Adiposity and Mortality among Patients Severely Ill with COVID-19 and non-COVID-19 Respiratory Conditions: A Cross-Context Comparison Study in the UK (preprint)

Objective: To assess the causality of adiposity for mortality among patients severely ill with COVID-19 and non-COVID-19 respiratory conditions by examining the consistency of associations across temporal and geographical contexts where biases vary Design: Prospective cohort study Setting: 297 intensive care units (ICUs) in England, Wales, and Northern Ireland monitored by the Intensive Care National Audit and Research Centre Case Mix Programme Participants: Patients aged [≥]16 years admitted to ICU with COVID-19 (N=33,352; Feb 2020-Aug 2021) and non-COVID-19 respiratory conditions (N=24,739; Feb 2018-Aug 2019) Main outcome measure: 30-day mortality post ICU admission Results: Compared with non-COVID-19 respiratory patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity (body mass index (BMI) [≥] 40kg/m2). COVID-19 patients had fewer comorbidities but higher mortality (35% vs. 23% mortality in non-COVID-19). Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than geographical region of ICU admission, particularly among COVID-19 patients. Among COVID-19 patients, higher BMI was associated with a small excess mortality (hazard ratio (HR) per standard deviation (SD)=1.05; 95% CI=1.03, 1.08), driven by extreme obesity (HR per SD=1.21; 95% CI=1.13, 1.31 vs. normal-weight). Extreme obesity was strongly associated with higher mortality only during Feb-April 2020 (HR=1.49, 95% CI=1.27, 1.73 vs. normal-weight); this association weakened thereafter (BMI-date interaction P=0.03). Among non-COVID-19 respiratory patients, higher BMI was associated with lower mortality (HR per SD=0.84; 95% CI=0.82, 0.87), seen across all overweight/obesity groups. These negative obesity-mortality associations were similar across most admission dates and regions. Conclusions: Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects. Among COVID-19 patients, unfavourable obesity-mortality associations differ by admission date. Among non-COVID-19 respiratory patients, favourable obesity-mortality associations may reflect comorbidity-induced weight loss.

"Alexa, I just ate a donut": A pilot study collecting food and drink intake data with voice input (preprint)

Background: Voice-based systems such as Amazon Alexa may be useful to collect self-reported information in real-time from participants of epidemiology studies, using verbal input. We demonstrate the technical feasibility of using Alexa, investigate participant acceptability, and provide an initial evaluation of the validity of the collected data. We use food and drink information as an exemplar. Methods We recruited 45 staff and students at the University of Bristol (UK). Participants were asked to tell Alexa what they ate or drank for 7 days, and also to submit this information using a web form. Questionnaires asked for basic demographic information and about their experience during the study and acceptability of using Alexa. Results Of the 37 participants with valid data, most were 20-39 years old (N=30; 81%) and 23 (62%) were female. Across 29 participants with Alexa and web entries corresponding to the same intake event, 357 Alexa entries (61%) contained the same food/drink information as the corresponding web entry. Participants often reported that Alexa interjected, and this was worse when entering the food and drink information compared with the event date and time. The majority said they would be happy to use a voice-controlled system for future research. Conclusions While usability of our skill was poor, largely due to the conversational nature and because Alexa interjected if there was a pause in speech, participants were mostly open to participating in future research studies using Alexa. Many more studies are needed, in particular, to trial less conversational interfaces.

Characterising patterns of COVID-19 and long COVID symptoms: Evidence from nine UK longitudinal studies (preprint)

Multiple studies across global populations have established the primary symptoms characterising COVID-19 (Coronavirus Disease 2019) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID, and the extent and length of time for which they are elevated after COVID-19, could not be examined. We analysed individual symptom prevalences and characterised patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups ('no COVID-19', 'COVID-19 in last 12 weeks', 'COVID-19 > 12 weeks ago'), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the 'COVID-19 in last 12 week,' and 'no COVID-19' groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the 'COVID-19 > 12 weeks ago' and 'no COVID-19' groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.

