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Zorn J, Simões M, Velders GJM, Gerlofs-Nijland M, Strak M, Jacobs J, Dijkema MBA, Hagenaars TJ, Smit LAM, Vermeulen R, Mughini-Gras L, Hogerwerf L, Klinkenberg D. Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands. ENVIRONMENTAL RESEARCH 2024; 252:118812. [PMID: 38561121 DOI: 10.1016/j.envres.2024.118812] [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: 02/04/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020-February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals' average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017-2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.
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Affiliation(s)
- Jelle Zorn
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Mariana Simões
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Guus J M Velders
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands; Institute for Marine and Atmospheric Research (IMAU), Utrecht University, Utrecht, the Netherlands
| | - Miriam Gerlofs-Nijland
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Maciek Strak
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - José Jacobs
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Marieke B A Dijkema
- Environment and Health in Overijssel and Gelderland, Public Health Services Gelderland-Midden, the Netherlands
| | | | - Lidwien A M Smit
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Lapo Mughini-Gras
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands; Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.
| | - Lenny Hogerwerf
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Don Klinkenberg
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
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2
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Simões M, Zorn J, Hogerwerf L, Velders GJM, Portengen L, Gerlofs-Nijland M, Dijkema M, Strak M, Jacobs J, Wesseling J, de Vries WJ, Mijnen-Visser S, Smit LAM, Vermeulen R, Mughini-Gras L. Outdoor air pollution as a risk factor for testing positive for SARS-CoV-2: A nationwide test-negative case-control study in the Netherlands. Int J Hyg Environ Health 2024; 259:114382. [PMID: 38652943 DOI: 10.1016/j.ijheh.2024.114382] [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: 01/22/2024] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Air pollution is a known risk factor for several diseases, but the extent to which it influences COVID-19 compared to other respiratory diseases remains unclear. We performed a test-negative case-control study among people with COVID-19-compatible symptoms who were tested for SARS-CoV-2 infection, to assess whether their long- and short-term exposure to ambient air pollution (AAP) was associated with testing positive (vs. negative) for SARS-CoV-2. We used individual-level data for all adult residents in the Netherlands who were tested for SARS-CoV-2 between June and November 2020, when only symptomatic people were tested, and modeled ambient concentrations of PM10, PM2.5, NO2 and O3 at geocoded residential addresses. In long-term exposure analysis, we selected individuals who did not change residential address in 2017-2019 (1.7 million tests) and considered the average concentrations of PM10, PM2.5 and NO2 in that period, and different sources of PM (industry, livestock, other agricultural activities, road traffic, other Dutch sources, foreign sources). In short-term exposure analysis, individuals not changing residential address in the two weeks before testing day (2.7 million tests) were included in the analyses, thus considering 1- and 2-week average concentrations of PM10, PM2.5, NO2 and O3 before testing day as exposure. Mixed-effects logistic regression analysis with adjustment for several confounders, including municipality and testing week to account for spatiotemporal variation in viral circulation, was used. Overall, there was no statistically significant effect of long-term exposure to the studied pollutants on the odds of testing positive vs. negative for SARS-CoV-2. However, significant positive associations of long-term exposure to PM10 and PM2.5 from specifically foreign and livestock sources, and to PM10 from other agricultural sources, were observed. Short-term exposure to PM10 (adjusting for NO2) and PM2.5 were also positively associated with increased odds of testing positive for SARS-CoV-2. While these exposures seemed to increase COVID-19 risk relative to other respiratory diseases, the underlying biological mechanisms remain unclear. This study reinforces the need to continue to strive for better air quality to support public health.
