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Abstract Purpose The COVID-19 pandemic increased psychiatric distress and impacts differed by family structure. We aimed to identify mechanisms contributing to these inequalities. Methods Survey data were from the UK Household Longitudinal Study. Psychiatric distress (GHQ-12) was measured in April 2020 (first UK lockdown; n = 10,516), and January 2021 (lockdown re-introduced following eased restrictions; n = 6,893). Pre-lockdown family structure comprised partner status and presence of children (

Abstract Background Employment disruptions can impact smoking and alcohol consumption. During the COVID-19 pandemic, many countries implemented furlough schemes to prevent job loss. We examine how furlough was associated with smoking, vaping and alcohol consumption in the UK. Methods Data from 27,841 participants in eight UK adult longitudinal surveys were analysed. Participants self-reported employment status and current smoking, current vaping and alcohol consumption (>4 days/week or 5+ drinks per typical occasion) both before and during the early stages of the pandemic (April–July 2020). Risk ratios were estimated within each study using modified Poisson regression, adjusting for a range of potential confounders, including pre-pandemic behaviour. Findings were synthesised using random effects meta-analysis. Results Compared to stable employment and after adjustment for pre-pandemic characteristics, furlough was not associated with smoking (ARR = 1.05; 95% CI: 0.95–1.16; I 2: 10%), vaping (ARR = 0.89; 95% CI: 0.74–1.08; I 2: 0%) or drinking (ARR = 1.03; 95% CI: 0.94–1.13; I 2: 48%). There were similar findings for no longer being employed, and stable unemployment, though this varied by sex: stable unemployment was associated with smoking for women (ARR = 1.35; 95% CI: 1.00–1.82; I 2: 47%) but not men (0.84; 95% CI: 0.67–1.05; I 2: 0%). No longer being employed was associated with vaping among women (ARR = 2.74; 95% CI: 1.59–4.72; I 2: 0%) but not men (ARR = 1.25; 95% CI: 0.83–1.87; I 2: 0%). Conclusions We found no clear evidence of furlough or unemployment having adverse impacts on smoking, vaping or drinking behaviours during the early stages of the COVID-19 pandemic in the UK. Differences in risk compared to those who remained employed were largely explained by pre-pandemic characteristics.

Abstract Background Advance care planning (ACP) is a process involving conversations between patients, loved ones, and healthcare providers that consider patient preferences for the types of medical therapies received at the end of life. Underserved populations, including Black, Hispanic, rural, and low-income communities are less likely to engage in ACP than other communities, a health inequity that results in lower-quality care and reduced hospice utilization. The purpose of this trial is to compare efficacy of two interventions intended to motivate ACP (particularly advance directive completion) for those living in underserved communities. Methods This 3-armed cluster, randomized controlled mixed methods design is being conducted in 75 community venues in underserved communities across the USA. The goal of the trial is to compare the efficacy of two interventions at motivating ACP. Arm 1 uses an end-of-life conversation game (Hello); Arm 2 uses a nationally utilized workshop format for ACP conversations (The Conversation Project); and Arm 3 uses an attention control game (TableTopics). Events are held in partnership with 75 local community-based host organizations and will involve 1500 participants (n=20 per event). The primary outcome is completion of a visually verified advance directive at 6 months post-event. Primary analyses compare efficacy of each intervention to each other and the control arm. Secondary mixed methods outcomes include (a) other ACP behaviors and engagement; (b) communication quality; (c) impact of sociocultural environment on ACP (via qualitative interviews); and (d) implementation and sustainability. Subgroup analyses examine outcomes for Black, Hispanic, and rural groups in particular. Discussion This trial will add to the evidence base behind various conversational ACP interventions, examine potential mechanisms of action for such interventions, and provide qualitative data to better understand the sociocultural environment of how community-based ACP interventions are experienced by underserved populations. Results will also provide important data for future researchers to learn whether visual verification of advance directives is necessary or whether reliance on self-reported outcomes is of comparable value. Data from this study will inform ways to effectively motivate underserved communities to participate in advance care planning. Trial registration ClinicalTrials.gov NCT04612738. Registered on October 12, 2020. All information from the WHO Trial Registration Data Set can be found within the protocol.

