Ethnic disparities in hospitalisation for COVID-19 in England: The role of socioeconomic factors, mental health, and inflammatory and pro-inflammatory factors in a community-based cohort study

Lassale, Camille; Gaye, Bamba; Hamer, Mark; Galé, Catharine R.

doi:10.1016/j.bbi.2020.05.074

Cited by 190 publications

(222 citation statements)

References 23 publications

Supporting

Mentioning

202

Contrasting

Unclassified

Order By: Relevance

“…Further, we were able to refine previous associations between infection and hospitalization rates with area-level household crowding. In fact, this association is consistent with smaller studies 33,34 and may partially explain racial disparities seen in aggregated county-level data. 19 For example, approximately one-fifth of US residential units are unsuitable for home quarantine based on the number of rooms available per individual, with racial and ethnic minorities overrepresented among these households.…”

Section: Baseline Characteristicssupporting

confidence: 88%

“…However, these studies have largely examined these socioeconomic measures in isolation, without adjusting for related area-level socioeconomic measures. Similarly, non-White race, household crowding, socioeconomic deprivation indices and lower income, and Medicaid or absent health insurance have been associated with hospitalization or death, 7,8,10,34,41,42 although it is not clear if these factors would be significant in models adjusted for other socioeconomic factors.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Cromer

Lakhani

Wexler

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

show abstract

Section: Baseline Characteristicssupporting

confidence: 88%

mentioning

confidence: 99%

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Cromer

Lakhani

Wexler

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…There are fewer reports on comorbid dyslipidemia. The most comprehensive analysis leveraged data from the UK Biobank as a control population (n=428,494) versus hospitalized COVID-19 patients (n=900) (39). Diabetes, HbA1c, CVD, hypertension, BMI, and waist-hip-ratio (WHR) were higher and cholesterol and high-density lipoprotein cholesterol (HDL-c) lower in COVID-19 patients.…”

Section: Comorbidities In General Covid-19 Patient Cohortsmentioning

confidence: 99%

COVID-19 and Diabetes: A Collision and Collusion of Two Diseases

et al. 2020

View full text Add to dashboard Cite

The COVID-19 pandemic has infected over 22.7 million and led to the deaths of 795,000 people worldwide. Diabetes patients are highly susceptible to COVID-19-induced adverse outcomes and complications. The COVID-19 pandemic is superimposing on the preexisting diabetes pandemic to create large and significantly vulnerable COVID-19 diabetes patient populations. This perspective provides an overview of the clinical evidence on the poorer clinical outcomes of COVID-19 infection in diabetes versus non-diabetes patients, including in specific patient populations, such as children, pregnant women, and racial minorities. It also draws parallels between COVID-19 and diabetes pathology, and suggests that preexisting complications or pathologies in diabetes patients might aggravate infection course. Finally, the perspective outlines the prospects for long-term sequelae after COVID-19 for vulnerable diabetes patient populations.

show abstract

“…[1][2][3][4][5][6][7] It is hypothesized that ethnic differences in COVID-19 infection and outcomes are driven by differences in factors such as living in deprived areas, working in high-exposure or frontline occupations, living in large, multigenerational households, a higher burden of underlying conditions, experiences of discrimination, or access to health and community services. 4,[8][9][10][11][12] As an example, ethnic minority healthcare workers in the UK have experienced higher rates of COVID-19-related death, which has been partly attributed to poorer access to personal protective equipment (PPE and fears around raising concerns about working in unsafe or high-exposure environments. 13,14 In the UK, the collection of ethnic group data is considered an essential first step towards identifying and actively reducing ethnic inequalities.…”

Section: Introductionmentioning

confidence: 99%

Ethnic differences in COVID-19 infection, hospitalisation, and mortality: an OpenSAFELY analysis of 17 million adults in England

Mathur

Rentsch

Morton

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: COVID-19 has had a disproportionate impact on ethnic minority populations, both in the UK and internationally. To date, much of the evidence has been derived from studies within single healthcare settings, mainly those hospitalised with COVID-19. Working on behalf of NHS England, the aim of this study was to identify ethnic differences in the risk of COVID-19 infection, hospitalisation and mortality using a large general population cohort in England. Methods: We conducted an observational cohort study using linked primary care records of 17.5 million adults between 1 February 2020 and 3 August 2020. Exposure was self-reported ethnicity collapsed into the 5 and 16 ethnicity categories of the English Census. Multivariable Cox proportional hazards regression was used to identify ethnic differences in the risk of being tested and testing positive for SARS-CoV-2 infection, COVID-19 related intensive care unit (ICU) admission, and COVID-19 mortality, adjusted for socio-demographic factors, clinical co-morbidities, geographic region, care home residency, and household size. Results: A total of 17,510,002 adults were included in the study; 63% white (n=11,030,673), 6% south Asian (n=1,034,337), 2% black (n=344,889), 2% other (n=324,730), 1% mixed (n=172,551), and 26% unknown (n=4,602,822). After adjusting for measured explanatory factors, south Asian, black, and mixed groups were marginally more likely to be tested (south Asian HR 1.08, 95%CI 1.07-1.09; black HR 1.08; 95%CI 1.06-1.09, mixed HR 1.03, 95%CI 1.01-1.05), and substantially more likely to test positive for SARS-CoV-2 compared with white adults (south Asian HR 2.02. 95% CI 1.97-2.07; black HR 1.68, 95%CI 1.61-1.76; mixed HR 1.46, 95%CI 1.36-1.56). The risk of being admitted to ICU for COVID-19 was substantially increased in all ethnic minority groups compared with white adults (south Asian HR 2.22, 95%CI 1.96-2.52; black HR 3.07, 95%CI 2.61-3.61; mixed HR 2.86, 95%CI 2.19-3.75, other HR 2.86, 95%CI 2.31-3.63). Risk of COVID-19 mortality was increased by 25-56% in ethnic minority groups compared with white adults (south Asian HR 1.27, 95%CI 1.17-1.38; black HR 1.55, 95%CI 1.38-1.75; mixed HR 1.40, 95%CI 1.12-1.76; other HR 1.25, 95%CI 1.05-1.49). We observed heterogeneity of associations after disaggregation into detailed ethnic groupings; Indian and African groups were at higher risk of all outcomes; Pakistani, Bangladeshi and Caribbean groups were less or equally likely to be tested for SARS-CoV-2, but at higher risk of all other outcomes, Chinese groups were less likely to be tested for and test positive for SARS-CoV-2, more likely to be admitted to ICU, and equally likely to die from COVID-19. Conclusions: We found evidence of substantial ethnic inequalities in the risk of testing positive for SARS-CoV-2, ICU admission, and mortality, which persisted after accounting for explanatory factors, including household size. It is likely that some of this excess risk is related to factors not captured in clinical records such as occupation, experiences of structural discrimination, or inequitable access to health and social services. Prioritizing linkage between health, social care, and employment data and engaging with ethnic minority communities to better understand their lived experiences is essential for generating evidence to prevent further widening of inequalities in a timely and actionable manner.

show abstract

Ethnic disparities in hospitalisation for COVID-19 in England: The role of socioeconomic factors, mental health, and inflammatory and pro-inflammatory factors in a community-based cohort study

Cited by 190 publications

References 23 publications

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

COVID-19 and Diabetes: A Collision and Collusion of Two Diseases

Ethnic differences in COVID-19 infection, hospitalisation, and mortality: an OpenSAFELY analysis of 17 million adults in England

Contact Info

Product

Resources

About