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Zhang Y, Li J, Wu C, Xiao Y, Wang X, Wang Y, Chen L, Ren L, Wang J. Impacts of environmental factors on the aetiological diagnosis and disease severity of community-acquired pneumonia in China: a multicentre, hospital-based, observational study. Epidemiol Infect 2024; 152:e80. [PMID: 38721832 PMCID: PMC11131030 DOI: 10.1017/s0950268824000700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
Environmental exposures are known to be associated with pathogen transmission and immune impairment, but the association of exposures with aetiology and severity of community-acquired pneumonia (CAP) are unclear. A retrospective observational study was conducted at nine hospitals in eight provinces in China from 2014 to 2019. CAP patients were recruited according to inclusion criteria, and respiratory samples were screened for 33 respiratory pathogens using molecular test methods. Sociodemographic, environmental and clinical factors were used to analyze the association with pathogen detection and disease severity by logistic regression models combined with distributed lag nonlinear models. A total of 3323 CAP patients were included, with 709 (21.3%) having severe illness. 2064 (62.1%) patients were positive for at least one pathogen. More severe patients were found in positive group. After adjusting for confounders, particulate matter (PM) 2.5 and 8-h ozone (O3-8h) were significant association at specific lag periods with detection of influenza viruses and Klebsiella pneumoniae respectively. PM10 and carbon monoxide (CO) showed cumulative effect with severe CAP. Pollutants exposures, especially PM, O3-8h, and CO should be considered in pathogen detection and severity of CAP to improve the clinical aetiological and disease severity diagnosis.
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Affiliation(s)
- Yichunzi Zhang
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Wu
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Xiao
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinming Wang
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Wang
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lan Chen
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lili Ren
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Ministry of Education), State Key Laboratory of Respiratory Health and Multimorbidity, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianwei Wang
- National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Feng Y, Castro E, Wei Y, Jin T, Qiu X, Dominici F, Schwartz J. Long-term exposure to ambient PM2.5, particulate constituents and hospital admissions from non-respiratory infection. Nat Commun 2024; 15:1518. [PMID: 38374182 PMCID: PMC10876532 DOI: 10.1038/s41467-024-45776-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 02/05/2024] [Indexed: 02/21/2024] Open
Abstract
The association between PM2.5 and non-respiratory infections is unclear. Using data from Medicare beneficiaries and high-resolution datasets of PM2.5 and its constituents across 39,296 ZIP codes in the U.S between 2000 and 2016, we investigated the associations between annual PM2.5, PM2.5 constituents, source-specific PM2.5, and hospital admissions from non-respiratory infections. Each standard deviation (3.7-μg m-3) increase in PM2.5 was associated with a 10.8% (95%CI 10.8-11.2%) increase in rate of hospital admissions from non-respiratory infections. Sulfates (30.8%), Nickel (22.5%) and Copper (15.3%) contributed the largest weights in the observed associations. Each standard deviation increase in PM2.5 components sourced from oil combustion, coal burning, traffic, dirt, and regionally transported nitrates was associated with 14.5% (95%CI 7.6-21.8%), 18.2% (95%CI 7.2-30.2%), 20.6% (95%CI 5.6-37.9%), 8.9% (95%CI 0.3-18.4%) and 7.8% (95%CI 0.6-15.5%) increases in hospital admissions from non-respiratory infections. Our results suggested that non-respiratory infections are an under-appreciated health effect of PM2.5.
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Affiliation(s)
- Yijing Feng
- Department of Epidemiology, 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
| | - Yaguang Wei
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tingfan Jin
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xinye Qiu
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joel Schwartz
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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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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Yu T, Wong TJ, Chang JW, Lao XQ. Trajectories of body mass index before the diagnosis of type 2 diabetes in a cohort of Taiwanese adults. Obes Res Clin Pract 2024; 18:21-27. [PMID: 38331596 DOI: 10.1016/j.orcp.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/18/2024] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Although the prevalence of overweight/obesity is lower in Asian countries, the risk of type 2 diabetes (T2DM) is disproportionally higher. We identified and characterized the trajectory patterns of body mass index (BMI) before the onset of T2DM in a Taiwanese population. METHODS Using the Taiwan MJ cohort study, we sampled the health examination data of 22,934 participants, including 7618 cases of T2DM and 15,316 controls. We used latent class trajectory analysis to identify distinct groups of pre-disease BMI trajectory. To compare the trajectories of cardiometabolic risk factors among different groups, we used linear mixed-effects models. RESULTS These 22,934 participants included 13,074 men (57%) and 9860 women (43%) who were on average followed for 9.0 years. We identified three distinct pre-disease BMI trajectories in cases: "stable overweight" (n = 7016, 92.1%), "weight gain" (n = 333, 4.4%) and "obesity" (n = 269, 3.5%). The "stable overweight" group had a mean BMI of 24.6 kg/m2 at 15 years prior to diagnosis, had a 1.2 unit increase during follow-up, and had a mean BMI of 25.8 kg/m2 at the time of diagnosis. The "weight gain" group had the most increasing trends in blood pressure/low-density lipoprotein cholesterol over time. CONCLUSION The BMI trajectory patterns among individuals who later developed diabetes in Taiwan seemed comparable to that of Western populations, but our population developed T2DM at a much lower BMI. Given that most cases belong to the "stable overweight" group, we also support using a population-based strategy for diabetes prevention instead of focusing on the high risk individuals.