Background Home working has increased since the Coronavirus Disease 2019 (COVID-19) pandemic’s onset with concerns that it may have adverse health implications. We assessed the association between home working and social and mental wellbeing among the employed population aged 16 to 66 through harmonised analyses of 7 UK longitudinal studies. Methods and findings We estimated associations between home working and measures of psychological distress, low life satisfaction, poor self-rated health, low social contact, and loneliness across 3 different stages of the pandemic (T1 = April to June 2020 –first lockdown, T2 = July to October 2020 –eased restrictions, T3 = November 2020 to March 2021 –second lockdown) using modified Poisson regression and meta-analyses to pool results across studies. We successively adjusted the model for sociodemographic characteristics (e.g., age, sex), job characteristics (e.g., sector of activity, pre-pandemic home working propensities), and pre-pandemic health. Among respectively 10,367, 11,585, and 12,179 participants at T1, T2, and T3, we found higher rates of home working at T1 and T3 compared with T2, reflecting lockdown periods. Home working was not associated with psychological distress at T1 (RR = 0.92, 95% CI = 0.79 to 1.08) or T2 (RR = 0.99, 95% CI = 0.88 to 1.11), but a detrimental association was found with psychological distress at T3 (RR = 1.17, 95% CI = 1.05 to 1.30). Study limitations include the fact that pre-pandemic home working propensities were derived from external sources, no information was collected on home working dosage and possible reverse association between change in wellbeing and home working likelihood. Conclusions No clear evidence of an association between home working and mental wellbeing was found, apart from greater risk of psychological distress during the second lockdown, but differences across subgroups (e.g., by sex or level of education) may exist. Longer term shifts to home working might not have adverse impacts on population wellbeing in the absence of pandemic restrictions but further monitoring of health inequalities is required. Jacques Wels and colleagues investigate the relationship between working from home and different indicators of mental and social wellbeing in the UK. Author summary Why was this study done? ➢ The number of workers working from home drastically increased during the Coronavirus Disease 2019 (COVID-19) pandemic. ➢ Little is known about the relationship between home working and mental and social wellbeing before and during the pandemic. ➢ As some home working schemes are likely to remain post-pandemic, understanding this association is important. What did the researchers do and find? ➢ We analysed data from 7 UK-based population surveys to better understand the relationship between working from home (partially or fully) and different indicators of mental and social wellbeing. ➢ We analysed these relationships over 3 time points—i.e., April to June 2020 (T1), July to October 2020 (T2), and November 2020 to March 2021 (T3)—and controlled for sociodemographic (e.g., age, sex, and ethnicity) and job characteristics (e.g., sector of activity, key worker status). ➢ We found that home working was not associated with psychological distress at T1 or T2, but at T3 it was associated with increased psychological distress (relative risk = 1.17, 95% CI = 1.05 to 1.30). No other meaningful associations were found. ➢ We stratified these associations by sex, education level, age, and full-time versus part-time employment and found that home working was associated with greater psychological distress in those with lower than degree level education. What do these findings mean? ➢ We found no clear evidence of a lasting association between home working, mental health, and social wellbeing which may indicate that, during the pandemic, home working was not detrimental nor beneficial for workers’ wellbeing. ➢ Differences across subgroups may exist and specific impacts by sex, age, and education level need to be investigated further.

Current understanding of Long COVID is limited, in part, due to lack of evidence from population-representative studies. Here, the authors analyse data from ten UK population-based studies and electronic health records, and find wide variation in the frequency of Long COVID between studies but some consistent risk factors.

Abstract Background In March 2020, the UK implemented the Coronavirus Job Retention Scheme (furlough) to minimise job losses. Our aim was to investigate associations between furlough and diet, physical activity, and sleep during the early stages of the COVID-19 pandemic. Methods We analysed data on 25,092 participants aged 16–66 years from eight UK longitudinal studies. Changes in employment, including being furloughed, were based on employment status before and during the first lockdown. Health behaviours included fruit and vegetable consumption, physical activity, and sleep. Study-specific estimates obtained using modified Poisson regression, adjusting for socio-demographic characteristics and pre-pandemic health and health behaviours, were statistically pooled using random effects meta-analysis. Associations were also stratified by sex, age, and education. Results Across studies, between 8 and 25% of participants were furloughed. Compared to those who remained working, furloughed workers were slightly less likely to be physically inactive (RR = 0.85; [95% CI 0.75–0.97]; I 2 = 59%) and did not differ overall with respect to low fruit and vegetable consumption or atypical sleep, although findings for sleep were heterogenous (I 2 = 85%). In stratified analyses, furlough was associated with lower fruit and vegetable consumption among males (RR = 1.11; [1.01–1.22]; I 2 = 0%) but not females (RR = 0.84; [0.68–1.04]; I 2 = 65%). Considering changes in quantity, furloughed workers were more likely than those who remained working to report increases in fruit and vegetable consumption, exercise, and hours of sleep. Conclusions Those furloughed exhibited similar health behaviours to those who remained in employment during the initial stages of the pandemic. There was little evidence to suggest that adoption of such social protection policies in the post-pandemic recovery period and during future economic crises had adverse effects on population health behaviours.

