This chapter provides an overview of the health and economic burden of mental ill health. Based on the results of the OECD Strategic Public Health Planning for Non-Communicable Diseases (SPHeP-NCDs) model, it presents the impact of mental ill health of three of the most prevalent mental disorders: major depressive disorders, generalised anxiety disorders and alcohol use disorders across EU countries. The analyses conducted using the OECD SPHeP-NCDs model gauge the impact of these conditions on health and economic outcomes in EU countries over the period 2025-2050, including effects on life expectancy, health expenditure and the labour market.
The Economic Case for Preventing Mental Ill Health
Abstract
Mental ill health gives rise to significant health, social and economic costs. Major depressive disorders, generalised anxiety disorders and alcohol use disorders are among the most common mental health conditions. They account for the largest share of the burden on individuals and the economy, and are the primary focus of this chapter.
Across EU countries, mental ill health is found to have a substantial impact on quality of life, reducing healthy life expectancy by an average of 2.5 years. Its impact on overall life expectancy is smaller – reducing it by an average of 0.25 years, or around 3 months. Nevertheless, this corresponds to approximately 28 000 premature deaths (among people aged under 75) each year, including deaths by suicide resulting from self-harm.
At the current level of coverage, mental ill health is estimated to account for 6% of the aggregate annual health budget in EU countries. This corresponds to an average expenditure of EUR 76 billion annually from 2025 to 2050, which is comparable to Belgium’s total annual healthcare budget.
Treatment coverage for mental ill health is estimated to be low, at around 33% across EU countries. Closing this gap would require an increase in the budget devoted to mental health, which could amount to a 41% increase (about EUR 72 per capita per year) compared to current spending, if EU countries aimed to achieve full coverage.
Beyond its costs to the healthcare system, mental ill health places a significant economic burden on the labour market due to the economic costs associated with absenteeism, presenteeism, unemployment and early retirement. These costs are equivalent to a loss of about 2.4 million full-time equivalent workers per year, which is similar to the Croatian working-age population.
GDP in EU countries is projected to be 1.7% lower each year than it would be in the absence of mental ill health. This is equivalent to an annual loss of EUR 313 billion – approximately the same as the GDP of Czechia in 2023.
As outlined in Chapter 2, prevalence of mental ill health is high across the population, with almost one in five people across EU and OECD countries experiencing a mental disorder in 2022. This high prevalence has significant negative impacts not only on health and quality of life but also on a country’s broader economic prosperity. In order to model the health and economic impacts, the OECD SPHeP-NCDs microsimulation model has been adapted to simulate the emergence of mental ill health across the 27 EU Member States, Iceland, Norway and Switzerland over the period 2025-2050 (Box 3.1). The scope of this modelling includes three of the most prevalent conditions:
major depressive disorders (including two levels of severity: mild and moderate, and severe)
generalised anxiety disorders
alcohol use disorders.
As discussed in Chapter 2, the Diagnostic and Statistical Manual of Mental Disorders (APA, 2013[1]) and the International Classification of Diseases (WHO, 2026[2]) also include the following major categories of mental disorders: substance use disorders (excluding alcohol use disorders); bipolar and related disorders, and all other disorders. The decision to limit the scope of modelling the impacts of mental ill health to the three leading conditions (major depressive, generalised anxiety and alcohol use disorders) reflects the complexity and resources required to extend the analysis to additional diseases. Consequently, the modelling of economic and cost impacts focusses on these three most prominent conditions, which account for the majority of diagnosed mental disorders. These conditions are referred to collectively as “mental ill health” in this chapter, acknowledging that the findings underestimate the true impact of all mental health conditions. This underestimation is further reinforced by the fact that the analysis does not capture several significant indirect social costs – including, for example, lower educational attainment, heightened risks of poverty and social exclusion, and broader impacts on family well-being and intergenerational outcomes.
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 produces a comprehensive set of key behavioural and physiological risk factors and their associated non-communicable diseases, including mental ill health.
The model focussing on mental ill health currently covers 30 countries: the 27 EU Member States, Iceland, Norway and Switzerland. Results also include averages for the 27 EU countries. For each of the 30 countries, the model uses demographic and risk factor characteristics by age and gender-specific population groups from international databases (see Figure 3.1). These inputs are used to generate synthetic populations, in which each individual is assigned demographic characteristics and a risk factor profile. Based on these characteristics, an individual has a certain risk of developing a disease each year. Incidence and prevalence of diseases in a specific country’s population were calibrated to match estimates from international datasets.
Note: This schematic is highly simplified and focusses on the disease component – it does not reflect some other components of the model (including births, immigration, emigration, deaths, remission and case‑fatality rates).
Source: For more information on the OECD SPHeP-NCDs model, see the SPHeP-NCDs Technical Documentation, available at: http://oecdpublichealthexplorer.org/ncd-doc.
Model outputs
For each year, a cross-sectional representation of the population can be obtained, to calculate health status indicators such as life expectancy, premature deaths (including as a result of self-harm), disease prevalence and DALYs using standard 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 costs of multimorbidity and end-of-life care are also calculated and applied.
The labour market module uses relative risks to relate disease status to the risk of 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 in number of full-time equivalent workers and costed based on a human capital approach,1 using national average wages. The output of the labour market module is also the main input for calculating the impact on GDP. Consistent with the approach used in the OECD’s long-term economic forecasting mode (Guillemette and Turner, 2017[3]) and other established long-term models, such as the Long-Term Growth Model of the World Bank, a Cobb-Douglas production function is used (Loayza and Pennings, 2022[4]). In practical terms, the labour component of the working-age population (those aged 15‑74) is modified based on the change of full-time equivalent workers.
The mental health module
The SPHeP-NCDs model includes three major mental health conditions: major depressive disorders, generalised anxiety disorders and alcohol use disorders. Mental disorders are modelled via specific modules developed for each disease.
For major depressive and generalised anxiety disorders, an integrative approach is used, combining multiple longitudinal datasets to reconstruct individuals’ mental health trajectories along a score curve that captures the range from no mental ill health to severe mental ill health. Each individual in the model is assigned with a PHQ‑8 and a GAD‑7 score (see Chapter 1, Box 1.2). Distribution of both scores is modelled using micro-level data with a zero‑inflated beta regression using parameters of age, sex and country for PHQ‑8 and age, sex and PHQ‑8 score for GAD‑7. The regression is performed in two steps: first, a logistic regression to predict the probability of zero and then, a beta regression to model the final score, taking into account the zero probability and scaling the score to between 0 and 1 to fit the distribution. Various models were tested to reproduce as closely as possible the observed PHQ‑8 and GAD‑7 score distributions, with the zero‑inflated beta regression producing the best results.
Based on their score, individuals are more or less likely to develop major depressive and generalised anxiety disorders. Scores are recalculated on a bi‑annual basis and following an episode of either disorder. For major depressive disorders, this link is based on a two‑step method combining PHQ‑8 screening and diagnostic criteria (Kroenke et al., 2009[5]). For generalised anxiety disorders, as no such empirical link was identified, a direct correspondence between the GAD‑7 anxiety score and a diagnosis of generalised anxiety disorder was assumed.
The ninth question of the PHQ‑9 score is modelled separately. Probability of having suicidal thoughts – identified via any “yes” response to question 9 on suicidal thoughts (from occasionally to almost every day) – is estimated based on micro-level data and modelled as a function of the PHQ‑8 score. When question 9 is present, individuals are at higher risk of self-harm.
Incidence of alcohol use disorders is instead linked to the volume and pattern of alcohol consumption, based on relative risks. This model does not include other highly disabling conditions such as schizophrenia and bipolar disorder; nor does it account for upstream determinants of mental health such as well-being and resilience, which means it likely underestimates the true burden of mental ill‑health.
For more information on the OECD SPHeP-NCDs model, see the SPHeP-NCDs Technical Documentation, available at: http://oecdpublichealthexplorer.org/ncd-doc.
1. The human capital approach is based on assumptions simplifying the economic dynamics leading to economic losses – including, for example, assumptions about reserve labour force, friction costs and the impact on reserve wages.
Mental ill health is a major public health concern, with a significant negative impact on the quality of life of those affected (Figure 3.2). As mental disorders are chronic in nature, subject to continuous relapses and recurrent episodes over many years, it is perhaps unsurprising that there is a substantial negative impact of mental ill health on so-called healthy life expectancy, which can be considered a proxy for quality of life (Abdin et al., 2020[6]). Healthy life expectancy uses disease disability weights to calculate the number of years lived in good health. The OECD SPHeP-NCDs model finds that across EU countries, average healthy life expectancy will be reduced by 2.5 years between 2025 and 2050, compared to a scenario with no mental ill health. All three conditions modelled affect healthy life expectancy, with major depressive disorders causing the greatest reduction (1.20 years), followed by generalised anxiety disorders (0.91 years) and alcohol use disorders (0.37 years). The smallest overall reductions in healthy life expectancy are projected in Greece and Bulgaria, at just under 1.2 years. In contrast, Sweden (3.37 years) and Austria (3.33 years) will see the largest reduction in healthy life years.
