The impact of grit on adolescent resilience in examining longitudinal mental health outcomes in peri-rural South Africa

The impact of grit on adolescent resilience in examining longitudinal mental health outcomes in peri-rural South Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The impact of grit on adolescent resilience in examining longitudinal mental health outcomes in peri-rural South Africa Sahba Besharati, Candice Ramsammy, Furzana Timol, Jeremy Kane, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6558246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Adolescents in Africa are the world’s fastest growing population group. Despite escalating rates of mental health disorders, little is known regarding the role of protective mechanisms that characterise resilience in adolescent mental health globally and in Africa, where there is heightened exposure to adversities. This study draws on two waves of a longitudinal population cohort from a peri-rural setting in KwaZulu Natal South Africa to investigate the relationship between grit – as a psychological resilience factor - and mental health outcomes in adolescents (N = 1174). Heightened mental health difficulty ratings for internalising factors were found across two study waves, with females reporting significantly higher rates of depression. Grit was found to be significant predictor of lower adolescent depression and anxiety, but dependent on the severity of internalising symptoms, sociodemographic factors and exposure to socioeconomic adversity. Potential differences in the mechanisms of adolescent resilience are highlighted that involve a dynamic interplay between bottom-up and top-down resilience factors in the African context. Biological sciences/Psychology/Human behaviour Health sciences/Health care/Public health Asenze cohort study adolescence mental health resilience depression multisystemic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Adolescents constitute more than 1.3 billion of the global population 1,2 , with adolescents in Africa representing the fastest growing population group in the world 3 . However, the study of adolescent health and development has only recently gained scientific traction 4 , 5 . The period of adolescence – roughly defined from 10 to 25 years 6 – is a unique developmental stage characterised by distinct changes in physical, neurocognitive and social-emotional development 7,8 . These developmental shifts also overlap with a heightened vulnerability to mental health disorders 9,10 . Recent estimates from UNICEF indicate that 20% of adolescents globally experience a mental health disorder 11 which accounts for at least 13% of the global burden of disease 12 within the period of early and middle adolescence 6 . Half of adult mental health disorders are also reported to begin during adolescence 9,13 . This escalation in mental health disorders in adolescents has been well documented in high-income counties 9,14 , highlighting the role of early life adversity and wider environmental factors, including social deprivation during the COVID-19 pandemic 15 and the use of social media 16,17 . However, far less attention has been afforded to the adolescent mental health crisis in low-to-middle income countries (LMICs) 18 - particularly in Africa 19 . The African context therefore represents both a high adversity context that can result in greater risk for mental health difficulties 20 , as well as an understudied social reality with varying cultural and contextual factors that may uncover different patterns of risk and vulnerability in adolescent mental health globally 7 , 21 . Consequently, in line with the 2030 United Nations Sustainable Development Goals 22 , action is needed in combating adolescent mental health difficulties particularly in Africa. The study of adolescent resilience against negative mental health outcomes in the face of adversity is therefore, a critical and underutilised approach in the current mental health literature 18 . Resilience research is based on the fundamental observation that mental health can be maintained despite heightened exposure to stress or adversity 23 . However, the protective factors underlying resilience are poorly understood, especially during adolescence, as a sensitive neurodevelopmental period. Increased focus on adolescent resilience research is even more urgent in low-resource and high-adversity settings 24 , especially across Africa 25 , which are subject to Majority World biases (i.e., limited research in LMICs that house 90% of the world’s population) 1,26,27 . A culmination of recent resilience research suggests that protective mechanisms that underpin resilience involve a dynamic interaction of internal processes (e.g., “bottom-up”; emotion regulation) 28 and external factors (e.g., “top-down”; social support and community structures) 29,30 . Aligned with this perspective, drawing on findings from foundational studies in Sub-Saharan Africa 31 and using cross-cultural comparisons 24,29 , Ungar and Theron 32 have proposed a multisystemic framework for understanding resilience that is culturally and contextually sensitive. Within this framework, resilience involves multiple interacting processes at a biological, psychological, social and ecological level. While recent resilience studies in African adolescents 31 have highlighted the importance of wider external factors at the social and ecological level, resilience also involves internal factors that function at a biological and psychological level 23,32 that have rarely been investigated in the African context. Emotional regulation for example has been thought to be a critical neurobiological mechanism underlying resilience, especially important in adolescence, where there are distinct changes in white matter pathways and volume, as well as developmental changes in the prefrontal cortex and limbic system 28,33 . Neuroimaging and experimental studies, have not only shown the importance of executive function (e.g., working memory) in adolescent development 34,35 , but also in relation to adolescent resilience and positive mental health outcomes 15 . These neurobiological factors also interact at a psychological level. Although there is consensus in the literature that resilience itself should not be understood as a stable personality trait 23,28 , individual resilience factors – such as cognitive reappraisal 36 and grit 37 – nevertheless form part of the psychological protective factors in Ungar and Theron 32 proposed multisystemic model that promote positive mental health outcomes. The concept of grit arises from a much longer historical tradition 38 , but it was first described in the scientific literature by Duckworth 39 as ‘perseverance and passion for long-term goals’. A recent meta-analysis 37 of the current literature has challenged traditional conceptions of grit or being “gritty” to extend beyond the original hierarchical two-factor model, namely perseverance and consistency, to include wider psychological constructs such as adaptability 37 that has been previously implicated in resilience and adolescence research 23,40 . Grit has been widely studied in the education literature as a predictor of academic success 41 , and more recent studies highlight the association between grit and positive mental health outcomes 37 , including internalising symptoms 42,43 (i.e., depression and anxiety). Interestingly, neuroimaging studies investigating the neuroanatomical correlates of grit 44–46 have identified structures and networks in the prefrontal cortex, limbic system and white matter pathways, which overlap with neural changes in the adolescent brain 9,35 and in adolescent resilience 28 . Although studies investigating the relationship between grit and mental health have been conducted in diverse country contexts that include Majority World countries 37 , cultural biases still extend to the study of grit 47 , with further research needed to include African populations and different cultural contexts. Long-standing birth and population cohort studies in Majority World LMICs 48 – including those in sub-Saharan Africa 19 – have played a profound role in understanding and promoting health across the lifecourse 9 . To this end, this study draws on data from the Asenze population cohort study, a prospective longitudinal study based in a peri-rural setting in KwaZulu-Natal South Africa that is characterised as a high adversity context 49 . The aims of this study are twofold. Firstly, to characterise the longitudinal mental health profile of internalising factors (i.e., depression and anxiety) of adolescents living in a high stress and adversity setting (e.g., socioeconomic adversity; increased exposure to crime; high rates of HIV) across two waves of the Asenze cohort study. Second, to investigate if grit as one measure of resilience, predicts positive mental health outcomes of African adolescents. This study also draws on Ungar Theron’s 32 proposed multisystemic approach that integrates culturally and contextually important factors, specifically sociodemographic characteristics and socioeconomic factors, in investigating adolescence resilience. Based on findings from previous cross-sectional studies, we hypothesised that grit would be a significant predictor of lower adolescent depression and anxiety longitudinally when examining internalising symptoms dichotomously and when using continuous scores. As previous longitudinal studies have reported sex differences in the prevalence of internalising symptoms, we also hypothesised that there would be higher rates of depression reported in female adolescents in the cohort across study waves. Results Participants We analysed data collected during waves 3 and 4. The cohort contained 1,174 participants in the third wave (average age = 15.87 years; SD = 0.92), of which 1,120 completed the fourth wave (average age = 17.87 years; SD = 0.72; see Table 1 for characteristics and Figure 1 for age distribution across both waves). The proportion of male to female participants remained nearly equal in both waves. By wave 4, a higher number of participants had completed high school, while those remaining in school were predominantly in their senior years (grades 11 and 12). Most participants were HIV negative. Socioeconomic adversity within this sample was assessed during wave 3, characterised by low asset index (24.1%), low caregiver education (32.5%), and food insecurity (11.0%). Table 1. Sociodemographic characteristics of adolescent participants at wave 3 and 4 Wave 3 1 Wave 4 1 N = 1,174 N = 1,120 p-value Age (years) Mean [SD] 15.87 [0.92] 17.87 [0.72] - Range 13 - 19 16 – 20 Gender Male 586 (49.9%) 546 (48.8%) 0.672 Female 588 (50.1%) 574 (51.2%) Attending school Yes 1111 (94.6%) 885 (79.0%) <0.001 Completed high school 2 (0.2%) 147 (13.1%) Technical and Vocational Education and Training 9 (0.8%) 34 (3.0%) No 52 (4.4%) 54 (4.8%) Grade Lower than Grade 8 23 (2.1%) 2 (0.2%) <0.001 Grade 8 90 (8.1%) 8 (0.9%) Grade 9 225 (20.3%) 39 (4.4%) Grade 10 384 (34.6%) 195 (22.0%) Grade 11 301 (27.1%) 302 (34.0%) Grade 12 85 (7.7%) 341 (38.4%) Missing 3 HIV Status <0.001 Positive 83 (7.1%) 88 (8.1%) Negative 1082 (92.9%) 996 (91.9%) Missing 9 36 Grit score 3.42 [0.57] - Missing 80 - Patient Health Questionnaire score Presence of depression symptoms* 257 (21.9%) 124 (11.1%) <0.001 Minimal /no depression symptoms 917 (78.1%) 996 (88.9%) Missing Generalised Anxiety Disorder Questionnaire Score Presence of anxiety symptoms* 172 (14.7%) 72 (6.4%) <0.001 Minimal /no anxiety symptoms 1002 (85.3%) 1048 (93.6%) Missing Socioeconomic adversity Low asset index 283 (24.1%) - Food insecurity 125 (11.0%) - Low caregiver education 382 (32.5%) - 1 n (%); Mean [SD] *Scores above the test cut-off (10 and higher) The table describes the characteristics of the sample during wave 3 and wave 4. P- values refer to the comparison of participant characteristics in wave 3 and wave 4 variables using Chi-square tests. The asset index was calculated using a factor analysis model and standardised (mean [SD], 0 [1]) with low assets defined as within the lower tertile, food insecurity refers to answering “often” to any one of the three questions, and low caregiver education was defined as the third quartile of