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      Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom

      research-article
      1 , 2 , 1 , 2 , 1 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 1 , 5 , 12 , 13 , 14 , 15 , 16 , 1 , 17 , 18 , 19 , 20 , 6 , 14 , 15 , 21 , 22 , 23 , 24 , 16 , 25 , 26 , 27 , 28 , 4 , 29 , 6 , 27 , 30 , 31 , 32 , 33 , 34 , 3 , 16 , 4 , 5 , 28 , 35 , 1 ,
      International Journal of Obesity (2005)
      Nature Publishing Group UK
      Epidemiology, Public health

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          Abstract

          Background

          A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity.

          Methods

          We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status.

          Results

          We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8−40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0−33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity.

          Conclusion

          We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

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          Most cited references29

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          Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

          There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).
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            OpenSAFELY: factors associated with COVID-19 death in 17 million patients

            COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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              An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

              The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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                Author and article information

                Contributors
                tduarte@idiapjgol.org
                Journal
                Int J Obes (Lond)
                Int J Obes (Lond)
                International Journal of Obesity (2005)
                Nature Publishing Group UK (London )
                0307-0565
                1476-5497
                15 July 2021
                15 July 2021
                : 1-11
                Affiliations
                [1 ]GRID grid.482253.a, ISNI 0000 0004 0450 3932, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), ; Barcelona, Spain
                [2 ]GRID grid.7080.f, Universitat Autònoma de Barcelona, ; Bellaterra, Spain
                [3 ]GRID grid.497530.c, ISNI 0000 0004 0389 4927, Janssen Research & Development, ; Titusville, NJ USA
                [4 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Medical Informatics, , Erasmus University Medical Center, ; Rotterdam, The Netherlands
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Centre for Statistics in Medicine, NDORMS, , University of Oxford, ; Oxford, UK
                [6 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, ; Oxford, UK
                [7 ]GRID grid.8391.3, ISNI 0000 0004 1936 8024, College of Medicine and Health, University of Exeter, St Luke’s Campus, ; Exeter, UK
                [8 ]GRID grid.442890.3, ISNI 0000 0000 9417 110X, Faculty of Medicine, , Islamic University of Gaza, ; Gaza, Palestine
                [9 ]GRID grid.443356.3, ISNI 0000 0004 1758 7661, College of Pharmacy, Riyadh Elm University, ; Riyadh, Saudi Arabia
                [10 ]GRID grid.38142.3c, ISNI 000000041936754X, Massachusetts General Hospital, Harvard Medical School, ; Boston, MA USA
                [11 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Nuffield Department of Clinical Neurosciences, , University of Oxford, ; Oxford, UK
                [12 ]Real-World Evidence, Trial Form Support, Barcelona, Spain
                [13 ]GRID grid.7776.1, ISNI 0000 0004 0639 9286, Cairo University, Faculty of Pharmacy, ; Cairo, Egypt
                [14 ]GRID grid.280807.5, ISNI 0000 0000 9555 3716, VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, ; Salt Lake City, UT USA
                [15 ]GRID grid.223827.e, ISNI 0000 0001 2193 0096, Department of Internal Medicine, , University of Utah School of Medicine, ; Salt Lake City, UT USA
                [16 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Biomedical Informatics, , Columbia University, ; New York, NY USA
                [17 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Epidemiology, , Johns Hopkins School of Public, ; Baltimore, MD USA
                [18 ]GRID grid.418961.3, ISNI 0000 0004 0472 2713, Pharmacoepidemiology, Regeneron Pharmaceuticals, ; Tarrytown, NY USA
                [19 ]DHC Technologies co, Ltd, Beijing, China
                [20 ]GRID grid.5379.8, ISNI 0000000121662407, Division of Cancer Sciences, School of Medical Sciences, , University of Manchester, ; Manchester, UK
                [21 ]GRID grid.413806.8, Tennessee Valley Healthcare System, Veterans Affairs Medical Center, ; Nashville, TN USA
                [22 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Department of Biomedical Informatics, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [23 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, College of Medicine, The University of Arizona, ; Tucson, AZ USA
                [24 ]GRID grid.8241.f, ISNI 0000 0004 0397 2876, Division of Population Health and Genomics, , University of Dundee, ; Dundee, UK
                [25 ]GRID grid.413734.6, ISNI 0000 0000 8499 1112, New York-Presbyterian Hospital, ; New York, NY USA
                [26 ]GRID grid.8761.8, ISNI 0000 0000 9919 9582, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, ; Gothenburg, Sweden
                [27 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Medicine, , Stanford University, ; Palo Alto, CA USA
                [28 ]GRID grid.418848.9, ISNI 0000 0004 0458 4007, Real World Solutions, IQVIA, ; Cambridge, MA USA
                [29 ]GRID grid.430503.1, ISNI 0000 0001 0703 675X, Data Science to Patient Value Program, Department of Medicine, , University of Colorado Anschutz Medical Campus, ; Aurora, CO USA
                [30 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, College of Engineering, The University of Arizona, ; Tucson, AZ USA
                [31 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, ; Beijing, China
                [32 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Melbourne School of Population and Global Health, , The University of Melbourne, ; Melbourne, VIC Australia
                [33 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, Institute of Health Management, Southern Medical University, ; Guangzhou, China
                [34 ]GRID grid.416466.7, Nanfang Hospital, Southern Medical University, ; Guangzhou, China
                [35 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, The OHDSI Center at the Roux Institute, Northeastern University, ; Portland, ME USA
                Author information
                http://orcid.org/0000-0002-1964-3546
                http://orcid.org/0000-0002-4467-0220
                http://orcid.org/0000-0001-8630-5347
                http://orcid.org/0000-0003-1202-9153
                http://orcid.org/0000-0002-5630-2468
                http://orcid.org/0000-0002-4668-7069
                Article
                893
                10.1038/s41366-021-00893-4
                8281807
                34267326
                50101153-770c-4f3e-93a4-22ae69835339
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 November 2020
                : 7 June 2021
                : 24 June 2021
                Categories
                Article

                Nutrition & Dietetics
                epidemiology,public health
                Nutrition & Dietetics
                epidemiology, public health

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