This chapter presents the results of modelling implementation of selected mental health policy interventions across three settings: primary healthcare, schools and workplaces. The analysis examines not only the health impacts of these interventions but also their broader economic implications, including changes in health expenditure, labour‑market outcomes and GDP. Results are reported for the 27 EU Member States, Iceland, Norway and Switzerland.
The Economic Case for Preventing Mental Ill Health
Abstract
Evidence from systematic reviews and meta‑analyses shows that interventions delivered in PHC, workplace and school settings can effectively mitigate mental ill health, although the quality and consistency of implementation vary – particularly in non‑clinical environments.
The OECD microsimulation model evaluated the impact of six scalable interventions. In PHC settings, these include a web-based programme for adults with mild to moderate depression or anxiety; group psychotherapy delivered by trained nurses for adults with mild to moderate depression; and combined psychotherapy and medication for severe depression cases not responding to treatment. Workplace interventions comprise an online programme for employees with mild to moderate depression or anxiety; and a universal programme based on CBT. Finally, in schools, the intervention comprises a universal resilience‑focussed CBT programme.
If implemented in 2025 and sustained through to 2050, these interventions are projected to yield larger gains in quality of life, measured in DALYs, than in life expectancy. The most effective intervention – combined psychotherapy and pharmacotherapy delivered in PHC settings – is associated with approximately 27 additional DALYs per 100 000 population, on average across countries. At a population level, this would create a total of 107 292 additional DALYs per year across EU countries over the modelled period.
Scaling up interventions is projected to reduce healthcare expenditure by between EUR 0.1 and EUR 10.4 per capita per year, with the most effective options generating total savings of around EUR 3.2 billion across EU countries over 2025-2050. However, these savings remain modest relative to the current cost of mental ill health: even the most impactful intervention only lowers mental‑health‑specific healthcare spending by 4.2%.
Interventions also enhance workforce productivity, thereby supporting economic growth. The most impactful option modelled – combined psychotherapy and pharmacotherapy in PHC settings – is estimated to be equivalent to adding 16 893 full‑time equivalent workers per year across EU countries. The associated gains in human capital are projected to raise economic output by around 0.013%, corresponding to approximately EUR 2.6 billion across EU countries.
Most interventions provide good value for money, meeting the EUR 50 000 per DALY cost-effectiveness threshold and, in some cases, even generating net savings. They also yield positive returns on investment, with economic gains usually exceeding the resources required for implementation. Interventions targeting the current workforce and delivered at relatively low cost, such as web‑based programmes, tend to perform particularly well. Interventions delivered in PHC settings also show strong results.
Improving population mental health requires the assessed interventions to be accompanied by complementary measures. First, strengthening intervention design and implementation by adhering to best‑practice models can enhance effectiveness. Second, more ambitious coverage targets are needed to maximise benefits, although scaling up will require significant upfront investment and increased workforce capacity. Finally, it is crucial to tackle underlying and persistent drivers of mental distress, such as economic insecurity and labour‑market instability.
Mental ill health – including major depressive, generalised anxiety and alcohol use disorders – has both substantial impacts on health and quality of life and broader social and economic consequences. Chapter 3 showed that mental ill health is projected to reduce healthy life years by an average of 2.5 years across EU countries and to account for around 6% of health spending between 2025 and 2050. Effective interventions that reduce the prevalence, severity or duration of mental ill health can therefore not only alleviate suffering and improve well-being but also help to mitigate these associated economic impacts.
This chapter analyses the cost-effectiveness of a range of mental health interventions to support governments in selecting policies that deliver strong health and economic returns. A wide spectrum of policy options has been shown to be effective in promoting good mental health and in preventing and treating mental ill health, as outlined in Chapter 4 and in a recent OECD report on mental health promotion and prevention (OECD, 2025[1]). This chapter seeks to provide a better understanding of the cost-effectiveness of a range of mental health interventions, including the impacts not only on health and quality of life but also on the broader economy. This is particularly important due to the broad range of policy options and levers that governments can consider when evaluating which policy approach to implement, such as intended outcomes (prevention or treatment), sector of implementation and target populations, among others. To provide governments with the data needed to identify which policy intervention, or combination of interventions, represents good value for money, a suite of common and evidence‑based interventions were modelled using the OECD SPHeP-NCDs model (for more information, see Chapter 3).
This chapter presents the modelled health and economic impacts of scaling up each selected intervention, assuming national‑level implementation. The analysis quantifies the outcomes of intervention scenarios relative to a business‑as‑usual baseline. Results are generated for the 27 EU Member States (for which averages are also reported) and for Iceland, Norway and Switzerland over the period 2025-2050, based on implementation beginning in 2025. To be included in the model, interventions were required to meet a set of criteria and parameter specifications ensuring their suitability for integration into the OECD SPHeP‑NCDs model, as summarised in Box 5.1.
The effectiveness of any policy intervention depends on a range of factors, some of which are context‑specific. For example, the value for money of a policy is influenced not only by its intrinsic efficacy but also by local conditions such as the cost of treating related diseases and complications, demographic structures, epidemiological patterns, and the costs associated with implementation. Within the OECD SPHeP‑NCDs model, policies are assessed using four key parameters:
The parameter of effectiveness at the individual level captures the extent to which mental health outcomes change following exposure to the intervention. Wherever possible, estimates are drawn from peer‑reviewed meta‑analyses, preferably based on randomised controlled trials.
Intervention effects may be time‑limited or time‑dependent, typically increasing initially before stabilising or diminishing. The parameter of time to maximum effect and evolution of effectiveness over time describes how effectiveness changes over the duration of exposure.
The parameter of intervention coverage includes defining the eligible population and the share of individuals exposed to the intervention. Some policies target specific groups (such as particular age cohorts or those with defined risk factors), and in many cases only a proportion of eligible individuals participate or receive the intervention.
The final parameter is implementation costs. Public health actions may incur costs related to planning, administration, monitoring, evaluation and provision of materials or equipment (such as brochures and stand‑up desks). Costs are estimated using the WHO‑CHOICE methodology (Bertram et al., 2021[2]), adjusted for cross‑country price differentials using purchasing power parity calculations and exchange rates. All costs are reported in 2019 EUR.
To assess the population‑level impact and benefit-cost performance of mental health policies, interventions are compared with a business‑as‑usual baseline in which no new measures are implemented, and preventive and health services continue at current, country‑specific levels. The difference between the business‑as‑usual and policy scenarios represents the impact of the intervention. This comparison encompasses all relevant dimensions, including health outcomes, healthcare expenditure, labour‑market productivity and other economic effects, thereby providing the basis for a comprehensive return‑on‑investment analysis.
In considering the findings of this analysis, it should be noted that the model includes only a subset of mental disorders, albeit the most prevalent ones (see Box 3.1 for additional information), and does not account for upstream determinants of mental health such as well-being and resilience. This means that the analysis is likely to underestimate the true impact of interventions, and is not designed to assess the effects of upstream efforts such as investments in positive health promotion and resilience‑building.
Note: For more information on the OECD SPHeP-NCDs model, please see the SPHeP-NCDs Technical Documentation, available at: http://oecdpublichealthexplorer.org/ncd-doc.
The analysis in this chapter examines six interventions selected on the basis of robust evidence demonstrating their effectiveness in improving mental health and/or treating mental ill health, as well as availability of data suitable for integration into the OECD SPHeP‑NCDs model (Barry et al., 2024[3]). These interventions are summarised in Table 5.1, and brief descriptions are provided below. The selected interventions correspond to the three priority settings identified in Chapter 4: PHC, schools and workplaces. The detailed model specifications and key assumptions underpinning each intervention are presented in the following section.
Three interventions in primary healthcare settings were selected
Web-based intervention to prevent major depressive and generalised anxiety disorders
Over the past decade, the rapid diffusion of internet‑enabled tools has created new opportunities to enhance accessibility, responsiveness and efficiency across health systems, while also reducing costs and associated carbon emissions (Ebert et al., 2015[9]; Purohit, Smith and Hibble, 2021[10]). Evidence shows that web‑based psychological interventions delivered in PHC settings can effectively prevent depression and anxiety, highlighting the value of digital channels for delivering accessible mental health support at scale and at a relatively low cost. To assess their cost-effectiveness, the modelling draws on a meta‑analysis indicating that eHealth interventions lead to a 0.493‑point reduction in depressive symptoms on the PHQ‑8 scale and a 1.023‑point reduction in anxiety on the GAD‑7 scale (see Box 1.2) (Deady et al., 2017[4]). These interventions are primarily based on CBT delivered through technology platforms, with a smaller share reflecting alternative modalities such as acceptance and commitment therapy. Given their preventive focus, the modelling assumes that GPs deliver these interventions to patients aged 15 and over who have mild or moderate symptoms of depression or anxiety (PHQ‑8 and/or GAD‑7 scores between 5 and 15). In line with previous OECD analyses (OECD, 2025[1]), it is assumed that 10% of GPs participate and that only 14% of eligible patients complete the programme, reflecting high dropout rates of around 86%. This results in an overall coverage rate of 1.12% of the eligible population, given that approximately 80% of individuals visit a GP at least once per year.
Face‑to-face psychological treatment for mild and moderate major depressive disorders
CBT is a psychological treatment that has proved effective for a wide range of mental health conditions, including depression, generalised anxiety disorders, panic disorder and post‑traumatic stress disorder (Butler et al., 2006[11]). CBT encompasses targeted therapeutic strategies aimed at modifying thought and behaviour patterns that contribute to negative emotions and maladaptive behaviours (David, Critsea and Hofmann, 2018[12]; Gaudiano, 2008[13]). A recent systematic review and meta‑analysis shows that CBT delivered by clinicians in PHC settings is an effective treatment for depression (Santoft et al., 2019[5]). It also shows that group‑based CBT sessions are generally more effective than other treatment modalities for mild and moderate depression, yielding an average 3.59‑point reduction on the PHQ‑8 scale compared to waiting list controls, and a 1.81‑point reduction compared to a care‑as‑usual scenario among patients already receiving treatment. Drawing on this evidence, the modelling assumes that psychotherapy interventions target individuals aged 15 and over diagnosed with mild to moderate major depressive disorders, defined as a PHQ‑8 score between 5 and 15. To align the intervention with best practice and current evidence, it is assumed that GPs refer eligible patients to group CBT consisting of 12 sessions, each involving around ten participants and led by two trained nurses; the intervention aims both to treat those currently receiving care and to increase overall treatment coverage by 5%.
Combined psychotherapy and pharmacotherapy for severe major depressive disorders
Severe depression is often resistant to treatment, and evidence indicates that combining psychotherapy with pharmacotherapy can more effectively reduce symptoms and support recovery (Santoft et al., 2019[5]). To assess the impact of scaling up this approach, combined therapy was modelled for the treatment of severe major depressive disorders. Drawing on findings from a previous meta‑analysis, the intervention targets individuals aged 15 and over who are already receiving treatment for severe major depressive disorders, defined as a PHQ‑8 score above 15. This research showed that combined treatment is associated with an average 2.88‑point reduction in PHQ‑8 scores compared with a care‑as‑usual scenario. Under the modelled scenario, targeted patients are referred by their GP to receive 12 sessions of individual CBT delivered by a trained therapist, alongside appropriate pharmacotherapy. Pharmacotherapy alone is treated as the care‑as‑usual baseline; it is therefore not included in the implementation costs of the combined intervention.
Two workplace‑based interventions were selected
Web-based intervention to prevent major depressive and generalised anxiety disorders
Workplace‑based interventions can be effective not only when delivered in person but also when provided through digital channels such as mobile applications or web‑based platforms. These approaches offer several advantages over face‑to‑face programmes, including lower delivery costs and improved discretion for employees, which may help to reduce perceived stigma. Evidence from a meta‑analysis suggests that web‑based workplace interventions may help to prevent major depressive and generalised anxiety disorders, yielding average reductions of 0.41 points on the PHQ‑8 scale and 1.12 points on the GAD‑7 scale (Stratton et al., 2017[6]). However, the robustness of this evidence remains limited due to the small number of high‑quality studies available. In line with existing research, the intervention is modelled as a targeted programme offered to employees of large companies who, following screening, are identified as being at risk of developing a mental illness – defined in the model as a PHQ‑8 score between 5 and 15 and/or a GAD‑7 score between 5 and 15 (National Mental Health Commission, 2021[14]). It is assumed that the intervention is delivered via a mobile application offering a combination of CBT‑based tools, mindfulness modules and/or stress‑management exercises. Drawing on OECD analysis of the 2020 Workforce Disclosure Initiative (OECD, 2022[15]) the model assumes that 68% of large companies offer such a mental health programme. Consistent with assumptions applied to web‑based interventions in PHC settings, only 14% of eligible employees are expected to participate.
Cognitive behavioural therapy delivered as an intervention to prevent major depressive disorders
Workplaces present a valuable opportunity to improve and support mental health, including through evidence‑based interventions such as CBT, which have been shown to reduce the severity of conditions like major depressive disorders. A systematic review and meta‑analysis found that workplace‑based CBT interventions reduce depressive symptoms by an average of 0.49 points on the PHQ‑8 scale (Tan et al., 2014[7]). The studies included in the review used different approaches to engage employees: in one pilot, human resources staff promoted the programme to all employees on an optional basis, resulting in a participation rate of around 3%; in another case, a company targeted lower‑ and middle‑level managers, and 72% agreed to participate. These findings highlight the importance for enterprises of assessing which strategies are most effective in increasing participation. Based on the review evidence, the workplace CBT intervention was modelled as a universal intervention offered to all employees aged 18 and over in large companies, regardless of their initial level of depressive symptoms. Following OECD analysis of the 2020 Workforce Disclosure Initiative (OECD, 2022[15]), the model assumes that 68% of large companies will offer such programmes and that 14% of employees will enrol. Consistent with the design used in the study by Ahola and colleagues (2012[16]), the intervention assumes that each company designates two staff members (human resources or occupational health and safety) to attend four full‑day training workshops (32 hours in total) with a psychologist to prepare them to deliver the programme. The intervention itself consists of four half‑day group sessions, each involving 5‑20 employees, conducted during working hours by the two trained facilitators from the organisation.
One school-based intervention was selected
Psychological resilience‑based intervention to prevent major depressive and generalised anxiety disorders
Resilience‑based interventions are psychological therapies designed to strengthen protective factors that support mental health and well-being, and to prevent the onset of conditions such as major depressive and generalised anxiety disorders. A recent systematic review of universal resilience‑focussed interventions found that these programmes can produce short‑term reductions in depressive and anxiety symptoms among children and adolescents, particularly when they incorporate CBT components (Dray et al., 2017[8]). These interventions were associated with a 0.41‑point reduction on the PHQ‑8 scale and a 1.12‑point reduction on the GAD‑7 scale. In line with the criteria applied in the systematic review, the modelled intervention is structured as a universal, school‑based programme delivered to all students, regardless of their initial symptom levels, with the aim of preventing the development of major depressive and generalised anxiety disorders. The model assumes that 75% of schools implement the intervention and that student attendance averages around 90%. As the literature suggests that the effects diminish after one year, the model further assumes that schools repeat the intervention annually to maintain its impact over time.
Modelling of the six interventions indicates that all generate positive impacts on population health. Improvements are observed primarily in quality of life, measured in DALYs, with comparatively smaller gains in life expectancy (Figure 5.1). This pattern is consistent with that discussed in Chapter 3, as mental ill health affects quality of life more substantially than it reduces life expectancy, measured in life years. The results further suggest that interventions delivered in PHC settings have the largest effects, relative to workplace‑ and school‑based approaches. Combined psychotherapy and pharmacotherapy delivered by clinicians emerges as the most effective intervention modelled: scaling it across EU countries is estimated to produce an average gain of 27 DALYs per 100 000 population every year, which is equivalent to 27 additional people per 100 000 living one additional year in a perfect state of health. Across EU countries, this corresponds to 107 292 additional DALYs per year during 2025-2050.
Note: LY is life year; phcWb is PHC web-based; phcPsy is PHC psychotherapy; phcComb is PHC combined psychotherapy and pharmacotherapy; wpWb is workplace web-based; wpCBT is workplace CBT; sCBT is school CBT.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2025.
The second most effective intervention is resilience‑focussed CBT delivered in school settings, which yields an average gain of 15 DALYs per 100 000 population per year, or a total of 62 163 DALYs across EU countries during 2025-2050. Psychotherapy treatment delivered in PHC settings also generates substantial benefits, with an average gain of 11 DALYs per 100 000 population per year, amounting to 41 656 DALYs across EU countries. Workplace‑based interventions, while still beneficial, produce relatively smaller improvements in quality of life: web‑based programmes increase DALYs by 6 per 100 000 population per year (23 010 DALYs across EU countries), and universal workplace CBT leads to a gain of 5 DALYs per 100 000 population per year (19 990 DALYs across EU countries).
