Mental ill health places a major burden across OECD and EU countries, with mild to moderate depressive symptoms affecting one in five adults. This chapter synthesises the key findings of the publication and highlights the policy implications derived from new OECD analyses of the health, social and economic impact of mental ill health. It outlines recent trends, presents the economic case for stronger investment in mental well-being, and reports the expected effectiveness, impact on health expenditure and value for money of six priority interventions.
The Economic Case for Preventing Mental Ill Health
Abstract
Mental ill health affects a significant proportion of the population. According to modelled data, in 2023 slightly over one in five individuals in OECD and EU countries experienced a mental disorder. This figure is likely to be an underestimate, as milder conditions often remain undiagnosed or unreported. Among diagnosed cases, anxiety disorders are the most common (around 40%), followed by depressive disorders (around 20%), substance use disorders (17%) and other mental health conditions (24%).
The prevalence of mental disorders is high and has been rising in certain population groups, such as children and young people. Across OECD countries, the rate of mental disorders has increased by nearly 21% over the past two decades. A notable acceleration occurred following the COVID‑19 pandemic, potentially due to both increased incidence and improved reporting. Particularly affected groups include individuals aged 15‑24, those with low socio‑economic status and women, who are more likely to report depression and anxiety. Men, on the other hand, are more likely to report alcohol and substance use disorders.
Multiple societal and environmental factors are driving this increase – particularly among young people. Available evidence suggests that the early pandemic restrictions, climate change anxiety (which affects 84% of young people globally), war, geopolitical instability and economic crises have all contributed to worsening mental health. Additionally, problematic social media use is emerging as a significant concern, especially among younger populations.
Access to mental health services remains a major challenge. Despite the growing need, nearly two‑thirds of individuals across EU countries who require mental healthcare are estimated to face inadequate access. This stands in stark contrast to the 3.8% of people reporting unmet needs for general medical examinations and treatment, highlighting a critical gap in mental health service provision.
All these factors negatively affect mental health, placing a heavy burden on both individuals and the wider economy. For example, in the 27 countries in the EU alone, OECD microsimulation work estimates that over a 25‑year horizon (2025-2050), major depressive disorders, generalised anxiety disorders and alcohol use disorders will result in:
a 2.5‑year reduction in healthy life expectancy and a 3‑month reduction in overall life expectancy, equivalent to approximately 28 000 premature deaths annually
additional healthcare costs of around EUR 76 billion per year, or 6% of total health budgets, comparable to the annual healthcare budget of Belgium – with about one‑quarter of these costs stemming from mental disorders exacerbating other health conditions
an average annual GDP reduction of approximately 1.7%, primarily due to decreased workforce participation and productivity.
In light of the significant impact of mental ill health, countries are stepping up efforts to address it. A survey by the OECD and the WHO Regional Office for Europe of 43 OECD and EU countries in 2023 showed that 41 had a national mental health policy and 38 also had an implementation strategy or action plan. Mental health interventions in primary healthcare (PHC), school and workplace settings, which are the focus of this publication, were relatively widespread: 35 out of 42 responding countries had implemented actions in PHC settings, 35 out of 39 in schools, and 27 out of 31 in workplaces.
Evidence‑based interventions to tackle depression and anxiety are available across these three settings. Cognitive behavioural therapy (CBT), which helps individuals reframe negative thinking patterns, is widely used for both prevention and treatment. Other effective approaches include mental health literacy programmes, psychological interventions, mindfulness-based techniques (such as meditation) and pharmaceutical treatments, which are typically delivered only in clinical settings.
Drawing on this evidence, 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 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 symptoms and a universal CBT programme. Finally, the intervention in schools is a resilience‑focussed CBT programme.
If implemented in 2025 and sustained to 2050, these interventions are estimated to improve population health and quality of life, reduce healthcare expenditure, and enhance workforce productivity, thereby supporting economic growth. Interventions in PHC settings tend to be most effective, followed by school-based programmes. Face‑to-face delivery generally outperforms online formats.
Most of these interventions offer good value for money: they are cost-effective under a EUR 50 000 per disability-adjusted life year threshold and, in some cases, even cost-saving. The main exception is workplace CBT, which is not cost-effective in about two‑thirds of countries, and is the only adult-focussed intervention where implementation costs exceed the GDP gains expected from improved workforce productivity.
The potential of these interventions to have a positive impact on health, social and economic factors is good news. However, despite their benefits, the aggregate impact of the interventions remains modest relative to the overall burden of mental ill health. For example, the most effective intervention (a combination of psychotherapy and pharmacotherapy in PHC settings) would reduce mental health-related healthcare costs by only 4%, assuming that 10% of general practitioners (GPs) deliver it.
These findings indicate that, to achieve a greater impact and address poor mental health, health systems need to go beyond implementing the interventions identified in the analysis. Efforts should concentrate on three key areas:
First, the design and implementation of interventions should be improved by focussing on best practice design. In particular, improving access to mental health services is more impactful when paired with complementary measures such as communication campaigns and peer-based programmes to destigmatise mental ill health.
Second, more ambitious coverage targets than those currently used for modelling the impact of interventions should be set. Expanding access to effective mental health services could deliver greater benefits at equal or better cost-effectiveness. However, such expansion would require careful upfront planning and substantial investment. For example, treating all individuals with mental disorders would demand a 41% increase in spending and a strengthened mental health workforce.
Third, the root and persistent causes of mental ill health must be addressed. Public health efforts should be complemented by policies tackling underlying drivers of mental ill health, such as economic insecurity and unemployment. For example, evidence shows that countries with more generous social welfare systems tend to have better mental health outcomes.
Modelled data indicate that in 2023 slightly over one in five individuals across OECD countries were estimated to experience a mental disorder – about the same proportion as in the 27 countries in the EU (Figure 1.1). The most prevalent conditions are anxiety disorders, accounting for around 39% of all cases, and depressive disorders, accounting for around 20%. These are followed by substance use disorders – which include alcohol use disorders – at 17% of all cases, and a heterogeneous group of other disorders at 24% (Box 1.1). These estimates are generally considered conservative, partly due to underreporting linked to stigma and limitations within health systems. In fact, many individuals with mild to moderate conditions may go undiagnosed because they do not meet formal clinical criteria (Annunziata, Krupsky and Lee, 2023[1]). There is some variability in the estimated prevalence level of these mental disorders across OECD and EU countries, with the lowest proportions in Japan (14.3%), Bulgaria (16.4%) and Romania (16.6%), and the highest levels in the United Kingdom (27.8%) and the Netherlands (27.1%). However, when comparing cross‑country differences, it is important to recognise that these patterns may also reflect other factors, such as variations in reporting – for example, driven by differing levels of stigma, as well as methodological differences – rather than true differences in underlying prevalence.
Note: Prevalence data are presented cumulatively, which may overestimate total mental disorder prevalence because individuals with multiple conditions can be counted more than once. Conversely, underreporting – for example, driven by stigma and health system limitations – may lead to underestimation of single‑condition prevalence. The data presented are modelled using a range of country‑specific sources, which may limit the degree of cross‑country comparability.
