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Xu Z, Wang M, Chang L, Pan K, Shen X, Zhong S, Xu J, Liu L, Li G, Chen L. Assessing the particulate matter emission reduction characteristics of small turbofan engine fueled with 100 % HEFA sustainable aviation fuel. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:174128. [PMID: 38908593 DOI: 10.1016/j.scitotenv.2024.174128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/16/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024]
Abstract
With the continuous increase in global air transportation, the impact of ultrafine particulate matter (PM) emissions from aviation on human health and environmental pollution is becoming increasingly severe. In addition to carbon reduction throughout the lifecycle, Sustainable Aviation Fuels (SAF) also represent a significant pathway for reducing PM emissions. However, due to issues such as airworthiness safety and adaptability, existing research has mostly focused on the emission performance of SAF when blended with traditional fuels at <50 %, leaving the emission characteristics of higher blending ratios to be explored. In this study, using measurement methods recommended by the International Civil Aviation Organization (ICAO), the PM emission reduction characteristics of small turbofan engines fueled with 100 % Hydroprocessed Esters and Fatty Acids (HEFA)-SAF were experimentally evaluated and compared with traditional fuels RP-3 and Diesel, while avoiding the interference of lubricant blending combustion. The results showed that the peak number concentration of particle size distribution (PSD), PM total number, as well as the number and mass concentration of non-volatile particulate matter (nvPM) decreased initially and then increased with rising thrust conditions. HEFA-SAF exhibits PSD with smaller diameters, and the Geometric Mean Diameter (GMD) ranges from 7.7 nm to 20.3 nm under all conditions. Both volatile particulates (vPM) and nvPM from HEFA-SAF are significantly reduced, with nvPM number emission index (EIn) being 92 % and 71 % lower than Diesel and RP-3, respectively. The nvPM mass emission index (EIm) also shows reductions of 96 % and 89 % compared to Diesel and RP-3. Microscopic characterization also indicated that using HEFA-SAF emitted fewer and smaller PMs. This study establishes a foundation for evaluating the effectiveness of 100 % SAF in reducing PM emissions within the aviation sector, and contributes to the airworthiness regulations development related to the use of SAF in a variety of application environments, alongside enhancing environmental protection measures.
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Affiliation(s)
- Zheng Xu
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Minghua Wang
- School of Energy and Power Engineering, Beihang University, Beijing 100083, China
| | - Liuyong Chang
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Kang Pan
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China; Tianmushan Laboratory, Yuhang District, Hangzhou 311115, China
| | - Xiaowei Shen
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Shenghui Zhong
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Jingsha Xu
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Lei Liu
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
| | - Guangze Li
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China.
| | - Longfei Chen
- School of Energy and Power Engineering, Beihang University, Beijing 100083, China.
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He Q, Cao J, Saide PE, Ye T, Wang W. Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39192575 DOI: 10.1021/acs.est.4c02926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well-correlated with ground-level ozone variation and available with spatiotemporal high-resolution data. This study introduces a high-resolution ozone modeling method utilizing the XGBoost algorithm with satellite-derived land surface temperature (LST) as the primary predictor. Focusing on China in 2019, our model achieved a cross-validation R2 of 0.91 and a root-mean-square error (RMSE) of 13.51 μg/m3. We provide detailed maps highlighting ground-level ozone concentrations in urban areas, uncovering spatial variations previously unresolved, along with time series aligning with established understandings of ozone dynamics. Our local interpretation of the machine learning model underscores the significant contribution of LST to spatiotemporal ozone variations, surpassing other meteorological, pollutant, and geographical predictors in its influence. Validation results indicate that model performance decreases as spatial resolution becomes coarser, with R2 decreasing from 0.91 for the 1 km model to 0.85 for the 25 km model. The methodology and data sets generated by this study offer new insights into ground-level ozone variability and mapping and can significantly aid in exposure assessment and epidemiological research related to this critical environmental challenge.
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Affiliation(s)
- Qingqing He
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
- Department of Atmospheric & Oceanic Sciences, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jingru Cao
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Pablo E Saide
- Department of Atmospheric & Oceanic Sciences, University of California, Los Angeles, Los Angeles, California 90095, United States
- Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Tong Ye
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Weihang Wang
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
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He G, Jiang M, Tian S, He L, Bai X, Chen S, Li G, Wang C, Zhang Z, Wu Y, Su M, Li X, Guo X, Yang Y, Zhang X, Cui J, Xu W, Song L, Yang H, He W, Zhang Y, Li X, Gao X, Chen L. Clean air policy reduces the atherogenic lipid profile levels: Results from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) Study. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135394. [PMID: 39128148 DOI: 10.1016/j.jhazmat.2024.135394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/16/2024] [Accepted: 07/30/2024] [Indexed: 08/13/2024]
Abstract
Evidence of the associations between long-term exposure to PM2.5 and O3 and human blood lipid concentrations is abundant yet inconclusive. Whether clean air policies could improve lipid profiles remains unclear. In total, 2979312 participants from a Chinese nationwide prospective study were included. For cross-sectional analyses, linear mixed-effects models were utilized to assess the associations of pollutants with lipid profiles (TC, LDL-C, TG, HDL-C). For longitudinal analyses, a quasi-experimental design and difference-in-differences models were employed to investigate the impact of China's Clean Air Act. In the cross-sectional analyses, each IQR increase in PM2.5 was associated with 2.49 % (95 % CI: 2.36 %, 2.62 %), 2.51 % (95 % CI: 2.26 %, 2.75 %), 3.94 % (95 % CI: 3.65 %, 4.23 %), and 1.54 % (95 % CI: 1.38 %, 1.70 %) increases in TC, LDL-C, TG, and HDL-C, respectively. For each IQR increase in O3, TC, LDL-C, TG, and HDL-C changed by 1.06 % (95 % CI: 0.95 %, 1.17 %), 1.21 % (95 % CI: 1.01 %, 1.42 %), 1.78 % (95 % CI: 1.54 %, 2.02 %), and -0.63 % (95 % CI: -0.76 %, -0.49 %), respectively. Longitudinal analyses showed that the intervention group experienced greater TC, LDL-C, and HDL-C reductions (1.77 %, 4.26 %, and 7.70 %, respectively). Our findings suggest that clean air policies could improve lipid metabolism and should be implemented in countries with heavy air pollution burdens.
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Affiliation(s)
- Guangda He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meijie Jiang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Sifan Tian
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Linkang He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueke Bai
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shi Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guangyu Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chunqi Wang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zenglei Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi Wu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingming Su
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangjie Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinxin Guo
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Yang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianlan Cui
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Xu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijuan Song
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hao Yang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyan He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xi Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
| | - Liang Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Troost JP, D’Souza J, Buxton M, Kshirsagar AV, Engel LS, O’Lenick CR, Smoyer WE, Klein J, Ju W, Eddy S, Helmuth M, Mariani LH, Kretzler M, Trachtman H. Elevated Exposure to Air Pollutants Accelerates Primary Glomerular Disease Progression. Kidney Int Rep 2024; 9:2527-2536. [PMID: 39156153 PMCID: PMC11328569 DOI: 10.1016/j.ekir.2024.05.013] [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: 02/09/2024] [Revised: 05/02/2024] [Accepted: 05/13/2024] [Indexed: 08/20/2024] Open
Abstract
Introduction Environmental contributors to kidney disease progression remain elusive. We explored how residential air pollution affects disease progression in patients with primary glomerulopathies. Methods Nephrotic Syndrome Study Network (NEPTUNE) and CureGlomerulonephropathy (CureGN) participants with residential census tract data and ≥2 years of follow-up were included. Using Cox proportional hazards models, the associations per doubling in annual average baseline concentrations of total particulate matter with diameter ≤2.5 μm (PM2.5) and its components, black carbon (BC), and sulfate, with time to ≥40% decline in estimated glomerular filtration rate (eGFR) or kidney failure were estimated. Serum tumour necrosis factor levels and kidney tissue transcriptomic inflammatory pathway activation scores were used as molecular markers of disease progression. Results PM2.5, BC, and sulfate exposures were comparable in NEPTUNE (n = 228) and CureGN (n = 697). In both cohorts, participants from areas with higher levels of pollutants had lower eGFR, were older and more likely self-reported racial and ethnic minorities. In a fully adjusted model combining both cohorts, kidney disease progression was associated with PM2.5 (adjusted hazard ratio 1.55 [95% confidence interval: 1.00-2.38], P = 0.0489) and BC (adjusted hazard ratio 1.43 [95% confidence interval: 0.98-2.07], P = 0.0608) exposure. Sulfate and PM2.5 exposure were positively correlated with serum tumour necrosis factor (TNF) (P = 0.003) and interleukin-1β levels (P = 0.03), respectively. Sulfate exposure was also directly associated with transcriptional activation of the TNF and JAK-STAT signaling pathways in kidneys (r = 0.55-0.67, P-value <0.01). Conclusion Elevated exposure to select air pollutants is associated with increased risk of disease progression and systemic inflammation in patients with primary.
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Affiliation(s)
- Jonathan P. Troost
- Michigan Institute for Clinical & Health Research, University of Michigan, Ann Arbor Michigan, USA
| | - Jennifer D’Souza
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Miatta Buxton
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Abhijit V. Kshirsagar
- Division of Nephrology and Hypertension, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Lawrence S. Engel
- Departments of Epidemiology and Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Cassandra R. O’Lenick
- Departments of Epidemiology and Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, North Carolina, USA
| | - William E. Smoyer
- Department of Pediatrics, Ohio State University, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, USA
| | - Jon Klein
- Division of Nephrology and Hypertension, Department of Medicine, Christina Lee Brown Environment Institute, University of Louisville School of Medicine, Louisville, Kentucky, USA
- Robley Rex Veterans Administration Medical Center, Louisville, Kentucky, USA
| | - Wenjun Ju
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan, USA
| | - Sean Eddy
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Margaret Helmuth
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura H. Mariani
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan, USA
| | - Howard Trachtman
- Division of Nephrology, Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA
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Boogaard H, Crouse DL, Tanner E, Mantus E, van Erp AM, Vedal S, Samet J. Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next? ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12767-12783. [PMID: 38991107 PMCID: PMC11270999 DOI: 10.1021/acs.est.3c09745] [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: 11/21/2023] [Revised: 06/14/2024] [Accepted: 06/14/2024] [Indexed: 07/13/2024]
Abstract
Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.
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Affiliation(s)
- Hanna Boogaard
- Health
Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Dan L. Crouse
- Health
Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Eva Tanner
- Health
Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Ellen Mantus
- Health
Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Annemoon M. van Erp
- Health
Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Sverre Vedal
- Department
of Environmental & Occupational Health Sciences, University of Washington, 4225 Roosevelt Way N.E., Seattle, Washington 98105, United States
| | - Jonathan Samet
- Department
of Environmental & Occupational Health, Department of Epidemiology, Colorado School of Public Health, 13001 East 17th Place, Aurora, Colorado 80045, United States
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Tang H, Chen S, Wei J, Guo T, Zhang Y, Wu W, Wang Y, Chen S, Chen D, Cai H, Du Z, Zhang W, Hao Y. How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 280:116478. [PMID: 38833984 DOI: 10.1016/j.ecoenv.2024.116478] [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: 10/20/2023] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. AIMS We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. METHODS We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. RESULTS With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. CONCLUSION This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.
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Affiliation(s)
- Hui Tang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Shirui Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA
| | - Tong Guo
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yuqin Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wenjing Wu
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Ying Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Shimin Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Dan Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Huanle Cai
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China; Center for Health Information Research, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China; Center for Health Information Research, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education.
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Yu Y, Tang Z, Huang Y, Zhang J, Wang Y, Zhang Y, Wang Q. Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis. Sci Rep 2024; 14:16220. [PMID: 39003417 PMCID: PMC11246484 DOI: 10.1038/s41598-024-66951-9] [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: 03/09/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024] Open
Abstract
Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.