Mental health outcomes following COVID-19 infection: Evidence from 11 UK longitudinal population studies (preprint)

Background Evidence on associations between COVID-19 illness and mental health is mixed. We examined longitudinal associations between COVID-19 and mental health while considering: 1) pre-pandemic mental health, 2) time since infection; 3) subgroup differences; and 4) confirmation of infection via self-reported test, and serology data. Methods Using data from 11 UK longitudinal studies, involving 54,442 participants, with 2 to 8 repeated measures of mental health and COVID-19 between April 2020 and April 2021, we standardised continuous mental health scales within each study across time. We investigated associations between COVID-19 (self-report, test-confirmed, serology-confirmed) and mental health using multilevel generalised estimating equations. We examined whether associations varied by age, sex, ethnicity, education and pre-pandemic mental health. Effect-sizes were pooled in random-effects meta-analyses. Outcomes Pooled estimates of the standardized difference in outcome between those with and without self-reported COVID-19 suggested associations with subsequent psychological distress (0.10 [95%CI: 0.06; 0.13], I 2 =42.8%), depression (0.08 [0.05; 0.10], I 2 =20.8%), anxiety (0.08 [0.05; 0.10], I 2 =0%), and lower life satisfaction (−0.06 [-0.08; -0.04], I 2 =29.2%). Associations did not vary by time since infection until 3+ months and were present in all age groups, with some evidence of stronger effects in those aged 50+. Self-reported COVID-19, whether suspected or test-confirmed and irrespective of serology status, was associated with poorer mental health. Interpretation Self-reporting COVID-19 was longitudinally associated with deterioration in mental health and life satisfaction. Our findings have important implications for mental health service provision, given the substantial prevalence of COVID-19 in the UK and worldwide. Funding MRC and NIHR

Mental-health before and during the COVID-19 pandemic in adults with neurodevelopmental disorders. (preprint)

The COVID-19 pandemic negatively impacted mental health globally. Individuals with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), are at elevated risk of mental health difficulties. Therefore, we investigated the impact of the pandemic on anxiety, depression and mental wellbeing in adults with NDDs using longitudinal data from the Avon Longitudinal Study of Parents and Children study (n=3,058). Mental health data were collected pre-pandemic (age 21-25) and at three timepoints during the pandemic (ages 27-28) using the Short Mood and Feelings Questionnaire, Generalised Anxiety Disorder Assessment-7, and Warwick Edinburgh Mental Wellbeing Scale. ADHD and ASD were defined using validated cut-points of the Strengths and Difficulties Questionnaire and Autism Spectrum Quotient, self-reported at age 25. We used multi-level mixed-effects models to investigate changes in mental health in those with ADHD and ASD compared to those without. Prevalences of depression, anxiety and poor mental wellbeing were higher at all timepoints (pre-pandemic and during pandemic) in those with ADHD and ASD compared to those without. Anxiety increased to a greater extent in those with ADHD ({beta}=0.8 [0.2,1.4], p=0.01) and ASD ({beta}=1.2 [-0.1,2.5], p=0.07), while depression symptoms decreased, particularly in females with ASD ({beta}=-3.1 [-4.6,-1.5], p=0.0001). On average, mental wellbeing decreased in all, but to a lesser extent in those with ADHD ({beta}=1.3 [0.2,2.5], p=0.03) and females with ASD ({beta}=3.0 [0.2,5.9], p=0.04). To conclude, anxiety disproportionately increased in adults with NDDs during the pandemic, however, the related lockdowns may have provided a protective environment for depressive symptoms in the same individuals.

