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Chen H, Xiao M. Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022. BMC Infect Dis 2024; 24:432. [PMID: 38654199 PMCID: PMC11036656 DOI: 10.1186/s12879-024-09301-4] [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: 11/28/2023] [Accepted: 04/07/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Influenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations. METHODS The multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise. RESULTS During the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2-4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0-1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time. CONCLUSION The ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.
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Affiliation(s)
- Huayong Chen
- School of Public Health, Research Center for Medical and Social Development, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, 400016, Chongqing, P. R. China
| | - Mimi Xiao
- School of Public Health, Research Center for Medical and Social Development, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, 400016, Chongqing, P. R. China.
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Kriit HK, Forsberg B, Nilsson Sommar J. Increase in sick leave episodes from short-term fine particulate matter exposure: A case-crossover study in Stockholm, Sweden. ENVIRONMENTAL RESEARCH 2024; 244:117950. [PMID: 38104916 DOI: 10.1016/j.envres.2023.117950] [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: 10/24/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023]
Abstract
Air pollution's short-term effects on a wide range of health outcomes have been studied extensively, primarily focused on vulnerable groups (e.g., children and the elderly). However, the air pollution effects on the adult working population through sick leave have received little attention. This study aims to 1) estimate the associations between particulate matter ≤2.5 μm3 (PM2.5) and sick leave episodes and 2) calculate the attributable number of sick leave days and the consequential productivity loss in the City of Stockholm, Sweden. Individual level daily sick leave data was obtained from Statistics Sweden for the years 2011-2019. Daily average concentrations of PM2.5 were obtained from the main urban background monitoring station in Stockholm. A case-crossover study design was applied to estimate the association between short-term PM2.5 and onset of sick leave episodes. Conditional logistic regression was used to estimate the relative increase in odds of onset per 10 μg/m3 of PM2.5, adjusting for temperature, season, and pollen. A human capital method was applied to estimate the PM2.5 attributable productivity loss. In total, 1.5 million (M) individual sick leave occurrences were studied. The measured daily mean PM2.5 concentration was 4.2 μg/m3 (IQR 3.7 μg/m3). The odds of a sick leave episode was estimated to increase by 8.5% (95% CI: 7.8-9.3) per 10 μg/m3 average exposure 2-4 days before. Sub-group analysis showed that private sector and individuals 15-24 years old had a lower increase in odds of sick leave episodes in relation to PM2.5 exposure. In Stockholm, 4% of the sick leave episodes were attributable to PM2.5 exposure, corresponding to €17 M per year in productivity loss. Our study suggests a positive association between PM2.5 and sick leave episodes in a low exposure area.
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Affiliation(s)
- Hedi Katre Kriit
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; Health Economics and Health Financing Group, Institute of Global Health, Heidelberg University, Heidelberg, Germany; Climate-Sensitive Infectious Disease Lab, Interdisciplinary Centre of Scientific Computing, Heidelberg University, Heidelberg, Germany; Climate-smart Health Systems, Institute of Global Health, Heidelberg University, Heidelberg, Germany.
| | - Bertil Forsberg
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Johan Nilsson Sommar
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
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Smyth T, Jaspers I. Diesel exhaust particles induce polarization state-dependent functional and transcriptional changes in human monocyte-derived macrophages. Am J Physiol Lung Cell Mol Physiol 2024; 326:L83-L97. [PMID: 38084400 PMCID: PMC11279754 DOI: 10.1152/ajplung.00085.2023] [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: 04/17/2023] [Revised: 10/30/2023] [Accepted: 11/23/2023] [Indexed: 01/10/2024] Open
Abstract
Macrophage populations exist on a spectrum between the proinflammatory M1 and proresolution M2 states and have demonstrated the ability to reprogram between them after exposure to opposing polarization stimuli. Particulate matter (PM) has been repeatedly linked to worsening morbidity and mortality following respiratory infections and has been demonstrated to modify macrophage function and polarization. The purpose of this study was to determine whether diesel exhaust particles (DEP), a key component of airborne PM, would demonstrate polarization state-dependent effects on human monocyte-derived macrophages (hMDMs) and whether DEP would modify macrophage reprogramming. CD14+CD16- monocytes were isolated from the blood of healthy human volunteers and differentiated into macrophages with macrophage colony-stimulating factor (M-CSF). Resulting macrophages were left unpolarized or polarized into the proresolution M2 state before being exposed to DEP, M1-polarizing conditions (IFN-γ and LPS), or both and tested for phagocytic function, secretory profile, gene expression patterns, and bioenergetic properties. Contrary to previous reports, we observed a mixed M1/M2 phenotype in reprogrammed M2 cells when considering the broader range of functional readouts. In addition, we determined that DEP exposure dampens phagocytic function in all polarization states while modifying bioenergetic properties in M1 macrophages preferentially. Together, these data suggest that DEP exposure of reprogrammed M2 macrophages results in a highly inflammatory, highly energetic subpopulation of macrophages that may contribute to the poor health outcomes following PM exposure during respiratory infections.NEW & NOTEWORTHY We determined that reprogramming M2 macrophages in the presence of diesel exhaust particles (DEP) results in a highly inflammatory mixed M1/M2 phenotype. We also demonstrated that M1 macrophages are particularly vulnerable to particulate matter (PM) exposure as seen by dampened phagocytic function and modified bioenergetics. Our study suggests that PM causes reprogrammed M2 macrophages to become a highly energetic, highly secretory subpopulation of macrophages that may contribute to negative health outcomes observed in humans after PM exposure.
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Affiliation(s)
- Timothy Smyth
- Curriculum in Toxicology & Environmental Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
- Center for Environmental Medicine, Asthma, and Lung Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Ilona Jaspers
- Curriculum in Toxicology & Environmental Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
- Center for Environmental Medicine, Asthma, and Lung Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
- Department of Pediatrics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
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Sun R, Tao J, Tang N, Chen Z, Guo X, Zou L, Zhou J. Air Pollution and Influenza: A Systematic Review and Meta-Analysis. IRANIAN JOURNAL OF PUBLIC HEALTH 2024; 53:1-11. [PMID: 38694869 PMCID: PMC11058381 DOI: 10.18502/ijph.v53i1.14678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/04/2023] [Indexed: 05/04/2024]
Abstract
Background Influenza is the first infectious disease that implements global monitoring and is one of the major public health issues in the world. Air pollutants have become an important global public health issue, in recent years, and much epidemiological and clinical evidence has shown that air pollutants are associated with respiratory diseases. Methods We comprehensively searched articles published up to 15 November 2022 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Database of Chinese sci-tech periodicals, and Wanfang Database. The search strategies were based on keyword combinations related to influenza and air pollutants. The air pollutants included particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Meta-analysis was performed using the R programming language (R4.2.1). Results A total of 2926 records were identified and 1220 duplicates were excluded. Finally, 19 studies were included in the meta-analysis according to inclusion and exclusion criteria. We observed a significant association between partial air pollutants (PM2.5, NO2, PM10 and SO2) and the incidence risk of influenza. The RRs were 1.0221 (95% CI: 1.0093~1.0352), 1.0395 (95% CI: 1.0131~1.0666), 1.007 (95% CI: 1.0009~1.0132), and 1.0352 (95% CI. 1.0076~1.0635), respectively. However, there was no significant relationship between CO and O3 exposure and influenza, and the RRs were 1.2272 (95% CI: 0.9253~1.6275) and 1.0045 (95% CI: 0.9930~1.0160), respectively. Conclusion Exposure to PM2.5, NO2, PM10, and SO2 was significantly associated with influenza, which may be risk factors for influenza. The association of CO and O3 with influenza needs further investigation.
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Affiliation(s)
- Rui Sun
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Juan Tao
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Na Tang
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Zhijun Chen
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Xiaowei Guo
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Lianhong Zou
- Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan 410013, P.R. China
| | - Junhua Zhou
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
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Yu LJ, Li XL, Wang YH, Zhang HY, Ruan SM, Jiang BG, Xu Q, Sun YS, Wang LP, Liu W, Yang Y, Fang LQ. Short-Term Exposure to Ambient Air Pollution and Influenza: A Multicity Study in China. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:127010. [PMID: 38078423 PMCID: PMC10711743 DOI: 10.1289/ehp12146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/02/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Air pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts. OBJECTIVE Our aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities. METHODS Based on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), NO 2 , SO 2 , CO, and O 3 ], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage. RESULTS A total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO 2 and NO 2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10 μ g / m 3 increase in the concentration at each of the lags of 1-7 d. Only NO 2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months. DISCUSSION Increased exposure to ambient air pollutants, particularly NO 2 , is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.
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Affiliation(s)
- Lin-Jie Yu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Xin-Lou Li
- Department of Medical Research, Key Laboratory of Environmental Sense Organ Stress and Health of the Ministry of Environmental Protection, PLA Strategic Support Force Medical Center, Beijing, P. R. China
| | - Yan-He Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Hai-Yang Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Shi-Man Ruan
- Jinan Center for Disease Control and Prevention, Jinan, P. R. China
| | - Bao-Gui Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Qiang Xu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Yan-Song Sun
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Li-Ping Wang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, P. R. China
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
| | - Yang Yang
- Department of Statistics, Franklin College of Arts and Science, University of Georgia, Athens, Georgia, USA
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China
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Colonna KJ, Alahmad B, Choma EF, Albahar S, Al-Hemoud A, Kinney PL, Koutrakis P, Evans JS. Acute exposure to total and source-specific ambient fine particulate matter and risk of respiratory disease hospitalization in Kuwait. ENVIRONMENTAL RESEARCH 2023; 237:117070. [PMID: 37666316 DOI: 10.1016/j.envres.2023.117070] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023]
Abstract
Many epidemiologic studies concerned with acute exposure to ambient PM2.5 have reported positive associations for respiratory disease hospitalization. However, few studies have investigated this relationship in Kuwait and extrapolating results from other regions may involve considerable uncertainty due to variations in concentration levels, particle sources and composition, and population characteristics. Local studies can provide evidence for strategies to reduce risks from episodic exposures to high levels of ambient PM2.5 and generating hypotheses for evaluating health risks from chronic exposures. Therefore, using speciated PM2.5 data from local samplers, we analyzed the impact of daily total and source-specific PM2.5 exposure on respiratory hospitalizations in Kuwait using a case-crossover design with conditional quasi-Poisson regression. Total and source-specific ambient PM2.5 were modeled using 0-5-day cumulative distributed lags. For total PM2.5, we observed a 0.16% (95% confidence interval [CI] = 0.05, 0.27%) increase in risk for respiratory hospitalization per 1 μg/m3 increase in concentration. Of the source factors assessed, dust demonstrated a statistically significant increase in risk (0.16%, 95% CI = 0.04, 0.29%), and the central estimate for regional PM2.5 was positive (0.11%) but not statistically significant (95% CI = -0.11, 0.33%). No effect was observed from traffic emissions and 'other' source factors. When hospitalizations were stratified by sex, nationality, and age, we found that female, Kuwaiti national, and adult groups had higher effect estimates. These results suggest that exposure to ambient PM2.5 is harmful in Kuwait and provide some evidence of differential toxicity and effect modification depending on the PM2.5 source and population affected.
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Affiliation(s)
- Kyle J Colonna
- Department of Environmental Health, Harvard University T.H. Chan School of Public Health, Boston, MA, USA.
| | - Barrak Alahmad
- Department of Environmental Health, Harvard University T.H. Chan School of Public Health, Boston, MA, USA; Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Ernani F Choma
- Department of Environmental Health, Harvard University T.H. Chan School of Public Health, Boston, MA, USA
| | - Soad Albahar
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Ali Al-Hemoud
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait City, Kuwait
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Petros Koutrakis
- Department of Environmental Health, Harvard University T.H. Chan School of Public Health, Boston, MA, USA
| | - John S Evans
- Department of Environmental Health, Harvard University T.H. Chan School of Public Health, Boston, MA, USA
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Yang J, Zhang T, Yang L, Han X, Zhang X, Wang Q, Feng L, Yang W. Association between ozone and influenza transmissibility in China. BMC Infect Dis 2023; 23:763. [PMID: 37932657 PMCID: PMC10626750 DOI: 10.1186/s12879-023-08769-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. METHODS We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility. RESULTS Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text]. CONCLUSIONS O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.
