Executive summary

Mental health plays a central role in people’s lives and is intrinsically tied to many other aspects of people’s wider well-being. The COVID-19 pandemic brought renewed attention to its importance, as direct health impacts and lost lives combined with social isolation, loss of work and financial insecurity all contributed to a significant worsening of people’s mental health. In the meantime, new threats to mental health, such as the cost-of-living crisis and climate change, have emerged or become more salient. Already well before 2020, it was estimated that half of the population will experience a mental health condition at least once in their lifetime and the economic costs of mental ill-health amounted to more than 4% of GDP annually. On the other hand, positive mental health, or having high levels of emotional and psychological well-being, is increasingly being recognised as a policy target in its own right by governments across the OECD.

Successful and people-centred strategies to promote good population mental health need to acknowledge that the ability to thrive depends on the broader living conditions experienced by individuals, families and communities. The recognition that these strategies hence need to involve a range of sectors across all of government is nothing new. Indeed, calls for comprehensive “health in all policies” approaches, which systematically integrate (mental) health considerations into policies across sectors, have been renewed at both national and international levels in recent years. Yet in practice, coalition-building with other sectors remains limited and often not implemented at scale. Some of the most commonly cited difficulties include the fact that inter-departmental task forces dealing with mental health are often time limited and lack decision-making power; furthermore, aspects such as accountability or plans for monitoring and evaluation of partnerships are often absent from high-level strategy documents, and resource constraints remain a challenge.

The OECD Well-being Framework has, for more than a decade, pioneered a multidimensional approach to measuring the outcomes that matter for people, the planet and future generations. Drawing on this conceptual framework and longstanding work of the organisation on integrated approaches to mental health, this report uses a “well-being lens” to underscore the reciprocal relationships between mental health and the outcomes typically under the responsibility of non-health government departments. Ultimately, recognising which policies under their mandate can or are already contributing to improving mental health as well as their own objectives can benefit the government’s broader policy goals, thus facilitating a move towards a “mental health for all policies” mindset.

Mapping the relationship between mental health and people’s economic, social, relational, civic and environmental experiences reveals that those with worse mental health outcomes also fare far worse in most other aspects of their well-being. For instance, compared to the general population, those at risk of mental distress are nearly twice as likely to be at the bottom of the income distribution, to be unemployed, or to be dissatisfied with the safety and availability of green spaces in their neighbourhoods. They are also more than twice as likely be unhappy with how they spend their time and to report low trust in other people, and their risk for feeling lonely is more than four times higher. Conversely, protective factors – such as being financially secure, being in good physical health, living in a safe and clean living environment, and having healthy social relationships – can provide resilience against poor mental health outcomes and support good emotional and psychological well-being.

There are several options for “win-win” policies that can jointly improve both mental health and other policy goals. Based on the evidence of the strong interlinkages between mental health and other well-being outcomes, and existing policy practices underway in OECD countries, this report identifies a range of illustrative examples of such co-benefits. More known practices cover aspects such as increased access to social assistance programmes, integrating mental health service provision into unemployment services, encouraging employers to prioritise mental health at work, or school-based interventions and the incorporation of social and emotional learning in curricula. More recent innovations for mental health promotion include the expansion of social prescribing programmes, the recognition of the value of unpaid work, interventions to tackle racism and discrimination, prioritising social connectedness as an explicit policy target, and accounting for the mental health costs of climate change (and the benefits of climate action).

Successful implementation of such “win-win” policies across sectors requires adequate resources, incentives and working arrangements that enable all relevant stakeholders to contribute. This report also reviews selected mental health initiatives across OECD countries to demonstrate how policy makers have in practice been aligning action across government agencies; redesigning policy formulation to address the joint factors influencing mental health through impact evaluation; refocusing efforts towards the promotion of positive mental health; and connecting with societal stakeholders, including those with lived experience, youth, civil society and researchers.

Lessons learned first show the importance of clearly defining mental health goals (i.e. what it is that should be improved, and who can contribute), for instance through using multidimensional frameworks to point out interlinkages and establish coordination with other sectors; formulating concrete implementation plans; or explicitly monitoring positive mental health. Second, intersectoral collaboration and partnership building – be it between different government agencies, levels of government or when supporting community actors – take resources, including time, to do well, and can be supported by a move away from short-term project cycles. Third, strategic grantmaking seems to be a promising approach for allocating funds to mental health promoting activities, including at the local level, that do not traditionally fall under the remit of the health sector. Lastly, provisions for impact evaluations should be integrated into programme design from the outset to improve learning and build the evidence base on successful interventions.