Source: OECD analyses based on the OECD SPHeP-NCDs model.
Of the three mental disorders modelled in the OECD SPHeP-NCDs model, two (major depressive and alcohol use disorders) are associated with increased likelihood of death, including deaths by suicide as a result of self-harm (GBD 2019 Diseases and Injuries Collaborators, 2020[7]). Based on this evidence, the OECD SPHeP-NCDs model estimates that, during 2025-2050, life expectancy across EU countries will be 0.25 years (roughly 3 months) lower than it would be in the absence of these two conditions, with roughly two‑thirds of this reduction attributable to major depressive disorders and one‑third to alcohol use disorders (Figure 3.2). Greece (0.05 years) and Cyprus (0.06 years) are projected to experience the smallest burden on life expectancy due to mental ill health, while Lithuania (0.50 years), Finland (0.43 years) and Latvia (0.41 years) will see the greatest impact. For context, life expectancy increased by an average of 0.18 years annually across OECD countries between 2010 and 2019 (OECD, 2023[8]). Thus, a 0.25‑year lower life expectancy effectively offsets more than a year of progress in longevity gains.
The estimated 0.25‑year lower life expectancy across EU countries also reflects a significant population-level impact. According to the OECD SPHeP-NCDs model, between 2025 and 2050, mental ill health will cause more than 709 300 premature deaths (deaths under the age of 75) or an average of 28 000 premature deaths per year, which is equivalent to 9 premature deaths per 100 000 population (Figure 3.3). These deaths are relatively evenly split between major depressive disorders (5.4 premature deaths per 100 000 people annually) and alcohol use disorders (4.6 per 100 000 people annually). The analyses also show great cross-country variability. The highest rates occur in Finland, Austria and Slovenia, each exceeding 14 premature deaths per 100 000 population annually (around 155% of the EU average), while Greece, Cyprus and Bulgaria show the lowest rates – all below 4 per 100 000 (less than 45% of the average).
Source: OECD analyses based on the OECD SPHeP-NCDs model.
In a business-as-usual scenario where major depressive, generalised anxiety 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 during 2025-2050. This is comparable to the annual healthcare budget of Belgium, and equivalent to about 6% of total EU healthcare spending (roughly EUR 175 per capita per year) (Figure 3.4). The estimate for EU countries is broadly in line with findings from other OECD contexts. For example, Milliken et al. (2024[9]) report costs of mental ill health in Canada amounting to approximately 6.4% of total health spending, while SAMHSA (2014[10]) estimates a similar burden for the United States at around 7%.
Source: OECD analyses based on the OECD SPHeP-NCDs model.
Differences in overall healthcare budgets mean that the share of spending on mental ill health can vary significantly across countries. For nations with smaller budgets, even modest absolute costs can represent a large proportion of total expenditure. For example, Slovenia is projected to spend EUR 127 per person annually on mental ill health, which is below the EU average, but this would account for 7% of its healthcare budget – a higher proportion than the EU average. Conversely, Norway has the highest absolute cost among the 30 countries in the OECD SPHeP-NCDs model analysis (EUR 594 per capita), although this represents around 9% of its overall healthcare expenditure.
The total cost of mental ill health comprises both direct treatment expenses and additional costs arising from comorbidities (Box 3.2). Treating major depressive, generalised anxiety and alcohol use disorders accounts for the majority of healthcare costs, but a significant share of overall expenditure stems from the extra resources required to manage other health conditions when a mental disorder is present (Cortaredona and Ventelou, 2017[11]). Model estimates indicate that of the EUR 175 per capita annual cost for mental ill health calculated by the OECD SPHeP-NCDs model, approximately 75% (EUR 131) is for treating mental disorders themselves, while the remaining 25% (EUR 44) reflects the added burden of comorbidities.
As shown in Figure 3.4, about 25% of total healthcare spending linked to mental ill health comes from the extra costs incurred when mental disorders occur alongside other chronic diseases as comorbidities. Research consistently shows that individuals with mental health conditions have higher healthcare expenditures than those without (Sartorius, 2018[12]). This is largely due to increased utilisation of outpatient services, emergency care and medications for comorbid conditions such as cardiovascular disease, diabetes and respiratory disorders (Simon et al., 2023[13]). Some studies focussing on people with severe mental ill health indicate that this group of patients incurs healthcare costs two to three times higher than those without such conditions (Figueroa et al., 2020[14]; Scott et al., 2016[15]).
Mental and physical health are strongly interconnected through a bi-directional relationship (Ogunmoroti et al., 2022[16]; Xiong and Qi, 2025[17]). Each can influence the other, creating a cycle that increases health risks and healthcare costs:
Mental ill health raises the risk of chronic physical conditions through biological and behavioural mechanisms. Stress and perceived threat trigger physiological changes that exacerbate chronic disease risk (Kyrou and Tsigos, 2009[18]), while behavioural challenges – such as poor adherence to medication, lack of exercise and unhealthy diets – further compound these risks (Baldessarini, 2020[19]; Loprinzi et al., 2013[20]).
Conversely, physical conditions such as cancer, autoimmune diseases and other chronic illnesses are also associated with mental ill health (Liao et al., 2022[21]). These conditions often lead to psychological distress, depression and anxiety, increasing the need for mental healthcare and, consequently, overall healthcare expenditure (Everard and Vuick, 2025[22]).
As discussed in Chapter 2, coverage across the 30 countries in the OECD SPHeP-NCDs model analysis remains low, at just 33% on average. Limited coverage means that many individuals with mental ill health do not receive timely care, leading to worse health outcomes, higher long-term costs and increased pressure on other parts of the healthcare system due to the impact of mental ill health as a comorbidity. Improving coverage could reduce these burdens, but it has resource implications. To illustrate the potential financial impact, the OECD SPHeP-NCDs model was used to estimate costs under a hypothetical scenario of 100% treatment coverage. While achieving full coverage is unrealistic due to practical and logistical barriers, modelling this scenario provides valuable insight into the magnitude of the financing gap and an upper-bound estimation of the resources needed to close this gap.
Achieving full treatment coverage would significantly increase mental health expenditure, though less than might be expected given the scale of expansion. Reaching 100% treatment coverage for the considered conditions would require treating roughly three times as many people as today. Nevertheless, as shown in Figure 3.5, total spending on mental ill health would rise by only 41% on average across EU countries, which is equal to an increase of EUR 72 per capita per year, bringing total expenditure to EUR 247 per person annually. This relatively moderate rise reflects the fact that current coverage already includes the most severe and costly cases, while the treatment gap consists mainly of mild to moderate conditions, which are less expensive to treat. Countries with the lowest current levels of coverage and spending, such as Bulgaria and Latvia, would experience the largest percentage increases, with mental health expenditure more than doubling under this scenario.
Note: HE is health expenditure. The light blue segment of each column represents the additional costs countries would incur if mental health service coverage were expanded to reach all individuals in need (100% coverage). This increase in healthcare spending is shown as a percentage relative to the current level of spending in the 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 health expenditure shown refers to the current level of coverage.
Source: OECD analyses based on the OECD SPHeP-NCDs model.
In addition to the costs to life expectancy, healthy life expectancy and government healthcare expenditure, mental ill health causes broader economic costs through its detrimental impacts on workforce productivity (OECD, 2021[23]). People with mental disorders are less likely to be employed; if they have a job, they are more likely to work part time, 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. In the case of absenteeism and presenteeism, the economic costs to the labour market are hidden, as they relate to wages paid to employees without a corresponding level of output from their work. The cumulative detrimental impact of mental ill health due to major depressive, generalised anxiety and alcohol use disorders on these four 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, which is equivalent to the Croatian working-age population (Figure 3.6).
Note: FTE is full-time equivalent workers; the “employment” category combines both effects on unemployment and part-time working.
Source: OECD analyses based on the OECD SPHeP-NCDs model.
Examining the drivers of labour market costs by disease and by type of productivity loss reveals important patterns. Across countries, the largest share of lost productivity is due to reduced employment, which accounts for nearly half of the total impact (48%), followed by presenteeism (36%) and absenteeism (14%). Early retirement contributes only a small fraction (around 3%). When looking at disease‑specific contributions, generalised anxiety disorders emerge as the primary driver, responsible for 56% of the total burden, followed by major depressive disorders at 39% and alcohol use disorders at just 5%. It is noteworthy that generalised anxiety disorders exert such a large impact despite having no effect on mortality; their influence on productivity stems entirely from symptoms that impair well-being and daily functioning.
