Dataset Information

Quantifying the mental health and economic impacts of prospective Universal Basic Income schemes among young people in the UK: a microsimulation modelling study.

ABSTRACT:

Objective

Universal Basic Income (UBI)-a largely unconditional, regular payment to all adults to support basic needs-has been proposed as a policy to increase the size and security of household incomes and promote mental health. We aimed to quantify its long-term impact on mental health among young people in England.

Methods

We produced a discrete-time dynamic stochastic microsimulation that models a close-to-reality open cohort of synthetic individuals (2010-2030) based on data from Office for National Statistics and Understanding Society. Three UBI scheme scenarios were simulated: Scheme 1-Starter (per week): £41 per child; £63 per adult over 18 and under 65; £190 per adult aged 65+; Scheme 2-Intermediate (per week): £63 per child; £145 per adult under 65; £190 per adult aged 65+; Scheme 3-Minimum Income Standard level (per week): £95 per child; £230 per adult under 65; £230 per adult aged 65+. We reported cases of anxiety and depression prevented or postponed and cost savings. Estimates are rounded to the second significant digit.

Results

Scheme 1 could prevent or postpone 200 000 (95% uncertainty interval: 180 000 to 210 000) cases of anxiety and depression from 2010 to 2030. This would increase to 420 000(400 000 to 440 000) for Scheme 2 and 550 000(520 000 to 570 000) for Scheme 3. Assuming that 50% of the cases are diagnosed and treated, Scheme 1 could save £330 million (£280 million to £390 million) to National Health Service (NHS) and personal social services (PSS), over the same period, with Scheme 2 (£710 million (£640 million to £790 million)) or Scheme 3 (£930 million (£850 million to £1000 million)) producing more considerable savings. Overall, total cost savings (including NHS, PSS and patients' related costs) would range from £1.5 billion (£1.2 billion to £1.8 billion) for Scheme 1 to £4.2 billion (£3.7 billion to £4.6 billion) for Scheme 3.

Conclusion

Our modelling suggests that UBI could substantially benefit young people's mental health, producing substantial health-related cost savings.

SUBMITTER: Chen T

PROVIDER: S-EPMC10551934 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying the mental health and economic impacts of prospective Universal Basic Income schemes among young people in the UK: a microsimulation modelling study.

Chen Tao T Reed Howard H Parra-Mujica Fiorella F Johnson Elliott Aidan EA Johnson Matthew M O'Flaherty Martin M Collins Brendan B Kypridemos Chris C

BMJ open 20231004 10

<h4>Objective</h4>Universal Basic Income (UBI)-a largely unconditional, regular payment to all adults to support basic needs-has been proposed as a policy to increase the size and security of household incomes and promote mental health. We aimed to quantify its long-term impact on mental health among young people in England.<h4>Methods</h4>We produced a discrete-time dynamic stochastic microsimulation that models a close-to-reality open cohort of synthetic individuals (2010-2030) based on data f ...[more]

PMID: 37793925

Similar Datasets

Project description:BackgroundPopulation 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 findingsAdults 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.ConclusionsUBI 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.

Project description:Background Produce prescription programs, providing free or discounted produce and nutrition education to patients with diet-related conditions within health care systems, have been shown to improve dietary quality and cardiometabolic risk factors. The potential impact of implementing produce prescription programs for patients with diabetes on long-term health gains, costs, and cost-effectiveness in the United States has not been established. Methods and Results We used a validated state-transition microsimulation model (Diabetes, Obesity, Cardiovascular Disease Microsimulation model), populated with national data of eligible individuals from the National Health and Nutrition Examination Survey 2013 to 2018, further incorporating estimated intervention effects and diet-disease effects from meta-analyses, and policy- and health-related costs from published literature. The model estimated that over a lifetime (mean=25 years), implementing produce prescriptions in 6.5 million US adults with both diabetes and food insecurity (lifetime treatment) would prevent 292 000 (95% uncertainty interval, 143 000-440 000) cardiovascular disease events, generate 260 000 (110000-411 000) quality-adjusted life-years, cost $44.3 billion in implementation costs, and save $39.6 billion ($20.5-58.6 billion) in health care costs and $4.8 billion ($1.84-$7.70 billion) in productivity costs. The program was highly cost effective from a health care perspective (incremental cost-effectiveness ratio: $18 100/quality-adjusted life-years) and cost saving from a societal perspective (net savings: $-0.05 billion). The intervention remained cost effective at shorter time horizons of 5 and 10 years. Results were similar in population subgroups by age, race or ethnicity, education, and baseline insurance status. Conclusions Our model suggests that implementing produce prescriptions among US adults with diabetes and food insecurity would generate substantial health gains and be highly cost effective.

Project description:BackgroundExcess caloric intake is linked to weight gain, obesity, and related diseases, including type 2 diabetes mellitus and cardiovascular disease (CVD). Obesity incidence is rising, with nearly 3 in 4 US adults being overweight or obese. In 2018, the US federal government finalized the implementation of mandatory labeling of calorie content on all menu items across major chain restaurants nationally as a strategy to support informed consumer choice, reduce caloric intake, and potentially encourage restaurant reformulations. Yet, the potential health and economic impacts of this policy remain unclear.Methods and resultsWe used a validated microsimulation model (CVD-PREDICT) to estimate reductions in CVD events, diabetes mellitus cases, gains in quality-adjusted life years, costs, and cost-effectiveness of the menu calorie labeling intervention, based on consumer responses alone, and further accounting for potential industry reformulation. The model incorporated nationally representative demographic and dietary data from National Health and Nutrition Examination Surveys 2009 to 2016; policy effects on consumer diets and body mass index-disease effects from published meta-analyses; and policy effects on industry reformulation, policy costs (policy administration, industry compliance, and reformulation), and health-related costs (formal and informal healthcare costs, productivity costs) from established sources or reasonable assumptions. We modeled change in calories to change in weight using an established dynamic weight-change model, assuming 50% of expected calorie reductions would translate to long-term reductions. Findings were evaluated over 5 years and a lifetime from healthcare and societal perspectives, with uncertainty incorporated in both 1-way and probabilistic sensitivity analyses. Between 2018 and 2023, implementation of the restaurant menu calorie labeling law was estimated, based on consumer response alone, to prevent 14 698 new CVD cases (including 1575 CVD deaths) and 21 522 new type 2 diabetes mellitus cases, gaining 8749 quality-adjusted life years. Over a lifetime, corresponding values were 135 781 new CVD cases (including 27 646 CVD deaths), 99 736 type 2 diabetes mellitus cases, and 367 450 quality-adjusted life years. Assuming modest restaurant item reformulation, both health and economic benefits were estimated to be about 2-fold larger than based on consumer response alone. The consumer response alone was estimated to be cost-saving by 2023, with net lifetime savings of $10.42B from a healthcare perspective and $12.71B from a societal perspective. Findings were robust in a range of sensitivity analyses.ConclusionsOur national model suggests that the full implementation of the US calorie menu labeling law will generate significant health gains and healthcare and societal cost-savings. Industry responses to modestly reformulate menu items would provide even larger additional benefits.

Dataset Information

Quantifying the mental health and economic impacts of prospective Universal Basic Income schemes among young people in the UK: a microsimulation modelling study.

Objective

Methods

Results

Conclusion

Publications

Quantifying the mental health and economic impacts of prospective Universal Basic Income schemes among young people in the UK: a microsimulation modelling study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets