chen Liu
- Peking University Third Hospital, Beijing, People's Republic of China
| | - Li Chen
- Institute of Child and Adolescent Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, People's Republic of China
| | - Yu Wu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, People's Republic of China
| | - Jun Li
- Institute of Quantitative and Technological Economics, Chinese Academy of Social Sciences, Beijing, People's Republic of China
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, People's Republic of China.
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12
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Cai M, Wei J, Zhang S, Liu W, Wang L, Qian Z, Lin H, Liu E, McMillin SE, Cao Y, Yin P. Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019. BMC Med 2023; 21:32. [PMID: 36694165 PMCID: PMC9875429 DOI: 10.1186/s12916-023-02734-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/11/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Long-term exposure to air pollution has been associated with the onset and progression of kidney diseases, but the association between short-term exposure to air pollution and mortality of kidney diseases has not yet been reported. METHODS A nationally representative sample of 101,919 deaths from kidney diseases was collected from the Chinese Center for Disease Control and Prevention from 2015 to 2019. A time-stratified case-crossover study was applied to determine the associations. Satellite-based estimates of air pollution were assigned to each case and control day using a bilinear interpolation approach and geo-coded residential addresses. Conditional logistic regression models were constructed to estimate the associations adjusting for nonlinear splines of temperature and relative humidity. RESULTS Each 10 µg/m3 increment in lag 0-1 mean concentrations of air pollutants was associated with a percent increase in death from kidney disease: 1.33% (95% confidence interval [CI]: 0.57% to 2.1%) for PM1, 0.49% (95% CI: 0.10% to 0.88%) for PM2.5, 0.32% (95% CI: 0.08% to 0.57%) for PM10, 1.26% (95% CI: 0.29% to 2.24%) for NO2, and 2.9% (95% CI: 1.68% to 4.15%) for SO2. CONCLUSIONS: Our study suggests that short-term exposure to ambient PM1, PM2.5, PM10, NO2, and SO2 might be important environmental risk factors for death due to kidney diseases in China.
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Affiliation(s)
- Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Wei Liu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Lijun Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Zhengmin Qian
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, St. Louis, 63103, USA
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Echu Liu
- Department of Health Management and Policy, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO, 63103, USA
| | - Stephen Edward McMillin
- School of Social Work, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO, 63103, USA
| | - Yu Cao
- Information Center, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
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