References

[13] Baksheev, G. et al. (2011), “Validity of the 12-item General Health Questionnaire (GHQ-12) in detecting depressive and anxiety disorders among high school students”, Psychiatry Research, Vol. 187/1-2, pp. 291-296, https://doi.org/10.1016/j.psychres.2010.10.010.

[42] Berthoud, R. et al. (2009), “Design of the Understanding Society ethnic minority boost sample”, Understanding Society Working Paper Series 2009-02, https://www.understandingsociety.ac.uk/sites/default/files/downloads/working-papers/2009-02.pdf.

[2] Berwick, D. et al. (1991), “Performance of a five-item mental health screening test”, Medical Care, Vol. 29/2, pp. 169-176, https://doi.org/10.1097/00005650-199102000-00008.

[46] Brown, T. (2015), Confirmatory Factor Analysis for Applied Research, Guilford Press, https://www.guilford.com/books/Confirmatory-Factor-Analysis-for-Applied-Research/Timothy-Brown/9781462515363.

[48] Chen, F. et al. (2008), “An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models”, Sociological Methods & Research, Vol. 36/4, pp. 462-494, https://doi.org/10.1177/0049124108314720.

[6] Elovanio, M. et al. (2020), “General Health Questionnaire (GHQ-12), Beck Depression Inventory (BDI-6), and Mental Health Index (MHI-5): Psychometric and predictive properties in a Finnish population-based sample”, Psychiatry Research, Vol. 289, p. 112973, https://doi.org/10.1016/j.psychres.2020.112973.

[45] Enders, C., D. Bandalos and D. And Bandalos (2001), “The relative performance of full information maximum likelihood estimation for missing data in structural equation models”, Structural Equation Modeling, Vol. 8/3, pp. 430-457, https://doi.org/10.1207/S15328007SEM0803_5.

[36] Eurofound (n.d.), European Quality of Life Surveys (EQLS) (database), https://www.eurofound.europa.eu/surveys/european-quality-of-life-surveys (accessed on 10 June 2022).

[35] Eurostat (n.d.), European Health Interview Survey (EHIS), https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:European_health_interview_survey_(EHIS) (accessed on 16 June 2021).

[34] Eurostat (n.d.), European Union Statistics on Income and Living Conditions (EU-SILC) (database), https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions (accessed on 10 June 2022).

[14] Gao, F. et al. (2004), “Does the 12-item General Health Questionnaire contain multiple factors and do we need them?”, Health and Quality of Life Outcomes, Vol. 2/1, p. 63, https://doi.org/10.1186/1477-7525-2-63.

[12] Gilbody, S. (2001), “Routinely administered questionnaires for depression and anxiety: Systematic review”, BMJ, Vol. 322/7283, pp. 406-409, https://doi.org/10.1136/bmj.322.7283.406.

[7] Gill, S. et al. (2007), “Validity of the mental health component scale of the 12-item Short-Form Health Survey (MCS-12) as measure of common mental disorders in the general population”, Psychiatry Research, Vol. 152/1, pp. 63-71, https://doi.org/10.1016/j.psychres.2006.11.005.

[49] Hallquist, M. and J. Wiley (2018), “MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus”, Structural Equation Modeling, Vol. 25/4, pp. 621-638, https://doi.org/10.1080/10705511.2017.1402334.

[11] Hankins, M. (2008), “The reliability of the twelve-item general health questionnaire (GHQ-12) under realistic assumptions”, BMC Public Health, Vol. 8/1, p. 355, https://doi.org/10.1186/1471-2458-8-355.

[24] Haroz, E. et al. (2017), “How is depression experienced around the world? A systematic review of qualitative literature”, Social Science & Medicine, Vol. 183, pp. 151-162, https://doi.org/10.1016/j.socscimed.2016.12.030.

[5] Hoeymans, N. et al. (2004), “Measuring mental health of the Dutch population: A comparison of the GHQ-12 and the MHI-5”, Health and Quality of Life Outcomes, Vol. 2/1, pp. 1-6, https://doi.org/10.1186/1477-7525-2-23/TABLES/4.