Source: IHME (2026[2]), GBD Compare Data Visualization, https://vizhub.healthdata.org/gbd-compare.
Mental disorders are a heterogeneous groups of diseases characterised by a clinically significant disturbance in an individual’s cognition, emotional regulation or behaviour, and are typically associated with impairment in important areas of functioning (WHO, 2022[3]). Mental disorders can be occasional or long-lasting (i.e. chronic) and can affect someone’s ability to relate to others and function each day.
This publication focusses only on the most common mental disorders that, taken together, account for approximately three‑quarters of all mental health conditions across OECD countries:
Depressive disorders are characterised by a depressed mood, such as feeling of sadness, irritability or emptiness, or by a loss of pleasure in activities (WHO, 2026[4]; APA, 2013[5]). Major depressive disorders are the most common subtype of depressive disorders (Li et al., 2023[6]). It is characterised as an episodic mood disorder, involving the experience of one of more major depressive episodes, which is either a depressed mood or loss of interest/pleasure, for most of every day, for at least two weeks. Major depressive disorders vary across levels of severity, ranging from mild through moderate to severe.
Anxiety disorders are characterised by excessive fear, anxiety and associated behaviours. This disorder spectrum covers separation anxiety disorders, selective mutism, specific phobias, social anxiety disorders, panic disorders, agoraphobia and generalised anxiety disorders (APA, 2013[5]). Generalised anxiety disorders are among the most common. These are characterised by persistent and excessive worry about a number of domains in a person’s life, which is present for at least six months and causes significant distress and/or impairment in an individual’s functioning (Spitzer et al., 2006[7]; Mortazavi et al., 2014[8]).
Substance use disorders are characterised by a cluster of cognitive, behavioural and physiological symptoms. These symptoms include the individual persisting in using the substance despite adverse consequences (APA, 2013[5]). Alcohol use disorders are a type of substance use disorder defined by an inability to control alcohol consumption, compulsive drinking behaviour and a negative emotional state during periods of abstinence. Drug use disorders are also a type of substance use disorder. Ten separate classes of drugs come under this umbrella – including, for example, cannabis, hallucinogens and opioids.
Other less prevalent disorders include severe disorders such as schizophrenia and bipolar disorders, as well as eating disorders and intellectual disabilities, among others. In addition, the landscape of mental health conditions includes neurodevelopmental disorders such as autism spectrum disorders and attention deficit hyperactivity disorders.
Research suggests that mental ill health has been increasing in recent decades (ten Have et al., 2023[9]; Richter et al., 2019[10]). Some countries saw an increase in prevalence of mental disorders between 2006 and 2019, and most, if not all, saw even greater increases during the COVID‑19 pandemic, when prevalence of anxiety and depression peaked. For example, according to modelled data, average prevalence of mental disorders in EU countries increased by 21.1% – from 17.0% in 1990 to 20.6% in 2023. Young people (OECD, 2026[11]), adult women and older adults (Eurofound, 2025[12]) are among the population groups for which the increase has been most noticeable. While identifying high-quality data using standard measures remains challenging, data from non-EU countries show a similar picture. Recent data from the United Kingdom and the United States indicate an overall upward trend over time, peaking during COVID‑19, followed by a partial recovery to levels higher than before the pandemic (NHS, 2025[13]; Saad, 2025[14]). Various shocks and factors – such as the impact of COVID‑19, climate change, war, conflict, political unrest and economic stressors – are considered to have negatively affected mental health in recent years. Other research argues, however, that mental ill health may have remained constant over this period, and that the rise in prevalence is a result of demographic changes and increased reporting of mental ill health due to increased mental health literacy, reduced stigma and other factors that have led to more accurate diagnosis and reporting – particularly of mild and moderate cases (Richter et al., 2019[10]; Baxter et al., 2014[15]).
Women, young people and people with lower socio‑economic status are at higher risk of mental disorders
Well-documented evidence shows that not everyone in the population has the same risk of developing mental disorders, and that certain groups are at higher risk. Consistent with results previously identified in the literature, the findings from OECD analyses of data from the Institute for Health Metrics and Evaluation (IHME) identify the following patterns of inequalities:
Women exhibit higher rates of depression and anxiety than men, while men have higher rates of alcohol use and substance use disorders (Vargas Lopes and Llena-Nozal, 2025[16]). The factors driving these gender differences are complex; they involve both biological elements, such as genetics and hormonal changes, and social elements, such as higher rates of sexual and domestic violence and societal norms (Farhane-Medina et al., 2022[17]). Additionally, interview techniques and self-reporting instruments may underdetect depressive symptoms in men, and women are more likely to seek help and report their symptoms, contributing to the observed disparities in mental health (Addis, 2008[18]; Kuehner, 2003[19]).
Over one in four adolescents and young adults (aged 15‑24) experience a mental disorder; prevalence declines consistently after this age, except for a slight increase among those aged 95 and above – particularly men. Mental disorders starting before the age of 24 are more likely to persist into adulthood, if untreated, with life‑long consequences. Accumulated adversities across the life course increase the risk of mental ill health. The accumulation of multiple adverse events has an additive and interactive effect, increasing the risk of mental disorder onset.
Recent OECD analyses of data from the European Health Interview Survey confirm previous evidence identifying income and education level as consistent predictors of mental ill health (Eurostat, 2019[20]; Eurostat, 2019[21]). Socio‑economic status disparities are more pronounced among men: those in the lowest income quintile are 5.3 times more likely to report depressive symptoms than their peers in the highest quintile (compared to a 3.7 times greater likelihood among women). Disparities by education level are smaller, and are more significant among women; women in the lowest education quintile are 3.3 times more likely to report depressive symptoms than their peers in the highest quintile (compared to 2.3 greater likelihood among men).
Several causes may lie behind the high burden of mental ill health
A rapid literature review undertaken for this publication identified several social, economic, environmental and lifestyle factors contributing to the observed trends. In some cases – such as recent conflicts and events – evidence is provided by ad hoc studies, although this may not yet be fully reflected in the available population-level statistics, which do not cover the most recent period. Overall, the evidence points to a worsening situation, with a peak during the COVID‑19 pandemic, and several factors probably contributing to a continued rise in prevalence of mental disorders over time, although the evidence on long-term trends in adult mental ill health prevalence remains unclear.
The COVID‑19 pandemic triggered anxiety and stress as a major public health crisis, and measures to contain the virus also had a significant impact on mental health (OECD, 2023[22]; Holmes et al., 2020[23]). Limitations on mobility and other measures had an impact on social lives, economic security and housing stability, significantly affecting quality of life and mental health (OECD, 2021[24]; OECD, 2021[25]). This led to an increase in mental disorders following the onset of the pandemic. At the time of writing, it is still too early to determine whether levels of mental ill health have returned to their pre‑pandemic state. Different analyses offer contrasting perspectives: while some evidence indicates some degree of recovery to pre‑pandemic levels, at least in Europe, other research – including OECD analyses presented in Chapter 2 – suggests lingering effects, especially among children and adolescents (Ahmed et al., 2023[26]; Kiviruusu et al., 2024[27]; Ma, Yao and Hao, 2022[28]).
