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Background There is a well-established cross-sectional association between income and health, but estimates of the causal effects of income vary substantially. Different definitions of income may lead to substantially different empirical results, yet research is often framed as investigating “the effect of income” as if it were a single, easily definable construct. Methods/Results The aim of this paper is to introduce a taxonomy for definitional and conceptual issues in studying individual- or household-level income for health research. We focus on (1) the definition of the income measure (earned and unearned; net, gross, and disposable; real and nominal; individual and household; relative and absolute income) and (2) the definition of the causal contrast (amount, functional form assumptions/transformations, direction, duration of change, and timing of exposure and follow-up). We illustrate the application of the taxonomy to four examples from the published literature. Conclusions Quantified estimates of causal effects of income on health and wellbeing have crucial relevance for policymakers to anticipate the consequences of policies targeting the social determinants of health. However, much prior evidence has been limited by lack of clarity in distinguishing between different causal questions. The present framework can help researchers explicitly and precisely articulate income-related exposures and causal questions.

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. 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. 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.

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. 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. 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.

IntroductionGovernment policies on taxation and social security are important determinants of population health outcomes and health inequalities. However, there is a shortage of evidence to inform policymakers of the health consequences of such policies. The Health Equity and Its Economic Determinants project aims to assess the potential impacts of different taxation and social security policies across Europe on population health and health inequalities using a computer-based simulation that provides projections over multiple health domains.Methods and analysisIn the first phase, key input parameters for the model will be estimated using estimation techniques that control for the effects of prior exposure on time-varying confounders and mediators (g-methods). The second phase will involve developing and validating the microsimulation model for the UK. Policy proposals, developed with policymakers, will be simulated in the third phase to investigate the impacts of income tax and social security changes on population health and health inequalities. In the final phase, the microsimulation model will be extended across other European countries.Ethics and disseminationThis project will use deidentified secondary data for which ethical approval and consents were received by the original data collectors. No further ethical approval will be required for our main analytical datasets. Dissemination plans include academic publications, conference presentations, accessible policy briefings, mass media engagement and a project website. Both the syntax and the underlying synthetic data for the HEED microsimulation model will be made freely available through GitHub and the project website.

ObjectivesTaxes and restrictions on promotions have recently been proposed as policy instruments to reduce consumption of unhealthy foods. The objective of this study is to add to the limited evidence on the comparative effectiveness of price changes, price promotions and volume promotions in changing household purchasing of unhealthy foods, using biscuits, crisps and savoury snacks as examples.DesignLongitudinal regression analysis of consumer microdata.SettingSecondary data on itemised household purchases of biscuits, crisps and savoury snacks from 2006 to 2012.ParticipantsSample of 3024 households in Scotland.Main outcome measuresChanges in the number of calories (kcal) purchased in the product category by a household caused by changes in the price for the product category, any temporary in-store price promotions and any temporary in-store volume promotions. Changes are measured at the mean, median, 25th percentile and 75th percentile of the household purchasing distribution for the full sample. Subgroup analyses were conducted by household income band and for households with and without children.ResultsBetween product categories, the scale of purchasing response to incentives varies significantly. Within product categories, the mean calories (kcal) purchased by a household are more responsive to any volume promotion than to price or any price promotion for all product categories. As the volume of items purchased increases, households are less responsive to price, less responsive to any volume promotion and more responsive to any price promotion. Statistically significant differences are observed between household income groups in their response to price and promotion incentives within the biscuits category only. In cases where statistically significant differences are observed, households with children are more responsive to promotion and price incentives than households without children.ConclusionsFor all product categories analysed (biscuits, crisps and savoury snacks), household purchasing is most responsive to any volume promotion. Therefore, assuming the response of consumers to incentives remains constant following legislation, the most effective policy instrument to reduce the calorie intake from these products may be a ban on volume promotions.