the distribution. Longitudinal mental health profiles First, using dichotomous rating of depression and anxiety as illustrated in Figure 2a, 21.9% of the overall sample presented with depression at wave 3, with a significant decrease to 11.1% at wave 4 [ X 2 (1, N = 1120) = 32.4, p < 0.001]. Females were disproportionately affected with a higher prevalence of depression at both waves [ X 2 (1, N = 1174) = 6.0, p = 0.015 and X 2 (1, N = 1120) = 4.0, p = 0.046 respectively]. Similarly, 14.7% of the overall sample presented with anxiety at wave 3, with a significant reduction to 6.4% at wave 4 [ X 2 (1, N = 1120) = 18.7, p<0.001, see Figure 2b]. No gender differences were observed for anxiety in wave 3 or 4 [ X 2 (1, N = 1174) = 0.001, p = 0.981 and X 2 (1, N = 1120) = 0.6, p = 0.450 respectively]. Additionally, of those that scored above the measures cut-off score at wave 3 for depression (N = 246) and anxiety (N = 163), 21% (N = 52) and 14% (N = 23) respectively, remained above the cut-off score at wave 4. The remaining 58% and 68% of the sample at wave 4 were newly reported cases of depression and anxiety. In comparison, using continuous scores for depression and anxiety (standardised z-scores; see methods and Figure 2c and 2d), females reported significantly higher depressive symptom severity at both waves [wave 3: t(1049) = -2.55, p <0.011, and wave 4: t(1118) = -2.32, p <0.020]. While males displayed an increasing trend in depressive symptom severity, the scores were not significantly higher in wave 4 [t(484) = -0.71, p = 0.476]. In contrast, no gender differences were observed for anxiety symptom severity [all p ’s > 0.360]. Grit as an individual resilience factor At wave 3, self-reported grit had an overall mean score of 3.42 (SD = 0.57; range 0-5) indicating average to high levels of grit in the overall sample. Females reported significantly higher levels of grit (means= 3.47, SD = 0.59) compared to male adolescents [mean = 3.38, SD = 0.55); p = 0.010; see supplementary Table 1 ]. Grit did not correlate with any indicators of socioeconomic adversity (all p ’s> 0.05 as shown in supplementary Table 2). Examining the role of grit in relation to internalising symptom severity, standardised z-scores (continuous scores) for depression and anxiety were used in linear regression models. Higher grit scores were significantly predictive of lower depressive symptom severity [F(1, 1049) = 12.39, p <0,001, R 2 =0,01] and lower anxiety symptom severity [F(1, 1049) = 11.54, p <0,001, R 2 =0,01; see supplementary Figure 1]. This held true when adjusted for age, gender and previous levels of internalising symptoms as shown in Figure 3a, for both depression severity [F(4, 998) = 19.94, p <0,001, R 2 =0,07] and anxiety severity in [Figure 3b, F(4, 965) = 9.25, p <0,001, R 2 =0,04] respectively. To further investigate the role of grit and mental health outcomes, we looked at depression and anxiety scores dichotomously using the measures cut-off scores. Logistic regression analysis showed that grit continued to significantly predict lower depression [ X 2 (1, N = 1051) = 8.30, p = 0.004 ], with the odds of scoring above the depression test cut-off decreasing with every 1-unit increase in grit as illustrated in Figure 4a. However, grit was no longer a significant predictor of lower depression once the model was adjusted for age, gender and previous depression. When examining anxiety on the other hand, grit significantly predicted lower anxiety [Figure 4b, X 2 (1, N = 1051) = 14.50, p <0.001 ], with this effect remaining significant when adjusting for age, gender and anxiety scores at wave 3 (Figure 4c). The odds of presenting with anxiety above the cut-off decreased for every 1 unit increase in grit scores. Socioeconomic adversity, grit and mental health outcomes Drawing on a multisystemic approach, the regression models were rerun controlling for age, gender and previous internalising symptoms, with the addition of household assets, caregiver education and food insecurity (see supplementary table 3 and table 4 for regression tables). Linear regression results showed that higher grit scores remained a significant predicter of lower depressive symptom severity [Figure 3c, F(7, 960) = 11.08, p <0,001, R 2 =0,07]. Although the model was significant when socioeconomic adversity was included, grit was no longer a significant predictor of lower anxiety symptom severity [Figure 3d, F(7, 931) = 5.41, p <0.001, R 2 =0,03]. To further investigate the role of grit, logistic regressions were run using dichotomous ratings. Grit became a significant predictor of depression [Figure 4d, X 2 (7, N = 968) = 52.13, p <0.001] when all three indexes of socioeconomic adversity were added to the model. This was not the case for anxiety, as the overall model was no longer significant [ X 2 (7, N = 236) = 13.28, p = 0.066 ]. Grit differences for those with high internalising symptoms. Adolescents scoring in the top 20% of the created global score at wave 3 (see methods below) were characterised as those presenting with the high levels of internalising symptoms [N=210; 14.9%] relative to the participants scoring in the remaining 80%. These groups have different patterns of internalising symptoms over time (see supplementary Figure 3). In examining adolescents presenting with the most severe cases of internalising symptoms – in the top 20% at wave 3 – linear regression analyses showed that grit was not a significant predictor of depression and anxiety (all p ’s > 0.05; see Figure 5). This held true when socioeconomic adversity indicators were included in the model. In the remaining 80% of the adolescent sample however, grit was again predictive of lower depression z-scores [F(3, 849) = 8.365, p <0.001, R 2 =0,03], and grit reduced the odds of presenting with depression and anxiety symptoms above the test cut-off scores [ X 2 (3, N = 853) = 25.076, p <0.001 and X 2 (13 N = 853) = 10.810, p = 0.013 respectively]. This once again held true when socioeconomic adversity was added. Discussion Our study provides the first longitudinal evidence of the role of grit as a psychological resilience factor in adolescent mental health globally, and in Africa. More specifically, drawing on data from the Asenze population cohort in a peri-rural context in KwaZulu-Natal, South African adolescents presented with high rates of depression and anxiety across two study waves, with females reporting significantly higher rates of depression. Linear regression analyses confirmed our study hypotheses, with grit consistently predicating lower depression and anxiety when using continues scores and controlling for previous internalising symptoms, gender and age, as well as when adding socioeconomic adversity to the model. However, in the most severe cases of adolescent depression and anxiety (top 20%), grit did not predict lower internalising symptoms. Furthermore, when using a dichotomous categorisation of depression and anxiety a slightly different pattern emerged. Grit predicted lower anxiety and not depression, when controlling for previous levels of internalising symptoms at wave 3, gender and age. In comparison, when socioeconomic adversity was added to the model with dichotomous scores, grit predicted lower depression and not anxiety. Taken together, these results suggest the differential predictive role of grit as a resilience factor on internalising symptoms in adolescent mental health depending on symptom severity, sociodemographic factors and socioeconomic adversity. The need to assume a multisystemic framework 32 is therefore further highlighted by our study results. Previous cross-sectional studies conducted in Majority World settings in China 43 and Thailand 42 showed similar associations between grit and lower levels of depression and anxiety. However, to the best of our knowledge, this is the first study to demonstrate longitudinal outcomes of grit on depression globally, and in the unique African context, while also focusing on a developmentally sensitive period of adolescence. Furthermore, the use of continuous scores offered a more sensitive analytical approach to changes of internalising symptoms overtime 19 , which also resulted in the most robust results of grit as a significant predictor of lower adolescent anxiety and depression. Nevertheless, our results also highlight that in the most severe cases of mental health difficulties, psychological resilience factors such as grit may not be enough 24 , 31 , and additional multifaceted interventions are needed 9 . Here, it is important to note that our results are not aligned to neo-liberal views that hold the individual solely responsible for their mental health 50 , 51 or that resilience is to be attributed to individual traits alone 40 . Rather, our study provides a new perspective on the role psychological protective factors, such a grit, within a multisystemic approach to the study of resilience 32 . Future studies are therefore needed to investigate the early determinants of protective factors like grit and what conditions might foster its development. Similarly, studies should be undertaken to investigate the role of similar psychological factors in resilience such as growth mindset 44 , 45 to help uncover the underlying mechanisms involved in the interplay between top-down and bottom-up processes in adolescent resilience. For example, recent findings 52 , 53 suggest that psychological resilience factors such as grit, emotion regulation and self-efficacy can be enhanced with resilience-based interventions, however the mechanisms underlying such changes, and the interaction with multisystemic factors, are still unclear. Furthermore, adolescent girls in this study reported significantly higher rates of grit, but also depression, which is consistent with prior studies showing increased vulnerability to depression in adolescent females 9 , 54 . These sex differences in depression may also explain why grit was no longer predictive of lower rates of depression in the adjusted model when looking at the dichotomised mental health outcomes for anxiety and depression. Future studies drawing on neuroimaging methods in the African context 21 could unlock novel insights into the underlying processes involved in potential sex differences or gender-related factors 55 , such as emotion regulation, hormonal changes or psychosocial stressors, in resilience research. For example, a recent protocol study using a longitudinal birth cohort in a peri-urban setting in Cape Town South Africa 56 , will draw on MRI methods to investigate longitudinal neurodevelopmental changes in resilient emotion regulation. Similarly, in looking at the dichotomised internalising scores, the degree of socioeconomic adversity changed the predictive power of grit, with only depression remaining significant in the model. The association between socioeconomic adversity and depression has been well documented in the literature 57 , however future studies are needed to explore the possible moderating relationship of socioeconomic adversity on grit and internalising symptoms. Furthermore, this study drew on objective and rigorous measures of socioeconomic levels – household assets, caregiver education and food security. However, these measures have not always been sensitive enough to capture the complexity of perceived socioeconomic inequality 58 , with recent studies advocated for the use of subjective fiscal appraisals 59 . The longitudinal mental health profile of this sample of South African adolescents is consistent with previous longitudinal studies based in peri-urban settings in South Africa as a Majority World LMIC 19 , 54 . However, our study is the first to provide longitudinal evidence of high rates of internalising symptoms in South African adolescents in peri-rural settings. The decline in reported anxiety and depression from wave 3 to wave 4 in the study could suggest potential differences in internalising problems in younger compared to older adolescents 19 , or increased stigma 31 in reporting symptoms in older adolescents within this context. However, this effect might also be attributed to the impact of the COVID-19 pandemic during data collection at wave 3 of the cohort study that ran from 2019 to 2021 49,60 . Data collection