Notes
Background
While certain infectious diseases have been linked to socioeconomic disadvantage, mental health problems, and lower cognitive function, relationships with COVID-19 are either uncertain or untested. Our objective was to examine the association of a range of psychosocial factors with hospitalisation for COVID-19.
Methods
UK Biobank, a prospective cohort study, comprises around half a million people who were aged 40-69 years at study induction between 2006 and 2010 when information on psychosocial factors and covariates were captured. Hospitalisations for COVID-19 were ascertained between 16th March and 26th April 2020.
Results
There were 908 hospitalisations for COVID-19 in an analytical sample of 431,051 England-based study members. In age- and sex-adjusted analyses, an elevated risk of COVID-19 was related to disadvantaged levels of education (odds ratio; 95% confidence interval: 2.05; 1.70, 2.47), income (2.00; 1.63, 2,47), area deprivation (2.20; 1.86, 2.59), occupation (1.39; 1.14, 1.69), psychological distress (1.58; 1.32, 1.89), mental health (1.50; 1.25, 1.79), neuroticism (1.19; 1.00, 1.42), and performance on two tests of cognitive function - verbal and numerical reasoning (2.66; 2.06, 3.34) and reaction speed (1.27; 1.08, 1.51). These associations were graded (p-value for trend ≤ 0.038) such that effects were apparent across the full psychosocial continua. After mutual adjustment for these characteristics plus ethnicity, comorbidity, and lifestyle factors, only the relationship between lower cognitive function as measured using the reasoning test and risk of the infection remained (1.98; 1.38, 2.85).
Conclusions
A range of psychosocial factors revealed associations with hospitalisation for COVID-19 of which the relation with cognitive function, a marker of health literacy, was most robust.
Application 10279
The relationship of cognitive function and negative emotions with morbidity and mortality: an aetiological investigation
The proposed research aims to understand why it is that poorer cognitive function and negative emotional factors are typically associated with poorer health and increased mortality. We shall use health outcome data to examine how all-cause mortality and incident cancer and cardiovascular disease (CVD)vary according to prior cognitive function and negative emotions. We shall investigate the extent to which relationships we find between cognition, emotions and these health outcomes are explained or modified by physical, biological, genetic, behavioural, and socio-demographic factors. Genetic analyses will incorporate multivariate genome-wide complex trait analysis and polygenic prediction of these relationships. Poorer cognitive function and negative emotional states and traits have been shown to increase mortality but the reasons for this are unclear. We anticipate that the proposed research will: 1) show us how mortality and morbidity from common health conditions vary according to prior cognitive abilities and emotional factors; 2) reveal potential mechanisms whereby poorer cognition and negative emotion increase risk; and 3) identify whether other characteristics can increase or reduce the risk of ill health in those with poorer cognition and negative emotions. This information could help inform intervention strategies for preventing or treating common health conditions. Using data on cognitive function and negative emotions together with data collected on health outcomes, scientists at the Centre for Cognitive Ageing and Cognitive Epidemiology will examine whether cognitive performance and emotional states predict risk of all-cause mortality and the onset of cancer and CVD. They will investigate whether other characteristics, such as lifestyle, socio-demographic, physical, behavioural or biological factors, help to explain any links between cognitive function and emotions and these health outcomes. They will estimate degree of genetic sharing between: 1) cognitive function/emotions and these characteristics, and 2) cognitive function/emotions and health outcomes. The full cohort
Lead investigator: | Dr Michelle Luciano |
Lead institution: | University of Edinburgh |
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