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Affiliation(s)
- Mariana Simões
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Jelle Zorn
- National Institute for Public Health and the Environment (RIVM), Centre for Infectious Disease Control (CIb), Bilthoven, the Netherlands
| | - Lenny Hogerwerf
- National Institute for Public Health and the Environment (RIVM), Centre for Infectious Disease Control (CIb), Bilthoven, the Netherlands
| | - Guus J M Velders
- Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands; National Institute for Public Health and the Environment (RIVM), Center for Environmental Quality (MIL), Bilthoven, the Netherlands
| | - Lützen Portengen
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Miriam Gerlofs-Nijland
- National Institute for Public Health and the Environment (RIVM), Center for Sustainability, Environment and Health (DMG), Bilthoven, the Netherlands
| | - Marieke Dijkema
- Municipal Health Services, Provinces of Overijssel and Gelderland, the Netherlands
| | - Maciek Strak
- National Institute for Public Health and the Environment (RIVM), Center for Sustainability, Environment and Health (DMG), Bilthoven, the Netherlands
| | - José Jacobs
- National Institute for Public Health and the Environment (RIVM), Center for Sustainability, Environment and Health (DMG), Bilthoven, the Netherlands
| | - Joost Wesseling
- National Institute for Public Health and the Environment (RIVM), Center for Environmental Quality (MIL), Bilthoven, the Netherlands
| | - Wilco J de Vries
- National Institute for Public Health and the Environment (RIVM), Center for Environmental Quality (MIL), Bilthoven, the Netherlands
| | - Suzanne Mijnen-Visser
- National Institute for Public Health and the Environment (RIVM), Center for Environmental Quality (MIL), Bilthoven, the Netherlands
| | - Lidwien A M Smit
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands
| | - Lapo Mughini-Gras
- Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; National Institute for Public Health and the Environment (RIVM), Centre for Infectious Disease Control (CIb), Bilthoven, the Netherlands.
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3
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Ali N. The recent burden of dengue infection in Bangladesh: A serious public health issue. J Infect Public Health 2024; 17:226-228. [PMID: 38113820 DOI: 10.1016/j.jiph.2023.12.003] [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: 09/04/2023] [Revised: 11/10/2023] [Accepted: 12/04/2023] [Indexed: 12/21/2023] Open
Abstract
Dengue is a rapidly increasing vector-borne disease with a high burden in South and Southeast Asia. This article presents an overview of the current dengue situation in Bangladesh, highlighting the critical public health challenges caused by this infectious disease. Between January and September 2023, a total of 203406 people were infected, and 989 people died, with a case fatality rate of 0.49%. Of these, 96.1% of infections and 95.2% of deaths occurred between July and September. Both infection and mortality rates showed a significant and positive correlation with population density and air quality index. Other environmental and socioeconomic factors may influence the burden of dengue infection. These include temperature, rainfall, humidity, unplanned urbanization, and water storage and waste management practices. To reduce transmission and mortality rates, it is urgent to prioritize early treatment of dengue cases and take measures to address the risk factors associated with dengue infection in the country.
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Affiliation(s)
- Nurshad Ali
- Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh.
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Houweling L, Maitland-Van der Zee AH, Holtjer JCS, Bazdar S, Vermeulen RCH, Downward GS, Bloemsma LD. The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis. ENVIRONMENTAL RESEARCH 2024; 240:117351. [PMID: 37852458 DOI: 10.1016/j.envres.2023.117351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/20/2023]
Abstract
BACKGROUND The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes. METHODS On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality. RESULTS A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality. CONCLUSION This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.
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Affiliation(s)
- Laura Houweling
- Department of Environmental Epidemiology, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands; Dept. of Pulmonary Medicine, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Anke-Hilse Maitland-Van der Zee
- Dept. of Pulmonary Medicine, Amsterdam UMC, Amsterdam, the Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands; Amsterdam Public Health, Amsterdam, the Netherlands
| | - Judith C S Holtjer
- Department of Environmental Epidemiology, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Somayeh Bazdar
- Dept. of Pulmonary Medicine, Amsterdam UMC, Amsterdam, the Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands; Amsterdam Public Health, Amsterdam, the Netherlands
| | - Roel C H Vermeulen
- Department of Environmental Epidemiology, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - George S Downward
- Department of Environmental Epidemiology, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Lizan D Bloemsma
- Dept. of Pulmonary Medicine, Amsterdam UMC, Amsterdam, the Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands; Amsterdam Public Health, Amsterdam, the Netherlands
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Saleh SAK, Adly HM. Impact of Ambient Air Pollution Exposure on Long COVID-19 Symptoms: A Cohort Study within the Saudi Arabian Population. Infect Dis Rep 2023; 15:642-661. [PMID: 37888141 PMCID: PMC10606867 DOI: 10.3390/idr15050060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
Evidence suggests that air pollution, specifically the particulate matters PM2.5 and PM10, plays a key role in exacerbating the risk of prolonged symptoms following COVID-19 infection. AIM This study endeavors to elucidate the potential interaction between chronic air pollution exposure and the manifestation of long COVID symptoms within a cohort based in Makkah, Saudi Arabia. METHODS Participants included residents from the Makkah region who had recovered from COVID-19 between 2022 and 2023. A comprehensive questionnaire was utilized to gather detailed demographic data and assess the persistent symptoms seen during the post-COVID period. To gauge the environmental exposure to potential risk factors, air sampling for PM10 and PM2.5 was systematically conducted in various locations in Makkah over a year. RESULTS Significant positive associations were found between PM2.5 and PM10 exposure and long COVID. Furthermore, specific symptom analysis revealed a significant association between air pollution and shortness of breath (for PM2.5). Only PM2.5 exposure remained statistically significant (RR = 1.32, 95% CI: 1.05, 1.67). In contrast, the association with PM10 remained on the cusp of significance, with an RR of 1.27 (95% CI: 1.00, 1.61). CONCLUSION This study highlights the importance of reducing air pollution levels to mitigate the long-term health consequences of COVID-19.
