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Qi Q, Yu F, Nair AA, Lau SSS, Luo G, Mithu I, Zhang W, Li S, Lin S. Hidden danger: The long-term effect of ultrafine particles on mortality and its sociodemographic disparities in New York State. J Hazard Mater 2024; 471:134317. [PMID: 38636229 DOI: 10.1016/j.jhazmat.2024.134317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/09/2024] [Accepted: 04/14/2024] [Indexed: 04/20/2024]
Abstract
Although previous studies have shown increased health risks of particulate matters, few have evaluated the long-term health impacts of ultrafine particles (UFPs or PM0.1, ≤ 0.1 µm in diameter). This study assessed the association between long-term exposure to UFPs and mortality in New York State (NYS), including total non-accidental and cause-specific mortalities, sociodemographic disparities and seasonal trends. Collecting data from a comprehensive chemical transport model and NYS Vital Records, we used the interquartile range (IQR) and high-level UFPs (≥75 % percentile) as indicators to link with mortalities. Our modified difference-in-difference model controlled for other pollutants, meteorological factors, spatial and temporal confounders. The findings indicate that long-term UFPs exposure significantly increases the risk of non-accidental mortality (RR=1.10, 95 % CI: 1.05, 1.17), cardiovascular mortality (RR=1.11, 95 % CI: 1.05, 1.18) particularly for cerebrovascular (RR=1.21, 95 % CI: 1.10, 1.35) and pulmonary heart diseases (RR=1.33, 95 % CI: 1.13, 1.57), and respiratory mortality (borderline significance, RR=1.09, 95 % CI: 1.00, 1.18). Hispanics (RR=1.13, 95 % CI: 1.00, 1.29) and non-Hispanic Blacks (RR=1.40, 95 % CI: 1.16, 1.68) experienced significantly higher mortality risk after exposure to UFPs, compared to non-Hispanic Whites. Children under five, older adults, non-NYC residents, and winter seasons are more susceptible to UFPs' effects.
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Affiliation(s)
- Quan Qi
- Department of Economics, University at Albany, State University of New York, Albany, NY, USA
| | - Fangqun Yu
- Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
| | - Arshad A Nair
- Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
| | - Sam S S Lau
- Research Centre for Environment and Human Health & College of International Education, School of Continuing Education, Hong Kong Baptist University, Hong Kong, China; Institute of Bioresource and Agriculture, Hong Kong Baptist University, Hong Kong, China
| | - Gan Luo
- Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
| | - Imran Mithu
- Community, Environment and Policy Division, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Sean Li
- Rausser College of Natural Resources, University of California, Berkeley, CA, USA
| | - Shao Lin
- Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA; Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA.
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Rafiee M, Jahangiri-Rad M, Mohseni-Bandpei A, Razmi E. Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran. Sci Rep 2024; 14:10604. [PMID: 38719879 PMCID: PMC11078954 DOI: 10.1038/s41598-024-61397-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024] Open
Abstract
Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.
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Affiliation(s)
- Mohammad Rafiee
- Air Quality and Climate Change Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahsa Jahangiri-Rad
- Department of Environmental Health Engineering, School of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
- Water Purification Research Center, Islamic Azad University, Tehran, Iran.
| | - Anoushiravan Mohseni-Bandpei
- Air Quality and Climate Change Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Elham Razmi
- Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
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Li Y, He Z, Wei J, Xu R, Liu T, Zhong Z, Liu L, Liang S, Zheng Y, Chen G, Lv Z, Huang S, Chen X, Sun H, Liu Y. Long-term exposure to ambient fine particulate matter constituents and mortality from total and site-specific gastrointestinal cancer. Environ Res 2024; 244:117927. [PMID: 38103778 DOI: 10.1016/j.envres.2023.117927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 11/22/2023] [Accepted: 12/10/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Ambient fine particulate matter (PM2.5) exposure has been associated with an increased risk of gastrointestinal cancer mortality, but the attributable constituents remain unclear. OBJECTIVES To investigate the association of long-term exposure to PM2.5 constituents with total and site-specific gastrointestinal cancer mortality using a difference-in-differences approach in Jiangsu province, China during 2015-2020. METHODS We split Jiangsu into 53 spatial units and computed their yearly death number of total gastrointestinal, esophagus, stomach, colorectum, liver, and pancreas cancer. Utilizing a high-quality grid dataset on PM2.5 constituents, we estimated 10-year population-weighted exposure to black carbon (BC), organic carbon (OC), sulfate, nitrate, ammonium, and chloride in each spatial unit. The effect of constituents on gastrointestinal cancer mortality was assessed by controlling time trends, spatial differences, gross domestic product (GDP), and seasonal temperatures. RESULTS Overall, 524,019 gastrointestinal cancer deaths were ascertained in 84.77 million population. Each interquartile range increment of BC (0.46 μg/m3), OC (4.56 μg/m3), and nitrate (1.41 μg/m3) was significantly associated with a 27%, 26%, and 34% increased risk of total gastrointestinal cancer mortality, respectively, and these associations remained significant in PM2.5-adjusted models and constituent-residual models. We also identified robust associations of BC, OC, and nitrate exposures with site-specific gastrointestinal cancer mortality. The mortality risk generally displayed increased trends across the total exposure range and rose steeper at higher levels. We did not identify robust associations for sulfate, ammonium, or chlorine exposure. Higher mortality risk ascribed to constituent exposures was identified in total gastrointestinal and liver cancer among women, stomach cancer among men, and total gastrointestinal and stomach cancer among low-GDP regions. CONCLUSIONS This study offers consistent evidence that long-term exposure to PM2.5-bound BC, OC, and nitrate is associated with total and site-specific gastrointestinal cancer mortality, indicating that these constituents need to be controlled to mitigate the adverse effect of PM2.5 on gastrointestinal cancer mortality.