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. We included adults 25-64 years from nine waves of the UK Household Longitudinal Study ( = 45 497/ = 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. 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)]. 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.

Policy Points Income 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.

BackgroundPopulation mental health has deteriorated in many high-income countries over the last decade. Novel welfare policies such as Universal Basic Income (UBI) have been suggested as potential approaches to improve mental health. However, no studies have trialled or modelled UBI at a whole population level or considered impacts on mental health inequalities. We simulated the effects of introducing a UBI on mental health for UK working-age adults.MethodsWe used the SimPaths microsimulation model, which integrates econometric and causal epidemiology analyses to model effects of UK tax/benefit policies on mental health. Adults aged 25–64 were simulated from 2022–2026. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population, and causal effect estimates of income/employment transitions on short-term mental health using marginal structural modelling. Three counterfactual UBI scenarios of increasing generosity were simulated from 2023: ‘Partial’ (equivalent to existing benefits), ‘Full’ (equivalent to the 2022 UK Minimum Income Standard), and ‘Full+’ (retained means-tested benefits for disability, housing, and childcare). The ‘Baseline’ scenario simulated planned tax/benefit policies for 2023–26. Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score ≥ 4). Relative and slope indices of inequality (RII/SII) by education were calculated. Simulations were run 1,000 times to generate 95% uncertainty intervals. Sensitivity analyses relaxed assumptions about likely employment effects of UBI in Full/Full+ scenarios.ResultsPartial UBI slightly reduced poverty (before housing costs) but had no impact on mental health. Full UBI scenarios substantially reduced poverty, from 9.1% (8.5–9.7) to 0.01% (0.00–0.03), but decreased employment from 78.9% (77.9–79.9) to 74.1% (72.6–75.4). In our primary analysis, with Full+ UBI absolute CMD prevalence increased by 0.38% (0.13–0.69), a rise of 158,004 cases (54,054–286,902). In sensitivity analyses assuming minimal employment effects, CMD prevalence instead fell by 0.26% (-0.46, -0.05), a reduction of 108,108 cases (20,790–191,268). In both analyses, effects waned by 2026. Despite a small gradient in effect by education, there was no significant effect of any scenario on mental health inequalities: for Full+ UBI, RII reduced from 1.33 (1.13, 1.56) in the Baseline scenario by 0.03 points (-0.09, 0.02) in primary analysis and 0.02 (-0.06, 0.02) in sensitivity analysis.ConclusionUBI has potential to improve short-term population mental health, but impacts are highly contingent on individual choices around employment following its introduction. In our simulation, UBI had no clear impact on mental health inequalities.

PurposeDistinguishing 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/approachUsing 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.FindingsThe 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/valueThis 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.

BackgroundMany complex public health evidence gaps cannot be fully resolved using only conventional public health methods. We aim to familiarise public health researchers with selected systems science methods that may contribute to a better understanding of complex phenomena and lead to more impactful interventions. As a case study, we choose the current cost-of-living crisis, which affects disposable income as a key structural determinant of health.MethodsWe first outline the potential role of systems science methods for public health research more generally, then provide an overview of the complexity of the cost-of-living crisis as a specific case study. We propose how four systems science methods (soft systems, microsimulation, agent-based and system dynamics models) could be applied to provide more in-depth understanding. For each method, we illustrate its unique knowledge contributions, and set out one or more options for studies that could help inform policy and practice responses.ResultsDue to its fundamental impact on the determinants of health, while limiting resources for population-level interventions, the cost-of-living crisis presents a complex public health challenge. When confronted with complexity, non-linearity, feedback loops and adaptation processes, systems methods allow a deeper understanding and forecasting of the interactions and spill-over effects common with real-world interventions and policies.ConclusionsSystems science methods provide a rich methodological toolbox that complements our traditional public health methods. This toolbox may be particularly useful in early stages of the current cost-of-living crisis: for understanding the situation, developing solutions and sandboxing potential responses to improve population health.