Economic crises have consistently been associated with a rise in mental health issues. Emerging evidence suggests that recent macroeconomic shocks – such as the economic downturn triggered by the COVID‑19 pandemic and its impact on unemployment (Lordan and Stringer, 2022[29]; Simonse et al., 2022[30]), as well as the 2022 inflation surge in OECD countries affecting individuals on low incomes (Causa et al., 2022[31]) – have contributed to higher rates of mental ill health, especially among vulnerable groups. A meta‑analysis of 26 studies from high-income countries revealed that rising income inequality is associated with approximately 19% higher rates of mental health problems across the population, with more pronounced effects on women and low-income groups (Patel et al., 2018[32]). Income and wealth inequality have been increasing since the 1980s; these trends are likely to contribute to the ongoing rise in mental health issues (Guschanski and Onaran, 2021[33]; Bourquin, Brewer and Wernham, 2024[34]).
Housing quality, affordability and stability are recognised as important determinants of mental health: inadequate or insecure housing is linked to heightened stress, anxiety and depressive symptoms – particularly among vulnerable groups (The Lancet Public Health, 2025[35]). Beyond the home itself, features of the surrounding built environment – such as access to green spaces and safe public areas – also support lower stress and improved well-being (Xian et al., 2024[36]). Higher levels of social cohesion and opportunities for social participation further strengthen these patterns by helping to protect against loneliness and isolation, which are both strongly associated with poor mental health. In particular, robust social support and larger social networks reduce the risk of depression and anxiety, whereas loneliness is predictive of more severe symptoms over time (Bogar, 2016[37]).
Extreme weather events, such as flooding and heatwaves, along with anticipated future risks, have been linked to rising levels of mental ill health, ranging from elevated stress to severe mental conditions. Children are particularly at risk: a global survey revealed that 84% of young people are moderately or extremely worried about climate change, and over 45% report negative impacts on their daily lives (Hickman et al., 2021[38]). Vulnerable populations are also at high risk of trauma caused by extreme weather events and the chronic stress of climate uncertainty. For instance, migration, driven by the loss of habitable land, exacerbates these issues, leading to grief, anxiety and loss of identity (Kanthee et al., 2024[39]). The impact of extreme weather events on population mental health is likely to worsen if these events become more frequent.
War, conflict and geopolitical unrest are strongly linked to increased rates of mental ill health, including generalised anxiety disorders, depression and post-traumatic stress. Recent conflicts in Ukraine, the Middle East and beyond have exacerbated these issues, with impacts on both the regions directly affected and others (Charlson, van Ommeren and Flaxman, 2019[40]; Carpiniello, 2023[41]). Studies confirm that exposure to war has a scarring effect on mental health, affecting individuals over their lifetime – including professional combatants and civilians. Children and adolescents are particularly vulnerable, with exposure to war significantly increasing their risk of developing mental disorders (Akbulut-Yuksel, Zimmer and Pandey, 2024[42]). Evidence also suggests that wartime events create long-term effects and intergenerational trauma, potentially affecting mental health for decades. Consequently, the mental health impacts of war and conflict are vast and far-reaching, extending beyond wartime into subsequent generations.
Problematic use of social media is increasingly a cause for concern regarding its impact on mental health – particularly among children and adolescents. A meta‑analysis of 143 studies identified a small but significant correlation between higher social media use and elevated levels of anxiety and depression (Fassi et al., 2024[43]). However, the evidence is not conclusive – for example, some longitudinal studies have not found a significant link (Coyne et al., 2020[44]). In parallel, recent legal action in the United States found major platforms liable for harms linked to addictive design features, setting an important precedent (Taylor, 2026[45]). Other research suggests that negative effects may be the result of factors such as poor sleep, lack of physical activity, online harassment, low self-esteem and poor body image, all of which may be exacerbated by social media use (Viner et al., 2019[46]; Kelly et al., 2019[47]). On the positive side, social media was also found to foster a sense of community (Ulvi et al., 2022[48]).
Suboptimal access to mental healthcare services contributes to the high burden of mental ill health
An additional important pattern contributing to the increasing burden of mental ill health is the suboptimal access to healthcare services for those in need. Many countries have attempted to improve access to mental health services in recent years, shifting the focus from hospitals to community and other non-medical settings. Despite these efforts, however, a substantial proportion of individuals with mental ill health still report significant unmet healthcare needs for their condition.
For example, 67.5% of individuals needing mental healthcare in EU countries are estimated not to have access to treatment (Figure 1.2). While potential caveats regarding the comparability of the underlying measures should be acknowledged, this proportion nevertheless appears to be almost 18 times higher than the share of unmet needs for medical examinations and treatment across the same group of countries. According to recent Eurostat estimates based on self-reported data from the EU statistics on income and living conditions (EU‑SILC) survey for 2024, 3.8% of people aged 16 and over in the EU reported an unmet need for a medical examination or treatment. The two most frequently cited reasons were waiting lists, which accounted for 37% of cases, followed by high costs, representing around 26% (Eurostat, 2025[49]).
Understanding the extent of unmet mental health needs is challenging owing to inconsistent data definitions. Without a standardised and uniformly adopted definition, this publication approximates the treatment gap using two complementary sources:
Previous OECD work (OECD, 2021[50]) investigated unmet needs for mental healthcare due to financial reasons, waiting times or transport availability. Based on that analysis, this publication calculates rates of access to mental health services as 100% minus the proportion of individuals identified as having unmet needs.
Treatment coverage was identified by the World Mental Health survey of the World Health Organization (WHO) (Evans-Lacko et al., 2018[51]), which asked respondents whether they had sought professional help for emotional, mental health, nerve or substance use disorders, and whether they had received treatment within the past 12 months.
Note: Extrapolation for countries lacking data was performed using an ensemble model based on a lasso regressor, incorporating the following country-specific indicators: suicide rates, depression rates, world happiness index, number of mental health professionals, universal healthcare service coverage index and GDP. Extrapolated data should only be considered as a high-level indicator of the possible coverage of services for mental ill health.
Source: OECD Mental Health Systems Performance Benchmark survey (OECD, 2021[50]), WHO World Mental Health survey (Evans-Lacko et al., 2018[51]).
Although the two surveys examine slightly different issues and cover marginally different mental disorders, comparing data from both sources for countries included in both analyses suggests very similar conclusions, as shown in Figure 1.2. After excluding a few countries with outlying results (such as Lithuania and the Slovak Republic) in one of the two datasets, the analysis supports broad comparability of data.
Various barriers contribute to unmet mental health needs, including financial, geographical and organisational obstacles (OECD, 2021[50]). Mental health services – especially psychological interventions – may not be covered by health insurance or public health coverage, requiring individuals to pay out of pocket. This financial burden is a significant barrier, particularly for those on low incomes. Geographical barriers also play a role, as individuals in rural areas are less likely to receive treatment than those in urban areas. Organisational barriers, such as a shortage of healthcare professionals, further hinder access as they lead to long waiting times and scarcity of available services; this can result in increased hospitalisations and higher risks of disability and suicide. Administrative requirements, like referrals from GPs, are often necessary for specialised care, and poor co‑ordination is also likely to result in longer waiting times. In several OECD countries, waiting-time targets or guarantees have been established for at least one area of mental healthcare, with most aiming to start treatment or make the first service contact within 1‑3 months.
Mental ill health places a heavy burden on people and the economy. Beyond the direct health and well-being impacts on individuals, poor mental health also causes significant costs on the economy through reduced employment, lower productivity and a range of other factors that impede economic growth. To assess the health and economic impacts of mental ill health for depressive disorders, anxiety disorders and alcohol use disorders, the OECD used its Strategic Public Health Planning for Non-Communicable Diseases (SPHeP-NCDs) microsimulation model, enhanced with a dedicated mental health module (Box 1.2). This analysis focusses on 30 EU and European Free Trade Association countries; however, findings from the cost-effectiveness assessment and subsequent discussions also provide evidence and insights that are broadly applicable across all OECD countries.
The OECD SPHeP-NCDs model is an advanced systems modelling tool for public health policy and strategic planning. It is used to predict the health and economic outcomes of the population of a country or a region up to 2050. The model uses a comprehensive set of key behavioural and physiological risk factors and their associated non-communicable diseases, including mental ill health. For the analyses of mental health, the model covers 30 countries: the 27 EU Member States, Iceland, Norway and Switzerland. Results also include averages for the 27 EU countries.
For each modelled country, the model uses demographic and risk factor characteristics by age and gender-specific population groups from international databases. These inputs are used to generate synthetic populations, in which each individual is assigned a profile with a certain risk of developing a disease each year. Incidence and prevalence of diseases in a specific country’s population are calibrated to match estimates from international datasets.
The model produces yearly cross-sectional representations of the population that can be used to calculate health status indicators such as life expectancy, premature deaths (including as a result of suicide), disease prevalence and disability-adjusted life years using disability weights. Healthcare costs of disease treatment are estimated based on a per case annual cost, which is extrapolated from national health-related expenditure data. The additional cost of multimorbidity, which is an important factor in the case of mental disorders, and the extra cost of end-of-life care are also considered. The labour market module uses relative risks relating disease status to absenteeism, presenteeism (where sick individuals, even if physically present at work, are not fully productive), early retirement and employment. These changes in employment and productivity are estimated as numbers of full-time equivalent workers and, with other parameters, contribute to calculating the impact on GDP by applying a Cobb-Douglas production function.
The model includes three leading mental health diseases: major depressive disorders (including three different levels of severity: mild, moderate and severe), generalised anxiety disorders (including three different levels of severity: mild, moderate and severe) and alcohol use disorders. Although these mental disorders represent over 72% of the total prevalence of mental health conditions across the 30 countries, the model’s results should be viewed as conservative since 28% of conditions remain excluded, and the model does not account for upstream determinants of mental health such as well-being and resilience. In addition, the model does not capture the burden suffered by people exposed to individuals with mental disorders, such as family and friends.
Mental disorders are modelled via specific modules created for each disease. Major depressive disorders and generalised anxiety disorders are modelled through the distribution of two international scales used for diagnosis: the 8‑item Patient Health Questionnaire (PHQ‑8) for major depressive disorders and the 7‑item Generalised Anxiety Disorder (GAD‑7) questionnaire for generalised anxiety disorders. Alcohol use disorders are modelled according to the pattern and volume of alcohol consumption, with higher consumption corresponding to a higher risk of developing the condition. All modules are calibrated to match prevalence data for the simulated diseases.
For more information on the OECD SPHeP-NCDs model, see the SPHeP-NCDs Technical Documentation, available at: http://oecdpublichealthexplorer.org/ncd-doc.
Depression, anxiety and alcohol use disorders reduce healthy life expectancy by an average of 2.5 years across EU countries
Unlike other non-communicable diseases (NCDs) such as cancer and cardiovascular diseases, mental disorders like major depressive disorders, generalised anxiety disorders and alcohol use disorders primarily affect quality of life rather than life expectancy. According to modelled data, mental ill health will reduce healthy life expectancy – which takes into account time spent in ill health – by an average of 2.5 years across EU countries over the period 2025-2050 (Figure 1.3). Approximately half of this reduction is due to major depressive disorders; generalised anxiety disorders contribute 36% and alcohol use disorders 15% of the burden. The overall reduction in life expectancy is about 0.25 years – roughly 3 months. Evidence indicates that only major depressive disorders (65% of the reduction in life expectancy) and alcohol use disorders (35%) increase patient fatality rates.
Source: OECD analyses using the SPHeP-NCD model.
While a 3‑month reduction in life expectancy may seem a minor detrimental impact, it should be noted that this value is calculated at the population level; it equates to nearly 783 000 premature deaths (among people aged under 75) in EU countries between 2025 and 2050, or around 28 000 premature deaths per year (equivalent to 9 per 100 000 people). The highest reductions are found in Lithuania, Estonia and Latvia, each with a reduction in life expectancy of over 5 months. Conversely, Malta, Cyprus and Greece show the smallest reductions – each lower than 1 month.
Mental disorders are estimated to account for 6% of total health expenditure in EU countries
In a business-as-usual scenario where major depressive disorders, generalised anxiety disorders and alcohol use disorders maintain current age‑specific prevalence and treatment levels, mental ill health is estimated to cost EUR 76 billion per year in EU countries from 2025 to 2050. This is comparable to the annual healthcare budget of Belgium (Eurostat, 2023[52]), and is equivalent to about a 6% share of total healthcare spending across EU countries (Figure 1.4). The estimate for EU countries is broadly comparable to what the literature suggests for other OECD countries such as Canada (Milliken et al., 2024[53]) and the United States (SAMHSA, 2014[54]).
Note: The light blue segment of each bar represents the additional costs countries would incur if mental health service coverage were expanded to reach all individuals in need (i.e. 100% coverage). This increase in healthcare spending is shown as a percentage relative to the current, business-as-usual scenario. For example, achieving full coverage would require a 41% increase in mental health expenditure across EU countries. It is important to note that the share of total healthcare expenditure shown refers only to the current level of coverage.
Source: OECD analyses using the SPHeP-NCD model.
Treating major depressive disorders, generalised anxiety disorders and alcohol use disorders represents the bulk of direct costs, but a substantial proportion of overall expenditure also stems from the adverse impact of mental ill health on other physical health conditions (or the cost of mental disorders as comorbidities) (Cortaredona and Ventelou, 2017[55]). Model outputs indicate that approximately 75% (EUR 131 per capita per year) of total mental healthcare expenditure in EU countries goes on treating mental disorders, while the remaining 25% (EUR 45 per capita per year) goes on the extra costs of mental disorders as comorbidities.
Treating all people with these three mental disorders would increase mental healthcare costs by 41% compared to current levels of expenditure. As discussed above, it is estimated that only 31% of those in need are currently receiving treatment in EU countries. The OECD SPHeP-NCD model was therefore used to estimate the costs of achieving full treatment coverage. While this remains a theoretical goal, as practical constraints currently prevent countries from achieving full coverage, this analysis is useful in highlighting the financing gap for mental disorders, and can be used for resource mobilisation and planning. The analysis suggests that to achieve full coverage, an additional 67% of patients – mainly with mild to moderate mental disorders – would need to be treated, at a cost increase of EUR 72 per capita per year, bringing the total mental health expenditure for major depressive disorders, generalised anxiety disorders and alcohol use disorders to EUR 247 per capita per year across EU countries. Countries with lower current coverage, such as Bulgaria and Latvia, would need to more than double their mental health expenditure to achieve full coverage (Figure 1.4).
Mental ill health has a negative impact on workforce productivity and the economy due to absenteeism, presenteeism, unemployment and early retirement
Mental ill health also creates broader economic costs through its detrimental impacts on workforce productivity and human capital. People with mental disorders are less likely to be employed; if they have a job, they are more likely to be absent from work or less productive than if they were in good health (also known as presenteeism), and to retire before the usual retirement age (OECD, 2021[56]). The cumulative impact of mental ill health due to major depressive disorders, generalised anxiety disorders and alcohol use disorders on all these factors and their consequent reduction in human capital has been calculated by the OECD SPHeP-NCDs model to represent almost 2.4 million workers per year, expressed in full-time equivalent workers. The model estimates that, across EU countries, the costs of mental ill health to the labour market are:
overwhelmingly driven by a reduced employment rate (48% of the reduction in full-time equivalent workers), followed by presenteeism (36%) and absenteeism (14%), while early retirement seems to account for a tiny fraction of the whole burden, corresponding to 3% of the total
caused primarily by generalised anxiety disorders (56%), followed by major depressive disorders (39%) and alcohol use disorders (5%).
The reduction in human capital and productivity has direct consequences for the economy (Figure 1.5). Outputs from the OECD SPHeP-NCD model were fed into the Long-Term Growth Model of the World Bank to calculate the impact of mental ill health on GDP. The resulting analysis suggests that in the business-as-usual scenario, the GDP of EU countries would be depressed by an average of 1.7% compared to a situation with no mental ill health, ranging from around 1.0% in countries including Bulgaria and Cyprus to 2.5% in Lithuania. This finding aligns with earlier OECD research indicating that, in 2015, reduced workforce productivity across the EU represented approximately 1.6% of GDP, out of an estimated total mental health cost of around 4% (OECD/European Union, 2018[57]).1 Studies from other OECD countries outside the EU, such as Australia (Beyond Blue and PwC, 2014[58]) and the United States (Abramson, Boerma and Tsyvinski, 2024[59]), indicate a comparable impact of mental ill health, once methodological differences and underlying assumptions are taken into account.
Average annual impact as a share of GDP due to major depressive disorders, generalised anxiety disorders and alcohol use disorders, 2025-2050
Source: OECD analyses using the SPHeP-NCD model and the World Bank Long-Term Growth Model.
Given the extensive and growing impact of mental disorders on population health and the economy, the need for policies to improve mental well-being is evident. Significant development and implementation of international strategies and action plans have occurred to support increased investment in mental health policies. In 2015, 38 OECD countries endorsed the Council’s Recommendation on Integrated Mental Health, Skills and Work Policy (OECD, 2015[60]), which provides guidelines to address the effects of mental ill health on health, education, employment and social outcomes. Similarly, the WHO’s Comprehensive Mental Health Action Plan 2013-2030 outlines objectives to guide international health systems and leaders in addressing mental ill health through policy (WHO, 2021[61]).
At the national level, governments in OECD and EU countries have placed an increased focus on mental health policy. This has been reflected in a substantial number of countries developing and implementing national strategies and action plans on mental health. In 2023, the OECD and the WHO Regional Office for Europe, with support from the European Commission’s Directorate‑General for Health and Food Safety, carried out a survey on mental health system capacity across EU countries, Iceland and Norway (WHO Regional Office for Europe, 2024[62]). The OECD thereafter extended the survey to all OECD Member countries (referred to in this report as the WHO/OECD Mental Health Survey). According to the survey, 41 of 43 (95%) national governments across OECD and EU countries reported that they had a national mental health policy in place and, in the vast majority of cases (88% of countries), that they also had a strategy or action plan in place to guide the implementation of the national mental health policy (Figure 1.6).
Source: WHO/OECD Mental Health Survey.
Proven evidence‑based policies are in place to improve mental well-being and managing mental disorders
At the patient level, national policies have led to implementation of a diverse range of interventions. These vary in terms of place of implementation (such as in PHC settings), intervention mechanisms (for example, psychological or pharmacological), delivery mode (including face‑to-face and phone‑based delivery) and intended outcomes (whether prevention or treatment). In terms of place of implementation, this report specifically focusses on interventions implemented in schools, workplaces and PHC settings. Emphasis is also placed on actions to prevent major depressive disorders and generalised anxiety disorders, although some interventions may also include some therapeutic component. The final criteria for selecting interventions were based on country priorities – in particular, availability of evidence, as evidence was often scarce, and it proved difficult to model many promising policies due to the lack of necessary quantitative inputs. At the same time, it is important to recognise that countries also implement other types of interventions beyond those selected for this analysis. These include, for example, parenting programmes and suicide prevention programmes, some of which are examined in the OECD publication on best practices in public health for mental health promotion and prevention (OECD, 2025[63]).
As shown in Figure 1.6, the vast majority of OECD and EU countries surveyed in 2023 were implementing mental health interventions in PHC settings: 8 of the 42 responding countries had fully implemented such interventions; 27 were in the process of implementation. The majority of countries (30 of the 39 responding countries) also reported that they were in the process of implementing school-based interventions; an additional 5 countries had fully implemented such programmes and policies. Although some form of workplace interventions had been implemented by the majority (27 of 31 responding countries), this option remained relatively underutilised.
Each of the three settings analysed in this report has unique characteristics, with both advantages and disadvantages. Various types of interventions (Box 1.3) can be also delivered in each setting.
Interventions in PHC settings are generally recognised as a potentially effective approach to reduce the burden of mental disorders such as major depressive disorders, generalised anxiety disorders and alcohol use disorders and, at least in some OECD countries, most mental disorders are treated in PHC settings. Patients prefer receiving treatment in PHC settings due to the flexibility, familiarity, lower cost, reduced stigma, comfort and immediate access to care provided by this setting. CBT and other psychosocial interventions have been found to be effective in mitigating symptoms of depression, anxiety and alcohol use disorders (Butler et al., 2006[64]). Research also suggests that patients often prefer psychosocial treatments over pharmacological ones, which, although effective, can have side effects (McHugh et al., 2013[65]). Several mental health promotion and prevention interventions are typically delivered in PHC settings, including mental health literacy interventions (Magallón-Botaya et al., 2023[66]) and mindfulness- and exercise‑based interventions (Demarzo et al., 2015[67]). When delivered online or via telehealth platforms, PHC interventions can be as effective as face‑to-face methods, and have been found to offer better cost-effectiveness (Hoifodt et al., 2011[68]). Common constraints in adopting PHC interventions include lack of resources, training and time among frontline workers (Weisberg et al., 2013[69]).