Abstract Background: We aimed to examine the attitudes of Pennsylvania rural residents toward data sharing in the setting of the COVID-19 pandemic. Specifically, we were interested in better understanding their willingness to provide personal information for contact tracing to public health staff investigating COVID-19 cases, as well as their concerns. We used a validated scale to describe the influence of distrust of healthcare organizations on their attitudes. Methods: We mailed 4000 surveys to rural residents identified from the electronic medical record of a healthcare system in central Pennsylvania. Data were entered into a REDCap database and analyzed using descriptive summaries, and both binomial and multivariable logistic regression. Results: Binomial logistic regression showed that both distrust in healthcare organizations and political values influence respondents’ willingness to share information with contact tracers as well as their concerns about sharing personal data. When our multivariable model was applied, political values remained and were consistently associated with willingness to share and concerns about sharing their data. Conclusion: This study is a first step in eliciting rural residents’ willingness to share personal data for contact tracing by public health officials. Understanding and addressing rural residents’ willingness to share personal data and their concerns about sharing those data will help public health officials identify effective strategies for managing COVID-19 and future pandemics in rural communities. By involving community members at the ground level, public health staff can ensure residents’ buy-in for the need to collect their personal data, thereby helping to mitigate the public health crises.

Abstract Background Concerns remain about potential negative impacts of e-cigarettes including possibilities that: youth e-cigarette use (vaping) increases risk of youth smoking; and vaping by parents may have impacts on their children’s vaping and smoking behaviour. Methods With panel data from 3291 youth aged 10–15 years from the 7th wave of the UK Understanding Society Survey (2015–2017), we estimated effects of youth vaping on youth smoking (ever, current and past year initiation), and of parental vaping on youth smoking and vaping, and examined whether the latter differed by parental smoking status. Propensity weighting was used to adjust for measured confounders and estimate average effects of vaping for all youth, and among youth who vaped. E-values were calculated to assess the strength of unmeasured confounding influences needed to negate our estimates. Results Associations between youth vaping and youth smoking were attenuated considerably by adjustment for measured confounders. Estimated average effects of youth vaping on youth smoking were stronger for all youth (e.g. OR for smoking initiation: 32.5; 95% CI: 9.8–107.1) than among youth who vaped (OR: 4.4; 0.6–30.9). Relatively strong unmeasured confounding would be needed to explain these effects. Associations between parental vaping and youth vaping were explained by measured confounders. Estimates indicated effects of parental vaping on youth smoking, especially for youth with ex-smoking parents (e.g. OR for smoking initiation: 11.3; 2.7–46.4) rather than youth with currently smoking parents (OR: 1.0; 0.2–6.4), but these could be explained by relatively weak unmeasured confounding. Conclusions While measured confounding accounted for much of the associations between youth vaping and youth smoking, indicating support for underlying propensities, our estimates suggested residual effects that could only be explained away by considerable unmeasured confounding or by smoking leading to vaping. Estimated effects of youth vaping on youth smoking were stronger among the general youth population than among the small group of youth who actually vaped. Associations of parental vaping with youth smoking and vaping were either explained by measured confounding or could be relatively easily explained by unmeasured confounding.