was halted for a period due to severe lockdown restrictions at the end of March 2020. The negative effect of the pandemic on adolescent mental health has been robustly shown globally 7 and even within this study cohort 60 . Therefore, one possibility is that marked higher rates of depression and anxiety were reported post the lockdown period when data collection resumed, which then reduced in data collected at wave 4 in 2022. However, a recent study 61 examined depression and anxiety symptoms in adolescents in the Asenze cohort throughout wave 3 and found no relationship between internalising symptoms and government-imposed lockdown restrictions. Therefore, although it is unlikely that the COVID-19 lockdown accounted for changes in internalising symptoms in adolescents in the cohort study, future studies should still explore the potential effect of lockdown restrictions during the pandemic as a source of social deprivation 15 in South African adolescents in the Asenze study and more broadly. Despite the novelty of our results, our study was not without limitations. The study was bound by the use of psychological measures developed in non-Majority World settings that often lack the cultural sensitivity needed in the current study context 26 , despite language translations used in the current study and South African validation studies of our measures e.g., 62 – 64 . Nevertheless, drawing on dual analytic strategies of using both dichotomous classification and continuous scores that were independent of the tests cut-off measures acted as a further buffer to counteract this limitation. Furthermore, the mental health measures used only included internalising mental health factors. Previous longitudinal studies in South Africa have shown a different pattern of mental health outcomes for male and female adolescents for externalising and internalising factors respectively 19 . Future studies are needed to test if grit is predictive of lower internalising and externalising symptoms and the possible sex differences involved. Lastly, none of the measures used were diagnostic in nature and were all based on self-report methods that are limited by inherent biases involved, which include mental health stigma 31 . Nevertheless, given the lack of longitudinal data on adolescent mental health in culturally diverse context 7 such as in this cohort study, the use of such screening tools is an acceptable limitation until more formal diagnostic interviews can be utilised 9 , 65 . Conclusion Housing the largest and fastest growing adolescent population in the world, safeguarding adolescent mental health across the African continent is a critical public health mandate. Resilience research offers an alternative approach to traditional intervention strategies, by investigating the protective mechanisms that promote positive mental health outcomes 18 , 23 . This study highlights the importance of adolescence and mental health research conducted in the African context that provides unique insights into the dynamic interplay between neurobiological and psychological protective factors, such as grit, which are embedded within wider socio-cultural processes and ecological structures. Methods Study setting and population This study draws on data from the Asenze cohort study, a longitudinal population-based study in KwaZulu-Natal, South Africa. This peri-rural site is characterised by high rates of HIV, food insecurity and unemployment 49 , 65 . The study follows the health, development, well-being and psychosocial functioning of children. Four waves of data collection occurred from 2008 to 2022: wave 1 (4–6 years old), wave 2 (6–8 years old), wave 3 (13–19 years old), and wave 4 (16–20 years old) 49 . This study draws on data collected in wave 3 (2019–2021) and wave 4 (2022). Data was primarily collected in person for wave 3, with a small portion interviewed telephonically in order to retain those participants who had relocated. Data was collected telephonically in wave 4. Caregiver consent where applicable, and participant assent/consent, were obtained at each wave of the study. Modest attrition was observed with 83.5% of the wave 2 cohort interviewed at wave 3, and 95% of the wave 3 cohort interviewed at wave 4. A fifth round of data collection is currently underway. Ethical approval was received from the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (BF 036/07 and BE 609/18) and from the Institutional Review Board of Columbia University (IRB No. AAAC2559). Initial approval was also received from local authority councils, the local district health committee, and the local district board of education. Data has been collected on demographic variables, including sex assigned at birth, age, education level completed, HIV status, and socioeconomic variables. Data was collected at wave 3 and 4 on grit, and mental health outcomes amongst others. All material was available in English and Zulu, the languages most used in the area. Assessment of mental health: Internalising symptoms Mental health assessments focused on internalising symptoms - specifically depression and anxiety - that were assessed using validated self-report questionnaires at wave 3 and wave 4. Depression. During wave 3, the Patient Health Questionnaire-9 (PHQ-9) was used to screen and measure the severity of depression symptoms based on the DSM-5 criteria for major depressive disorder 66 . Participants rated nine items in reference to the past two weeks, using a 4-point Likert scale (0 = not at all; 1 = several days; 2 = more than half the days; and 3 = nearly every day). The scores for each item were summed, resulting in a total score ranging from 0 to 27, with higher scores indicating greater symptom severity. The PHQ-9 has strong psychometric properties (Cronbach’s alpha: 0.71–0.89) and has been validated in multicultural environments, including African contexts 62 , 63 , 67 . During wave 4, participants completed the brief PHQ-2, which consists of the first two questions of the PHQ-9. Total scores on both measures ranged from 0–6, with higher scores indicating more greater symptom severity. Anxiety. At wave 3 Generalized Anxiety Disorder questionnaire- 7 (GAD-7) was used to screen for generalised anxiety disorder according to the DSM-5 68 . The seven items are in reference to the past two weeks and were reported on a 4-point Likert scale from 0 (“not at all”) to 3 (“nearly every day”). The scores were summed, resulting in a total ranging from 0 to 21, with higher scores indicating greater symptom severity. The GAD-7 has been shown to have strong psychometric properties (Cronbach’s alpha: 0.69–0.87), and has been validated in South Africa 62 , 69 . For wave 4, participants completed the brief GAD-2, which consists of the first two questions of the GAD-7. Total scores range from 0 to 6, with higher scores indicating more frequent symptoms 70 . Since different depression and anxiety measures were used at each wave, scores were standardised in two ways. First, using the measures cut-off scores, depression and anxiety was looked at dichotomously, with participants scoring above the measure’s cut-off score being categorised as having depression or anxiety respectively. For the PHQ-9 and GAD-7, scores of 10 or higher were categorised as presenting with depression or anxiety symptoms. If lower, it was categorised as minimal or no depression or anxiety symptoms respectively. For the PHQ-2 and GAD-2, scores of three and higher indicated the presence of depression or anxiety symptoms respectively, while lower scores indicated minimal or no symptoms. Dichotomising the scores enabled us to describe them longitudinally. Second, the depression and anxiety scores were standardised (z-score transformed) allowing us to look at the data as a continuous measure of depression and anxiety symptom severity overtime. A global score for internalising symptoms was also created by averaging the depression and anxiety standardised z-score at each wave, with higher scores indicating a greater presence of internalising symptoms (supplementary Fig. 1). Assessment of grit Grit was assessed during wave 3 using the 8-item Short GRIT Scale (GRIT-S) 39 , 71 . Participants rated how much each statement described them, from 1 (“Not like me at all”) to 5 (“very much like me”). The eight items were averaged to create a total score, ranging from 1 to 5, with higher scores indicating more grit. Although internal consistency was poor in this study (Cronbach’s alpha = 0.297), previous research has demonstrated good validity and reliability globally (Cronbach’s alpha: 0.75–0.81) 72 , 73 , as well as within South Africa (Cronbach’s alpha: 0.71–0.72) 64 , 74 . Socioeconomic adversity Drawing on previous methods, socioeconomic adversity was measured using the following indicators: (1) asset index (using household characteristics, assets, and source of heating), (2) low caregiver education (the third quartile of the distribution) and (3) food insecurity (answering “often” to any one of the three questions). The asset index was calculated using a factor analysis model and standardised (mean [SD], 0 [1]) 65 . Socioeconomic adversity indicators were dichotomised as follows: low assets (within the third quartile of the distribution), low caregiver education (the third quartile of the distribution), and food insecurity (answering “often” to any one of the three questions: 1) In the past 4 weeks how often was there no food to eat of any kind in your house because of lack of money; 2) In the past 4 weeks, how often did you or any member of your household go to sleep hungry because of lack of food; 3) In the past 4 weeks, how often did you or any of your household go a whole day and night without eating because of lack of food). Analysis Data were analysed using IBM SPSS software version 29.0.2.0, while regression models and figures were generated in RStudio 2024.09.0 + 375. Descriptive statistics were run to characterise the sample, using independent sample t-tests for comparison and chi-square tests for dichotomised variables (i.e., PHQ and GAD variables). Linear regression models were performed using depressive and anxiety z-scores (continuous scores) at wave 4 as the outcome measure. Grit was the predictor variable, with age, gender, and depressive or anxiety z-scores at wave 3 as covariates. When examining the effects of socioeconomic adversity, grit was the predictor variable, with age, gender, depressive or anxiety z-scores at wave 3, low assets, food insecurity and low caregiver education as covariates. Logistic regressions were performed with the outcome variables being dichotomised depression and anxiety scores at wave 4. Similar to the linear regressions, grit was the predictor variable, with age, gender, depressive or anxiety z-scores at wave 3, low assets, food insecurity and low caregiver education as covariates. Models were performed on the whole sample, as well as a subset of participants who scored in the top 20% of the global internalising scores at wave 3, and with participants who scored in the remaining 80%. P values were evaluated using 2-sided 2-sample t tests and χ2 tests, and significance was set at P < 0.05. Lastly, although the mean age for adolescents in the study at wave 3 and wave 4 were 15.87 and 17.87 years respectively, the age range of the sample includes a much broader range (13 to 20 years across both waves). As previous studies have shown developmental differences according to age distribution, the same set of analysis were run in a subset of the sample to include only 15–17-year-old adolescents across both waves, finding the same pattern of results. Declarations Author Contributions All authors: interpretation, drafting of manuscript and editing; SB, CWR, CD, LLD: conceptualisation, design and analysis; CD, LLD, JK, FT: funding acquisition, data acquisition. Funding Research reported in this publication was supported by the Fogarty International Center, NIH, Office of Behavioral and Social Sciences Research, Office of Disease Prevention of the National Institutes of Health under Award Number R01 TW011228, and the National Institute of Mental Health of the National Institutes of Health under Award Number RF1MH134561 and P30-MH43520. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Acknowledgements The authors would like to thank the adolescents and young adults who participated in this research. Kathryn G. Watt for her assistance with data acquisition. Caleb Miles and Massimiliano Orri for their helpful statistical consultations. Statements and declarations The authors declare no conflicts of interest. Data Availability De-identified study data is available on request. Code Availability Code available on request. 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J Adolesc Health Stockton MA et al (2024) Validation of screening instruments for common mental disorders and suicide risk in south African primary care settings. J Affect Disord 362:161–168 Anum A, Adjorlolo S, Kugbey N (2019) Depressive symptomatology in adolescents in Ghana: Examination of psychometric properties of the Patient Health Questionnaire-9. J Affect Disord 256:213–218 Young KA, Archer E (2023) Validating the Grit-S scale among postgraduate students in a South African distance education institution. Front Educ 8:1229433 Nazareth ML et al (2022) Adverse childhood experiences (ACEs) and child behaviour problems in KwaZulu-Natal, South Africa. Child Care Health Dev 48:494–502 Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16:606–613 Adewuya AO, Ola BA, Afolabi OO (2006) Validity of the patient health questionnaire (PHQ-9) as a screening tool for depression amongst Nigerian university students. J Affect Disord 96:89–93 Spitzer RL, Kroenke K, Williams JBW, Löwe B (2006) A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Arch Intern Med 166:1092 Kigozi G (2021) Construct validity and reliability of the generalised anxiety disorder-7 scale in a sample of tuberculosis patients in the Free State Province, South Africa. South Afr J Infect Dis 36 Kroenke K, Spitzer RL, Williams JB (2003) The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 41:1284–1292 Duckworth AL, Quinn PD (2009) Development and Validation of the Short Grit Scale (Grit–S). J Pers Assess 91:166–174 Alhadabi A et al (2019) Psychometric Assessment and Cross-Cultural Adaptation of the Grit-S Scale among Omani and American Universities’ Students. Eur J Educ Res volume–8–2019:1175–1191 Gonzalez O, Canning JR, Smyth H, MacKinnon DP (2020) A Psychometric Evaluation of the Short Grit Scale: A Closer Look at its Factor Structure and Scale Functioning. Eur J Psychol Assess 36:646–657 Urban B, Pendame R (2016) Perseverance among university students as an indicator or entrepreneurial intent. South Afr J High Educ 29 Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarymaterialsubmission.docx Supplementary material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6558246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452273583,"identity":"d5ce8f7e-b92f-4dfd-9f0f-6b0b9c457f15","order_by":0,"name":"Sahba Besharati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFACHjYGhooDDAwSYJ4EsVrOwLQkEKuFsQ2uhQgN8u1njz34Oe+OnPzs5mcffv6wYOBv78av0eBMXrph77ZnxgZ3jhnP7AE6TOLM2Q34tTDkmEnwbjucuEEiwZiBB6jFQCIXvxb5/jdmkn/nHK6fPyP9M+MfYrQw3Mgxk+ZtOJwAZBgzE2WLwY035sYyx54ZbriRU8wskybBQ9Av8v05Zg/f1NyRl5+RvpnxjU2dHH97LwGHoQMe0pSPglEwCkbBKMAKAC18Rv82pWPoAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2836-7982","institution":"University of the Witwatersrand","correspondingAuthor":true,"prefix":"","firstName":"Sahba","middleName":"","lastName":"Besharati","suffix":""},{"id":452273584,"identity":"f251c5fb-5afa-4ecf-92cf-be43bce01818","order_by":1,"name":"Candice Ramsammy","email":"","orcid":"https://orcid.org/0000-0003-4822-3054","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Candice","middleName":"","lastName":"Ramsammy","suffix":""},{"id":452273585,"identity":"48a444aa-2f27-41e6-95c6-ddddc359c1b2","order_by":2,"name":"Furzana Timol","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Furzana","middleName":"","lastName":"Timol","suffix":""},{"id":452273586,"identity":"d0ca3461-db64-474b-b29e-d1bc61da572e","order_by":3,"name":"Jeremy Kane","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"","lastName":"Kane","suffix":""},{"id":452273587,"identity":"c6217163-e2f5-4713-ae0a-cdecfb116d93","order_by":4,"name":"Leslie Davidson","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Leslie","middleName":"","lastName":"Davidson","suffix":""},{"id":452273588,"identity":"ff8d29bc-5abe-4e95-9dc4-2135e5a0dcbb","order_by":5,"name":"Chris Desmond","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Desmond","suffix":""}],"badges":[],"createdAt":"2025-04-29 16:11:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6558246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6558246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82131513,"identity":"730016ea-44a1-4053-aedf-3d8606265f64","added_by":"auto","created_at":"2025-05-07 05:36:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge distribution of the sample at wave 3 and wave 4. \u003c/strong\u003eThis study used data from the Asenze cohort study, which had completed four waves of data collection at the time of publishing. While the first wave focused on a narrow age range, subsequent waves covered longer timeframes, leading to a wider age distribution in later waves. The histogram illustrates the age distribution of study participants during wave 3 (N = 1174) and wave 4 (N =1120) used in this study. The mean age is indicated by the dotted red line for wave 3 (15.87 [0.92]) and by the dotted blue line in wave 4 (17.87 [0.72]).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/64b1a2526da2c77a9808c167.jpg"},{"id":82131514,"identity":"98cf51a8-150e-498e-b4ae-7956d796870d","added_by":"auto","created_at":"2025-05-07 05:36:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of depression and anxiety symptoms amongst adolescents over 2 time points.\u003c/strong\u003e Depression and anxiety symptom presence and severity was examined by gender. 2a and 2b) Across all participants, 21.9% scored above the depression measure cut-off score and 13% scored above the anxiety measure cut-off scores at wave 3; this reduced to 11.1% and 6.4% respectively in wave 4. 2c) Depressive symptom z-scores across all participants at wave 3 and wave 4; females scored significantly higher at both time points, while males trended towards a higher symptom severity at wave 4. 2d) Anxiety symptom z-scores across all participants at wave 3 and wave 4; there were no differences gender differences in z-scores between wave 3 and wave 4.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/8cadd59491d88b09dd9c97bb.jpg"},{"id":82131519,"identity":"0a82197a-9b4f-450a-a598-273c0f666cec","added_by":"auto","created_at":"2025-05-07 05:36:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":498899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear regression analyses to predict depressive and anxiety symptom severity based on grit.\u003c/strong\u003e Grit scores were regressed onto continuous depressive symptoms z-scores (a) and anxiety symptom z-scores (b) at wave 4. Both models controlled for age, gender, and previous internalising symptoms at wave 3. Both models were significant, with higher grit contributing significantly to lower internalising symptom severity. When adding socioeconomic adversity (low asset index, food insecurity and low caregiver education) alongside the demographic variables (age, gender and previous internalising symptoms), the grit remained a significant predictor for depression (c) but not anxiety (d).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/892be9613d764ff5e33be1df.jpg"},{"id":82132564,"identity":"f915fad5-065d-45de-9321-113fa64166f2","added_by":"auto","created_at":"2025-05-07 05:44:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":349217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLogistic regression analyses to predict dichotomised rating of depression and anxiety based on grit.\u003c/strong\u003e Logistic regression analysis showed that grit continued to significantly predict lower depressive symptoms at wave 4 [\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (1, N = 1051) = 8.30, \u003cem\u003ep\u003c/em\u003e = \u003cem\u003e0.004\u003c/em\u003e], with the odds of scoring above the depression measure cut-off score decreasing with every 1-unit increase in grit. In addition, grit significantly predicted lower anxiety symptoms at wave 4 [\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (1, N = 1051) = 14.50, \u003cem\u003ep\u003c/em\u003e \u003cem\u003e\u0026lt;0.001]\u003c/em\u003e. c) When adjusting for adjusted for age, gender and previous levels of internalising symptoms, grit remained as a significant predictor for anxiety [\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e = 1051) = 14.50, \u003cem\u003ep\u003c/em\u003e \u003cem\u003e\u0026lt;0.001\u003c/em\u003e] and not depression. The partial residuals shows that grit further reduces the odds of presenting with anxiety after controlling for demographic factors. d) When adding socioeconomic adversity (low asset index, food insecurity and low caregiver education) alongside the demographic variables (age, gender and previous internalising symptoms), grit became a significant predictor for depression [\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (7, \u003cem\u003eN\u003c/em\u003e = 968) = 52.13, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001], with the partial residuals illustrating that effect. The anxiety model was no longer significant.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/c62c447cc962d92f38541b46.jpg"},{"id":82133876,"identity":"445ecf64-fa72-422f-ab9a-37b6d7891c4f","added_by":"auto","created_at":"2025-05-07 06:00:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":228764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential predictive role of grit on internalising symptoms in adolescents across symptom severity.\u003c/strong\u003e Grit was regressed onto the depressive and anxiety symptoms using z-scores for those presenting with the most severe cases of internalising scores (i.e., in the top 20%) at wave 3, and to the remaining 80%. Linear regression analyses showed that grit was a significant predictor of depressive (a) and anxiety (b) symptom severity in adolescents in the lower 80% range. However, grit was not a significant predictor for lower depressive (c) or anxiety (d) symptom severity in the most severe cases scoring in the top 20%.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/99ec3f2f425844c6b649963b.jpg"},{"id":89417184,"identity":"c6ea930b-44cb-4797-b060-ab74dc289d41","added_by":"auto","created_at":"2025-08-19 17:29:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2465885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/6d756bbd-016d-4353-9265-988f54ec21c7.pdf"},{"id":82131515,"identity":"cb5bddf6-cbfa-44c4-a542-6dca853a3c39","added_by":"auto","created_at":"2025-05-07 05:36:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":329117,"visible":true,"origin":"","legend":"Supplementary material","description":"","filename":"Supplementarymaterialsubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-6558246/v1/6ab508d7c563b36d1be494eb.