[20] Huang, F. et al. (2006), “Using the Patient Health Questionnaire-9 to measure depression among racially and ethnically diverse primary care patients”, Journal of General Internal Medicine, Vol. 21/6, pp. 547-552, https://doi.org/10.1111/j.1525-1497.2006.00409.x.

[47] Hu, L. and P. Bentler (2009), “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives”, Structural Equation Modeling: A Multidisciplinary Journal, Vol. 6/1, pp. 1-55, https://doi.org/10.1080/10705519909540118.

[15] Hystad, S. and B. Johnsen (2020), “The dimensionality of the 12-Item General Health Questionnaire (GHQ-12): Comparisons of factor structures and invariance across samples and time”, Frontiers in Psychology, Vol. 11, https://doi.org/10.3389/fpsyg.2020.01300.

[43] ISER (n.d.), BHPS User Documentation and Questionnaires, https://www.iser.essex.ac.uk/bhps/documentation (accessed on 19 September 2021).

[3] Kelly, M. et al. (2008), “Evaluating cutpoints for the MHI-5 and MCS using the GHQ-12: A comparison of five different methods”, BMC Psychiatry, Vol. 8/1, p. 10, https://doi.org/10.1186/1471-244X-8-10.

[31] Koushede, V. et al. (2019), “Measuring mental well-being in Denmark: Validation of the original and short version of the Warwick-Edinburgh mental well-being scale (WEMWBS and SWEMWBS) and cross-cultural comparison across four European settings”, Psychiatry Research, Vol. 271, pp. 502-509, https://doi.org/10.1016/j.psychres.2018.12.003.

[21] Kroenke, K., R. Spitzer and J. Williams (2001), “The PHQ-9: Validity of a brief depression severity measure”, Journal of General Internal Medicine, Vol. 16/9, https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

[19] 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.

[44] Lynn, P. (2009), “Sample design for understanding society”, Understanding Society Working Paper Series 2009-01, https://www.understandingsociety.ac.uk/sites/default/files/downloads/working-papers/2009-01.pdf.

[41] Lynn, P. and M. Borkowska (2018), “Some indicators of sample representativeness and attrition bias for BHPS and Understanding Society”, Understanding Society Working Paper Series 2018-01, https://www.understandingsociety.ac.uk/sites/default/files/downloads/working-papers/2018-01.pdf.

[17] Manea, L., S. Gilbody and D. McMillan (2012), “Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): A meta-analysis”, Canadian Medical Association Journal, Vol. 184/3, pp. E191-E196, https://doi.org/10.1503/cmaj.110829.

[37] Mayer, L. (1986), “On cross-lagged panel models with serially correlated errors”, Journal of Business and Economic Statistics, Vol. 4/3, pp. 347-357, https://doi.org/10.1080/07350015.1986.10509531.

[18] Moriarty, A. et al. (2015), “Screening and case finding for major depressive disorder using the Patient Health Questionnaire (PHQ-9): A meta-analysis”, General Hospital Psychiatry, Vol. 37/6, pp. 567-576, https://doi.org/10.1016/j.genhosppsych.2015.06.012.

[50] Mund, M., M. Johnson and S. Nestler (2021), “Changes in size and interpretation of parameter estimates in within-person models in the presence of time-invariant and time-varying covariates”, Frontiers in Psychology, Vol. 1/12, https://doi.org/10.3389/FPSYG.2021.666928.

[33] Ng Fat, L. et al. (2017), “Evaluating and establishing national norms for mental wellbeing using the short Warwick–Edinburgh Mental Well-being Scale (SWEMWBS): Findings from the Health Survey for England”, Quality of Life Research, Vol. 26/5, pp. 1129-1144, https://doi.org/10.1007/s11136-016-1454-8.

[1] OECD (2023), Measuring Population Mental Health, OECD Publishing, Paris, https://doi.org/10.1787/5171eef8-en.

[22] Richardson, L. et al. (2010), “Evaluation of the Patient Health Questionnaire-9 item for detecting major depression among adolescents”, Pediatrics, Vol. 126/6, pp. 1117-1123, https://doi.org/10.1542/peds.2010-0852.