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Affiliation(s)
- Yong Yu
- Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Shiyan, 442000, China
| | - Ziqing Tang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yuqian Huang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Jingjing Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yixiang Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yunquan Zhang
- Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Shiyan, 442000, China.
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Qun Wang
- Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Shiyan, 442000, China.
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8
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Geldsetzer P, Fridljand D, Kiang MV, Bendavid E, Heft-Neal S, Burke M, Thieme AH, Benmarhnia T. Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors. Nat Med 2024:10.1038/s41591-024-03117-0. [PMID: 38951636 DOI: 10.1038/s41591-024-03117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 06/05/2024] [Indexed: 07/03/2024]
Abstract
There are large differences in premature mortality in the USA by race/ethnicity, education, rurality and social vulnerability index groups. Using existing concentration-response functions, published particulate matter (PM2.5) air pollution estimates, population estimates at the census tract level and county-level mortality data from the US National Vital Statistics System, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in PM2.5-attributable mortality were consistently more pronounced by race/ethnicity than by education, rurality or social vulnerability index, with the Black American population having the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Our model estimates that over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic white population was attributable to PM2.5 in the years 2000 to 2011. This difference decreased only marginally between 2000 and 2015, from 53.4% (95% confidence interval 51.2-55.9%) to 49.9% (95% confidence interval 47.8-52.2%), respectively. Our findings underscore the need for targeted air quality interventions to address environmental health disparities.
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Affiliation(s)
- Pascal Geldsetzer
- Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
| | - Daniel Fridljand
- Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA
- Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
- Department of Mathematics, Yale University, New Haven, CT, USA
| | - Mathew V Kiang
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Eran Bendavid
- Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Health Policy, Stanford University, Stanford, CA, USA
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Marshall Burke
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
- Doerr School of Sustainability, Stanford University, Stanford, CA, USA
| | - Alexander H Thieme
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santéenvironnement et travail) - UMR_S 1085, Rennes, France
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9
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Zhu L, Fang J, Yao Y, Yang Z, Wu J, Ma Z, Liu R, Zhan Y, Ding Z, Zhang Y. Long-term ambient ozone exposure and incident cardiovascular diseases: National cohort evidence in China. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134158. [PMID: 38636234 DOI: 10.1016/j.jhazmat.2024.134158] [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: 08/26/2023] [Revised: 03/07/2024] [Accepted: 03/27/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Long-term ozone (O3) exposure has been associated with cardiovascular disease (CVD) mortality in mounting cohort evidence, yet its relationship with incident CVD was poorly understood, especially in low- and middle-income countries (LMICs) experiencing high ambient air pollution. METHODS We carried out a nationwide perspective cohort study from 2010 through 2018 by dynamically enrolling 36948 participants across Chinese mainland. Warm-season (April-September) O3 concentrations were estimated using satellite-based machine-learning models with national coverage. Cox proportional hazards model with time-varying exposures was employed to evaluate the association of long-term O3 exposure with incident CVD (overall CVD, hypertension, stroke, and coronary heart disease [CHD]). Assuming causality, a counterfactual framework was employed to estimate O3-attributable CVD burden based on the exposure-response (E-R) relationship obtained from this study. Decomposition analysis was utilized to quantify the contributions of four key direct driving factors (O3 exposure, population size, age structure, and incidence rate) to the net change of O3-related CVD cases between 2010 and 2018. RESULTS A total of 4428 CVD, 2600 hypertension, 1174 stroke, and 337 CHD events were reported during 9-year follow-up. Each 10-μg/m³ increase in warm-season O3 was associated with an incident risk of 1.078 (95% confidence interval [CI]: 1.050-1.106) for overall CVD, 1.098 (95% CI: 1.062-1.135) for hypertension, 1.073 (95% CI: 1.019-1.131) for stroke, and 1.150 (95% CI: 1.038-1.274) for CHD, respectively. We observed no departure from linear E-R relationships of O3 exposure with overall CVD (Pnonlinear= 0.22), hypertension (Pnonlinear= 0.19), stroke (Pnonlinear= 0.70), and CHD (Pnonlinear= 0.44) at a broad concentration range of 60-160 µg/m3. Compared with rural dwellers, those residing in urban areas were at significantly greater O3-associated incident risks of overall CVD, hypertension, and stroke. We estimated 1.22 million (10.6% of overall CVD in 2018) incident CVD cases could be attributable to ambient O3 pollution in 2018, representing an overall 40.9% growth (0.36 million) compared to 2010 (0.87 million, 9.7% of overall CVD in 2010). This remarkable rise in O3-attributable CVD cases was primary driven by population aging (+24.0%), followed by increase in O3 concentration (+10.5%) and population size (+6.7%). CONCLUSIONS Long-term O3 exposure was associated with an elevated risk and burden of incident CVD in Chinese adults, especially among urban dwellers. Our findings underscored policy priorities of implementing joint control measures for fine particulate matter and O3 in the context of accelerated urbanization and population aging in China.
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Affiliation(s)
- Lifeng Zhu
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Jiaying Fang
- Huadu District People's Hospital of Guangzhou, Guangzhou 510800, China
| | - Yao Yao
- China Center for Health Development Studies, Peking University, Beijing 100871, China
| | - Zhiming Yang
- School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
| | - Jing Wu
- China Center for Health Development Studies, Peking University, Beijing 100871, China
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Riyang Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Zan Ding
- Baoan Central Hospital of Shenzhen, Shenzhen 518102, China.
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China.
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10
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Wang L, Wang G, Gao AS. Exploring heterogeneity and dynamics of meteorological influences on US PM 2.5: A distributed learning approach with spatiotemporal varying coefficient models. SPATIAL STATISTICS 2024; 61:100826. [PMID: 38779141 PMCID: PMC11108057 DOI: 10.1016/j.spasta.2024.100826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Particulate matter (PM) has emerged as a primary air quality concern due to its substantial impact on human health. Many recent research works suggest that PM2.5 concentrations depend on meteorological conditions. Enhancing current pollution control strategies necessitates a more holistic comprehension of PM2.5 dynamics and the precise quantification of spatiotemporal heterogeneity in the relationship between meteorological factors and PM2.5 levels. The spatiotemporal varying coefficient model stands as a prominent spatial regression technique adept at addressing this heterogeneity. Amidst the challenges posed by the substantial scale of modern spatiotemporal datasets, we propose a pioneering distributed estimation method (DEM) founded on multivariate spline smoothing across a domain's triangulation. This DEM algorithm ensures an easily implementable, highly scalable, and communication-efficient strategy, demonstrating almost linear speedup potential. We validate the effectiveness of our proposed DEM through extensive simulation studies, demonstrating that it achieves coefficient estimations akin to those of global estimators derived from complete datasets. Applying the proposed model and method to the US daily PM2.5 and meteorological data, we investigate the influence of meteorological variables on PM2.5 concentrations, revealing both spatial and seasonal variations in this relationship.
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Affiliation(s)
- Lily Wang
- Department of Statistics, George Mason University, 4400 University Drive, MS 4A7, Fairfax, 22030, VA, USA
| | - Guannan Wang
- Department of Mathematics, William & Mary, 120 Jones Hall, Williamsburg, 23185, VA, USA
| | - Annie S. Gao
- McLean High School, 1633 Davidson Rd, McLean, 22101, VA, USA
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11
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Cox LA. An AI assistant to help review and improve causal reasoning in epidemiological documents. GLOBAL EPIDEMIOLOGY 2024; 7:100130. [PMID: 38188038 PMCID: PMC10767365 DOI: 10.1016/j.gloepi.2023.100130] [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: 10/04/2023] [Revised: 11/08/2023] [Accepted: 11/24/2023] [Indexed: 01/09/2024] Open
Abstract
Drawing sound causal inferences from observational data is often challenging for both authors and reviewers. This paper discusses the design and application of an Artificial Intelligence Causal Research Assistant (AIA) that seeks to help authors improve causal inferences and conclusions drawn from epidemiological data in health risk assessments. The AIA-assisted review process provides structured reviews and recommendations for improving the causal reasoning, analyses and interpretations made in scientific papers based on epidemiological data. Causal analysis methodologies range from earlier Bradford-Hill considerations to current causal directed acyclic graph (DAG) and related models. AIA seeks to make these methods more accessible and useful to researchers. AIA uses an external script (a "Causal AI Booster" (CAB) program based on classical AI concepts of slot-filling in frames organized into task hierarchies to complete goals) to guide Large Language Models (LLMs), such as OpenAI's ChatGPT or Google's LaMDA (Bard), to systematically review manuscripts and create both (a) recommendations for what to do to improve analyses and reporting; and (b) explanations and support for the recommendations. Review tables and summaries are completed systematically by the LLM in order. For example, recommendations for how to state and caveat causal conclusions in the Abstract and Discussion sections reflect previous analyses of the Study Design and Data Analysis sections. This work illustrates how current AI can contribute to reviewing and providing constructive feedback on research documents. We believe that such AI-assisted review shows promise for enhancing the quality of causal reasoning and exposition in epidemiological studies. It suggests the potential for effective human-AI collaboration in scientific authoring and review processes.
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Affiliation(s)
- Louis Anthony Cox
- Cox Associates, Entanglement, and University of Colorado, United States of America
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12
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Cheng SL, Hedges M, Keski-Rahkonen P, Chatziioannou AC, Scalbert A, Chung KF, Sinharay R, Green DC, de Kok TMCM, Vlaanderen J, Kyrtopoulos SA, Kelly F, Portengen L, Vineis P, Vermeulen RCH, Chadeau-Hyam M, Dagnino S. Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8771-8782. [PMID: 38728551 PMCID: PMC11112755 DOI: 10.1021/acs.est.3c09148] [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: 11/02/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024]
Abstract
This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.
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Affiliation(s)
- Sibo Lucas Cheng
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
| | - Michael Hedges
- MRC
Centre for Environment and Health, Environmental Research Group, Imperial College London, London W12 0BZ, U.K.
| | | | | | - Augustin Scalbert
- International
Agency for Research on Cancer (IARC), Lyon 69366 Cedex, France
| | - Kian Fan Chung
- National
Heart & Lung Institute, Imperial College
London, London SW7 2AZ, U.K.
- Royal Brompton
& Harefield NHS Trust, London SW3 6NP, U.K.
| | - Rudy Sinharay
- National
Heart & Lung Institute, Imperial College
London, London SW7 2AZ, U.K.
- Imperial
College Healthcare NHS Trust, London W2 1NY, U.K.
| | - David C. Green
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Environmental Research Group, Imperial College London, London W12 0BZ, U.K.
| | - Theo M. C. M. de Kok
- Department
of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht 6229 ER, The Netherlands
| | - Jelle Vlaanderen
- Division
of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CS, The Netherlands
| | | | - Frank Kelly
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Environmental Research Group, Imperial College London, London W12 0BZ, U.K.
| | - Lützen Portengen
- Division
of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CS, The Netherlands
| | - Paolo Vineis
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
| | - Roel C. H. Vermeulen
- Division
of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CS, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University
Medical
Centre, Utrecht University, Utrecht 3584 CG, The Netherlands
| | - Marc Chadeau-Hyam
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
| | - Sonia Dagnino
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
- Transporters
in Imaging and Radiotherapy in Oncology (TIRO), School
of Medicine, Direction de la Recherche Fondamentale (DRF), Institut
des Sciences du Vivant Fréderic Joliot, Commissariat à
l’Energie Atomique et aux Énergies Alternatives (CEA), Université Côte d’Azur (UniCA), Nice 06107, France
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13
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Klompmaker JO, Hart JE, Dominici F, James P, Roscoe C, Schwartz J, Yanosky JD, Zanobetti A, Laden F. Associations of fine particulate matter with incident cardiovascular disease; comparing models using ZIP code-level and individual-level fine particulate matter and confounders. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171866. [PMID: 38521279 PMCID: PMC11034806 DOI: 10.1016/j.scitotenv.2024.171866] [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: 12/19/2023] [Revised: 02/23/2024] [Accepted: 03/19/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND PM2.5 has been positively associated with cardiovascular disease (CVD) incidence. Most evidence has come from cohorts and administrative databases. Cohorts typically have extensive information on potential confounders and residential-level exposures. Administrative databases are usually more representative but typically lack information on potential confounders and often only have exposures at coarser geographies (e.g., ZIP code). The weaknesses in both types of studies have been criticized for potentially jeopardizing the validity of their findings for regulatory purposes. METHODS We followed 101,870 participants from the US-based Nurses' Health Study (2000-2016) and linked residential-level PM2.5 and individual-level confounders, and ZIP code-level PM2.5 and confounders. We used time-varying Cox proportional hazards models to examine associations with CVD incidence. We specified basic models (adjusted for individual-level age, race and calendar year), individual-level confounder models, and ZIP code-level confounder models. RESULTS Residential- and ZIP code-level PM2.5 were strongly correlated (Pearson r = 0.88). For residential-level PM2.5, the hazard ratio (HR, 95 % confidence interval) per 5 μg/m3 increase was 1.06 (1.01, 1.11) in the basic and 1.04 (0.99, 1.10) in the individual-level confounder model. For ZIP code-level PM2.5, the HR per 5 μg/m3 was 1.04 (0.99, 1.08) in the basic and 1.02 (0.97, 1.08) in the ZIP code-level confounder model. CONCLUSION We observed suggestive positive, but not statistically significant, associations between long-term PM2.5 and CVD incidence, regardless of the exposure or confounding model. Although differences were small, associations from models with individual-level confounders and residential-level PM2.5 were slightly stronger than associations from models with ZIP code-level confounders and PM2.5.
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Affiliation(s)
- Jochem O Klompmaker
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Jaime E Hart
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Peter James
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA
| | - Charlie Roscoe
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jeff D Yanosky
- Department of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Francine Laden
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
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14
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Ai B, Zhang J, Zhang S, Chen G, Tian F, Chen L, Li H, Guo Y, Jerath A, Lin H, Zhang Z. Causal association between long-term exposure to air pollution and incident Parkinson's disease. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:133944. [PMID: 38457975 DOI: 10.1016/j.jhazmat.2024.133944] [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: 11/02/2023] [Revised: 02/23/2024] [Accepted: 02/29/2024] [Indexed: 03/10/2024]
Abstract
Epidemiological evidence for long-term air pollution exposure and Parkinson's disease (PD) is controversial, and analysis of causality is limited. We identified 293,888 participants who were free of PD at baseline in the UK Biobank (2006-2010). Time-varying air pollution [fine particulate (PM2.5) and ozone (O3)] exposures were estimated using spatio-temporal models. Incident cases of PD were identified using validated algorithms. Four methods were used to investigate the associations between air pollution and PD, including (1) standard time-varying Cox proportional-hazard model; (2) Cox models weighted by generalized propensity score (GPS) and inverse-probability weights (IPW); (3) instrumental variable (IV) analysis; and (4) negative control outcome analysis. During a median of 11.6 years of follow-up, 1822 incident PD cases were identified. Based on standard Cox regression, the hazard ratios (95% confidence interval) for a 1 µg/m3 or ppb increase in PM2.5 and O3 were 1.23 (1.17, 1.30) and 1.02 (0.98, 1.05), respectively. Consistent results were found in models weighted by GPS and IPW, and in IV analysis. There were no significant associations between air pollution and negative control outcomes. This study provides evidence to support a causal association between PM2.5 exposure and PD. Mitigation of air pollution could be a protective measure against PD.
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Affiliation(s)
- Baozhuo Ai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jiayue Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Ge Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Haitao Li
- Shenzhen University General Hospital, Shenzhen, China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Angela Jerath
- Schulich Heart Program, Sunnybrook Research Institute, Toronto, ON, Canada; ICES, Toronto, ON, Canada; Department of Anesthesiology and Pain Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
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15
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Geldsetzer P, Fridljand D, Kiang MV, Bendavid E, Heft-Neal S, Burke M, Thieme AH, Benmarhnia T. Sociodemographic and geographic variation in mortality attributable to air pollution in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305943. [PMID: 38699349 PMCID: PMC11065005 DOI: 10.1101/2024.04.17.24305943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
There are large differences in premature mortality in the USA by racial/ethnic, education, rurality, and social vulnerability index groups. Using existing concentration-response functions, particulate matter (PM2.5) air pollution, population estimates at the tract level, and county-level mortality data, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in mortality attributable to PM2.5 were consistently more pronounced between racial/ethnic groups than by education, rurality, or social vulnerability index, with the Black American population having by far the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic White population was attributable to PM2.5 in the years 2000 to 2011.
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Affiliation(s)
- Pascal Geldsetzer
- Division of Primary Care and Population Health, Department of Medicine, Stanford University; Stanford, CA 94305, USA
- Department of Epidemiology and Population Health, Stanford University; Stanford, CA 94305, USA
- Chan Zuckerberg Biohub; San Francisco, CA 94158, USA
| | - Daniel Fridljand
- Division of Primary Care and Population Health, Department of Medicine, Stanford University; Stanford, CA 94305, USA
- Heidelberg Institute of Global Health (HIGH), Heidelberg University; 69120 Heidelberg, Germany
- Department of Mathematics, Yale University; New Haven, CT 06511, USA
| | - Mathew V. Kiang
- Department of Epidemiology and Population Health, Stanford University; Stanford, CA 94305, USA
| | - Eran Bendavid
- Division of Primary Care and Population Health, Department of Medicine, Stanford University; Stanford, CA 94305, USA
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University; Stanford, CA 94305, USA
| | - Marshall Burke
- Center on Food Security and the Environment, Stanford University; Stanford, CA 94305, USA
- Department of Earth System Science, Stanford University; Stanford, CA 94305, USA
| | - Alexander H. Thieme
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University; Stanford, CA 94305, USA
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin; 10117 Berlin, Germany
- Berlin Institute of Health at Charité — Universitätsmedizin Berlin; 10117 Berlin, Germany
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego; La Jolla, CA 92093, USA
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
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16
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Zorzetto D, Bargagli-Stoffi FJ, Canale A, Dominici. F. Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology. Biometrics 2024; 80:ujae025. [PMID: 38640436 PMCID: PMC11028589 DOI: 10.1093/biomtc/ujae025] [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: 02/27/2023] [Revised: 01/24/2024] [Accepted: 03/14/2024] [Indexed: 04/21/2024]
Abstract
Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous.
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Affiliation(s)
- Dafne Zorzetto
- Department of Statistics, University of Padova, Padova 35121, Italy
| | - Falco J Bargagli-Stoffi
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115 MA, United States
| | - Antonio Canale
- Department of Statistics, University of Padova, Padova 35121, Italy
| | - Francesca Dominici.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115 MA, United States
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17
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Guo T, Cheng X, Wei J, Chen S, Zhang Y, Lin S, Deng X, Qu Y, Lin Z, Chen S, Li Z, Sun J, Chen X, Chen Z, Sun X, Chen D, Ruan X, Tuohetasen S, Li X, Zhang M, Sun Y, Zhu S, Deng X, Hao Y, Jing Q, Zhang W. Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 274:116212. [PMID: 38489900 DOI: 10.1016/j.ecoenv.2024.116212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/17/2024]
Abstract
Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.
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Affiliation(s)
- Tong Guo
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xi Cheng
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 20740, USA
| | - Shirui Chen
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuqin Zhang
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Shao Lin
- Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Xinlei Deng
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Yanji Qu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, China
| | - Ziqiang Lin
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Shimin Chen
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Zhiqiang Li
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jie Sun
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xudan Chen
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Zhibing Chen
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xurui Sun
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Dan Chen
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xingling Ruan
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Shaniduhaxi Tuohetasen
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xinyue Li
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Man Zhang
- Department of nosocomial infection management, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Yongqing Sun
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Shuming Zhu
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xueqing Deng
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China.
| | - Qinlong Jing
- Guangzhou Municipal Health Commission, Guangzhou, China.
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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18
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Guo T, Chen S, Wang Y, Zhang Y, Du Z, Wu W, Chen S, Ju X, Li Z, Jing Q, Hao Y, Zhang W. Potential causal links of long-term air pollution with lung cancer incidence: From the perspectives of mortality and hospital admission in a large cohort study in southern China. Int J Cancer 2024; 154:251-260. [PMID: 37611179 DOI: 10.1002/ijc.34699] [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: 04/10/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 08/25/2023]
Abstract
Evidence on the potential causal links of long-term air pollution exposure with lung cancer incidence (reflected by mortality and hospital admission) was limited, especially based on large cohorts. We examined the relationship between lung cancer and long-term exposure to particulate matter (PM, including PM2.5 , PM10 and PM10-2.5 ) and nitrogen dioxide (NO2 ) among a large cohort of general Chinese adults using causal inference approaches. The study included 575 592 participants who were followed up for an average of 8.2 years. The yearly exposure of PM and NO2 was estimated through satellite-based random forest approaches and the ordinary kriging method, respectively. Marginal structural Cox models were used to examine hazard ratios (HRs) of mortality and hospital admission due to lung cancer following air pollution exposure, adjusting for potential confounders. The HRs of mortality due to lung cancer were 1.042 (95% confidence interval [CI]: 1.033-1.052), 1.032 (95% CI:1.024-1.041) and 1.052 (95% CI:1.041-1.063) for each 1 μg/m3 increase in PM2.5 , PM10 and NO2 , respectively. In addition, we observed statistically significant effects of PMs on hospital admission due to lung cancer. The HRs (95%CI) were 1.110 (1.027-1.201), 1.067 (1.020-1.115) and 1.079 (1.010-1.153) for every 1 μg/m3 increase in PM2.5 , PM10 , PM10-2.5 , respectively. Furthermore, we found larger effect estimates among the elderly and those who exercised more frequently. We provided the most comprehensive evidence of the potential causal links between two outcomes of lung cancer and long-term air pollution exposure. Relevant policies should be developed, with special attention to protecting the vulnerable groups of the population.
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Affiliation(s)
- Tong Guo
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shirui Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ying Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuqin Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wenjing Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shimin Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xu Ju
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhiqiang Li
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qinlong Jing
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
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19
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Chan EAW, Fann N, Kelly JT. PM 2.5-Attributable Mortality Burden Variability in the Continental U.S. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2023; 315:1-9. [PMID: 38299035 PMCID: PMC10829079 DOI: 10.1016/j.atmosenv.2023.120131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
Epidemiologic studies have consistently observed associations between fine particulate matter (PM2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM2.5-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM2.5-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ~3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM2.5-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.
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Affiliation(s)
- Elizabeth A W Chan
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, USA
| | - Neal Fann
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, USA
| | - James T Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, USA
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20
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Wei J, Wang J, Li Z, Kondragunta S, Anenberg S, Wang Y, Zhang H, Diner D, Hand J, Lyapustin A, Kahn R, Colarco P, da Silva A, Ichoku C. Long-term mortality burden trends attributed to black carbon and PM 2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. Lancet Planet Health 2023; 7:e963-e975. [PMID: 38056967 DOI: 10.1016/s2542-5196(23)00235-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. METHODS In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. FINDINGS Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. INTERPRETATION Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. FUNDING National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.
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Affiliation(s)
- Jing Wei
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA; Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA.
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Shobha Kondragunta
- Center for Satellite Applications and Research, NOAA National Environmental Satellite, Data, and Information Service, College Park, MD, USA
| | - Susan Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, USA
| | - Yi Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - Huanxin Zhang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - David Diner
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jenny Hand
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - Alexei Lyapustin
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Ralph Kahn
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Peter Colarco
- Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Arlindo da Silva
- Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Charles Ichoku
- Department of Geography and Environmental Systems, University of Maryland Baltimore County, Baltimore, MD, USA
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21
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Henneman L, Choirat C, Dedoussi I, Dominici F, Roberts J, Zigler C. Mortality risk from United States coal electricity generation. Science 2023; 382:941-946. [PMID: 37995235 PMCID: PMC10870829 DOI: 10.1126/science.adf4915] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 10/02/2023] [Indexed: 11/25/2023]
Abstract
Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.