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The impact of grit on adolescent resilience in examining longitudinal mental health outcomes in peri-rural South Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescents constitute more than 1.3 billion of the global population\u003csup\u003e1,2\u003c/sup\u003e, with adolescents in Africa representing the fastest growing population group in the world\u003csup\u003e3\u003c/sup\u003e. However, the study of adolescent health and development has only recently gained scientific traction\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e. The period of adolescence – roughly defined from 10 to 25 years\u003csup\u003e6\u003c/sup\u003e – is a unique developmental stage characterised by distinct changes in physical, neurocognitive and social-emotional development\u003csup\u003e7,8\u003c/sup\u003e. These developmental shifts also overlap with a heightened vulnerability to mental health disorders\u003csup\u003e9,10\u003c/sup\u003e. Recent estimates from UNICEF indicate that 20% of adolescents globally experience a mental health disorder\u003csup\u003e11\u003c/sup\u003e which accounts for at least 13% of the global burden of disease\u003csup\u003e12\u003c/sup\u003e within the period of early and middle adolescence\u003csup\u003e6\u003c/sup\u003e. Half of adult mental health disorders are also reported to begin during adolescence\u003csup\u003e9,13\u003c/sup\u003e. This escalation in mental health disorders in adolescents has been well documented in high-income counties\u003csup\u003e9,14\u003c/sup\u003e, highlighting the role of early life adversity and wider environmental factors, including social deprivation during the COVID-19 pandemic\u003csup\u003e15\u003c/sup\u003e and the use of social media\u003csup\u003e16,17\u003c/sup\u003e. However, far less attention has been afforded to the adolescent mental health crisis in low-to-middle income countries (LMICs)\u003csup\u003e18\u003c/sup\u003e - particularly in Africa\u003csup\u003e19\u003c/sup\u003e. The African context therefore represents both a high adversity context that can result in greater risk for mental health difficulties\u003csup\u003e20\u003c/sup\u003e, as well as an understudied social reality with varying cultural and contextual factors that may uncover different patterns of risk and vulnerability in adolescent mental health globally\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsequently, in line with the 2030 United Nations Sustainable Development Goals\u003csup\u003e22\u003c/sup\u003e, action is needed in combating adolescent mental health difficulties particularly in Africa. The study of adolescent resilience against negative mental health outcomes in the face of adversity is therefore, a critical and underutilised approach in the current mental health literature\u003csup\u003e18\u003c/sup\u003e. Resilience research is based on the fundamental observation that mental health can be maintained despite heightened exposure to stress or adversity\u003csup\u003e23\u003c/sup\u003e. However, the protective factors underlying resilience are poorly understood, especially during adolescence, as a sensitive neurodevelopmental period. Increased focus on adolescent resilience research is even more urgent in low-resource and high-adversity settings\u003csup\u003e24\u003c/sup\u003e, especially across Africa\u003csup\u003e25\u003c/sup\u003e, which are subject to Majority World biases (i.e., limited research in LMICs that house 90% of the world’s population)\u003csup\u003e1,26,27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA culmination of recent resilience research suggests that protective mechanisms that underpin resilience involve a dynamic interaction of internal processes (e.g., “bottom-up”; emotion regulation)\u003csup\u003e28\u003c/sup\u003e and external factors (e.g., “top-down”; social support and community structures)\u003csup\u003e29,30\u003c/sup\u003e. Aligned with this perspective, drawing on findings from foundational studies in Sub-Saharan Africa\u003csup\u003e31\u003c/sup\u003e and using cross-cultural comparisons\u003csup\u003e24,29\u003c/sup\u003e, Ungar and Theron\u003csup\u003e32\u003c/sup\u003e have proposed a multisystemic framework for understanding resilience that is culturally and contextually sensitive. Within this framework, resilience involves multiple interacting processes at a biological, psychological, social and ecological level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile recent resilience studies in African adolescents\u003csup\u003e31\u003c/sup\u003e have highlighted the importance of wider external factors at the social and ecological level, resilience also involves internal factors that function at a biological and psychological level\u003csup\u003e23,32\u003c/sup\u003e that have rarely been investigated in the African context. Emotional regulation for example has been thought to be a critical neurobiological mechanism underlying resilience, especially important in adolescence, where there are distinct changes in white matter pathways and volume, as well as developmental changes in the prefrontal cortex and limbic system\u003csup\u003e28,33\u003c/sup\u003e. Neuroimaging and experimental studies, have not only shown the importance of executive function (e.g., working memory) in adolescent development\u003csup\u003e34,35\u003c/sup\u003e, but also in relation to adolescent resilience and positive mental health outcomes\u003csup\u003e15\u003c/sup\u003e. These neurobiological factors also interact at a psychological level. Although there is consensus in the literature that resilience itself should not be understood as a stable personality trait\u003csup\u003e23,28\u003c/sup\u003e, individual resilience factors – such as cognitive reappraisal\u003csup\u003e36\u003c/sup\u003e and grit\u003csup\u003e37\u003c/sup\u003e – nevertheless form part of the psychological protective factors in Ungar and Theron\u003csup\u003e32\u003c/sup\u003e proposed multisystemic model that promote positive mental health outcomes.\u003c/p\u003e\n\u003cp\u003eThe concept of grit arises from a much longer historical tradition\u003csup\u003e38\u003c/sup\u003e, but it was first described in the scientific literature by Duckworth\u003csup\u003e39\u003c/sup\u003e as ‘perseverance and passion for long-term goals’. A recent meta-analysis\u003csup\u003e37\u003c/sup\u003e of the current literature has challenged traditional conceptions of grit or being “gritty” to extend beyond the original hierarchical two-factor model, namely perseverance and consistency, to include wider psychological constructs such as adaptability\u003csup\u003e37\u003c/sup\u003e that has been previously implicated in resilience and adolescence research\u003csup\u003e23,40\u003c/sup\u003e. Grit has been widely studied in the education literature as a predictor of academic success\u003csup\u003e41\u003c/sup\u003e, and more recent studies highlight the association between grit and positive mental health outcomes\u003csup\u003e37\u003c/sup\u003e, including internalising symptoms\u003csup\u003e42,43\u003c/sup\u003e (i.e., depression and anxiety). Interestingly, neuroimaging studies investigating the neuroanatomical correlates of grit\u003csup\u003e44–46\u003c/sup\u003e have identified structures and networks in the prefrontal cortex, limbic system and white matter pathways, which overlap with neural changes in the adolescent brain\u003csup\u003e9,35\u003c/sup\u003e and in adolescent resilience\u003csup\u003e28\u003c/sup\u003e. Although studies investigating the relationship between grit and mental health have been conducted in diverse country contexts that include Majority World countries\u003csup\u003e37\u003c/sup\u003e, cultural biases still extend to the study of grit\u003csup\u003e47\u003c/sup\u003e, with further research needed to include African populations and different cultural contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLong-standing birth and population cohort studies in Majority World LMICs\u003csup\u003e48\u003c/sup\u003e – including those in sub-Saharan Africa\u003csup\u003e19\u003c/sup\u003e – have played a profound role in understanding and promoting health across the lifecourse\u003csup\u003e9\u003c/sup\u003e. To this end, this study draws on data from the Asenze population cohort study, a prospective longitudinal study based in a peri-rural setting in KwaZulu-Natal South Africa that is characterised as a high adversity context\u003csup\u003e49\u003c/sup\u003e. \u0026nbsp;The aims of this study are twofold. Firstly, to characterise the longitudinal mental health profile of internalising factors (i.e., depression and anxiety) of adolescents living in a high stress and adversity setting (e.g., socioeconomic adversity; increased exposure to crime; high rates of HIV) across two waves of the Asenze cohort study. Second, to investigate if grit as one measure of resilience, predicts positive mental health outcomes of African adolescents. This study also draws on Ungar Theron’s\u003csup\u003e32\u003c/sup\u003e proposed multisystemic approach that integrates culturally and contextually important factors, specifically sociodemographic characteristics and socioeconomic factors, in investigating adolescence resilience. Based on findings from previous cross-sectional studies, we hypothesised that grit would be a significant predictor of lower adolescent depression and anxiety longitudinally when examining internalising symptoms dichotomously and when using continuous scores. As previous longitudinal studies have reported sex differences in the prevalence of internalising symptoms, we also hypothesised that there would be higher rates of depression reported in female adolescents in the cohort across study waves.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe analysed data collected during waves 3 and 4. The cohort contained 1,174 participants in the third wave (average age = 15.87 years; SD = 0.92), of which 1,120 completed the fourth wave (average age = 17.87 years; SD = 0.72; see Table 1 for characteristics and Figure 1 for age distribution across both waves). The proportion of male to female participants remained nearly equal in both waves. By wave 4, a higher number of participants had completed high school, while those remaining in school were predominantly in their senior years (grades 11 and 12). Most participants were HIV negative. Socioeconomic adversity within this sample was assessed during wave 3, characterised by low asset index (24.1%), low caregiver education (32.5%), and food insecurity (11.0%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sociodemographic characteristics of adolescent participants at wave 3 and 4\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWave 3\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWave 4\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN = 1,174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eN = 1,120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003e(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMean [SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e15.87 [0.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e17.87 [0.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e13 - 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16 \u0026ndash; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e586 (49.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e546 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e588 (50.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e574 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eAttending school\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1111 (94.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e885 (79.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eCompleted high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e147 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eTechnical and Vocational Education and Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e34 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e52 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e54 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eLower than Grade 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e23 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eGrade 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e90 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e8 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eGrade 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e225 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e39 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eGrade 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e384 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e195 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eGrade 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e301 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e302 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eGrade 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e85 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e341 (38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eHIV Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e83 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e88 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1082 (92.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e996 (91.