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Bayram H, Konyalilar N, Elci MA, Rajabi H, Aksoy GT, Mortazavi D, Kayalar Ö, Dikensoy Ö, Taborda-Barata L, Viegi G. Issue 4 - Impact of air pollution on COVID-19 mortality and morbidity: An epidemiological and mechanistic review. Pulmonology 2024:S2531-0437(24)00051-5. [PMID: 38755091 DOI: 10.1016/j.pulmoe.2024.04.005] [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: 03/28/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/18/2024] Open
Abstract
Air pollution is a major global environment and health concern. Recent studies have suggested an association between air pollution and COVID-19 mortality and morbidity. In this context, a close association between increased levels of air pollutants such as particulate matter ≤2.5 to 10 µM, ozone and nitrogen dioxide and SARS-CoV-2 infection, hospital admissions and mortality due to COVID 19 has been reported. Air pollutants can make individuals more susceptible to SARS-CoV-2 infection by inducing the expression of proteins such as angiotensin converting enzyme (ACE)2 and transmembrane protease, serine 2 (TMPRSS2) that are required for viral entry into the host cell, while causing impairment in the host defence system by damaging the epithelial barrier, muco-ciliary clearance, inhibiting the antiviral response and causing immune dysregulation. The aim of this review is to report the epidemiological evidence on impact of air pollutants on COVID 19 in an up-to-date manner, as well as to provide insights on in vivo and in vitro mechanisms.
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Affiliation(s)
- Hasan Bayram
- Koç University Research Centre for Translational Medicine (KUTTAM), Zeytinburnu, Istanbul, Turkey; Department of Pulmonary Medicine, School of Medicine, Koç University, Zeytinburnu, Istanbul, Turkey.
| | - Nur Konyalilar
- Koç University Research Centre for Translational Medicine (KUTTAM), Zeytinburnu, Istanbul, Turkey
| | | | - Hadi Rajabi
- Koç University Research Centre for Translational Medicine (KUTTAM), Zeytinburnu, Istanbul, Turkey
| | - G Tuşe Aksoy
- Koç University Research Centre for Translational Medicine (KUTTAM), Zeytinburnu, Istanbul, Turkey
| | - Deniz Mortazavi
- Koç University Research Centre for Translational Medicine (KUTTAM), Zeytinburnu, Istanbul, Turkey
| | - Özgecan Kayalar
- Koç University Research Centre for Translational Medicine (KUTTAM), Zeytinburnu, Istanbul, Turkey
| | - Öner Dikensoy
- Department of Pulmonary Medicine, School of Medicine, Koç University, Zeytinburnu, Istanbul, Turkey
| | - Luis Taborda-Barata
- UBIAir - Clinical and Experimental Lung Centre UBIMedical, University of Beira Interior, Covilhã, Portugal; CICS-UBI - Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal
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Jiang S, Tang L, Lou Z, Wang H, Huang L, Zhao W, Wang Q, Li R, Ding Z. The changing health effects of air pollution exposure for respiratory diseases: a multicity study during 2017-2022. Environ Health 2024; 23:36. [PMID: 38609898 PMCID: PMC11015632 DOI: 10.1186/s12940-024-01083-1] [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: 08/27/2023] [Accepted: 04/10/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Multifaceted SARS-CoV-2 interventions have modified exposure to air pollution and dynamics of respiratory diseases. Identifying the most vulnerable individuals requires effort to build a complete picture of the dynamic health effects of air pollution exposure, accounting for disparities across population subgroups. METHODS We use generalized additive model to assess the likely changes in the hospitalisation and mortality rate as a result of exposure to PM2.5 and O3 over the course of COVID-19 pandemic. We further disaggregate the population into detailed age categories and illustrate a shifting age profile of high-risk population groups. Additionally, we apply multivariable logistic regression to integrate demographic, socioeconomic and climatic characteristics with the pollution-related excess risk. RESULTS Overall, a total of 1,051,893 hospital admissions and 34,954 mortality for respiratory disease are recorded. The findings demonstrate a transition in the association between air pollutants and hospitalisation rates over time. For every 10 µg/m3 increase of PM2.5, the rate of hospital admission increased by 0.2% (95% CI: 0.1-0.7%) and 1.4% (1.0-1.7%) in the pre-pandemic and dynamic zero-COVID stage, respectively. Conversely, O3-related hospitalization rate would be increased by 0.7% (0.5-0.9%) in the pre-pandemic stage but lowered to 1.7% (1.5-1.9%) in the dynamic zero-COVID stage. Further assessment indicates a shift of high-risk people from children and young adolescents to the old, primarily the elevated hospitalization rates among the old people in Lianyungang (RR: 1.53, 95%CI: 1.46, 1.60) and Nantong (RR: 1.65, 95%CI: 1.57, 1.72) relative to those for children and young adolescents. Over the course of our study period, people with underlying diseases would have 26.5% (22.8-30.3%) and 12.7% (10.8-14.6%) higher odds of having longer hospitalisation and over 6 times higher odds of deaths after hospitalisation. CONCLUSIONS Our estimates provide the first comprehensive evidence on the dynamic pollution-health associations throughout the pandemic. The results suggest that age and underlying diseases collectively determines the disparities of pollution-related health effect across population subgroups, underscoring the urgency to identifying the most vulnerable individuals to air pollution.
