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      Trends in weight gain recorded in English primary care before and during the Coronavirus-19 pandemic: An observational cohort study using the OpenSAFELY platform

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          Abstract

          Background

          Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic.

          Methods and findings

          With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before (δ-prepandemic) and during (δ-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m 2/year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period (δ-change = δ-pandemic—δ-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain (δ-change ≥1.84 kg/m 2/year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m 2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m 2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data.

          Conclusions

          We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning.

          Abstract

          Miriam Samuel and team analyzed health records of 17 million adults in England, examining patterns of weight change before and during COVID-19 and identifying groups most prone to rapid weight gain.

          Author summary

          Why was this study done?
          • Restrictions imposed during the Coronavirus Disease 2019 (COVID-19) pandemic may have led to lifestyle changes that are associated with weight gain.

          • Some studies have suggested that women, younger adults (18 to 29 years), and those living in more deprived areas were at greatest risk of weight gain during the pandemic, but these were limited by small sample size.

          • There are currently no large-scale analyses of how the pandemic impacted preexisting patterns of weight gain, and how this varied by sociodemographic and clinical characteristics.

          What did the researchers do and find?
          • We used data from the routinely collected health records of 17 million adults living in England to investigate patterns of weight change before and during the pandemic and describe which groups were most likely to have experienced unhealthy patterns of weight gain.

          • We found that, among adults with measures of weight recorded in their healthcare records, women, younger adults (18 to 29 years), those living in the most deprived areas, and those of white British ethnicity were most likely to have gained weight rapidly before and during the pandemic.

          • The same groups were also most likely to have experienced extreme acceleration in their rate of weight gain during the pandemic.

          • Almost all the long-term conditions (LTCs) increased the risk of unhealthy patterns of weight gain, with mental health conditions, such as depression, having the greatest estimated effect.

          What do these findings mean?
          • The COVID-19 pandemic appears to have had the greatest impact on women, young adults, and those living in the most deprived areas in terms of unhealthy patterns of weight gain.

          • We present, to our knowledge, new evidence that people with mental health conditions were more likely to have unhealthy patterns of weight gain during the pandemic, highlighting the need to consider sociodemographic, physical, and mental health characteristics in research, policies, and interventions around weight.

          • We recommend that future pandemic planning should consider how to mitigate the unequal indirect impact of a pandemic on patterns of weight gain to prevent preexisting health inequalities being further exacerbated.

          • Changes in healthcare activity during the pandemic affected patterns of weight monitoring in primary care, which may have introduced new bias to the analyses.

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          Most cited references33

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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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            Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

            Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
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              Collider bias undermines our understanding of COVID-19 disease risk and severity

              Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ResourcesRole: SoftwareRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                PLOS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                24 June 2024
                June 2024
                : 21
                : 6
                : e1004398
                Affiliations
                [1 ] Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
                [2 ] Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
                [3 ] MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom
                [4 ] Leicester Diabetes Centre, Leicester General Hospital, Leicester, United Kingdom
                [5 ] Diabetes Research Centre, College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester, United Kingdom
                [6 ] Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Chelsea and Westminster Hospital Campus, Imperial College London, London, United Kingdom
                Wuqu’ Kawoq | Maya Health Alliance, GUATEMALA
                Author notes