It is important to recognise that the overall impact of a mental health intervention depends not only on the mechanism and setting in which it is delivered but also on several additional factors related to its design and implementation. Three main elements help to explain the observed patterns:
The most influential factor is how effective the intervention is for each person who receives it. Interventions that produce substantial and sustained reductions in symptoms of depression or anxiety have a larger aggregate impact. For example, psychotherapy combined with pharmacotherapy in PHC settings yields an effect on targeted individuals that is nearly seven times greater than that of school‑based CBT.
The next factor is the number of people reached. Universal interventions typically influence more people, and therefore have the potential for a larger population‑level effect. For instance, CBT programmes in schools can reach all students aged 8‑18, which is a large cohort. However, because these programmes are delivered to everyone, many participants may have limited need and may thus derive smaller individual benefits.
The third factor is the type and severity of mental health needs addressed. Targeted interventions focussing on individuals with more severe mental health problems – such as combined psychotherapy and pharmacotherapy in PHC settings – reach fewer people overall, but concentrate resources on those with the greatest level of need. This targeted approach can generate a stronger overall impact despite the smaller population reached.
In general, findings from the model suggest that, when scaled up, the most effective interventions fall into two broad categories: highly effective, targeted treatments for individuals with significant mental health needs, and broader, lower‑intensity programmes that reach large segments of the population.
Compared with the scale of the overall burden, the estimated impacts of the modelled interventions remain relatively modest. Across EU countries, and at the assumed levels of implementation, the interventions reduce the projected burden of mental disorders by up to around 1%, with the largest effects observed for combined psychotherapy and pharmacotherapy delivered in PHC settings. Expanding population coverage beyond modelled levels would generate further health gains; however, the coverage assumptions used in the analysis reflect what is considered realistic under real‑world conditions. Scaling up these programmes may be constrained by factors such as shortages of trained professionals, resource pressures within PHC and educational settings, and challenges in maintaining sustained engagement among target populations.
Mental health interventions can reduce healthcare costs, but their impact is relatively small compared to the overall scale of the burden
Modelling the scale‑up of mental health interventions across EU countries shows that doing so would generate an overall reduction in healthcare expenditure (Figure 5.2). These savings arise from lower spending on other forms of treatment and from reductions in the healthcare costs associated with mental ill health as a comorbidity. The estimates do not, however, include the direct costs of scaling up the interventions themselves. The interventions that generate the largest reductions in healthcare expenditure are the same as those that deliver the greatest health gains. In both cases, combined psychotherapy and pharmacotherapy delivered in PHC settings has the largest impact, reducing healthcare expenditure by an estimated EUR 10.4 per capita per year, or around EUR 3.2 billion across EU countries over 2025-2050.
Note: phcWb is PHC web-based; phcPsy is PHC psychotherapy; phcComb is PHC combined psychotherapy and pharmacotherapy; wpWb is workplace web-based; wpCBT is workplace CBT; sCBT is school CBT.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2025.
By contrast, the smallest reductions are associated with web‑based interventions delivered in workplace settings, which are estimated to generate savings of EUR 0.1 per capita per year – equivalent to EUR 79 million across EU countries between 2025 and 2050. For most other interventions, reductions range between EUR 0.1 and EUR 0.3 per capita per year, with the exception of psychotherapy delivered in PHC settings, which is estimated to reduce spending by EUR 2.6 per capita per year. In total, this corresponds to savings of between EUR 24 million and EUR 738 million across EU countries during 2025-2050. Significant variation is also observed across countries, reflecting differences in baseline health expenditure and in the magnitude of potential savings, as illustrated in Figure 5.2.
It is important to note that, as with the modelled effects on population health, the overall impact of the interventions on healthcare expenditure remains small relative to the total cost of mental ill health. Even the intervention generating the largest reduction in healthcare spending lowers mental health‑related expenditure by only 4.2%. This underscores that, while the modelled interventions can make a meaningful contribution to reducing the burden of mental ill health, they are unlikely on their own to reduce the overall healthcare costs associated with these conditions substantially.
Mental health interventions can enhance productivity by increasing workforce participation and productivity
The modelled interventions are estimated to generate positive productivity gains, driven by improvements in workforce participation and performance. As shown in Figure 5.3, most of these gains stem from increased productivity, reflecting reductions in absenteeism and presenteeism, followed by higher labour market participation and decreases in early retirement. The intervention with the largest impact is combined psychotherapy and pharmacotherapy delivered in PHC settings, which would yield productivity gains equivalent to an additional 16 893 full‑time equivalent workers per year across EU countries. The next most effective intervention is workplace‑based CBT, which would generate the equivalent of 9 032 additional full‑time‑equivalent workers annually. This is followed by psychotherapy delivered in PHC settings (7 604), universal workplace‑based CBT (5 726) and school‑based CBT (1 827). The smallest estimated productivity gain, equivalent to 1 163 full‑time‑equivalent workers, is associated with web‑based interventions delivered in PHC settings.
Note: The “employment” category combines both effects on unemployment and part-time working; phcWb is PHC web-based; phcPsy is PHC psychotherapy; phcComb is PHC combined psychotherapy and pharmacotherapy; wpWb is workplace web-based; wpCBT is workplace CBT; sCBT is school CBT.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2025.
