Dataset Information

Online survey comparing coping responses to SARS-CoV-2 by people with and without existing health conditions in the UK.

ABSTRACT:

Objectives

To investigate the impact of SARS-CoV-2 on self-reported mood, coping and health behaviours of people living with existing health conditions in the UK to understand how to improve coping responses to the threat of SARS-CoV-2.

Design

Quantitative design using a cross-sectional survey.

Setting

Online survey in the UK.

Participants

UK adults (18+ years) were eligible to participate. A total of 9110 people participated. Of these, 4377 (48%) reported at least one existing health condition, 874 (10%) reported having two or more existing conditions, and 715 (8%) reported having an existing mental health condition.

Primary and secondary outcome measures

Multivariable linear regression and sequential multiple mediation analysis were used to estimate differences in average scores for active and avoidant coping response scores due to pre-existing health conditions, and to investigate the extent to which these differences are explained by differences in perceptions, beliefs, concerns and mood.

Results

People with pre-existing physical (+1.11 higher; 95% CI 0.88 to 1.34) and especially mental health conditions (3.06 higher; 95% CI 2.65 to 3.48) reported poorer health and used more avoidant coping compared with healthy participants. Under some strong untestable assumptions, we estimate that experiencing low mood or concern related to SARS-CoV-2 mostly explained the relationship between existing health conditions and avoidant coping.

Conclusion

Psychological support and interventions including behaviour change are required to mitigate the psychological burden of the SARS-CoV-2 pandemic and increase autonomy in people with and without pre-existing conditions during this highly uncertain time. Psychologists are well placed to support clinicians and people with existing health conditions to minimise the psychological impact of SARS-CoV-2, in order to alleviate the subsequent strain on healthcare services.

SUBMITTER: Hewitt RM

PROVIDER: S-EPMC8845092 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Online survey comparing coping responses to SARS-CoV-2 by people with and without existing health conditions in the UK.

Hewitt Rachael Marie RM Pattinson Rachael R Daniel Rhian R Carrier Judith J Sanders Oliver O Bundy Christine C

BMJ open 20220210 2

<h4>Objectives</h4>To investigate the impact of SARS-CoV-2 on self-reported mood, coping and health behaviours of people living with existing health conditions in the UK to understand how to improve coping responses to the threat of SARS-CoV-2.<h4>Design</h4>Quantitative design using a cross-sectional survey.<h4>Setting</h4>Online survey in the UK.<h4>Participants</h4>UK adults (18+ years) were eligible to participate. A total of 9110 people participated. Of these, 4377 (48%) reported at least o ...[more]

PMID: 35144948

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Project description:Background and objectivesAmyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function and a cure for this devastating disease remains elusive. Early detection and risk stratification are crucial for timely intervention and improving patient outcomes. This study aimed to identify predisposing genetic, phenotypic, and exposure-related factors for Amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential.MethodsUtilizing data from the UK Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates.ResultsBoth PRSs modestly predicted ALS diagnosis, but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a 4-fold higher ALS risk (95% CI: [2.04, 7.73]) versus those in the 40%-60% range.DiscussionsBy leveraging UK Biobank data, our study uncovers predisposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.

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Dataset Information

Online survey comparing coping responses to SARS-CoV-2 by people with and without existing health conditions in the UK.

Objectives

Design

Setting

Participants

Primary and secondary outcome measures

Results

Conclusion

Publications

Online survey comparing coping responses to SARS-CoV-2 by people with and without existing health conditions in the UK.

Similar Datasets

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