Socioeconomic inequality in SARS-CoV-2 testing and COVID-19 outcomes in UK Biobank over the first year of the pandemic: can inequalities be explained byselection bias? (preprint)

Background Structural barriers to testing may introduce selection bias in COVID-19 research. We explore whether changes to testing and lockdown restrictions introduce time-specific selection bias into analyses of socioeconomic position (SEP) and SARS-CoV-2 infection. Methods Using UK Biobank (N = 420 231; 55 % female; mean age = 56.3 [SD=8.01]) we estimated the association between SEP and i) being tested for SARS-CoV-2 infection versus not being tested ii) testing positive for SARS-CoV-2 infection versus testing negative and iii) testing negative for SARS-CoV-2 infection versus not being tested, at four distinct time-periods between March 2020 and March 2021. We explored potential selection bias by examining the same associations with hypothesised positive (ABO blood type) and negative (hair colour) control exposures. Finally, we conducted a hypothesis-free phenome-wide association study to investigate how individual characteristics associated with testing changed over time. Findings The association between low SEP and SARS-CoV-2 testing attenuated across time-periods. Compared to individuals with a degree, individuals who left school with GCSEs or less had an OR of 1.05 (95% CI: 0.95 to 1.16) in March-May 2020 and 0.98 (95% CI: 0.94 to 1.02) in January-March 2021. The magnitude of the association between low SEP and testing positive for SARS-CoV-2 infection increased over the same time-periods. For the same comparisons, the OR for testing positive increased from 1.27 (95% CI: 1.08 to 1.50), to 1.73 (95% CI: 1.59 to 1.87). We found little evidence of an association between both control exposures and all outcomes considered. Our phenome-wide analysis highlighted a broad range of individual traits were associated with testing, which were distinct across time-periods. Interpretation The association between SEP (and indeed many individual traits) and SARS-CoV-2 testing changed over time, indicating time-specific selection pressures in COVID-19. However, positive, and negative control analyses suggest that changes in the magnitude of the association between SEP and SARS-CoV-2 infection over time were unlikely to be explained by selection bias and reflect true increases in socioeconomic inequalities.

The relationship between BMI and COVID-19: exploring misclassification and selection bias in a two-sample Mendelian randomisation study (preprint)

ObjectiveTo use the example of the effect of body mass index (BMI) on COVID-19 susceptibility and severity to illustrate methods to explore potential selection and misclassification bias in Mendelian randomisation (MR) of COVID-19 determinants. DesignTwo-sample MR analysis. SettingSummary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) and COVID-19 Host Genetics Initiative (HGI) consortia. Participants681,275 participants in GIANT and more than 2.5 million people from the COVID-19 HGI consortia. ExposureGenetically instrumented BMI. Main outcome measuresSeven case/control definitions for SARS-CoV-2 infection and COVID-19 severity: very severe respiratory confirmed COVID-19 vs not hospitalised COVID-19 (A1) and vs population (those who were never tested, tested negative or had unknown testing status (A2)); hospitalised COVID-19 vs not hospitalised COVID-19 (B1) and vs population (B2); COVID-19 vs lab/self-reported negative (C1) and vs population (C2); and predicted COVID-19 from self-reported symptoms vs predicted or self-reported non-COVID-19 (D1). ResultsWith the exception of A1 comparison, genetically higher BMI was associated with higher odds of COVID-19 in all comparison groups, with odds ratios (OR) ranging from 1.11 (95%CI: 0.94, 1.32) for D1 to 1.57 (95%CI: 1.57 (1.39, 1.78) for A2. As a method to assess selection bias, we found no strong evidence of an effect of COVID-19 on BMI in a no-relevance analysis, in which COVID-19 was considered the exposure, although measured after BMI. We found evidence of genetic correlation between COVID-19 outcomes and potential predictors of selection determined a priori (smoking, education, and income), which could either indicate selection bias or a causal pathway to infection. Results from multivariable MR adjusting for these predictors of selection yielded similar results to the main analysis, suggesting the latter. ConclusionsWe have proposed a set of analyses for exploring potential selection and misclassification bias in MR studies of risk factors for SARS-CoV-2 infection and COVID-19 and demonstrated this with an illustrative example. Although selection by socioeconomic position and arelated traits is present, MR results are not substantially affected by selection/misclassification bias in our example. We recommend the methods we demonstrate, and provide detailed analytic code for their use, are used in MR studies assessing risk factors for COVID-19, and other MR studies where such biases are likely in the available data. SummaryO_ST_ABSWhat is already known on this topicC_ST_ABS- Mendelian randomisation (MR) studies have been conducted to investigate the potential causal relationship between body mass index (BMI) and COVID-19 susceptibility and severity. - There are several sources of selection (e.g. when only subgroups with specific characteristics are tested or respond to study questionnaires) and misclassification (e.g. those not tested are assumed not to have COVID-19) that could bias MR studies of risk factors for COVID-19. - Previous MR studies have not explored how selection and misclassification bias in the underlying genome-wide association studies could bias MR results. What this study adds- Using the most recent release of the COVID-19 Host Genetics Initiative data (with data up to June 2021), we demonstrate a potential causal effect of BMI on susceptibility to detected SARS-CoV-2 infection and on severe COVID-19 disease, and that these results are unlikely to be substantially biased due to selection and misclassification. - This conclusion is based on no evidence of an effect of COVID-19 on BMI (a no-relevance control study, as BMI was measured before the COVID-19 pandemic) and finding genetic correlation between predictors of selection (e.g. socioeconomic position) and COVID-19 for which multivariable MR supported a role in causing susceptibility to infection. - We recommend studies use the set of analyses demonstrated here in future MR studies of COVID-19 risk factors, or other examples where selection bias is likely.

