Cumulative childhood trauma exposure and depressive and anxiety symptoms among Kenyan adolescents: A multilevel school-based study

Cumulative childhood trauma exposure and depressive and anxiety symptoms among Kenyan adolescents: A multilevel school-based study | 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 Research Article Cumulative childhood trauma exposure and depressive and anxiety symptoms among Kenyan adolescents: A multilevel school-based study Kofi Nyantakyi Appiah, Nathanael Adu, Divyanshu Kumar Singh, Edward Edem Nartey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9327795/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 low- and middle-income countries experience a high burden of depression and anxiety, often in the context of exposure to multiple childhood traumas including abuse, neglect, and household dysfunction. This study examined how cumulative childhood trauma exposure (ACEs) and perceived social support relate to depressive and anxiety symptoms among Kenyan secondary school students using a large, school-based dataset. A cross-sectional secondary analysis was conducted using survey data from 15,177 students in 23 secondary schools. Depressive (PHQ-8) and anxiety (GAD-7) symptoms were regressed on a cumulative ACE index (ACEStotal) and perceived social support (MSPSStotal) using multilevel linear models with random school intercepts, adjusting for age, gender, and school form. Single-level ordinary least squares models with cluster-robust standard errors were estimated as sensitivity analyses. Intraclass correlations were modest (ICCPHQ = .024; ICCGAD = .015). Higher ACEStotal predicted higher PHQtotal and GADtotal (b ≈ 0.40–0.46, p < .001), whereas MSPSStotal showed no independent association with either outcome in adjusted models. Sensitivity analyses yielded a similar pattern of findings. Cumulative childhood trauma is a strong and consistent correlate of depressive and anxiety symptoms among Kenyan secondary school students. These findings suggest that trauma-informed mental health services in low- and middle-income settings should prioritize young people exposed to multiple traumas and consider integrating trauma-informed screening and stepped-care pathways within school-linked services. adolescents childhood trauma adverse childhood experiences depression anxiety social support resilience Figures Figure 1 Figure 2 Figure 3 Introduction Adolescence is a developmental period marked by heightened vulnerability to the psychological effects of childhood trauma, including the emergence of depression and anxiety, which together account for a substantial proportion of disease burden worldwide (Kieling et al., 2011 ). In many countries, these conditions emerge during secondary school years and can have lasting consequences for educational attainment, social functioning, and risk of chronic mental disorders in adulthood (Von Simson et al., 2022 ). In low- and middle-income countries such as Kenya, this burden is compounded by limited access to mental health services, high rates of exposure to violence, neglect, and household dysfunction, and ongoing educational pressures within school systems (Fazel et al., 2014 ; Kisito et al., 2026 ; Tele et al., 2025 ). Recent school-based surveys in Kenya report that between roughly one fifth and over half of adolescents meet criteria for probable depression, depending on region and methodology, underscoring the urgency of understanding modifiable risk and protective factors in school settings where young people can be routinely reached (Khasakhala et al., 2012 ; Magige et al., 2025 ; Osborn, Venturo-Conerly, Wasil, Schleider, et al., 2020). Adverse childhood experiences (ACEs) — encompassing childhood traumas such as abuse, neglect, and household dysfunction — are consistently linked to elevated depressive and anxiety symptoms and other negative outcomes across the life course (Dube et al., 2001 ; Felitti et al., 1998 ). Cumulative ACE exposure shows a dose-response association with depressive and anxiety symptoms, substance use, and suicidality in diverse adolescent samples (Matthews et al., 2024 ). Emerging evidence from sub-Saharan Africa suggests that Kenyan adolescents experience high rates of adversity, with higher ACE counts associated with more severe depression, anxiety, and bullying victimization (Byansi et al., 2025 ; P. Ferrajão et al., 2024 ). A recent study in Nairobi’s informal settlements, for example, found that many youths had experienced multiple forms of violence and deprivation, and that cumulative ACEs were strongly associated with symptoms of depression, anxiety, and stress (Byansi et al., 2025 ). Taken together, these findings highlight childhood trauma as a central risk factor for adolescent mental health in Kenya and raise the question of which resources within and around schools might support resilience and recovery among trauma-exposed youth. Perceived social support is widely recognized as a key protective factor in adolescent mental health, reflecting the extent to which young people feel valued, cared for, and embedded in supportive relationships with family, friends, teachers, and other significant figures (Jakobsen et al., 2022 ; Petersen et al., 2023 ). Higher perceived support is typically associated with lower depressive and anxiety symptoms, greater wellbeing, and better academic outcomes in school-based samples (Demaray & Malecki, 2002 ; Rueger et al., 2010 ). In Kenya, recent work using the Multidimensional Scale of Perceived Social Support (MSPSS) has shown that social support is both directly associated with fewer internalizing symptoms and indirectly associated with better mental health via mediators such as perceived control and gratitude (Zhao et al., 2025 ). However, less is known about whether social support independently predicts mental health once cumulative trauma exposure is considered, and whether its role among trauma-exposed adolescents is best conceptualized as a direct buffer or as part of a broader resilience process that facilitates recovery. Despite the growing literature on adolescent mental health in Kenya, several important gaps remain. First, many studies focus on small or geographically restricted samples, limiting the generalizability of findings to the diverse population of Kenyan secondary school students (Muhati, 2012 ; Pattullo et al., 1994 ). Notably, the Journal of Child & Adolescent Trauma has published latent class analyses of ACEs among Kenyan adolescents (Ferrajão et al., 2024 ), but no study in this journal has yet examined cumulative ACE dose-response associations with both depression and anxiety in a large Kenyan school sample. Third, school-based surveys often rely on single-level analyses that do not explicitly account for the clustering of students within schools, which can bias standard errors and yield misleading inferences when intraclass correlations are non-trivial (Cheong et al., 2001 ; McCoach & Adelson, 2010 ). Finally, there is limited evidence on how these risk and protective processes operate in ways that are directly informative for early identification and mental health care for adolescents in routine service and school-linked settings. The present study addresses these gaps by conducting a secondary analysis of a large, school-based dataset on the mental health and wellbeing of Kenyan adolescents collected by the Shamiri Institute and collaborators (Baseke et al., 2026 ). The dataset includes more than 17,000 secondary school students from multiple counties, providing one of the most comprehensive sources of information on adolescent internalizing symptoms and psychosocial adversity in a low- and middle-income context to date (Baseke et al., 2026 ). Using validated measures of depressive symptoms (PHQ-8), anxiety symptoms (GAD-7), adverse childhood experiences, and perceived social support (MSPSS), this study examines how cumulative ACE exposure and perceived social support relate to depressive and anxiety symptoms, while accounting for age, gender, school form, and the clustering of students within schools (Dube et al., 2001 ; Marks, 2008 ). The study is guided by a cumulative trauma and resilience framework. From a cumulative trauma perspective, each additional traumatic experience is expected to incrementally increase the likelihood of depressive and