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Zhang Y, Hu M, Xiang B, Yu H, Wang Q. Urban-rural disparities in the association of nitrogen dioxide exposure with cardiovascular disease risk in China: effect size and economic burden. Int J Equity Health 2024; 23:22. [PMID: 38321458 PMCID: PMC10845777 DOI: 10.1186/s12939-024-02117-3] [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: 07/11/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Together with rapid urbanization, ambient nitrogen dioxide (NO2) exposure has become a growing health threat. However, little is known about the urban-rural disparities in the health implications of short-term NO2 exposure. This study aimed to compare the association between short-term NO2 exposure and hospitalization for cardiovascular disease (CVD) among urban and rural residents in Shandong Province, China. Then, this study further explored the urban-rural disparities in the economic burden attributed to NO2 and the explanation for the disparities. METHODS Daily hospitalization data were obtained from an electronic medical records dataset covering a population of 5 million. In total, 303,217 hospital admissions for CVD were analyzed. A three-stage time-series analytic approach was used to estimate the county-level association and the attributed economic burden. RESULTS For every 10-μg/m3 increase in NO2 concentrations, this study observed a significant percentage increase in hospital admissions on the day of exposure of 1.42% (95% CI 0.92 to 1.92%) for CVD. The effect size was slightly higher in urban areas, while the urban-rural difference was not significant. However, a more pronounced displacement phenomenon was found in rural areas, and the economic burden attributed to NO2 was significantly higher in urban areas. At an annual average NO2 concentration of 10 μg/m3, total hospital days and expenses in urban areas were reduced by 81,801 (44,831 to 118,191) days and 60,121 (33,002 to 86,729) thousand CNY, respectively, almost twice as much as in rural areas. Due to disadvantages in socioeconomic status and medical resources, despite similar air pollution levels in the urban and rural areas of our sample sites, the rural population tended to spend less on hospitalization services. CONCLUSIONS Short-term exposure to ambient NO2 could lead to considerable health impacts in either urban or rural areas of Shandong Province, China. Moreover, urban-rural differences in socioeconomic status and medical resources contributed to the urban-rural disparities in the economic burden attributed to NO2 exposure. The health implications of NO2 exposure are a social problem in addition to an environmental problem. Thus, this study suggests a coordinated intervention system that targets environmental and social inequality factors simultaneously.
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Affiliation(s)
- Yike Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Mengxiao Hu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Bowen Xiang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Haiyang Yu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Qing Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
- National Institute of Health Data Science of China, Shandong University, Jinan, China.
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Yang J, Dong H, Yu C, Li B, Lin G, Chen S, Cai D, Huang L, Wang B, Li M. Mortality Risk and Burden From a Spectrum of Causes in Relation to Size-Fractionated Particulate Matters: Time Series Analysis. JMIR Public Health Surveill 2023; 9:e41862. [PMID: 37812487 PMCID: PMC10637369 DOI: 10.2196/41862] [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: 08/12/2022] [Revised: 07/07/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND There is limited evidence regarding the adverse impact of particulate matters (PMs) on multiple body systems from both epidemiological and mechanistic studies. The association between size-fractionated PMs and mortality risk, as well as the burden of a whole spectrum of causes of death, remains poorly characterized. OBJECTIVE We aimed to examine the wide range of susceptible diseases affected by different sizes of PMs. We also assessed the association between PMs with an aerodynamic diameter less than 1 µm (PM1), 2.5 µm (PM2.5), and 10 µm (PM10) and deaths from 36 causes in Guangzhou, China. METHODS Daily data were obtained on cause-specific mortality, PMs, and meteorology from 2014 to 2016. A time-stratified case-crossover approach was applied to estimate the risk and burden of cause-specific mortality attributable to PMs after adjusting for potential confounding variables, such as long-term trend and seasonality, relative humidity, temperature, air pressure, and public holidays. Stratification analyses were further conducted to explore the potential modification effects of season and demographic characteristics (eg, gender and age). We also assessed the reduction in mortality achieved by meeting the new air quality guidelines set by the World Health Organization (WHO). RESULTS Positive and monotonic associations were generally observed between PMs and mortality. For every 10 μg/m3 increase in 4-day moving average concentrations of PM1, PM2.5, and PM10, the risk of all-cause mortality increased by 2.00% (95% CI 1.08%-2.92%), 1.54% (95% CI 0.93%-2.16%), and 1.38% (95% CI 0.95%-1.82%), respectively. Significant effects of size-fractionated PMs were observed for deaths attributed to nonaccidental causes, cardiovascular disease, respiratory disease, neoplasms, chronic rheumatic heart diseases, hypertensive diseases, cerebrovascular diseases, stroke, influenza, and pneumonia. If daily concentrations of PM1, PM2.5, and PM10 reached the WHO target levels of 10, 15, and 45 μg/m3, 7921 (95% empirical CI [eCI] 4454-11,206), 8303 (95% eCI 5063-11,248), and 8326 (95% eCI 5980-10690) deaths could be prevented, respectively. The effect estimates of PMs were relatively higher during hot months, among female individuals, and among those aged 85 years and older, although the differences between subgroups were not statistically significant. CONCLUSIONS We observed positive and monotonical exposure-response curves between PMs and deaths from several diseases. The effect of PM1 was stronger on mortality than that of PM2.5 and PM10. A substantial number of premature deaths could be preventable by adhering to the WHO's new guidelines for PMs. Our findings highlight the importance of a size-based strategy in controlling PMs and managing their health impact.