Exploring selection bias in COVID-19 research: Simulations and prospective analyses of two UK cohort studies (preprint)

Background Non-random selection into analytic subsamples could introduce selection bias in observational studies of SARS-CoV-2 infection and COVID-19 severity (e.g. including only those have had a COVID-19 PCR test). We explored the potential presence and impact of selection in such studies using data from self-report questionnaires and national registries. Methods Using pre-pandemic data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (mean age=27.6 (standard deviation [SD]=0.5); 49% female) and UK Biobank (UKB) (mean age=56 (SD=8.1); 55% female) with data on SARS-CoV-2 infection and death-with-COVID-19 (UKB only), we investigated predictors of selection into COVID-19 analytic subsamples. We then conducted empirical analyses and simulations to explore the potential presence, direction, and magnitude of bias due to selection when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. Results In both ALSPAC and UKB a broad range of characteristics related to selection, sometimes in opposite directions. For example, more educated participants were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB. We found bias in many simulated scenarios. For example, in one scenario based on UKB, we observed an expected odds ratio of 2.56 compared to a simulated true odds ratio of 3, per standard deviation higher BMI. Conclusion Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depends on the outcome definition, the true effect of the risk factor, and the assumed selection mechanism.

Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK (preprint)

The impact of long COVID is increasingly recognised, but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID, as part of the UK National Core Study for Longitudinal Health and Wellbeing. Methods Analysis was conducted on 6,899 adults self-reporting COVID-19 from 45,096 participants of the UK LS, and on 3,327 cases assigned a long COVID code in primary care EHR out of 1,199,812 adults diagnosed with acute COVID-19. In LS, we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma) with these two outcomes were assessed, using logistic regression, with meta-analyses of findings presented alongside equivalent results from EHR analyses. Results Functionally limiting long COVID for 12+ weeks affected between 1.2% (age 20), and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%). Conclusions Long COVID is clearly distributed differentially according to several sociodemographic and pre-existing health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID.

Trajectories of Child Emotional and Behavioural Difficulties Before and During the COVID-19 Pandemic in a Longitudinal UK Cohort (preprint)

