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Studies that examine the relationship between connectedness or school climate and education outcomes are relatively rare. One such study is by Panayiotou et al. (2019), discussed in the next section. Connectedness, school climate, mental health and education outcomes Panayiotou et al. (2019) explored the linkages between socioemotional competence, school connectedness, mental health difficulties and academic achievement. The model showing these relationships is illustrated in Fig 3.1.
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The results are drawn from an RCT of a universal SEL intervention (PATHS) used to raise the socioemotional competencies of students aged 8–12 and test the impact on school connectedness and mental health difficulties as mediators for academic attainment. The results are complex. The interventions had a significant impact on both mental health issues and school connectedness. However, only mental health issues had a statistically significant path to academic attainment.
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The impact of school connectedness on academic performance was through mental health issues, which has a two-way relationship with school connectedness. The study suggested that a reduction in mental health difficulties was the sole mediator between the SEL interventions and academic attainment. 36 Adolescents in a changing world. The case for urgent investment Fig. 3.1 Structural equation model Note: Indirect effect, β = 0.05, p < 0.001, 95% CI [0.03, 0.08]. **p < 0.01, ***p < 0.001.
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**p < 0.01, ***p < 0.001. Source: Panayiotou et al. (2019, p201). Both the Singla et al. (2021) study based on the SEHER intervention programme and Panayiotou et al. (2019) support the proposition that a favourable school climate has a beneficial effect on mental health outcomes. However, the Panayiotou et al. (2019) study also suggests a direct relationship between the interventions and mental health.
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This evidence suggests that the SEL programmes delivered in the SEHER project also, in all likelihood, had a positive impact on academic outcomes, but the project did not measure this. The next section discusses the evidence for the relationship between SEL and education outcomes, employing the results of the structured reviews by Durlak et al. (2011), Taylor et al. (2017) and Corcoran et al. (2017).
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(2017) and Corcoran et al. (2017). Results of studies on the relationship between SEL interventions and academic outcomes The characteristics and results of the three meta-analyses are summarized in Table 3.2. Each of the studies has demonstrated statistically significant positive outcomes in academic performance from the SEL intervention programmes. The academic performance scores in the Durlak et al. (2011) study were based on standardized reading and mathematics achievement test scores.
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The results of eight follow-up studies found that after 150 weeks, the academic performance had been maintained with an effect size of 0.32. Some of the studies reviewed by Taylor et al. (2017) reported after long-term follow-up. This yielded an effect size of 0.12 for secondary school completion and 0.22 for college graduation. The Corcoran et al. (2018) study reported on academic outcomes with results of studies for mathematics, reading and science.
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In summary, the results indicate effect sizes of between 0.25 (reading) and 0.33 (total sample). The average effect size of the studies weighted by the number of studies is 0.26, which is used in the cost-benefit analysis discussed in the next section.
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0.23*** 0.11*** -0.07*** 0.76*** 0.18*** 0.11** -0.43*** -0.22*** 0.22*** -0.25*** -0.02 -0.41*** -0.05 0.46*** Prior attainment School connectedness R2 = 0.31*** Gender Prior attainment Gender Prior attainment Prior attainment Gender Gender Social-emotional competence R2 = 0.07*** Academic attainment R2 = 0.77 Mental health difficulties R2 = 0.35*** 37 3.
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Building connectedness, agency and resilience Table 3.2 Effect size of SEL programmes on academic performance, results summary Study # schools # students ES CI 95% N Durlak et al. (2011) 213 270 034 Total sample 0.27 0.16 to 0.39 35 Taylor et al. (2017) 82 97 407 Total sample 0.33 0.17 to 0.49 8 Corcoran et al. (2018) N/A N/A Reading 0.25 0.14 to 0.36 35 Maths 0.26 0.18 to 0.34 33 Science 0.19 0.05 to 0.33 5 Weighted average 0.26 0.19 to 0.33 116 Notes: ES = effect size (Hedges g).
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CI is confidence interval. For the WWC (2014), ES of 0.25 standard deviations or larger are considered substantively important, N = number of studies. 3.3.3 Cost-benefit analysis of the impact of SEL programmes on school connectedness The evidence provided by the above analysis of the relevant literature suggests that SEL programmes with a focus on school climate could provide a causal link between school connectedness and mental health and education outcomes.
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While most of the studies providing evidence of these relationships are based on HICs, the SEHER study, conducted in the Indian state of Bihar (Shinde et al., 2020), provides a relatively unique opportunity to undertake a cost-benefit analysis for an LMIC. The evidence for the relationships between SEL interventions, school climate, mental health outcomes and academic performance is both direct and indirect, as illustrated in Fig. 3.2. Fig. 3.2, captures the direct effects identified by Shinde et al.