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Affiliation(s)
- Siyu Jiang
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Longjuan Tang
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Zhe Lou
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Ling Huang
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Wei Zhao
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Qingqing Wang
- Jiangsu Provincial Center for Disease Prevention and Control, 172 Jiangsu Rd, Nanjing, 210009, Jiangsu, China
| | - Ruiyun Li
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China.
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China.
| | - Zhen Ding
- Jiangsu Provincial Center for Disease Prevention and Control, 172 Jiangsu Rd, Nanjing, 210009, Jiangsu, China.
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Yıldırım E. The relationship between PM 10 and SO 2 exposure and Covid-19 infection rates in Turkey using nomenclature of territorial units for statistics level 1 regions. Heliyon 2023; 9:e21795. [PMID: 38034777 PMCID: PMC10682619 DOI: 10.1016/j.heliyon.2023.e21795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/17/2023] [Accepted: 10/28/2023] [Indexed: 12/02/2023] Open
Abstract
The Covid-19 pandemic, which has been affecting the world since December 2019, has become one of the biggest problems of the 21st century. There are studies stating that the contagiousness of the Covid-19 pandemic, which is transmitted from person to person, increases more with environmental factors such as air pollution, and accordingly, there is an increase in the number of cases. In this study, a panel regression model to investigate the effect of air pollution concentrations such as PM10 and SO2 as environmental factors and population density on the monthly mean number of Covid-19 cases for 12 regions at the nomenclature of territorial units for statistics (NUTS) level 1 in Turkey between June 2020 and November 2020, and a linear regression model to investigate the effect at the regional level. we used. Based on the model results, we concluded that a small increase in air pollution indicators led to an increase in the number of Covid-19 cases in Turkey and its regions. It is very important to identify preventable environmental factors in order to prevent and minimize the effects of respiratory tract diseases and rapidly spreading pandemic diseases such as Covid-19. Accordingly, we can conclude that countries should take some measures, especially on air pollution, in order to develop public health and pandemic/disease management strategies and to reduce the risk of respiratory diseases.
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Affiliation(s)
- Elif Yıldırım
- Department of Statistics and Quality Coordinator, Konya Technical University, 42250, Konya, Turkey
- Department of Statistics, Hacettepe University, 06800, Ankara, Turkey
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Hyman S, Zhang J, Andersen ZJ, Cruickshank S, Møller P, Daras K, Williams R, Topping D, Lim YH. Long-term exposure to air pollution and COVID-19 severity: A cohort study in Greater Manchester, United Kingdom. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 327:121594. [PMID: 37030601 PMCID: PMC10079212 DOI: 10.1016/j.envpol.2023.121594] [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: 01/19/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 05/19/2023]
Abstract
Exposure to outdoor air pollution may affect incidence and severity of coronavirus disease 2019 (COVID-19). In this retrospective cohort based on patient records from the Greater Manchester Care Records, all first COVID-19 cases diagnosed between March 1, 2020 and May 31, 2022 were followed until COVID-19 related hospitalization or death within 28 days. Long-term exposure was estimated using mean annual concentrations of particulate matter with diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10), nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2) and benzene (C6H6) in 2019 using a validated air pollution model developed by the Department for Environment, Food and Rural Affairs (DEFRA). The association of long-term exposure to air pollution with COVID-19 hospitalization and mortality were estimated using multivariate logistic regression models after adjusting for potential individual, temporal and spatial confounders. Significant positive associations were observed between PM2.5, PM10, NO2, SO2, benzene and COVID-19 hospital admissions with odds ratios (95% Confidence Intervals [CI]) of 1.27 (1.25-1.30), 1.15 (1.13-1.17), 1.12 (1.10-1.14), 1.16 (1.14-1.18), and 1.39 (1.36-1.42), (per interquartile range [IQR]), respectively. Significant positive associations were also observed between PM2.5, PM10, SO2, or benzene and COVID-19 mortality with odds ratios (95% CI) of 1.39 (1.31-1.48), 1.23 (1.17-1.30), 1.18 (1.12-1.24), and 1.62 (1.52-1.72), per IQR, respectively. Individuals who were older, overweight or obese, current smokers, or had underlying comorbidities showed greater associations between all pollutants of interest and hospital admission, compared to the corresponding groups. Long-term exposure to air pollution is associated with developing severe COVID-19 after a positive SARS-CoV-2 infection, resulting in hospitalization or death.