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Affiliation(s)
- Lucas Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University Volgenau School of Engineering, Fairfax, VA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard Data Science Initiative, Harvard University, Boston, MA, USA
| | - Christine Choirat
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Irene Dedoussi
- Section Aircraft Noise and Climate Effects, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard Data Science Initiative, Harvard University, Boston, MA, USA
| | - Jessica Roberts
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Corwin Zigler
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard Data Science Initiative, Harvard University, Boston, MA, USA
- Department of Statistics and Data Sciences, University of Texas, Austin, TX, USA
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22
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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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23
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Wang X, Deng X, Wu Y, Qian Z, Cai M, Li H, Lin H. Low-level ambient sulfur dioxide exposure and genetic susceptibility associated with incidence of idiopathic pulmonary fibrosis: A national prospective cohort study. CHEMOSPHERE 2023; 337:139362. [PMID: 37414299 DOI: 10.1016/j.chemosphere.2023.139362] [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: 03/07/2023] [Revised: 06/14/2023] [Accepted: 06/27/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND The association between long-term air pollution exposure and the development of idiopathic pulmonary fibrosis (IPF) has been established, but the evidence regarding the effect of low levels of air pollution, especially ambient sulfur dioxide (SO2), is limited. Besides, the combined effect and interaction between genetic susceptibility and ambient SO2 on IPF remain uncertain. METHODS This study retrieved data from 402,042 participants who were free of IPF at baseline in the UK Biobank. The annual mean concentration of ambient SO2 was estimated for each participant based on their residential addresses using a bilinear interpolation method. Cox proportional hazard models were used to examine the relationship between ambient SO2 and incident IPF. We further generated a polygenic risk score (PRS) for IPF and estimated the combined effects of genetic susceptibility and ambient SO2 on incident IPF. RESULTS After a median follow-up of 11.78 years, 2562 cases of IPF were identified. The results indicated that each 1 μg/m3 increase in ambient SO2 was associated with a hazard ratio (HR) (95% confidence interval [CI]) of 1.67 (1.58, 1.76) for incident IPF. The study found statistically significant synergistic additive interaction between genetic susceptibility and ambient SO2. Individuals with high genetic risk and high ambient SO2 exposure had a higher risk of developing IPF (HR = 7.48, 95% CI:5.66, 9.90). CONCLUSION The study suggests that long-term exposure to ambient SO2, even at concentrations lower than current air quality guidelines set by the Word Health Organization and European Union, may be an important risk factor for IPF. This risk is more pronounced among people with a high genetic risk. Therefore, these findings emphasize the need to consider the potential health effects of SO2 exposure and the necessity for stricter air quality standards.
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Affiliation(s)
- Xiaojie Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, China
| | - Xu Deng
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, China
| | - Yinglin Wu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, China
| | - Zhengmin Qian
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, USA
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, China
| | - Haitao Li
- Department of Social Medicine and Health Service Management, Shenzhen University General Hospital, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, China.
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Marais EA, Kelly JM, Vohra K, Li Y, Lu G, Hina N, Rowe EC. Impact of Legislated and Best Available Emission Control Measures on UK Particulate Matter Pollution, Premature Mortality, and Nitrogen-Sensitive Habitats. GEOHEALTH 2023; 7:e2023GH000910. [PMID: 37885915 PMCID: PMC10599219 DOI: 10.1029/2023gh000910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023]
Abstract
Past emission controls in the UK have substantially reduced precursor emissions of health-hazardous fine particles (PM2.5) and nitrogen pollution detrimental to ecosystems. Still, 79% of the UK exceeds the World Health Organization (WHO) guideline for annual mean PM2.5 of 5 μg m-3 and there is no enforcement of controls on agricultural sources of ammonia (NH3). NH3 is a phytotoxin and an increasingly large contributor to PM2.5 and nitrogen deposited to sensitive habitats. Here we use emissions projections, the GEOS-Chem model, high-resolution data sets, and contemporary exposure-risk relationships to assess potential human and ecosystem health co-benefits in 2030 relative to the present day of adopting legislated or best available emission control measures. We estimate that present-day annual adult premature mortality attributable to exposure to PM2.5 is 48,625 (95% confidence interval: 45,188-52,595), that harmful amounts of reactive nitrogen deposit to almost all (95%) sensitive habitat areas, and that 75% of ambient NH3 exceeds levels safe for bryophytes and lichens. Legal measures decrease the extent of the UK above the WHO guideline to 58% and avoid 6,800 premature deaths by 2030. This improves with best available measures to 36% of the UK and 13,300 avoided deaths. Both legal and best available measures are insufficient at reducing the extent of damage of nitrogen pollution to sensitive habitats. Far more ambitious reductions in nitrogen emissions (>80%) than is achievable with best available measures (34%) are required to halve the amount of excess nitrogen deposited to sensitive habitats.
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Affiliation(s)
| | - Jamie M. Kelly
- Department of GeographyUniversity College LondonLondonUK
- Now at Centre for Research and Clean AirHelsinkiFinland
| | - Karn Vohra
- Department of GeographyUniversity College LondonLondonUK
| | - Yifan Li
- Reading AcademyNanjing University of Information Science and TechnologyNanjingChina
| | - Gongda Lu
- Department of GeographyUniversity College LondonLondonUK
| | - Naila Hina
- UK Centre for Ecology & HydrologyEnvironment Centre WalesBangorUK
| | - Ed C. Rowe
- UK Centre for Ecology & HydrologyEnvironment Centre WalesBangorUK
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25
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Wang Y, Jiang J, Chen L, Guo T, Chen S, Du Z, Wei J, Zhang W, Hao Y. Is COPD mortality in South China causally linked to the long-term PM 1 exposure? Evidence from a large community-based cohort. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115299. [PMID: 37499383 DOI: 10.1016/j.ecoenv.2023.115299] [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: 04/10/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND Long-term ambient particulate matter (PM) exposure has been found associated with chronic obstructive pulmonary disease (COPD) mortality in an increasing body of research. However, limited evidence was available on the potential causal links between PM1 and COPD mortality, especially in highly exposed areas. OBJECTIVES To examine the COPD mortality risk following long-term ambient PM1 exposure in south China. METHODS The cohort included 580,757 participants recruited during 2009-2015. Satellite-based annual concentrations of PM1 were estimated at a spatial resolution of 1 km × 1 km and assigned to each participant based on their residential addresses. We analyzed the potential causal links between time-varying PM1 exposure and COPD mortality using marginal structural cox models within causal frameworks. Stratified analyses were also performed to identify the potential susceptible groups. RESULTS The annual average PM1 concentration continuously decreased over time. After adjusting for confounders, each 1 μg/m3 increase in PM1 concentration corresponded to an 8.1 % (95% confidence interval: 6.4-9.9 %) increment in the risk of COPD mortality. The impact of PM1 was more pronounced among the elderly and those with low exercise frequency, with a 1.9-6.9 % higher risk than their counterparts. We further observed a 0.1-9.7 % greater risk among those who lived in lower greenness settings. Additionally, we observed higher effect estimates in participants with long-term low PM1 exposure compared to the general population. CONCLUSIONS COPD mortality risk significantly increased following long term ambient PM1 exposure, particularly among groups with certain demographics or long-term low exposure.
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Affiliation(s)
- Ying Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangzhou, China
| | - Jie Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Liufu Chen
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Tong Guo
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangzhou, China
| | - Shimin Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangzhou, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, United States.
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangzhou, China.
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China.
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26
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Nethery RC, Josey K, Gandhi P, Kim JH, Visaria A, Bates B, Schwartz J, Robinson D, Setoguchi S. Air Pollution and Cardiovascular and Thromboembolic Events in Older Adults With High-Risk Conditions. Am J Epidemiol 2023; 192:1358-1370. [PMID: 37070398 PMCID: PMC10666966 DOI: 10.1093/aje/kwad089] [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: 02/07/2022] [Revised: 01/11/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
Little epidemiologic research has focused on pollution-related risks in medically vulnerable or marginalized groups. Using a nationwide 50% random sample of 2008-2016 Medicare Part D-eligible fee-for-service participants in the United States, we identified a cohort with high-risk conditions for cardiovascular and thromboembolic events (CTEs) and linked individuals with seasonal average zip-code-level concentrations of fine particulate matter (particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5)). We assessed the relationship between seasonal PM2.5 exposure and hospitalization for each of 7 CTE-related causes using history-adjusted marginal structural models with adjustment for individual demographic and neighborhood socioeconomic variables, as well as baseline comorbidity, health behaviors, and health-service measures. We examined effect modification across geographically and demographically defined subgroups. The cohort included 1,934,453 individuals with high-risk conditions (mean age = 77 years; 60% female, 87% White). A 1-μg/m3 increase in PM2.5 exposure was significantly associated with increased risk of 6 out of 7 types of CTE hospitalization. Strong increases were observed for transient ischemic attack (hazard ratio (HR) = 1.039, 95% confidence interval (CI): 1.034, 1.044), venous thromboembolism (HR = 1.031, 95% CI: 1.027, 1.035), and heart failure (HR = 1.019, 95% CI: 1.017, 1.020). Asian Americans were found to be particularly susceptible to thromboembolic effects of PM2.5 (venous thromboembolism: HR = 1.063, 95% CI: 1.021, 1.106), while Native Americans were most vulnerable to cerebrovascular effects (transient ischemic attack: HR = 1.093, 95% CI: 1.030, 1.161).
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Affiliation(s)
- Rachel C Nethery
- Correspondence to Dr. Rachel C. Nethery, Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA 02115 (e-mail: )
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27
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Fletcher J, Jajtner KM. Multidimensional intergenerational mobility. Soc Sci Med 2023; 328:115966. [PMID: 37244022 PMCID: PMC10330858 DOI: 10.1016/j.socscimed.2023.115966] [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: 10/31/2022] [Revised: 05/02/2023] [Accepted: 05/12/2023] [Indexed: 05/29/2023]
Abstract
In this paper, we present novel evidence of the extent to which intergenerational mobility is generalized or specific across domains of human and health capital. That is, do children who experience greater mobility in one domain (e.g., income) also experience mobility in other domains (education, health status, health behaviors, crime). Using rich data in Add Health, we find evidence against generalized mobility-families that are more mobile in one domain are not more mobile in others. We then ask a place-based version of this question, motivated by Chetty et al. (2014)'s work showing high levels of geographically-based income mobility in the US. The school-based sampling combined with parent-child links across many outcome domains of the Add Health allows us to use a common dataset between the two analyses. Like our individual-based results, we find limited evidence of generalized mobility by place-indeed, most estimates suggest close-to-zero correlations between many of the ten domains we explore.
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Affiliation(s)
- Jason Fletcher
- University of Wisconsin - Madison, 1180 Observatory Drive, Room 4408, Madison, WI, 53706, USA
| | - Katie M Jajtner
- University of Wisconsin - Madison, 1180 Observatory Drive, Room 4408, Madison, WI, 53706, USA.