                MS salary costs have been supported through a National Institute for Health and Care Research (NIHR) funded academic clinical fellowship in primary care (ACF-2017-19-006) and NIHR grant funding (NIHR AI-MULTIPLY Consortium NIHR203982) https://www.nihr.ac.uk/. RYP is supported by the EPSRC Centre for Doctoral Training in Health Data Science (EP/S02428X/1). RYP was previously employed as a data scientist for the Bennet Institute which is funded by grants from the Bennett Foundation, Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, Mohn-Westlake Foundation. SVE has been funded by a Diabetes UK Sir George Alberti research training fellowship (grant number: 17/0005588) and University College London Hospitals Biomedical Research Centre, Cardiovascular theme. FE salary cost is supported by MRC (MR/S027297/1) “Multimorbidity, clusters, trajectories and genetic risk in British south Asians, 2020-2023”. DS is funded by the NIHR (NIHR203982). AM is a senior clinical researcher at the University of Oxford in the Bennett Institute, which is funded by grants from the Bennett Foundation, Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, Mohn-Westlake Foundation. AM has consulted for https://inductionhealthcare.com/. AM has represented the RCGP in the health informatics group and the Professional Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter role is paid. AM is a former employee and Chief Medical Officer of NHS Digital (having left NHS Digital in January 2020). AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY. RM is supported by Barts Charity (MGU0504). JV was the National Clinical Director for Diabetes and Obesity at National Health Service (NHS) England from April 2013 to September 2023 and is funded by the Imperial National Institute for Health Research (NIHR) Biomedical Research Centre and North-West London NIHR Applied Research Collaboration. BMK is also employed by NHS England. KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and the NIHR Leicester Biomedical Research Centre (BRC). KK has acted as a consultant, speaker or received grants for investigator-initiated studies for Astra Zeneca, Bayer, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly and Merck Sharp & Dohme, Boehringer Ingelheim, Oramed Pharmaceuticals, Roche and Applied Therapeutics. SF has received grants from the NIHR (NIHR 31672, NIHR 202635) and MRC (MR/W014416/1, MR/V004905/1, MR/S027297/1). SF, RM, CM are part of the Genes & Health programme, which is part-funded (including salary contributions) by a Life Sciences Consortium comprising Astra Zeneca PLC, Bristol-Myers Squibb Company, GlaxoSmithKline Research and Development Limited, Maze Therapeutics Inc, Merck Sharp & Dohme LLC, Novo Nordisk A/S, Pfizer Inc, Takeda Development Centre Americas Inc. This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058). In addition, the OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157).

                ¶ Membership of the OpenSAFELY collaborative is provided in the Acknowledgements.

                Author information
                https://orcid.org/0000-0003-1258-435X
                https://orcid.org/0000-0002-1883-8771
                https://orcid.org/0000-0002-0203-8649
                https://orcid.org/0000-0002-9534-4521
                https://orcid.org/0000-0002-2098-1278
                https://orcid.org/0000-0001-5221-4770
                https://orcid.org/0000-0002-7784-1719
                https://orcid.org/0000-0002-9162-4999
                https://orcid.org/0000-0002-3817-8790
                https://orcid.org/0000-0002-3786-9063
                https://orcid.org/0000-0002-2684-4653
                Article
                PMEDICINE-D-23-01514
                10.1371/journal.pmed.1004398
                11249215
                38913709
                878d2db7-842f-4304-a1d0-5fbba3109a6f
                © 2024 Samuel et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 31 May 2023
                : 5 April 2024
                Page count
                Figures: 4, Tables: 1, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health and Care Research;
                Award ID: ACF-2017-19-006
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health and Care Research;
                Award ID: NIHR203982
                Award Recipient :
                During the period of the analysis MS salary costs salary costs have been supported through a National Institute for Health and Care Research (NIHR) funded academic clinical fellowship in primary care (ACF-2017-19-006) and NIHR grant funding (NIHR AI-MULTIPLY Consortium NIHR203982) https://www.nihr.ac.uk/. There was no direct funding for this study. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Weight Gain
                Medicine and Health Sciences
                Epidemiology
                Pandemics
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Health Care
                Primary Care
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Medicine and Health Sciences
                Epidemiology
                Ethnic Epidemiology
                Custom metadata
                vor-update-to-uncorrected-proof
                2024-07-15
                Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in OpenSAFELY-TPP is drawn from General Practice data across England where TPP is the data processor. TPP developers (CB, JC, JP, FH, and SH) initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. Bennett Institute for Applied Data Science developers and PIs (BG, LS, CEM, SB, AJW, WH, HJC, DE, PI, SD, GH, KB, and CTR) holding contracts with NHS England have access to the OpenSAFELY pseudonymised data tables as needed to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY Data Access Agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline—from raw data to completed results for this analysis—and for the OpenSAFELY platform as a whole is available for review at https://github.com/OpenSAFELY. The data management and analysis code for this paper was led by MS and RYP and is available for scientific review and re-use under MIT open licence. https://github.com/opensafely/BMI-and-Metabolic-Markers.
                COVID-19

                Medicine
                Medicine

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