anxiety symptoms, in line with prior ACE research in both high-income and Kenyan settings (Byansi et al., 2025 ; Wado et al., 2025 ). From a resilience perspective, perceived social support is conceptualized as a potentially protective factor that may attenuate the association between trauma exposure and symptoms, either directly or through mediators such as perceived control and gratitude, thereby facilitating recovery and positive adaptation in the face of childhood trauma (Lee, 2019 ; Yuan et al., 2018 ). Clarifying how cumulative trauma and perceived support jointly relate to depressive and anxiety symptoms in adolescents is therefore directly relevant for trauma-informed mental health services seeking to identify high-risk youth and to design contextually appropriate interventions in low- and middle-income settings (Pigatto et al., 2025 ; Suliman et al., 2009 ). Accordingly, the primary aims of this study are threefold. First, to describe levels of depressive symptoms, anxiety symptoms, cumulative ACE exposure, and perceived social support in a large sample of Kenyan secondary school students. Second, to estimate the associations between ACEStotal and both PHQtotal and GADtotal, after adjusting for sociodemographic covariates and school clustering. Third, to assess whether MSPSStotal shows an independent association with depressive and anxiety symptoms when modeled alongside cumulative ACEs and demographic factors. By addressing these aims, the study seeks to clarify the relative contributions of adversity and perceived social support to Kenyan adolescents’ mental health and to inform ACE-informed approaches to risk stratification, early identification, and referral within adolescent mental health and school-linked services. Methods Design and Data Source This study is a secondary analysis of anonymised survey data drawn from a large, school-based study of adolescent mental health and wellbeing conducted by the Shamiri Institute and collaborators in Kenya (Baseke et al., 2026 ). The original project employed a cross-sectional design to characterize mental health symptoms, psychosocial risk and protective factors, and school context among Kenyan secondary school students in multiple counties (Baseke et al., 2026 ). Secondary analyses of existing datasets are increasingly used in adolescent mental health research to maximize the scientific value of large surveys and to address new questions without imposing additional burden on participants (Kelly et al., 2024 ; Kievit et al., 2022 ). For the present study, the existing dataset was restricted to variables relevant to depressive symptoms, anxiety symptoms; adverse childhood experiences (ACEs), perceived social support, sociodemographic characteristics, and school identifiers. No new data were collected; all procedures described below for sampling, recruitment, and data collection summarize the methods of the original investigators as reported in their study documentation. The data analyzed in this study were drawn from the open, de-identified project “A Dataset on Adolescent Mental Health in Kenya,” hosted on the Open Science Framework (OSF; https://osf.io/k3xtd/files/8zh4g ). The original dataset (MHS_merged 1.csv) includes survey responses from 17,089 Kenyan adolescents (ages 12–19) across four counties, with measures of depressive and anxiety symptoms (PHQ-8, GAD-7), psychosocial stressors (including adverse childhood experiences), social support, and sociodemographic characteristics. For the present secondary analysis, we used a derived restricted file (MHS_merged-1.csv) that focused on variables relevant to depressive and anxiety symptoms, cumulative ACE exposure, perceived social support, and core sociodemographic indicators. Sampling and Participants In the parent study, schools were recruited in collaboration with county education officials to ensure representation of different school types (e.g., day versus boarding, county versus extra-county), single-sex and mixed-sex schools, and diverse geographic regions (Baseke et al., 2026 ). Within participating schools, entire classes or grade streams were invited to participate to minimize selection bias, a common approach in school-based epidemiological surveys (Booth et al., 2011 ; Strugnell et al., 2018 ). The full dataset includes responses from 17,089 adolescents enrolled in Forms 1–4. For the current secondary analysis, cases with missing data on key study variables were removed via listwise deletion, resulting in an analytic sample of 15,177 adolescents nested within 23 school clusters. The mean age of adolescents in the analytic sample was approximately 16 years, and students were distributed across Forms 1–4, consistent with the typical age-grade structure of Kenyan secondary education (Baseke et al., 2026 ). Descriptive statistics for the sample and key study variables are presented in Table 1 . Gender was coded as male (1) and female (2) based on school administrative records. This binary classification reflects the categories available in the original dataset and does not capture the full spectrum of gender identities. A small number of cases with missing or other codes were excluded from models involving gender. Ethical Considerations The original data collection was approved by Kenyan institutional review boards and partnering universities and conducted in accordance with national guidelines for research in schools (Onyango-Ouma et al., 2005 ). Head teachers provided school-level consent, parents or guardians provided consent according to school policy, and adolescents gave written assent after being informed about the voluntary and confidential nature of participation (Bonell et al., 2023 ). The original study implemented a safety protocol whereby students reporting severe distress or suicidality were referred to school counselors or local services, which is consistent with best practice for school-based mental health research (Randhawa et al., 2024 ). For the present secondary analysis, only de-identified data were used. No attempt was made to link responses back to individual students or schools beyond the anonymous school_id variable required for multilevel modeling. The secondary analysis was reviewed and judged exempt from additional full ethics review by the authors’ institution because it involved analysis of pre-existing, anonymised data collected under prior ethical approvals (Herington et al., 2024 ). Measures Depressive symptoms . Depressive symptoms were assessed in the original survey using the eight-item Patient Health Questionnaire (PHQ-8), which asks how often core depressive symptoms were experienced over the past two weeks (Arias De La Torre et al., 2023). Items are rated from 0 (“not at all”) to 3 (“nearly every day”) and summed to yield a total score, with higher scores indicating more severe depressive symptoms (Kroenke et al., 2001 ). The PHQ-8 has demonstrated good reliability and validity in adolescent populations and has been used extensively in Kenyan youth in prior Shamiri trials (Osborn, Venturo-Conerly, Arango G., et al., 2021 ; Osborn, Venturo-Conerly, Wasil, Rodriguez, et al., 2020 ). For this secondary analysis, PHQtotal was calculated as the sum of PHQ-8 items for each participant. Anxiety symptoms . Anxiety symptoms were measured using the seven-item Generalized Anxiety Disorder scale (GAD-7), which assesses the frequency of common anxiety symptoms over the past two weeks (Spitzer et al., 2006 ). Items are rated from 0 to 3 and summed to produce a total score, with higher scores reflecting greater anxiety (Spitzer et al., 2006 ). The GAD-7 has shown strong psychometric properties in adolescent and young adult samples and has been used in Kenyan school-based intervention studies (Osborn, Roe, et al., 2020 ). GADtotal was computed as the sum of GAD-7 items in the existing dataset. Adverse childhood experiences . ACEs were captured using a set of items adapted from standard ACE checklists to reflect experiences relevant in the Kenyan context, including household dysfunction, abuse, neglect, and exposure to violence (Mutiso et al., 2025 ; Samia et al., 2020 ). Each item was coded to indicate the presence or absence of the adverse experience, and ACEStotal was constructed as a simple count of endorsed ACEs, consistent with the cumulative risk approach commonly used in ACE research (Anda et al., 2006 ; Bethell et al., 2017 ). Higher ACEStotal scores therefore indicate exposure to a greater number of distinct adversities. Previous analyses of this dataset have shown that the ACE index exhibits acceptable internal consistency and strong predictive validity for internalizing symptoms among Kenyan adolescents (Wado et al., 2025 ). Perceived social support . Perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS), a 12-item instrument that assesses support from family, friends, and a significant other (Koğar & Yılmaz Koğar, 2024 ). Items are rated on a 7-point Likert scale from 1 (“very strongly disagree”) to 7 (“very strongly agree”) and summed or averaged to yield a global support score, with higher scores indicating greater perceived social support (Zimet et al., 1988 ). The MSPSS has been widely validated, including in adolescent school samples, and typically demonstrates high internal consistency (Pina et al., 2025 ). In this secondary analysis, MSPSStotal was calculated as the sum of all MSPSS items; prior work using the same dataset has reported good reliability for this total score (Cartwright et al., 2022 ). Sociodemographic and school variables . Adolescents reported their age (in years), gender, and school form (1–4), which were treated as covariates because mental health symptoms vary, systematically across these dimensions during adolescence (Barbieri et al., 2023 ; Barene et al., 2022 ). School-level information (e.g., boarding versus day, school type, demographic composition, county) was derived from administrative records in the original study and combined to create an anonymous school_id variable representing unique school clusters. This variable was used to specify clustering in multilevel and cluster-robust models. Data Preparation For the current secondary analysis, data were imported into R (version 4.5.2) from the original CSV file. After preliminary checks, cases with missing values on PHQtotal, GADtotal, ACEStotal, MSPSStotal, or core covariates (age, gender, form, school_id) were excluded using listwise deletion, yielding a final analytic sample of 15,177 adolescents. Listwise deletion is commonly used in secondary analyses of large surveys when missingness is low and when more complex imputation procedures are not feasible without access to the full original study documentation (Langkamp et al., 2010 ). All scale scores (PHQtotal, GADtotal, ACEStotal, MSPSStotal) were recalculated from item-level data to ensure consistency and to allow sensitivity checks if needed, following standard scoring procedures (Chae et al., 2025 ; Stochl et al., 2022 ). Analytic Strategy The analytic approach for this secondary analysis mirrored and extended the methods used in the original study, with a specific focus on modeling the associations of ACEStotal and MSPSStotal with depressive and anxiety symptoms while accounting for school clustering (Giri et al., 2025 ). First, descriptive statistics and distribution plots were generated to summarize symptom levels and key predictors, including Fig. 1 showing PHQtotal and GADtotal distributions by gender. Second, null random-intercept multilevel models were estimated for PHQtotal and GADtotal to compute intraclass correlation coefficients (ICCs) and quantify the proportion of variance attributable to between-school differences (Hale et al., 2014 ). Third, main multilevel models were fitted for each outcome including age, gender, school form, ACEStotal, and MSPSStotal as fixed effects and random intercepts for school_id. Models were estimated using restricted maximum likelihood in the lme4 package, which is recommended for continuous outcomes in multilevel designs (Bates et al., 2015 ). Fourth, single-level ordinary least squares models with cluster-robust standard errors were estimated as sensitivity analyses to evaluate the robustness of fixed-effect estimates to modeling choices (Abe & Gee, 2014 ; Hazlett & Wainstein, 2022 ). This secondary-analysis design allowed the leveraging of a rich, existing dataset to address new questions about the joint roles of ACEs and perceived social support in Kenyan adolescents’ mental health while avoiding additional data-collection burden and adhering to ethical standards for the use of de-identified data. Results Descriptive Statistics A total of 15,177 adolescents from 23 school clusters were included in the analytic sample after listwise deletion of incomplete cases. Participants’ mean age was 15.90 years (SD = 1.42, range 11–25), and students were drawn from Forms 1–4 of Kenyan secondary schools. The mean PHQtotal score was 7.77 (SD = 4.83, range 0–27), indicating mild but heterogeneous depressive symptoms. The mean GAD_total score was 7.15 (SD = 4.94, range 0–27). Exposure to adverse childhood experiences was generally low but skewed, with ACEStotal showing a mean of 0.33 (SD = 1.06, range 0–10). Perceived social support (MSPSS_total) had a mean of 3.85 (SD = 8.78, range 0–28). This low mean and wide range suggest that many participants endorsed minimal social support on the items available in the dataset. The scoring reflects the sum of recoded or selected MSPSS items as provided in the original dataset; because the standard 12-item MSPSS (scored 1–7 per item) yields summed totals of 12–84, the observed range indicates that the original investigators may have used a subset of items or an alternative scoring scheme. Readers should interpret absolute MSPSS_total values with caution, though relative associations remain valid. Gender distribution was 54.2% male (n = 8,223) and 45.8% female (n = 6,954), and students were distributed across Forms 1–4 as follows: 36.6% (n = 5,555), 32.6% (n = 4,952), 19.3% (n = 2,932), and 11.5% (n = 1,738), respectively. <> Table 1 Descriptive statistics for key study variables (N = 15,177) Variable n M SD Min Max Age (years) 15,177 15.90 1.42 11 25 PHQtotal (depression) 15,177 7.77 4.83 0 27 GAD_total (anxiety) 15,177 7.15 4.94 0 27 ACEStotal (adversity) 15,177 0.33 1.06 0 10 MSPSS_total (social support) 15,177 3.85 8.78 0 28 Note. PHQ = Patient Health Questionnaire-8; GAD = Generalized Anxiety Disorder-7; ACES = Adverse Childhood Experiences Scale; MSPSS = Multidimensional Scale of Perceived Social Support. Data were drawn from the Shamiri Institute dataset (Baseke et al., 2026 ). <> Intraclass Correlations Prior to fitting the substantive multilevel models, null (intercept-only) random-intercept models were estimated for each outcome to quantify the proportion of variance attributable to between-school differences. For PHQtotal, the intraclass correlation coefficient (ICC) was .024, indicating that approximately 2.4% of the variance in depressive symptoms lay between schools and 97.6% within schools. For GAD_total, the ICC was .015, indicating that about 1.5% of the variance in anxiety symptoms was between schools. Although these values are modest, they are consistent with ICCs reported in school-based mental health studies internationally, where values in the .01-.05 range are common (Pina et al., 2025 ). ICCs of this magnitude can meaningfully inflate Type I error rates when clustering is ignored (McCoach & Adelson, 2010 ), thereby justifying the use of multilevel modeling or, at minimum, cluster-robust standard errors. Multilevel Models for Depressive Symptoms (PHQtotal) The main multilevel model for PHQtotal included Age, Gender, Form, ACEStotal, and MSPSS_total as fixed effects with a random intercept for school cluster. The results are presented in Table 2 . <> Table 2 Multilevel model predicting PHQtotal (Depression) Predictor b SE t Intercept 9.36 1.27 7.39 Age 0.37 0.08 4.52 Gender (female vs. male) −1.94 0.19 −10.46 Form 2 1.29 0.20 6.29 Form 3 1.53 0.28 5.54 Form 4 2.45 0.36 6.75 ACEStotal 0.40 0.04 10.88 MSPSS_total −0.00 0.00 −0.32 Random effects School-level variance (τ₀₀) 2.49 — — Residual variance (σ²) 89.11 — — ICC .024 — — Note. Gender reference category = male; Form reference category = Form 1. Model estimated with REML using lme4 (Bates et al., 2015 ). N = 15,177 students in 23 school clusters. Older age was positively associated with depressive symptoms ( b = 0.37, SE = 0.08), indicating that each additional year of age corresponded to higher PHQtotal scores. This age effect is consistent with evidence that older adolescents in Kenya report higher levels