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      Ethnicity, household composition and COVID-19 mortality: a national linked data study

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          Abstract

          Objective

          To estimate the proportion of ethnic inequalities explained by living in a multi-generational household.

          Design

          Causal mediation analysis.

          Setting

          Retrospective data from the 2011 Census linked to Hospital Episode Statistics (2017-2019) and death registration data (up to 30 November 2020).

          Participants

          Adults aged 65 years or over living in private households in England from 2 March 2020 until 30 November 2020 (n=10,078,568).

          Main outcome measures

          Hazard ratios were estimated for COVID-19 death for people living in a multi-generational household compared with people living with another older adult, adjusting for geographic factors, socioeconomic characteristics and pre-pandemic health.

          Results

          Living in a multi-generational household was associated with an increased risk of COVID-19 death. After adjusting for confounding factors, the hazard ratios for living in a multi-generational household with dependent children were 1.17 (95% confidence interval [CI] 1.06–1.30) and 1.21 (95% CI 1.06–1.38) for elderly men and women. The hazard ratios for living in a multi-generational household without dependent children were 1.07 (95% CI 1.01–1.13) for elderly men and 1.17 (95% CI 1.07–1.25) for elderly women. Living in a multi-generational household explained about 11% of the elevated risk of COVID-19 death among elderly women from South Asian background, but very little for South Asian men or people in other ethnic minority groups.

          Conclusion

          Elderly adults living with younger people are at increased risk of COVID-19 mortality, and this is a contributing factor to the excess risk experienced by older South Asian women compared to White women. Relevant public health interventions should be directed at communities where such multi-generational households are highly prevalent.

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          Most cited references18

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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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            A general approach to causal mediation analysis.

            Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. PsycINFO Database Record (c) 2010 APA, all rights reserved.
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              The impact of ethnicity on clinical outcomes in COVID-19: A systematic review

              Background The relationship between ethnicity and COVID-19 is uncertain. We performed a systematic review to assess whether ethnicity has been reported in patients with COVID-19 and its relation to clinical outcomes. Methods We searched EMBASE, MEDLINE, Cochrane Library and PROSPERO for English-language citations on ethnicity and COVID-19 (1st December 2019-15th May 2020). We also reviewed: COVID-19 articles in NEJM, Lancet, BMJ, JAMA, clinical trial protocols, grey literature, surveillance data and preprint articles on COVID-19 in MedRxiv to evaluate if the association between ethnicity and clinical outcomes were reported and what they showed. PROSPERO:180654. Findings Of 207 articles in the database search, five reported ethnicity; two reported no association between ethnicity and mortality. Of 690 articles identified from medical journals, 12 reported ethnicity; three reported no association between ethnicity and mortality. Of 209 preprints, 34 reported ethnicity – 13 found Black, Asian and Minority Ethnic (BAME) individuals had an increased risk of infection with SARS-CoV-2 and 12 reported worse clinical outcomes, including ITU admission and mortality, in BAME patients compared to White patients. Of 12 grey literature reports, seven with original data reported poorer clinical outcomes in BAME groups compared to White groups. Interpretation Data on ethnicity in patients with COVID-19 in the published medical literature remains limited. However, emerging data from the grey literature and preprint articles suggest BAME individuals are at an increased risk of acquiring SARS-CoV-2 infection compared to White individuals and also worse clinical outcomes from COVID-19. Further work on the role of ethnicity in the current pandemic is of urgent public health importance. Funding NIHR
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                Author and article information

                Journal
                J R Soc Med
                J R Soc Med
                JRS
                spjrs
                Journal of the Royal Society of Medicine
                SAGE Publications (Sage UK: London, England )
                0141-0768
                1758-1095
                24 March 2021
                : 0141076821999973
                Affiliations
                [1 ]Office for National Statistics, Newport, UK
                [2 ]Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, UK
                [3 ]Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
                [4 ]Diabetes Research Centre, University of Leicester, Leicester, UK
                [5 ]MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
                [6 ]6Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
                [7 ]LSE Health, London School of Economics and Political Sciences, London, UK
                [8 ]Swansea University, Swansea, UK
                [9 ]UCL Institute of Health Equity, University College London, London, UK
                [10 ]Institute of Health Informatics, University College London, London, UK
                Author notes
                [*]Vahé Nafilyan. Email: vahe.nafilyan@ 123456ons.gov.uk
                Author information
                https://orcid.org/0000-0003-0160-217X
                https://orcid.org/0000-0003-3982-4325
                https://orcid.org/0000-0002-3538-4417
                https://orcid.org/0000-0001-8741-3411
                https://orcid.org/0000-0001-6067-3811
                Article
                10.1177_0141076821999973
                10.1177/0141076821999973
                7994923
                33759630
                0a4667f3-02d7-4ae3-bdf3-25e0ca3a5d2e
                © The Royal Society of Medicine

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 5 February 2021
                : 15 February 2021
                Categories
                Research
                Custom metadata
                corrected-proof
                ts2

                Medicine
                clinical,ethnic studies,housing and health,infectious diseases,public health
                Medicine
                clinical, ethnic studies, housing and health, infectious diseases, public health

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