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eGrit score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.42 [0.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 250px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003ePatient Health Questionnaire score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003ePresence of depression symptoms*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e257 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e124 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMinimal /no depression symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e917 (78.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e996 (88.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 427px;\"\u003e\n \u003cp\u003eGeneralised Anxiety Disorder Questionnaire Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003ePresence of anxiety symptoms*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e172 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e72 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMinimal /no anxiety symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1002 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1048 (93.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eSocioeconomic adversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eLow asset index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e283 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eFood insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e125 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 251px;\"\u003e\n \u003cp\u003eLow caregiver education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e382 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 651px;\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%); Mean [SD]\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e*Scores above the test cut-off (10 and higher)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe table describes the characteristics of the sample during wave 3 and wave 4. \u003cem\u003eP-\u003c/em\u003evalues refer to the comparison of participant characteristics in wave 3 and wave 4 variables using \u003cem\u003eChi-square\u003c/em\u003e tests. The asset index was calculated using a factor analysis model and standardised (mean [SD], 0 [1]) with low assets defined as within the lower tertile, food insecurity refers to answering \u0026ldquo;often\u0026rdquo; to any one of the three questions, and low caregiver education was defined as the third quartile of the distribution.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eLongitudinal mental health profiles\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFirst, using dichotomous rating of depression and anxiety as illustrated in Figure 2a, 21.9% of the overall sample presented with depression at wave 3, with a significant decrease to 11.1% at wave 4 [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, N = 1120) = 32.4, \u003cem\u003ep\u003c/em\u003e \u003cem\u003e\u0026lt;\u003c/em\u003e0.001]. Females were disproportionately affected with a higher prevalence of depression at both waves [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, N = 1174) = 6.0, \u003cem\u003ep\u003c/em\u003e = 0.015 and\u003cem\u003e\u0026nbsp;X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, N = 1120) = 4.0, \u003cem\u003ep\u003c/em\u003e = 0.046\u003cem\u003e\u0026nbsp;\u003c/em\u003erespectively]. Similarly, 14.7% of the overall sample presented with anxiety at wave 3, with a significant reduction to 6.4% at wave 4 [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, N = 1120) = 18.7, \u003cem\u003ep\u0026lt;0.001, see\u0026nbsp;\u003c/em\u003eFigure 2b]. No gender differences were observed for anxiety in wave 3 or 4 [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, N = 1174) = 0.001, \u003cem\u003ep\u003c/em\u003e = \u003cem\u003e0.981\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, N = 1120) = 0.6, \u003cem\u003ep\u003c/em\u003e = 0.450\u003cem\u003e\u0026nbsp;\u003c/em\u003erespectively].\u0026nbsp;Additionally, of those that scored above the measures cut-off score at wave 3 for depression (N = 246) and anxiety (N = 163), 21% (N = 52) and 14% (N = 23) respectively, remained above the cut-off score at wave 4. The remaining 58% and 68% of the sample at wave 4 were newly reported cases of depression and anxiety.\u003c/p\u003e\n\u003cp\u003eIn comparison, using continuous scores for depression and anxiety (standardised z-scores; see methods and Figure 2c and 2d), females reported significantly higher depressive symptom severity at both waves [wave 3: t(1049) = -2.55, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.011, and wave 4: t(1118) = -2.32, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.020]. While males displayed an increasing trend in depressive symptom severity, the scores were not significantly higher in wave 4 [t(484) = -0.71, \u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.476]. In contrast, no gender differences were observed for anxiety symptom severity [all \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s \u0026gt; 0.360].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eGrit as an individual resilience factor\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAt wave 3, self-reported grit had an overall mean score of 3.42 (SD = 0.57; range 0-5)\u0026nbsp;indicating average to high levels of grit in the overall sample. Females reported significantly higher levels of grit (means= 3.47, SD = 0.59) compared to male adolescents [mean = 3.38, SD = 0.55);\u003cem\u003e\u0026nbsp;p = 0.010; see supplementary Table 1\u003c/em\u003e]. Grit did not correlate with any indicators of socioeconomic adversity (all \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026gt; 0.05 as shown in supplementary Table 2).\u003c/p\u003e\n\u003cp\u003eExamining the role of grit in relation to internalising symptom severity, standardised z-scores (continuous scores) for depression and anxiety were used in linear regression models. Higher grit scores were significantly predictive of lower depressive symptom severity [F(1, 1049) = 12.39, \u003cem\u003ep\u003c/em\u003e \u0026lt;0,001, R\u003csup\u003e2\u003c/sup\u003e=0,01] and lower anxiety symptom severity [F(1, 1049) = 11.54, \u003cem\u003ep\u003c/em\u003e \u0026lt;0,001, R\u003csup\u003e2\u003c/sup\u003e=0,01; see supplementary Figure 1]. This held true when adjusted for age, gender and previous levels of internalising\u0026nbsp;symptoms as shown in Figure 3a, for both depression severity [F(4, 998) = 19.94, p \u0026lt;0,001, R\u003csup\u003e2\u003c/sup\u003e=0,07] and anxiety severity in [Figure 3b, F(4, 965) = 9.25, \u003cem\u003ep\u003c/em\u003e \u0026lt;0,001, R\u003csup\u003e2\u003c/sup\u003e=0,04] respectively.\u003c/p\u003e\n\u003cp\u003eTo further investigate the role of grit and mental health outcomes, we looked at depression and anxiety scores dichotomously using the measures cut-off scores. Logistic regression analysis showed that grit continued to significantly predict lower depression [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, \u003cem\u003eN\u003c/em\u003e = 1051) = 8.30, \u003cem\u003ep\u003c/em\u003e = \u003cem\u003e0.004\u003c/em\u003e], with the odds of scoring above the depression test cut-off decreasing with every 1-unit increase in grit as illustrated in Figure 4a. However, grit was no longer a significant predictor of lower depression once the model was adjusted for age, gender and previous depression. When examining anxiety on the other hand, grit significantly predicted lower anxiety [Figure 4b, \u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (1, \u003cem\u003eN\u003c/em\u003e = 1051) = 14.50, \u003cem\u003ep\u003c/em\u003e \u003cem\u003e\u0026lt;0.001\u003c/em\u003e], with this effect remaining significant when adjusting for age, gender and anxiety scores at wave 3 (Figure 4c). The odds of presenting with anxiety above the cut-off decreased for every 1 unit increase in grit scores.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eSocioeconomic adversity, grit and mental health outcomes\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDrawing on a multisystemic approach, the regression models were rerun controlling for age, gender and previous internalising symptoms, with the addition of household assets, caregiver education and food insecurity (see supplementary table 3 and table 4 for regression tables). Linear regression results showed that higher grit scores remained a significant predicter of lower depressive symptom severity [Figure 3c, F(7, 960) = 11.08, \u003cem\u003ep\u003c/em\u003e \u0026lt;0,001, R\u003csup\u003e2\u003c/sup\u003e=0,07]. Although the model was significant when socioeconomic adversity was included, grit was no longer a significant predictor of lower anxiety symptom severity [Figure 3d, F(7, 931) = 5.41, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001, R\u003csup\u003e2\u003c/sup\u003e=0,03]. To further investigate the role of grit, logistic regressions were run using dichotomous ratings. Grit became a significant predictor of depression [Figure 4d, \u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (7, \u003cem\u003eN\u003c/em\u003e = 968) = 52.13, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001] when all three indexes of socioeconomic adversity were added to the model. This was not the case for anxiety, as the overall model was no longer significant [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (7, \u003cem\u003eN\u003c/em\u003e = 236) = 13.28, \u003cem\u003ep\u003c/em\u003e = \u003cem\u003e0.066\u003c/em\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGrit differences for those with high internalising symptoms.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdolescents scoring in the top 20% of the created global score at wave 3 (see methods below) were characterised as those presenting with the high levels of internalising symptoms [N=210; 14.9%] relative to the participants scoring in the remaining 80%. These groups have different patterns of internalising symptoms over time (see supplementary Figure 3). In examining adolescents presenting with the most severe cases of internalising symptoms \u0026ndash; in the top 20% at wave 3 \u0026ndash; linear regression analyses showed that grit was not a significant predictor of depression and anxiety (all \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s \u0026gt; 0.05; see Figure 5). This held true when socioeconomic adversity indicators were included in the model. In the remaining 80% of the adolescent sample however, grit was again predictive of lower depression z-scores [F(3, 849) = 8.365, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001, R\u003csup\u003e2\u003c/sup\u003e=0,03], and grit reduced the odds of presenting with depression and anxiety symptoms above the test cut-off scores [\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (3, \u003cem\u003eN\u003c/em\u003e = 853) = 25.076, \u003cem\u003ep\u003c/em\u003e \u003cem\u003e\u0026lt;0.001\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e (13 \u003cem\u003eN\u003c/em\u003e = 853) = 10.810, \u003cem\u003ep\u003c/em\u003e \u003cem\u003e= 0.013\u0026nbsp;\u003c/em\u003erespectively]. This once again held true when socioeconomic adversity was added.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides the first longitudinal evidence of the role of grit as a psychological resilience factor in adolescent mental health globally, and in Africa. More specifically, drawing on data from the Asenze population cohort in a peri-rural context in KwaZulu-Natal, South African adolescents presented with high rates of depression and anxiety across two study waves, with females reporting significantly higher rates of depression. Linear regression analyses confirmed our study hypotheses, with grit consistently predicating lower depression and anxiety when using continues scores and controlling for previous internalising symptoms, gender and age, as well as when adding socioeconomic adversity to the model. However, in the most severe cases of adolescent depression and anxiety (top 20%), grit did not predict lower internalising symptoms. Furthermore, when using a dichotomous categorisation of depression and anxiety a slightly different pattern emerged. Grit predicted lower anxiety and not depression, when controlling for previous levels of internalising symptoms at wave 3, gender and age. In comparison, when socioeconomic adversity was added to the model with dichotomous scores, grit predicted lower depression and not anxiety.