of depression (Osborn, Venturo-Conerly, Wasil, Schleider, et al., 2020). Gender was also significantly associated with PHQtotal: female participants reported lower depressive symptoms than males (b = − 1.94, SE = 0.19), consistent with prior findings of gender differences in adolescent depression (Cindik-Herbrueggen, 2025 ). A monotonic increase in depressive symptoms was observed across school forms: relative to Form 1, Form 2 ( b = 1.29), Form 3 ( b = 1.53), and Form 4 ( b = 2.45) were each associated with progressively higher PHQtotal scores. This gradient may reflect increasing academic and psychosocial demands in higher grades (Tang et al., 2019 ). Critically, ACEStotal was strongly and positively associated with PHQtotal ( b = 0.40, SE = 0.04 , t = 10.88), indicating that each additional childhood trauma exposure was associated with a 0.40-point increase in depressive symptoms, even after adjusting for demographics and school clustering. This finding is consistent with the cumulative risk model of ACEs and aligns with evidence from both Kenyan (Byansi et al., 2025 ; Tsehay et al., 2020 ) and international (Carbone et al., 2026 ; Templeton et al., 2025 ) studies demonstrating dose-response relationships between ACE exposure and internalizing symptoms. In contrast, MSPSS_total showed no clear independent association with PHQtotal ( b = − 0.00, SE = 0.00 , t = − 0.32), suggesting that perceived social support did not independently predict depressive symptoms once adversity and demographics were included in the model. This null finding diverges from bivariate associations reported elsewhere (Acoba, 2024 ; Suh et al., 2012 ) and raises important questions about the conditions under which social support functions as a resilience factor for trauma-exposed adolescents. The protective role of MSPSS may operate through indirect or mediated pathways — such as perceived control, gratitude, or cognitive reappraisal — rather than as a direct buffer against trauma-related symptoms. Figure 2 illustrates the predicted depressive symptom scores across the observed range of ACEs, separately for male and female participants, based on the multilevel model. <> Multilevel Models for Anxiety Symptoms (GAD_total) Results for GAD_total are presented in Table 3 . The pattern of associations was broadly similar to that observed for PHQtotal, with some differences in magnitude. <> Table 3 Multilevel model predicting GAD_total (Anxiety) Predictor b SE t Intercept 6.24 1.28 4.87 Age 0.56 0.08 6.64 Gender (female vs. male) −2.70 0.19 −14.31 Form 2 0.35 0.21 1.67 Form 3 −0.00 0.28 −0.01 Form 4 1.25 0.37 3.35 ACEStotal 0.46 0.04 12.25 MSPSS_total 0.00 0.00 0.02 Random effects School-level variance (τ₀₀) 1.69 — — Residual variance (σ²) 93.35 — — ICC .015 — — Note. Gender reference category = male; Form reference category = Form 1. Model estimated with REML using lme4. N = 15,177 students in 23 school clusters. Age was positively associated with anxiety symptoms ( b = 0.56, SE = 0.08), with a larger effect than that observed for depression. Gender differences were more pronounced for GAD_total than for PHQtotal: female participants had lower anxiety scores (b = − 2.70, SE = 0.19). The Form gradient was less consistent for anxiety than for depression; while Form 4 showed a positive association with GAD_total ( b = 1.25, SE = 0.37), Forms 2 and 3 had weaker or non-significant effects, suggesting that grade-related differences in anxiety may be less linear. As with depression, ACEStotal showed a robust positive association with GAD_total ( b = 0.46, SE = 0.04, t = 12.25), indicating that cumulative adversity was strongly related to anxiety symptoms. This is consistent with prior evidence of differential but significant ACE-anxiety associations in both Kenyan (Byansi et al., 2025 ) and US (Matthews et al., 2024 ) adolescent samples. The magnitude of the ACEStotal coefficient was slightly larger for anxiety than for depression, mirroring patterns reported by Ferrajão et al. (P. Ferrajão et al., 2024 ), who found that indirect ACE exposure was more strongly related to anxiety symptoms. MSPSS_total again showed no independent association with GAD_total ( b = 0.00, t = 0.02), replicating the null finding observed for depression. Random effects for the GAD model showed school-level variance of 1.69 ( SD = 1.30) and residual variance of 93.35 ( SD = 9.66), implying slightly lower between-school variability for anxiety than for depression. These patterns are consistent with evidence that individual-level adversity is a more potent correlate of internalizing symptoms than school context in Kenyan samples (P. Ferrajão et al., 2024 ). Figure 3 shows the predicted anxiety symptom scores across the range of ACEs by gender from the multilevel model. <> Sensitivity Analyses: Single-Level Models with Cluster-Robust Standard Errors To assess robustness, single-level ordinary least squares (OLS) regression models were fitted for PHQtotal and GAD_total with the same fixed-effect structure, and cluster-robust standard errors were computed using the sandwich estimator clustered by school_id (Colin Cameron & Miller, 2015 ). The results are presented in Table 4 . <> Table 4 Single-level OLS models with cluster-robust standard errors Predictor PHQtotal GAD_total b SECR t p b SECR t p Intercept 10.30 1.54 6.70 < .001 6.39 1.33 4.82 < .001 Age 0.31 0.09 3.35 < .001 0.53 0.09 6.28 < .001 Gender (female vs. male) −1.81 0.35 −5.16 < .001 −2.43 0.35 −6.95 < .001 Form 2 1.20 0.27 4.46 < .001 0.28 0.22 1.25 .213 Form 3 1.37 0.38 3.64 < .001 −0.16 0.36 −0.44 .659 Form 4 2.68 0.51 5.23 < .001 1.32 0.65 2.01 .044 ACEStotal 0.39 0.05 7.27 < .001 0.46 0.05 9.40 < .001 MSPSS_total −0.00 0.01 −0.77 .441 0.00 0.00 0.11 .911 Note. SECR = cluster-robust standard error (sandwich estimator, clustered by school_id). Gender reference category = male; Form reference category = Form 1. N = 15,177. The pattern of estimates closely matched the multilevel results. For PHQtotal, ACEStotal remained strongly associated with depressive symptoms ( b = 0.39, SECR = 0.05, p < .001), and Gender, Age, and Form retained similar directions and magnitudes. For GAD_total, ACEStotal again showed a strong positive association ( b = 0.46, SECR = 0.05, p < .001), and gender differences were pronounced. MSPSS_total remained non-significant in both models ( p = .441 and p = .911, respectively). These sensitivity analyses confirm that the observed associations between ACEStotal and both depressive and anxiety symptoms are not artifacts of the specific modeling approach used to handle clustering. Summary of Key Findings Across all models and both outcomes, ACEStotal emerged as the strongest and most consistent predictor of depressive and anxiety symptoms among Kenyan adolescents. Each additional adverse childhood experience was associated with 0.40-0.46-point increases in symptom scores, after adjustment for age, gender, school form, perceived social support, and school-level clustering. This dose-response pattern aligns with the cumulative risk framework and with growing evidence from sub-Saharan African adolescent samples (L. Cluver et al., 2015 ; Tsehay et al., 2020 ). MSPSS_total did not independently predict either outcome in adjusted models, suggesting that its protective effects may operate through mediating mechanisms such as perceived control or gratitude rather than as a direct buffer (Zhao et al., 2025 ). Gender differences were consistently observed, with female participants reporting lower symptoms on both measures. School-level variability was modest (ICCs of .015-.024) but was appropriately accounted for through multilevel modeling and confirmed via cluster-robust sensitivity analyses. Discussion The present study examined how adverse childhood experiences and perceived social support relate to depressive and anxiety symptoms among Kenyan secondary school students using a large, school-based sample and a multilevel analytic approach. Overall, the findings highlight a robust and graded association between cumulative adversity and internalizing symptoms, whereas perceived social support showed no independent association once adversity and demographics were controlled. These patterns have important implications for theory, prevention, and the organization of child and adolescent mental health care, including school-linked services, in low- and middle-income contexts such as Kenya (Fazel et al., 2014 ; Wahdi et al., 2025 ). Interpretation of Main Findings Across all models and both outcomes, ACEStotal emerged as the strongest and most consistent correlate of adolescents’ mental health. Each additional adverse childhood experience was associated with approximately 0.40-0.46-point increases in PHQtotal and GADtotal scores, even after adjustment for age, gender, school form, perceived social support, and school-level clustering. This pattern is consistent with the cumulative risk framework, which posits that the accumulation of multiple adversities exerts a more detrimental effect on mental health than any single stressor in isolation (Atkinson et al., 2015 ; Plamondon et al., 2022 ). Similar dose-response relationships between ACE exposure and internalizing symptoms have been documented among adolescents in both high-income and sub-Saharan African settings, including recent Kenyan samples using the same or closely related datasets (Brown et al., 2024 ; Youssef et al., 2017 ). Age and school form showed a coherent pattern, particularly for depressive symptoms. Older adolescents and those in higher forms reported more severe depressive symptoms, which may reflect increased academic pressure, high-stakes examinations, and developmental transitions during mid- to late adolescence (Lazaratou et al., 2010 ; Lipps et al., 2010 ). For anxiety, age effects were larger than for depression, and form effects were strongest in Form 4, suggesting that anxiety may be especially sensitive to the demands associated with terminal examination years (Wuthrich et al., 2021 ). Gender differences were pronounced for both outcomes: students coded as Gender 2 reported significantly lower PHQtotal and GADtotal scores than those in the reference category, mirroring gender-patterned internalizing symptom profiles in other Kenyan and global adolescent studies (Osborn, Venturo-Conerly, Gan, et al., 2021 ; Osborn, Venturo-Conerly, Wasil, Schleider, et al., 2020). In contrast to expectations, MSPSStotal did not show a statistically significant independent association with either depressive or anxiety symptoms once ACEs and demographics were included in the models. This finding diverges from bivariate evidence and mediation work suggesting that higher perceived social support is associated with better mental health among Kenyan adolescents, partly through increased perceived control and gratitude (Zhao et al., 2025 ). However, it is consistent with emerging evidence that social support may function as a resilience factor primarily through indirect pathways rather than as a simple additive predictor. For instance, among HIV-affected adolescents in Uganda, Ashaba et al. ( 2021 ) found that social support moderated the trauma-depression association only among those with low support, suggesting a threshold effect rather than a linear protective gradient. In the present sample, the magnitude of cumulative trauma-related risk may be sufficiently large that it attenuates or overwhelms the unique contribution of perceived support when both are entered simultaneously (Sloss et al., 2025 ). This raises the possibility that resilience among trauma-exposed Kenyan adolescents depends less on perceived support per se and more on the interplay between support, cognitive appraisals, and agentic coping — a question that warrants investigation using mediation and moderation designs (Batthyany & Mokoena, 2025 ; Panagou & MacBeth, 2022 ). School Context and Clustering The intraclass correlation coefficients indicated that 2.4% of the variance in depressive symptoms and 1.5% of the variance in anxiety symptoms were attributable to differences between schools. Although modest, these ICC values fall within the range reported for adolescent psychosocial outcomes in school-based studies, where ICCs between .01 and .05 are typical (Scheier et al., 2002 ; Shackleton et al., 2016 ). Even small ICCs can meaningfully inflate Type I error rates when clustering is ignored, particularly in large samples (Shackleton et al., 2016 ). The use of random-intercept multilevel models therefore follows best-practice recommendations for analyzing clustered adolescent health data and allows more accurate estimation of standard errors and confidence intervals (Krull & MacKinnon, 2001 ). The parallel single-level models with cluster-robust standard errors yielded highly similar estimates, further reinforcing that the observed ACE-symptom associations are not artifacts of model specification. At the same time, the low ICCs and modest school-level variance components suggest that most of the variability in symptoms lies within schools rather than between them. This pattern implies that, although school context matters, individual-level experiences of adversity remain the dominant correlate of internalizing symptoms in this sample (Smolkowski et al., 2006 ; Wickrama & Vazsonyi, 2011 ). Nevertheless, schools remain a strategic and efficient delivery platform for ACE-informed identification and early support, given the feasibility of reaching large numbers of adolescents through existing educational structures and student support services (Blodgett & Lanigan, 2018 ). Comparison with Previous Research The strong associations between cumulative ACEs and both depression and anxiety are consistent with a growing body of literature from Kenya and other low- and middle-income countries. Recent work in informal settlements and rural Kenyan counties has shown that adolescents exposed to multiple adversities such as parental death, violence, poverty, and neglect are more likely to meet criteria for clinically significant internalizing symptoms (Byansi et al., 2025 ; P. Ferrajão et al., 2024 ; Wado et al., 2025 ). Similar patterns have been reported for young people in urban slums in other African countries, as well as among marginalized youth in high-income contexts, supporting the cross-cultural relevance of trauma-focused ACE frameworks (Byansi et al., 2025 ; Swahn et al., 2020 ). Within the Journal of Child & Adolescent Trauma specifically, latent class analyses have identified distinct victimization profiles among Kenyan and Ugandan adolescents, with high-risk classes showing markedly elevated PTSD rates (Ferrajão et al., 2024 ), and studies of South African adolescents have demonstrated associations between childhood trauma and multiple domains of psychopathology (Sevenoaks et al., 2022 ). The present findings extend this evidence by showing that these dose-response relationships persist in a large, multi-county school sample and remain robust after accounting for age, gender, school form, perceived social support, and clustering by school. The absence of a main effect of MSPSStotal contrasts with some Kenyan studies reporting that high-perceived social support is associated with lower depressive symptoms and better wellbeing (Okawa et al., 2011 ; Zhao et al., 2025 ). However, those studies often examine bivariate relationships or mediation models rather than simultaneous prediction in the presence of cumulative adversity, and they frequently conceptualize social support as part of a broader resilience process (Fritz et al., 2018 ). In this context, the current null findings may indicate that support alone is insufficient to offset the mental health burden associated with high ACE exposure, particularly when support is not accompanied by structural changes, targeted psychological interventions, or changes in school climate and safety practices. Strengths and Limitations Key strengths of this study include the large, multi-site sample; the use of validated measures such as the PHQ-8, GAD-7, ACE items, and MSPSS; and the application of multilevel models with appropriate handling of school clustering. The convergence between multilevel and cluster-robust OLS estimates provides additional confidence in the robustness of the main findings (Hazlett & Wainstein, 2022 ). Furthermore, the focus on a Kenyan adolescent school sample contributes to the limited but rapidly expanding evidence base on ACEs and mental health in sub-Saharan Africa (Mutamba et al., 2026 ). Several limitations should also be acknowledged. First, the cross-sectional design precludes causal inference; it is not possible to determine the temporal ordering of