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Affiliation(s)
- Saleh A. K. Saleh
- Biochemistry Department, College of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia;
- Oncology Diagnostic Unit, College of Medicine, Ain Shams University, Cairo 11435, Egypt
| | - Heba M. Adly
- Community Medicine and Pilgrims Healthcare Department, College of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia
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Learoyd AE, Nicholas J, Hart N, Douiri A. Revisiting ethnic discrepancies in COVID-19 hospitalized cohorts: a correction for collider bias. J Clin Epidemiol 2023; 161:94-103. [PMID: 37385305 PMCID: PMC10299938 DOI: 10.1016/j.jclinepi.2023.06.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES Studies from the first waves of the coronavirus disease 2019 (COVID-19) pandemic suggest that individuals from minority ethnicities are at an increased risk of worse outcomes. Concerns exist that this relationship is potentially driven by bias from analyzing hospitalized patients only. We investigate this relationship and the possible presence of bias. STUDY DESIGN AND SETTING Using data from South London hospitals across two COVID-19 waves (February 2020 - May 2021), the relationship between ethnicity and COVID-19 outcomes was examined using regression models. Three iterations of each model were completed: 1) an unadjusted analysis, 2) adjusting for covariates (medical history and deprivation), and 3) adjusting for covariates and bias induced by conditioning on hospitalization. RESULTS Among 3,133 patients, those who were Asian had a two-fold increased risk of death during the hospital stay that was consistent across the two COVID-19 waves and was not affected by correcting for conditioning on hospitalization. However, wave-specific effects demonstrate significant differences between ethnic groups until bias from using a hospitalized cohort was corrected for. CONCLUSION Worsened COVID-19 outcomes in minority ethnicities may be minimized by correcting for bias induced by conditioning on hospitalization. Consideration of this bias should be a key component of study design.
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Affiliation(s)
| | - Jennifer Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Nicholas Hart
- Lane Fox Clinical Respiratory Physiology Research Centre, Guy's & St Thomas' NHS Foundation Trust, London, UK; Centre for Human and Applied Physiological Sciences, King's College London, London, UK
| | - Abdel Douiri
- School of Life Course and Population Sciences, King College London, London, UK
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7
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Woodward SM, Mork D, Wu X, Hou Z, Braun D, Dominici F. Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002178. [PMID: 37531330 PMCID: PMC10395946 DOI: 10.1371/journal.pgph.0002178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/21/2023] [Indexed: 08/04/2023]
Abstract
Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.
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Affiliation(s)
- Sophie M. Woodward
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Daniel Mork
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Xiao Wu
- Department of Biostatistics, Columbia University, New York, New York, United States of America
| | - Zhewen Hou
- Department of Statistics, Columbia University, New York, New York, United States of America
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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8
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Islam ARMT, Al Awadh M, Mallick J, Pal SC, Chakraborty R, Fattah MA, Ghose B, Kakoli MKA, Islam MA, Naqvi HR, Bilal M, Elbeltagi A. Estimating ground-level PM 2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh. AIR QUALITY, ATMOSPHERE, & HEALTH 2023; 16:1117-1139. [PMID: 37303964 PMCID: PMC9961308 DOI: 10.1007/s11869-023-01329-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/16/2023] [Indexed: 06/13/2023]
Abstract
Fine particulate matter (PM2.5) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM2.5 concentrations. However, statistical models have shown inconsistency in PM2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NOX, SO2, CO, and O3) on the dynamics of PM2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO2, NOX, and O3. Precipitation, relative humidity, and temperature have negative correlations with PM2.5. The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM2.5 concentrations. This study will help quantify ground-level PM2.5 concentration exposure and recommend regional government actions to prevent and regulate PM2.5 air pollution. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01329-w.