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Affiliation(s)
- Jiao Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Liuyang Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Xuan Han
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Xingxing Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Qing Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China.
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China.
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China.
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China.
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Verma D, Nichakornpong N, Kraiwitwattana U, Okhawilai M, Kasemsiri P, Potiyaraj P, Rangkupan R. High performance filtration membranes from electrospun poly (3-hydroxybutyrate)-based fiber membranes for fine particulate protection. ENVIRONMENTAL RESEARCH 2023; 231:116144. [PMID: 37201705 DOI: 10.1016/j.envres.2023.116144] [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: 02/09/2023] [Revised: 04/19/2023] [Accepted: 05/13/2023] [Indexed: 05/20/2023]
Abstract
PM2.5 (particulate matter with a size of <2.5 μm) pollution has become a critical issue owing to its adverse health effects, including bronchitis, pneumonopathy, and cardiovascular diseases. Globally, around 8.9 million premature casualties related to exposure to PM2.5 were reported. Face masks are the only option that may restrict exposure to PM2.5. In this study, a PM2.5 dust filter was developed via the electrospinning technique using the poly (3-hydroxybutyrate) (PHB) biopolymer. Smooth and continuous fibers without beads were formed. The PHB membrane was further characterized, and the effects of the polymer solution concentration, applied voltage, and needle-to-collector distance were analyzed via the design of experiments technique, with three factors and three levels. The concentration of the polymer solution had the most significant effect on the fiber size and the porosity. The fiber diameter increased with increasing concentration, but decreases the porosity. The sample with a fiber diameter of ∼600 nm exhibited a higher PM2.5 filtration efficiency than the samples with a diameter of 900 nm, according to an ASTM F2299-based test. The PHB fiber mats fabricated at a concentration of 10%w/v, applied voltage of 15 kV, and needle tip-to-collector distance of 20 cm exhibited a high filtration efficiency of 95% and a pressure drop of <5 mmH2O/cm2. The tensile strength of the developed membranes ranged from 2.4 to 5.01 MPa, higher than those of the mask filters available in the market. Therefore, the prepared electrospun PHB fiber mats have great potential for the manufacture of PM2.5 filtration membranes.
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Affiliation(s)
- Deepak Verma
- International Graduate Program of Nanoscience & Technology, Chulalongkorn University, Bangkok 10330, Thailand
| | - Nichakan Nichakornpong
- Department of Materials Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Unchalee Kraiwitwattana
- Department of Materials Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Manunya Okhawilai
- Metallurgy and Materials Science Research Institute, Chulalongkorn University, Bangkok, 10330, Thailand; Research Unit on Polymeric Materials for Medical Devices, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Pornnapa Kasemsiri
- Sustainable Infrastructure Research and Development Center and Department of Chemical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Pranut Potiyaraj
- Department of Materials Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand; Metallurgy and Materials Science Research Institute, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Ratthapol Rangkupan
- Metallurgy and Materials Science Research Institute, Chulalongkorn University, Bangkok, 10330, Thailand
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Amnuaylojaroen T, Parasin N. Perspective on Particulate Matter: From Biomass Burning to the Health Crisis in Mainland Southeast Asia. TOXICS 2023; 11:553. [PMID: 37505519 PMCID: PMC10384564 DOI: 10.3390/toxics11070553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/29/2023]
Abstract
Air pollution, notably particulate matter pollution, has become a serious concern in Southeast Asia in recent decades. The combustion of biomass has been recognized to considerably increase air pollution problems from particulate matter in this region. Consequently, its effect on people in this area is significant. This article presents a synthesis of several datasets obtained from satellites, global emissions, global reanalysis, and the global burden of disease (GBD) to highlight the air quality issue and emphasize the health crisis in mainland Southeast Asia. We found that the death rates of people have increased significantly along with the rise of hotspots in mainland Southeast Asia over the last two decades (2000-2019). In comparison, most countries saw a considerable increase in the predicted fatality rates associated with chronic respiratory illnesses during those two decades. Several reports highlight the continued prevalence of chronic respiratory diseases likely related to poor air quality in Southeast Asia.
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Affiliation(s)
- Teerachai Amnuaylojaroen
- School of Energy and Environment, University of Phayao, Phayao 56000, Thailand
- Atmospheric Pollution and Climate Research Unit, School of Energy and Environment, University of Phayao, Phayao 56000, Thailand
| | - Nichapa Parasin
- School of Allied Health Science, University of Phayao, Phayao 56000, Thailand
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10
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Zhang R, Li Y, Bi P, Wu S, Peng Z, Meng Y, Wang Y, Wang S, Huang Y, Liang J, Wu J. Seasonal associations between air pollutants and influenza in 10 cities of southern China. Int J Hyg Environ Health 2023; 252:114200. [PMID: 37329817 DOI: 10.1016/j.ijheh.2023.114200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 05/20/2023] [Accepted: 06/05/2023] [Indexed: 06/19/2023]
Abstract
Few studies have explored the associations between air pollutants and influenza across seasons, especially at large scales. This study aimed to evaluate seasons' modifying effects on associations between air pollutants and influenza from 10 cities of southern China. Through scientific evidence, it provides mitigation and adaptation strategies with practical guidelines to local health authorities and environmental protection agencies. Daily influenza incidence, meteorological, and air pollutants data from 2016 to 2019 were collected. Quasi-Poisson regression with a distributed lag nonlinear model was used to evaluate city-specific air pollutants and influenza associations. Meta-analysis was used to pool site-specific estimates. Attributable fractions (AFs) of influenza incidence due to pollutants were calculated. Stratified analyses were conducted by season, sex, and age. Overall, the cumulative relative risk (CRR) of influenza incidence for a 10-unit increase in PM2.5, PM10, SO2, NO2, and CO was 1.45 (95% CI: 1.25, 1.68), 1.53 (95% CI: 1.29, 1.81), 1.87 (95% CI: 1.40, 2.48), 1.74 (95% CI: 1.49, 2.03), and 1.19 (95% CI: 1.04, 1.36), respectively. Children aged 0-17 were more sensitive to air pollutants in spring and winter. PM10 had greater effect on influenza than PM2.5 in autumn, winter, and overall, lesser in spring. The overall AF due to PM2.5, PM10, SO2, NO2, and CO was 4.46% (95% eCI: 2.43%, 6.43%), 5.03% (95% eCI: 2.33%, 7.56%), 5.36% (95% eCI: 3.12%, 7.58%), 24.88% (95% eCI: 18.02%, 31.67%), and 23.22% (95% eCI: 17.56%, 28.61%), respectively. AF due to O3 was 10.00% (95% eCI: 4.76%, 14.95%) and 3.65% (95% eCI: 0.50%, 6.59%) in spring and summer, respectively. The seasonal variations in the associations between air pollutants and influenza in southern China would provide evidence to service providers for tailored intervention, especially for vulnerable populations.
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Affiliation(s)
- Rui Zhang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yonghong Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Peng Bi
- School of Public Health, The University of Adelaide, South Australia, Australia
| | - Siyuan Wu
- Sprott School of Business, Carleton University, Ottawa Ontario, Canada
| | - Zhibin Peng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yujie Meng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yu Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Songwang Wang
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yushu Huang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Juan Liang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jing Wu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Chinese Center for Disease Control and Prevention, Beijing, China.
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11
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Zhong S, Ma F, Gao J, Bian L. Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105865. [PMID: 37239591 DOI: 10.3390/ijerph20105865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/27/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023]
Abstract
Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.
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Affiliation(s)
- Shiran Zhong
- Department of Geography, University of Western Ontario, London, ON N6A 3K7, Canada
| | - Fenglong Ma
- College of Information Sciences and Technology, Pennsylvania State University, University Park, State College, PA 16802, USA
| | - Jing Gao
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Ling Bian
- Department of Geography, University at Buffalo, The State University of New York, Buffalo, NY 14261, USA
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12
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Chen D, Sun X, Cheke RA. Inferring a Causal Relationship between Environmental Factors and Respiratory Infections Using Convergent Cross-Mapping. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050807. [PMID: 37238562 DOI: 10.3390/e25050807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023]
Abstract
The incidence of respiratory infections in the population is related to many factors, among which environmental factors such as air quality, temperature, and humidity have attracted much attention. In particular, air pollution has caused widespread discomfort and concern in developing countries. Although the correlation between respiratory infections and air pollution is well known, establishing causality between them remains elusive. In this study, by conducting theoretical analysis, we updated the procedure of performing the extended convergent cross-mapping (CCM, a method of causal inference) to infer the causality between periodic variables. Consistently, we validated this new procedure on the synthetic data generated by a mathematical model. For real data in Shaanxi province of China in the period of 1 January 2010 to 15 November 2016, we first confirmed that the refined method is applicable by investigating the periodicity of influenza-like illness cases, an air quality index, temperature, and humidity through wavelet analysis. We next illustrated that air quality (quantified by AQI), temperature, and humidity affect the daily influenza-like illness cases, and, in particular, the respiratory infection cases increased progressively with increased AQI with a time delay of 11 days.
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Affiliation(s)
- Daipeng Chen
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
- Mathematical Institute, Leiden University, 2333 CA Leiden, The Netherlands
| | - Xiaodan Sun
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Robert A Cheke
- Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Chatham ME4 4TB, Kent, UK
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13
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Ma P, Zhou N, Wang X, Zhang Y, Tang X, Yang Y, Ma X, Wang S. Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China. ENVIRONMENTAL RESEARCH 2023; 219:115100. [PMID: 36565842 DOI: 10.1016/j.envres.2022.115100] [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: 10/04/2022] [Revised: 12/10/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Air pollution was indicated to be a key factor contributing to the aggressive spread of influenza viruses, whereas uncertainty still exists regarding to whether distinctions exist between influenza subtypes. Our study quantified the impact of five air pollutants on influenza subtype outbreaks in Shenzhen, China, a densely populated and highly urbanized megacity. Daily influenza outbreak data of laboratory-confirmed positive cases were obtained from the Shenzhen CDC, from May 1, 2013 to Dec 31, 2015. Concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matters ≤2.5 μm (PM2.5), particulate matters ≤10 μm (PM10), and ozone (O3), were retrieved from the 18 national monitoring stations. The generalized additive model (GAM) and distributed lag non-linear model (DLNM) were used to calculate the concentration-response relationships between environmental inducers and outbreak epidemics, respectively for influenza A (Flu-A) and B (Flu-B). There were 1687 positive specimens were confirmed during the study period. The cold season was restricted from Nov. 4th to Apr. 20th, covering all seasons other than the long-lasting summer. Relatively heavy fine particle matter (PM2.5) and NO2 pollution was observed in cold months, with mean concentrations of 46.06 μg/m3 and 40.03 μg/m3, respectively. Time-series analysis indicated that high concentrations of NO2, PM2.5, PM10, and O3 were associated with more influenza outbreaks at short lag periods (0-5 d). Although more Flu-B (679 cases) epidemics occurred than Flu-A (382 cases) in the cold season, Flu-A generally showed higher susceptibility to air pollutants. A 10 μg/m3 increment in concentrations of PM2.5, PM10, and O3 at lag 04, was associated with a 2.103 (95%CI: 1.528-2.893), 1.618 (95%CI: 1.311-1.996), and 1.569 (95%CI: 1.214-2.028) of the relative risk (RR) of Flu-A, respectively. A 5 μg/m3 increase in NO2 was associated with higher risk of Flu-A at lag 03 (RR = 1.646, 95%CI: 1.295-2.092) and of Flu-B at lag 04 (RR = 1.319, 95%CI: 1.095-1.588). Nevertheless, barely significant effect of particulate matters (PM2.5, PM10) on Flu-B and SO2 on both subtypes was detected. Further, the effect estimates of NO2 increased for both subtypes when coexisting with other pollutants. This study provides evidence that declining concentrations of main pollutants including NO2, O3, and particulate matters, could substantially decrease influenza risk in subtropical Shenzhen, especially for influenza A.
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Affiliation(s)
- Pan Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China; Chengdu Plain Urban Meteorology and Environment Scientific Observation and Research Station of Sichuan Province, Chengdu, 610225, Sichuan, China.
| | - Ning Zhou
- The First People's Hospital of Lanzhou, Lanzhou, 730050, Gansu, China.
| | - Xinzi Wang
- Meteorological Bureau of Jinnan District, Tianjin, 300350, China.
| | - Ying Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China; Chengdu Plain Urban Meteorology and Environment Scientific Observation and Research Station of Sichuan Province, Chengdu, 610225, Sichuan, China.
| | - Xiaoxin Tang
- Shenzhen National Climate Observatory, Shenzhen, 518000, China.
| | - Yang Yang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Xiaolu Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Shigong Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
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14
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Lian XY, Xi L, Zhang ZS, Yang LL, Du J, Cui Y, Li HJ, Zhang WX, Wang C, Liu B, Yang YN, Cui F, Lu QB. Impact of air pollutants on influenza-like illness outpatient visits under COVID-19 pandemic in the subcenter of Beijing, China. J Med Virol 2023; 95:e28514. [PMID: 36661040 DOI: 10.1002/jmv.28514] [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: 10/10/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023]
Abstract
This study aimed to explore the association between air pollutants and outpatient visits for influenza-like illnesses (ILI) under the coronavirus disease 2019 (COVID-19) stage in the subcenter of Beijing. The data on ILI in the subcenter of Beijing from January 1, 2018 to December 31, 2020 were obtained from the Beijing Influenza Surveillance Network. A generalized additive Poisson model was applied to examine the associations between the concentrations of air pollutants and daily outpatient visits for ILI when controlling meteorological factors and temporal trend. A total of 171 943 ILI patients were included. In the pre-coronavirus disease 2019 (COVID-19) stage, an increased risk of ILI outpatient visits was associated to a high air quality index (AQI) and the high concentrations of particulate matter less than 2.5 (PM2.5 ), particulate matter 10 (PM10 ), sulphur dioxide (SO2 ), nitrogen dioxide (NO2 ), and carbon monoxide (CO), and a low concentration of ozone (O3 ) on lag0 day and lag1 day, while a higher increased risk of ILI outpatient visits was observed by the air pollutants in the COVID-19 stage on lag0 day. Except for PM10 , the concentrations of other air pollutants on lag1 day were not significantly associated with an increased risk of ILI outpatient visits during the COVID-19 stage. The findings that air pollutants had enhanced immediate effects and diminished lag-effects on the risk of ILI outpatient visits during the COVID-19 pandemic, which is important for the development of public health and environmental governance strategies.
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Affiliation(s)
- Xin Yao Lian
- Department of Laboratorial Science and Technology, Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Lu Xi
- Beijing Tongzhou Center for Diseases Prevention and Control, Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing, People's Republic of China
| | - Zhong Song Zhang
- Department of Laboratorial Science and Technology, Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Li Li Yang
- Beijing Tongzhou Center for Diseases Prevention and Control, Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing, People's Republic of China
| | - Juan Du
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, People's Republic of China
| | - Yan Cui
- Beijing Tongzhou Center for Diseases Prevention and Control, Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing, People's Republic of China
| | - Hong Jun Li
- Beijing Tongzhou Center for Diseases Prevention and Control, Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing, People's Republic of China
| | - Wan Xue Zhang
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, People's Republic of China
| | - Chao Wang
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, People's Republic of China
| | - Bei Liu
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, People's Republic of China
| | - Yan Na Yang
- Center for Disease Control and Prevention of Beijing Economic and Technological Development Area, Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing, People's Republic of China
| | - Fuqiang Cui
- Department of Laboratorial Science and Technology, Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China.,Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, People's Republic of China
| | - Qing Bin Lu
- Department of Laboratorial Science and Technology, Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China.,Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, People's Republic of China
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15
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Burbank AJ. Risk Factors for Respiratory Viral Infections: A Spotlight on Climate Change and Air Pollution. J Asthma Allergy 2023; 16:183-194. [PMID: 36721739 PMCID: PMC9884560 DOI: 10.2147/jaa.s364845] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Climate change has both direct and indirect effects on human health, and some populations are more vulnerable to these effects than others. Viral respiratory infections are most common illnesses in humans, with estimated 17 billion incident infections globally in 2019. Anthropogenic drivers of climate change, chiefly the emission of greenhouse gases and toxic pollutants from burning of fossil fuels, and the consequential changes in temperature, precipitation, and frequency of extreme weather events have been linked with increased susceptibility to viral respiratory infections. Air pollutants like nitrogen dioxide, particulate matter, diesel exhaust particles, and ozone have been shown to impact susceptibility and immune responses to viral infections through various mechanisms, including exaggerated or impaired innate and adaptive immune responses, disruption of the airway epithelial barrier, altered cell surface receptor expression, and impaired cytotoxic function. An estimated 90% of the world's population is exposed to air pollution, making this a topic with high relevance to human health. This review summarizes the available epidemiologic and experimental evidence for an association between climate change, air pollution, and viral respiratory infection.
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Affiliation(s)
- Allison J Burbank
- Division of Pediatric Allergy and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Correspondence: Allison J Burbank, 5008B Mary Ellen Jones Building, 116 Manning Dr, CB#7231, Chapel Hill, NC, 27599, USA, Tel +1 919 962 5136, Fax +1 919 962 4421, Email
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16
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Wang X, Cai J, Liu X, Wang B, Yan L, Liu R, Nie Y, Wang Y, Zhang X, Zhang X. Impact of PM 2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10426-10443. [PMID: 36076137 PMCID: PMC9458314 DOI: 10.1007/s11356-022-22814-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/27/2022] [Indexed: 05/03/2023]
Abstract
Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM2.5 was the highest (621 μg/m3), the relative risk was the highest (RR: 2.39, 95% CI: 1.10-5.17). For extremely high concentration PM2.5 (348 μg/m3), analysis of cumulative lag effect showed statistical significance from cumulative lag0-1 to lag0-6 day, and the minimum cumulative lag effect appeared in lag0-2 (RR: 0.760, 95% CI: 0.655-0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O3 concentration was 310 μg/m3, and the RR was 1.65(1.26,2.15), when O3 concentration was 0 μg/m3. The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0-7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.
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Affiliation(s)
- Xue Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Jianning Cai
- The Department of Epidemic Treating and Preventing, Center for Disease Prevention and Control of Shijiazhuang City, Shijiazhuang, China
| | - Xuehui Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Binhao Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Lina Yan
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Ran Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Yaxiong Nie
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Yameng Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Xinzhu Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Xiaolin Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China.
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17
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Min KD, Kim SY, Cho SI. Ambient PM 2.5 exposures could increase risk of tuberculosis recurrence. Environ Health Prev Med 2023; 28:48. [PMID: 37648454 PMCID: PMC10480611 DOI: 10.1265/ehpm.23-00131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 07/28/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND The effect of ambient PM2.5 on the incidence of tuberculosis (TB) has been investigated in epidemiological studies. However, they did not separately study new and relapsed TB infection and focused on relatively short-term effects of PM2.5. In this regard, we examined the associations of long-term PM2.5 exposures with both new and relapsed TB incidences in South Korea, where the disease burden of TB is greatest among high-income countries. METHODS An area-level ecological study of 250 districts was conducted from 2015 to 2019. Age- and sex-standardized TB incidence ratios for each district and year were used as outcome variables, and their associations with PM2.5 concentrations for one to five-year average were examined. Negative binomial regression models incorporating spatiotemporal autocorrelation were employed using integrated nested Laplace approximations. Stratified analyses were conducted by type of TB (total, new, and relapsed cases). RESULTS Districts with higher PM2.5 concentrations tended to have significantly higher TB recurrence rate. The relative risks per 10 µg/m3 PM2.5 increase were 1.218 (95% credible interval 1.051-1.411), 1.260 (1.039-1.527) and 1.473 (1.015-2.137) using the two, three and five-year average PM2.5 exposures, respectively. CONCLUSIONS The results imply that interventions for reducing air pollution might help prevent TB recurrence.
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Affiliation(s)
- Kyung-Duk Min
- College of Veterinary Medicine, Chungbuk National University
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center
| | - Sung-il Cho
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University
- Institute of Health and Environment, Graduate School of Public Health, Seoul National University
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18
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Seah A, Loo LH, Jamali N, Maiwald M, Aik J. The influence of air quality and meteorological variations on influenza A and B virus infections in a paediatric population in Singapore. ENVIRONMENTAL RESEARCH 2023; 216:114453. [PMID: 36183790 DOI: 10.1016/j.envres.2022.114453] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 09/11/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION Influenza is an important cause of paediatric illness across the globe. However, information about the relationships between air pollution, meteorological variability and paediatric influenza A and B infections in tropical settings is limited. METHODS We analysed all daily reports of influenza A and B infections in children <5 years old obtained from the largest specialist women and children's hospital in Singapore. In separate negative binomial regression models, we assessed the dependence of paediatric influenza A and B infections on air quality and meteorological variability, using multivariable fractional polynomial modelling and adjusting for time-varying confounders. RESULTS Approximately 80% of 7329 laboratory-confirmed reports were caused by influenza A. We observed positive associations between sulphur dioxide (SO2) exposure and the subsequent risk of infection with both influenza types. We observed evidence of a harvesting effect of SO2 on Influenza A but not Influenza B. Ambient temperature was associated with a decline in influenza A reports (Relative Risk at lag 5 [RRlag5]: 0.949, 95% CI: 0.916-0.983). Rainfall was positively associated with a subsequent increase in influenza A reports (RRlag3: 1.044, 95% CI: 1.017-1.071). Nitrogen dioxide (NO2) concentration was positively associated with influenza B reports (RRlag5: 1.015, 95% CI: 1.005-1.025). There was a non-linear association between CO and influenza B reports. Absolute humidity increased the ensuing risk of influenza B (RRlag5: 4.799, 95% CI: 2.277-10.118). Influenza A and B infections displayed dissimilar but predictable within-year seasonal patterns. CONCLUSIONS We observed different independent associations between air quality and meteorological variability with paediatric influenza A and B infections. Anticipated seasonal infection peaks and variations in air quality and meteorological parameters can inform the timing of community measures aimed at reducing influenza infection risk.