[8] Rumpf, H. et al. (2001), “Screening for mental health: Validity of the MHI-5 using DSM-IV Axis I psychiatric disorders as gold standard”, Psychiatry Research, Vol. 105/3, pp. 243-253, https://doi.org/10.1016/S0165-1781(01)00329-8.

[39] Saeri, A., T. Cruwys and C. Sibley (2018), “Social connectedness improves public mental health: Investigating bidirectional relationships in the New Zealand attitudes and values survey”, The Australian and New Zealand Journal of Psychiatry, Vol. 52/4, pp. 365-374, https://doi.org/10.1177/0004867417723990.

[27] Sándor, E. et al. (2021), “Impact of COVID-19 on young people in the EU”, Eurofound, https://www.eurofound.europa.eu/en/publications/2020/impact-covid-19-young-people-eu.

[38] Selig, J. and T. Little (2013), “Autoregressive and cross-lagged panel analysis for longitudinal data”, in Laursen, B., T. Little and N. Card (eds.), Handbook of Developmental Research Methods, Guilford Press, https://www.guilford.com/books/Handbook-of-Developmental-Research-Methods/Laursen-Little-Card/9781462513932/reviews.

[30] Shannon, S. et al. (2020), “Testing the factor structure of the Warwick-Edinburgh Mental Well-Being Scale in adolescents: A bi-factor modelling methodology”, Psychiatry Research, Vol. 293, p. 113393, https://doi.org/10.1016/j.psychres.2020.113393.

[32] Stewart-Brown, S. et al. (2009), “Internal construct validity of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): A Rasch analysis using data from the Scottish Health Education Population Survey”, Health and Quality of Life Outcomes, Vol. 7/15, https://doi.org/10.1186/1477-7525-7-15.

[9] Strand, B. et al. (2003), “Measuring the mental health status of the Norwegian population: A comparison of the instruments SCL-25, SCL-10, SCL-5 and MHI-5 (SF-36)”, Nordic Journal of Psychiatry, Vol. 57/2, pp. 113-118, https://doi.org/10.1080/08039480310000932.

[16] Sunderland, M. et al. (2019), “Self-Report Scales for Common Mental Disorders”, in The Cambridge Handbook of Clinical Assessment and Diagnosis, Cambridge University Press, https://doi.org/10.1017/9781108235433.019.

[10] Thorsen, S. et al. (2013), “The predictive value of mental health for long-term sickness absence: The Major Depression Inventory (MDI) and the Mental Health Inventory (MHI-5) compared”, BMC Medical Research Methodology, Vol. 13/1, p. 115, https://doi.org/10.1186/1471-2288-13-115.

[26] Topp, C. et al. (2015), “The WHO-5 well-being index: A systematic review of the literature”, Psychotherapy and Psychosomatics, Vol. 84/3, pp. 167-176, https://doi.org/10.1159/000376585.

[29] Warwick Medical School (2021), The Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), https://warwick.ac.uk/fac/sci/med/research/platform/wemwbs/.

[25] WHO (1998), Wellbeing measures in primary health care/the DepCare Project: Report on a WHO meeting: Stockholm, Sweden, 12-13 February 1998, https://apps.who.int/iris/handle/10665/349766.

[28] WHO Collaborating Center for Mental Health (1998), WHO (Five) Well-Being Index (1998 version), WHO Collaborating Center for Mental Health, Frederiksborg General Hospital, DK, https://www.psykiatri-regionh.dk/who-5/Documents/WHO-5%20questionaire%20-%20English.pdf.

[4] Yamazaki, S., S. Fukuhara and J. Green (2005), “Usefulness of five-item and three-item Mental Health Inventories to screen for depressive symptoms in the general population of Japan”, Health and Quality of Life Outcomes, Vol. 8/3, p. 48, https://doi.org/10.1186/1477-7525-3-48.

[40] Yu, G. et al. (2015), “A multilevel cross-lagged structural equation analysis for reciprocal relationship between social capital and health”, Social Science & Medicine, Vol. 142, pp. 1-8, https://doi.org/10.1016/j.socscimed.2015.08.004.

[23] Zimmerman, M. et al. (2012), “How can we use depression severity to guide treatment selection when measures of depression categorize patients differently?”, The Journal of Clinical Psychiatry, Vol. 73/10, pp. 1287-1291, https://doi.org/10.4088/JCP.12m07775.

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