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Background: Population mental health in the United Kingdom (UK) has deteriorated, alongside worsening socioeconomic conditions, over the last decade. Policies such as Universal Basic Income (UBI) have been suggested as an alternative economic approach to improve population mental health and reduce health inequalities. UBI may improve mental health (MH), but to our knowledge, no studies have trialled or modelled UBI in whole populations. We aimed to estimate the short-term effects of introducing UBI on mental health in the UK working-age population. Methods and findings: Adults aged 25 to 64 years were simulated across a 4-year period from 2022 to 2026 with the SimPaths microsimulation model, which models the effects of UK tax/benefit policies on mental health via income, poverty, and employment transitions. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population (n = 25,000) and causal effect estimates. Three counterfactual UBI scenarios were modelled from 2023: "Partial" (value equivalent to existing benefits), "Full" (equivalent to the UK Minimum Income Standard), and "Full+" (retaining means-tested benefits for disability, housing, and childcare). Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score ≥4). Relative and slope indices of inequality were calculated, and outcomes stratified by gender, age, education, and household structure. Simulations were run 1,000 times to generate 95% uncertainty intervals (UIs). Sensitivity analyses relaxed SimPaths assumptions about reduced employment resulting from Full/Full+ UBI. Partial UBI had little impact on poverty, employment, or mental health. Full UBI scenarios practically eradicated poverty but decreased employment (for Full+ from 78.9% [95% UI 77.9, 79.9] to 74.1% [95% UI 72.6, 75.4]). Full+ UBI increased absolute CMD prevalence by 0.38% (percentage points; 95% UI 0.13, 0.69) in 2023, equivalent to 157,951 additional CMD cases (95% UI 54,036, 286,805); effects were largest for men (0.63% [95% UI 0.31, 1.01]) and those with children (0.64% [95% UI 0.18, 1.14]). In our sensitivity analysis assuming minimal UBI-related employment impacts, CMD prevalence instead fell by 0.27% (95% UI −0.49, −0.05), a reduction of 112,228 cases (95% UI 20,783, 203,673); effects were largest for women (−0.32% [95% UI −0.65, 0.00]), those without children (−0.40% [95% UI −0.68, −0.15]), and those with least education (−0.42% [95% UI −0.97, 0.15]). There was no effect on educational mental health inequalities in any scenario, and effects waned by 2026. The main limitations of our methods are the model's short time horizon and focus on pathways from UBI to mental health solely via income, poverty, and employment, as well as the inability to integrate macroeconomic consequences of UBI; future iterations of the model will address these limitations. Conclusions: UBI has potential to improve short-term population mental health by reducing poverty, particularly for women, but impacts are highly dependent on whether individuals choose to remain in employment following its introduction. Future research modelling additional causal pathways between UBI and mental health would be beneficial. Rachel M Thomson and colleagues used a policy model to simulate how a Universal Basic Income might influence mental health for working-age adults in the UK. Author summary: Why was this study done?: Universal Basic Income (UBI) is a radical social security policy proposal where everyone in a society would receive a regular, unconditional cash payment. It has been suggested that UBI might be beneficial for mental health. However, there has never been a trial of a true UBI in a high-income country to know what its potential mental health impacts might be. What did the researchers do and find?: We used a policy model to simulate how a UBI might influence mental health for working-age adults in the United Kingdom. We found that the effects of UBI were very sensitive to assumptions we made about how people's employment choices might respond to receiving the money—if people choose to stay in work, UBI may have small benefits for population mental health, but if people are more likely to leave work, population mental health may actually worsen. The groups most likely to experience positive mental health effects of UBI were women and those with the lowest educational qualifications. What do these findings mean?: UBI may have potential to improve population mental health in high-income countries, but only if people do not choose to leave work in response to the policy. More real-world research is needed to know how people are likely to respond to receiving a UBI in reality. The main limitation of our modelling study is that it looks at how UBI would influence mental health only through income and employment, and other pathways might also be important to include in future research. [ABSTRACT FROM AUTHOR]