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Affiliation(s)
- Annabel Seah
- Environmental Epidemiology and Toxicology Division, National Environment Agency, 40 Scotts Road, Environment Building, #13-00, 228231, Singapore.
| | - Liat Hui Loo
- Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, 100 Bukit Timah Road, 229899, Singapore; Duke-NUS Graduate Medical School, 8 College Road, 169857, Singapore.
| | - Natasha Jamali
- Environmental Monitoring and Modelling Division, National Environment Agency, 40 Scotts Road, #13-00, 228231, Singapore.
| | - Matthias Maiwald
- Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, 100 Bukit Timah Road, 229899, Singapore; Duke-NUS Graduate Medical School, 8 College Road, 169857, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, NUHS Tower Block, 1E Kent Ridge Road Level 11, 119228, Singapore.
| | - Joel Aik
- Environmental Epidemiology and Toxicology Division, National Environment Agency, 40 Scotts Road, Environment Building, #13-00, 228231, Singapore; Pre-Hospital & Emergency Research Centre, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
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19
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Yang J, Yang Z, Qi L, Li M, Liu D, Liu X, Tong S, Sun Q, Feng L, Ou CQ, Liu Q. Influence of air pollution on influenza-like illness in China: a nationwide time-series analysis. EBioMedicine 2022; 87:104421. [PMID: 36563486 PMCID: PMC9800295 DOI: 10.1016/j.ebiom.2022.104421] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/21/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Evidence concerning effects of air pollution on influenza-like illness (ILI) from multi-center is limited and little is known about how regional factors might modify this relationship. METHODS In this ecological study, ILI cases defined as outpatients with temperature ≥38 °C, accompanied by cough or sore throat, were collected from National Influenza Surveillance Network in China. We adopted generalized additive model with quasi-Poisson to estimate province-specific association between air pollution and ILI in 30 Chinese provinces during 2015-2019, after adjusting for time trend and meteorological factors. We then pooled province-specific association by using random-effect meta-analysis. Potential effect modifications of season and regional characteristics were explored. FINDINGS A total of 26, 004, 853 ILI cases and 777, 223, 877 hospital outpatients were collected. In general, effects of air pollutants were acute. An inter-quartile range increase of PM2.5, SO2, PM10, NO2 and CO at lag0, and O3 at lag0-2 was associated with 3.08% (95% CI: 1.91%, 4.27%), 3.00% (1.86%, 4.16%), 6.46% (4.71%, 8.25%), 7.21% (5.73%, 8.71%), 4.37% (3.05%, 5.70%), and -9.26% (-11.32%, -7.14%) change of ILI at national level, respectively. Associations between air pollutants and ILI varied by season and regions, with higher effect estimates in cold season, eastern and central regions and provinces with more humid condition and larger population. INTERPRETATION This study indicated that most air pollutants increased the risk of ILI in China. Our findings might provide implications for the development of policies to protect public health from air pollution and influenza. FUNDING National Natural Science Foundation of China and Chongqing Health Commission Program.
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Affiliation(s)
- Jun Yang
- School of Public Health, Guangzhou Medical University, Guangzhou, 511436, China,Corresponding author.
| | - Zhou Yang
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Li Qi
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Mengmeng Li
- Department of Cancer Prevention, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Di Liu
- School of Public Health, Guangzhou Medical University, Guangzhou, 511436, China
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Shilu Tong
- Shanghai Children's Medical Center, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Qinghua Sun
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China,Corresponding author.
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China,Corresponding author.
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20
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Zhang Y, Wang S, Feng Z, Song Y. Influenza incidence and air pollution: Findings from a four-year surveillance study of prefecture-level cities in China. Front Public Health 2022; 10:1071229. [PMID: 36530677 PMCID: PMC9755172 DOI: 10.3389/fpubh.2022.1071229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/14/2022] [Indexed: 12/04/2022] Open
Abstract
Background Influenza is a serious public health problem, and its prevalence and spread show significant spatiotemporal characteristics. Previous studies have found that air pollutants are linked to an increased risk of influenza. However, the mechanism of influence and the degree of their association have not been determined. This study aimed to determine the influence of the air environment on the spatiotemporal distribution of influenza. Methods The kernel density estimation and Getis-Ord Gi * statistic were used to analyze the spatial distribution of the influenza incidence and air pollutants in China. A simple analysis of the correlation between influenza and air pollutants was performed using Spearman's correlation coefficients. A linear regression analysis was performed to examine changes in the influenza incidence in response to air pollutants. The sensitivity of the influenza incidence to changes in air pollutants was evaluated by performing a gray correlation analysis. Lastly, the entropy weight method was used to calculate the weight coefficient of each method and thus the comprehensive sensitivity of influenza incidence to six pollution elements. Results The results of the sensitivity analysis using Spearman's correlation coefficients showed the following ranking of the contributions of the air pollutants to the influenza incidence in descending order: SO2 >NO2 >CO> PM2.5 >O3 >PM10. The sensitivity results obtained from the linear regression analysis revealed the following ranking: CO>NO2 >SO2 >O3 >PM2.5 >PM10. Lastly, the sensitivity results obtained from the gray correlation analysis showed the following ranking: NO2 >CO>PM10 >PM2.5 >SO2 >O3. According to the sensitivity score, the study area can be divided into hypersensitive, medium-sensitive, and low-sensitive areas. Conclusion The influenza incidence showed a strong spatial correlation and associated sensitivity to changes in concentrations of air pollutants. Hypersensitive areas were mainly located in the southeastern part of northeastern China, the coastal areas of the Yellow River Basin, the Beijing-Tianjin-Hebei region and surrounding areas, and the Yangtze River Delta. The influenza incidence was most sensitive to CO, NO2, and SO2, with the occurrence of influenza being most likely in areas with elevated concentrations of these three pollutants. Therefore, the formulation of targeted influenza prevention and control strategies tailored for hypersensitive, medium-sensitive, low-sensitive, and insensitive areas are urgently needed.
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Affiliation(s)
- Yu Zhang
- School of Geographical Sciences, Northeast Normal University, Changchun, China
| | - Shijun Wang
- School of Geographical Sciences, Northeast Normal University, Changchun, China
- Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, Changchun, China
| | - Zhangxian Feng
- School of Geographical Sciences, Northeast Normal University, Changchun, China
- Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, Changchun, China
| | - Yang Song
- School of Geographical Sciences, Northeast Normal University, Changchun, China
- Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, Changchun, China
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21
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Chandy M, Obal D, Wu JC. Elucidating effects of environmental exposure using human-induced pluripotent stem cell disease modeling. EMBO Mol Med 2022; 14:e13260. [PMID: 36285490 PMCID: PMC9641419 DOI: 10.15252/emmm.202013260] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Induced pluripotent stem cells (iPSCs) are a powerful modeling system for medical discovery and translational research. To date, most studies have focused on the potential for iPSCs for regenerative medicine, drug discovery, and disease modeling. However, iPSCs are also a powerful modeling system to investigate the effects of environmental exposure on the cardiovascular system. With the emergence of e-cigarettes, air pollution, marijuana use, opioids, and microplastics as novel cardiovascular risk factors, iPSCs have the potential for elucidating the effects of these toxins on the body using conventional two-dimensional (2D) arrays and more advanced tissue engineering approaches with organoid and other three-dimensional (3D) models. The effects of these environmental factors may be enhanced by genetic polymorphisms that make some individuals more susceptible to the effects of toxins. iPSC disease modeling may reveal important gene-environment interactions that exacerbate cardiovascular disease and predispose some individuals to adverse outcomes. Thus, iPSCs and gene-editing techniques could play a pivotal role in elucidating the mechanisms of gene-environment interactions and understanding individual variability in susceptibility to environmental effects.
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Affiliation(s)
- Mark Chandy
- Stanford Cardiovascular InstituteStanford University School of MedicineStanfordCAUSA
- Department of MedicineWestern UniversityLondonONCanada
- Department of Physiology and PharmacologyWestern UniversityLondonONCanada
| | - Detlef Obal
- Stanford Cardiovascular InstituteStanford University School of MedicineStanfordCAUSA
- Department of Anesthesiology, Perioperative, and Pain MedicineStanford UniversityStanfordCAUSA
| | - Joseph C Wu
- Stanford Cardiovascular InstituteStanford University School of MedicineStanfordCAUSA
- Department of Medicine, Division of Cardiovascular MedicineStanford University School of MedicineStanfordCAUSA
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22
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Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020. Trop Med Infect Dis 2022; 7:tropicalmed7110341. [DOI: 10.3390/tropicalmed7110341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/27/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM2.5, and PM10). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM2.5 and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM2.5 lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM10 and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
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23
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Application Effect of Transparent Supervision Based on Informatization in Prevention and Control of Carbapenem-Resistant Klebsiella pneumoniae Nosocomial Infection. THE CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY = JOURNAL CANADIEN DES MALADIES INFECTIEUSES ET DE LA MICROBIOLOGIE MEDICALE 2022; 2022:2193430. [PMID: 36329985 PMCID: PMC9626236 DOI: 10.1155/2022/2193430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 08/04/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Objective To explore the effect of transparent supervision model on the prevention and control of carbapenem-resistant Klebsiella pneumoniae (CRKP) nosocomial infection and the value of the autoregressive integrated moving average (ARIMA) model in predicting the incidence of CRKP infection. Methods A total of 46,873 inpatients from Jiawang District People's Hospital of Xuzhou between January 2019 and December 2019 (prior to COVID-19 prevention and control) were selected as the preintervention group and 45,217 inpatients from January 2020 to December 2020 (after the COVID-19 prevention and control) as the postintervention group. We performed transparent supervision on CRKP patients detected by the real-time monitoring system for nosocomial infection. Incidence and detection rate of CRKP, utilization rate of special grade hydrocarbon enzyme alkene antibiotics, hand hygiene compliance rate, qualified rate of ATP tests on surface of environmental objects, and execution rate of CRKP core prevention and control were compared between the two groups. Results Transparent supervision of CRKP-infected patients was conducted daily from January to December 2020, which resulted in the following: (a) the infection rate of CRKP decreased in a fluctuating manner, and the actual value of hydrocarbon alkene use rate was basically the same as the predicted value with an overall decreasing trend; (b) after the intervention, hand hygiene compliance rate increased from 53.30% to 70.24% (P < 0.001) and the ATP qualified rate increased from 53.77% to 92.24% (P < 0.001); (c) the fitted value of the ARIMA model was in good agreement with the actual value. The incidence of CRKP infection and the utilization rate of carbene antibiotics were also in good agreement with the predicted value. The average relative errors were 11% and 10.78%. Conclusions During the COVID-19 outbreak in 2020, the ARIMA model effectively fit and predicted the CRKP infection rate, thereby providing scientific guidance for the prevention and control of CRKP infection. In addition, the transparent supervision intervention model improved the hand hygiene compliance and environmental hygiene qualification rates of medical staff, effectively reducing CRKP cross-infection in the hospital.
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24
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Milani GP, Cafora M, Favero C, Luganini A, Carugno M, Lenzi E, Pistocchi A, Pinatel E, Pariota L, Ferrari L, Bollati V. PM 2 .5, PM 10 and bronchiolitis severity: A cohort study. Pediatr Allergy Immunol 2022; 33:e13853. [PMID: 36282132 PMCID: PMC9827836 DOI: 10.1111/pai.13853] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND A few studies suggest that particulate matter (PM) exposure might play a role in bronchiolitis. However, available data are mostly focused on the risk of hospitalization and come from retrospective studies that provided conflicting results. This prospective study investigated the association between PM (PM2.5 and PM10 ) exposure and the severity of bronchiolitis. METHODS This prospective cohort study was conducted between November 2019 and February 2020 at the pediatric emergency department of the Fondazione IRCCS Ca' Ospedale Maggiore Policlinico, Milan, Italy. Infants <1 year of age with bronchiolitis were eligible. The bronchiolitis severity score was assessed in each infant and a nasal swab was collected to detect respiratory viruses. The daily PM10 and PM2.5 exposure in the 29 preceding days were considered. Adjusted regression models were employed to evaluate the association between the severity score and PM10 and PM2.5 exposure. RESULTS A positive association between the PM2.5 levels and the severity score was found at day-2 (β 0.0214, 95% CI 0.0011-0.0417, p = .0386), day-5 (β 0.0313, 95% CI 0.0054-0.0572, p = .0179), day-14 (β 0.0284, 95% CI 0.0078-0.0490, p = .0069), day-15 (β 0.0496, 95% CI 0.0242-0.0750, p = .0001) and day-16 (β 0.0327, 95% CI 0.0080-0.0574, p = .0093).Similar figures were observed considering the PM10 exposure and limiting the analyses to infants with respiratory syncytial virus. CONCLUSION This study shows for the first time a direct association between PM2.5 and PM10 levels and the severity of bronchiolitis.