To estimate the macroeconomic impact of mental ill health, outputs from the OECD SPHeP-NCDs model were integrated into the Long-Term Growth Model of the World Bank (Box 3.1). This approach allows for a comprehensive assessment of how health outcomes translate into economic performance over time. The analysis focussed on the combined effects of major depressive, generalised anxiety and alcohol use disorders on two critical dimensions that capture both the availability and effectiveness of human capital: life expectancy and workforce productivity. As previously discussed, mental ill health is projected to reduce life expectancy by an average of 0.25 years across EU countries and to cause labour market losses equivalent to 2.4 million full-time workers annually between 2025 and 2050. By linking these health and productivity impacts to long-term economic growth, the modelling provides valuable insights into the scale of GDP losses attributable to mental ill health.
On average, GDP across EU countries is projected to be 1.7% lower each year due to the impact of mental ill health compared to a scenario without these conditions (Figure 3.7). This translates into an annual loss of EUR 313 billion across EU countries, which is roughly equivalent to the entire GDP of Czechia in 2023 (Eurostat, 2026[24]). Most of this reduction is driven by generalised anxiety disorders (0.9% of GDP loss) and major depressive disorders (0.7%), while alcohol use disorders contribute around 0.1%. The greatest declines are expected in Lithuania, Finland, Latvia and Estonia, each facing reductions of more than 2%, whereas Bulgaria, Cyprus and Greece would experience the smallest impact, at about 1%.
Source: OECD analyses based on the OECD SPHeP-NCDs model.
This finding is consistent with earlier OECD research, which estimated that in 2015 reduced workforce productivity across the EU accounted for approximately 1.6% of GDP, out of a total mental health-related cost of around 4% (OECD/European Union, 2018[25]). This suggests that productivity losses represent a major component of the economic burden of mental ill health. Evidence from other OECD countries reinforces this pattern: studies in Australia (Beyond Blue and PwC, 2015[26]) and the United States (Abramson, Boerma and Tsyvinski, 2024[27]) report comparable impacts once methodological differences in study design and in the cost components considered are considered. Both these studies also identify reduced human capital and the resulting decline in productivity as crucial drivers of economic losses from mental ill health. However, the US study also highlights additional channels, including changes in consumption patterns and portfolio allocations, which would further amplify the detrimental economic impact of mental ill health.
References
[6] Abdin, E. et al. (2020), “Impact of mental disorders and chronic physical conditions on quality-adjusted life years in Singapore”, Scientific Reports, Vol. 10/1, p. 2695, https://doi.org/10.1038/s41598-020-59604-0.
[27] Abramson, B., J. Boerma and A. Tsyvinski (2024), “Macroeconomics of Mental Health”, NBER Working Paper, No. 32354, https://www.nber.org/papers/w32354.
[1] APA (2013), Diagnostic and Statistical Manuel of Mental Disorders, American Psychiatric Association, Arlington, VA, https://www.psychiatry.org/psychiatrists/practice/dsm.
[19] Baldessarini, R. (2020), “Epidemiology of suicide: Recent developments”, Epidemiology and Psychiatric Sciences, Vol. 29, p. e71, https://doi.org/10.1017/S2045796019000672.
[26] Beyond Blue and PwC (2015), Creating a Mentally Healthy Workplace: Return on Investment Analysis, Pricewaterhousecoopers, Sydney, https://www.nrspp.org.au/resources/creating-a-mentally-healthy-workplace-return-on-investment-analysis/.
[11] Cortaredona, S. and B. Ventelou (2017), “The extra cost of comorbidity: Multiple illnesses and the economic burden of non-communicable diseases”, BMC Medicine, Vol. 15/1, p. 216, https://doi.org/10.1186/s12916-017-0978-2.
[24] Eurostat (2026), “Gross domestic product (GDP) and main components (output, expenditure and income)”, https://ec.europa.eu/eurostat/databrowser/product/page/NAMA_10_GDP (accessed on 2 April 2026).
[22] Everard, C. and S. Vuick (2025), “Exploring the relationship between non-communicable diseases and depression”, OECD Health Working Papers, No. 178, OECD Publishing, Paris, https://doi.org/10.1787/02a1cfc5-en.
[14] Figueroa, J. et al. (2020), “Association of mental health disorders with health care spending in the medicare population”, JAMA Network Open, Vol. 3/3, p. e201210, https://doi.org/10.1001/jamanetworkopen.2020.1210.