These findings are consistent with existing evidence on the economic benefits of investing in mental health. Because mental ill health affects quality of life more substantially than life expectancy, the most significant productivity improvements arise from reductions in absenteeism and presenteeism – two of the key channels through which mental ill health generates economic losses. Interventions that successfully mitigate the adverse impacts of mental ill health on daily functioning therefore offer considerable potential to boost economic performance.
Improving mental well-being has a positive impact on the economy
The productivity improvements generated by mental health interventions are expected to translate into measurable gains in GDP, primarily through increased human capital resulting from reduced presenteeism, absenteeism and early retirement and higher labour market participation. These enhancements in workforce performance are projected to raise economic output by between 0.001% for the least impactful intervention and 0.013% for the most effective (Figure 5.4). In monetary terms, this corresponds to increases of between EUR 166 million and EUR 2.6 billion across EU countries during 2025-2050. The smallest GDP gains are associated with scaling up web‑based interventions in PHC settings and school‑based CBT, each generating an estimated 0.001% increase in GDP – equivalent to EUR 166 million for PHC web‑based interventions and EUR 324 million for school‑based CBT. Larger impacts are observed for workplace‑based CBT (0.004% or EUR 880 million), web‑based interventions delivered in the workplace (0.006% or EUR 1.4 billion) and psychotherapy delivered in PHC settings (0.006% or EUR 1.2 billion). The greatest GDP gains would result from scaling up combined psychotherapy and pharmacotherapy in PHC settings, which is estimated to increase GDP by 0.013% per year, equivalent to EUR 2.6 billion across EU countries over the modelled timeframe.
Note: Blue dots represent single country estimates; the red dot represents the EU average; WP is workplace.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2025.
As discussed in the previous section, results from the OECD SPHeP‑NCDs model indicate that, even when scaled up to realistic coverage levels that reflect workforce and budget constraints, the assessed interventions would reduce the burden of mental ill health associated with generalised anxiety and major depressive disorders only modestly. Across EU countries, the most effective interventions are estimated to lower the health burden by up to roughly 1% and to reduce related healthcare expenditure by up to 4.2%. While affordable, implementation costs are also not negligible, ranging from EUR 0.2 to EUR 3.1 per capita per year. These findings may prompt reflection across countries on whether such interventions constitute a worthwhile investment, including whether mobilising additional funding and adopting enabling policies – such as strengthening the mental health workforce – to support scaled‑up implementation is economically justified.
To respond to such questions, governments and public health authorities usually rely on cost-effectiveness analysis and benefit-cost metrics. These economic evaluations help to ensure that limited resources are allocated to programmes that deliver the greatest social benefits at the lowest cost. Specifically:
Cost-effectiveness analysis evaluates the improvement in quality of life, measured in DALYs gained, relative to the cost of an intervention, net of any potential savings in healthcare expenditure. In line with thresholds commonly used at a national level, OECD analyses consider an intervention cost‑effective when it delivers one additional DALY (i.e. an additional year in full health) at a cost below roughly EUR 50 000.
Benefit-cost ratios reflect how investment in a public health intervention contributes to an economy’s competitiveness by strengthening human capital through improved health. It is calculated as the total increase in GDP resulting from higher workforce productivity, divided by the cost of implementing the intervention; values above 1 indicate that the economic return exceeds the investment cost.
The majority of assessed interventions demonstrate good value for money, making them viable options for policymakers seeking efficient use of public resources (Figure 5.5). Interventions delivered in PHC settings generally perform best: psychotherapy is cost-saving in 53% of countries; combined psychotherapy and pharmacotherapy is cost‑saving in 83% of countries and cost‑effective in all others. School‑based CBT and web‑based programmes delivered in workplace settings also show favourable value for money, with all countries reporting cost-effectiveness ratios below the EUR 50 000 per DALY threshold, and workplace web‑based programmes being cost‑saving in about one‑third of countries. Other interventions display a more heterogeneous pattern. Web‑based programmes delivered in PHC settings yield mixed results, with countries distributed across all cost-effectiveness categories, although only four countries fall above the cost-effectiveness threshold. Workplace‑based CBT shows the widest variation, and is considered not cost‑effective in about 70% of countries. These differences suggest that while several interventions reliably deliver value for public investment, others are more sensitive to country context, implementation capacity and delivery conditions.
Note: CS is cost-saving; phcWb is PHC web-based; phcPsy is PHC psychotherapy; phcCom is PHC combined psychotherapy and pharmacotherapy; wpWb is workplace web-based; wpCBT is workplace CBT; sCBT is school CBT.
Source: OECD analyses based on the OECD SPHeP-NCDs model, 2025.
Considering the benefit-cost results (Figure 5.6), a clear pattern emerges: interventions that target the current workforce and can be implemented at relatively low cost deliver the strongest economic returns. All options except school‑based and workplace‑based CBT yield a benefit-cost ratio greater than 1. Workplace web‑based programmes perform particularly well, generating EUR 10.3 in GDP for every euro invested, reflecting both their low implementation cost and their direct effect on workforce productivity. Web‑based interventions in PHC settings also show strong returns, with a benefit-cost ratio of 3.9, followed by combined psychotherapy and pharmacotherapy (3.3) and psychotherapy (2.6) in PHC settings. By contrast, school‑based and workplace‑based CBT both produce benefit-cost ratios below one (0.6), with the former reflecting the absence of short‑term productivity gains among beneficiaries who are not yet in the labour market, and the latter combining higher implementation costs with more limited productivity impacts.
Note: Estimates for the benefit-cost ratio are the result of the total increase in GDP produced by the policy divided by the total cost of implementing the policy in these countries; phcWb is PHC web-based; phcPsy is PHC psychotherapy; phcCom is PHC combined psychotherapy and pharmacotherapy; wpWb is workplace web-based; wpCBT is workplace CBT; sCBT is school CBT.