ACE exposure, social support, and mental health outcomes, and bidirectional relationships are likely (Panagou & MacBeth, 2022 ). Future longitudinal and intervention studies will be essential to test whether modifying adversity or support meaningfully changes symptom trajectories in this context. Second, all measures were based on self-report, which may be influenced by recall bias, social desirability, or cultural variations in symptom expression. Third, the school identifier was derived from school-level fields rather than an independently verified code, which could introduce minor misclassification of clusters, although the small ICCs make large distortions unlikely. Fourth, the analyses focused on main effects of ACEStotal and MSPSStotal; interactions and mediated pathways involving social support, resilience, or cognitive processes were not examined in depth and may reveal more nuanced protective mechanisms (Kobrinsky & Siedlecki, 2023 ; Lee et al., 2016 ). Finally, digital stressors, smartphone-related difficulties, and other contemporary risk factors present in the broader dataset were not included in the current models, which may underestimate the full range of influences on adolescent mental health. Implications and Future Directions The consistent and sizeable association between cumulative ACEs and both depressive and anxiety symptoms highlights the importance of prioritizing ACE-informed prevention and intervention strategies for adolescents in Kenyan schools and communities. At the policy level, efforts to reduce exposure to violence, neglect, and other adversities in childhood through social protection programs, parenting support, and community-based violence prevention are likely to yield substantial mental health benefits for adolescents (L. D. Cluver et al., 2016 ; Wahlbeck et al., 2017 ). Within schools, integrating trauma-informed screening, establishing clear referral pathways, and offering low-intensity, scalable psychological interventions could help address the needs of students with a history of childhood trauma and reduce the risk of symptom escalation (Fletcher-Janzen & Harrington, 2021 ). The dose-response pattern observed here — each additional ACE adding approximately 0.40–0.46 points to symptom scores — could inform stepped-care models in which adolescents endorsing higher ACE counts are prioritized for support that is more intensive. Practically, this might involve training teachers and school counselors to recognize trauma-related distress using brief ACE screeners, followed by referral to evidence-based interventions already shown to be effective in Kenyan school settings, such as the Shamiri lay-provider intervention (Osborn, Venturo-Conerly, Arango G., et al., 2021 ). Embedding such trauma-informed identification and care pathways within existing school counseling and life-skills programs offers a feasible approach in resource-constrained settings where specialized mental health services remain scarce. Given the ambiguous role of perceived social support in the current adjusted models, future research should examine how support interacts with other resilience factors, such as perceived control, gratitude, and school connectedness, rather than assuming a simple direct buffering effect (Zhao et al., 2025 ). Longitudinal designs would allow a clearer test of whether supportive relationships in families, peer groups, and school communities attenuate the long-term mental health consequences of early adversity, and whether school-based interventions that enhance both support and psychological skills can shift trajectories for high-risk youth. Conclusion In summary, this large multilevel study of Kenyan secondary school students demonstrates that cumulative childhood trauma exposure is a powerful and consistent predictor of both depressive and anxiety symptoms; whereas perceived social support does not show an independent main effect once, trauma exposure and demographics are controlled. These findings reinforce the centrality of childhood trauma in adolescent mental health and highlight the need for trauma-informed policies and school-based interventions that both reduce exposure to adversity and support the recovery and resilience of adolescents who have already experienced multiple traumas (Bomysoad & Francis, 2020 ; Meeker et al., 2021 ). At the same time, they point to the importance of moving beyond simple main-effect models of social support to consider more complex, mediated resilience processes — including the roles of agency, cognitive appraisal, and community connectedness — in future research and in the design of trauma-informed child and adolescent mental health services, including school-linked care (Rueger et al., 2016 ; Wills & Cleary, 1996 ). Abbreviations ACEs Adverse childhood experiences ICC Intraclass correlation coefficient LMICs Low‑ and middle‑income countries MSPSS Multidimensional Scale of Perceived Social Support PHQ‑8 Patient Health Questionnaire‑8 GAD‑7 Generalized Anxiety Disorder‑7 OSF Open Science Framework SD Standard deviation CI Confidence interval Declarations Author Contribution K.N.A. conceived and designed the secondary analysis, performed the statistical analyses, and drafted the main manuscript text. N.A. contributed to data curation, investigation, and critical revision of the manuscript. D.K.S. supervised the project, validated the findings, and reviewed and edited the manuscript. E.E.N. contributed to methodological decisions and software support, and reviewed and edited the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Acknowledgement The authors wish to thank the Shamiri Institute and collaborating schools for providing access to the dataset. We also acknowledge the adolescents who participated in the original survey. Data Availability The original de-identified dataset “A Dataset on Adolescent Mental Health in Kenya” (file MHS_merged (1).csv) is openly available on OSF at https://osf.io/k3xtd/files/8zh4g. For this secondary analysis, a derived restricted file (MHS_merged-1.csv) and all R code (R/01_analysis_PHQ_GAD.R) are available at https://github.com/nyantakyiappiah-eng/kenya-adolescent-mental-health. References Abe, Y., & Gee, K. A. (2014). Sensitivity analyses for clustered data: An illustration from a large-scale clustered randomized controlled trial in education. Evaluation and Program Planning , 47 , 26–34. https://doi.org/10.1016/j.evalprogplan.2014.07.001 Acoba, E. F. (2024). Social support and mental health: The mediating role of perceived stress. Frontiers in Psychology , 15 , 1330720. https://doi.org/10.3389/fpsyg.2024.1330720 Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., Dube, S. R., & Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience , 256 (3), 174–186. https://doi.org/10.1007/s00406-005-0624-4 De La Arias, J., Vilagut, G., Ronaldson, A., Valderas, J. M., Bakolis, I., Dregan, A., Molina, A. J., Navarro-Mateu, F., Pérez, K., Bartoll-Roca, X., Elices, M., Pérez-Sola, V., Serrano-Blanco, A., Martín, V., & Alonso, J. (2023). Reliability and cross-country equivalence of the 8-item version of the Patient Health Questionnaire (PHQ-8) for the assessment of depression: Results from 27 countries in Europe. The Lancet Regional Health - Europe , 31 , 100659. https://doi.org/10.1016/j.lanepe.2023.100659 Ashaba, S., Cooper-Vince, C. E., Maling, S., Satinsky, E. N., Baguma, C., Akena, D., Nansera, D., Bajunirwe, F., & Tsai, A. C. (2021). Childhood trauma, major depressive disorder, suicidality, and the modifying role of social support among adolescents living with HIV in rural Uganda. Journal of Affective Disorders Reports , 4 , 100094. https://doi.org/10.1016/j.jadr.2021.100094 Atkinson, L., Beitchman, J., Gonzalez, A., Young, A., Wilson, B., Escobar, M., Chisholm, V., Brownlie, E., Khoury, J. E., Ludmer, J., & Villani, V. (2015). Cumulative Risk, Cumulative Outcome: A 20-Year Longitudinal Study. PLOS ONE , 10 (6), e0127650. https://doi.org/10.1371/journal.pone.0127650 Barbieri, V., Piccoliori, G., Mahlknecht, A., Plagg, B., Ausserhofer, D., Engl, A., & Wiedermann, C. J. (2023). Adolescent Mental Health during the COVID-19 Pandemic: The Interplay of Age, Gender, and Mental Health Outcomes in Two Consecutive Cross-Sectional Surveys in Northern Italy. Behavioral Sciences , 13 (8), 643. https://doi.org/10.3390/bs13080643 