University of South Florida Ethnic Disparities in Hospitalization for COVID 19 HW I do have biostatistics papers and you will find all the details in the f

University of South Florida Ethnic Disparities in Hospitalization for COVID 19 HW I do have biostatistics papers and you will find all the details in the files below Brain, Behavior, and Immunity xxx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Brain, Behavior, and Immunity
journal homepage: www.elsevier.com/locate/ybrbi
Ethnic disparities in hospitalisation for COVID-19 in England: The role of
socioeconomic factors, mental health, and inflammatory and proinflammatory factors in a community-based cohort study
Camille Lassalea,b,c,⁎, Bamba Gayed, Mark Hamere, Catharine R. Galef,g, G David Battyc
a
Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
CIBER of Pathophysiology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
c
Department of Epidemiology and Public Health, University College London, UK
d
Integrative Epidemiology of Cardiovascular Diseases, Paris Cardiovascular Research Center-INSERM U970 (PARCC), France
e
Division of Surgery and Interventional Sciences, Faculty Medical Sciences, University College London, London, UK
f
MRC Lifecourse Epidemiology Unit, University of Southampton, UK
g
Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
b
ARTICLE INFO
ABSTRACT
Keywords:
COVID-19
Ethnicity
Inflammatory factors
Background: Differentials in COVID-19 hospitalisations and mortality according to ethnicity have been reported
but their origin is uncertain. We examined the role of socioeconomic, mental health, and pro-inflammatory
factors in a community-based sample.
Methods: We used data on 340,966 men and women (mean age 56.2 years) from the UK Biobank study, a
prospective cohort study with linkage to hospitalisation for COVID-19. Logistic regression models were used to
estimate associations between ethnicity and hospitalisation for COVID-19.
Results: There were 640 COVID-19 cases (571/324,306 White, 31/4,485 Black, 21/5,732 Asian, 17/5,803
Other). Compared to the White study members and after adjusting for age and sex, Black individuals had over a
4-fold increased risk of COVID-19 infection (odds ratio; 95% confidence interval: 4.32; 3.00–6.23), and there
was a doubling of risk in the Asian group (2.12; 1.37, 3.28) and the ‘other’ non-white group (1.84; 1.13, 2.99).
After controlling for potential explanatory factors which included neighbourhood deprivation, household
crowding, smoking, body size, inflammation, glycated haemoglobin, and mental illness, these effect estimates
were attenuated by 33% for Blacks, 52% for Asians and 43% for Other, but remained raised for Blacks (2.66;
1.82, 3.91), Asian (1.43; 0.91, 2.26) and other non-white groups (1.41; 0.87, 2.31).
Conclusions: There were clear ethnic differences in risk of COVID-19 hospitalisation and these do not appear to
be fully explained by measured factors. If replicated, our results have implications for health policy, including
the targeting of prevention advice and vaccination coverage.
1. Introduction
et al., 2020) In the US, a pooling of hospital data from 38 states also
shows that minorities have a greater rate of deaths involving COVID-19
and this is particularly so for African-Americans. (Yancy, 2020;
Research, 2020)
With neighbourhood deprivation and comorbidity only partially
explaining these ethnic differentials, (Williamson et al., 2020) other
causes need to be examined. These include individual socioeconomic
status such as education, overrepresentation of minorities in in publicfacing occupations, overcrowded living and working conditions, and
greater prevalence of pro-inflammatory unhealthy lifestyle and chronic
disease. (Kirby, 2020; Ross et al., 2020; Platt and Warwick, 2020;
Ethnic disparities in health have traditionally been examined for
non-communicable disease, chiefly obesity (Adult Obesity Facts, 2020;
Public Health England, 2020), diabetes (Goff, 2019) and cardiovascular
disease (George et al., 2017), however, there is emerging evidence that
COVID-19 might disproportionately affect people from ethnic backgrounds. (Kirby, 2020; Aldridge et al., 2020) In the UK, inequalities in
COVID-19 in prognostic studies have been reported such that, in cohorts of hospitalised patients, minority groups appear to have the
greatest risk of progression to intensive care and death. (Williamson
⁎
Corresponding author at: Hospital del Mar Medical Research Institute, Carrer Doctor Aiguader 88, 08003 Barcelona, Spain.