School-based interventions are ideal for mental health initiatives, as adolescents spend much of their time at school. These interventions can remove barriers to accessing mental health treatment, such as cost and time. They are generally delivered as either universal or targeted programmes: universal programmes are easier to implement, less stigmatising and less costly, although they are also less effective (Calear and Christensen, 2010[70]). Interventions can be delivered by school staff or medical professionals, with the latter typically showing a larger effect but being more costly. Implemented interventions include psychosocial education, skill-building, mindfulness, exercise‑based programmes, mental health literacy, stigma reduction and CBT, which is particularly effective in preventing depression and anxiety (Hetrick and Merry, 2015[71]). Evidence suggests that school-based interventions often have a small impact. A meta‑analysis showed improvements in depression by 1.2% and in anxiety by 1.1% (Werner-Seidler et al., 2017[72]).
With nearly 60% of the global population employed, and about 15% of working-age adults living with a mental disorder, workplace interventions can play a crucial role in improving mental well-being (OECD, 2021[50]). Health and well-being initiatives at work can boost employee productivity and reduce absenteeism, benefiting both governments and employers (OECD, 2022[73]). Intergovernmental organisations, including the OECD, have highlighted a wide range of evidence‑based workplace interventions. Commonly implemented strategies include physical activity, mindfulness, meditation and psychosocial interventions like CBT, all of which are effective for depression and anxiety (Harris, Harris and Cavanagh, 2017[74]). These interventions are equally effective whether delivered in person or online. Interventions can be tailored to individual workers or implemented at the organisational level. While most evidence focusses on targeted interventions, organisation-level strategies are also valuable for addressing psychosocial risk factors such as unsafe working conditions and long working hours (Rugulies, 2019[75]). Some countries have also enacted legislation to protect workers’ mental well-being, such as the “right to disconnect” from work-related communications during non-work hours (Fricke, 2023[76]).
Various intervention mechanisms can be implemented to improve mental well-being. A review of the most common interventions in school, workplace and PHC settings identified several key strategies. It is important to note that the following list of five categories of interventions does not aim to be exhaustive but simply highlights commonly implemented interventions. These can be delivered face to face; increasingly, since the COVID‑19 pandemic, they can also be delivered online via digital platforms, which in some cases has improved their cost-effectiveness.
Mental health literacy and stigma-reduction interventions aim to promote individuals’ social and cognitive skills, resulting in personal motivation and ability to access, understand and apply information that promotes and maintains good mental health. These interventions are generally implemented as awareness-raising activities, training or media campaigns. Often, they are associated with stigma-reduction activities that seek to reduce or remove embarrassment and shame associated with seeking mental healthcare, thus increasing health promotion and help-seeking behaviours (Moreira, 2018[77]).
Mindfulness-based interventions have gained popularity, with growing evidence supporting their role in promoting mental well-being. These practices involve paying attention purposefully and non-judgementally to the present moment; they include meditation training, mindful activities like body scans and sitting meditation, and participatory learning processes. Mindfulness-based interventions have shown efficacy in reducing psychological distress, anxiety and depression. Studies indicate that these interventions can be as effective as other standard treatments, offering potential cost savings due to their less resource‑intensive nature (Zhang et al., 2022[78]).
Psychological interventions encompass a wide and heterogeneous range of approaches, including cognitive, behavioural, humanistic and systemic methods. Commonly used interventions include psychodynamic therapy and eye movement desensitisation and reprocessing (Lovelock et al., 2018[79]), along with CBT. Utilising frontline actors such as teachers and social workers to deliver these interventions has proved effective. Overall, psychological interventions are valuable for both preventing and treating mental ill health.
CBT is widely used to prevent and treat a range of different mental health conditions such as depression, generalised anxiety disorders, panic disorders and posttraumatic stress disorders. CBT involves targeted strategies to alter negative thinking and behavioural patterns, such as problem-solving therapy, dialectic behaviour therapy and meta-cognitive therapy. Some evidence suggests that CBT has a similar effect size to pharmacotherapies in the short term, but could be more effective over 6‑12 months (Cuijpers et al., 2023[80]).
Pharmaceutical interventions are essential for treating mental disorders, including depression, anxiety disorders and substance use disorders. These treatments are typically administered in clinical settings, as opposed to a broader community context such as a school, owing to the need for prescriptions. Efficacy varies depending on a range of factors, such as the treated condition and its severity and the treatment used (Cheng et al., 2020[81]). Combining pharmaceutical treatments with psychological interventions, such as CBT, can enhance efficacy, and patient preference for treatment type has a significant impact on completion rates (Cuijpers, 2017[82]).
Note: For a detailed overview of the various interventions, please refer to Chapter 4.
To improve the mental well-being of the population, countries should consider stepping up their efforts by introducing new policy options and strengthening existing ones. The focus of the analysis in this report is on improving mental well-being and, specifically, addressing major depressive disorders and generalised anxiety disorders in PHC settings, schools and workplaces, as these have been shown to be especially well suited to implementation of effective interventions that improve mental well-being. The analysis assumes that these interventions will be implemented at the beginning of 2025, and their impact assessed over a 25‑year period to 2050 (Box 1.4).
The OECD used its SPHeP-NCDs microsimulation model, based on available evidence, to assess the impact of policy actions on population health, health expenditure and the broader economy. The policies were selected based on several criteria, including alignment with global guidelines and national policy priorities. Availability of quantitative evidence showing a statistically significant improvement for the targeted mental disorder was also a key factor determining the inclusion of an intervention in the modelling-based analyses, given that evidence was often scarce: it proved difficult to model many promising policies due to the lack of necessary quantitative inputs.
Simulation models, such as the one employed in the OECD analysis, offer numerous advantages. They can generate evidence in areas where direct empirical investigation is challenging or unfeasible, and they enable estimation of population-level impacts from interventions that are typically evaluated at the individual level within small groups. Additionally, these models can integrate data from various sources, allowing for conclusions that individual studies might not achieve. However, modelling-based analyses come with limitations. They require assumptions and depend on diverse input data, which can vary in quality. This is particularly true for mental health studies, where data on the longer-term impact of interventions is often limited. Moreover, models simplify certain aspects based on available data, such as underestimating the total impact of interventions by not fully capturing very mild forms of disease and overall “happiness”. Finally, it is also important to note that a 25‑year simulation period may be limited in terms of full assessment of the impact of interventions targeting children – especially in terms of improvements in human capital.
Interventions carried out in schools, workplaces and primary healthcare settings help to enhance people’s mental health
A total of six interventions were modelled, including three in PHC settings, two in workplaces and one targeting children in school environments. These interventions align with the criteria outlined above – their aim is to improve mental well-being and, specifically, to address major depressive disorders and generalised anxiety disorders – and aim to provide a comprehensive set of options. For example, the analysis includes a mix of face‑to-face and online interventions. Similarly, both interventions targeting high-risk individuals and universal interventions were modelled. Moreover, the analysis encompasses mental well-being-promoting interventions for conditions at various stages of severity and development, adopting a comprehensive prevention approach from primary prevention (preventing disease development) to tertiary prevention (preventing further exacerbation of the condition in already affected individuals).