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Affiliation(s)
| | - Mohammed Al Awadh
- Department of Industrial Engineering, College of Engineering, King Khalid University, Abha, 61421 Saudi Arabia
| | - Javed Mallick
- Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal 713104 India
| | - Rabin Chakraborty
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal 713104 India
| | - Md. Abdul Fattah
- Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh
| | - Bonosri Ghose
- Department of Disaster Management, Begum Rokeya University, Rangpur, Rangpur, 5400 Bangladesh
| | | | - Md. Aminul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, Rangpur, 5400 Bangladesh
| | - Hasan Raja Naqvi
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia (A Central University), New Delhi, 110025 India
| | - Muhammad Bilal
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 45003 China
| | - Ahmed Elbeltagi
- Agricultural Engineering Dept., Faculty of Agriculture, Mansoura University, Mansoura, 35516 Egypt
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9
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Bonilla JA, Lopez-Feldman A, Pereda PC, Rivera NM, Ruiz-Tagle JC. Association between long-term air pollution exposure and COVID-19 mortality in Latin America. PLoS One 2023; 18:e0280355. [PMID: 36649353 PMCID: PMC9844883 DOI: 10.1371/journal.pone.0280355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/27/2022] [Indexed: 01/18/2023] Open
Abstract
Recent studies have shown a relationship between air pollution and increased vulnerability and mortality due to COVID-19. Most of these studies have looked at developed countries. This study examines the relationship between long-term exposure to air pollution and COVID-19-related deaths in four countries of Latin America that have been highly affected by the pandemic: Brazil, Chile, Colombia, and Mexico. Our results suggest that an increase in long-term exposure of 1 μg/m3 of fine particles is associated with a 2.7 percent increase in the COVID-19 mortality rate. This relationship is found primarily in municipalities of metropolitan areas, where urban air pollution sources dominate, and air quality guidelines are usually exceeded. By focusing the analysis on Latin America, we provide a first glimpse on the role of air pollution as a risk factor for COVID-19 mortality within a context characterized by weak environmental institutions, limited health care capacity and high levels of inequality.
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Affiliation(s)
- Jorge A. Bonilla
- Department of Economics, Universidad de Los Andes, Bogota, Colombia
| | - Alejandro Lopez-Feldman
- Environment for Development, University of Gothenburg, Göteborg, Sweden
- Department of Economics, Centro de Investigacion y Docencia Economicas, Mexico City, Mexico
| | - Paula C. Pereda
- Department of Economics, University of São Paulo, São Paulo, Brazil
| | | | - J. Cristobal Ruiz-Tagle
- Department of Geography & Environment, London School of Economics and Political Science, London, United Kingdom
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10
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Di Ciaula A, Moshammer H, Lauriola P, Portincasa P. Environmental health, COVID-19, and the syndemic: internal medicine facing the challenge. Intern Emerg Med 2022; 17:2187-2198. [PMID: 36181580 PMCID: PMC9525944 DOI: 10.1007/s11739-022-03107-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/16/2022] [Indexed: 11/29/2022]
Abstract
Internists are experts in complexity, and the COVID-19 pandemic is disclosing complex and unexpected interactions between communicable and non-communicable diseases, environmental factors, and socio-economic disparities. The medicine of complexity cannot be limited to facing comorbidities and to the clinical management of multifaceted diseases. Evidence indicates how climate change, pollution, demographic unbalance, and inequalities can affect the spreading and outcomes of COVID-19 in vulnerable communities. These elements cannot be neglected, and a wide view of public health aspects by a "one-health" approach is strongly and urgently recommended. According to World Health Organization, 35% of infectious diseases involving the lower respiratory tract depend on environmental factors, and infections from SARS-Cov-2 is not an exception. Furthermore, environmental pollution generates a large burden of non-communicable diseases and disabilities, increasing the individual vulnerability to COVID-19 and the chance for the resilience of large communities worldwide. In this field, the awareness of internists must increase, as privileged healthcare providers. They need to gain a comprehensive knowledge of elements characterizing COVID-19 as part of a syndemic. This is the case when pandemic events hit vulnerable populations suffering from the increasing burden of chronic diseases, disabilities, and social and economic inequalities. Mastering the interplay of such events requires a change in overall strategy, to adequately manage not only the SARS-CoV-2 infection but also the growing burden of non-communicable diseases by a "one health" approach. In this context, experts in internal medicine have the knowledge and skills to drive this change.