Background: Employment and income are important determinants of mental health (MH), but the extent that unemployment effects are mediated by reduced income is unclear. We estimated the total effect (TE) of unemployment on MH and the controlled direct effect (CDE) not acting via income. Methods: We included adults 25–64 years from nine waves of the UK Household Longitudinal Study (n = 45 497/ obs = 202 297). Unemployment was defined as not being in paid employment; common mental disorder (CMD) was defined as General Health Questionnaire-12 score ≥4. We conducted causal mediation analysis using double-robust marginal structural modelling, estimating odds ratios (OR) and absolute differences for effects of unemployment on CMD in the same year, before (TE) and after (CDE) blocking the income pathway. We calculated percentage mediation by income, with bootstrapped standard errors. Results: The TE of unemployment on CMD risk was OR 1.66 (95% CI 1.57–1.76), with 7.09% (6.21–7.97) absolute difference in prevalence; equivalent CDEs were OR 1.55 (1.46–1.66) and 6.08% (5.13–7.03). Income mediated 14.22% (8.04–20.40) of the TE. Percentage mediation was higher for job losses [15.10% (6.81–23.39)] than gains [8.77% (0.36–17.19)]; it was lowest for those 25–40 years [7.99% (−2.57 to 18.51)] and in poverty [2.63% (−2.22 to 7.49)]. Conclusions: A high proportion of the short-term effect of unemployment on MH is not explained by income, particularly for younger people and those in poverty. Population attributable fractions suggested 16.49% of CMD burden was due to unemployment, with 13.90% directly attributable to job loss rather than resultant income changes. Similar analytical approaches could explore how this differs across contexts, by other factors, and consider longer-term effects. [ABSTRACT FROM AUTHOR]

Background Addressing poverty through taxation or welfare policies is likely important for public mental health; however, few studies assess poverty's effects using causal epidemiology. We estimated the effect of poverty on mental health. Methods We used data on working-age adults (25–64 years) from nine waves of the UK Household Longitudinal Survey (2009–19; n  = 45 497/observations = 202 207 following multiple imputation). We defined poverty as a household equivalized income <60% median, and the outcome likely common mental disorder (CMD) as a General Health Questionnaire-12 score ≥4. We used double-robust marginal structural modelling with inverse probability of treatment weights to generate absolute and relative effects. Supplementary analyses separated transitions into/out of poverty, and stratified by gender, education, and age. We quantified potential impact through population attributable fractions (PAFs) with bootstrapped standard errors. Results Good balance of confounders was achieved between exposure groups, with 45 830 observations (22.65%) reporting poverty. The absolute effect of poverty on CMD prevalence was 2.15% [%-point change; 95% confidence interval (CI) 1.45, 2.84]; prevalence in those unexposed was 20.59% (95% CI 20.29%, 20.88%), and the odds ratio was 1.17 (95% CI 1.12, 1.24). There was a larger absolute effect for transitions into poverty [2.46% (95% CI 1.56, 3.36)] than transitions out of poverty [–1.49% (95% CI –2.46, –0.53)]. Effects were also slightly larger in women than men [2.34% (95% CI 1.41, 3.26) versus 1.73% (95% CI 0.72, 2.74)]. The PAF for moving into poverty was 6.34% (95% CI 4.23, 8.45). Conclusions PAFs derived from our causal estimates suggest moves into poverty account for just over 6% of the burden of CMD in the UK working-age population, with larger effects in women. [ABSTRACT FROM AUTHOR]

Economic determinants are important for population health, but actionable evidence of how policies can utilise these pathways remains scarce. This study employs a microsimulation framework to evaluate the effects of taxation and social security policies on population mental health. The UK economic crisis caused by the COVID-19 pandemic provides an informative context involving an economic shock accompanied by one of the strongest discretionary fiscal responses amongst OECD countries. The analytical setup involves a dynamic, stochastic, discrete-time microsimulation model (SimPaths) projecting changes in psychological distress given predicted economic outcomes from a static tax-benefit microsimulation model (UKMOD) based on different policy scenarios. We contrast projections of psychological distress for the working-age population from 2017 to 2025 given the observed policy environment against a counterfactual scenario where pre-crisis policies remained in place. Levels of psychological distress and potential cases of common mental disorders (CMDs) were assessed with the 12-item General Health Questionnaire (GHQ-12). The UK policy response to the economic crisis is estimated to have prevented a substantial fall (over 12 percentage points, %pt) in the employment rate in 2020 and 2021. In 2020, projected psychological distress increased substantially (CMD prevalence increase >10%pt) under both the observed and the counterfactual policy scenarios. Through economic pathways, the policy response is estimated to have prevented a further 3.4%pt [95%UI 2.8%pt, 4.0%pt] increase in the prevalence of CMDs, approximately 1.2 million cases. Beyond 2021, as employment levels rapidly recovered, psychological distress returned to the pre-pandemic trend. Sustained preventative effects on poverty are estimated, with projected levels 2.1%pt [95%UI 1.8%pt, 2.5%pt] lower in 2025 than in the absence of the observed policy response. The study shows that policies protecting employment during an economic crisis are effective in preventing short-term mental health losses and have lasting effects on poverty levels. This preventative effect has substantial public health benefits. • Open-source microsimulation framework focusing on economic determinants of health. • Evaluates the health benefits of UK policy response during economic crisis. • Quantifies preventative effect of social security policies on mental health. • Projections of health and economic outcomes to 2025. [ABSTRACT FROM AUTHOR]