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Affiliation(s)
- Gregorio P Milani
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.,Pediatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Marco Cafora
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.,Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
| | - Chiara Favero
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Anna Luganini
- Department of Life Science and System Biology, Università degli Studi di Torino, Turin, Italy
| | - Michele Carugno
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.,Occupational Health Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Erica Lenzi
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Anna Pistocchi
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
| | - Eva Pinatel
- Institute of Biomedical Technologies, National Research Council, Milan, Italy
| | - Luigi Pariota
- Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy
| | - Luca Ferrari
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.,Occupational Health Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Valentina Bollati
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.,Occupational Health Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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25
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Burns CJ, LaKind JS, Naiman J, Boon D, Clougherty JE, Rule AM, Zidek A. Research on COVID-19 and air pollution: A path towards advancing exposure science. ENVIRONMENTAL RESEARCH 2022; 212:113240. [PMID: 35390303 PMCID: PMC8979614 DOI: 10.1016/j.envres.2022.113240] [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: 02/28/2022] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 05/26/2023]
Abstract
The COVID-19 pandemic has resulted in an extraordinary incidence of morbidity and mortality, with almost 6 million deaths worldwide at the time of this writing (https://covid19.who.int/). There has been a pressing need for research that would shed light on factors - especially modifiable factors - that could reduce risks to human health. At least several hundred studies addressing the complex relationships among transmission of SARS-CoV-2, air pollution, and human health have been published. However, these investigations are limited by available and consistent data. The project goal was to seek input into opportunities to improve and fund exposure research on the confluence of air pollution and infectious agents such as SARS-CoV-2. Thirty-two scientists with expertise in exposure science, epidemiology, risk assessment, infectious diseases, and/or air pollution responded to the outreach for information. Most of the respondents expressed value in developing a set of common definitions regarding the extent and type of public health lockdown. Traffic and smoking ranked high as important sources of air pollution warranting source-specific research (in contrast with assessing overall ambient level exposures). Numerous important socioeconomic factors were also identified. Participants offered a wide array of inputs on what they considered to be essential studies to improve our understanding of exposures. These ranged from detailed mechanistic studies to improved air quality monitoring studies and prospective cohort studies. Overall, many respondents indicated that these issues require more research and better study design. As an exercise to solicit opinions, important concepts were brought forth that provide opportunities for scientific collaboration and for consideration for funding prioritization. Further conversations on these concepts are needed to advance our thinking on how to design research that moves us past the documented limitations in the current body of research and prepares us for the next pandemic.
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Affiliation(s)
- Carol J Burns
- Burns Epidemiology Consulting, LLC, 255 W Sunset Ct., Sanford, MI, 48657, USA.
| | - Judy S LaKind
- LaKind Associates, LLC, 106 Oakdale Avenue, Catonsville, MD, 21228, USA; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
| | - Josh Naiman
- Naiman Consulting, LLC, 504 S 44th St, Apt 2, Phila, PA, 19104, USA.
| | - Denali Boon
- Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, IN, 46268, USA.
| | - Jane E Clougherty
- Department of Environmental and Occupational Health, 3215 Market St, Dornsife School of Public Health, Drexel University, Philadelphia, PA, 19104, USA.
| | - Ana M Rule
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, The Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, USA.
| | - Angelika Zidek
- Existing Substances Risk Assessment Bureau, 269 Laurier Ave, West, Health Canada, Ottawa, Ontario, Canada.
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26
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Manik S, Mandal M, Pal S. Impact of air pollutants on COVID-19 transmission: a study over different metropolitan cities in India. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2022; 25:1-13. [PMID: 35975212 PMCID: PMC9371967 DOI: 10.1007/s10668-022-02593-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/22/2022] [Indexed: 05/16/2023]
Abstract
India is affected strongly by the Coronavirus and within a short period, it becomes the second-highest country based on the infected case. Earlier, there was an indication of the impact of pollution on COVID-19 transmission from a few studies with early COVID-19 data. The study of the effect of pollution on COVID-19 in Indian metropolitan cities is ideal due to the high level of pollution and COVID-19 transmission in these cities. We study the impact of different air pollutants on the spread of coronavirus in different cities in India. A correlation is studied with daily confirmed COVID-19 cases with a daily mean of ozone, particle matter (PM) in size ≤ 10 μ m, carbon monoxide, sulfur dioxide, and nitrogen dioxide of different cities. It is found that particulate matter concentration decreases during the nationwide lockdown period and the air quality index improves for different Indian regions. A correlation between the daily confirmed cases with particulate matter (PM2.5 and PM10 both) is observed. The air quality index also shows a positive correlation with the daily confirmed cases for most of the metropolitan Indian cities. The correlation study also indicates that different air pollutants may have a role in the spread of the virus.
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Affiliation(s)
- Souvik Manik
- Midnapore City college, Kuturia, Bhadutala, Paschim Medinipur, West Bengal 721129 India
| | - Manoj Mandal
- Midnapore City college, Kuturia, Bhadutala, Paschim Medinipur, West Bengal 721129 India
| | - Sabyasachi Pal
- Midnapore City college, Kuturia, Bhadutala, Paschim Medinipur, West Bengal 721129 India
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27
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Zhang R, Lai KY, Liu W, Liu Y, Lu J, Tian L, Webster C, Luo L, Sarkar C. Community-level ambient fine particulate matter and seasonal influenza among children in Guangzhou, China: A Bayesian spatiotemporal analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 826:154135. [PMID: 35227720 DOI: 10.1016/j.scitotenv.2022.154135] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Influenza is a major preventable infectious respiratory disease. However, there is little detailed long-term evidence of its associations with PM2.5 among children. We examined the community-level associations between exposure to ambient PM2.5 and incident influenza in Guangzhou, China. METHODS We used data from the city-wide influenza surveillance system collected by Guangzhou Centre for Disease Control and Prevention (GZCDC) over the period 2013 and 2019. Incident influenza was defined as daily new influenza (both clinically diagnosed and laboratory confirmed) cases as per standard diagnostic criteria. A 200-meter city-wide grid of daily ambient PM2.5 exposure was generated using a random forest model. We developed spatiotemporal Bayesian hierarchical models to examine the community-level associations between PM2.5 and the influenza adjusting for meteorological and socioeconomic variables and accounting for spatial autocorrelation. We also calculated community-wide influenza cases attributable to PM2.5 levels exceeding the China Grade 1 and World Health Organization (WHO) regulatory thresholds. RESULTS Our study comprised N = 191,846 children from Guangzhou aged ≤19 years and diagnosed with influenza between January 1, 2013 and December 31, 2019. Each 10 μg/m3 increment in community-level PM2.5 measured on the day of case confirmation (lag 0) and over a 6-day moving average (lag 0-5 days) was associated with higher risks of influenza (RR = 1.05, 95% CI: 1.05-1.06 for lag 0 and RR = 1.15, 95% CI: 1.14-1.16 for lag 05). We estimated that 8.10% (95%CI: 7.23%-8.57%) and 20.11% (95%CI: 17.64%-21.48%) influenza cases respectively were attributable to daily PM2.5 exposure exceeding the China Grade I (35 μg/m3) and the WHO limits (25 μg/m3). The risks associated with PM2.5 exposures were more pronounced among children of the age-group 10-14 compared to other age groups. CONCLUSIONS More targeted non-pharmaceutical interventions aimed at reducing PM2.5 exposures at home, school and during commutes among children may constitute additional influenza prevention and control polices.
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Affiliation(s)
- Rong Zhang
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wenhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Jianyun Lu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Linwei Tian
- School of Public Health, The University of Hong Kong, Patrick Mason Building, Sassoon Road, Pokfulam, Hong Kong, China
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China.
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28
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Akan AP. Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:41695-41712. [PMID: 35098452 PMCID: PMC8801283 DOI: 10.1007/s11356-021-18403-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 12/25/2021] [Indexed: 05/21/2023]
Abstract
The study aims to investigate associations between air pollution, climate parameters, and the diffusion of COVID-19-confirmed cases in Turkey using Spearman's correlation test as an empirical methodology by Statgraphics Centurion XVI (version 16.1) and to determine the risk factors accelerating the spread of SARS-CoV-2 virus. The present study demonstrates the strong impacts of air pollutants and weather conditions on the transmission of COVID-19 morbidity. Particularly, O3 and PM10 from air quality parameters exhibited the strongest correlation with the number of daily cases in Kütahya (rs = -0.62; p < 0.05) and Sivas (rs = -0.62; p < 0.05) provinces, respectively. In meteorological parameters, rainfall showed the highest impact (rs = 0.76; p < 0.05) on the number of daily COVID-19 cases in Denizli distinct. Moreover, this study suggested that the diffusion of the novel coronavirus SARS-CoV-2 in regions with high levels of air pollution and low wind speed is dominant. To prevent the negative effects of the future pandemic crisis on public health and economic systems, manifold implications to encourage strategies to reduce air pollution in the polluted region such as being prevalent the usage of renewable energy technologies in particular electricity generation and sustainable policies such as improving the health system should be implemented by decision-makers.
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Affiliation(s)
- Aytac Perihan Akan
- Department of Environmental Engineering, Hacettepe University, 06800, Ankara, Turkey.
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29
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Leirião LFL, Debone D, Miraglia SGEK. Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:275. [PMID: 35286482 PMCID: PMC8918908 DOI: 10.1007/s10661-022-09924-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/05/2022] [Indexed: 05/05/2023]
Abstract
Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM10, PM2.5, and NO2 contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM2.5 concentration and fatality (R2 = 0.416; p = 0.003), NO2 concentration and fatality (R2 = 0.232; p = 0.005), and NO2 concentration and mortality (R2 = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.
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Affiliation(s)
- Luciana Ferreira Leite Leirião
- Laboratory of Economics, Health and Environmental Pollution, Institute of Environmental, Chemical and Pharmaceutical Sciences, Federal University of São Paulo, R São Nicolau, 210, Cep 09913-030, SP, Diadema, Brazil.
| | - Daniela Debone
- Laboratory of Economics, Health and Environmental Pollution, Institute of Environmental, Chemical and Pharmaceutical Sciences, Federal University of São Paulo, R São Nicolau, 210, Cep 09913-030, SP, Diadema, Brazil
| | - Simone Georges El Khouri Miraglia
- Laboratory of Economics, Health and Environmental Pollution, Institute of Environmental, Chemical and Pharmaceutical Sciences, Federal University of São Paulo, R São Nicolau, 210, Cep 09913-030, SP, Diadema, Brazil
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Li Y, Wu J, Hao J, Dou Q, Xiang H, Liu S. Short-term impact of ambient temperature on the incidence of influenza in Wuhan, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:18116-18125. [PMID: 34677763 DOI: 10.1007/s11356-021-16948-y] [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: 07/12/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure-response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0-1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2-4 days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases.
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Affiliation(s)
- Yanbing Li
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Jingtao Wu
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Jiayuan Hao
- Department of Biostatistics, Harvard University, Cambridge, MA, 02138, USA
| | - Qiujun Dou
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
| | - Hao Xiang
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
| | - Suyang Liu
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China.
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Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion. SUSTAINABILITY 2022. [DOI: 10.3390/su14052803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.
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Mondal S, Chaipitakporn C, Kumar V, Wangler B, Gurajala S, Dhaniyala S, Sur S. COVID-19 in New York state: Effects of demographics and air quality on infection and fatality. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 807:150536. [PMID: 34628294 PMCID: PMC8461036 DOI: 10.1016/j.scitotenv.2021.150536] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/28/2021] [Accepted: 09/19/2021] [Indexed: 05/07/2023]
Abstract
The coronavirus disease 2019 (COVID-19) has had a global impact that has been unevenly distributed among and even within countries. Multiple demographic and environmental factors have been associated with the risk of COVID-19 spread and fatality, including age, gender, ethnicity, poverty, and air quality among others. However, specific contributions of these factors are yet to be understood. Here, we attempted to explain the variability in infection, death, and fatality rates by understanding the contributions of a few selected factors. We compared the incidence of COVID-19 in New York State (NYS) counties during the first wave of infection and analyzed how different demographic and environmental variables associate with the variation observed across the counties. We observed that infection and death rates, two important COVID-19 metrics, to be highly correlated with both being highest in counties located near New York City, considered as one of the epicenters of the infection in the US. In contrast, disease fatality was found to be highest in a different set of counties despite registering a low infection rate. To investigate this apparent discrepancy, we divided the counties into three clusters based on COVID-19 infection, death, or fatality, and compared the differences in the demographic and environmental variables such as ethnicity, age, population density, poverty, temperature, and air quality in each of these clusters. Furthermore, a regression model built on this data reveals PM2.5 and distance from the epicenter are significant risk factors for infection, while disease fatality has a strong association with age and PM2.5. Our results demonstrate that for the NYS, demographic components distinctly associate with specific aspects of COVID-19 burden and also highlight the detrimental impact of poor air quality. These results could help design and direct location-specific control and mitigation strategies.