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Affiliation(s)
- Agostino Di Ciaula
- Clinica Medica “A. Murri”, Department of Biomedical Sciences and Human Oncology, University of Bari Medical School, Bari, Italy
- International Society of Doctors for Environment (ISDE), Geneva, Switzerland
| | - Hanns Moshammer
- International Society of Doctors for Environment (ISDE), Geneva, Switzerland
- Department of Environmental Health, Center for Public Health, Medical University Vienna, 1090 Vienna, Austria
- Department of Hygiene, Medical University of Karakalpakstan, Nukus, Uzbekistan 230100
| | - Paolo Lauriola
- International Society of Doctors for Environment (ISDE), Geneva, Switzerland
| | - Piero Portincasa
- Clinica Medica “A. Murri”, Department of Biomedical Sciences and Human Oncology, University of Bari Medical School, Bari, Italy
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11
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Marian B, Yan Y, Chen Z, Lurmann F, Li K, Gilliland F, Eckel SP, Garcia E. Independent associations of short- and long-term air pollution exposure with COVID-19 mortality among Californians. ENVIRONMENTAL ADVANCES 2022; 9:100280. [PMID: 35966412 PMCID: PMC9361629 DOI: 10.1016/j.envadv.2022.100280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/30/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
The growing literature demonstrating air pollution associations on COVID-19 mortality contains studies predominantly examining long-term exposure, with few on short-term exposure, and rarely both together to estimate independent associations. Because mechanisms by which air pollution may impact COVID-19 mortality risk function over timescales ranging from years to days, and given correlation among exposure time windows, consideration of both short- and long-term exposure is of importance. We assessed the independent associations between COVID-19 mortality rates with short- and long-term air pollution exposure by modeling both concurrently. Using California death certificate data COVID-19-related deaths were identified, and decedent residential information used to assess short- (4-week mean) and long-term (6-year mean) exposure to particulate matter <2.5µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3). Negative binomial mixed models were fitted on weekly census tract COVID-19 mortality adjusting for potential confounders with random effects for county and census tract and an offset for population. Data were evaluated separately for two time periods March 16, 2020-October 18, 2020 and October 19, 2020-April 25, 2021, representing the Spring/Summer surges and Winter surge. Independent positive associations with COVID-19 mortality were observed for short- and long-term PM2.5 in both study periods, with strongest associations observed in the first study period: COVID-19 mortality rate ratio for a 2-μg/m3 increase in long-term PM2.5 was 1.13 (95%CI:1.09,1.17) and for a 4.7-μg/m3 increase in short-term PM2.5 was 1.05 (95%CI:1.02,1.08). Statistically significant positive associations were seen for both short- and long-term NO2 in study period 1, but short-term NO2 was not statistically significant in study period 2. Results for long-term O3 indicate positive associations, however, only marginal significance is achieved in study period 1. These findings support an adverse effect of long-term PM2.5 and NO2 exposure on COVID-19 mortality risk, independent of short-term exposure, and a possible independent effect of short-term PM2.5.
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Affiliation(s)
- Brittney Marian
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States of America
| | - Ying Yan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States of America
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States of America
| | - Fred Lurmann
- Sonoma Technology, Inc, Petaluma, CA, United States of America
| | - Kenan Li
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Frank Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States of America
| | - Sandrah P Eckel
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States of America
| | - Erika Garcia
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States of America
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12
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English PB, Von Behren J, Balmes JR, Boscardin J, Carpenter C, Goldberg DE, Horiuchi S, Richardson M, Solomon G, Valle J, Reynolds P. Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A. ENVIRONMENTAL ADVANCES 2022; 9:100270. [PMID: 35912397 PMCID: PMC9316717 DOI: 10.1016/j.envadv.2022.100270] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 07/06/2022] [Accepted: 07/25/2022] [Indexed: 05/08/2023]
Abstract
Previous studies have reported associations between air pollution and COVID-19 morbidity and mortality, but most have limited their exposure assessment to a large area, have not used individual-level variables, nor studied infections. We examined 3.1 million SARS-CoV-2 infections and 49,691 COVID-19 deaths that occurred in California from February 2020 to February 2021 to evaluate risks associated with long-term neighborhood concentrations of particulate matter less than 2.5 μm in diameter (PM2.5). We obtained individual address data on SARS-CoV-2 infections and COVID-19 deaths and assigned 2000-2018 1km-1km gridded PM2.5 surfaces to census block groups. We included individual covariate data on age and sex, and census block data on race/ethnicity, air basin, Area Deprivation Index, and relevant comorbidities. Our analyses were based on generalized linear mixed models utilizing a Poisson distribution. Those living in the highest quintile of long-term PM2.5 exposure had risks of SARS-CoV-2 infections 20% higher and risks of COVID-19 mortality 51% higher, compared to those living in the lowest quintile of long-term PM2.5 exposure. Those living in the areas of highest long-term PM2.5 exposure were more likely to be Hispanic and more vulnerable, based on the Area Deprivation Index. The increased risks for SARS-CoV-2 Infections and COVID-19 mortality associated with highest long-term PM2.5 concentrations at the neighborhood-level in California were consistent with a growing body of literature from studies worldwide, and further highlight the importance of reducing levels of air pollution to protect public health.
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Affiliation(s)
- Paul B English
- Tracking California Public Health Institute, 555 12th St., Suite 290, Oakland, CA 94607, United States
| | - Julie Von Behren
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
| | - John R Balmes
- Department of Medicine, University of California, San Francisco, CA, United States
| | - John Boscardin
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
| | - Catherine Carpenter
- Tracking California Public Health Institute, 555 12th St., Suite 290, Oakland, CA 94607, United States
| | - Debbie E Goldberg
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
| | - Sophia Horiuchi
- Tracking California Public Health Institute, 555 12th St., Suite 290, Oakland, CA 94607, United States
| | - Maxwell Richardson
- Tracking California Public Health Institute, 555 12th St., Suite 290, Oakland, CA 94607, United States
| | - Gina Solomon
- Tracking California Public Health Institute, 555 12th St., Suite 290, Oakland, CA 94607, United States
- Department of Medicine, University of California, San Francisco, CA, United States
| | - Jhaqueline Valle
- Tracking California Public Health Institute, 555 12th St., Suite 290, Oakland, CA 94607, United States
| | - Peggy Reynolds
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
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13
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Marcus SM, Marcus TS. Infrastructural Inequality and Household COVID-19 Vulnerability in a South African Urban Settlement. J Urban Health 2022; 99:571-581. [PMID: 35445280 PMCID: PMC9020544 DOI: 10.1007/s11524-022-00625-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/25/2022] [Indexed: 12/24/2022]
Abstract
COVID-19 has highlighted the importance of household infrastructure in containing the spread of SARS-CoV-2, with Global South urban settlements particularly vulnerable. Targeted interventions have used area or dwelling type as proxies for infrastructural vulnerability, potentially missing vulnerable households. We use infrastructural determinants of COVID-19 (crowding, water source, toilet facilities, and indoor pollution) to create an Infrastructural Vulnerability Index using cross-sectional household data (2018-2019) from Mamelodi, a low-income urban settlement in South Africa. Households were stratified into vulnerability groups by index results; sociodemographic variables were assessed as predictors of index scores; and inequality analysis and decomposition were conducted. Thirty-three percent of households fell in the lowest risk group, 32% in the second, 21% in the third, and 14% in the highest. Dwelling type and geographical ward were associated with changes in index scores, with a shack (adjusted β (aβ) = 3.45, CI = 3.39-3.51) associated with highest increase compared to a house. Wards in more developed areas were not consistently associated with lower index scores in the final regression model. The infrastructural vulnerability of the top 10% of households was greater than the bottom 40%, and inequality was predominantly within (80%) rather than between (20%) wards, and more between (60%) than within (40%) dwelling types. Our results show a minority of households account for the majority of infrastructural vulnerability, with its distribution only partially explained by area and dwelling type. Efforts to contain COVID-19 can be improved by using local-level data, and a vulnerability index, to target infrastructural support to households in greatest need.
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Affiliation(s)
- Simon M. Marcus
- Division of Family Medicine, Department of Family Medicine and Primary Care, School of Clinical Medicine, Faculty of Health Science, University of the Witwatersrand, Johannesburg, 2000 South Africa
| | - Tessa S. Marcus
- COPC Research Unit, School of Medicine, Faculty of Health Science, University of Pretoria, Pretoria, 0028 South Africa
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14
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Refisch L, Lorenz F, Riedlinger T, Taubenböck H, Fischer M, Grabenhenrich L, Wolkewitz M, Binder H, Kreutz C. Data-driven prediction of COVID-19 cases in Germany for decision making. BMC Med Res Methodol 2022; 22:116. [PMID: 35443607 PMCID: PMC9019290 DOI: 10.1186/s12874-022-01579-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/15/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. METHODS We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. RESULTS The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. CONCLUSIONS We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.