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(2020) between the SEHER intervention programme and school climate and mental health components, bullying and depression. The indirect effects between SEL interventions and mental health components are mediated by school climate, as estimated by Singla et al. (2021) (shown in red). These are small but statistically significant.
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These are small but statistically significant. These outcomes are supplemented by the results from the meta-analysis for the direct effect on academic performance summarized in Table 3.2 (effect size = 0.26) and the evidence provided by Panayiotou et al. (2019) of mental health issues on academic performance (effect size = 0.25). These latter two results are based on studies in HICs employing conventional SEL intervention programmes.
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Compared with many SEL programmes, the SEHER interventions have two enhancements. One is the employment of lay counsellors, and the other is the inclusion of school-wide interventions (Shinde et al., 2018; Singla et al., 2017). Modelling methodology Fig 3.2 suggests that a modelling methodology, which estimates the benefits of SEL interventions for improved adolescent mental health and academic performance mediated through school climate, could have two components.
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One could be based on the improved education outcomes and employment implications and the other on the value of mental health benefits. While the evidence suggests complex interrelationships between school climate, mental health and academic outcomes, a simpler approach is to conduct the modelling in two parts.
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The first step is to analyse the relationship between the SEL interventions and education outcomes together with the estimates of economic benefits arising from the employment implications of better school performance. The second is to estimate the benefits of enhanced mental health arising from the SEL interventions and indirectly through improved school climate. The particular mental health conditions for which effect sizes are available are depression, bullying and violence.
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To estimate these mental health benefits, we converted the effect sizes to DALYs from GBD studies (IHME, 2019). Given that a large proportion of the evidence was derived from a Bihar-based study, we situate the analysis in Bihar, employing demographics, school and economic parameters from Bihar. 38 Adolescents in a changing world. The case for urgent investment Fig.
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The case for urgent investment Fig. 3.2 Relationship between SEL interventions, school climate and academic performance, direct (purple) and indirect (orange) effects SEL interventions School climate Participation Belonging Relationships Mental health outcomes Bullying Violence Depression Academic performance ES 0.26 ES 0.25 ES 2.23 ES -2.22 ES -1.19 ES -0.040 ES -0.044 ES -0.033 ES -0.105 ES -0.071 Sources: Developed from Shinde et al. (2018); Singla et al. (2021); Panayiotou et al. (2019, p201).
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(2021); Panayiotou et al. (2019, p201). Results for the education and health effect of improvements in mental health in Bihar Two effects were modelled: the intrinsic value of health and the productivity and earning gains due to improved academic performance and increased retention. The intrinsic value of the health effect was modelled through the impact on the disease burden of depression and bullying as measured by a reduction in the DALY rate. The Shinde et al.
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The Shinde et al. (2020) study reported that the effect size of the improved school climate on depression was -1.19 (CI 95% -1.56 to -0.82) and bullying -2.22 (-2.84 to -0.60) after 17 months. The results after only eight months were only --0.27 (-0.44 to- 0.11) for depression and -0.47 (-0.61 to -0.33) for bullying (Shinde et al. 2018).
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2018). Indian DALY rates for depression and bullying for those aged 10–19 years were used as representative of Bihar to estimate the decline of depression and bullying in the Bihar secondary school population, which would occur should the SEL programme be progressively rolled out to all schools in Bihar. It was assumed that it would be administered each year with the results for the first assumed to be as measured at eight months and the second as measured at 17 months.
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For school leavers, it was assumed that the programme would have a positive effect on depression and anxiety (at the 2-year level) for 3.7 years (Taylor et al., 2017). Each DALY saved was valued at Indian GDP per capita in US dollars (Bihar GDP per capita not available) projected over the assumed period of the intervention to 2035, in accordance with current practice (Stelmach et al., 2022).
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The intrinsic health effect for the period 2024 to 2050 was estimated in present value terms (3% discount rate) at US$ 1.93 billion. The estimated cost of the Bihar intervention programme was US$ 2.26 billion. For the health effects only, the BCR would be just under one (0.85) meaning that the estimated health benefits based on estimated DALYs saved would be somewhat less than the estimated costs. As discussed, the SEHER study did not attempt to measure the education effect of the intervention.
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To estimate a likely outcome for education performance for such an initiative applied to Bihar state, the education effect was 39 3. Building connectedness, agency and resilience modelled using the VISES Education Model (VEM) (discussed in Chapter 6), assuming an effect size of 0.26 for SEL derived from the meta-analyses summarized in Table 3.2. This results in a reduced dropout rate of 3% and a reduction in what are known as learning gaps of 11%.
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The VEM applies assumptions about productivity gains from increased years of education and the likelihood of increased formal employment to those likely to successfully enter the workforce as a result of improved education outcomes. This results in an estimated earnings gain of US$ 55.9 billion in present value terms for the period to 2050, increasing to US$ 118.8 billion, again in present value terms, when the estimation of benefits is extended to 2100 (for example, at retirement).
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The mental health benefits are added to these education benefits for a total of US$ 57.9 billion. This compares with the present value of the costs of the programme of US$ 2.26 billion for the period to 2050. On this basis, the estimated BCR is 25.6. For the employment gains from participating in the SEHER programme, it can be reasonably assumed that a good proportion of school leavers will generate earnings until retirement.
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The duration of the impact of the SEL programme on mental health is much less clear. Taylor et al. (2017) referenced studies with an impact of up to almost four years. 3.3.4 Discussion Programmes such as the Gatehouse Project and SEHER were initiated to address mental health issues for students in school. This would be achieved by improving the school climate and, with it, the connectedness of its students to school and their fellow students.
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The analysis presented in this chapter suggests that this strategy provides high returns relative to the costs of such programmes. However, it appears that the largest economic benefits arise from education rather than the health effects. This should not be surprising. Gains through additional time spent in school and improved performance in school potentially deliver lifetime benefits in the form of better-paying and higher quality jobs.
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The mental health benefits of addressing depression and other issues have been assumed to only last a few years based on studies reported by Taylor et al. (2017). A recent paper by Bailey et al. (2023), however, reported that SEL has intergenerational effects. If it could be established that the intervention had a lifetime effect, this would increase the estimated value of the mental health benefits substantially.
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The estimated health benefits may underestimate the broader consequences and therefore the benefit of the SEHER intervention programme. The estimated benefits are confined to only bullying and depression. This excludes other possible mental health effects. SEL programmes generally address a wider range of mental health issues, such as conduct disorder, which if quantified in the SEHER study may have generated a broader range of benefits than those quantified in this study.
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This study provides support for the concept of the school as a broad-based platform for the delivery of a range of interventions that do not have as their immediate objective narrow education outcomes, but which in fact, deliver substantial education and health benefits in a cost-effective way.
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3.4 Conclusion The purpose of this chapter, despite the challenges of quantifying the concepts of connectedness, agency and resilience, is to extend the existing literature to develop a case for investing in interventions to support improved outcomes in these domains.
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While the extension of the SEHER study is largely about connectedness, the links between these concepts and measurable outcomes that generate quantifiable economic benefits can support the case for increased investment in these areas, just as the case can be developed for health and education. 40 Adolescents in a changing world. The case for urgent investment 4.
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The case for urgent investment 4. Investing in adolescent mental health5 4.1 Introduction This chapter provides a detailed analysis of the quantitative assessment of programmes for the prevention and treatment of adolescent mental health. As the WHO World mental health report (WHO, 2022a) pointed out, mental health conditions represent one of the leading causes of disease burden. This has remained so despite many of the same issues being raised and recommendations made two decades before (WHO, 2001).
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Even so, the approach to mental health care remains very much business as usual (WHO, 2022a) in the sense that most countries continue to rely on a biomedical and hospital-based service model. For adolescents, public health consequences of mental health conditions are greater relative to the rest of the population. Mental health disorders represent 13% of the global adolescent burden of disease, as measured by DALYs (WHO, 2021a).
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There are wide disparities in the burden of mental disorders between countries. Some possible reasons include income per capita and political instability/terrorism, which are significantly correlated with the level of mental disorders. Inequality and the level of youth unemployment also play a role (Viner et al., 2012). For adolescents in many countries, these structural determinants of mental health may have a very large impact on mental health outcomes.
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However, in formulating preventative interventions to address adolescent issues, it is the determinants much closer to home that are the focus of preventative intervention programmes. Adverse childhood experiences can have a tremendous impact on future violence victimization and perpetration, and lifelong health and opportunity. They are linked to chronic health problems, mental health conditions and substance use problems in adolescence and adulthood.
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Adverse childhood experiences can also negatively impact education, job opportunities and earning potential (CDC, 2023). Reducing violence in early childhood can have lifelong benefits. Connectedness to school is a powerful protective factor against mental health risks such as bullying and mental disorders, such as depression and anxiety (Patton, 2000; Bond et al., 2004; Shinde et al., 2018).