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Affiliation(s)
- Tsung Yu
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Tzu-Jung Wong
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taoyuan, Taiwan
| | - Jen-Wen Chang
- Department of Pharmacy, Chi Mei Medical Center, Tainan, Taiwan
| | - Xiang-Qian Lao
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong; School of Public Health, Zhengzhou University, Zhengzhou, China
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Liu C, Yu Y, Liu C, Tang L, Zhao K, Zhang P, He F, Wang M, Shi C, Lu Z, Zhang B, Wei J, Xue F, Guo X, Jia X. Effect of neighbourhood greenness on the association between air pollution and risk of stroke first onset: A case-crossover study in shandong province, China. Int J Hyg Environ Health 2023; 254:114262. [PMID: 37776760 DOI: 10.1016/j.ijheh.2023.114262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/11/2023] [Accepted: 09/19/2023] [Indexed: 10/02/2023]
Abstract
BACKGROUND Higher neighbourhood greenness is associated with beneficial health outcomes, and short-term exposure to air pollution is associated with an elevated risk of stroke onset. However, little is known about their interactions. METHODS Daily data on stroke first onset were collected from 20 counties in Shangdong Province, China, from 2013 to 2019. The enhanced vegetation index (EVI) and concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were calculated for each individual at the village or community level based on their home address to measure their neighbourhood exposure to greenness and air pollution. EVI was categorised as low or high, and a time-stratified case-crossover design was used to estimate the percent excess risk (ER%) of stroke associated with short-term exposure to air pollution. We further stratified greenness on the basis of EVI values into quartiles and introduced interaction terms between air pollutant concentrations and the median EVI values of the quartiles to assess the effect of greenness on the associations between short-term exposure and stroke. RESULTS Individuals living in the high-greenness areas had weaker associations between total stroke risk and exposure to NO2 (low greenness: ER% = 1.765% [95% CI 1.205%-2.328%]; high greenness: ER% = 0.368% [95% CI -0.252% to 0.991%]; P = 0.001), O3 (low greenness: 0.476% [95% CI 0.246%-0.706%]; high greenness: ER% = 0.085% [95% CI -0.156% to 0.327%]; P = 0.011), and SO2 (low greenness: 0.632% [95% CI 0.138%-1.129%]; high greenness: ER% = -0.177% [95% CI -0.782% to 0.431%]; P = 0.035). CONCLUSION Residence in areas with higher greenness was related to weaker associations between air pollution and stroke risk, suggesting that effectively planning green spaces can improve public health.
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Affiliation(s)
- Chao Liu
- Department of Epidemiology and Statistics, Bengbu Medical College, China
| | - Ying Yu
- Department of Physiology, School of Basic Medicine, Bengbu Medical College, China
| | - Chengrong Liu
- Department of Epidemiology and Statistics, Bengbu Medical College, China
| | - Lulu Tang
- Department of Epidemiology and Statistics, Bengbu Medical College, China
| | - Ke Zhao
- Department of Epidemiology and Statistics, Bengbu Medical College, China
| | - Peiyao Zhang
- Department of Epidemiology and Statistics, Bengbu Medical College, China
| | - Fenfen He
- Department of Epidemiology and Statistics, Bengbu Medical College, China
| | - Meng Wang
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Chunxiang Shi
- Meteorological Data Laboratory, National Meteorological Information Center, Beijing, China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, Jinan, China
| | - Bingyin Zhang
- Shandong Center for Disease Control and Prevention, Jinan, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Healthcare Big Data Research Institute, Jinan, China.
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, Jinan, China.
| | - Xianjie Jia
- Department of Epidemiology and Statistics, Bengbu Medical College, China.
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Lin C, Jiang W, Gao X, He Y, Li J, Zhou C, Yang L. Attributable risk and economic burden of pneumonia among older adults admitted to hospital due to short-term exposure to airborne particulate matter: a time-stratified case-crossover study from China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:45342-45352. [PMID: 36705825 DOI: 10.1007/s11356-023-25530-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 01/18/2023] [Indexed: 01/28/2023]
Abstract
Many studies have proven the relationship between air pollutants and respiratory diseases, but few studies have assessed the impacts of air particulate matter exposure on older patients with pneumonia. This study aimed to reveal the impacts of short-term exposure to air particulate matter on the daily number of older adult patients hospitalized due to pneumonia and calculate the economic costs attributable to this exposure. We collected inpatient data from 9 city hospitals in Sichuan Province, China, from January 1, 2018, to December 31, 2019, and calculated odds ratios and 95% confidence intervals using a time-stratified case-crossover study design and an attributable risk model to calculate the economic burden due to particulate matter pollution. It was found that for every 10 μg/m3 increase in PM2.5 and PM10 concentrations, the daily number of older adult pneumonia inpatients increased by 1.5% (95% CI: 1.010-1.021) and 1.0% (95% CI: 1.006-1.014), respectively. Those 65 ~ 79 years old were more susceptible to air particulate pollutants (P < 0.05). During the study period, the total hospitalization costs and out-of-pocket expenses attributable to PM2.5 and PM10 exposure were 44.60 million CNY (6.22%) and 16.03 million CNY (6.21%), respectively, with PM2.5 being the primary influencing factor. This study revealed the relationship between particulate matter pollution and pneumonia among older adults. The role of policies to limit particulate matter concentrations in reducing disease burden among older adults can be further explored.
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Affiliation(s)
- Chengwei Lin
- School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China
| | - Wanyanhan Jiang
- School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China
| | - Xi Gao
- School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China
| | - Yi He
- School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China
| | - Jia Li
- School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China
| | - Chengchao Zhou
- School of Public Health, Shandong University, Jinan, 250100, Shandong, China
| | - Lian Yang
- School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China.
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