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Affiliation(s)
- Yingxin Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhimin He
- Department of Environmental Health, Nantong Center for Disease Control and Prevention, Nantong, Jiangsu, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | - Ruijun Xu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tingting Liu
- 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
| | - Sihan Liang
- 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
| | - Gongbo Chen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Ziquan Lv
- Central Laboratory of Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Suli Huang
- Department of Environment and Health, Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Xi Chen
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hong Sun
- Institute of Environment and Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, China.
| | - Yuewei Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Guo B, Gao Q, Pei L, Guo T, Wang Y, Wu H, Zhang W, Chen M. Exploring the association of PM 2.5 with lung cancer incidence under different climate zones and socioeconomic conditions from 2006 to 2016 in China. Environ Sci Pollut Res Int 2023; 30:126165-126177. [PMID: 38008841 DOI: 10.1007/s11356-023-31138-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/16/2023] [Indexed: 11/28/2023]
Abstract
Air pollution generated by urbanization and industrialization poses a significant negative impact on public health. Particularly, fine particulate matter (PM2.5) has become one of the leading causes of lung cancer mortality worldwide. The relationship between air pollutants and lung cancer has aroused global widespread concerns. Currently, the spatial agglomeration dynamic of lung cancer incidence (LCI) has been seldom discussed, and the spatial heterogeneity of lung cancer's influential factors has been ignored. Moreover, it is still unclear whether different socioeconomic levels and climate zones exhibit modification effects on the relationship between PM2.5 and LCI. In the present work, spatial autocorrelation was adopted to reveal the spatial aggregation dynamic of LCI, the emerging hot spot analysis was introduced to indicate the hot spot changes of LCI, and the geographically and temporally weighted regression (GTWR) model was used to determine the affecting factors of LCI and their spatial heterogeneity. Then, the modification effects of PM2.5 on the LCI under different socioeconomic levels and climatic zones were explored. Some findings were obtained. The LCI demonstrated a significant spatial autocorrelation, and the hot spots of LCI were mainly concentrated in eastern China. The affecting factors of LCI revealed an obvious spatial heterogeneity. PM2.5 concentration, nighttime light data, 2 m temperature, and 10 m u-component of wind represented significant positive effects on LCI, while education-related POI exhibited significant negative effects on LCI. The LCI in areas with low urbanization rates, low education levels, and extreme climate conditions was more easily affected by PM2.5 than in other areas. The results can provide a scientific basis for the prevention and control of lung cancer and related epidemics.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China.
| | - Qian Gao
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, 710068, Shaanxi, China
| | - Tengyue Guo
- Department of Geological Engineering, Qinghai University, Xining, 810016, Qinghai, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
| | - Wencai Zhang
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Miaoyi Chen
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
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Yu Z, Liu H, Liu X, Tao Y, Zhang X, Zhao X, Chang H, Huang J, Zhao Y, Zhang H, Huang C. Dynamic changes in ambient PM 2.5 and body mass index among old adults: a nationwide cohort study. Environ Sci Pollut Res Int 2023; 30:115929-115937. [PMID: 37897584 DOI: 10.1007/s11356-023-30620-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/18/2023] [Indexed: 10/30/2023]
Abstract
Outdoor air pollution has been considered as a severe environmental health issue that almost affecting everyone in the world, and intensive actions were launched. However, little is known about the association between dynamic changes in ambient fine particulate matter (PM2.5) exposure and body mass index (BMI) among old adults. To investigate the dynamic changes in ambient PM2.5 and body mass index among the elderly, we included a total of 7204 participants from 28 provinces of China during 2011-2015 in the China Health and Retirement Longitudinal Study (CHARLS). Ambient fine particle matter (PM2.5) was estimated using a well-validated space-time extremely randomized trees model. Change in PM2.5 and BMI (ΔPM2.5 and ΔBMI) were calculated as the value at a follow-up visit minus value at baseline. Linear mixed-effects models were applied to quantify the associations, controlling for sociodemographic factors. We found that per 1 μg/m3 increase in PM2.5 exposure was associated with a 0.031-0.044 kg/m2 increase in BMI among the elderly. We observed an approximate linear concentration-response relationship of PM2.5 and BMI in each visit. Each 1 μg/m3 increase in ΔPM2.5 exposure was associated with an increase in ΔBMI (β = 0.040, 95% CI 0.030, 0.049), while per 1 μg/m3 decrease in the ΔPM2.5 exposure level was associated with a decrease in ΔBMI (β = -0.016, 95% CI -0.027, -0.004). Our findings suggest that dynamic changes in ambient PM2.5 was positively associated with changes in BMI among old Chinese population.
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Affiliation(s)
- Zengli Yu
- School of Public Health, Zhengzhou University, 100 Science Avenue, Zhengzhou, 450001, China
| | - Hongyan Liu
- Department of Medical Genetics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaozhuan Liu
- Department of Medical Genetics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuchang Tao
- Department of Medical Genetics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoan Zhang
- The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Zhao
- The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Chang
- The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jia Huang
- Department of Medical Genetics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuanfang Zhao
- School of Public Health, Zhengzhou University, 100 Science Avenue, Zhengzhou, 450001, China
| | - Huanhuan Zhang
- Department of Medical Genetics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
- School of Public Health, Zhengzhou University, 100 Science Avenue, Zhengzhou, 450001, China.
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
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