A brief overview of each intervention, highlighting its main features, is provided below, while more detailed descriptions are available in Chapter 5. Key input data used to model the interventions are detailed in Table 1.1.
A web-based intervention in PHC settings targets adults with mild to moderate major depressive disorders or generalised anxiety disorders, recruited by GPs. The 6‑week programme includes weekly 30‑minute CBT-based sessions such as self-monitoring and relaxation techniques.
A psychological treatment in PHC settings involves 12 one‑hour group sessions led by trained nurses for adults with mild to moderate major depressive disorders, referred by primary care physicians. It reaches patients not currently covered by any service, and transitions eligible patients from traditional care.
A combined psychotherapy and pharmacotherapy intervention in PHC settings targets adults with severe major depressive disorders who are not responding fully to medication. The intervention consists of adding 12 individual CBT sessions, delivered in ambulatory care, to existing pharmacological treatment.
A web-based intervention in workplace settings supports employees with mild to moderate major depressive disorders or generalised anxiety disorders through a 4‑8‑week online programme. Activities include CBT, mindfulness and stress management, and it is accessible via the web or a mobile app.
A CBT intervention in workplace settings is organised as a universal programme managed by human resources. The intervention is delivered in 4 half-day sessions by trained staff, and includes stress management, acceptance and commitment therapy, and mental health first aid; it focusses on preventive action for major depressive disorders.
A resilience‑focussed CBT intervention in school settings is organised as a universal intervention for students aged 8‑18, delivered by trained teachers in 10 classroom sessions. Activities focus on resilience, problem-solving, communication and coping skills, supported by a workbook. This intervention focusses on preventive action for both major depressive disorders and generalised anxiety disorders.
Note: MDD is major depressive disorders; GAD is generalised anxiety disorders.
Source: OECD analyses of the literature: see Chapter 5 for full details.
Scaling up the six interventions to increase population coverage enhances people’s lives
All the modelled interventions are estimated to have a positive impact on population health. As with the findings related to the burden of mental ill health, most of the impact of interventions is on quality of life – measured in disability-adjusted life years (DALYs) – rather on life expectancy (Figure 1.7). PHC interventions tend to have a larger impact on population health than interventions implemented in other settings. Combined psychotherapy and pharmacotherapy treatment by clinicians is the most effective intervention (yielding gains of approximately 27 DALYs per 100 000 population, on average across countries, which is equivalent to 27 people per 100 000 living an additional year in good health). The resilience‑focussed CBT in school settings is the second most effective intervention, producing a gain of 15 DALYs per 100 000 population on average across countries. Psychotherapy treatment in PHC settings also results in gains in population health, with an average increase of 11 DALYs per 100 000 population across countries. Workplace‑based interventions have a lower impact on people’s quality of life, and web-based interventions (6 DALYs per 100 000 population) are marginally more effective than universal CBT interventions (5 DALYs per 100 000 population).
Annual average across countries, 2025-2050
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.
Source: OECD analyses using the SPHeP-NCD model.
The overall impact of a mental health intervention depends on several factors, including how it is designed and put into practice. In general, the most effective approaches fall into two categories: highly effective targeted treatments for people with high mental health needs, and broader, lower-intensity interventions that reach very large parts of the population. Three main factors help to explain this pattern:
1. The most important factor is how effective the intervention is for individuals: how well it works for each person. If it helps people feel significantly better, like reducing symptoms of depression or anxiety, and for a longer period, it is likely to have a bigger impact overall. For example, combined psychotherapy and pharmacotherapy in PHC settings has an impact on the targeted individuals that is nearly seven time greater than school-based CBT.
2. The next factor is how many people the intervention reaches. Universal interventions available to more people tend to have a larger effect at the population level. For example, CBT in schools can reach all students aged 8‑18, which is a large group. But because it is offered to everyone, many children who do not need it may not benefit much.
3. The third factor is how many people in the target group need help. Targeted interventions focussing on people who are likely to have more severe mental health problems, such as combined psychotherapy and pharmacotherapy in PHC settings, may reach fewer people, but they concentrate resources on those who need it most, which can lead to a stronger overall impact.
When compared to the size of the burden, the impact of the assessed interventions tends to be small. This suggests that, while helpful, actions to improve mental well-being can play a complementary role, but would be insufficient to address the overall burden of mental ill health on their own. On average across countries, at the modelled levels of implementation, the interventions can reduce the projected burden of mental disorders by around 1% in the case of the most effective intervention (combined psychotherapy and pharmacotherapy in PHC settings). While increasing population coverage beyond the modelled levels will further improve the gains in population health, the coverage targets used in these analyses are grounded in available evidence of what is realistically achievable in real-world settings. Further expansion of the simulated programmes may be hindered by factors such as limited availability of trained personnel, resource constraints in primary care and educational settings, and challenges in engaging target populations consistently over time.
The interventions contribute to reducing healthcare costs and enhancing productivity, although the impacts are small relative to the size of the burden
Implementation of the interventions produces a positive impact on the economy – both through a reduction in healthcare expenditure and, more consistently, through an increase in human capital that can positively contribute to economic growth.
The reduction in average healthcare expenditure at the population level during 2025-2050 is estimated to range from EUR 0.1 per capita per year for interventions such as web-based interventions in PHC settings to EUR 10.4 per capita per year for combined psychotherapy and pharmacotherapy treatment in PHC settings (Figure 1.7). For all other interventions, the impact ranges between EUR 0.1 and EUR 0.3 per capita per year, except for psychotherapy in PHC settings, which is estimated to produce a reduction of EUR 2.6 per capita per year. This estimate includes both the reduction in costs for other forms of treatment and the lowering of the costs produced by mental ill health as a comorbidity, but does not include the costs of scaling up the interventions. As noted for the impact on population health, overall, the impact of the interventions is small compared to the current cost of mental disorders: the intervention producing the largest impact only decreases the healthcare costs for mental ill health by 4.2%.
The impact on productivity is also positive, with interventions improving workforce participation and productivity equivalent to an additional 1 163 full-time equivalent workers per year across the 30 OECD and EU countries included in the analysis (for web-based interventions in PHC settings) and up to 18 230 full-time equivalent workers per year for combined psychotherapy and pharmacotherapy treatment in PHC settings. Most of this gain is produced by increased productivity, due to improvements in absenteeism and presenteeism, followed by increases in workforce participation and reductions in early retirements. When all these improvements in human capital are considered, the average improvement in yearly productivity is expected to equal 0.013% of GDP for combined psychotherapy and pharmacotherapy treatment in PHC settings, and less for the other considered interventions (Figure 1.7).