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28
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Chen S, Lin X, Du Z, Zhang Y, Zheng L, Ju X, Guo T, Wang X, Chen L, Jiang J, Hu W, Zhang W, Hao Y. Potential causal links between long-term ambient particulate matter exposure and cerebrovascular mortality: Insights from a large cohort in southern China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 328:121336. [PMID: 36822305 DOI: 10.1016/j.envpol.2023.121336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 05/09/2023]
Abstract
Cohort studies conducted in North America and Europe have linked cerebrovascular mortality to long-term exposure to particulate matter (PM). However, limited evidence from large cohorts in high-exposure areas and the traditional approach of association assessment may cause residual confounding issues. In this study, we aimed to investigate the causal links between cerebrovascular mortality and long-term exposure to PM2.5, PM10, and PM2.5-10 in an ongoing cohort study with 580,757 participants in southern China. Using satellite-based estimates of PM concentration at a 1-km2 spatial resolution, we assigned exposure levels to each participant and used the marginal structural Cox model to assess the association between PM exposure and cerebrovascular mortality while accounting for time-varying covariates. We also explored the potential modification effects of sociodemographic and behavioral factors on the PM-health associations. Adjusted hazard ratios (HR) for overall cerebrovascular mortality were 1.041 (95% confidence interval (CI): 1.034-1.049) and 1.032 (95% CI: 1.026-1.038) for each 1 μg/m3 increase in PM2.5, and PM10, respectively. Similar trends were observed in the mortality risk from stroke and ischemic stroke, with HRs ranging from 1.040 to 1.069 and 1.025 to 1.052, respectively, across 2 p.m. exposures. The impact of PM exposure was generally more apparent among women, participants with primary school diplomas and below, and the subgroup under low-exposure. Multiple sensitivity analyses confirmed the robustness of the results. In conclusion, this sizable prospective cohort study hypothesizes causal links between long-term PM exposure and cerebrovascular mortality, particularly among vulnerable participants, supporting the rationale for reducing PM concentration in China to reduce cerebrovascular mortality.
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Affiliation(s)
- Shirui Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Xiao Lin
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yuqin Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Lingling Zheng
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Xu Ju
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Tong Guo
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Xinran Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Lichang Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Jie Jiang
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China
| | - Weihua Hu
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China.
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29
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Buxton MA, Fleischer NL, Ro A, O’Neill MS. Structural racism, air pollution and the association with adverse birth outcomes in the United States: the value of examining intergenerational associations. FRONTIERS IN EPIDEMIOLOGY 2023; 3:1190407. [PMID: 38455927 PMCID: PMC10910959 DOI: 10.3389/fepid.2023.1190407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/26/2023] [Indexed: 03/09/2024]
Abstract
Structurally racist policies and practices of the past are likely to be a driving factor in current day differences in exposure to air pollution and may contribute to observed racial and ethnic disparities in adverse birth outcomes in the United States (U.S.). Non-Hispanic Black women in the U.S. experience poorer health outcomes during pregnancy and throughout the life course compared to non-Hispanic White women. This disparity holds even among non-Hispanic Black women with higher socioeconomic status. Reasons for this finding remain unclear, but long-term environmental exposure, either historical exposure or both historical and ongoing exposure, may contribute. Structural racism likely contributes to differences in social and environmental exposures by race in the U.S. context, and these differences can affect health and wellbeing across multiple generations. In this paper, we briefly review current knowledge and recommendations on the study of race and structural racism in environmental epidemiology, specifically focused on air pollution. We describe a conceptual framework and opportunities to use existing historical data from multiple sources to evaluate multi-generational influences of air pollution and structurally racist policies on birth and other relevant health outcomes. Increased analysis of this kind of data is critical for our understanding of structural racism's impact on multiple factors, including environmental exposures and adverse health outcomes, and identifying how past policies can have enduring legacies in shaping health and well-being in the present day. The intended purpose of this manuscript is to provide an overview of the widespread reach of structural racism, its potential association with health disparities and a comprehensive approach in environmental health research that may be required to study and address these problems in the U.S. The collaborative and methodological approaches we highlight have the potential to identify modifiable factors that can lead to effective interventions for health equity.
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Affiliation(s)
- Miatta A. Buxton
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Nancy L. Fleischer
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Annie Ro
- Department of Health, Society, and Behavior, Program in Public Health, University of California, Irvine, Irvine, CA, United States
| | - Marie S. O’Neill
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, United States
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30
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Wolf K, Rodopoulou S, Chen J, Andersen ZJ, Atkinson RW, Bauwelinck M, Janssen NAH, Kristoffersen DT, Lim YH, Oftedal B, Strak M, Vienneau D, Zhang J, Brunekreef B, Hoek G, Stafoggia M, Samoli E. Comparison of traditional Cox regression and causal modeling to investigate the association between long-term air pollution exposure and natural-cause mortality within European cohorts. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 327:121515. [PMID: 36967008 DOI: 10.1016/j.envpol.2023.121515] [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: 01/23/2023] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 06/18/2023]
Abstract
Most studies investigating the health effects of long-term exposure to air pollution used traditional regression models, although causal inference approaches have been proposed as alternative. However, few studies have applied causal models and comparisons with traditional methods are sparse. We therefore compared the associations between natural-cause mortality and exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) using traditional Cox and causal models in a large multicenter cohort setting. We analysed data from eight well-characterized cohorts (pooled cohort) and seven administrative cohorts from eleven European countries. Annual mean PM2.5 and NO2 from Europe-wide models were assigned to baseline residential addresses and dichotomized at selected cut-off values (PM2.5: 10, 12, 15 μg/m³; NO2: 20, 40 μg/m³). For each pollutant, we estimated the propensity score as the conditional likelihood of exposure given available covariates, and derived corresponding inverse-probability weights (IPW). We applied Cox proportional hazards models i) adjusting for all covariates ("traditional Cox") and ii) weighting by IPW ("causal model"). Of 325,367 and 28,063,809 participants in the pooled and administrative cohorts, 47,131 and 3,580,264 died from natural causes, respectively. For PM2.5 above vs. below 12 μg/m³, the hazard ratios (HRs) of natural-cause mortality were 1.17 (95% CI 1.13-1.21) and 1.15 (1.11-1.19) for the traditional and causal models in the pooled cohort, and 1.03 (1.01-1.06) and 1.02 (0.97-1.09) in the administrative cohorts. For NO2 above vs below 20 μg/m³, the HRs were 1.12 (1.09-1.14) and 1.07 (1.05-1.09) for the pooled and 1.06 (95% CI 1.03-1.08) and 1.05 (1.02-1.07) for the administrative cohorts. In conclusion, we observed mostly consistent associations between long-term air pollution exposure and natural-cause mortality with both approaches, though estimates partly differed in individual cohorts with no systematic pattern. The application of multiple modelling methods might help to improve causal inference. 299 of 300 words.
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Affiliation(s)
- Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München GmbH - German Research Center for Environmental Health, Neuherberg, Germany.
| | - Sophia Rodopoulou
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Jie Chen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Zorana J Andersen
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Richard W Atkinson
- Population Health Research Institute, St George's, University of London, London, UK
| | - Mariska Bauwelinck
- Interface Demography, Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Nicole A H Janssen
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | | | - Youn-Hee Lim
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Bente Oftedal
- Department of Air Quality and Noise, Norwegian Institute of Public Health, Oslo, Norway
| | - Maciek Strak
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Danielle Vienneau
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Jiawei Zhang
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Region Health Service / ASL Roma 1, Rome, Italy; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Evangelia Samoli
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
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31
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Deng S, Lau J, Wang Z, Wargocki P. Associations between illness-related absences and ventilation and indoor PM 2.5 in elementary schools of the Midwestern United States. ENVIRONMENT INTERNATIONAL 2023; 176:107944. [PMID: 37216835 DOI: 10.1016/j.envint.2023.107944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/30/2023] [Accepted: 04/19/2023] [Indexed: 05/24/2023]
Abstract
This study monitored indoor environmental data in 144 classrooms in 31 schools in the Midwestern United States for two consecutive days every fall, winter, and spring during a two-year period; 3,105 pupils attended classrooms where the measurements were conducted. All classrooms were ventilated with mechanical systems that had recirculation; there were no operable exterior windows or doors. The daily absence rate at the student level and demographic data at the classroom level were collected. The overall mean ventilation rate, using outdoor air, was 5.5 L/s per person (the corresponding mean carbon dioxide concentrations were < 2,000 ppm), and the mean indoor PM2.5 was 3.6 μg/m3. The annual illness-related absence rate at the classroom level was extracted from the student-level absence data and regressed on measured indoor environmental parameters. Significant associations were found. Every 1 L/s per person increase in ventilation rate was associated with a 5.59 decrease in days with absences per year. This corresponds to a 0.15% increase in the annual daily attendance rate. Every additional 1 μg/m3 of indoor PM2.5 was associated with a 7.37 increase in days with absences per year. This corresponds to a 0.19% decrease in the annual daily attendance rate. No other relationships were significant. Present results agree with the previously demonstrated benefits of reduced absence rates when classroom ventilation is improved and provide additional evidence on the potential benefits of reducing indoor inhalable particles. Overall, reduced absence rates are expected to provide socioeconomic benefits and benefits for academic achievements, while higher ventilation rates and reduced particle levels will also contribute to reduced health risks, including those related to airborne respiratory pathogens.
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Affiliation(s)
- Shihan Deng
- Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, 727 South Jingming Road, Chenggong District, Kunming, Yunnan, 650500, China; Durham School of Architectural Engineering and Construction, University of Nebraska - Lincoln, 1110 S. 67th Street, Omaha, NE, 68182-0816, United States
| | - Josephine Lau
- Durham School of Architectural Engineering and Construction, University of Nebraska - Lincoln, 1110 S. 67th Street, Omaha, NE, 68182-0816, United States.
| | - Zhihao Wang
- Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, 727 South Jingming Road, Chenggong District, Kunming, Yunnan, 650500, China
| | - Pawel Wargocki
- Department of Environmental and Resource Engineering (DTU Sustain), Technical University of Denmark, Koppels Allé, 402, 2800 Kgs. Lyngby, Denmark
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32
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Li S, Guo B, Dong K, Huang S, Wu J, Zhou H, Wu K, Han X, Liang X, Pei X, Zuo H, Lin H, Zhao X. Association of long-term exposure to ambient PM 2.5 and its constituents with gut microbiota: Evidence from a China cohort. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 884:163577. [PMID: 37084912 DOI: 10.1016/j.scitotenv.2023.163577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/29/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
Accumulating animal experiments and epidemiological studies have found that exposure to fine particulate matter (PM2.5) is associated with altered gut microbiota (GM). However, it is unclear what kind of role the PM2.5 constituents play in the PM2.5-GM association. Therefore, this study aimed to investigate the association of long-term exposure to PM2.5 and its constituents (PMcons) with GM. This study included 1583 participants from a cohort in Southwest China. Satellite remote sensing and chemical transport modelling were used to determine the yearly average concentrations of PMcons. GM data were derived from 16 s sequencing based on stool samples. Generalized propensity score weighting regression and Bayesian Kernel Machine Regression (BKMR) were used to estimate the individual and joint association of exposure to PMcons with the Shannon index. The weighted correlation analysis was used to estimate the association of PMcons with the composition of GM. The result showed that an interquartile range increase of 3-year average black carbon (BC), ammonium, nitrate, organic matter (OM), sulfate, and soil particles (SOIL) were negatively associated with Shannon index with mean difference (95 % confidence interval) being -0.144 (-0.208, -0.080), -0.141 (-0.205, -0.078), -0.126 (-0.184, -0.068), -0.117 (-0.172, -0.062), -0.153 (-0.221, -0.085), and - 0.153 (-0.222, -0.085). BKMR indicated joint exposure to PMcons was associated with decreased Shannon index, and BC had the largest posterior inclusion probability (0.578). Weighted correlation analyses indicated PMcons were associated with decreased Bacteroidetes (r = -0.204, P < 0.001 for PM2.5) and increased Proteobacteria (r = 0.273, P < 0.001 for PM2.5). These results revealed that long-term exposure to PMcons was associated with GM. BC was the most important constituent in the association, indicating that the source of BC should be controlled to mitigate the negative effects of PM2.5 on GM.