\u003c/p\u003e \u003cp\u003eTaken together, these results suggest the differential predictive role of grit as a resilience factor on internalising symptoms in adolescent mental health depending on symptom severity, sociodemographic factors and socioeconomic adversity. The need to assume a multisystemic framework\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e is therefore further highlighted by our study results. Previous cross-sectional studies conducted in Majority World settings in China\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and Thailand\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e showed similar associations between grit and lower levels of depression and anxiety. However, to the best of our knowledge, this is the first study to demonstrate longitudinal outcomes of grit on depression globally, and in the unique African context, while also focusing on a developmentally sensitive period of adolescence. Furthermore, the use of continuous scores offered a more sensitive analytical approach to changes of internalising symptoms overtime\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, which also resulted in the most robust results of grit as a significant predictor of lower adolescent anxiety and depression. Nevertheless, our results also highlight that in the most severe cases of mental health difficulties, psychological resilience factors such as grit may not be enough\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and additional multifaceted interventions are needed\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Here, it is important to note that our results are not aligned to neo-liberal views that hold the individual solely responsible for their mental health\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e or that resilience is to be attributed to individual traits alone\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Rather, our study provides a new perspective on the role psychological protective factors, such a grit, \u003cem\u003ewithin\u003c/em\u003e a multisystemic approach to the study of resilience\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Future studies are therefore needed to investigate the early determinants of protective factors like grit and what conditions might foster its development. Similarly, studies should be undertaken to investigate the role of similar psychological factors in resilience such as growth mindset\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e to help uncover the underlying mechanisms involved in the interplay between top-down and bottom-up processes in adolescent resilience. For example, recent findings\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e suggest that psychological resilience factors such as grit, emotion regulation and self-efficacy can be enhanced with resilience-based interventions, however the mechanisms underlying such changes, and the interaction with multisystemic factors, are still unclear.\u003c/p\u003e \u003cp\u003eFurthermore, adolescent girls in this study reported significantly higher rates of grit, but also depression, which is consistent with prior studies showing increased vulnerability to depression in adolescent females\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. These sex differences in depression may also explain why grit was no longer predictive of lower rates of depression in the adjusted model when looking at the dichotomised mental health outcomes for anxiety and depression. Future studies drawing on neuroimaging methods in the African context\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e could unlock novel insights into the underlying processes involved in potential sex differences or gender-related factors\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, such as emotion regulation, hormonal changes or psychosocial stressors, in resilience research. For example, a recent protocol study using a longitudinal birth cohort in a peri-urban setting in Cape Town South Africa\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, will draw on MRI methods to investigate longitudinal neurodevelopmental changes in resilient emotion regulation. Similarly, in looking at the dichotomised internalising scores, the degree of socioeconomic adversity changed the predictive power of grit, with only depression remaining significant in the model. The association between socioeconomic adversity and depression has been well documented in the literature\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, however future studies are needed to explore the possible moderating relationship of socioeconomic adversity on grit and internalising symptoms. Furthermore, this study drew on objective and rigorous measures of socioeconomic levels \u0026ndash; household assets, caregiver education and food security. However, these measures have not always been sensitive enough to capture the complexity of \u003cem\u003eperceived\u003c/em\u003e socioeconomic inequality\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, with recent studies advocated for the use of subjective fiscal appraisals\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe longitudinal mental health profile of this sample of South African adolescents is consistent with previous longitudinal studies based in peri-urban settings in South Africa as a Majority World LMIC\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. However, our study is the first to provide longitudinal evidence of high rates of internalising symptoms in South African adolescents in peri-rural settings. The decline in reported anxiety and depression from wave 3 to wave 4 in the study could suggest potential differences in internalising problems in younger compared to older adolescents\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, or increased stigma\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e in reporting symptoms in older adolescents within this context. However, this effect might also be attributed to the impact of the COVID-19 pandemic during data collection at wave 3 of the cohort study that ran from 2019 to 2021\u003csup\u003e49,60\u003c/sup\u003e. Data collection was halted for a period due to severe lockdown restrictions at the end of March 2020. The negative effect of the pandemic on adolescent mental health has been robustly shown globally\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and even within this study cohort\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Therefore, one possibility is that marked higher rates of depression and anxiety were reported post the lockdown period when data collection resumed, which then reduced in data collected at wave 4 in 2022. However, a recent study\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e examined depression and anxiety symptoms in adolescents in the Asenze cohort throughout wave 3 and found no relationship between internalising symptoms and government-imposed lockdown restrictions. Therefore, although it is unlikely that the COVID-19 lockdown accounted for changes in internalising symptoms in adolescents in the cohort study, future studies should still explore the potential effect of lockdown restrictions during the pandemic as a source of social deprivation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e in South African adolescents in the Asenze study and more broadly.\u003c/p\u003e \u003cp\u003eDespite the novelty of our results, our study was not without limitations. The study was bound by the use of psychological measures developed in non-Majority World settings that often lack the cultural sensitivity needed in the current study context\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, despite language translations used in the current study and South African validation studies of our measures \u003csup\u003ee.g.,\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Nevertheless, drawing on dual analytic strategies of using both dichotomous classification and continuous scores that were independent of the tests cut-off measures acted as a further buffer to counteract this limitation. Furthermore, the mental health measures used only included internalising mental health factors. Previous longitudinal studies in South Africa have shown a different pattern of mental health outcomes for male and female adolescents for externalising and internalising factors respectively\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Future studies are needed to test if grit is predictive of lower internalising and externalising symptoms and the possible sex differences involved. Lastly, none of the measures used were diagnostic in nature and were all based on self-report methods that are limited by inherent biases involved, which include mental health stigma\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Nevertheless, given the lack of longitudinal data on adolescent mental health in culturally diverse context\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e such as in this cohort study, the use of such screening tools is an acceptable limitation until more formal diagnostic interviews can be utilised\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHousing the largest and fastest growing adolescent population in the world, safeguarding adolescent mental health across the African continent is a critical public health mandate. Resilience research offers an alternative approach to traditional intervention strategies, by investigating the protective mechanisms that promote positive mental health outcomes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study highlights the importance of adolescence and mental health research conducted in the African context that provides unique insights into the dynamic interplay between neurobiological and psychological protective factors, such as grit, which are embedded within wider socio-cultural processes and ecological structures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and population\u003c/h2\u003e \u003cp\u003eThis study draws on data from the Asenze cohort study, a longitudinal population-based study in KwaZulu-Natal, South Africa. This peri-rural site is characterised by high rates of HIV, food insecurity and unemployment\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The study follows the health, development, well-being and psychosocial functioning of children. Four waves of data collection occurred from 2008 to 2022: wave 1 (4\u0026ndash;6 years old), wave 2 (6\u0026ndash;8 years old), wave 3 (13\u0026ndash;19 years old), and wave 4 (16\u0026ndash;20 years old)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This study draws on data collected in wave 3 (2019\u0026ndash;2021) and wave 4 (2022). Data was primarily collected in person for wave 3, with a small portion interviewed telephonically in order to retain those participants who had relocated. Data was collected telephonically in wave 4. Caregiver consent where applicable, and participant assent/consent, were obtained at each wave of the study. Modest attrition was observed with 83.5% of the wave 2 cohort interviewed at wave 3, and 95% of the wave 3 cohort interviewed at wave 4. A fifth round of data collection is currently underway.\u003c/p\u003e \u003cp\u003eEthical approval was received from the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (BF 036/07 and BE 609/18) and from the Institutional Review Board of Columbia University (IRB No. AAAC2559). Initial approval was also received from local authority councils, the local district health committee, and the local district board of education. Data has been collected on demographic variables, including sex assigned at birth, age, education level completed, HIV status, and socioeconomic variables. Data was collected at wave 3 and 4 on grit, and mental health outcomes amongst others. All material was available in English and Zulu, the languages most used in the area.