Source: OECD analyses using the SPHeP-NCDs model.
The analyses presented in this chapter highlight that, although initiatives to improve mental well‑being are beneficial and offer good value for money, they should be considered as one element of a broader, multi‑component mental health strategy rather than standalone solutions. Even when implemented in combination, these interventions are unlikely to generate sizeable reductions in the substantial and growing burden of mental ill health. Their impact depends not only on the scale of investment but also on implementing the most effective versions of the policies, acknowledging that the systematic reviews informing this work point to considerable variation in intervention design and outcomes. In addition, countries may wish to adopt more ambitious coverage targets, with corresponding investments to support wider implementation. Finally, efforts to improve mental well‑being need to be accompanied by policy action that addresses the underlying determinants of mental ill health, including those discussed in Chapter 2. The remainder of this section examines these complementary options in greater detail.
Implementing best practices for improving mental well-being will deliver better results
A central priority for countries aiming to strengthen population mental health is to invest in interventions that reflect validated best practices and are adapted to national contexts. While the interventions modelled in this report provide a useful benchmark, their real‑world impact depends on how effectively they are designed, targeted and delivered. OECD evidence shows that core public health principles – such as ensuring accessibility, promoting equity and integrating services across sectors – are critical determinants of success (OECD, 2025[1]).
Impact is further enhanced when clinical services are supported by system‑level measures that promote awareness, early detection and timely access to care. Improving service availability alone is insufficient if stigma, poor mental health literacy or social barriers continue to hinder help‑seeking. Communication campaigns, literacy initiatives and peer‑support programmes have proved effective in normalising discussions about mental health and encouraging individuals to seek care when needed. These complementary actions expand the reach and effectiveness of clinical interventions. For example, scaling up Norway’s Prompt Mental Health Care programme, combining rapid access with evidence‑based psychotherapy, could yield roughly 33 DALYs per 100 000 people annually across EU countries – three times the effect of the generic psychotherapy intervention in PHC settings modelled in this report.
Countries can also strengthen intervention performance by investing in key implementation enablers. Workforce development is especially important, with best practice programmes emphasising the need to expand mental health capacity, support multidisciplinary teams and introduce new roles that improve continuity and access. In many countries, teleconsultations, digital tools and integrated care networks have become vital to extending services to underserved groups and managing rising demand. These measures improve both coverage and resource efficiency at a time when workforce shortages are widespread.
Reducing financial barriers is another crucial enabler. Expanding reimbursement for psychotherapy and lowering out‑of‑pocket payments have been associated with higher service uptake, earlier engagement in care and improved equity. When combined with destigmatisation and literacy initiatives, such reforms create an environment where interventions are more likely to achieve their intended outcomes. Without these enabling conditions, even highly effective programmes may fail to reach those who would benefit most.
Finally, scaling up mental health interventions requires sustained investment in research and evaluation. Although the interventions modelled in this report draw on well‑established evidence, ongoing trials and implementation studies remain essential to refine existing approaches and identify promising new ones. A stronger evidence base will support more informed decision‑making about which interventions to prioritise, the contexts in which they perform best and the system features that facilitate success. This is particularly important for digital and workplace‑based interventions, where evidence is evolving rapidly but remains uneven across countries.
Achieving greater impact through higher coverage and system preparedness
A second avenue for increasing the impact of mental health interventions is to raise coverage levels beyond those simulated in this analysis. The modelling presented in this chapter adopts deliberately conservative assumptions, such as assuming that 10% of GPs participate in PHC‑based interventions. This reflects short‑term feasibility and current evidence on workforce availability and service organisation across OECD and EU health systems. However, the potential benefits of higher coverage are substantial. Expanding participation beyond these baseline levels could generate significantly greater health gains and productivity benefits for the population, especially if countries simultaneously adopt best‑practice versions of interventions. Previous OECD work on mental health performance (OECD, 2021[17]) has shown that countries with higher coverage of evidence‑based psychological therapies and preventive programmes are likely to achieve better outcomes at similar or even lower marginal cost, due in part to reduced unmet needs and earlier engagement in care.
Scaling up coverage, however, has important implications for planning and financing. As highlighted in Chapter 3, providing treatment to all individuals with mental disorders would require an estimated 41% increase in mental‑health-specific health spending compared with current levels. Such resources would need to be invested upfront to enable the necessary expansion in capacity and service provision. This finding is consistent with broader OECD analyses showing that mental health services are structurally under‑resourced relative to disease burden: more than half of countries for which data were available allocate less than 6% of government health spending to mental health, despite mental disorders accounting for a much larger share of the total disease burden (OECD, 2021[17]). Ambitious expansion would therefore require sustained financial commitment, but these investments are likely to generate positive long‑term returns by improving productivity, reducing absenteeism and lowering downstream healthcare costs.
Any substantial increase in coverage must also be preceded by deliberate efforts to strengthen the mental healthcare workforce. Without expanded workforce capacity, scaling up risks exacerbating already high levels of unmet needs and service bottlenecks. OECD work (OECD, 2023[18]) highlights persistent shortages of health professionals, uneven geographical distribution of providers and limited availability of specialised competencies. These challenges are evident in the mental healthcare workforce: two‑thirds of WHO Member States globally report having only one psychiatrist per 200 000 people, and even in high‑income countries the median number of specialised mental healthcare workers is only around 67 per 100 000 population (WHO, 2025[19]). Addressing these gaps requires a combination of strategies: expanding training capacity, improving retention, creating new professional roles, investing in multidisciplinary models, and leveraging digital tools and stepped‑care approaches to increase efficiency. Lessons from best‑practice interventions across OECD countries also indicate that rapid‑access pathways, triage systems and low‑threshold digital entry points can alleviate pressure on specialist services while maintaining high levels of effectiveness.