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Affiliation(s)
- Lukas Refisch
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan Meier Str. 26, Freiburg, 79104 Germany
- Institute of Physics, University of Freiburg, Hermann-Herder-Str. 3, Freiburg, 79104 Germany
| | - Fabian Lorenz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan Meier Str. 26, Freiburg, 79104 Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), Schänzlestr. 18, Freiburg, 79104 Germany
| | - Torsten Riedlinger
- German Aerospace Center, Earth Observation Center, Münchener Str. 20, Weßling, 82234 Germany
| | - Hannes Taubenböck
- German Aerospace Center, Earth Observation Center, Münchener Str. 20, Weßling, 82234 Germany
- Institute for Geography and Geology, Julius-Maximilians-Universität Würzburg, Am Hubland, Würzburg, 97074 Germany
| | - Martina Fischer
- Robert-Koch-Institute, Department for Methodology and Research Infrastructure, Nordufer 20, Berlin, 13353 Germany
| | - Linus Grabenhenrich
- Robert-Koch-Institute, Department for Methodology and Research Infrastructure, Nordufer 20, Berlin, 13353 Germany
- Charité - Universitätsmedizin Berlin, Department of Dermatology, Venerology and Allergology, Luisenstraße 2, Berlin, 10117 Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan Meier Str. 26, Freiburg, 79104 Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan Meier Str. 26, Freiburg, 79104 Germany
- Freiburg Center for Data Analysis and Modelling (FDM), University of Freiburg, Ernst-Zermelo-Str. 1, Freiburg, 79104 Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan Meier Str. 26, Freiburg, 79104 Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), Schänzlestr. 18, Freiburg, 79104 Germany
- Freiburg Center for Data Analysis and Modelling (FDM), University of Freiburg, Ernst-Zermelo-Str. 1, Freiburg, 79104 Germany
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15
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Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19094989. [PMID: 35564384 PMCID: PMC9100265 DOI: 10.3390/ijerph19094989] [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: 02/20/2022] [Revised: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 01/09/2023]
Abstract
The aim of this study was to investigate the relationship between meteorological parameters, air quality and daily COVID-19 transmission in Morocco. We collected daily data of confirmed COVID-19 cases in the Casablanca region, as well as meteorological parameters (average temperature, wind, relative humidity, precipitation, duration of insolation) and air quality parameters (CO, NO2, 03, SO2, PM10) during the period of 2 March 2020, to 31 December 2020. The General Additive Model (GAM) was used to assess the impact of these parameters on daily cases of COVID-19. A total of 172,746 confirmed cases were reported in the study period. Positive associations were observed between COVID-19 and wind above 20 m/s and humidity above 80%. However, temperatures above 25° were negatively associated with daily cases of COVID-19. PM10 and O3 had a positive effect on the increase in the number of daily confirmed COVID-19 cases, while precipitation had a borderline effect below 25 mm and a negative effect above this value. The findings in this study suggest that significant associations exist between meteorological factors, air quality pollution (PM10) and the transmission of COVID-19. Our findings may help public health authorities better control the spread of COVID-19.
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16
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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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17
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Norouzi N, Asadi Z. Air pollution impact on the Covid-19 mortality in Iran considering the comorbidity (obesity, diabetes, and hypertension) correlations. ENVIRONMENTAL RESEARCH 2022; 204:112020. [PMID: 34509488 PMCID: PMC8426329 DOI: 10.1016/j.envres.2021.112020] [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: 08/15/2021] [Revised: 09/03/2021] [Accepted: 09/04/2021] [Indexed: 05/09/2023]
Abstract
Since the rise of the Covid-19 pandemic, several researchers stated the possibility of a positive relationship between Covid-19 spread and climatic parameters. An ecological study in 12 Iranian cities using the report of daily deaths from Covid-19 (March to August 2020) and validated data on air pollutants, considering average concentrations in each city in the last year used to analyze the association between chronic exposure to air pollutants and the death rate from Covid-19 in Iran. Poisson regression models were used, with generalized additive models and adjustment variables. A significant increase of 2.7% (IC(95%) 2.6-4.4) was found in the mortality rate due to Covid-19 due to an increase of 1 μg/m3 of NO2. The results suggest an association between Covid-19 mortality and NO2 exposure. As a risk approximation associated with air pollution, more precise analysis is done. The results also show a good consistency with studies from other regions; this paper's results can be useful for the public health policymakers and decision-making to control the Covid-19 spread.