Policies to improve mental well-being generally offer good value for money
Governments and public health authorities usually rely on cost-effectiveness analyses and benefit-cost metrics to guide decisions about which health interventions to fund. These economic evaluations help to ensure that limited resources are allocated to programmes that deliver the greatest social benefits at the lowest cost. 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 contrast, benefit-cost ratio is calculated as the increase in GDP resulting from enhanced workforce productivity, divided by the costs of implementing the intervention.
The economic evaluation of the assessed interventions reveals a case for scaling up investment in mental health promotion. Although, as discussed in previous sections, their impact on population health, healthcare expenditure and productivity appears relatively modest when compared to the overall burden of mental ill health, these interventions nonetheless perform well once implementation costs are considered. The majority of the assessed interventions demonstrate value for money, making them options for policymakers seeking efficient use of public resources.
All interventions, apart from school-based and workplace‑based CBT, yield a benefit-cost ratio greater than 1, meaning that their contribution to GDP surpasses the cost of implementation (Figure 1.7). The web-based intervention in workplace settings demonstrates the highest benefit-cost ratio, returning EUR 10.3 in GDP for every EUR 1 invested. This strong performance is largely due to its relatively low implementation cost, which is significantly lower than, for example, that of the workplace‑based CBT intervention. Moreover, by targeting the workforce directly, it enhances productivity, further boosting economic returns. In contrast, the CBT intervention in school settings yields a benefit-cost ratio below 1, as it targets young individuals who are not yet part of the workforce and therefore do not directly contribute to productivity gains in the short term.
When assessing cost-effectiveness, which considers the cost of improving healthy life expectancy, interventions delivered in PHC settings generally offer the greatest health impact relative to investment. Psychotherapy and combined psychotherapy and pharmacotherapy in PHC settings consistently emerge as either cost-saving, where healthcare savings exceed implementation costs, or cost-effective, based on a threshold of EUR 50 000 per DALY gained. Other interventions that are largely cost-effective or cost-saving include CBT in school settings and web-based interventions in workplace settings. The remaining interventions present a more mixed picture, with cost-effectiveness varying by country. For example, workplace‑based CBT is considered not cost-effective in up to 70% of countries (Figure 1.8).
Note: ICER is incremental cost-effectiveness ratio.
Source: OECD analyses using the SPHeP-NCD model.
Analyses presented in this chapter suggest that initiatives implemented by health systems, including healthcare providers (particularly GPs), and public health initiatives implemented in workplaces and schools can contribute meaningfully to improving mental well-being across the population. Many of these interventions appear to be cost-effective, with some even generating cost savings, and they demonstrate value for money comparable to other public health measures. However, these analyses also suggest that it is unlikely that the health system alone can make a fundamental difference in addressing the burden of mental ill health. The findings outlined here point to three key policy implications.
First, countries should prioritise investments in interventions that are designed and implemented according to best practices. While the interventions outlined in this report are generic models, their effectiveness and efficiency depend greatly on how they are designed and implemented in practice. OECD work on best practices for mental health promotion and prevention (OECD, 2025[63]) shows that efforts to improve access to mental health services must be accompanied by complementary measures, such as communication campaigns and peer-based programmes to destigmatise mental ill health. These actions are essential to enhance the impact of interventions – particularly by addressing stigma and other barriers to care (Box 1.5). Additional research, including rigorously designed clinical trials, is also required to advance the identification and validation of more effective mental health interventions.
The OECD report Mental Health Promotion and Prevention: Best Practices in Public Health identifies 11 interventions that meet validated best practice criteria, co-developed by OECD Member countries. Each intervention demonstrates strong performance across five key indicators: effectiveness, efficiency, equity, evidence quality, coverage and transferability.
These assessed interventions focus on creating school environments that support mental well-being and resilience; enhancing mental health literacy and training frontline professionals to identify and support individuals in distress; preventing suicide; and improving access to mental healthcare.
OECD analysis suggests these approaches can deliver significant health benefits at costs that are manageable for many health systems, making them cost-effective. For instance, scaling up Norway’s Prompt Mental Health Care programme could yield an average of 33 DALYs gained per 100 000 people annually across EU countries. This programme shares some broad design elements and characteristics with the standard psychotherapy intervention in PHC settings modelled in this report, which is estimated to deliver approximately one‑third of the health impact observed in Norway’s best practice.
The 2025 OECD report also highlights common features that contribute to the definition of the best practices, including:
expanding low-threshold and specialised mental health services, such as multidisciplinary networks, teleconsultations and digital tools
reducing financial barriers – for example, through psychotherapy reimbursement
destigmatising mental ill health and improving mental health literacy across the population
rolling out peer-based programmes to train students and frontline workers (such as teachers) to support individuals in distress, reduce stigma and encourage help-seeking
investing in workforce planning and development, including creating new roles within existing professions.
Source: OECD (2025[63]), Mental Health Promotion and Prevention: Best Practices in Public Health, https://doi.org/10.1787/88bbe914-en.
Second, countries may choose to set more ambitious targets and scale up interventions beyond the coverage levels simulated in this analysis. For example, the model assumed that 10% of GPs would participate in primary care‑based interventions – a pragmatic estimate based on current evidence and short-term feasibility. Expanding coverage beyond this level could generate greater health and economic benefits at the population level, probably with equal or improved cost-effectiveness if countries invest in best practice interventions. However, such expansion would carry broader financial and planning implications. As discussed earlier in this chapter, providing treatment to all individuals with mental disorders would require a 41% increase in mental health spending compared to current levels. Moreover, any significant scale‑up should be preceded by policies aimed at strengthening the capacity of the mental health workforce to ensure effective and sustainable implementation. In other words, achieving more impact requires significant upfront investments. One way to keep this additional cost at an affordable level would be to ensure that expanded services are deployed first where they are most needed. For example, targeting resources toward populations and settings with the highest unmet needs, rather than expanding services uniformly, can improve the efficiency of intervention delivery while containing overall expenditure.
Third, countries should address the root causes of mental ill health – for example, with strong welfare systems. The interventions analysed primarily aim to strengthen individual and population-level resilience to mental distress and support those already affected by mental disorders. While these efforts are essential for improving mental well-being, they do not address the root causes of mental ill health. To be fully effective, these actions should be complemented by broader policies that foster environments conducive to mental well-being (OECD, 2023[83]). The OECD literature review highlights several determinants of mental distress, some of which – such as economic crises and problematic social media use – are likely to persist in the future. Tackling these enduring factors can help to reduce incidence of mental health issues. For example, welfare systems with more generous social expenditure are generally associated with better mental health outcomes (Ribanszki et al., 2022[84]). Similarly, The OECD Council Recommendation on Integrated Mental Health, Skills and Work Policy (OECD, 2015[60]) recognises that integrating mental health, skills and employment services is essential to improve outcomes for individuals who are not in employment, and encourages multi‑sectoral strategies that promote early engagement in education and work, and support the return to employment for people with mental health conditions.
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← 1. Beside indirect productivity losses such as those estimated in this publication, the estimates in Health at a Glance: Europe 2018 report also included healthcare expenditure and social benefits, providing an estimate for indirect labour market costs of 1.6% of GDP, based on lower 2015 prevalence.