Background: There are concerns that the COVID-19 pandemic and its associated public health mitigation measures will have detrimental effects on emotional and behavioural problems in children. However, longitudinal studies with pre-pandemic data are scarce. In a UK cohort, we quantify the impact of the COVID-19 pandemic on trajectories of children’s emotional and behavioural difficulties measured before and during the pandemic.Methods: Data were from 708 children (Mean age = 3·45 years, SD = 3·13) part of the third generation of a birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). Study sample comprised children whose parents provided previous pre-pandemic surveys and a survey between 26 May and 5 July 2020 that focused on the impact of the COVID-19 pandemic as restrictions in the UK were eased. The children had up to seven measurements of emotional and behavioural difficulties from infancy to late childhood (including the most recent measures during the pandemic). We employed multi-level mixed effects modelling with random intercepts and slopes to examine whether children’s trajectories of emotional and behavioural difficulties (a combined “total difficulties score”) during the pandemic differ from expected pre-pandemic trajectories. Findings: We found that children’s emotional and behavioural difficulties increased during infancy, peaked around the age of 2, and then declined throughout the rest of childhood. Pre-pandemic, the decline in difficulties scores after age 2 was 0·6 points per month; but was approximately one third of that in post-pandemic trajectories (there was a difference in mean rate of decline after age 2 of 0·2 points per month in pre vs during pandemic trajectories [95 % CI: 0·1 to 0·3, p <0·001]). This lower decline in scores over the years translated to older children having pandemic difficulty scores higher than would be expected from pre-pandemic trajectories (for example, an estimated 10-point higher score (95% CI: 5·0 to 15·0) by age 8·5 years). Results remained similar although somewhat attenuated after adjusting for maternal anxiety and age. Interpretation: The COVID-19 pandemic may be associated with greater persistence of emotional and behavioural difficulties after the age 2. Further evidence and monitoring of emotional and behavioural difficulties are required to fully understand the impact of the pandemic on this population, given ongoing and likely further periods of restrictions.Funding: This work was supported by the UK Medical Research Council and Wellcome (Grants 217065/Z/19/Z and 102215), the European Research Commission grant (Grant Ref: 758813 MHINT), the Elizabeth Blackwell Institute, University of Bristol, with funding from QR SPF (Quality-Related Strategic Priorities Fund), and UKRI Research England the Faculty Research Director’s discretionary fund and the University of Bristol. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf).Declaration of Interests: All authors declare no conflicts of interest.Ethics Approval Statement: Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. All participants provided fully informed consent and the study is GDPR compliant.

Mental health during the COVID-19 pandemic in two longitudinal UK population cohorts (preprint)

Background: The impact of COVID-19 on mental health is unclear. Evidence from longitudinal studies with pre pandemic data are needed to address (1) how mental health has changed from pre-pandemic levels to during the COVID-19 pandemic and (2), whether there are groups at greater risk of poorer mental health during the pandemic? Methods: We used data from COVID-19 surveys (completed through April/May 2020), nested within two large longitudinal population cohorts with harmonised measures of mental health: two generations of the Avon Longitudinal Study of Parents and Children (ALPSAC): the index generation ALSPAC-G1 (n= 2850, mean age 28) and the parents generation ALSPAC-G0 (n= 3720, mean age = 59) and Generation Scotland: Scottish Family Health Study (GS, (n= 4233, mean age = 59), both with validated pre-pandemic measures of mental health and baseline factors. To answer question 1, we used ALSPAC-G1, which has identical mental health measures before and during the pandemic. Question 2 was addressed using both studies, using pre-pandemic and COVID-19 specific factors to explore associations with depression and anxiety in COVID-19. Findings: In ALSPAC-G1 there was evidence that anxiety and lower wellbeing, but not depression, had increased in COVID-19 from pre-pandemic assessments. The percentage of individuals with probable anxiety disorder was almost double during COVID-19: 24% (95% CI 23%, 26%) compared to pre-pandemic levels (13%, 95% CI 12%, 14%), with clinically relevant effect sizes. In both ALSPAC and GS, depression and anxiety were greater in younger populations, women, those with pre-existing mental and physical health conditions, those living alone and in socio-economic adversity. We did not detect evidence for elevated risk in key workers or health care workers. Interpretation: These results suggest increases in anxiety and lower wellbeing that may be related to the COVID-19 pandemic and/or its management, particularly in young people. This research highlights that specific groups may be disproportionally at risk of elevated levels of depression and anxiety during COVID-19 and supports recent calls for increasing funds for mental health services. Funding: The UK Medical Research Council (MRC), the Wellcome Trust and University of Bristol.

Collider bias undermines our understanding of COVID-19 disease risk and severity (preprint)

Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.