E-mail address: classale@imim.es (C. Lassale).
https://doi.org/10.1016/j.bbi.2020.05.074
Received 26 May 2020; Received in revised form 27 May 2020; Accepted 28 May 2020
0889-1591/ © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
Please cite this article as: Camille Lassale, et al., Brain, Behavior, and Immunity, https://doi.org/10.1016/j.bbi.2020.05.074
Downloaded for Anonymous User (n/a) at New York Medical College from ClinicalKey.com by Elsevier on July 06, 2020.
For personal use only. No other uses without permission. Copyright ©2020. Elsevier Inc. All rights reserved.
Brain, Behavior, and Immunity xxx (xxxx) xxx–xxx
C. Lassale, et al.
Centers for Disease Control and Prevention, 2019) Mental health problems, also more common in minorities, (NHS, 2014; Diaz-Venegas
et al., 2016) may be related to infection and severity of respiratory
infections via impaired innate and adaptive immunity. (Hamer et al.,
2019; Gale et al., 2019) Finally, biological differences, such as impaired
immunologic response functioning, (Webb Hooper and Napoles, 2020)
are amplified in the present of racism and chronic stress.
With existing studies focusing on disease prognosis, it is unclear if
people from ethnic groups also experience an elevated risk of disease
onset, and, if so, what explains this burden. Accordingly, our aim was to
assess the ethnic differences in serious cases of COVID-19 in a wellcharacterized, large, community-based cohort study in the UK, and
investigate which underlying factors drive the observed associations.
of the household had four groups (living alone; with two people; with
three people; and four or more).
2.6. Lifestyle measures
Physical activity, smoking, and alcohol consumption were assessed
by questionnaire. Participants were categorised into never, former, and
current smokers. We grouped alcohol intake into three categories:
never/rarely, and below or above current UK guidelines (≥14 units in
women and ≥ 21 units in men). Leisure time physical activity was
assessed using the short form version of the International Physical
Activity Questionnaire (IPAQ). (Craig et al., 2003) Measuring duration
and frequency of moderate-to-vigorous physical activity in the last
week, data were grouped in 3 categories: inactive, somewhat active
below the guidelines, and meeting activity guidelines (≥150 min/week
moderate-to-vigorous physical activity or ≥ 75 min/week vigorous
activity). (Nyberg et al., 2020)
2. Methods
2.1. Study population
We used data from UK Biobank, a prospective cohort study, the
sampling and procedures of which have been well described. (Sudlow
et al., 2015) Baseline data collection took place between 2006 and 2010
across twenty-two research assessment centres in the UK giving rise to a
sample of 502,655 people aged 40 to 69 years (response rate 5.5%).
(Sudlow et al., 2015) Ethical approval was received from the NorthWest Multi-centre Research Ethics Committee, and the research was
carried out in accordance with the Declaration of Helsinki of the World
Medical Association, and participants gave informed consent. For the
present analysis, participants residing in Scotland and Wales were excluded as COVID-19 test data were only available for England.
2.7. Comorbidities
Body weight was measured using Tanita BC418MA scales and
standing height using a Seca height measure, and body mass index
(BMI) calculated [weight (kilograms)/height2 (meters2) squared].