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Affiliation(s)
- Sumona Mondal
- Department of Mathematics, Clarkson University, Potsdam, NY, USA
| | | | - Vijay Kumar
- Department of Mathematics, Clarkson University, Potsdam, NY, USA
| | - Bridget Wangler
- David D. Reh School of Business, Clarkson University, Potsdam, NY, USA
| | | | - Suresh Dhaniyala
- Department of Mechanical and Aeronautical Engineering, Clarkson University, Potsdam, NY, USA
| | - Shantanu Sur
- Department of Biology, Clarkson University, Potsdam, NY, USA.
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Assessment of Meteorological Variables and Air Pollution Affecting COVID-19 Cases in Urban Agglomerations: Evidence from China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19010531. [PMID: 35010793 PMCID: PMC8744893 DOI: 10.3390/ijerph19010531] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/14/2021] [Accepted: 12/30/2021] [Indexed: 12/23/2022]
Abstract
The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM2.5, PM10, sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.
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Xie W, Zhao H, Shu C, Wang B, Zeng W, Zhan Y. Association between ozone exposure and prevalence of mumps: a time-series study in a Megacity of Southwest China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:64848-64857. [PMID: 34318412 PMCID: PMC8315250 DOI: 10.1007/s11356-021-15473-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
In the present study, we aim to evaluate the delayed and cumulative effect of ozone (O3) exposure on mumps in a megacity with high population density and high humidity. We took Chongqing, a megacity in Southwest China, as the research area and 2013-2017 as the research period. A total of 49,258 confirmed mumps cases were collected from 122 hospitals of Chongqing. We employed the distributed lag nonlinear models with quasi-Poisson link to investigate the relationship between prevalence of mumps and O3 exposure after adjusting for the effects of meteorological conditions. The results show that the effect of O3 exposure on mumps was mainly manifested in the lag of 0-7 days. The single-day ;lag effect was the most obvious on the 4th day, with the relative risk (RR) of mumps occurs of 1.006 (95% CI: 1.003-1.007) per 10 μg/m3 in the O3 exposure. The cumulative RR within 7 days was 1.025 (95% CI: 1.013-1.038). Our results suggest that O3 exposure can increase the risk of mumps infection, which fills the gap of relevant research in mountainous areas with high population density and high humidity.
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Affiliation(s)
- Wenjun Xie
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, China
| | - Han Zhao
- Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Chang Shu
- Ministry of Education Key Laboratory of Child Development and Disorders; National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Bin Wang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, China
- Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin, China
| | - Wen Zeng
- Sichuan University-the Hong Kong Polytechnic University Institute for Disaster Management and Reconstruction, Chengdu, Sichuan, China.
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, China.
- Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin, China.
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Exposure to PM2.5 and PM10 and COVID-19 Infection Rates and Mortality: a one-year observational study in Poland. Biomed J 2021; 44:S25-S36. [PMID: 34801766 PMCID: PMC8603332 DOI: 10.1016/j.bj.2021.11.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/09/2021] [Accepted: 11/12/2021] [Indexed: 01/26/2023] Open
Abstract
Background Atmospheric contamination, especially particulate matter (PM), can be associated viral infections connected with respiratory failure. Literature data indicates that intensity of SARS-CoV-2 infections worldwide can be associated with PM pollution levels. Objectives The aim of the study was to examine the relationship between atmospheric contamination, measured as PM2.5 and PM10 levels, and the number of COVID-19 cases and related deaths in Poland in a one-year observation study. Methods Number and geographical distribution of COVID-19 incidents and related deaths, as well as PM2.5 and PM10 exposure levels in Poland were obtained from publicly accessible databases. Average monthly values of these parameters for individual provinces were calculated. Multiple regression analysis was performed for the period between March 2020 and February 2021, taking into account average monthly exposure to PM2.5 and PM10, monthly COVID-19 incidence and mortality rates per 100,000 inhabitants and the population density across Polish provinces. Results Only December 2020 the number of new infections was significantly related to the three analyzed factors: PM2.5, population density and the number of laboratory COVID-19 tests (R2 = 0.882). For COVID-19 mortality, a model with all three significant factors: PM10, population density and number of tests was obtained as significant only in November 2020 (R2 = 0.468). Conclusion The distribution of COVID-19 incidents across Poland was independent from annual levels of particulate matter concentration in provinces. Exposure to PM2.5 and PM10 was associated with COVID-19 incidence and mortality in different provinces only in certain months. Other cofactors such as population density and the number of performed COVID-19 tests also corresponded with both COVID-19-related infections and deaths only in certain months. Particulate matter should not be treated as the sole determinant of the spread and severity of the COVID-19 pandemic but its importance in the incidence of infectious diseases should not be forgotten.
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Alamri AA, Almutairi AB, Hawsah AM, Aljarullah AH, Almeerabdullah YW, Alenezi MA, Ahmed S. Evaluation of Reduction Protocols in Managing Aerosol Generation in Caries Management in COVID 19 in Riyadh: An Original Research. J Pharm Bioallied Sci 2021; 13:S1655-S1658. [PMID: 35018049 PMCID: PMC8687041 DOI: 10.4103/jpbs.jpbs_390_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 05/23/2021] [Accepted: 05/28/2021] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION External high-volume extraction (HVE) devices may offer a way to reduce any aerosol particulate generated. The aim of this study was to measure the particle count during dental aerosol-generating procedures and compare the results with when a HVE device is used. MATERIALS AND METHODS Design A comparative clinical study measuring the amount of PM1, PM2.5, and PM10 aerosol particulate with and without the use of an external HVE device was undertaken. Materials and methods in total, ten restorative procedures were monitored with an industrial Trotec PC220 particle counter. The intervention was an external HVE device. Main outcome methods the air sampler was placed at the average working distance of the clinicians involved in the study - 420 mm. RESULTS In the present study, aerosol particulate was recorded at statistically significantly increased levels during dental procedures without an external HVE device versus with the device. Discussion The null hypothesis was rejected, in that significant differences were found between the results of the amount of aerosol particle count with and without a HVE device. CONCLUSION If the results of the present study are repeated in an in vivo setting, an external high-volume suction device may potentially show a lower risk of transmission of viral particulate.
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Affiliation(s)
| | | | | | | | | | | | - Suhael Ahmed
- Department of Oral and Maxillofacial Surgery, Riyadh Elm University, Riyadh, Kingdom of Saudi Arabia,Address for correspondence: Dr. Suhael Ahmed, Department of Oral and Maxillofacial Surgery, Riyadh Elm University, Riyadh, Kingdom of Saudi Arabia. E-mail:
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Chen C, Zhang X, Jiang D, Yan D, Guan Z, Zhou Y, Liu X, Huang C, Ding C, Lan L, Huang X, Li L, Yang S. Associations between Temperature and Influenza Activity: A National Time Series Study in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010846. [PMID: 34682590 PMCID: PMC8535740 DOI: 10.3390/ijerph182010846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
Previous studies have reported that temperature is the main meteorological factor associated with influenza activity. This study used generalized additive models (GAMs) to explore the relationship between temperature and influenza activity in China. From the national perspective, the average temperature (AT) had an approximately negative linear correlation with the incidence of influenza, as well as a positive rate of influenza H1N1 virus (A/H1N1). Every degree that the monthly AT rose, the influenza cases decreased by 2.49% (95%CI: 1.24%–3.72%). The risk of influenza cases reached a peak at −5.35 °C with RRs of 2.14 (95%CI: 1.38–3.33) and the monthly AT in the range of −5.35 °C to 18.31 °C had significant effects on the incidence of influenza. Every degree that the weekly AT rose, the positive rate of A/H1N1 decreased by 5.28% (95%CI: 0.35%–9.96%). The risk of A/H1N1 reached a peak at −3.14 °C with RRs of 4.88 (95%CI: 1.01–23.75) and the weekly AT in the range of −3.14 °C to 17.25 °C had significant effects on the incidence of influenza. Our study found that AT is negatively associated with influenza activity, especially for A/H1N1. These findings indicate that temperature could be integrated into the current influenza surveillance system to develop early warning systems to better predict and prepare for the risks of influenza.
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Affiliation(s)
- Can Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xiaobao Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Daixi Jiang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Danying Yan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Zhou Guan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Yuqing Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xiaoxiao Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Chenyang Huang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Lei Lan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xihui Huang
- Subject Teaching (English), College of Foreign Languages, Fujian Normal University, Fujian 350117, China;
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
- Correspondence: (L.L.); (S.Y.); Tel.: +86-13605705640 (S.Y.)
| | - Shigui Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
- Correspondence: (L.L.); (S.Y.); Tel.: +86-13605705640 (S.Y.)
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Zhao D, Chen M, Shi K, Ma M, Huang Y, Shen J. A long short-term memory-fully connected (LSTM-FC) neural network for predicting the incidence of bronchopneumonia in children. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:56892-56905. [PMID: 34076817 DOI: 10.1007/s11356-021-14632-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/25/2021] [Indexed: 06/12/2023]
Abstract
Bronchopneumonia is the most common infectious disease in children, and it seriously endangers children's health. In this paper, a deep neural network combining long short-term memory (LSTM) layers and fully connected layers was proposed to predict the prevalence of bronchopneumonia in children in Chengdu based on environmental factors and previous prevalence rates. The mean square error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (R) were used to detect the performance of the deep learning model. The values of MSE, MAE, and R in the test dataset are 0.0051, 0.053, and 0.846, respectively. The results show that the proposed model can accurately predict the prevalence of bronchopneumonia in children. We also compared the proposed model with three other models, namely, a fully connected (FC) layer neural network, a random forest model, and a support vector machine. The results show that the proposed model achieves better performance than the three other models by capturing time series and mitigating the lag effect.
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Affiliation(s)
- Dongzhe Zhao
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing, Jiangsu Province, 210046, China
| | - Kaifang Shi
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Mingguo Ma
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Yang Huang
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Jingwei Shen
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China.
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China.
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Baboli Z, Neisi N, Babaei AA, Ahmadi M, Sorooshian A, Birgani YT, Goudarzi G. On the airborne transmission of SARS-CoV-2 and relationship with indoor conditions at a hospital. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 261:118563. [PMID: 34177342 PMCID: PMC8215890 DOI: 10.1016/j.atmosenv.2021.118563] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/06/2023]
Abstract
The limited knowledge about the mechanism of SARS-CoV-2 transmission is a current challenge on a global scale. Among possible transmission routes, air transfer of the virus is thought to be prominent. To investigate this further, measurements were conducted at Razi hospital in Ahvaz, Iran, which was selected to treat COVID-19 severe cases in the Khuzestan province. Passive and active sampling methods were employed and compared with regard to their efficiency for collection of airborne SARS-COV-2 virus particles. Fifty one indoor air samples were collected in two areas, with distances of less than or equal to 1 m (patient room) and more than 3 m away (hallway and nurse station) from patient beds. A simulation method was used to obtain the virus load released by a regularly breathing or coughing individual including a range of microdroplet emissions. Using real-time reverse transcription polymerase chain reaction (RT-PCR), 11.76% (N = 6) of all indoor air samples (N = 51) collected in the COVID-19 ward tested positive for SARS-CoV-2 virus, including 4 cases in patient rooms and 2 cases in the hallway. Also, 5 of the 6 positive cases were confirmed using active sampling methods with only 1 based on passive sampling. The results support airborne transmission of SARS-CoV-2 bioaerosols in indoor air. Multivariate analysis showed that among 15 parameters studied, the highest correlations with PCR results were obtained for temperature, relative humidity, PM levels, and presence of an air cleaner.