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Affiliation(s)
- Sicheng Li
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bing Guo
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ke Dong
- Department of Public Health Laboratory Sciences, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shourui Huang
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jialong Wu
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hanwen Zhou
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kunpeng Wu
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xinyu Han
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xian Liang
- Chengdu Center for Disease Control and Prevention, Chengdu, Sichuan, China
| | - Xiaofang Pei
- Department of Public Health Laboratory Sciences, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Haojiang Zuo
- Department of Public Health Laboratory Sciences, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
| | - Xing Zhao
- Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Josey KP, Delaney SW, Wu X, Nethery RC, DeSouza P, Braun D, Dominici F. Air Pollution and Mortality at the Intersection of Race and Social Class. N Engl J Med 2023; 388:1396-1404. [PMID: 36961127 PMCID: PMC10182569 DOI: 10.1056/nejmsa2300523] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
BACKGROUND Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 μm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM2.5 standards. METHODS We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible). RESULTS Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 from 12 μg per cubic meter to 8 μg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval [CI], 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation. CONCLUSIONS Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).
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Affiliation(s)
- Kevin P Josey
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
| | - Scott W Delaney
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
| | - Xiao Wu
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
| | - Rachel C Nethery
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
| | - Priyanka DeSouza
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
| | - Danielle Braun
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
| | - Francesca Dominici
- From the Departments of Biostatistics (K.P.J., R.C.N., D.B., F.D.) and Environmental Health (S.W.D.), Harvard T.H. Chan School of Public Health, Boston; the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York (X.W.); and the Department of Urban and Regional Planning, University of Colorado Denver, Denver (P.D.)
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Zhang Y, Wang Y, Du Z, Chen S, Qu Y, Hao C, Ju X, Lin Z, Wu W, Xiao J, Chen X, Lin X, Chen S, Chen L, Jiang J, Zhang W, Hao Y. Potential causal links between long-term ambient particulate matter exposure and cardiovascular mortality: New evidence from a large community-based cohort in South China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 254:114730. [PMID: 36905844 DOI: 10.1016/j.ecoenv.2023.114730] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Cardiovascular disease (CVD) mortality is associated with long-term particulate matter (PM) exposure. However, evidence from large, highly-exposed population cohort and observational-data-based causal inference approaches remains limited. AIMS We examined the potential causal links between PM exposure and the CVD mortality in South China. METHODS 580,757 participants were recruited during 2009-2015 and followed up through 2020. Satellite-based annual concentrations of PM2.5, PM10, and PMcoarse (i.e., PM10 - PM2.5) at 1 km2 spatial resolution were estimated and assigned to each participant. Marginal structural Cox models with time-varying covariates, adjusted using inverse probability weighting, were developed to evaluate the association between prolonged PM exposure and CVD mortality. RESULTS For overall CVD mortality, the hazard ratios and 95% confidence interval for each 1 μg/m3 increase in the annual average concentration of PM2.5, PM10, and PMcoarse were 1.033 (1.028-1.037), 1.028 (1.024-1.032), and 1.022 (1.012-1.033), respectively. All three PMs were linked to a higher mortality risk for myocardial infarction and ischemic heart disease (IHD). The mortality risk of chronic IHD and hypertension was linked to PM2.5 and PM10. Significant association between PMcoarse and other heart disease mortality was also observed. The older, women, less-educated participants, or inactive participants exhibited particularly higher susceptibility. Participants who were generally exposed to PM10 concentrations below 70 μg/m3 were more vulnerable to PM2.5-, PM10- and PMcoarse-CVD mortality risks. CONCLUSION This large cohort study provides evidence for the potential causal links between increased CVD mortality and ambient PM exposure, as well as socio-demographics linked to the highest vulnerability.
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Affiliation(s)
- Yuqin Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Ying Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Shirui Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yanji Qu
- Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, China
| | - Chun Hao
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Xu Ju
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Ziqiang Lin
- Department of Preventive Medicine, School of Basic Medicine and Public Health, Jinan University, Guangzhou, China
| | - Wenjing Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xiuyuan Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Xiao Lin
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Shimin Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Lichang Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Jie Jiang
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China.
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Stafoggia M, Analitis A, Chen J, Rodopoulou S, Brunekreef B, Hoek G, Wolf K, Samoli E. Comparing "causal" and "traditional" approaches in the association of long-term exposure to ambient air pollution on mortality: How sensitive are the results? ENVIRONMENT INTERNATIONAL 2023; 174:107872. [PMID: 36934573 DOI: 10.1016/j.envint.2023.107872] [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: 01/11/2023] [Revised: 02/24/2023] [Accepted: 03/08/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Few comparisons between causal inference and traditional approaches have been performed. We applied "causal" and "traditional" methods to investigate the association between long-term air pollution exposure (PM2.5 and NO2) and mortality. METHODS We analyzed pooled data from eight well-characterized cohorts and one administrative cohort. We defined the generalized propensity score (GPS) as the conditional likelihood of exposure given confounders, and derived corresponding inverse-probability weights (IPW). We applied Cox-proportional hazard models weighted by IPW, adjusted for GPS, and directly adjusting for all confounders. RESULTS In IPW models, PM2.5 5 µg/m3 increases were associated with hazard ratios (HR) = 1.141 (95% confidence interval (CI): 1.107, 1.176) and 1.050 (1.014, 1.088) in the pooled and administrative cohorts. Corresponding estimates for traditional Cox models were 1.132 (1.107, 1.158) and 1.057 (1.025, 1.089). Almost identical results were found for all approaches and both pollutants, when unbalanced covariates were adjusted for in causal models. CONCLUSIONS Traditional and causal approaches provided consistent associations between long-term exposure to air pollution and mortality.
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Affiliation(s)
- Massimo Stafoggia
- Department of Epidemiology, Lazio Region Health Service / ASL Roma 1, Rome, Italy; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Antonis Analitis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Jie Chen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Sophia Rodopoulou
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München GmbH - German Research Center for Environmental Health, Neuherberg, Germany
| | - Evangelia Samoli
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
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Wang Y, Du Z, Zhang Y, Chen S, Lin S, Hopke PK, Rich DQ, Zhang K, Romeiko XX, Deng X, Qu Y, Liu Y, Lin Z, Zhu S, Zhang W, Hao Y. Long-term exposure to particulate matter and COPD mortality: Insights from causal inference methods based on a large population cohort in southern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 863:160808. [PMID: 36502970 DOI: 10.1016/j.scitotenv.2022.160808] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/17/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Evidence of the association between long-term exposure to particulate matter (PM) and chronic obstructive pulmonary disease (COPD) mortality from large population-based cohort study is limited and often suffers from residual confounding issues with traditional statistical methods. We hereby assessed the casual relationship between long-term PM (PM2.5, PM10 and PM10-2.5) exposure and COPD mortality in a large cohort of Chinese adults using state-of-the-art causal inference approaches. METHODS A total of 580,757 participants in southern China were enrolled in a prospective cohort study from 2009 to 2015 and followed up until December 2020. Exposures to PM at each residential address were obtained from the Long-term Gap-free High-resolution Air Pollutant Concentration dataset. Marginal structural Cox models were used to investigate the association between COPD mortality and annual average exposure levels of PM exposure. RESULTS During an average follow-up of 8.0 years, 2250 COPD-related deaths occurred. Under a set of causal inference assumptions, the hazard ratio (HR) for COPD mortality was estimated to be 1.046 (95 % confidence interval: 1.034-1057), 1.037 (1.028-1.047), and 1.032 (1.006-1.058) for each 1-μg/m3 increase in annual average concentrations of PM2.5, PM10, and PM10-2.5 respectively. Additionally, the detrimental effects appeared to be more pronounced among the elderly (age ≥ 65) and inactive participants. The effect estimates of PM2.5, PM10, and PM10-2.5 tend to be greater among participants who were generally exposed to PM10 concentrations below 70 μg/m3 than that among the general population. CONCLUSION Our results support causal links between long-term PM exposure and COPD mortality, highlighting the urgency for more effective strategies to reduce PM exposure, with particular attention on protecting potentially vulnerable groups.
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Affiliation(s)
- Ying Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yuqin Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Shirui Chen
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Shao Lin
- Department of Environmental Health Sciences, School of Public Health, University at Albany, the State University of New York, Rensselaer, NY, USA
| | - Philip K Hopke
- Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, USA; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - David Q Rich
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, the State University of New York, Rensselaer, NY, USA
| | - Xiaobo X Romeiko
- Department of Environmental Health Sciences, School of Public Health, University at Albany, the State University of New York, Rensselaer, NY, USA
| | - Xinlei Deng
- Department of Environmental Health Sciences, School of Public Health, University at Albany, the State University of New York, Rensselaer, NY, USA
| | - Yanji Qu
- Department of Cardiovascular Epidemiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yu Liu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Ziqiang Lin
- Department of Psychiatry, New York University School of Medicine, NY, USA
| | - Shuming Zhu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking, China.
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Josey KP, deSouza P, Wu X, Braun D, Nethery R. Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS 2023; 28:20-41. [PMID: 37063643 PMCID: PMC10103900 DOI: 10.1007/s13253-022-00508-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 07/08/2022] [Accepted: 07/23/2022] [Indexed: 10/14/2022]
Abstract
Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.
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Affiliation(s)
- Kevin P. Josey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado, Denver, CO
| | - Xiao Wu
- Department of Statistics, Stanford University, Stanford, CA
- Stanford Data Science, Stanford University, Stanford, CA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Rachel Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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Trickey KS, Chen Z, Sanghavi P. Hospitalisations for cardiovascular and respiratory disease among older adults living near unconventional natural gas development: a difference-in-differences analysis. Lancet Planet Health 2023; 7:e187-e196. [PMID: 36889860 DOI: 10.1016/s2542-5196(23)00009-8] [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: 12/03/2021] [Revised: 01/14/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND During 2008-15, the Marcellus shale region of the US state of Pennsylvania experienced a boom in unconventional natural gas development (UNGD) or "fracking". However, despite much public debate, little is known about the effects of UNGD on population health in local communities. Among other mechanisms, air pollution from UNGD might affect individuals living nearby through cardiovascular or respiratory disease, and older adults could be particularly susceptible. METHODS To study the health impacts of Pennsylvania's fracking boom, we exploited the ban on UNGD in neighbouring New York state. Using 2002-15 Medicare claims, we conducted difference-in-differences analyses over multiple timepoints to estimate the risk of living near UNGD for hospitalisation with acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD) and bronchiectasis, heart failure, ischaemic heart disease, and stroke among older adults (aged ≥65 years). FINDINGS Pennsylvania ZIP codes that started UNGD in 2008-10 were associated with more hospitalisations for cardiovascular diseases in 2012-15 than would be expected in the absence of UNGD. Specifically, in 2015, we estimated an additional 11·8, 21·6, and 20·4 hospitalisations for AMI, heart failure, and ischaemic heart disease, respectively, per 1000 Medicare beneficiaries. Hospitalisations increased even as UNGD growth slowed. Results were robust in sensitivity analyses. INTERPRETATION Older adults living near UNGD could be at high risk of poor cardiovascular outcomes. Mitigation policies for existing UNGD might be needed to address current and future health risks. Future consideration of UNGD should prioritise local population health. FUNDING University of Chicago and Argonne National Laboratories.
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Affiliation(s)
- Kevin S Trickey
- Department of Public Health Sciences, Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Zihan Chen
- Department of Public Health Sciences, Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Prachi Sanghavi
- Department of Public Health Sciences, Biological Sciences Division, University of Chicago, Chicago, IL, USA.
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Chen C, Chen H, van Donkelaar A, Burnett RT, Martin RV, Chen L, Tjepkema M, Kirby-McGregor M, Li Y, Kaufman JS, Benmarhnia T. Using Parametric g-Computation to Estimate the Effect of Long-Term Exposure to Air Pollution on Mortality Risk and Simulate the Benefits of Hypothetical Policies: The Canadian Community Health Survey Cohort (2005 to 2015). ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:37010. [PMID: 36920446 PMCID: PMC10016347 DOI: 10.1289/ehp11095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
BACKGROUND Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges. METHOD We applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication. RESULTS All hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 μg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications. DISCUSSION We found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.