One way to keep the additional cost at an affordable level is to ensure that any expansion of services is directed first to the areas where needs are greatest. Prioritising the deployment of new capacity toward population groups, geographical areas and service settings with the highest unmet needs would allow countries to achieve stronger marginal gains in health and economic outcomes from each unit of investment. For example, by targeting resources at PHC practices with high caseloads, schools or workplaces with identified needs, or regions with clear service shortages, systems can improve the efficiency of intervention delivery while containing overall expenditure. This targeted approach helps to avoid the higher costs and logistical challenges associated with uniform expansion, promotes the alignment of new resources with the parts of the system where they can deliver the greatest value, and supports more efficient and affordable implementation.
Addressing root causes by creating environments that sustain mental well-being
A third pillar for strengthening population mental health is addressing the underlying social and economic determinants that contribute to mental distress. As outlined in Chapter 2, evidence from a rapid literature review highlights a wide range of social, economic, environmental and lifestyle factors associated with rising rates of mental ill health. Several longer‑term macro‑trends are also shaping mental health trajectories. These include rapid digitalisation and problematic use of social media – which disproportionately affects younger people – as well as economically stressful environments that encompass both persistent pressures such as economic insecurity and labour‑market instability and sudden shocks like the 2022 inflation surge across many OECD countries, which placed a heavy burden on low‑income households (Causa et al., 2022[20]).
Collectively, these factors point to the need for upstream policies capable of mitigating both acute shocks and persistent structural drivers of mental distress. The interventions assessed in this report focus primarily on building individual and community resilience and on supporting people already experiencing mental health difficulties. While these measures are essential for improving well-being and reducing the severity of mental disorders, they cannot on their own counteract the factors that give rise to mental ill health in the first place. For this reason, preventive and clinical interventions need to be embedded within a wider set of policies that create social, economic and environmental conditions supportive of mental well-being (OECD, 2023[21]).
Social protection systems play a crucial role in shielding individuals from stressors that may precipitate or worsen mental health problems. A literature review covering OECD and European Economic Area countries finds a strong association between welfare system generosity and population mental health, with countries that devote higher levels of social expenditure generally achieving better outcomes across indicators such as suicide rates and deaths related to substance use (Ribanszki et al., 2022[22]). The review suggests that one important driver of these positive results is the way these systems mitigate social inequality, particularly by preventing those who are least advantaged from falling below critical socio‑economic thresholds. Additional factors – including trust in the welfare system and the overall cohesiveness of societies – also contribute to the beneficial impact of welfare policies on mental well-being, even when they are not directly tied to specific spending measures.
Previous OECD work has highlighted that integrating mental health support into skills and employment services is essential for improving outcomes among individuals who are not in employment, underscoring the importance of co‑ordinated responses across health, education and labour market systems. In 2015, the OECD Council adopted the OECD Recommendation of the Council on Integrated Mental Health, Skills and Work Policy (OECD, 2015[23]), which includes a detailed set of policy principles to address the impact of mental ill health on employment, education, health and social outcomes. These policy principles encourage adherents to embrace approaches to mental health policy, including seeking to improve mental well-being, prevent mental health conditions, and provide appropriate and timely services that recognise the benefits of meaningful work for people living with mental health conditions (Box 5.2). All OECD countries adhere to the Recommendation, along with a number of non-Members. This Recommendation is the recognition by OECD countries that good policies can make a significant difference when it comes to preventing mental illness at all ages, including in youth and adolescence; in supporting those experiencing mental illness to stay in the workplace; and in supporting those who have left employment to return to the labour market.
Addressing the root and persistent causes of mental ill health requires an integrated, cross‑sectoral policy approach that should also strengthen individuals’ ability to engage meaningfully in the labour market. The OECD Recommendation of the Council on Integrated Mental Health, Skills and Work Policy underscores the fact that mental well‑being, prevention and timely services must be linked to the benefits of meaningful work for people with mental health conditions (OECD, 2015[23]).
Since the Recommendation’s adoption, most OECD countries had introduced multi‑sectoral mental health strategies, with 19 of 26 countries reporting integrated cross-government approaches to mental health governance, and at least 24 countries report that ministries other than the ministry of health have a dedicated mental health strategy. Reforms increasingly aim to prevent early disengagement from work or education. For example, Denmark, Hungary, Latvia and the United Kingdom have programmes to support students’ transition into the labour market, while Canada, Estonia and Finland have adapted work‑capacity assessments to facilitate partial return to work following mental health‑related absences.
Several countries have adopted individual placement and support to combine co‑ordinated clinical and employment assistance, demonstrating the effectiveness of integrating mental health and labour market support. However, most OECD countries still lack systematic inclusion of employment outcomes in mental health frameworks, and social protection systems rarely reflect the high prevalence of mental ill health among benefit recipients. Evidence shows that mental health treatment alone does not improve employment outcomes, while integrated approaches do (OECD, 2015[24]).
Preventing long‑term unemployment remains critical, especially for young people, because (as discussed in Chapter 2) prolonged joblessness worsens mental health. Active labour market programmes can mitigate these effects by providing routine, structure and social connection. As countries respond to the growing detrimental impact of mental ill health, the OECD analysis stresses that integrated services and early intervention across welfare, education, health and employment systems are essential to tackling persistent drivers of mental ill health while enabling individuals to reconnect with meaningful work.