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of mental health: Internalising symptoms\u003c/h3\u003e\n\u003cp\u003eMental health assessments focused on internalising symptoms - specifically depression and anxiety - that were assessed using validated self-report questionnaires at wave 3 and wave 4.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDepression.\u003c/em\u003e During wave 3, the Patient Health Questionnaire-9 (PHQ-9) was used to screen and measure the severity of depression symptoms based on the DSM-5 criteria for major depressive disorder\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Participants rated nine items in reference to the past two weeks, using a 4-point Likert scale (0\u0026thinsp;=\u0026thinsp;not at all; 1\u0026thinsp;=\u0026thinsp;several days; 2\u0026thinsp;=\u0026thinsp;more than half the days; and 3\u0026thinsp;=\u0026thinsp;nearly every day). The scores for each item were summed, resulting in a total score ranging from 0 to 27, with higher scores indicating greater symptom severity. The PHQ-9 has strong psychometric properties (Cronbach\u0026rsquo;s alpha: 0.71\u0026ndash;0.89) and has been validated in multicultural environments, including African contexts\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. During wave 4, participants completed the brief PHQ-2, which consists of the first two questions of the PHQ-9. Total scores on both measures ranged from 0\u0026ndash;6, with higher scores indicating more greater symptom severity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnxiety.\u003c/em\u003e At wave 3 Generalized Anxiety Disorder questionnaire- 7 (GAD-7) was used to screen for generalised anxiety disorder according to the DSM-5\u003csup\u003e68\u003c/sup\u003e. The seven items are in reference to the past two weeks and were reported on a 4-point Likert scale from 0 (\u0026ldquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every day\u0026rdquo;). The scores were summed, resulting in a total ranging from 0 to 21, with higher scores indicating greater symptom severity. The GAD-7 has been shown to have strong psychometric properties (Cronbach\u0026rsquo;s alpha: 0.69\u0026ndash;0.87), and has been validated in South Africa \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. For wave 4, participants completed the brief GAD-2, which consists of the first two questions of the GAD-7. Total scores range from 0 to 6, with higher scores indicating more frequent symptoms \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince different depression and anxiety measures were used at each wave, scores were standardised in two ways. First, using the measures cut-off scores, depression and anxiety was looked at dichotomously, with participants scoring above the measure\u0026rsquo;s cut-off score being categorised as having depression or anxiety respectively. For the PHQ-9 and GAD-7, scores of 10 or higher were categorised as presenting with depression or anxiety symptoms. If lower, it was categorised as minimal or no depression or anxiety symptoms respectively. For the PHQ-2 and GAD-2, scores of three and higher indicated the presence of depression or anxiety symptoms respectively, while lower scores indicated minimal or no symptoms. Dichotomising the scores enabled us to describe them longitudinally. Second, the depression and anxiety scores were standardised (z-score transformed) allowing us to look at the data as a continuous measure of depression and anxiety symptom severity overtime. A global score for internalising symptoms was also created by averaging the depression and anxiety standardised z-score at each wave, with higher scores indicating a greater presence of internalising symptoms (supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of grit\u003c/h2\u003e \u003cp\u003eGrit was assessed during wave 3 using the 8-item Short GRIT Scale (GRIT-S)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Participants rated how much each statement described them, from 1 (\u0026ldquo;Not like me at all\u0026rdquo;) to 5 (\u0026ldquo;very much like me\u0026rdquo;). The eight items were averaged to create a total score, ranging from 1 to 5, with higher scores indicating more grit. Although internal consistency was poor in this study (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.297), previous research has demonstrated good validity and reliability globally (Cronbach\u0026rsquo;s alpha: 0.75\u0026ndash;0.81)\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, as well as within South Africa (Cronbach\u0026rsquo;s alpha: 0.71\u0026ndash;0.72) \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSocioeconomic adversity\u003c/h3\u003e\n\u003cp\u003eDrawing on previous methods, socioeconomic adversity was measured using the following indicators: (1) asset index (using household characteristics, assets, and source of heating), (2) low caregiver education (the third quartile of the distribution) and (3) food insecurity (answering \u0026ldquo;often\u0026rdquo; to any one of the three questions). The asset index was calculated using a factor analysis model and standardised (mean [SD], 0 [1])\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Socioeconomic adversity indicators were dichotomised as follows: low assets (within the third quartile of the distribution), low caregiver education (the third quartile of the distribution), and food insecurity (answering \u0026ldquo;often\u0026rdquo; to any one of the three questions: 1) In the past 4 weeks how often was there no food to eat of any kind in your house because of lack of money; 2) In the past 4 weeks, how often did you or any member of your household go to sleep hungry because of lack of food; 3) In the past 4 weeks, how often did you or any of your household go a whole day and night without eating because of lack of food).\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eData were analysed using IBM SPSS software version 29.0.2.0, while regression models and figures were generated in RStudio 2024.09.0\u0026thinsp;+\u0026thinsp;375. Descriptive statistics were run to characterise the sample, using independent sample t-tests for comparison and chi-square tests for dichotomised variables (i.e., PHQ and GAD variables).\u003c/p\u003e \u003cp\u003eLinear regression models were performed using depressive and anxiety z-scores (continuous scores) at wave 4 as the outcome measure. Grit was the predictor variable, with age, gender, and depressive or anxiety z-scores at wave 3 as covariates. When examining the effects of socioeconomic adversity, grit was the predictor variable, with age, gender, depressive or anxiety z-scores at wave 3, low assets, food insecurity and low caregiver education as covariates. Logistic regressions were performed with the outcome variables being dichotomised depression and anxiety scores at wave 4. Similar to the linear regressions, grit was the predictor variable, with age, gender, depressive or anxiety z-scores at wave 3, low assets, food insecurity and low caregiver education as covariates. Models were performed on the whole sample, as well as a subset of participants who scored in the top 20% of the global internalising scores at wave 3, and with participants who scored in the remaining 80%. \u003cem\u003eP\u003c/em\u003e values were evaluated using 2-sided 2-sample \u003cem\u003et\u003c/em\u003e tests and χ2 tests, and significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eLastly, although the mean age for adolescents in the study at wave 3 and wave 4 were 15.87 and 17.87 years respectively, the age range of the sample includes a much broader range (13 to 20 years across both waves). As previous studies have shown developmental differences according to age distribution, the same set of analysis were run in a subset of the sample to include only 15\u0026ndash;17-year-old adolescents across both waves, finding the same pattern of results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors: interpretation, drafting of manuscript and editing; SB, CWR, CD, LLD: conceptualisation, design and analysis; CD, LLD, JK, FT: funding acquisition, data acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch reported in this publication was supported by the Fogarty International Center, NIH, Office of Behavioral and Social Sciences Research, Office of Disease Prevention of the National Institutes of Health under Award Number R01 TW011228, and the National Institute of Mental Health of the National Institutes of Health under Award Number RF1MH134561 and P30-MH43520. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the adolescents and young adults who participated in this research. Kathryn G. Watt for her assistance with data acquisition. Caleb Miles and Massimiliano Orri for their helpful statistical consultations. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified study data is available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbubakar A et al (2024) Towards a decolonial developmental science: Adolescent development in the Majority World taking center stage. 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Med Care 41:1284\u0026ndash;1292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuckworth AL, Quinn PD (2009) Development and Validation of the Short Grit Scale (Grit\u0026ndash;S). J Pers Assess 91:166\u0026ndash;174\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhadabi A et al (2019) Psychometric Assessment and Cross-Cultural Adaptation of the Grit-S Scale among Omani and American Universities\u0026rsquo; Students. Eur J Educ Res volume\u0026ndash;8\u0026ndash;2019:1175\u0026ndash;1191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez O, Canning JR, Smyth H, MacKinnon DP (2020) A Psychometric Evaluation of the Short Grit Scale: A Closer Look at its Factor Structure and Scale Functioning. Eur J Psychol Assess 36:646\u0026ndash;657\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrban B, Pendame R (2016) Perseverance among university students as an indicator or entrepreneurial intent. South Afr J High Educ 29\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Asenze cohort study, adolescence, mental health, resilience, depression, multisystemic","lastPublishedDoi":"10.21203/rs.3.rs-6558246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6558246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdolescents in Africa are the world\u0026rsquo;s fastest growing population group. Despite escalating rates of mental health disorders, little is known regarding the role of protective mechanisms that characterise resilience in adolescent mental health globally and in Africa, where there is heightened exposure to adversities. This study draws on two waves of a longitudinal population cohort from a peri-rural setting in KwaZulu Natal South Africa to investigate the relationship between grit \u0026ndash; as a psychological resilience factor - and mental health outcomes in adolescents (N\u0026thinsp;=\u0026thinsp;1174). Heightened mental health difficulty ratings for internalising factors were found across two study waves, with females reporting significantly higher rates of depression. Grit was found to be significant predictor of lower adolescent depression and anxiety, but dependent on the severity of internalising symptoms, sociodemographic factors and exposure to socioeconomic adversity. Potential differences in the mechanisms of adolescent resilience are highlighted that involve a dynamic interplay between bottom-up and top-down resilience factors in the African context.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"The impact of grit on adolescent resilience in examining longitudinal mental health outcomes in peri-rural South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:36:13","doi":"10.21203/rs.3.rs-6558246/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"884b8151-567a-45dd-98aa-b464553ba8b8","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48099316,"name":"Biological sciences/Psychology/Human behaviour"},{"id":48099317,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-08-19T17:21:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 05:36:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6558246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6558246","identity":"rs-6558246","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}