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Zamanian A, von Kleist H, Ciora OA, Piperno M, Lancho G, Ahmidi N. Analysis of Missingness Scenarios for Observational Health Data. J Pers Med 2024; 14:514. [PMID: 38793096 PMCID: PMC11122060 DOI: 10.3390/jpm14050514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 04/29/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains.
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Affiliation(s)
- Alireza Zamanian
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany;
- Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany; (O.-A.C.); (M.P.); (G.L.); (N.A.)
| | - Henrik von Kleist
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany;
- Institute of Computational Biology, Helmholtz Center Munich, 80939 Munich, Germany
| | - Octavia-Andreea Ciora
- Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany; (O.-A.C.); (M.P.); (G.L.); (N.A.)
| | - Marta Piperno
- Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany; (O.-A.C.); (M.P.); (G.L.); (N.A.)
| | - Gino Lancho
- Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany; (O.-A.C.); (M.P.); (G.L.); (N.A.)
| | - Narges Ahmidi
- Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany; (O.-A.C.); (M.P.); (G.L.); (N.A.)
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Garmoe W, Rao K, Gorter B, Kantor R. Neurocognitive Impairment in Post-COVID-19 Condition in Adults: Narrative Review of the Current Literature. Arch Clin Neuropsychol 2024; 39:276-289. [PMID: 38520374 DOI: 10.1093/arclin/acae017] [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: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/25/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 virus has, up to the time of this article, resulted in >770 million cases of COVID-19 illness worldwide, and approximately 7 million deaths, including >1.1 million in the United States. Although defined as a respiratory virus, early in the pandemic, it became apparent that considerable numbers of people recovering from COVID-19 illness experienced persistence or new onset of multi-system health problems, including neurologic and cognitive and behavioral health concerns. Persistent multi-system health problems are defined as Post-COVID-19 Condition (PCC), Post-Acute Sequelae of COVID-19, or Long COVID. A significant number of those with PCC report cognitive problems. This paper reviews the current state of scientific knowledge on persisting cognitive symptoms in adults following COVID-19 illness. A brief history is provided of the emergence of concerns about persisting cognitive problems following COVID-19 illness and the definition of PCC. Methodologic factors that complicate clear understanding of PCC are reviewed. The review then examines research on patterns of cognitive impairment that have been found, factors that may contribute to increased risk, behavioral health variables, and interventions being used to ameliorate persisting symptoms. Finally, recommendations are made about ways neuropsychologists can improve the quality of existing research.
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Affiliation(s)
- William Garmoe
- Director of Psychology, MedStar National Rehabilitation Network, Washington, DC, USA
| | - Kavitha Rao
- Clinical Neuropsychologist, MedStar Good Samaritan Hospital, Baltimore, MD, USA
| | - Bethany Gorter
- Neuropsychology Post-Doctoral Fellow, MedStar National Rehabilitation Hospital, Washington, DC, USA
| | - Rachel Kantor
- Neuropsychology Post-Doctoral Fellow, MedStar National Rehabilitation Hospital, Washington, DC, USA
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3
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Kempaiah P, Libertin CR, Chitale RA, Naeyma I, Pleqi V, Sheele JM, Iandiorio MJ, Hoogesteijn AL, Caulfield TR, Rivas AL. Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes. Biomedicines 2024; 12:871. [PMID: 38672225 PMCID: PMC11048687 DOI: 10.3390/biomedicines12040871] [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: 02/10/2024] [Revised: 03/14/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. MATERIALS AND METHODS Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. RESULTS While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. DISCUSSION The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.
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Affiliation(s)
- Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA; (P.K.); (V.P.)
| | | | - Rohit A. Chitale
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Islam Naeyma
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA; (I.N.); (T.R.C.)
| | - Vasili Pleqi
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA; (P.K.); (V.P.)
| | | | - Michelle J. Iandiorio
- Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA;
| | | | - Thomas R. Caulfield
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA; (I.N.); (T.R.C.)