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School-based SEL programmes conducted by teachers or other trained staff are effective interventions for reducing depression and anxiety (Durlak et al., 2011; Taylor et al., 2017; Corcoran et al., 2018; Lee et al., 2023). Such interventions have been included in the recent WHO guidelines on mental health promotive and preventive interventions for adolescents (WHO, 2020a).
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As noted earlier, social and emotional skills are being recognized increasingly as important for child development, providing support for their universal delivery.
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In evaluating SEL programmes, attempts are being made to capture mental health benefits, such as reductions in delinquency, conduct disorder, depression and anxiety; education benefits due to improved academic performance and enrolment retention; and direct earnings benefits through higher self-esteem and enhanced soft skills, as they are known (Belfield et al., 2015). Belfield et al.
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Belfield et al. (2015) is one of only a few benefit-cost studies of SEL programmes that have extended the estimation of benefits to education and direct earnings outcomes. This study is discussed further in section 4.4.1. Stelmach et al. (2022) conducted a more comprehensive cost-benefit analysis of the returns on investing in treating and preventing adolescent disorders, discussed in section 4.4.2. 5.
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5. Mental health interventions for anxiety, depression, psychosis, bipolar disorder, conduct disorder and harmful substance use are included in the OHT modelling reports, discussed in Chapter 2. They are listed in Table A2.2 in Annex 2. OHT does not include prevention interventions. In this chapter, we do address prevention. 41 4.
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In this chapter, we do address prevention. 41 4. Investing in adolescent mental health While treating conduct disorder and attention-deficit/hyperactivity disorder is a common objective of most SEL and other school-based programmes, there are cost-effective community-based programmes directed towards improving positive parenting and parenting competence to behavioural problems amongst adolescents. This includes the Connect Program in Sweden with a BCR of 10.61 (95%CI 10.29–10.93) (Nystrand et al., 2020).
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Table 4.1 summarizes the results of investment evaluations employing cost-benefit analysis. They illustrate the generally high returns obtained by mental health interventions. The most comprehensive of the studies was conducted by Stelmach et al. (2022), which estimated a BCR of 23.6 for a wide-ranging group of interventions as well as a comprehensive set of benefits. Other studies have established a case for the economic value of targeted intervention programmes, such as SEL and harmful substance use.
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Other than the multi-country Stelmach et al. (2022) study, the results of the other studies listed in Table 4.1 were conducted in HICs, providing a poor guide to the investment case for mental health programmes in LICs and LMICs. However, the results from the SEHER programme conducted in Bihar, India, discussed in Chapter 3, support the effectiveness of school-based SEL-type intervention programmes in LMICs despite not including an investment evaluation.
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Table 4.1 Summary, BCRs for mental health interventions in adolescents Author Location Interventions Benefits BCR Stelmach et al. (2022) Multi-country (36) Wide-ranging (teacher-led SEL-type, CBT, internet-based self-guided) Comprehensive (health, productivity) 23.6 Belfield et al. (2015) United States of America SEL (life skill, 4Rs, SE training) Reduced depression, bullying and education outcomes 3.5–13.9 Nystrand et al.
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(2020) Sweden Positive parenting and parenting competence Improved adolescent behaviours 10.6 Spoth et al. (2002) Mid-west United States of America Parenting programme Reduced harmful substance use 9.6, 5.85 Kuklinski et al. (2015) United States of America CTC programme Reduced harmful substance use, delinquency, violence and other problem behaviours 8.2 Notes: BCR is benefit-cost ratio. SEL is social and emotional learning. CBT is cognitive behavioural therapy.
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CBT is cognitive behavioural therapy. 4Rs is the Program on Reading, Writing, Respect and Resolution. CTC is Communities that Care. Mental disorders represent the largest health burden for adolescents. However, the application of known successful intervention programs with demonstrably high BCRs is limited in both LICs and HICs. The barriers to the adoption of universal programs through schools, for instance, are impediments that need to be addressed.
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4.2 Importance of mental health Mental disorders represent 13% of the adolescent GBD (WHO, 2021a). Fig 4.1 provides data on the GBD for adolescents classified by disease (tier 2).
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If suicide and harmful substance use are included, then the percentage would be increased to almost 17% of total disease burden (IHME, 2019).6 Expressed as a DALY rate (DALY per 100 000), the estimate for global mental disorders is 1384, which is almost twice as high as the DALY rate of the next highest causes, neurological disorders and skin diseases, which have DALY rates of 825 and 719, respectively.