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Affiliation(s)
- Zeynab Baboli
- Student Research Committee, Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Niloofar Neisi
- Clinical Sciences Research Institute, Alimentary Tract Research Center, Department of Medical Virology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Ali Akbar Babaei
- Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Environmental Technologies Research Center (ETRC), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mehdi Ahmadi
- Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Environmental Technologies Research Center (ETRC), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Armin Sorooshian
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
- Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
| | - Yaser Tahmasebi Birgani
- Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Environmental Technologies Research Center (ETRC), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Gholamreza Goudarzi
- Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Environmental Technologies Research Center (ETRC), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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Sundaram ME, Calzavara A, Mishra S, Kustra R, Chan AK, Hamilton MA, Djebli M, Rosella LC, Watson T, Chen H, Chen B, Baral SD, Kwong JC. Déterminants individuels et sociaux du test de dépistage du SRAS-CoV-2 et de l’obtention d’un résultat positif en Ontario, au Canada: une étude populationnelle. CMAJ 2021; 193:E1261-E1276. [PMID: 34400488 PMCID: PMC8386493 DOI: 10.1503/cmaj.202608-f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2021] [Indexed: 11/08/2022] Open
Abstract
Contexte: Optimiser la réponse de la santé publique pour diminuer le fardeau de la COVID-19 nécessite la caractérisation de l’hétérogénéité du risque posé par la maladie à l’échelle de la population. Cependant, l’hétérogénéité du dépistage du SRAS-CoV-2 peut fausser les estimations selon le modèle d’étude analytique utilisé. Notre objectif était d’explorer les biais collisionneurs dans le cadre d’une vaste étude portant sur les déterminants de la maladie et d’évaluer les déterminants individuels, environnementaux et sociaux du dépistage et du diagnostic du SRAS-CoV-2 parmi les résidents de l’Ontario, au Canada. Méthodes: Nous avons exploré la présence potentielle de biais collisionneurs et caractérisé les déterminants individuels, environnementaux et sociaux de l’obtention d’un test de dépistage et d’un résultat positif à la présence de l’infection au SRAS-CoV-2 à l’aide d’analyses transversales parmi les 14,7 millions de personnes vivant dans la collectivité en Ontario, au Canada. Parmi les personnes ayant obtenu un diagnostic, nous avons utilisé des études analytiques distinctes afin de comparer les prédicteurs pour les personnes d’obtenir un résultat de test de dépistage positif plutôt que négatif, pour les personnes symptomatiques d’obtenir un résultat de test de dépistage positif plutôt que négatif et pour les personnes d’obtenir un résultat de test de dépistage positif plutôt que de ne pas obtenir un résultat positif (c.-à-d., obtenir un résultat de test de dépistage négatif ou ne pas obtenir de test de dépistage). Nos analyses comprennent des tests de dépistage réalisés entre le 1er mars et le 20 juin 2020. Résultats: Sur 14 695 579 personnes, nous avons constaté que 758 691 d’entre elles ont passé un test de dépistage du SRAS-CoV-2, parmi lesquelles 25 030 (3,3 %) ont obtenu un résultat positif. Plus la probabilité d’obtenir un test de dépistage s’éloignait de zéro, plus la variabilité généralement observée dans la probabilité d’un diagnostic était grande parmi les modèles d’études analytiques, particulièrement en ce qui a trait aux facteurs individuels. Nous avons constaté que la variabilité dans l’obtention d’un test de dépistage était moins importante en fonction des déterminants sociaux dans l’ensemble des études analytiques. Les facteurs tels que le fait d’habiter dans une région ayant une plus haute densité des ménages (rapport de cotes corrigé 1,86; intervalle de confiance [IC] à 95 % 1,75–1,98), une plus grande proportion de travailleurs essentiels (rapport de cotes corrigé 1,58; IC à 95 % 1,48–1,69), une population atteignant un plus faible niveau de scolarité (rapport de cotes corrigé 1,33; IC à 95 % 1,26–1,41) et une plus grande proportion d’immigrants récents (rapport de cotes corrigé 1,10; IC à 95 % 1,05–1,15), étaient systématiquement corrélés à une probabilité plus importante d’obtenir un diagnostic de SRAS-CoV-2, peu importe le modèle d’étude analytique employé. Interprétation: Lorsque la capacité de dépister est limitée, nos résultats suggèrent que les facteurs de risque peuvent être estimés plus adéquatement en utilisant des comparateurs populationnels plutôt que des comparateurs de résultat négatif au test de dépistage. Optimiser la lutte contre la COVID-19 nécessite des investissements dans des interventions structurelles déployées de façon suffisante et adaptées à l’hétérogénéité des déterminants sociaux du risque, dont le surpeuplement des ménages, l’occupation professionnelle et le racisme structurel.
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Affiliation(s)
- Maria E Sundaram
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Andrew Calzavara
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Sharmistha Mishra
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Rafal Kustra
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Adrienne K Chan
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Mackenzie A Hamilton
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Mohamed Djebli
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Laura C Rosella
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Tristan Watson
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Hong Chen
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Branson Chen
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Stefan D Baral
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md
| | - Jeffrey C Kwong
- ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md.
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Chan TC, Tang JH, Hsieh CY, Chen KJ, Yu TH, Tsai YT. Approaching precision public health by automated syndromic surveillance in communities. PLoS One 2021; 16:e0254479. [PMID: 34358241 PMCID: PMC8345830 DOI: 10.1371/journal.pone.0254479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 06/29/2021] [Indexed: 11/23/2022] Open
Abstract
Background Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.
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Affiliation(s)
- Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan
- Institute of Public Health, School of Medicine, Yang-Ming Chiao Tung University, Taipei, Taiwan
- * E-mail:
| | - Jia-Hong Tang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Cheng-Yu Hsieh
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan
| | - Kevin J. Chen
- Department of Health, Taipei City Government, Taipei, Taiwan
| | - Tsan-Hua Yu
- Department of Health, Taipei City Government, Taipei, Taiwan
| | - Yu-Ting Tsai
- Department of Health, Taipei City Government, Taipei, Taiwan
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Borisova T, Komisarenko S. Air pollution particulate matter as a potential carrier of SARS-CoV-2 to the nervous system and/or neurological symptom enhancer: arguments in favor. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:40371-40377. [PMID: 33051841 PMCID: PMC7552951 DOI: 10.1007/s11356-020-11183-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 10/07/2020] [Indexed: 05/04/2023]
Abstract
Entry receptor for SARS-CoV-2 is expressed in nasal epithelial cells, and nasal delivery pathway can be a key feature of transmission. Here, a possibility of interaction of SARS-CoV-2 with air pollution particulate matter (PM) was considered. It was shown in our recent studies that water-suspended plastic and wood smoke aerosol PM and carbon-containing nanoparticles from burning organics can interact with the plasma membrane of brain nerve terminals presumably due to their lipid components. COVID-19 patients have neurological symptoms, viral particles were found in the brain, SARS-CoV-2 enters the cells via fusion of lipid viral envelope with the plasma membranes of infected cells, and so viral envelop can contain lipid components of the host neuronal membranes. Therefore, interaction of SARS-CoV-2 envelope with PM is possible in water surrounding. After drying, PM can serve as a carrier for transmission of SARS-CoV-2 immobilized at their surface. Moreover, PM and SARS-CoV-2 per se can enter human organism during nasal inhalation, and they both use the same nose-to-brain delivery pathways moving along axons directly to the brain, influencing the nervous system and exocytosis/endocytosis in nerve cells. Thus, PM can aggravate neurological symptoms of SARS-CoV-2 and vice versa, due to their identical nose-to-brain delivery mechanism and possible interference of neuronal effects. In addition, different types of PM because of their ability to interact with the plasma membranes of nerve cells can facilitate unspecific SARS-CoV-2 entrance to the cells, and can influence envelope features of SARS-CoV-2. Detailed studies are required to analyze interaction of SARS-CoV-2 with PM.
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Affiliation(s)
- Tatiana Borisova
- Palladin Institute of Biochemistry, National Academy of Sciences of Ukraine, 9 Leontovicha Street, Kiev, 01054, Ukraine.
| | - Serhiy Komisarenko
- Palladin Institute of Biochemistry, National Academy of Sciences of Ukraine, 9 Leontovicha Street, Kiev, 01054, Ukraine
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Xu Y, Cui W. WITHDRAWN: Health risk assessment and countermeasure analysis of the elderly population exposed to PM2.5 microenvironment. Work 2021:WOR205361. [PMID: 34275922 DOI: 10.3233/wor-205361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Ahead of Print article withdrawn by publisher.
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Affiliation(s)
- Yan Xu
- Department of Economics, Liaoning University, Liaoning, China
| | - Wantian Cui
- Department of Economics, Liaoning University, Liaoning, China
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Wu Q, He J, Zhang WY, Zhao KF, Jin J, Yu JL, Chen QQ, Hou S, Zhu M, Xu Z, Pan HF. The contrasting relationships of relative humidity with influenza A and B in a humid subtropical region. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:36828-36836. [PMID: 33710490 DOI: 10.1007/s11356-021-13107-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/18/2021] [Indexed: 05/19/2023]
Abstract
Influenza is an acute respiratory disease that seriously threatens public health. The occurrence of influenza has been proved to be related to a variety of meteorological factors. However, less attention has been paid to the effect of relative humidity (RH) on different types of influenza, especially in subtropical regions. Daily data on laboratory-confirmed influenza cases, weather variables, and air pollutants in Hefei covering the 2014-2019 period were collected. The seasonality and trend of daily influenza cases were explored by the time series seasonal decomposition method. Generalized linear model was fitted in conjunction with distributed lag nonlinear model to quantify the associations of RH with influenza A and influenza B. Subgroup analyses were conducted by sex, age (0-4, 5-17, and ≥18 years), and season (cold and warm seasons). A total of 5238 influenza cases including 2847 influenza A cases and 2391 influenza B cases were recorded. The epidemic of influenza presented a distinct seasonal pattern, and the number of daily influenza cases increased steadily since 2016. High RH was related to an increased risk of influenza A (maximum RR = 1.683, 95%CI: 1.365-2.076), especially among males, females, and school-age children. Low RH was associated with an increased risk of influenza B (maximum RR = 1.252, 95%CI: 1.169-1.340). The contrasting relationships of RH with influenza A and B remained significant in cold seasons. High RH and low RH were significantly associated with the increased risk of influenza A and B, respectively. The findings of our study may provide clues for proposing new effective interventions.
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Affiliation(s)
- Qian Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China
| | - Jun He
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
- Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Wen-Yan Zhang
- Hefei Center for Disease Control and Prevention, Anhui, China
| | - Ke-Fu Zhao
- Hefei Center for Disease Control and Prevention, Anhui, China
| | - Jing Jin
- Hefei Center for Disease Control and Prevention, Anhui, China
| | - Jun-Ling Yu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
- Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Qing-Qing Chen
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
- Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Sai Hou
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Meng Zhu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia.
| | - Hai-Feng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China.