Abstract Background Smoking contributes significantly to socioeconomic health inequalities. Vaping has captured much interest as a less harmful alternative to smoking, but may be harmful relative to non-smoking. Examining inequalities in vaping by smoking status, may offer insights into potential impacts of vaping on socioeconomic inequalities in health. Methods Data were from 3291 youth (aged 10–15) and 35,367 adults (aged 16+) from wave 7 (2015–17) of the UK Household Longitudinal Study. In order to adjust for biases that could be introduced by stratifying on smoking status, marginal structural models were used to estimate controlled direct effects of an index of socioeconomic disadvantage (incorporating household education, occupation and income) on vaping by smoking status (among adults and youth), adjusting for relevant confounders and for selection into smoking states. We also estimated controlled direct effects of socioeconomic disadvantage on being an ex-smoker by vaping status (among adult ever-smokers; n = 18,128). Results Socioeconomic disadvantage was associated with vaping among never smoking youth (OR for a unit increase in the socioeconomic index: 1.17; 95%: 1.03–1.34), and among ex-smoking adults (OR: 1.17; 95% CI: 1.09–1.26), with little to no association among never smoking (OR: 0.98; 95% CI: 0.91–1.07) and current smoking (OR: 1.00; 95% CI: 0.93–1.07) adults. Socioeconomic disadvantage was also associated with reduced odds of being an ex-smoker among adult ever-smokers, but this association was moderately weaker among those who vaped (OR: 0.88; 95% CI: 0.82–0.95) than those who did not (OR: 0.82; 95% CI: 0.80–0.84; p-value for difference = 0.081). Conclusions Inequalities in vaping among never smoking youth and adult ex-smokers, suggest potential to widen health inequalities, while weaker inequalities in smoking cessation among adult vapers indicate e-cigarettes could help narrow inequalities. Further research is needed to understand the balance of these opposing potential impacts, and how any benefits can be maximised whilst protecting the vulnerable.

Background: The relative importance of income, poverty and unemployment status for mental health is unclear, and understanding this has implications for income and welfare policy design. We aimed to assess the association between changes in these exposures and mental health. Methods: We measured effects of three transition exposures between waves of the UK Household Longitudinal Study from 2010/11–2019/20 (n=38,697, obs=173,859): income decreases/increases, moving in/out of poverty, and job losses/gains. The outcome was General Health Questionnaire (GHQ), which measures likelihood of common mental disorder (CMD) as a continuous (GHQ-36) and binary measure (score ≥4 = case). We used fixed-effects linear and linear probability models to adjust for time invariant and time-varying confounders. To investigate effect modification, we stratified analyses by age, sex and highest education. Results: A 10% income decrease/increase was associated with a 0.02% increase (95% CI 0.00, 0.04) and 0.01% reduction (95% CI -0.03, 0.02) in likelihood of CMD respectively. Effect sizes were larger for moving into poverty (+1.8% [0.2, 3.5]), out of poverty (−1.8%, [-3.2, −0.3]), job loss (+15.8%, [13.6, 18.0]) and job gain (−11.4%, [-14.4, −8.4]). The effect of new poverty was greater for women (+2.3% [0.8, 3.9] versus +1.2% [-1.1, 3.5] for men) but the opposite was true for job loss (+17.8% [14.4, 21.2] for men versus +13.5% [9.8, 17.2] for women). There were no clear differences by age, but those with least education experienced the largest effects from poverty transitions, especially moving out of poverty (−2.9%, [-5.7, −0.0]). Conclusions: Moving into unemployment was most strongly associated with CMD, with poverty also important but income effects generally much smaller. Men appear most sensitive to employment transitions, but poverty may have larger impacts on women and those with least education. As the COVID-19 pandemic recedes, minimising unemployment as well as poverty is crucial for population mental health.

Abstract Research into the effects of Socioeconomic Position (SEP) on health will sometimes compare effects from multiple, different measures of SEP in “mutually adjusted” regression models. Interpreting each effect estimate from such models equivalently as the “independent” effect of each measure may be misleading, a mutual adjustment (or Table 2) fallacy. We use directed acyclic graphs (DAGs) to explain how interpretation of such models rests on assumptions about the causal relationships between those various SEP measures. We use an example DAG whereby education leads to occupation and both determine income, and explain implications for the interpretation of mutually adjusted coefficients for these three SEP indicators. Under this DAG, the mutually adjusted coefficient for education will represent the direct effect of education, not mediated via occupation or income. The coefficient for occupation represents the direct effect of occupation, not mediated via income, or confounded by education. The coefficient for income represents the effect of income, after adjusting for confounding by education and occupation. Direct comparisons of mutually adjusted coefficients are not comparing like with like. A theoretical understanding of how SEP measures relate to each other can influence conclusions as to which measures of SEP are most important. Additionally, in some situations adjustment for confounding from more distal SEP measures (like education and occupation) may be sufficient to block unmeasured socioeconomic confounding, allowing for greater causal confidence in adjusted effect estimates for more proximal measures of SEP (like income).