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After that, the next highest is for a cluster of injuries, transport accidents and unintentional injuries, which together have a DALY rate of 1347. This is high but still lower than the rate for mental disorders. 6. Suicide is dealt with separately in Chapter 7 of this report. 42 Adolescents in a changing world. The case for urgent investment Fig. 4.1 Adolescent* global burden by cause of disease, proportion of global disease burden, tier 2 classification level, 2019 Notes: *Aged 10–19.
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^Harmful substance use classified separately following GBD. Source: IHME (2019). The disease burden of mental disorders, as measured by the DALY rate, is higher for females at 1440, than for males at 1331. However, this difference is less marked than for a number of other diseases. Fig 4.2 provides a comparison in terms of the DALY rate for mental disorders and a range of other causes of disease where the differences are much more marked (greater than 15% in the DALY rate).
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Neurological disorders, nutritional deficiencies and musculoskeletal disorders create a greater burden for girls while injuries from transport accidents, IPV and unintentional injuries in general have a greater impact on adolescent boys than girls. Fig.
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4.2 DALY rates for the global adolescent (10–19 years) burden of disease by sex and selected cause, 2019 Mental disorders 12.9% Neurological disorders 7.7% Unintentional injuries 6.6% Other NCDs 6.5% Transport injuries 6.0% Enteric infections 5.9% Skin and subcutaneous diseases 6.7% Self-harm and IPV 6.1% Neglected tropical diseases and malaria 4.1% Maternal and neonatal disorders 3.8% Nutritional deficiencies 4.7% Neoplasms 3.3% Cardiovascular diseases 2.8% HIV/AIDS and sexually transmitted infections 2.5% Musculoskeletal disorders 4.4% Digestive diseases 2.2% Other infectious diseases 2.8% Chronic respiratory diseases 2.3% Respiratory infections and tuberculosis 4.4% Sense organ diseases 2.0% Diabetes and kidney diseases 1.4% Substance use disorders 1.0% Note: *Harmful substance use classified separately following GBD.
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Source: IHME (2019). Female Male Mental disorders Neurological disorders Unintentional injuries Other non-communicable diseases Self-harm and interpersonal violence Transport injuries Nutritional deficiencies Musculoskeletal disorders Substance use disorders* 0 200 400 600 800 1 000 1 200 1 400 1 600 43 4.
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Investing in adolescent mental health 4.3 Structural factors affecting mental health Social determinants of health are analysed through structural factors such as national wealth, income inequality and access to education and those relating to families and communities, supportive schools and positive relations with peers (Viner et al., 2012).
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In general, adolescents who live in countries with high levels of country wealth, equitable levels of income distribution and which also have good education and accessible health systems, have better overall health outcomes. This appears not to be the case for adolescent mental health. The country-level GBD data permits analysis of the correlation between the level of adolescent mental disorders and macro socioeconomic variables at the national level, as detailed in the next section.
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4.3.1 Country comparisons of mental health for adolescents Income per capita The GBD of mental disorders for adolescents is subject to wide country variation. The DALY rate ranges from a high of 2500 for New Zealand to 1018 for Viet Nam. Only a limited number of studies have attempted to explain these large country differences using macro socioeconomic variables (Öztürk et al., 2020), employed GDP per capita, unemployment and household debt to help explain the use of anti-depressants in nineteen HICs.
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There is an association between country income per capita and mental health disease burden, as shown in Fig 4.3 for cross-country data for 202 countries, including HICs, MICs and LICs. It indicates that there is a reasonably strong correlation between the mental health disease burden and per capita income. A simple regression of the gross national income (GNI) per capita against the country DALY rate is significant at the 99% level. Fig.
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4.3 Scatter diagram of adolescent mental health DALY rate (2019) versus GNI per capita (Atlas method), 202 countries, 2018 GNI per capita y = 0.0095x + 1389 R2 = 0.3169 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600 0 10,000 20,000 30,000 40,000 50,000 New Zealand Portugal Greece Bahrain Saudi Arabia Oman Uruguay Kuwait Singapore Japan Viet Nam Republic of Korea Bahamas Puerto Rico Brunei Darussalam Lithuania Sweden Germany Finland France United Kingdom of Great Britain and Northern Ireland Belgium Israel Denmark United States of America Switzerland Iceland Luxembourg Norway Qatar Canada United Arab Emirates Iran (Islamic Republic of) Tunisia Syrian Arab Republic Afghanistan Lebanon Brazil Libya Chile Spain Malta Italy Cyprus Australia Ireland Netherlands (Kingdom of the) Austria 60,000 70,000 80,000 90,000 DALY rate Trinidad and Tobago Sources: GBD data from IHME (2019); GNI per capita from World Development Indicators (World Bank, 2023c).