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Affiliation(s)
- Chen Chen
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA
| | - Hong Chen
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Aaron van Donkelaar
- Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Richard T. Burnett
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Randall V. Martin
- Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Li Chen
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Michael Tjepkema
- Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
| | - Megan Kirby-McGregor
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Yi Li
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Jay S. Kaufman
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA
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Yang Q, Yuan Q, Gao M, Li T. A new perspective to satellite-based retrieval of ground-level air pollution: Simultaneous estimation of multiple pollutants based on physics-informed multi-task learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159542. [PMID: 36265618 DOI: 10.1016/j.scitotenv.2022.159542] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 09/13/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Remote sensing of air pollution is essential for air quality management and health risk assessment. Many machine-learning-based retrieval models have been established for estimating various kinds of air pollutants. These methods mainly aimed to estimate a single pollutant (single-pollutant approach). However, different air pollutants interact with each other and are highly correlated. Building a unified model and conducting a joint retrieval of multiple pollutant can make a better use of these connections and improve the model efficiency. This study proposed a physics-informed multi-task deep neural network (phyMTDNN) for the joint retrieval of six main air pollutants, i.e., PM2.5, PM10, SO2, NO2, CO, and O3. The relationships among these pollutants were used to design the physics-informed network structure and loss function. Top-of-atmosphere reflectance which can generate retrieval results at ultrahigh resolution was used as model input. Experiments for mainland China in 2019 showed that the proposed model successfully achieved simultaneous estimation of six air pollutants, with the cross-validated R2 for the six pollutants varying from 0.72 to 0.90. The contrast experiments proved that physics-informed network structure contributed to the most of the model performance improvement. Compared to the single-pollutant approach, phyMTDNN ameliorated the model accuracy on traces gases retrieval. Furthermore, the modeling efficiency was largely improved in that a lot of repetitive work was avoided and modeling method was optimized. The proposed new multiple-pollutant retrieval frame can be applied to various fields for multi-variate retrieval or estimation.
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Affiliation(s)
- Qianqian Yang
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China; Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, Hubei 430079, China.
| | - Meng Gao
- Department of Geography, Hong Kong Baptist University, 999077, Hong Kong
| | - Tongwen Li
- School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, Guangzhou 519082, China
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Mork D, Braun D, Zanobetti A. Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias. ENVIRONMENT INTERNATIONAL 2023; 171:107694. [PMID: 36521347 PMCID: PMC9885762 DOI: 10.1016/j.envint.2022.107694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/11/2022] [Accepted: 12/11/2022] [Indexed: 05/09/2023]
Abstract
Alzheimer's disease and related dementias (ADRD) poses substantial health challenges among an aging population. One of the primary challenges in studying ADRD is that biological processes underlying these ailments begin decades prior to diagnosis. Previous studies indicate a relationship between ADRD and air pollution exposure to both fine particulate matter (PM2.5) and nitrogen dioxide (NO2) but are limited in their interpretation because they consider exposure measurements at a single time point. Our retrospective cohort study considered 27 + million Medicare enrollees in the United States followed up to 17 years and matched with highly accurate annual air pollution exposure measurements for PM2.5, NO2, and summer ozone. We applied distributed lag models and estimated the lagged associations between air pollution and odds of first hospitalization with ADRD. We found significantly increased odds due to overall PM2.5 and NO2 exposure and time-lagged exposure 10 and 8 years prior to admission, respectively. Furthermore, we found the connection between air pollution exposure and increased odds of first hospitalization with ADRD exists at air pollution levels below current National Ambient Air Quality Standards set by the US Environmental Protection Agency, with the steepest increase in odds occurring at low concentrations of PM2.5. Our findings are the first to show that air pollution exposures from as many as 10 years prior to the admission are related to increased odds of hospitalizations with ADRD. As there are no clear treatments available for ADRD, identifying modifiable risk factors such as air pollution exposure may make significant contributions towards prevention or delayed disease progression.
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Affiliation(s)
- Daniel Mork
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Jung EJ, Lim AY, Kim JH. Decreased birth weight after prenatal exposure to wildfires on the eastern coast of Korea in 2000. Epidemiol Health 2023; 45:e2023003. [PMID: 36596738 PMCID: PMC10106538 DOI: 10.4178/epih.e2023003] [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: 09/01/2022] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVES In April 2000, a series of wildfires occurred simultaneously in five adjacent small cities located on the eastern coast of Korea. These wildfires burned approximately 23,794 hectares of forestland over several days. We investigated the effects of prenatal exposure to the by-products generated by wildfire disasters on birth weight. METHODS Birth weight data were obtained for 1999-2001 from the birth registration database of the Korean National Statistical Office and matched with the zip code and exposed/unexposed pregnancy week for days of the wildfires. Generalized linear models were then used to assess the associations between birth weight and exposure to wildfires after adjusting for fetal sex, gestational age, parity, maternal age, maternal education, paternal education, and average exposed atmospheric temperature. RESULTS Compared with unexposed pregnancies before and after the wildfires, mean birth weight decreased by 41.4 g (95% confidence interval [CI], -72.4 to -10.4) after wildfire exposure during the first trimester, 23.2 g (95% CI, -59.3 to 13.0) for exposure during the second trimester, and 27.0 g (95% CI, -63.8 to 9.8) during the third trimester. In the adjusted model for infants exposed in utero during any trimester, the mean birth weight decreased by 32.5 g (95% CI, -53.2 to -11.7). CONCLUSIONS We observed a 1% reduction in birth weight after wildfire exposure. Thus, exposure to by-products generated during a wildfire disaster during pregnancy may slow fetal growth and cause developmental delays.
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Affiliation(s)
- En-Joo Jung
- Department of Public Medical Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ah-Young Lim
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea
| | - Jong-Hun Kim
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea
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Yang S, Wen L, Chai X, Song Y, Chen X, Chen ZF, Li R, Dong C, Qi Z, Cai Z. The protective effects of taurine and fish oil supplementation on PM 2.5-induced heart dysfunction among aged mice: A random double-blind study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:157966. [PMID: 35964740 DOI: 10.1016/j.scitotenv.2022.157966] [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: 06/10/2022] [Revised: 07/24/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
As it is nearly impossible to reduce PM2.5 concentrations in most cities to safe limits in a short period of time, dietary supplementation presents a promising approach for mitigating the adverse effects of PM2.5 exposure. A cross-sectional study showed that the elderly population of Linfen (PM2.5: 102 μg/m3) exhibited significantly lower serum taurine levels, as well as higher oxidative stress levels and cardiovascular health risks, than the corresponding population in Guangzhou (PM2.5: 39 μg/m3). We conducted a random double-blind study on aged mice that employed a "real-world" PM2.5 exposure system to simulate the conditions of Linfen with the aim of investigating the protective effects of taurine and fish oil supplementation on PM2.5-induced heart dysfunction. When compared with the placebo group, supplementation with taurine and fish oil not only maintained normal taurine levels, but also suppressed oxidative stress and inflammation in aged mice subjected to high concentrations of PM2.5. Variations in heart rate, contractile function, cardiac oxidative stress, inflammation and fibrosis among different groups of aged mice were used to clarify the beneficial effects of taurine and fish oil supplementation. Our results not only revealed the protective effects of taurine and fish oil supplementation on heart dysfunction induced by PM2.5 exposure from the aged mice experiments and also provided new means for the elderly to resist PM2.5 pollution at the individual level.
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Affiliation(s)
- Shiyi Yang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Luyao Wen
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Xuyang Chai
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Yuanyuan Song
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Xin Chen
- The Center for Reproductive Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), 528300 Foshan, Guangdong, China
| | - Zhi-Feng Chen
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Ruijin Li
- Institute of Environmental Science, Shanxi University, Taiyuan, China
| | - Chuan Dong
- Institute of Environmental Science, Shanxi University, Taiyuan, China
| | - Zenghua Qi
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
| | - Zongwei Cai
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China.
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Lin Y, Li M, Lin R. Can urban rail transit reduce haze pollution? A spatial difference-in-differences approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:81430-81440. [PMID: 35732892 DOI: 10.1007/s11356-022-21490-6] [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: 02/10/2022] [Accepted: 06/11/2022] [Indexed: 06/15/2023]
Abstract
This paper explores the influence and mechanism of urban rail transit on haze pollution in mainland China. Based on the satellite remote sensing dataset released by the Earth Observing System of Data and Information System (EOSDIS) of the National Aeronautics and Space Administration (NASA) and urban rail transit network data of Robert Schwan, the prefecture-year urban rail transit and haze concentration-related dataset from 2001 to 2018 is collected. Considering the significant spatial autocorrelation of urban haze pollution, a spatial difference-in-differences (SDID) approach is applied to empirically investigate the influence of urban rail transit on haze pollution. The results show that the connection of urban rail transit significantly reduces the urban PM2.5 concentration, and the effect has significant regional heterogeneity. Furthermore, it is found that the substitution effect on motor vehicles is the mechanism in which urban rail transit impacts haze pollution. Based on our findings, accelerating urban rail transit network support, appropriately relaxing the subway application in some large cities, and taking comprehensive measures to attract more residents to choose subway travel is proposed.
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Affiliation(s)
- Yumei Lin
- School of Marxism, Shanghai University of International Business and Economics, Shanghai, China
| | - Meiling Li
- Department of Economics, University of Melbourne, Melbourne, Australia
| | - Ruofei Lin
- School of Economic and Management, Tongji University, Shanghai, China.
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Zhu J, Sun H, Ren W, He X, Wang L, Yang Y, Shi G. Emission Characterization of Particulate Matters from the Combustion of Pulverized Coals in a Simulated Fluidized Bed Boiler. ACS OMEGA 2022; 7:37922-37932. [PMID: 36312405 PMCID: PMC9609066 DOI: 10.1021/acsomega.2c05080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Coal-fired power generation is one of the main causes of air pollution, and the fluidized bed technology is currently a commercially used coal-fired technology. Therefore, it is of great significance to investigate the characteristics of particulate matter released from the fluidized bed boiler. In this study, lignite, bituminous coal and anthracite with particle sizes of <75 μm and 180-830 μm were selected and burned completely at 700, 800, and 900 °C for the purpose of simulating the process of pulverized coal combustion in a small sized simulated fluidized bed boiler and exploring the effects of coal rank, particle size, and burning temperature on the characteristics of the released particulate matter. The results show that, under the same mass, bituminous coal combustion releases the most PM1, PM2.5, and PM10, followed by lignite and anthracite. For all combusted coals the released PM1 accounts for half and one-third of the PM2.5 and PM10, respectively, and the released PM2.5 accounts for half of the PM10. A smaller particle size of pulverized coal and a higher burning temperature correspond to the release of more submicron to micron particulate matter. The mass concentration of released particulate matter for lignite and bituminous coal shows a bimodal distribution, with the two peak values in the ranges of 0.1-0.18 and 3.2-10 μm, respectively. As the burning temperature increases and the particle size of pulverized coal decreases, the first peak value falls and the second peak shifts to a small particle size range. This study can serve as reference for diminishing the emission of submicron to micron particulate matter by coal-fired power plants and preventing air pollution.