Waist and hip circumference were measured with a non-elastic tape,
and their ratio computed. The following self-reported physician diagnosed chronic diseases were used: cardiovascular diseases (heart attack,
angina, stroke), chronic bronchitis and diabetes. Hypertension was
defined as elevated measured blood pressure (≥140/90 mmHg) and
/or use of anti-hypertensive medication. We used two indicators of
mental health: contact with a psychiatrist for any disorder and symptoms of psychological distress as measured using the four-item version
of the Patient Health Questionnaire (PHQ-4) in which scores ranged
from 0 to 12 (categorised as 0, 1–2, ≥3 [high]). A verbal numerical
reasoning task was used as a marker of cognitive function. (Gale et al.,
2019)
2.2. Hospitalisation for COVID-19
Provided by Public Health England, data on COVID-19 status covered the period from 16th March to 26thApril 2020 (http://biobank.
ndph.ox.ac.uk/showcase/field.cgi?id = 40100), during which testing
was largely restricted to those with symptoms in hospital. COVID-19
tests were performed on samples from combined nose/throat swabs
using real time polymerase chain reaction (RT-PCR) in accredited laboratories. (NHS England and NHS, 2020) These data can therefore be
regarded as a proxy for hospitalisations for severe COVID-19 cases.
2.8. Biomarkers
Non-fasting venous blood samples were drawn and assayed for Creactive protein, glycated haemoglobin, and total and high-density lipoprotein cholesterol. (Mindell et al., 2012; Elliott and Peakman, 2008)
Forced expiratory volume in 1 s, a marker of lung function, was
quantified using spirometry with the best of three technically satisfactory exhalations used.
2.3. Ethnicity
Ethnicity was self-reported at baseline assessment and based on 6
categories: White (including White British, White Irish, any other white
background), Mixed (White and Black Caribbean, White and Black
African, White and Asian, any other mixed background), Asian or Asian
British (thereafter termed “Asian”, including Indian, Pakistani,
Bangladeshi, any other Indian background), Black or Black British
(“Black”, Caribbean, African, any other Black background), Chinese,
and Other. To maintain statistical power in our analyses, we grouped
together Chinese, Mixed and Other under the “Other” category.
3. Statistical analyses
To compare participants’ characteristics between non-hospitalised
and hospitalised patients, we performed t-tests for continuous variables
and Chi-square tests for categorical variables. We fitted logistic regression models to estimate odds ratios and 95% confidence intervals
for associations between ethnicity and hospitalisation for COVID-19.
With the outcome being rare, odds ratios (OR) can be interpreted as
relative risks. To quantify the contribution of factors to the ethnic differences, we used a simple approach to quantify the change in coefficient. Beginning with a comparator model where ORs were adjusted for
age and sex, we subsequently fitted 5 models corresponding to groups
of covariates: 1) socioeconomic, 2) lifestyle, 3) comorbidities, 4) biomarkers, and 5) all covariates. Percentage change in effect estimate was
calculated as 100*(βmodel x – βbase model)/ βbase model. With the aim being
to compare attenuation of ORs by inclusion of various sets of factors, we
selected all participants with non-missing values to run all five models.
In a first sensitivity analysis, we present the estimates in samples with
the maximum number of observations for each model. The cognitive
function variable was only available in a subset of participants, therefore we present as a sensitivity analysis for the complete-case model
2.4. Covariates
All variables were obtained at baseline and were grouped into 4
clusters.
2.5. Socioeconomic factors
Socioeconomic factors included highest educational attainment,
household income, occupation, number of people living in the household, and the Townsend index of area deprivation (Townsend, 2017)
(higher values denote deprivation). We created binary variables for
education (university degree yes/no), total household income before
tax ( 10 min not reaching
guideline
Inactive
Alcohol intake
Within guideline
Never/rarely
Heavy drinking
Cigarette Smoking
Never
Past
Current
Hypertension
Diabetes
Cardiovascular disease
Chronic bronchitis
Seen a psychiatrist
Psychological distress (PHQ4 ≥ 3)
BMI, kg/m2
Waist to hip ratio
C-reactive protein (mg/L)
HbA1c (mmol/mol)
Cholesterol (mmol/L)
HDL-cholesterol (mmol/L)
Forced expiratory volume in 1 sec
(L)
Not hospitalised
Hospitalised
427,594
900
1.8
2.2
1.9
94.1
55.0
56.4 (8.1)
Percent
32.6
19.3
19.6
6.0
5.1
3.1
85.8
44.4
57.2 (9.0)

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