CovidLife: a resource to understand mental health, well-being and behaviour during the COVID-19 pandemic in the UK

Fawns-Ritchie, Chloë; Campbell, Archie; Huggins, Charlotte; Nangle, Clifford; Dawson, Rebecca; Edwards, Robin R; Flaig, Robin; Hartley, Louise; Levein, Christie; McCartney, Daniel L.; Bell, David; Douglas, Elaine; Deary, Ian J.; Hayward, Caroline; Marioni, Riccardo E.; McIntosh, Andrew M.; Sudlow, Cathie; Porteous, David J.

doi:10.12688/wellcomeopenres.16987.1

Cited by 16 publications

(12 citation statements)

References 39 publications

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“…Four of the studies are birth cohorts, containing participants of a similar age (age-homogeneous): National Child Development Study (NCDS; born 1958) (16,17), British Cohort Study (BCS70; born 1970) (17,18), Next Steps (NS; born 1989-90) (17,19) and Millennium Cohort Study (MCS; born 2000-02) (17,20). The remaining five studies covered a wider range of ages (age-heterogeneous): Avon Longitudinal Study of Parents and Children (ALSPAC) 1 (21,22), TwinsUK (23,24), Born in Bradford (BiB) (25,26), Understanding Society (USoc) (27,28) and Generation Scotland (GS) (29,30). Full details of the studies are provided in Table S1 (Supplementary Material), with ethics and data access statements in Table S2 (Supplementary Material).…”

Section: Datamentioning

confidence: 99%

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

Bowyer

Huggins

Toms

et al. 2022

Preprint

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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.

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Section: Datamentioning

confidence: 99%

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

Bowyer

Huggins

Toms

et al. 2022

Preprint

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“…The subset of the Generation Scotland cohort with reported COVID-19 infection (clinically-diagnosed or positive test from linked test data), who had also participated in the CovidLife study [33] were used for prediction of ongoing symptomatic COVID-19 and hospitalisation from COVID-19 (n=713). Ongoing symptomatic COVID-19 cases were defined here as individuals with self-reported symptoms lasting ≥ 4 weeks [34].…”

Section: Methodsmentioning

confidence: 99%

“…Self-reported COVID-19 phenotypes were available in a subset of individuals from the Generation Scotland DNA methylation sample who had also participated in the CovidLife surveys (n=2,399) [33]. Ongoing symptomatic COVID-19 phenotypes were ascertained from CovidLife survey 3, (n=1,802 Generation Scotland participants with DNAm profiled), where participants were asked about the total overall time they experienced symptoms in their first/only episode of illness, as well as the whole of their COVID-19 illness.…”

Section: Supplementary Methodsmentioning

confidence: 99%

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Development and validation of DNA Methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes

Cheng

Gadd

Gieger

et al. 2021

Preprint

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Type 2 diabetes mellitus (T2D) is one of the most prevalent diseases in the world and presents a major health and economic burden, a notable proportion of which could be alleviated with improved early prediction and intervention. While standard risk factors including age, obesity, and hypertension have shown good predictive performance, we show that the use of CpG DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk.Whilst previous studies have been largely constrained by linear assumptions and the use of CpGs one-at-the-time, we have adopted a more flexible approach based on a range of linear and tree-ensemble models for classification and time-to-event prediction. Using the Generation Scotland cohort (n=9,537) our best performing model (Area Under the Curve (AUC)=0.880, Precision Recall AUC (PRAUC)=0.539, McFadden’s R2=0.316) used a LASSO Cox proportional-hazards predictor and showed notable improvement in onset prediction, above and beyond standard risk factors (AUC=0.860, PRAUC=0.444 R2=0.261). Replication of the main finding was observed in an external test dataset (the German-based KORA study, p=3.7×10−4). Tree-ensemble methods provided comparable performance and future improvements to these models are discussed.Finally, we introduce MethylPipeR, an R package with accompanying user interface, for systematic and reproducible development of complex trait and incident disease predictors. While MethylPipeR was applied to incident T2D prediction with DNA methylation in our experiments, the package is designed for generalised development of predictive models and is applicable to a wide range of omics data and target traits.

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“…Moreover, longitudinal population studies such as Generation Scotland are particularly well-positioned to study the COVID-19 pandemic 3 . This led to the formulation of the CovidLife project, studying the impact of COVID-19 on over 18,000 adults in the UK 4 .…”

Section: Introductionmentioning

confidence: 99%

TeenCovidLife: a resource to understand the impact of the COVID-19 pandemic on adolescents in Scotland

Huggins

Fawns-Ritchie²,

Campbell³

et al. 2022

Wellcome Open Res

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TeenCovidLife is part of Generation Scotland’s CovidLife projects, a set of longitudinal observational studies designed to assess the psychosocial and health impacts of the COVID-19 pandemic. TeenCovidLife focused on how adolescents in Scotland were coping during the pandemic. As of September 2021, Generation Scotland had conducted three TeenCovidLife surveys. Participants from previous surveys were invited to participate in the next, meaning the age ranges shifted over time. TeenCovidLife Survey 1 consists of data from 5,543 young people age 12 to 17, collected from 22 May to 5 July 2020, during the first school closures period in Scotland. TeenCovidLife Survey 2 consists of data from 2,245 young people aged 12 to 18, collected from 18 August to 14 October 2020, when the initial lockdown measures were beginning to ease, and schools reopened in Scotland. TeenCovidLife Survey 3 consists of data from 597 young people age 12 to 19, collected from 12 May to 27 June 2021, a year after the first survey, after the schools returned following the second lockdown in 2021. A total of 316 participants took part in all three surveys. TeenCovidLife collected data on general health and well-being, as well as topics specific to COVID-19, such as adherence to COVID-19 health guidance, feelings about school closures, and the impact of exam cancellations. Limited work has examined the impact of the COVID-19 pandemic on young people. TeenCovidLife provides relevant and timely data to assess the impact of the pandemic on young people in Scotland. The dataset is available under authorised access from Generation Scotland; see the Generation Scotland website for more information.

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CovidLife: a resource to understand mental health, well-being and behaviour during the COVID-19 pandemic in the UK

Cited by 16 publications

References 39 publications

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

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

Development and validation of DNA Methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes

TeenCovidLife: a resource to understand the impact of the COVID-19 pandemic on adolescents in Scotland

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