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44 Adolescents in a changing world. The case for urgent investment Low-income Lower middle-income Upper middle-income High-income 0 500 1000 1500 2000 Autism spectrum disorders Depressive disorders Other mental disorders Bipolar disorder Anxiety disorders Attention-deficit/hyperactivity disorder Conduct disorder Idiopathic developmental intellectual disability Eating disorders Schizophrenia It is, however, counterintuitive that disease burden is higher for HICs.
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Some of the factors that are regarded as protective to mental health for adolescents, such as quality education and accessible health services, are more readily available in HICs. Indeed, the country with the highest measured adolescent mental health burden is New Zealand, which is noted for the quality and wide accessibility of both its education services and health system.
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Other HICs with high mental health DALY rates for adolescents (above 2000) include Finland, Netherlands (Kingdom of the), Norway, Sweden, Switzerland and the United States of America. Overall, the main cause for the higher DALY rates for HICs is due to much higher levels of anxiety and depression disorders compared with LICs. Fig 4.4 shows average (unweighted) DALY rates for each country by income group. The average DALY rate for HICs is 1777 compared with 1420 for the remaining countries.
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Most of this difference is explained by the higher levels of anxiety (144) and depression (70). Eating disorders are also markedly higher in HICs. Fig. 4.4 Adolescent* mental disorders by type, DALY rate, 202 countries, 2019 Note: *Aged 10–19. Source: IHME (2019). Other factors suggested to explain high mental disorder DALY rates for mental health in HICs discussed in the literature (Viner et al., 2012) include income inequality and limited youth employment opportunities.
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However, analysis of country-level indicators suggests a relatively low correlation between mental disorder DALY rates for adolescents and variables such as the Gini coefficient, a widely accepted measure of income inequality (World Bank, 2023b). Contrary to expectations (Viner et al., 2012; WHO, 2022a), at the national level, in this data set, there is little correlation between youth unemployment and mental disorders for HICs and UMICs.
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However, the coefficient is statistically significant for LICs and LMICs (t=4.9, p=<0.001). The correlation is particularly strong for LICs, as shown in Fig. 4.5 with an R2 of 0.398. This suggests that for LICs, the rate of youth unemployment explains a significant percentage of the level of mental disorders, largely depression and anxiety. One other characteristic of note in Fig. 4.5 is that the vast majority of LICs had relatively low levels of both youth unemployment and mental disorders.
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In most LICs where work of any kind, including on family farms and in micro-businesses, is necessary for survival, unemployment is low and family connectedness is high. However, these lower recorded levels of mental disorders in LICs may also be due to shortcomings in the data. Erskine et al. (2017) have drawn attention to the relatively small number of studies on which the GBD mental health data is based for many LICs and MICs.
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Moreover, concern has been expressed about methodological factors, such as the need to pay attention to cultural differences in the survey instruments (Baxter et al., 2013). 45 4. Investing in adolescent mental health Moreover, in countries without a culture or language to describe mental disorders, even the appreciation that an individual’s experiences might reflect an illness to be addressed may not occur to someone feeling sadness.
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There are deep-seated cultural beliefs about the causes of mental disorders that are a barrier to modern psychiatric care (Sorketti et al., 2013) with competition from faith healers (Ikwuka et al., 2016) and family counsellors (Bwanika et al., 2022). The stigma attached to mental illness in LICs and the limited facilities available reduces the propensity of sufferers to present for treatment (Whiteford et al., 2013; Saxena et al., 2007). Fig.
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4.5 Scatter diagram of adolescent mental health DALY rate versus youth unemployment rate, 26 LICs, 2019 Morocco 1,000 1,200 1,400 1,600 1,800 2,000 2,200 0 5 10 15 20 25 30 35 DALY rate Unemployment rate % Togo Lao People's Democratic Republic Ethiopia Somalia Benin Jordan Nicaragua Pakistan Kyrgyzstan United Republic of Tanzania Papua New Guinea Uzbekistan Myanmar Sri Lanka Comoros Ukraine Cambodia Cote d'Ivoire Angola Guinea Mauritania Zambia Kiribati y = 15.912x + 1316.4 R2 = 0.3976 Sources: IHME (2019); World Development Indicators (World Bank, 2023c).
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4.3.2 Political instability/conflict There has been significant interest in the impacts of war and conflict on mental health (Lopez-Ibor et al., 2005; UNICEF, 2005, 2021). A few studies have documented the impacts of war by country (Murthy and Lakshminarayana, 2006; WHO, 2003; Baingana et al., 2005; Charlson et al.
