{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:09:51Z","timestamp":1790615391678,"version":"4.1.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018517","name":"Office for National Statistics","doi-asserted-by":"publisher","award":["2002569"],"award-info":[{"award-number":["2002569"]}],"id":[{"id":"10.13039\/100018517","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J R Soc Med"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To estimate the risk of Long COVID by socioeconomic deprivation and to further examine the inequality by sex and occupation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>We conducted a retrospective population-based cohort study using data from the ONS COVID-19 Infection Survey between 26 April 2020 and 31 January 2022. This is the largest nationally representative survey of COVID-19 in the UK with longitudinal data on occupation, COVID-19 exposure and Long COVID.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Community-based survey in the UK.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>A total of 201,799 participants aged 16 to 64 years and with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main outcome measures<\/jats:title>\n                    <jats:p>The risk of Long COVID at least 4 weeks after SARS-CoV-2 infection by index of multiple deprivation (IMD) and the modifying effects of socioeconomic deprivation by sex and occupation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Nearly 10% ( n\u2009=\u200919,315) of participants reported having Long COVID. Multivariable logistic regression models, adjusted for a range of variables (demographic, co-morbidity and time), showed that participants in the most deprived decile had a higher risk of Long COVID (11.4% vs. 8.2%; adjusted odds ratio (aOR): 1.46; 95% confidence interval (CI): 1.34, 1.59) compared to the least deprived decile. Significantly higher inequalities (most vs. least deprived decile) in Long COVID existed in healthcare and patient-facing roles (aOR: 1.76; 95% CI: 1.27, 2.44), in the education sector (aOR: 1.68; 95% CI: 1.31, 2.16) and in women (aOR: 1.56; 95% CI: 1.40, 1.73) than men (aOR: 1.32; 95% CI: 1.15, 1.51).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study provides insights into the heterogeneous degree of inequality in Long COVID by deprivation, sex and occupation. These findings will help inform public health policies and interventions in incorporating a social justice and health inequality lens.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/01410768231168377","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T19:05:17Z","timestamp":1683745517000},"page":"263-273","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":91,"title":["Socioeconomic inequalities of Long COVID: a retrospective population-based cohort study in the United Kingdom"],"prefix":"10.1177","volume":"116","author":[{"given":"Sharmin","family":"Shabnam","sequence":"first","affiliation":[{"name":"Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cameron","family":"Razieh","sequence":"additional","affiliation":[{"name":"Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW, UK"},{"name":"Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK"},{"name":"NIHR Leicester Biomedical Research Centre (BRC), University Hospitals of Leicester NHS Trust and University of Leicester, Leicester, LE5 4PW, UK"},{"name":"Office for National Statistics, Newport, NP10 8XG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hajira","family":"Dambha-Miller","sequence":"additional","affiliation":[{"name":"Primary Care Research Centre, Faculty of Medicine, University of Southampton, SO16 5ST, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Yates","sequence":"additional","affiliation":[{"name":"Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK"},{"name":"NIHR Leicester Biomedical Research Centre (BRC), University Hospitals of Leicester NHS Trust and University of Leicester, Leicester, LE5 4PW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clare","family":"Gillies","sequence":"additional","affiliation":[{"name":"Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogini V","family":"Chudasama","sequence":"additional","affiliation":[{"name":"Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manish","family":"Pareek","sequence":"additional","affiliation":[{"name":"Department of Respiratory Sciences, University of Leicester, Leicester, LE1 9HN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-3411","authenticated-orcid":false,"given":"Amitava","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London, London, NW1 2DA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ichiro","family":"Kawachi","sequence":"additional","affiliation":[{"name":"Harvard TH Chan School of Public Health, Harvard University, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Lacey","sequence":"additional","affiliation":[{"name":"Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva JA","family":"Morris","sequence":"additional","affiliation":[{"name":"Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, OX3 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of Southampton, SO16 5ST, UK"},{"name":"Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"bibr1-01410768231168377","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0952-y"},{"key":"bibr2-01410768231168377","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n1137"},{"key":"bibr3-01410768231168377","doi-asserted-by":"publisher","DOI":"10.1038\/s41584-021-00608-z"},{"key":"bibr4-01410768231168377","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(21)00031-X"},{"key":"bibr5-01410768231168377","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-95565-8"},{"key":"bibr6-01410768231168377","unstructured":"Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK \u2013 Office for National Statistics. 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