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Affiliation(s)
- Jun Yang
- School of Public Health, Guangzhou Medical University, Guangzhou, China
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Hang Dong
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Chao Yu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Bixia Li
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
- Guangdong University of Science and Technology, Dongguan, China
| | - Guozhen Lin
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Sujuan Chen
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Dongjie Cai
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Lin Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Boguang Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Mengmeng Li
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
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Wongnakae P, Chitchum P, Sripramong R, Phosri A. Application of satellite remote sensing data and random forest approach to estimate ground-level PM 2.5 concentration in Northern region of Thailand. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:88905-88917. [PMID: 37442931 DOI: 10.1007/s11356-023-28698-0] [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: 02/15/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023]
Abstract
Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.
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Affiliation(s)
- Pimchanok Wongnakae
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand
| | - Pakkapong Chitchum
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand
| | - Rungduen Sripramong
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand
| | - Arthit Phosri
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand.
- Center of Excellence on Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Research, Science and Innovation, Bangkok, Thailand.
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Liu T, Gong W, Zhou C, Bai G, Meng R, Huang B, Zhang H, Xu Y, Hu R, Hou Z, Xiao Y, Li J, Xu X, Jin D, Qin M, Zhao Q, Xu Y, Hu J, Xiao J, He G, Rong Z, Zeng F, Yang P, Liu D, Yuan L, Cao G, Chen Z, Yu S, Yang S, Huang C, Du Y, Yu M, Lin L, Liang X, Ma W. Mortality burden based on the associations of ambient PM 2.5 with cause-specific mortality in China: Evidence from a death-spectrum wide association study (DWAS). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 259:115045. [PMID: 37235896 DOI: 10.1016/j.ecoenv.2023.115045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Although studies have estimated the associations of PM2.5 with total mortality or cardiopulmonary mortality, few have comprehensively examined cause-specific mortality risk and burden caused by ambient PM2.5. Thus, this study investigated the association of short-term exposure to PM2.5 with cause-specific mortality using a death-spectrum wide association study (DWAS). Individual information of 5,450,764 deaths during 2013-2018 were collected from six provinces in China. Daily PM2.5 concentration in the case and control days were estimated by a random forest model. A time-stratified case-crossover study design was applied to estimate the associations (access risk, ER) of PM2.5 with cause-specific mortality, which was then used to calculate the population-attributable fraction (PAF) of mortality and the corresponding mortality burden caused by PM2.5. Each 10 μg/m3 increase in PM2.5 concentration (lag03) was associated with a 0.80 % [95 % confidence interval (CI): 0.73 %, 0.86 %] rise in total mortality. We found greater mortality effect at PM2.5 concentrations < 50 μg/m3. Stratified analyses showed greater ERs in females (1.01 %, 95 %CI: 0.91 %, 1.11 %), children ≤ 5 years (2.17 %, 95 %CI: 0.85 %, 3.51 %), and old people ≥ 70 years. We identified 33 specific causes (level 2) of death which had significant associations with PM2.5, including 16 circulatory diseases, 9 respiratory diseases, and 8 other causes. The PAF estimated based on the overall association between PM2.5 and total mortality was 3.16 % (95 %CI: 2.89 %, 3.40 %). However, the PAF was reduced to 2.88 % (95 %CI: 1.88 %, 3.81 %) using the associations of PM2.5 with 33 level 2 causes of death, based on which 250.15 (95 %CI: 163.29, 330.93) thousand deaths were attributable to short-term PM2.5 exposure across China in 2019. Overall, this study provided a comprehensive picture on the death-spectrum wide association between PM2.5 and morality in China. We observed robust positive cause-specific associations of PM2.5 with mortality risk, which may provide more precise basis in assessing the mortality burden of air pollution.
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Affiliation(s)
- Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Weiwei Gong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Chunliang Zhou
- Department of Environment and Health, Hunan Provincial Center for Disease Control and Prevention, Changsha 450001, China
| | - Guoxia Bai
- Institute of Non-communicable Diseases Prevention and Control,Tibet Center for Disease Control and Prevention, Lhasa 850000, China
| | - Ruilin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Biao Huang
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun 130062, China
| | - Haoming Zhang
- Yunnan Center for Disease Control and Prevention, Kunming 650022, China
| | - Yanjun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Ruying Hu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Zhulin Hou
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun 130062, China
| | - Yize Xiao
- Yunnan Center for Disease Control and Prevention, Kunming 650022, China
| | - Junhua Li
- Department of Environment and Health, Hunan Provincial Center for Disease Control and Prevention, Changsha 450001, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Donghui Jin
- Department of Environment and Health, Hunan Provincial Center for Disease Control and Prevention, Changsha 450001, China
| | - Mingfang Qin
- Yunnan Center for Disease Control and Prevention, Kunming 650022, China
| | - Qinglong Zhao
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun 130062, China
| | - Yiqing Xu
- Department of Environment and Health, Hunan Provincial Center for Disease Control and Prevention, Changsha 450001, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Guanghao He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Zuhua Rong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Fangfang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Pan Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Dan Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Lixia Yuan
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Ganxiang Cao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Zhiqing Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Siwen Yu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Shangfeng Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Yaodong Du
- Guangdong Provincial Climate Center, Guangzhou 510080, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Xiaofeng Liang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health,School of Medicine, Jinan University, Guangzhou 510632, China.
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