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2019) provided new estimates of the prevalence of mental disorders in conflict settings, which showed that age-standardized years lived with disability (YLDs) for conflict-affected populations was substantially higher than the global average.
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For instance, they estimated age-standardized YLDs for depression in conflict-affected populations at a rate of 24.8 YLDs per 1000 population (95% UI16.4–36.0) in contrast to the GBD 2016 global age-standardized estimate of 4.6 YLDs per 1000 population (3.2–6.2) (p245).
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The development of the Political Stability and Absence of Violence/Terrorism index, published by the World Bank as one of its World Development Indicators (Kaufman et al., 2010), provides an opportunity to examine the relationship between the severity of mental disorders and the intensity of political instability/ conflict at the global level. 46 Adolescents in a changing world.
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The case for urgent investment Political instability/terrorism Mental disorders 2,400.00 2,200.00 2,000.00 1,800.00 1,600.00 1,400.00 1,200.00 1,000.00 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 Iran (Islamic Republic of) Libya Yemen Iraq Afghanistan Lebanon Sudan Egypt Algeria Türkiye Tunisia Brazil Morocco Paraguay Jordan Argentina Suriname Malaysia Ecuador Dominica Dominican Republic World Nicaragua South Sudan Somalia Pakistan Mali Cameroon Nigeria Myanmar Kenya Ukraine Niger Burundi Ethiopia Chad Democratic Republic of the Congo Zimbabwe Central African Republic Syrian Arab Republic Burkina Faso 7.
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The relationship shows signs of heteroscedasticity, so the increasing spread of data either side of the trend line may be a product of measurement problems. A scatter diagram (Fig. 4.6) of the two indicators shows that while there is much variability in the relationship,7 the overall trend is that mental disorder is positively associated with political instability/terrorism.
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In particular, there are a number of countries with high levels of political instability/terrorism that are amongst those with the highest mental disorder rates. These include Afghanistan, Iran (Islamic Republic of), Lebanon, Syrian Arab Republic and Yemen, which have all recently experienced civil wars, other violent conflict or governance failure.
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Moreover, they are neighbouring countries with similar problems and large adolescent populations (more than twice the proportion of HICs) with limited opportunities. They are also unstable countries with low levels of reported mental disorders, thus creating measurement issues, as previously discussed. The scatter chart is, therefore, indicative of the relationship between mental disorders and political instability, not conclusive. Fig.
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Fig. 4.6 Scatter diagram of adolescent mental health, DALY rate, versus political instability/ terrorism, 193 countries, 2019 Sources: GBD data (IHME, 2019); Political Stability and Absence of Violence/Terrorism Index (Kaufman et al., 2010). The broader issue of the impact of violence and terrorism on mental health and well-being is taken up at both the country and local levels in Chapter 7.
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This analysis has demonstrated the wide disparities in the burden of mental disorders between countries and some possible reasons for these differences. The three areas analysed here (youth unemployment in LICs, income per capita and political instability/terrorism) are significantly correlated with the level of mental disorders. For adolescents in many countries, these structural determinants of mental health may have a very large impact on mental health outcomes.
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While these issues are beyond the scope of most intervention programmes, it does emphasize the need for existing programmes to be applied more intensely in low-income and politically unstable contexts where violence and economic instability seriously degrade the well-being of adolescents and the communities in which they live. 47 4.
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47 4. Investing in adolescent mental health 4.3.3 Harmful substance use Harmful substance use refers to excessive use of a drug in a way that is detrimental to self, society or both. It can also be termed substance use disorder. Substance use disorders are when use of a substance is heavy, prolonged and creates personal or social problems (Health Direct, 2023).
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Substance use disorder is a recognized medical brain disorder that refers to the use of illegal substances, such as marijuana, heroin, cocaine or methamphetamine as well as legal substances, such as alcohol, nicotine or prescription medicines. Alcohol is the most common legal drug associated with substance use disorder (John Hopkins Medicine, n.d.). The GBD database provides an estimate of the worldwide prevalence of alcohol and drug use disorders as being 10.4 million adolescents (10–19-year-olds).
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The disease burden of harmful substance use is estimated by GBD as 107 DALYs per 100 000 with 132 for males and 80 for females. As with mental disorders, the country DALY rate is highly correlated with GDP per capita. Indeed, a number of HICs, including Canada, the United Kingdom of Great Britain and Northern Ireland and the United States of America, have especially high disease burdens from harmful substance use.
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The rate for the United States of America, for instance, is 805 and that for Canada is 594, both well above the global average. WHO (2022b) also indicates that adolescent harmful substance use has wide disease and injury implications, as well as intergenerational effects. The harmful use of alcohol is a causal factor in more than 200 disease and injury conditions. Worldwide, three million deaths every year result from harmful use of alcohol.