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Sahu SK, Mangaraj P, Beig G, Tyagi B, Tikle S, Vinoj V. Establishing a link between fine particulate matter (PM 2.5) zones and COVID -19 over India based on anthropogenic emission sources and air quality data. URBAN CLIMATE 2021; 38:100883. [PMID: 34131558 PMCID: PMC8189761 DOI: 10.1016/j.uclim.2021.100883] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/29/2021] [Accepted: 06/07/2021] [Indexed: 05/20/2023]
Abstract
The spread of coronavirus disease of 2019 (COVID-19) pandemic around the globe is affecting people. The majority of Indian urban complexes are reeling under high emissions of deadly fine particulate matter PM2.5 and resulting in poor air quality. These fine particles penetrate deep into the body and fuel inflammation in the lungs and respiratory tract, leading to the risk of having cardiovascular and respiratory problems, including a weak immune system. In the present study, we report the first national-scale study over India, which establishes a strong relationship between the PM2.5 emission load and COVID-19 infections and resulting deaths. We find a significant correlation (R2 = 0.66 & 0.60) between the states as well as districts having varied levels of PM2.5 emissions with corresponding COVID-19 positive cases respectively, and R2 = 0.61 between wavering air quality on a longer time scale and the number of COVID-19 related deaths till 5 November 2020. This study provides practical evidence that cities having pollution hotspot where fossil fuel emissions are dominating are highly susceptible to COVID-19 cases.
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Affiliation(s)
- Saroj Kumar Sahu
- P.G. Environment Sciences, Utkal University, Bhubaneswar 751004, India
| | - Poonam Mangaraj
- P.G. Environment Sciences, Utkal University, Bhubaneswar 751004, India
| | - Gufran Beig
- Indian Institute of Tropical Meteorology, Pune 411008, India
| | - Bhishma Tyagi
- Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, 769008, India
| | - Suvarna Tikle
- Indian Institute of Tropical Meteorology, Pune 411008, India
| | - V Vinoj
- School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, 752050, India
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Lau SY, Cheng W, Yu Z, Mohammad KN, Wang MH, Zee BC, Li X, Chong KC, Chen E. Independent association between meteorological factors, PM2.5, and seasonal influenza activity in Hangzhou, Zhejiang province, China. Influenza Other Respir Viruses 2021; 15:513-520. [PMID: 33342077 PMCID: PMC8189232 DOI: 10.1111/irv.12829] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 11/28/2020] [Accepted: 12/01/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Due to variations in climatic conditions, the effects of meteorological factors and PM2.5 on influenza activity, particularly in subtropical regions, vary in existing literature. In this study, we examined the relationship between influenza activity, meteorological parameters, and PM2.5 . METHODS A total of 20 165 laboratory-confirmed influenza cases in Hangzhou, Zhejiang province, were documented in our dataset and aggregated into weekly counts for downstream analysis. We employed a combination of the quasi-Poisson-generalized additive model and the distributed lag non-linear model to examine the relationship of interest, controlling for long-term trends, seasonal trends, and holidays. RESULTS A hockey-stick association was found between absolute humidity and the risk of influenza infections. The overall cumulative adjusted relative risk (ARR) was statistically significant when weekly mean absolute humidity was low (<10 µg/m3 ) and high (>17.5 µg/m3 ). A slightly higher ARR was observed when weekly mean temperature reached over 30.5°C. A statistically significantly higher ARR was observed when weekly mean relative humidity dropped below 67%. ARR increased statistically significantly with increasing rainfall. For PM2.5 , the ARR was marginally statistically insignificant. In brief, high temperature, wet and dry conditions, and heavy rainfall were the major risk factors associated with a higher risk of influenza infections. CONCLUSIONS The present study contributes additional knowledge to the understanding of the effects of various environmental factors on influenza activities. Our findings shall be useful and important for the development of influenza surveillance and early warning systems.
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Affiliation(s)
- Steven Yuk‐Fai Lau
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
| | - Wei Cheng
- Zhejiang Province Centre for Disease Control and PreventionHangzhouChina
| | - Zhao Yu
- Zhejiang Province Centre for Disease Control and PreventionHangzhouChina
| | - Kirran N. Mohammad
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
| | - Maggie Haitian Wang
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
- Clinical Trials and Biostatistics LaboratoryShenzhen Research InstituteThe Chinese University of Hong KongHong KongChina
| | - Benny Chung‐Ying Zee
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
- Clinical Trials and Biostatistics LaboratoryShenzhen Research InstituteThe Chinese University of Hong KongHong KongChina
| | - Xi Li
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
| | - Ka Chun Chong
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
- Clinical Trials and Biostatistics LaboratoryShenzhen Research InstituteThe Chinese University of Hong KongHong KongChina
- Centre for Health Systems and Policy ResearchThe Chinese University of Hong KongHong KongChina
| | - Enfu Chen
- Zhejiang Province Centre for Disease Control and PreventionHangzhouChina
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Mahapatra B, Walia M, Avis WR, Saggurti N. Effect of exposure to PM 10 on child health: evidence based on a large-scale survey from 184 cities in India. BMJ Glob Health 2021; 5:bmjgh-2020-002597. [PMID: 32816954 PMCID: PMC7437942 DOI: 10.1136/bmjgh-2020-002597] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/22/2020] [Accepted: 07/12/2020] [Indexed: 01/07/2023] Open
Abstract
Introduction Air pollution is increasingly becoming a serious global public health concern. Prior studies examining the effect of air pollution on health have ignored the role of households’ hygienic practices and socioeconomic condition, which are key determinants of the health status of a country like India. This study examines the effects of air pollution, measured in levels of particulate matters of size below 10 µg/m3 (PM10), on child-health outcomes after adjusting for hygiene practices. Methods Health data from the National Family Health Survey-4 (NFHS-4) and PM10 levels provided by the Central Pollution Control Board were matched for 184 Indian towns/cities. Child health outcomes included neonatal mortality, post-neonatal mortality, premature births, children with symptoms of acute respiratory infections (ARI) and low birth weight. Multilevel mixed-effects models were used to estimate the risk associated with exposure to PM10. Result Analyses based on 23 954 births found that every 10-unit increase in PM10 level, increased the risk of neonatal mortality by 6% (adjusted RR (95% CI): 1.02 (1.02 to 1.09)), and the odds of symptoms of ARI among children by 7% (adjusted OR (95% CI): 1.07 (1.03 to 1.12)), and premature births by 8% (adjusted OR (95% CI): 1.08 (1.03 to 1.12)). There was no statistically significant difference in the effect of PM10 on child health regardless of household’s hygienic practices. Effects of PM10 on child health outcomes remained similar for cities whether or not they were part of the National Clean Air Program (NCAP). Conclusion Exposure to PM10, regardless of hygienic practices, increases the risk of adverse child health outcomes. Study findings suggest that the focus of mitigating the effects of air pollution should be beyond the towns/cities identified under NCAP. Given the increasing industrialisation and urbanisation, a systemic, coherent approach is required to address the issue of air pollution in India.
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Affiliation(s)
| | | | - Wiliam Robert Avis
- International Development Department, University of Birmingham, Birmingham, UK
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Short-term exposure to air pollution and hospital admission for heart failure among older adults in metropolitan cities: a time-series study. Int Arch Occup Environ Health 2021; 94:1605-1615. [PMID: 34089350 DOI: 10.1007/s00420-021-01724-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/26/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE We aimed to investigate the association between air pollution concentration levels and hospital admissions for heart failure (HF) among older adults in metropolitan cities in South Korea. METHODS We used hospital admission data of 1.8 million older adults in seven metropolitan cities from 2008 to 2016, derived from the National Health Insurance Service of South Korea. Daily HF admission data were linked to air pollutants concentrations for the respective dates, including particulate matter less than 2.5 μm in size (PM2.5), 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone. We estimated the association between air pollutants and daily HF admissions using quasi-Poisson generalized additive models for each city. RESULTS During the study period, 142,490 hospital admissions for HF were noted. Increases of 10 μg/m3 of PM2.5 and PM10, and 10 ppb of SO2, NO2, and CO were associated with an increased risk of HF admission by 0.93% ([95% confidence intervals 0.51-1.36], 0.55% [0.31-0.80], 6.04% [2.15-10.08], 1.10% [0.38-1.82], and 0.05% [0.01-0.09]), respectively, on the same day. Increases in mean exposure to PM2.5, PM10, and SO2 for 8 days from the concurrent day were also significantly associated with HF admissions. During the warm season, the risk of HF admissions increased shortly after an increase in PM2.5, whereas prolonged effects were observed during the cold season. CONCLUSION Our study suggests the adverse effects of air pollution on HF. Moreover, the evidence of seasonality may help tailor protection guidelines for older adults.
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Li X, Xu J, Wang W, Liang JJ, Deng ZH, Du J, Xie MZ, Wang XR, Liu Y, Cui F, Lu QB. Air pollutants and outpatient visits for influenza-like illness in Beijing, China. PeerJ 2021; 9:e11397. [PMID: 34141466 PMCID: PMC8179240 DOI: 10.7717/peerj.11397] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/12/2021] [Indexed: 11/20/2022] Open
Abstract
Background Air pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms. Previous studies are limited as to whether air pollutants were associated to influenza-like illness (ILI). This study aimed to explore the association between air pollutants and outpatient visits for ILI, especially during an outbreak of influenza. Methods Daily counts of hospital visits for ILI were obtained from Peking University Third Hospital between January 1, 2015, and March 31, 2018. A generalized additive Poisson model was applied to examine the associations between air pollutants concentrations and daily outpatient visits for ILI when adjusted for the meteorological parameters. Results There were 35862 outpatient visits at the fever clinic for ILI cases. Air quality index (AQI), PM2.5, PM10, CO and O3 on lag0 days, as well as nitrogen dioxide (NO2) and sulfur dioxide (SO2) on lag1 days, were significantly associated with an increased risk of outpatient visits for ILI from January 2015 to November 2017. From December 2017 to March 2018, on lag0 days, air pollutants PM2.5 [risk ratio (RR) = 0.971, 95% CI: 0.963-0.979], SO2 (RR = 0.892, 95% CI: 0.840–0.948) and CO (RR = 0.306, 95% CI: 0.153–0.612) were significantly associated with a decreased risk of outpatient visits for ILI. Interestingly, on the lag2 days, all the pollutants were significantly associated with a reduced risk of outpatient visits for ILI except for O3. We did not observe the linear correlations between the outpatient visits for ILI and any of air pollutants, which were instead associated via a curvilinear relationship. Conclusions We found that the air pollutants may be associated with an increased risk of outpatient visits for ILI during the non-outbreak period and with a decreased risk during the outbreak period, which may be linked with the use of disposable face masks and the change of outdoor activities. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants during an influenza pandemic.
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Affiliation(s)
- Xiaoguang Li
- Department of Infectious Diseases, Peking University Third Hospital, Beijing, China
| | - Jie Xu
- Department of Infectious Diseases, Peking University Third Hospital, Beijing, China
| | - Wei Wang
- Department of Infectious Diseases, Peking University Third Hospital, Beijing, China
| | - Jing-Jin Liang
- Department of Infectious Diseases, Peking University Third Hospital, Beijing, China
| | - Zhong-Hua Deng
- Department of Infectious Diseases, Peking University Third Hospital, Beijing, China
| | - Juan Du
- Department of Laboratorial of Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Ming-Zhu Xie
- Department of Laboratorial of Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Xin-Rui Wang
- Department of Laboratorial of Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Yaqiong Liu
- Department of Laboratorial of Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Fuqiang Cui
- Department of Laboratorial of Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Qing-Bin Lu
- Department of Laboratorial of Science and Technology, School of Public Health, Peking University, Beijing, China
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Lindner-Cendrowska K, Bröde P. Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2021; 65:929-944. [PMID: 33454853 PMCID: PMC8149351 DOI: 10.1007/s00484-021-02076-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 05/13/2023]
Abstract
In order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland-Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013-2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasing UTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM and UTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.
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Affiliation(s)
- Katarzyna Lindner-Cendrowska
- Institute of Geography and Spatial Organization, Polish Academy of Sciences, Twarda 51/55, 00-818 Warsaw, Poland
| | - Peter Bröde
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund (IfADo), Dortmund, Germany
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