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ariel L. Rivas
- Center for Global Health, Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA
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4
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Ranzani O, Alari A, Olmos S, Milà C, Rico A, Basagaña X, Dadvand P, Duarte-Salles T, Forastiere F, Nieuwenhuijsen M, Vivanco-Hidalgo RM, Tonne C. Who is more vulnerable to effects of long-term exposure to air pollution on COVID-19 hospitalisation? ENVIRONMENT INTERNATIONAL 2024; 185:108530. [PMID: 38422877 DOI: 10.1016/j.envint.2024.108530] [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: 09/06/2023] [Revised: 01/23/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Factors that shape individuals' vulnerability to the effects of air pollution on COVID-19 severity remain poorly understood. We evaluated whether the association between long-term exposure to ambient NO2, PM2.5, and PM10 and COVID-19 hospitalisation differs by age, sex, individual income, area-level socioeconomic status, arterial hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. METHODS We analysed a population-based cohort of 4,639,184 adults in Catalonia, Spain, during 2020. We fitted Cox proportional hazard models adjusted for several potential confounding factors and evaluated the interaction effect between vulnerability indicators and the 2019 annual average of NO2, PM2.5, and PM10. We evaluated interaction on both additive and multiplicative scales. RESULTS Overall, the association was additive between air pollution and the vulnerable groups. Air pollution and vulnerability indicators had a synergistic (greater than additive) effect for males and individuals with low income or living in the most deprived neighbourhoods. The Relative Excess Risk due to Interaction (RERI) was 0.21, 95 % CI, 0.15 to 0.27 for NO2 and 0.16, 95 % CI, 0.11 to 0.22 for PM2.5 for males; 0.13, 95 % CI, 0.09 to 0.18 for NO2 and 0.10, 95 % CI, 0.05 to 0.14 for PM2.5 for lower individual income and 0.17, 95 % CI, 0.12 to 0.22 for NO2 and 0.09, 95 % CI, 0.05 to 0.14 for PM2.5 for lower area-level socioeconomic status. Results for PM10 were similar to PM2.5. Results on multiplicative scale were inconsistent. CONCLUSIONS Long-term exposure to air pollution had a larger synergistic effect on COVID-19 hospitalisation for males and those with lower individual- and area-level socioeconomic status.
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Affiliation(s)
- Otavio Ranzani
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Anna Alari
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sergio Olmos
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carles Milà
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Alex Rico
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Xavier Basagaña
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Payam Dadvand
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Francesco Forastiere
- National Research Council, IFT, Palermo, Italy; Environmental Research Group, Imperial College London, London, UK
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Cathryn Tonne
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
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Allen NE, Lacey B, Lawlor DA, Pell JP, Gallacher J, Smeeth L, Elliott P, Matthews PM, Lyons RA, Whetton AD, Lucassen A, Hurles ME, Chapman M, Roddam AW, Fitzpatrick NK, Hansell AL, Hardy R, Marioni RE, O’Donnell VB, Williams J, Lindgren CM, Effingham M, Sellors J, Danesh J, Collins R. Prospective study design and data analysis in UK Biobank. Sci Transl Med 2024; 16:eadf4428. [PMID: 38198570 PMCID: PMC11127744 DOI: 10.1126/scitranslmed.adf4428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.
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Affiliation(s)
- Naomi E Allen
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ben Lacey
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Scotland
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
- Dementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Paul Elliott
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Chemical Radiation Threats and Hazards, Imperial College London, UK
| | - Paul M Matthews
- UK Dementia Research Centre Institute and Department of Brain Sciences, Imperial College London, London, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, Wales
| | - Anthony D Whetton
- Veterinary Health Innovation Engine, University of Surrey, Guildford, UK
| | - Anneke Lucassen
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Southampton University, Southampton, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | | | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
| | | | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, Wales
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | | | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Rory Collins
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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6
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Carter AR, Clayton GL, Borges MC, Howe LD, Hughes RA, Smith GD, Lawlor DA, Tilling K, Griffith GJ. Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias? BMC Public Health 2023; 23:1863. [PMID: 37752486 PMCID: PMC10521522 DOI: 10.1186/s12889-023-16767-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection. METHODS Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures. RESULTS The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome. CONCLUSIONS The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - M Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rachael A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gareth J Griffith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
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7