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In terms of the impacts of tobacco, WHO (2023b) states that: the tobacco epidemic is one of the biggest public health threats facing the world, killing over eight million people a year around the world; and the substantial economic costs of tobacco use include significant health care costs for treating the diseases caused by tobacco use, as well as the lost human capital that results from tobacco-attributable morbidity and mortality.
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Harmful substance use is both a cause of and a response to other mental disorders. The proximal determinants of harmful substance use and the interventions to address it have much in common and significantly overlap with depression, anxiety and other mental disorders. These are discussed in the next section.
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These are discussed in the next section. In formulating preventative interventions to address adolescent mental health issues, it is the determinants much closer to home, rather than generalized structural determinants, that are the focus of preventative intervention programmes. These are also discussed in the next section. 4.4 Proximal determinants Most studies of the causes of poor mental health in adolescents focus on the individual, the community in which they live and the school they attend.
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These proximal determinants form the basis of intervention programmes typically focused on family, school, peer and broader community relationships. While the provision of education services is an important aspect of the structural factors determining mental health outcomes for adolescents, so is the role of the school environment (Viner et al., 2012; Sawyer et al., 2021).
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As discussed in Chapter 3, connectedness to school is a powerful protective factor against mental health risks such as bullying and mental disorders including depression and anxiety (Patton, 2000; Bond et al., 2004; Shinde et al., 2018; Raniti et al., 2022). 4.4.1 Social and emotional learning SEL programmes are not homogeneous, each incorporating different components depending on school objectives and circumstances.
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Most programmes incorporate both skills—behavioural and other mental health objectives—but their relative emphases may vary. The objective of some is to reduce depression and anxiety while others have a focus on delinquency, conduct disorders and reducing harmful substance use. Others, such as Responsive Classroom, have a greater focus on improved academic achievement (Belfield et al., 2015). This variability may lead to evaluation challenges and inconsistencies.
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However, there are two main categories of SEL interventions: universal, which are delivered to all of the relevant population and indicated, which are delivered only to those who have been screened and found to be high risk. 48 Adolescents in a changing world. The case for urgent investment Social and emotional skills are often regarded as personality traits that, according to Heckman and Kautz (2012), predict success in school, the labour market and in life.
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SEL is often embedded into a school curriculum and is seen to help reduce risky behaviours, such as violence and drug abuse (Durlak et al., 2011; CASEL, 2003). It may form one aspect of programmes designed to enhance non-cognitive skills and improve, among other things, academic performance (Gutman and Schoon, 2013b). Zins, Bloodworth et al.
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Zins, Bloodworth et al. (2004) sought to examine the relationship between SEL and school success as measured by school attitudes (e.g., motivation and responsibility), school behaviour (e.g., engagement, attendance, and study habits) and school performance (e.g., grades, subject mastery and test performance) (p194). A CASEL (2003) review of 80 nationally available programmes found that 83% produced academic gains.
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As noted in Chapter 1, when evaluating SEL programmes, attempts are being made to capture mental health benefits, education benefits and direct earnings benefits. A recent study by Lee et al. (2023) focused on the mental health benefits of SEL. Their research presents a cost-effectiveness analysis of universal and indicated school-based SEL programmes designed to prevent the onset of depression/anxiety and suicide deaths amongst adolescents in 20 countries.
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The findings suggest that universal programmes are more cost-effective than indicated programmes. The cost of the programmes ranged from US$ 0.10 per capita for LLMICs to US$ 0.16 for UMHICs for the universal programmes, which converted to a cost of US$ 958 per HLYG to US$ 2006 per HLGY for UMHICs.
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This is compared to a cost of only US$ 0.06 per capita among LLMICs to US$ 0.09 per capita among UMHICs of the indicated programme, but the cost per HLYG was considerably higher at US$ 11 123 and US$ 18 473, respectively. This demonstrates that the universal programme was much more cost-effective in both LLMICs and UMHICs. The costs were based on WHO CHOICE estimates and the interventions on a meta-analysis by Skeen et al.
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(2019), which reported effect sizes of -0.10 standard mean difference (SMD) and -0.19 SMD for universal and indicated, respectively. The interventions in the meta-analysis were varied, including cognitive and interpersonal therapy, yoga-based, mindfulness, empowerment and optimism programmes. The modelling of benefits was exclusively focused on improvements to health, and did not include any productivity gains from the reduction in depression and anxiety or gains from other potentially beneficial effects.
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