Source: OECD (2015[23]), Recommendation of the Council on Integrated Mental Health, Skills and Work Policy, https://legalinstruments.oecd.org/en/instruments/334.
References
[16] Ahola, K. et al. (2012), “Resource-enhancing group intervention against depression at workplace: Who benefits? A randomised controlled study with a 7-month follow-up”, Occupational and Environmental Medicine, Vol. 69/12, pp. 870-876, https://doi.org/10.1136/oemed-2011-100450.
[3] Barry, M. et al. (2024), “Priority actions for promoting population mental health and wellbeing”, Mental Health & Prevention, Vol. 33, p. 200312, https://doi.org/10.1016/j.mhp.2023.200312.
[2] Bertram, M. et al. (2021), “Methods for the economic evaluation of health care interventions for priority setting in the health system: An update from WHO CHOICE”, International Journal of Health Policy and Management, Vol. 10/11, pp. 673-677, https://doi.org/10.34172/ijhpm.2020.244.
[11] Butler, A. et al. (2006), “The empirical status of cognitive-behavioral therapy: A review of meta-analyses”, Clinical Psychology Review, Vol. 26/1, pp. 17-31, https://doi.org/10.1016/j.cpr.2005.07.003.
[20] Causa, O. et al. (2022), “A cost-of-living squeeze? Distributional implications of rising inflation”, OECD Economics Department Working Papers, No. 1744, OECD Publishing, Paris, https://doi.org/10.1787/4b7539a3-en.
[12] David, D., I. Critsea and S. Hofmann (2018), “Why cognitive behavioural therapy is the current gold standard of psychotherapy”, Frontiers in Psychology, Vol. 9/4, https://doi.org/10.3389/fpsyt.2018.00004.
[4] Deady, M. et al. (2017), “Health interventions for the prevention of depression and anxiety in the general population: A systematic review and meta-analysis”, BMC Psychiatry, Vol. 17, p. 310, https://doi.org/10.1186/s12888-017-1473-1.
[8] Dray, J. et al. (2017), “Systematic review of universal resilience-focused interventions targeting child and adolescent mental health in the school setting”, Journal of the American Academy of Child and Adolescent Psychiatry, Vol. 56/10, pp. 813-824, https://doi.org/10.1016/j.jaac.2017.07.780.
[9] Ebert, D. et al. (2015), “Increasing the acceptance of internet-based mental health interventions in primary care patients with depressive symptoms: A randomized controlled trial”, Journal of Affective Disorders, Vol. 176, pp. 9-17, https://doi.org/10.1016/j.jad.2015.01.056.
[13] Gaudiano, B. (2008), “Cognitive-behavioural therapies: Achievements and challenges”, BMJ Mental Health, Vol. 11/1, pp. 5-7, https://doi.org/10.1136/ebmh.11.1.5.
[14] National Mental Health Commission (2021), e-Health Workplace Interventions for the Prevention of Depression, National Mental Health Commission, Sydney, https://www.mentalhealthcommission.gov.au/publications/e-health-workplace-interventions-prevention-depression.
[1] OECD (2025), Mental Health Promotion and Prevention: Best Practices in Public Health, OECD Publishing, Paris, https://doi.org/10.1787/88bbe914-en.
[21] OECD (2023), How to Make Societies Thrive? Coordinating Approaches to Promote Well-being and Mental Health, OECD Publishing, Paris, https://doi.org/10.1787/fc6b9844-en.
[18] OECD (2023), Ready for the Next Crisis? Investing in Health System Resilience, OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/1e53cf80-en.
[15] OECD (2022), Promoting Health and Well-being at Work: Policy and Practices, OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/e179b2a5-en.
[17] OECD (2021), A New Benchmark for Mental Health Systems: Tackling the Social and Economic Costs of Mental Ill-Health, OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/4ed890f6-en.
[24] OECD (2015), Fit Mind, Fit Job: From Evidence to Practice in Mental Health and Work, Mental Health and Work, OECD Publishing, Paris, https://doi.org/10.1787/9789264228283-en.
[23] OECD (2015), Recommendation of the Council on Integrated Mental Health, Skills and Work Policy, OECD Publishing, Paris, https://legalinstruments.oecd.org/en/instruments/334.
[10] Purohit, A., J. Smith and A. Hibble (2021), “Does telemedicine reduce the carbon footprint of healthcare? A systematic review”, Future Healthcare Journal, Vol. 8/1, pp. e85-e95, https://doi.org/10.7861/fhj.2020-0080.
[22] Ribanszki, R. et al. (2022), “Welfare systems and mental health in OECD and EEA countries: A scoping review”, Humanities and Social Sciences Communications, Vol. 9, p. 431, https://doi.org/10.1057/s41599-022-01391-2.
[5] Santoft, F. et al. (2019), “Cognitive behaviour therapy for depression in primary care: Systematic review and meta-analysis”, Psychological Medicine, Vol. 49/8, pp. 1266-1274, https://doi.org/10.1017/S0033291718004208.
[6] Stratton, E. et al. (2017), “Effectiveness of eHealth interventions for reducing mental health conditions in employees: A systematic review and meta-analysis”, PLoS One, Vol. 12/12, p. e0189904, https://doi.org/10.1371/journal.pone.0189904.
[7] Tan, L. et al. (2014), “Preventing the development of depression at work: A systematic review and meta-analysis of universal interventions in the workplace”, BMC Medicine, Vol. 12/74, https://doi.org/10.1186/1741-7015-12-74.
[19] WHO (2025), World Mental Health Today: Latest Data, World Health Organization, Geneva, https://iris.who.int/handle/10665/382343.