Suchergebnisse

BackgroundEmployment is a wider determinant of health, and one of the most striking societal transformations witnessed during the SARS-CoV-2 (COVID-19) pandemic was the sudden shift to working from home (WFH). Within the COVID-19 recovery and efforts to tackle climate change, having large proportions of the workforce WFH has clear benefits. However, the wider adoption of such measures should account for inequalities in accessing home working, the workforce's preferences for the future, and crucially, the impact that WFH might have on the workforce's health.
MethodsA nationally-representative household survey was undertaken across Wales (Public Health Wales' COVID-19, Employment and Health in Wales study), with cross-sectional data on home-working being collected between November 2020 and January 2021 from 615 employed working-aged adults in Wales (63.7% female, 32.7% aged 50-59). Respondents were asked about their ability to WFH, their perceptions of its impact on their health and their preferences for time spent home-working in future.
FindingsOver 50% were able to WFH, and showed a preference towards home-working to some capacity, with over a third wishing to WFH at least half the time. However, those living in the most deprived areas, in atypical employment, with high wage precarity or with limiting pre-existing conditions were less likely to report being able to WFH. Of those that could WFH, over 40% reported that it worsened their mental well-being and loneliness, and for people in poorer health, home-working negatively impacted their diet, physical activity, smoking and alcohol use. People aged 30 to 39 and those who lived alone were more likely to report wanting to spend some time working in an office/base instead of at home.
ConclusionsThe new way of working introduces new challenges to preserving workforce mental well-being, regulating health behaviours and tackling inequalities. Our findings shine a light on the negative health impacts associated with WFH (particularly for those already in poorer health), highlight how disadvantaged population sub-groups face further inequalities through being less able to WFH, and provide novel observations of the preferred working models of different sub-groups within the population. Hybrid approaches, combining home and work-based employment are preferred and could alleviate the negative health impacts associated with WFH. Addressing inequalities across the workforce to ensure that everyone is as able to participate in this transformative shift in ways of working is key.

AbstractClinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative prediction: predictions inform interventions which influence the outcome they were trying to predict, leading to a potential underestimation of risk in some patients if a model is updated on this data. One suggested resolution to this is the use of hold-out sets, in which a set of patients do not receive model derived risk scores, such that a model can be safely retrained. We present an overview of clinical and research ethics regarding potential implementation of hold-out sets for clinical prediction models in health settings. We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss informed consent, clinical equipoise, and truth-telling. We present illustrative cases of potential hold-out set implementations and discuss statistical issues arising from different hold-out set sampling methods. We also discuss differences between hold-out sets and randomised control trials, in terms of ethics and statistical issues. Finally, we give practical recommendations for researchers interested in the use hold-out sets for clinical prediction models.

Funding Funding for this work was received from Public Health Wales NHS Trust as part of a report on migration and health. Support for the report was also received from the National Centre for Population Health and Wellbeing Research (NCPHWR). The eCATALYsT multiagency dataset and the baseline survey was supported by the Wales Office of Research and Development (SCC99/1/105 and R00/1/017). The follow-up survey was supported by a Welsh Assembly Government/Medical Research Council Health Research Partnership Award (H07-3-030), and the electronic cohort is supported by a National Institute for Social Care and Health Research Welsh Assembly Government Translational Health Research Platform Award (TPR08-020). Data availability statement Data may be obtained from a third party and are not publicly available. The electronic cohort is securely stored and maintained on the Secure Anonymised Information Linkage (SAIL) databank at Swansea University Medical School. The authors welcome general enquiries and ideas for new collaborations. Readers with an interest in further details should contact Professor Shantini Paranjothy, Principal Investigator. ; Peer reviewed ; Publisher PDF

Background: We aimed to estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy makers and decision makers allocate resources efficiently and effectively. Methods: We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors [alcohol intake, body mass index (BMI), cholesterol, systolic blood pressure, smoking] on 19 social and socioeconomic outcomes in 336 997 men and women of White British ancestry in UK Biobank, aged between 39 and 72 years. Outcomes included annual household income, employment, deprivation [measured by the Townsend deprivation index (TDI)], degree-level education, happiness, loneliness and 13 other social and socioeconomic outcomes. Results: Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income [mean difference = -£22 838, 95% confidence interval (CI): -£31 354 to -£14 321] and the chance of owning accommodation [absolute percentage change (APC) = -20.8%, 95% CI: -28.2% to -13.4%], of being satisfied with health (APC = -35.4%, 95% CI: -51.2% to -19.5%) and of obtaining a university degree (APC = -65.9%, 95% CI: -81.4% to -50.4%), while also increasing deprivation (mean difference in TDI = 1.73, 95% CI: 1.02 to 2.44, approximately 216% of a decile of TDI). There was evidence that asthma decreased household income, the chance of obtaining a university degree and the chance of cohabiting, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions: Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.

Background To estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy-makers and decision-makers allocate resources efficiently and effectively. Methods We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors (alcohol intake, body mass index [BMI], cholesterol, systolic blood pressure, smoking) on 19 social and socioeconomic outcomes in 336,997 men and women of white British ancestry in UK Biobank, aged between 39 and 72 years. Outcomes included annual household income, employment, deprivation (measured by the Townsend deprivation index [TDI]), degree level education, happiness, loneliness, and 13 other social and socioeconomic outcomes. Results Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income (mean difference = -£22,838, 95% confidence interval (CI): -£31,354 to -£14,321), the chance of owning accommodation (absolute percentage change [APC] = -20.8%, 95% CI: -28.2% to -13.4%), being satisfied with health (APC = -35.4%, 95% CI: -51.2% to -19.5%), and of obtaining a university degree (APC = -65.9%, 95% CI: -81.4% to -50.4%), while also increasing deprivation (mean difference in TDI = 1.73, 95% CI: 1.02 to 2.44, approximately 216% of a decile of TDI). There was evidence that asthma decreased household income, the chance of obtaining a university degree and the chance of cohabiting, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.

Objectives: To estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy-makers and decision-makers allocate resources efficiently and effectively. Design: We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors (alcohol intake, body mass index [BMI], cholesterol, systolic blood pressure, smoking) on 19 social and socioeconomic outcomes. Setting: UK Biobank. Participants: 337,009 men and women of white British ancestry, aged between 39 and 72 years. Main outcome measures: Annual household income, employment, deprivation (measured by the Townsend deprivation index [TDI]), degree level education, happiness, loneliness, and 13 other social and socioeconomic outcomes. Results: Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income (mean difference = -24,394, 95% confidence interval (CI): -33,403 to -15,384), the chance of owning accommodation (absolute percentage change [APC] = -21.5%, 95% CI: -29.3% to -13.6%), being satisfied with health (APC = -32.4%, 95% CI: -48.9% to -15.8%), and of obtaining a university degree (APC = -73.8%, 95% CI: -90.7% to -56.9%), while also increasing deprivation (mean difference in TDI = 1.89, 95% CI: 1.13 to 2.64, approximately 236% of a decile of TDI). There was evidence that asthma increased deprivation and decreased both household income and the chance of obtaining a university degree, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions: Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.