Centre-based childcare may benefit pre-school children and alleviate inequalities in early childhood development, but evidence on socio-emotional and physical health outcomes is limited. Data were from the UK Millennium Cohort Study (n = 14,376). Inverse-probability weighting was used to estimate confounder-adjusted population-average effects of centre and non-centre-based childcare (compared to parental care only) between ages 26–31 months on (age 3): internalising and externalising symptoms, pro-social behaviour, independence, emotional dysregulation, vocabulary, school readiness, and body mass index. To assess impacts on inequalities, controlled direct effects of low parental education and lone parenthood on all outcomes were estimated under two hypothetical scenarios: 1) universal take-up of centre-based childcare; and 2) parental care only. On average, non-centre based childcare improved vocabulary and centre-based care improved school readiness, with little evidence of other benefits. However, socio-economic inequalities were observed for all outcomes and were attenuated in scenario 1 (universal take-up). For example, inequalities in externalising symptoms (according to low parental education) were reduced from a confounder-adjusted standard deviation difference of 7.8 (95% confidence intervals: 6.7–8.8), to 1.7 (0.6–2.7). Inequalities by parental education in scenario 2 (parental care only) were wider than in scenario 1 for externalising symptoms (at 3.4; 2.4–4.4), and for emotional dysregulation and school readiness. Inequalities by lone parenthood, which were smaller, fell in scenario 1, and fell further in scenario 2. Universal access to centre-based pre-school care may alleviate inequalities, while restricted access (e.g. during lockdown for a pandemic such as Covid-19) may widen some inequalities in socioemotional and cognitive development.

Background: Social contact, including remote contact (by telephone, email, letter or text), could help reduce social inequalities in depressive symptoms and loneliness among older adults. Methods: Data were from the 8th wave of the English Longitudinal Study of Aging (2016/17), stratified by age (n = 1578 aged

Abstract Background Screening and counseling for genetic conditions is an increasingly important part of primary care practice, particularly given the paucity of genetic counselors in the United States. However, primary care physicians (PCPs) often have an inadequate understanding of evidence-based screening; communication approaches that encourage shared decision-making; ethical, legal, and social implication (ELSI) issues related to screening for genetic mutations; and the basics of clinical genetics. This study explored whether an interactive, web-based genetics curriculum directed at PCPs in non-academic primary care settings was superior at changing practice knowledge, attitudes, and behaviors when compared to a traditional educational approach, particularly when discussing common genetic conditions. Methods One hundred twenty one PCPs in California and Pennsylvania physician practices were randomized to either an Intervention Group (IG) or Control Group (CG). IG physicians completed a 6 h interactive web-based curriculum covering communication skills, basics of genetic testing, risk assessment, ELSI issues and practice behaviors. CG physicians were provided with a traditional approach to Continuing Medical Education (CME) (clinical review articles) offering equivalent information. Results PCPs in the Intervention Group showed greater increases in knowledge compared to the Control Group. Intervention PCPs were also more satisfied with the educational materials, and more confident in their genetics knowledge and skills compared to those receiving traditional CME materials. Intervention PCPs felt that the web-based curriculum covered medical management, genetics, and ELSI issues significantly better than did the Control Group, and in comparison with traditional curricula. The Intervention Group felt the online tools offered several advantages, and engaged in better shared decision making with standardized patients, however, there was no difference in behavior change between groups with regard to increases in ELSI discussions between PCPs and patients. Conclusion While our intervention was deemed more enjoyable, demonstrated significant factual learning and retention, and increased shared decision making practices, there were few differences in behavior changes around ELSI discussions. Unfortunately, barriers to implementing behavior change in clinical genetics is not unique to our intervention. Perhaps the missing element is that busy physicians need systems-level support to engage in meaningful discussions around genetics issues. The next step in promoting active engagement between doctors and patients may be to put into place the tools needed for PCPs to easily access the materials they need at the point-of-care to engage in joint discussions around clinical genetics.