Evidence map›Paper›PMID 37592225›Full record

ArticleBMC public health2023

Comorbidities associated with the severity of COVID-19, and differences across ethnic groups: a UK Biobank cohort study.

Rahul Patel, Jaspal S Kooner, Weihua Zhang

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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12citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Rahul PatelDepartment of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK. rpatel209@googlemail.com.
Jaspal S KoonerDepartment of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UB1 3HW, UK.
Weihua ZhangDepartment of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK. weihua.zhang@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDisparities in COVID-19 outcomes exist on the basis of ethnicity and comorbidities. Minority ethnic groups in the UK are known to have poorer COVID-19 outcomes, but also an increased prevelance of certain comorbidities associated with severe outcomes. Additionally, despite the prevalence of certain psychiatric disorders there is a lack of research establishing their relationship with COVID-19 outcomes.

methodsWe used UK Biobank data, involving 472,182 participants, to test for an association between comorbidities and COVID-19 diagnosis (n = 30,901); and to test for an association between comorbidities and severe COVID-19 (n = 3182). This was done by performing univariable and multivariable logistic regression analysis, estimating odds ratios (ORs) and their 95% confidence intervals (95% CIs). The comorbidities studied were coronary heart disease (CHD), hypertension, type II diabetes mellitus (T2DM), obesity, chronic kidney disease (CKD), depression and anxiety. Multivariable models were adjusted for various socioeconomic, demographic and health-related confounders. We then performed sub-group analysis by common UK ethnic groups (White, South Asian, and Black).

resultsIncreased prevalence of all studied comorbidities was seen in both outcomes, compared to the rest of the cohort. All studied comorbidities were associated with an increased risk of COVID-19 infection and severity across all models. For example, the adjusted ORs (95% CI) for depression were 1.112 (1.083 - 1.161) for COVID-19 diagnosis and 2.398 (2.163 - 2.658) for severe COVID-19. Sub-group analysis revealed stronger associations of COVID-19 diagnosis and severe COVID-19 for South-Asian participants for CHD (OR 1.585 [95% CI 1.194-2.105] for COVID-19 diagnosis and 3.021 [1.683-5.390] for severe COVID-19), hypertension (1.488 [1.231-1.799]; 3.399 [1.862-6.206]) and T2DM (1.671 [1.346-2.076]; 5.412 [3.130-9.357]) compared to White participants (1.264 [1.195-1.336] and 1.627 [1.441-1.837] for CHD; 1.131 [1.097-1.116] and 2.075 [1.885-2.284] for hypertension; 1.402 [1.331-1.476] and 2.890 [2.596-3.216] for T2DM). Similar results were seen for Black participants with CKD and hypertension.

conclusionSpecific comorbidities are risk factors for poorer COVID-19 outcomes, supporting targeted interventions and policy aimed at individuals with these comorbidities. Although further research is required, there's also a need for targeted policies for ethnic minorities assessing the unique reasons they are at greater risk of poor COVID-19 outcomes.

Indexed as

COVID-19Diabetes Mellitus, Type 2HypertensionBiological Specimen BanksCohort StudiesCOVID-19 TestingEthnicityHumansUnited KingdomCohortComorbiditiesCOVID-19DisparityEthnicityUK Biobank

Identifiers

PMID37592225
PMCPMC10436456

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.