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Affiliation(s)
- Nima Norouzi
- Bournemouth University, Fern Barrow, Poole, Dorset, BH12 5BB, UK.
| | - Zahra Asadi
- Al-Ameen College of Pharmacy, Rajiv Gandhi University of Health Science (RGUHS), Bangalore, India
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18
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Prinz AL, Richter DJ. Long-term exposure to fine particulate matter air pollution: An ecological study of its effect on COVID-19 cases and fatality in Germany. ENVIRONMENTAL RESEARCH 2022; 204:111948. [PMID: 34464613 PMCID: PMC8400616 DOI: 10.1016/j.envres.2021.111948] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 05/18/2023]
Abstract
BACKGROUND COVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health. RESEARCH QUESTION This paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany. STUDY DESIGN Data from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included. MAIN FINDINGS An increase in the average long-term air pollution of 1 μg/m3 particulate matter PM2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM2.5 and PM10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants). CONCLUSION Long-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity.
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Affiliation(s)
- Aloys L Prinz
- Institute of Public Economics, University of Muenster, Wilmergasse 6-8, 48143, Muenster, Germany.
| | - David J Richter
- Institute of Public Economics, University of Muenster, Wilmergasse 6-8, 48143, Muenster, Germany.
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19
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Marquès M, Domingo JL. Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences. ENVIRONMENTAL RESEARCH 2022; 203:111930. [PMID: 34425111 PMCID: PMC8378989 DOI: 10.1016/j.envres.2021.111930] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/19/2021] [Indexed: 05/04/2023]
Abstract
In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM2.5, PM10, O3, NO2, SO2 and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM2.5 and PM10 as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.
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Affiliation(s)
- Montse Marquès
- Laboratory of Toxicology and Environmental Health, Universitat Rovira i Virgili, School of Medicine, Sant Llorens 21, 43201, Reus, Catalonia, Spain.
| | - José L Domingo
- Laboratory of Toxicology and Environmental Health, Universitat Rovira i Virgili, School of Medicine, Sant Llorens 21, 43201, Reus, Catalonia, Spain
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20
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Berg K, Romer Present P, Richardson K. Long-term air pollution and other risk factors associated with COVID-19 at the census tract level in Colorado. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 287:117584. [PMID: 34153607 PMCID: PMC8202820 DOI: 10.1016/j.envpol.2021.117584] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/28/2021] [Accepted: 06/09/2021] [Indexed: 05/07/2023]
Abstract
Previous nationwide studies have reported links between long-term concentrations of fine particulate matter (PM2.5) and COVID-19 infection and mortality rates. In order to translate these results to the state level, we use Bayesian hierarchical models to explore potential links between long-term PM2.5 concentrations and census tract-level rates of COVID-19 outcomes (infections, hospitalizations, and deaths) in Colorado. We explicitly consider how the uncertainty in PM2.5 estimates affects our results by comparing four different PM2.5 surfaces from academic and governmental organizations. After controlling for 20 census tract-level covariates, we find that our results depend heavily on the choice of PM2.5 surface. Using PM2.5 estimates from the United States EPA, we find that a 1 μg/m3 increase in long-term PM2.5 concentrations is associated with a statistically significant 26% increase in the relative risk of hospitalizations and a 34% increase in mortality. Results for all other surfaces and outcomes were not statistically significant. At the same time, we find a clear association between communities of color and COVID-19 outcomes at the Colorado census tract level that is minimally affected by the choice of PM2.5 surface. A per-interquartile range (IQR) increase in the percent of non-African American people of color was associated with a 31%, 43%, and 56% increase in the relative risk of infection, hospitalization, and mortality respectively, while a per-IQR increase in the proportion of non-Hispanic African Americans was associated with a 4% and 7% increase in the relative risk of infections and hospitalizations. The current disagreement among the different PM2.5 estimates is a key factor limiting our ability to link environmental exposures and health outcomes at the census tract level. These results have strong implications for the implementation of an equitable public health response during the crisis and suggest targeted areas for additional air monitoring in Colorado.
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Affiliation(s)
- Kevin Berg
- Colorado Department of Public Health and Environment, United States.
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