Barene, S., Ruud-Tronsmoen, A., & Johansen, P. F. (2022). Associations between Demographic Characteristics, Lifestyle Factors and School-Related Conditions and Symptoms of Mental Health Problems in Norwegian Upper Secondary School Students. International Journal of Environmental Research and Public Health , 19 (15), 9575. https://doi.org/10.3390/ijerph19159575 Baseke, R., Kilonzo, R., Ngesa, M., Mwende, P., & Osborn, T. (2026). A dataset on adolescent mental health in Kenya. Data in Brief , 65 , 112513. https://doi.org/10.1016/j.dib.2026.112513 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software , 67 (1). https://doi.org/10.18637/jss.v067.i01 Batthyany, K., & Mokoena, T. (2025). The Impact of Social Support on Academic Resilience Mediated by Positive Affect in University Students. Journal of Adolescent and Youth Psychological Studies , 6 (7), 1–9. https://doi.org/10.61838/kman.jayps.4358 Bethell, C. D., Carle, A., Hudziak, J., Gombojav, N., Powers, K., Wade, R., & Braveman, P. (2017). Methods to Assess Adverse Childhood Experiences of Children and Families: Toward Approaches to Promote Child Well-being in Policy and Practice. Academic Pediatrics , 17 (7), S51–S69. https://doi.org/10.1016/j.acap.2017.04.161 Blodgett, C., & Lanigan, J. D. (2018). The association between adverse childhood experience (ACE) and school success in elementary school children. School Psychology Quarterly , 33 (1), 137–146. https://doi.org/10.1037/spq0000256 Bomysoad, R. N., & Francis, L. A. (2020). Adverse Childhood Experiences and Mental Health Conditions Among Adolescents. Journal of Adolescent Health , 67 (6), 868–870. https://doi.org/10.1016/j.jadohealth.2020.04.013 Bonell, C., Humphrey, N., Singh, I., Viner, R. M., & Ford, T. (2023). Approaches to consent in public health research in secondary schools. British Medical Journal Open , 13 (6), e070277. https://doi.org/10.1136/bmjopen-2022-070277 Booth, M. L., Okely, A. D., & Denney-Wilson, E. (2011). Validation and application of a novel method of measuring non-response bias in school-based surveys of paediatric overweight and obesity. International Journal of Pediatric Obesity , 6 (2–2), e87–e93. https://doi.org/10.3109/17477166.2010.498917 Brown, C., Nkemjika, S., Ratto, J., Dube, S. R., Gilbert, L., Chiang, L., Picchetti, V., Coomer, R., Kambona, C., McOwen, J., Akani, B., Kamagate, M. F., Low, A., Manuel, P., Agusto, A., & Annor, F. B. (2024). Adverse childhood experiences and associations with mental health, substance use, and violence perpetration among young adults in sub-Saharan Africa. Child Abuse & Neglect , 150 , 106524. https://doi.org/10.1016/j.chiabu.2023.106524 Byansi, W., Musyoka, C. M., Baidoo, C. E., Okumu, M., Mutavi, T., Mbwayo, A., Mulwa, A. S., Kyalo, D. N., & Mathai, M. (2025). Cumulative adverse childhood experiences and their association with depression and anxiety: A cross-sectional study of youth living in informal urban settings in Kenya. Frontiers in Psychiatry , 16 , 1641321. https://doi.org/10.3389/fpsyt.2025.1641321 Carbone, G. A., Adenzato, M., Dell’Acqua, C., Imperatori, C., Lo Presti, A., De Rossi, E., Allegrini, G., Messerotti Benvenuti, S., Farina, B., & Ardito, R. B. (2026). Breaking the cycle: The protective role of perceived social support in the relationship between adverse childhood experiences, emotional dysregulation and depression in university students. Journal of Affective Disorders , 395 , 120669. https://doi.org/10.1016/j.jad.2025.120669 Cartwright, A. V., Pione, R. D., Stoner, C. R., & Spector, A. (2022). Validation of the multidimensional scale of perceived social support (MSPSS) for family caregivers of people with dementia. Aging & Mental Health , 26 (2), 286–293. https://doi.org/10.1080/13607863.2020.1857699 Chae, D., Lee, J., & Lee, E. H. (2025). Internal Structure of the Patient Health Questionnaire-9: A Systematic Review and Meta-analysis. Asian Nursing Research , 19 (1), 1–12. https://doi.org/10.1016/j.anr.2024.12.005 Cheong, Y. F., Fotiu, R. P., & Raudenbush, S. W. (2001). Efficiency and Robustness of Alternative Estimators for Two- and Three-level Models: The Case of NAEP. Journal of Educational and Behavioral Statistics , 26 (4), 411–429. https://doi.org/10.3102/10769986026004411 Cindik-Herbrueggen, E. D. (2025). Gender Differences in Depressive Symptomatology Among Turkish Immigrants: An Analysis of PHQ9 Scores. European Psychiatry , 68 (S1), S645–S645. https://doi.org/10.1192/j.eurpsy.2025.1312 Cluver, L. D., Orkin, F. M., Meinck, F., Boyes, M. E., Yakubovich, A. R., & Sherr, L. (2016). Can Social Protection Improve Sustainable Development Goals for Adolescent Health? PLOS ONE , 11 (10), e0164808. https://doi.org/10.1371/journal.pone.0164808 Cluver, L., Orkin, M., Boyes, M. E., & Sherr, L. (2015). Child and Adolescent Suicide Attempts, Suicidal Behavior, and Adverse Childhood Experiences in South Africa: A Prospective Study. Journal of Adolescent Health , 57 (1), 52–59. https://doi.org/10.1016/j.jadohealth.2015.03.001 Colin Cameron, A., & Miller, D. L. (2015). A Practitioner’s Guide to Cluster-Robust Inference. Journal of Human Resources , 50 (2), 317–372. https://doi.org/10.3368/jhr.50.2.317 Demaray, M. K., & Malecki, C. K. (2002). Critical levels of perceived social support associated with student adjustment. School Psychology Quarterly , 17 (3), 213–241. https://doi.org/10.1521/scpq.17.3.213.20883 Dube, S. R., Anda, R. F., Felitti, V. J., Chapman, D. P., Williamson, D. F., & Giles, W. H. (2001). Childhood Abuse, Household Dysfunction, and the Risk of Attempted Suicide Throughout the Life Span: Findings From the Adverse Childhood Experiences Study. Journal Of The American Medical Association , 286 (24), 3089. https://doi.org/10.1001/jama.286.24.3089 Fazel, M., Patel, V., Thomas, S., & Tol, W. (2014). Mental health interventions in schools in low-income and middle-income countries. The Lancet Psychiatry , 1 (5), 388–398. https://doi.org/10.1016/S2215-0366(14)70357-8 Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., Koss, M. P., & Marks, J. S. (1998). Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults. American Journal of Preventive Medicine , 14 (4), 245–258. https://doi.org/10.1016/S0749-3797(98)00017-8 Ferrajão, P. C., Tourais, B., Faria, I., Dias, J., & Elklit, A. (2024). A Latent Class Analysis of Adverse Life Events for Kenyan Adolescents. Journal of Child & Adolescent Trauma , 17 (2), 261–273. https://doi.org/10.1007/s40653-023-00603-4 Ferrajão, P., Dias, J., & Elklit, A. (2024). Defense mechanisms mediate associations between exposure to adverse childhood experiences and anxiety and depression in Kenyan adolescents. Traumatology , 30 (3), 238–250. https://doi.org/10.1037/trm0000426 Fletcher-Janzen, E., & Harrington, E. (2021). Translating ACE research into multi-tiered systems of supports for at-risk high-school students. Journal of Pediatric Neuropsychology , 7 (3), 89–101. https://doi.org/10.1037/h0101953 Fritz, J., De Graaff, A. M., Caisley, H., Van Harmelen, A. L., & Wilkinson, P. O. (2018). A Systematic Review of Amenable Resilience Factors That Moderate and/or Mediate the Relationship Between Childhood Adversity and Mental Health in Young People. Frontiers in Psychiatry , 9 , 230. https://doi.org/10.3389/fpsyt.2018.00230 Giri, S., Ross, N., Kornhaber, R., Ahmed, K. Y., & Thapa, S. (2025). The effects of parental adverse childhood experiences (ACEs) and childhood threat and deprivation on adolescent depression and anxiety: An analysis of the longitudinal study of Australian children. Epidemiology and Psychiatric Sciences , 34 , e49. https://doi.org/10.1017/S2045796025100255 Swahn, H., Babihuga, M., Dube, N. K., S. R., & Kasirye, R. (2020). Adverse Childhood Experiences and Alcohol Use Consumption Patterns among Vulnerable Urban Youth in Uganda. Addiction Research and Adolescent Behaviour , 3 (1), 01–08. https://doi.org/10.31579/2688-7517/017 Hale, D. R., Patalay, P., Fitzgerald-Yau, N., Hargreaves, D. S., Bond, L., Görzig, A., Wolpert, M., Stansfeld, S. A., & Viner, R. M. (2014). School-Level Variation in Health Outcomes in Adolescence: Analysis of Three Longitudinal Studies in England. Prevention Science , 15 (4), 600–610. https://doi.org/10.1007/s11121-013-0414-6 Hazlett, C., & Wainstein, L. (2022). Understanding, Choosing, and Unifying Multilevel and Fixed Effect Approaches. Political Analysis , 30 (1), 