Purpose: Distinguishing what employers in different areas of Great Britain need to pay to attract and retain labour has been a central component of public-sector resource allocation decisions. This paper examines how changes in the pattern of spatial wage differentials following the global financial crisis have impacted on the formulae which allocate government funding to local government and health providers in the NHS. Design/methodology/approach: Using employer-reported data on earnings, we examine spatial patterns of private-sector wages in Great Britain between 2007 and 2017. The method permits the analysis of finely defined geographical areas and controls for differences in industry and workforce composition to distinguish those differences that are attributable from unmeasured characteristics, such as differences between areas in the cost of living and amenities. These standardised spatial wage differentials (SSWDs) underpin the funding allocation formulae. Findings: The analysis shows that since 2007 private-sector wage dispersion, both within and between regions, has reduced: lower paid areas have experienced a relative increase in wages and higher paid a relative decline. Over the period, there was a significant reduction in the London wage premium. Originality/value: This paper demonstrates the importance of ensuring established policies are applied using contemporary data. The SSWDs used to distribute government funds have not been re-estimated for some time. As a result, the current resource allocation model has overcompensated the London region and undercompensated others during this period. [ABSTRACT FROM AUTHOR]

Policy PointsIncome is thought to impact a broad range of health outcomes. However, whether income inequality (how unequal the distribution of income is in a population) has an additional impact on health is extensively debated.Studies that use multilevel data, which have recently increased in popularity, are necessary to separate the contextual effects of income inequality on health from the effects of individual income on health.Our systematic review found only small associations between income inequality and poor self‐rated health and all‐cause mortality. The available evidence does not suggest causality, although it remains methodologically flawed and limited, with very few studies using natural experimental approaches or examining income inequality at the national level. Context: Whether income inequality has a direct effect on health or is only associated because of the effect of individual income has long been debated. We aimed to understand the association between income inequality and self‐rated health (SRH) and all‐cause mortality (mortality) and assess if these relationships are likely to be causal. Methods: We searched Medline, ISI Web of Science, Embase, and EconLit (PROSPERO: CRD42021252791) for studies considering income inequality and SRH or mortality using multilevel data and adjusting for individual‐level socioeconomic position. We calculated pooled odds ratios (ORs) for poor SRH and relative risk ratios (RRs) for mortality from random‐effects meta‐analyses. We critically appraised included studies using the Risk of Bias in Nonrandomized Studies – of Interventions tool. We assessed certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework and causality using Bradford Hill (BH) viewpoints. Findings: The primary meta‐analyses included 2,916,576 participants in 38 cross‐sectional studies assessing SRH and 10,727,470 participants in 14 cohort studies of mortality. Per 0.05‐unit increase in the Gini coefficient, a measure of income inequality, the ORs and RRs (95% confidence intervals) for SRH and mortality were 1.06 (1.03‐1.08) and 1.02 (1.00‐1.04), respectively. A total of 63.2% of SRH and 50.0% of mortality studies were at serious risk of bias (RoB), resulting in very low and low certainty ratings, respectively. For SRH and mortality, we did not identify relevant evidence to assess the specificity or, for SRH only, the experiment BH viewpoints; evidence for strength of association and dose–response gradient was inconclusive because of the high RoB; we found evidence in support of temporality and plausibility. Conclusions: Increased income inequality is only marginally associated with SRH and mortality, but the current evidence base is too methodologically limited to support a causal relationship. To address the gaps we identified, future research should focus on income inequality measured at the national level and addressing confounding with natural experiment approaches. [ABSTRACT FROM AUTHOR]

Objectives: The usefulness of discrete choice experiments (DCEs) to inform clinical guidelines rests on the assumption that patients facing the same treatment choice at different points in time will express the same preferences. This study provides the first investigation, to our knowledge, to specifically focus on the stability of patients' treatment preferences over the course of a clinical trial.Methods: The same DCE was completed by participants at baseline and final post-treatment assessment in a trial of the efficacy of alternative topical treatments for actinic keratosis as a means for the prevention of skin cancer. The study assesses both the consistency of stated treatment choices and the stability of population-level preference parameter estimates and analyzes how the former is influenced by design aspects of the DCE.Results: No evidence was found of population-level preference parameter instability over the course of the trial despite only a moderate strength of choice consistency. Choice consistency is negatively related to task difficulty with weak evidence of the existence of ordering effects over the sequence of choice tasks.Conclusions: The results provide no evidence that the timing of a DCE within a clinical trial significantly influences population-level treatment preference estimates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]