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Demirer I, Pförtner TK. The Covid-19 pandemic as an accelerator of economic worries and labor-related mental health polarization in Germany? A longitudinal interacted mediation analysis with a difference-in-difference comparison. SSM Popul Health 2023; 23:101469. [PMID: 37538051 PMCID: PMC10393830 DOI: 10.1016/j.ssmph.2023.101469] [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: 04/03/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023] Open
Abstract
Objectives Labor-related mental health polarization refers to exposure to low-paid employment and unemployment decreasing mental health. Previous research identified economic worries as a key mediator. Against this background, the Covid-19 pandemic is often assumed to have accelerated already existing processes and affected vulnerable populations the most. Our study sought to investigate whether the Covid-19 pandemic accelerated the mediation by economic worries between employment type and mental health. Method Using the German Socioeconomic Panel (GSOEP) from 2016 onwards, we created a pre-Covid-19 sample (N = 8266) and a per-Covid-19 sample (N = 7294), with each having a t0 wave (2016/2018) and a t1 wave (2018/2020). We applied the mediational g-formula for longitudinal mediation with exposure-mediator (XM) interaction between employment type (X) and economic worries (M). We decomposed the total effect into a direct, indirect, and interacted effect of employment on mental health and provided a difference-in-difference comparison of the effects. Results During the Covid-19 pandemic, economic worries increased, and mental health decreased. However, the mediation by economic worries reduced by approx. 18.0% (e.g., from 35.0% to 28.9%). A decreased indirect effect caused the reduction in mediation, while the direct and interacted effect remained rather stable. We also found stark gender differences towards males having a higher total effect but a lower mediated effect during the Covid-19 pandemic. Conclusion Our results highlight that mediators competing to economic worries must have emerged during the Covid-19 pandemic. Such mediators could be the risk of infection at the workplace, the possibility of remote work, and gender-specific mediators. Our study is also the first to extend the mediational g-formula with the difference-in-difference approach. It can be used as a blueprint for researchers interested in evaluating the impact of events, such as the Covid-19 pandemic, on preexisting processes.
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Affiliation(s)
- Ibrahim Demirer
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Institute of Medical Sociology, Health Services Research, and Rehabilitation Science (IMVR), Chair of Medical Sociology, Germany
| | - Timo-Kolja Pförtner
- Department of Research Methods, Faculty of Human Sciences, University of Cologne, Cologne, Germany
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8
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Ranzani O, Alari A, Olmos S, Milà C, Rico A, Ballester J, Basagaña X, Chaccour C, Dadvand P, Duarte-Salles T, Foraster M, Nieuwenhuijsen M, Sunyer J, Valentín A, Kogevinas M, Lazcano U, Avellaneda-Gómez C, Vivanco R, Tonne C. Long-term exposure to air pollution and severe COVID-19 in Catalonia: a population-based cohort study. Nat Commun 2023; 14:2916. [PMID: 37225741 DOI: 10.1038/s41467-023-38469-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/02/2023] [Indexed: 05/26/2023] Open
Abstract
The association between long-term exposure to ambient air pollutants and severe COVID-19 is uncertain. We followed 4,660,502 adults from the general population in 2020 in Catalonia, Spain. Cox proportional models were fit to evaluate the association between annual averages of PM2.5, NO2, BC, and O3 at each participant's residential address and severe COVID-19. Higher exposure to PM2.5, NO2, and BC was associated with an increased risk of COVID-19 hospitalization, ICU admission, death, and hospital length of stay. An increase of 3.2 µg/m3 of PM2.5 was associated with a 19% (95% CI, 16-21) increase in hospitalizations. An increase of 16.1 µg/m3 of NO2 was associated with a 42% (95% CI, 30-55) increase in ICU admissions. An increase of 0.7 µg/m3 of BC was associated with a 6% (95% CI, 0-13) increase in deaths. O3 was positively associated with severe outcomes when adjusted by NO2. Our study contributes robust evidence that long-term exposure to air pollutants is associated with severe COVID-19.
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Affiliation(s)
- Otavio Ranzani
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Anna Alari
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sergio Olmos
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carles Milà
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Alex Rico
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Joan Ballester
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
| | - Xavier Basagaña
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carlos Chaccour
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universidad de Navarra, Pamplona, Spain
- CIBER Enfermedades Infecciosas (CIBERINFEC), Madrid, Spain
| | - Payam Dadvand
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Foraster
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- PHAGEX Research Group, Blanquerna School of Health Science, Universitat Ramon Llull (URL), Barcelona, Spain
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jordi Sunyer
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Antònia Valentín
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Manolis Kogevinas
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Uxue Lazcano
- Instituto Biodonostia, Grupo Atención Primaria, San Sebastian, Spain
- Agency for Health Quality and Assessment of Catalonia (AQuAS), Barcelona, Spain
| | | | - Rosa Vivanco
- Agency for Health Quality and Assessment of Catalonia (AQuAS), Barcelona, Spain
| | - Cathryn Tonne
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
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