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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 [greater than or equal to]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., Author(s): Rachel M. Thomson 1,*, Daniel Kopasker 1, Patryk Bronka 2, Matteo Richiardi 2, Vladimir Khodygo 1, Andrew J. Baxter 1, Erik Igelström 1, Anna Pearce 1, Alastair H. Leyland [...]

In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called 'causal reviews', but there is currently no consensus on how these should be conducted. We conducted a methodological review of self-identifying 'causal reviews' within the field of population health to establish: (1) which CAAs are used; (2) differences in how CAAs are implemented; (3) how methods were modified to incorporate causal assessment in SRs. Three databases were searched and two independent reviewers selected reviews for inclusion. Data were extracted using a standardised form and summarised using tabulation and narratively. Fifty-three reviews incorporated CAAs: 46/53 applied Bradford Hill (BH) viewpoints/criteria, with the remainder taking alternative approaches: Medical Research Council guidance on natural experiments (2/53, 3.8%); realist reviews (2/53, 3.8%); horizontal SRs (1/53, 1.9%); 'sign test' of causal mechanisms (1/53, 1.9%); and a causal cascade model (1/53, 1.9%). Though most SRs incorporated BH, there was variation in application and transparency. There was considerable overlap across the CAAs, with a trade-off between breadth (BH viewpoints considered a greater range of causal characteristics) and depth (many alternative CAAs focused on one viewpoint). Improved transparency in the implementation of CAA in SRs in needed to ensure their validity and allow robust assessments of causality within evidence synthesis.

Background: Bradford Hill (BH) viewpoints are widely used to assess causality in systematic reviews, but their application has often lacked reproducibility. We describe an approach for assessing causality within systematic reviews ('causal' reviews), illustrating its application to the topic of income inequality and health. Our approach draws on principles of process tracing, a method used for case study research, to harness BH viewpoints to judge evidence for causal claims. Methods: In process tracing, a hypothesis may be confirmed by observing highly unique evidence and disconfirmed by observing highly definitive evidence. We drew on these principles to consider the value of finding supportive or contradictory evidence for each BH viewpoint characterised by its uniqueness and definitiveness. Results: In our exemplar systematic review, we hypothesised that income inequality adversely affects self‐rated health and all‐cause mortality. BH viewpoints 'analogy' and 'coherence' were excluded from the causal assessment because of their low uniqueness and low definitiveness. The 'experiment' viewpoint was considered highly unique and highly definitive, and thus could be particularly valuable. We propose five steps for using BH viewpoints in a 'causal' review: (1) define the hypothesis; (2) characterise each viewpoint; (3) specify the evidence expected for each BH viewpoint for a true or untrue hypothesis; (4) gather evidence for each viewpoint (e.g., systematic review meta‐analyses, critical appraisal, background knowledge); (5) consider if each viewpoint was met (supportive evidence) or unmet (contradictory evidence). Conclusions: Incorporating process tracing has the potential to provide transparency and structure when using BH viewpoints in 'causal' reviews. [ABSTRACT FROM AUTHOR]

Background We compared the quality of ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other National Health Service datasets, with the 2011 Scottish Census. Methods Measures of quality included the level of missingness and misclassification. We examined the impact of misclassification using Cox proportional hazards to compare the risk of severe coronavirus disease (COVID-19) (hospitalization & death) by ethnic group. Results Misclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared with the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was highest among the White Other British group [39%] and lowest among the Pakistani group [17%]. PHS-EL data often underestimated severe COVID-19 risk compared with Census data. e.g. in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared with the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL. Conclusions Poor quality ethnicity coding in health records can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health. [ABSTRACT FROM AUTHOR]

Background To estimate the effect of social media use in 14 year olds on risk of and inequalities in alcohol use and binge drinking at 17 years. Methods Using the UK-representative Millennium Cohort Study, the relationship between time spent on social media (assessed using questionnaires [ n  = 8987] and time-use-diaries [ n  = 2520]) with frequency of alcohol use in the past month and binge drinking was estimated using adjusted odds ratios (AORs) or adjusted relative risk ratios (ARRRs). Associations within low and high parental education groups were compared to examine effect modification. Analyses accounted for pre-specified confounders, baseline outcome measures (to address reverse causality), sample design, attrition and item-missingness (through multiple imputation). Results Questionnaire-reported time spent on social media was associated with increased risk of alcohol use and binge drinking in a dose–response manner. Compared to 1-< 30 min/day social media users, 30 min-<1 h/day users were more likely to report alcohol use ≥6 times/month (ARRR 1.62 [95% confidence interval 1.20 to 2.20]) and binge drinking (AOR 1.51 [1.22 to 1.87]), as were 1–<2 h/day users (ARRR 2.61 [1.90 to 3.58]; AOR 2.06 [1.69 to 2.52]) and ≥2 h/day users (ARRR 4.80 [3.65 to 6.32]; AOR 3.07 [2.54 to 3.70]). Social media measured by time-use-diary was associated with higher risks, although not always demonstrating a dose–response relationship. The effect of social media use (vs no-use) on binge drinking was larger in the higher (vs lower) parental education groups. Analyses repeated in complete case samples, and with adjustment for baseline outcome measures revealed consistent findings. Conclusions Findings suggest social media use may increase risk of alcohol use and binge drinking. Regulatory action protecting adolescents from harmful alcohol-related social media content is necessary. [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]