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Affiliation(s)
- Samuel Hyman
- Department of Earth and Environmental Science, Centre for Atmospheric Science, School of Natural Sciences, The University of Manchester, Manchester, UK; Institute of Immunology and Inflammation, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK.
| | - Jiawei Zhang
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Zorana Jovanovic Andersen
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Sheena Cruickshank
- Institute of Immunology and Inflammation, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK
| | - Peter Møller
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Konstantinos Daras
- Department of Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Richard Williams
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; NIHR Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; NIHR Applied Research Collaboration Greater Manchester, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - David Topping
- Department of Earth and Environmental Science, Centre for Atmospheric Science, School of Natural Sciences, The University of Manchester, Manchester, UK
| | - Youn-Hee Lim
- Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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Ghobakhloo S, Khoshakhlagh AH, Mostafaii GR, Chuang KJ, Gruszecka-Kosowska A, Hosseinnia P. Critical air pollutant assessments and health effects attributed to PM 2.5 during and after COVID-19 lockdowns in Iran: application of AirQ + models. Front Public Health 2023; 11:1120694. [PMID: 37304093 PMCID: PMC10249069 DOI: 10.3389/fpubh.2023.1120694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 04/28/2023] [Indexed: 06/13/2023] Open
Abstract
Objectives The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic. Methods Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5). Results The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM2.5 in 2019-2021 decreased. Conclusion Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.
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Affiliation(s)
- Safiye Ghobakhloo
- Department of Environmental Health Engineering, School of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Amir Hossein Khoshakhlagh
- Department of Occupational Health Engineering, School of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Gholam Reza Mostafaii
- Department of Environmental Health Engineering, School of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Kai-Jen Chuang
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Agnieszka Gruszecka-Kosowska
- Faculty of Geology, Geophysics, and Environmental Protection, Department of Environmental Protection, AGH University of Science and Technology, Krakow, Poland
| | - Pariya Hosseinnia
- Department of Public Health, Garmsar Branch, Islamic Azad University, Garmsar, Iran
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Gilardi L, Marconcini M, Metz-Marconcini A, Esch T, Erbertseder T. Long-term exposure and health risk assessment from air pollution: impact of regional scale mobility. Int J Health Geogr 2023; 22:11. [PMID: 37208713 PMCID: PMC10196305 DOI: 10.1186/s12942-023-00333-8] [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: 04/04/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND The negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities. METHODS In this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level. RESULTS The effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric. CONCLUSIONS This indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.
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Affiliation(s)
- Lorenza Gilardi
- German Remote Sensing Data Center, Department Atmosphere (DFD-ATM), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany.
| | - Mattia Marconcini
- German Remote Sensing Data Center, Department Land Surface Dynamics (DFD-LAX), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
| | - Annekatrin Metz-Marconcini
- German Remote Sensing Data Center, Department Land Surface Dynamics (DFD-LAX), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
| | - Thomas Esch
- German Remote Sensing Data Center, Department Land Surface Dynamics (DFD-LAX), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
| | - Thilo Erbertseder
- German Remote Sensing Data Center, Department Atmosphere (DFD-ATM), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
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Suligowski R, Ciupa T. Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland. ENVIRONMENTAL RESEARCH 2023; 216:114662. [PMID: 36374652 PMCID: PMC9617687 DOI: 10.1016/j.envres.2022.114662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/18/2022] [Accepted: 10/23/2022] [Indexed: 05/19/2023]
Abstract
Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m2/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.
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Affiliation(s)
- Roman Suligowski
- Institute of Geography and Environmental Sciences, Jan Kochanowski University in Kielce, Poland.
| | - Tadeusz Ciupa
- Institute of Geography and Environmental Sciences, Jan Kochanowski University in Kielce, Poland.
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Hayet-Otero M, García-García F, Lee DJ, Martínez-Minaya J, España Yandiola PP, Urrutia Landa I, Nieves Ermecheo M, Quintana JM, Menéndez R, Torres A, Zalacain Jorge R, Arostegui I. Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques. PLoS One 2023; 18:e0284150. [PMID: 37053151 PMCID: PMC10101453 DOI: 10.1371/journal.pone.0284150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/26/2023] [Indexed: 04/14/2023] Open
Abstract
With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.
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Affiliation(s)
- Miren Hayet-Otero
- Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain
- Department of Electronic Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Basque Research and Technology Alliance (BRTA), TECNALIA, Derio, Basque Country, Spain
| | | | - Dae-Jin Lee
- Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain
- School of Science and Technology, IE University, Madrid, Madrid, Spain
| | - Joaquín Martínez-Minaya
- Department of Applied Statistics and Operational Research, and Quality, Universitat Politècnica de València (UPV), Valencia, Valencian Community, Spain
| | | | | | - Mónica Nieves Ermecheo
- BioCruces Bizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Research Unit, Galdakao-Usansolo University Hospital, Galdakao, Basque Country, Spain
| | - José María Quintana
- Research Unit, Galdakao-Usansolo University Hospital, Galdakao, Basque Country, Spain
| | - Rosario Menéndez
- Pneumology Department, La Fe University and Polytechnic Hospital, Valencia, Valencian Community, Spain
| | - Antoni Torres
- Pneumology Department, Hospital Clínic of Barcelona, Barcelona, Catalonia, Spain
| | | | - Inmaculada Arostegui
- Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain
- Department of Mathematics, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
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