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Affiliation(s)
- Jintuo Zhu
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Haisong Sun
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Wanxing Ren
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Xinjian He
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Liang Wang
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Yongliang Yang
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Guoqing Shi
- Key
Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- National
Professional Laboratory for Fundamental Research of Mine Gas and Dust
Control Technology, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
- School
of Safety Engineering, China University
of Mining and Technology, Xuzhou, Jiangsu 221116, China
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Danesh Yazdi M, Wei Y, Di Q, Requia WJ, Shi L, Sabath MB, Dominici F, Schwartz J. The effect of long-term exposure to air pollution and seasonal temperature on hospital admissions with cardiovascular and respiratory disease in the United States: A difference-in-differences analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 843:156855. [PMID: 35750164 PMCID: PMC10007814 DOI: 10.1016/j.scitotenv.2022.156855] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/31/2022] [Accepted: 06/17/2022] [Indexed: 04/14/2023]
Abstract
BACKGROUND Few studies have simultaneously examined the effect of long-term exposure to air pollution and ambient temperature on the rate of hospital admissions with cardiovascular and respiratory disease using causal inference methods. METHODS We used a variation of a difference-in-difference (DID) approach to assess the effects of long-term exposure to warm-season temperature, cold-season temperature, NO2, O3, and PM2.5 on the rate of hospital admissions for cardiovascular disease (CVD), myocardial infarction (MI), ischemic stroke, and respiratory diseases from 2001 to 2016 among Medicare beneficiaries who use fee-for-service programs. We computed the rate of admissions by zip code and year. Covariates included demographic and socioeconomic variables which were obtained from the decennial Census, the American Community Survey, the Behavioral Risk Factor Surveillance System, and the Dartmouth Health Atlas. As a secondary analysis, we restricted the analysis to zip code-years that had exposure to low concentrations of our pollutants. RESULTS PM2.5 was associated with a significant increase in the absolute rate of annual admissions with cardiovascular disease by 47.71 admissions (95 % CI: 41.25-56.05) per 100,000 person-years, myocardial infarction by 7.44 admissions (95 % CI: 5.53-9.63) per 100,000 person-years, and 18.58 respiratory admissions (95 % CI: 12.42-23.72) for each one μg/m3 increase in two-year average levels. O3 significantly increased the rates of all the studied outcomes. NO2 was associated with a decreased rate of admissions with MI by 0.83 admissions (95 % CI: 0.10-1.55) per 100,000 person-years but increased rate of admissions for respiratory disease by 3.16 admissions (95 % CI: 1.34-5.24) per 100,000 person-years. Warmer cold-season temperature was associated with a decreased admissions rate for all outcomes. CONCLUSION Air pollutants, particularly PM2.5 and O3, increased the rate of hospital admissions with cardiovascular and respiratory disease among the elderly, while higher cold-season temperatures decreased the rate of admissions with these conditions.
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Affiliation(s)
- Mahdieh Danesh Yazdi
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States of America.
| | - Yaguang Wei
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States of America
| | - Qian Di
- Vanke School of Public Health, Tsinghua University, Beijing, China; Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States of America; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States of America
| | - Weeberb J Requia
- School of Public Policy and Government, Fundação Getúlio Vargas, Brasília, Distrito Federal, Brazil; Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States of America
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States of America
| | - Matthew Benjamin Sabath
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States of America
| | - Francesca Dominici
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States of America
| | - Joel Schwartz
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States of America; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States of America
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Jin T, Amini H, Kosheleva A, Danesh Yazdi M, Wei Y, Castro E, Di Q, Shi L, Schwartz J. Associations between long-term exposures to airborne PM 2.5 components and mortality in Massachusetts: mixture analysis exploration. Environ Health 2022; 21:96. [PMID: 36221093 PMCID: PMC9552465 DOI: 10.1186/s12940-022-00907-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/14/2022] [Accepted: 10/02/2022] [Indexed: 05/05/2023]
Abstract
BACKGROUND Numerous studies have documented PM2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM2.5 components. The lack of exposure measurements and high correlation among different PM2.5 components are two limitations. METHODS We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO42-, NO3-, NH4+, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM2.5 components. Total PM2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models. RESULTS Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM2.5 mass analysis, and single component associations. CONCLUSION We have found positive associations between the mixture of 15 PM2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.
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Affiliation(s)
- Tingfan Jin
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Heresh Amini
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anna Kosheleva
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mahdieh Danesh Yazdi
- Department of Family, Population, & Preventive Medicine, Program in Public Health, Stony Brook University, New York, NY, USA
| | - Yaguang Wei
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Edgar Castro
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qian Di
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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48
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Liu S, Yang X, Duan F, Zhao W. Changes in Air Quality and Drivers for the Heavy PM 2.5 Pollution on the North China Plain Pre- to Post-COVID-19. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912904. [PMID: 36232204 PMCID: PMC9566441 DOI: 10.3390/ijerph191912904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 06/03/2023]
Abstract
Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.
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49
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Li S, Meng Q, Laba C, Guan H, Wang Z, Pan Y, Wei J, Xu H, Zeng C, Wang X, Jiang M, Lu R, Guo B, Zhao X. Associations between long-term exposure to ambient air pollution and renal function in Southwest China: The China Multi-Ethnic Cohort (CMEC) study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 242:113851. [PMID: 35816844 DOI: 10.1016/j.ecoenv.2022.113851] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/21/2022] [Accepted: 07/01/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Limited studies have examined associations between air pollutants exposure and renal function, especially in China, with the most extensive chronic kidney disease (CKD) disease burden worldwide. OBJECTIVES This study examines associations between long-term exposure to ambient PM2.5, NO2, CO, O3, SO2 and renal function. METHODS We included 80,225 participants aged 30-79 years from the baseline data of the China Multi-Ethnic Cohort (CMEC) study. Three-year average concentrations of PM2.5, NO2, CO, O3, and SO2 were estimated using satellite-based spatiotemporal models. Renal function is determined by the estimated glomerular filtration rate (eGFR) using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. After adjusting for covariates, generalized propensity scores (GPS) weighting regression was used to estimate associations between ambient air pollutants and renal function. RESULTS An increase of 0.1 mg/m3 CO (OR [odds ratio] =1.20 95% CI [confidence interval], 1.05-1.37) was positively associated with CKD. An increase of 1 μg/m3 in SO2 (1.07, 1.00-1.14) concentration was positively associated with CKD. An increase of 10 μg/m3 in PM2.5 (1.17, 0.99-1.38), NO2 (1.12, 0.83-1.51) and O3 (1.10, 0.81-1.50) concentration was not associated with CKD. These effects are stronger in those younger than 65, smoking and with low BMI. CONCLUSIONS In this study, we found that long-term exposure to ambient CO and SO2 were positively associated with CKD. Gaseous pollutants should also arouse the concern of relevant departments.
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Affiliation(s)
- Sicheng Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiong Meng
- School of Public Health, Kunming Medical University, Kunming, Yunnan, China
| | - Ciren Laba
- Tibet Center for Disease Control and Prevention CN, Lhasa, Tibet, China
| | - Han Guan
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou, China
| | - Zhenghong Wang
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | | | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland at College Park, College Park, MD, USA
| | - Huan Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chunmei Zeng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xing Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Min Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Rong Lu
- Chengdu Center for Disease Control & Prevention, Chengdu, Sichuan, China.
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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50
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Piscitelli P, Miani A, Setti L, De Gennaro G, Rodo X, Artinano B, Vara E, Rancan L, Arias J, Passarini F, Barbieri P, Pallavicini A, Parente A, D'Oro EC, De Maio C, Saladino F, Borelli M, Colicino E, Gonçalves LMG, Di Tanna G, Colao A, Leonardi GS, Baccarelli A, Dominici F, Ioannidis JPA, Domingo JL. The role of outdoor and indoor air quality in the spread of SARS-CoV-2: Overview and recommendations by the research group on COVID-19 and particulate matter (RESCOP commission). ENVIRONMENTAL RESEARCH 2022; 211:113038. [PMID: 35231456 PMCID: PMC8881809 DOI: 10.1016/j.envres.2022.113038] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/24/2022] [Accepted: 02/24/2022] [Indexed: 05/29/2023]
Abstract
There are important questions surrounding the potential contribution of outdoor and indoor air quality in the transmission of SARS-CoV-2 and perpetuation of COVID-19 epidemic waves. Environmental health may be a critical component of COVID-19 prevention. The public health community and health agencies should consider the evolving evidence in their recommendations and statements, and work to issue occupational guidelines. Evidence coming from the current epidemiological and experimental research is expected to add knowledge about virus diffusion, COVID-19 severity in most polluted areas, inter-personal distance requirements and need for wearing face masks in indoor or outdoor environments. The COVID-19 pandemic has highlighted the need for maintaining particulate matter concentrations at low levels for multiple health-related reasons, which may also include the spread of SARS-CoV-2. Indoor environments represent even a more crucial challenge to cope with, as it is easier for the SARS-COV2 to spread, remain vital and infect other subjects in closed spaces in the presence of already infected asymptomatic or mildly symptomatic people. The potential merits of preventive measures, such as CO2 monitoring associated with natural or controlled mechanical ventilation and air purification, for schools, indoor public places (restaurants, offices, hotels, museums, theatres/cinemas etc.) and transportations need to be carefully considered. Hospital settings and nursing/retirement homes as well as emergency rooms, infectious diseases divisions and ambulances represent higher risk indoor environments and may require additional monitoring and specific decontamination strategies based on mechanical ventilation or air purification.
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Affiliation(s)
- Prisco Piscitelli
- Italian Society of Environmental Medicine (SIMA), Milan, Italy; UNESCO Chair on Health Education and Sustainable Development, University of Naples Federico II, Naples, Italy.
| | - Alessandro Miani
- Italian Society of Environmental Medicine (SIMA), Milan, Italy; Department of Environmental Science and Policy, University of Milan, Milan, Italy.
| | - Leonardo Setti
- Italian Society of Environmental Medicine (SIMA), Milan, Italy; Department of Industrial Chemistry, University of Bologna, Bologna, Italy.
| | - Gianluigi De Gennaro
- Italian Society of Environmental Medicine (SIMA), Milan, Italy; Department of Biology, University of Bari "Aldo Moro", Bari, Italy.
| | - Xavier Rodo
- ICREA and Climate & Health Program, ISGlobal, Barcelona, Spain.
| | - Begona Artinano
- Unit Atmospheric Pollution and POP Characterization, CIEMAT, Madrid, Spain.
| | - Elena Vara
- Department of Biochemistry and Molecular Biology, School of Medicine, Complutense University, Madrid, Spain.
| | - Lisa Rancan
- Department of Biochemistry and Molecular Biology, School of Medicine, Complutense University, Madrid, Spain.
| | - Javier Arias
- School of Medicine, Complutense University, Madrid, Spain.
| | - Fabrizio Passarini
- Interdepartmental Centre for Industrial Research "Renewable Sources, Environment, Blue Growth, Energy", University of Bologna, Rimini, Italy.
| | - Pierluigi Barbieri
- Italian Society of Environmental Medicine (SIMA), Milan, Italy; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy.
| | | | - Alessandro Parente
- Université libre de Bruxelles (ULB), Ecole Polytechnique de Bruxelles, Département d'Aéro-Thermo-Mécanique, Brussels, Belgium; Brussels Institute for Thermal-fluid systems and clean Energy (BRITE), Brussels, Belgium.
| | - Edoardo Cavalieri D'Oro
- Chemical, Biological, Radiological and Nuclear Unit (NBCRE), Italian National Fire and Rescue Service, Milan, Italy.
| | - Claudio De Maio
- Chemical, Biological, Radiological and Nuclear Unit (NBCRE), Italian National Fire and Rescue Service, Milan, Italy.
| | - Francesco Saladino
- Chemical, Biological, Radiological and Nuclear Unit (NBCRE), Italian National Fire and Rescue Service, Milan, Italy.
| | - Massimo Borelli
- UMG School of PhD Programmes, University Magna Graecia of Catanzaro, Italy.
| | - Elena Colicino
- Department of Environmental Medicine and Public Health at the Icahn School of Medicine at Mount Sinai, New York, USA.
| | | | - Gianluca Di Tanna
- BioStatistics & Data Science Division, Meta-Research and Evidence Synthesis Unit, The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
| | - Annamaria Colao
- UNESCO Chair on Health Education and Sustainable Development, University of Naples Federico II, Naples, Italy.
| | - Giovanni S Leonardi
- Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTP), London, UK.
| | - Andrea Baccarelli
- Chair of the Department of Environmental Health Sciences, Columbia University, New York, USA.
| | | | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science and of Statistics, Stanford University, Stanford, CA, USA.
| | - Josè L Domingo
- Laboratory of Toxicology and Environmental Health, Universitat Rovira I Virgili, School of Medicine, Reus, Spain.
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