[7] GBD 2019 Diseases and Injuries Collaborators (2020), “Global burden of 369 diseases and injuries in 204 countriesand territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019”, The Lancet, Vol. 396/10258, pp. 1204-1222, https://doi.org/10.1016/S0140-6736(20)30925-9.
[3] Guillemette, Y. and D. Turner (2017), “The fiscal projection framework in long-term scenarios”, OECD Economics Department Working Papers, No. 1440, OECD Publishing, Paris, https://doi.org/10.1787/8eddfa18-en.
[5] Kroenke, K. et al. (2009), “The PHQ-8 as a measure of current depression in the general population”, Journal of Affective Disorders, Vol. 114/1-3, pp. 163-173, https://doi.org/10.1016/j.jad.2008.06.026.
[18] Kyrou, I. and C. Tsigos (2009), “Stress hormones: Physiological stress and regulation of metabolism”, Current Opinion in Pharmacology, Vol. 9/6, pp. 787-93, https://doi.org/10.1016/j.coph.2009.08.007.
[21] Liao, B. et al. (2022), “Association of mental distress with chronic diseases in 1.9 million individuals: A population-based cross-sectional study”, Journal of Psychosomatic Research, Vol. 162, p. 111040, https://doi.org/10.1016/j.jpsychores.2022.111040.
[4] Loayza, N. and S. Pennings (2022), The Long Term Growth Model: Fundamentals, Extensions, and Applications (English), World Bank Group, Washington, D.C., http://documents.worldbank.org/curated/en/099627211072228496.
[20] Loprinzi, P. et al. (2013), “Physical activity and the brain: A review of this dynamic, bi-directional relationship”, Brain Research, Vol. 1539, pp. 95-104, https://doi.org/10.1016/j.brainres.2013.10.004.
[9] Milliken, O. et al. (2024), “Mental health expenditure in Canada”, Journal of Mental Health Policy and Economics, Vol. 27/3, pp. 75-84, http://www.icmpe.org/test1/journal/issues/v27i3/v27i3abs02.html.
[8] OECD (2023), Health at a Glance 2023: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/7a7afb35-en.
[23] OECD (2021), Fitter Minds, Fitter Jobs: From Awareness to Change in Integrated Mental Health, Skills and Work Policies, Mental Health and Work, OECD Publishing, Paris, https://doi.org/10.1787/a0815d0f-en.
[25] OECD/European Union (2018), Health at a Glance: Europe 2018: State of Health in the EU Cycle, OECD Publishing, Paris, https://doi.org/10.1787/health_glance_eur-2018-en.
[16] Ogunmoroti, O. et al. (2022), “A systematic review of the bidirectional relationship between depressive symptoms and cardiovascular health”, Preventive Medicine, Vol. 154, p. 106891, https://doi.org/10.1016/j.ypmed.2021.106891.
[10] SAMHSA (2014), Projections of National Expenditures for Treatment of Mental and Substance Use Disorders, 2010-2020, Substance Abuse and Mental Health Services Administration, Rockville, MD, https://library.samhsa.gov/product/projections-national-expenditures-treatment-mental-and-substance-use-disorders-2010-2020.
[12] Sartorius, N. (2018), “Comorbidity of mental and physical disorders: A key problem for medicine in the 21st century”, Acta Psychiatrica Scandinavia, Vol. 137/5, pp. 369-370, https://doi.org/10.1111/acps.12888.
[15] Scott, K. et al. (2016), “Association of mental disorders with subsequent chronic physical conditions: World mental health surveys from 17 countries”, JAMA Psychiatry, Vol. 73/2, pp. 150-158, https://doi.org/10.1001/jamapsychiatry.2015.2688.
[13] Simon, J. et al. (2023), “Excess resource use and costs of physical comorbidities in individuals with mental health disorders: A systematic literature review and meta-analysis”, European Neuropsychopharmacology, Vol. 66, pp. 14-27, https://doi.org/10.1016/j.euroneuro.2022.10.001.
[2] WHO (2026), “ICD-11: International Classification of Diseases 11th Revision”, https://www.who.int/standards/classifications/classification-of-diseases (accessed on 2 April 2026).
[17] Xiong, Y. and Y. Qi (2025), “The unequal loop: Socioeconomic status and the dynamic bidirectional relationship between physical and mental health”, Journal of Health and Social Behavior, Vol. 66/3, pp. 410-427, https://doi.org/10.1177/00221465241300303.