46–65. https://doi.org/10.1017/pan.2020.41 Herington, J., Li, K., & Pisani, A. R. (2024). Expanding the role of justice in secondary research using digital psychological data. American Psychologist , 79 (1), 123–136. https://doi.org/10.1037/amp0001190 Jakobsen, A. L., Hansen, C. D., & Andersen, J. H. (2022). The association between perceived social support in adolescence and positive mental health outcomes in early adulthood: A prospective cohort study. Scandinavian Journal of Public Health , 50 (3), 404–411. https://doi.org/10.1177/1403494821993718 Kelly, M. M., Martin-Peters, T., & Farber, J. S. (2024). Secondary Data Analysis: Using existing data to answer new questions. Journal of Pediatric Health Care , 38 (4), 615–618. https://doi.org/10.1016/j.pedhc.2024.03.005 Khasakhala, L., Ndetei, D., Mutiso, V., Mbwayo, A., & Mathai, M. (2012). The prevalence of depressive symptoms among adolescents in Nairobi public secondary schools: Association with perceived maladaptive parental behaviour. African Journal of Psychiatry , 15 (2), 106–113. https://doi.org/10.4314/ajpsy.v15i2.14 Kieling, C., Baker-Henningham, H., Belfer, M., Conti, G., Ertem, I., Omigbodun, O., Rohde, L. A., Srinath, S., Ulkuer, N., & Rahman, A. (2011). Child and adolescent mental health worldwide: Evidence for action. The Lancet , 378 (9801), 1515–1525. https://doi.org/10.1016/S0140-6736(11)60827-1 Kievit, R. A., McCormick, E. M., Fuhrmann, D., Deserno, M. K., & Orben, A. (2022). Using large, publicly available data sets to study adolescent development: Opportunities and challenges. Current Opinion in Psychology , 44 , 303–308. https://doi.org/10.1016/j.copsyc.2021.10.003 Kisito, J. M., Ogodo, F. M., & Gatumu, E. P. W. (2026). Economic Precariousness, Racialized Gender Inequality, and Child Mental Health Outcomes in Kenya: A systematic Literature Review. International Journal of Research and Innovation in Social Science , 10 (1), 5637–5652. https://doi.org/10.47772/IJRISS.2026.10100434 Kobrinsky, V., & Siedlecki, K. L. (2023). Mediators of the Relationship Between Adverse Childhood Experiences (ACEs) and Symptoms of Anxiety, Depression, and Suicidality among Adults. Journal of Child & Adolescent Trauma , 16 (2), 233–246. https://doi.org/10.1007/s40653-022-00510-0 Koğar, H., & Yılmaz Koğar, E. (2024). The structure of the multidimensional scale of perceived social support: A meta-analytic confirmatory factor analysis. Cambridge Prisms: Global Mental Health , 11 , e126. https://doi.org/10.1017/gmh.2024.118 Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine , 16 (9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x Krull, J. L., & MacKinnon, D. P. (2001). Multilevel Modeling of Individual and Group Level Mediated Effects. Multivariate Behavioral Research , 36 (2), 249–277. https://doi.org/10.1207/S15327906MBR3602_06 Langkamp, D. L., Lehman, A., & Lemeshow, S. (2010). Techniques for Handling Missing Data in Secondary Analyses of Large Surveys. Academic Pediatrics , 10 (3), 205–210. https://doi.org/10.1016/j.acap.2010.01.005 Lazaratou, H., Dikeos, D. G., Anagnostopoulos, D. C., & Soldatos, C. R. (2010). Depressive Symptomatology in High School Students: The Role of Age, Gender and Academic Pressure. Community Mental Health Journal , 46 (3), 289–295. https://doi.org/10.1007/s10597-010-9296-2 Lee, J. E. C., Phinney, B., Watkins, K., & Zamorski, M. A. (2016). Psychosocial Pathways Linking Adverse Childhood Experiences to Mental Health in Recently Deployed Canadian Military Service Members. Journal of Traumatic Stress , 29 (2), 124–131. https://doi.org/10.1002/jts.22085 Lee, J. S. (2019). Perceived social support functions as a resilience in buffering the impact of trauma exposure on PTSD symptoms via intrusive rumination and entrapment in firefighters. PLOS ONE , 14 (8), e0220454. https://doi.org/10.1371/journal.pone.0220454 Lipps, G. E., Lowe, G. A., Halliday, S., Morris-Patterson, A., Clarke, N., & Wilson, R. N. (2010). The association of academic tracking to depressive symptoms among adolescents in three Caribbean countries. Child and Adolescent Psychiatry and Mental Health , 4 (1), 16. https://doi.org/10.1186/1753-2000-4-16 Magige, L., Bukania, Z., Mwangi, M., Chuchu, V., Wanjihia, V., Karanja, S., Onteri, S., Macharia, A., & Ongeri, L. (2025). Prevalence and predictors of depressive symptoms among school-going adolescents in Kenya. PLOS Mental Health , 2 (11), e0000474. https://doi.org/10.1371/journal.pmen.0000474 Marks, G. N. (2008). Accounting for the gender gaps in student performance in reading and mathematics: Evidence from 31 countries. Oxford Review of Education , 34 (1), 89–109. https://doi.org/10.1080/03054980701565279 Matthews, T. A., Shao, H., Forster, M., & Kim, I. (2024). Associations of adverse childhood experiences with depression and anxiety among children in the United States: Racial and ethnic disparities in mental health. Journal of Affective Disorders , 362 , 645–651. https://doi.org/10.1016/j.jad.2024.07.121 McCoach, D. B., & Adelson, J. L. (2010). Dealing With Dependence (Part I): Understanding the Effects of Clustered Data. Gifted Child Quarterly , 54 (2), 152–155. https://doi.org/10.1177/0016986210363076 Meeker, E. C., O’Connor, B. C., Kelly, L. M., Hodgeman, D. D., Scheel-Jones, A. H., & Berbary, C. (2021). The impact of adverse childhood experiences on adolescent health risk indicators in a community sample. Psychological Trauma: Theory Research Practice and Policy , 13 (3), 302–312. https://doi.org/10.1037/tra0001004 Muhati, L. I. (2012). An investigation into the strategies used in addressing drug abuse problems’’ a case study of Nairobi provincial boys secondary schools [Thesis, Kenyatta University]. http://ir-library.ku.ac.ke/handle/123456789/2492 Mutamba, E. M., Mbwayo, A., & Njiru, L. (2026). Adverse Childhood Experiences and Associated Correlates among Adolescents at a Rural Private High School in Kenya. International Journal of Research and Innovation in Social Science , 10 (1), 1538–1551. https://doi.org/10.47772/IJRISS.2026.10100125 Mutiso, V., Ndetei, D., Jeremiah, E., Nyamai, P., Walusaka, S., Onyango, V., Musyimi, C., Bhui, K., & Mamah, D. (2025). The moderating and mediating role of resilience in the relationship between adverse childhood experiences and depression, PTSD, and suicidality in Kenyan youth. Cambridge Prisms: Global Mental Health , 12 , e88. https://doi.org/10.1017/gmh.2025.10041 Okawa, S., Yasuoka, J., Ishikawa, N., Poudel, K. C., Ragi, A., & Jimba, M. (2011). Perceived social support and the psychological well-being of AIDS orphans in urban Kenya. Aids Care , 23 (9), 1177–1185. https://doi.org/10.1080/09540121.2011.554530 Onyango-Ouma, W., Birungi, H., & Geibel, S. (2005). Understanding the HIV/STI risks and prevention needs of men who have sex with men in Nairobi, Kenya. Population Council . https://doi.org/10.31899/HIV2.1031 Osborn, T. L., Roe, E., Alemu, R., Venturo-Conerly, K., Gan, J., Arango, S., Johnson, N., Campbell, S., & Rodriguez, M. (2020). Depression and Anxiety Symptoms in Kenyan Adolescents (2020) . https://doi.org/10.17605/OSF.IO/KSX6Y Osborn, T. L., Venturo-Conerly, K. E., Arango, G., Roe, S., Rodriguez, E., Alemu, M., Gan, R. G., Wasil, J., Otieno, A. R., Rusch, B. H., Ndetei, T., Wasanga, D. M., Schleider, C., J. L., & Weisz, J. R. (2021). Effect of Shamiri Layperson-Provided Intervention vs Study Skills Control Intervention for Depression and Anxiety Symptoms in Adolescents in Kenya: A Randomized Clinical Trial. JAMA Psychiatry , 78 (8), 829. https://doi.org/10.1001/jamapsychiatry.2021.1129 Osborn, T. L., Venturo-Conerly, K. E., Gan, J., Rodriguez, M., Alemu, R. G., Roe, E., Arango, S., Wasil, A. R., Campbell, S., Weisz, J. R., & Wasanga, C. (2021). Depression and Anxiety Symptoms Amongst Kenyan Adolescents: Psychometric Properties, Prevalence, Sociodemographic Factors, and Psychological Wellbeing . PsyArXiv. https://doi.org/10.31234/osf.io/ze8tf Osborn, T. L., Venturo-Conerly, K. E., Wasil, A. R., Rodriguez, M., Roe, E., Alemu, R., Arango, G., Gan, S., Wasanga, J., Schleider, C., J. L., & Weisz, J. R. (2020). The Shamiri group intervention for adolescent anxiety and depression: Study protocol for a randomized controlled trial of a lay-provider-delivered, school-based intervention in Kenya. Trials , 21 (1), 938. https://doi.org/10.1186/s13063-020-04732-1 Osborn, T. L., Venturo-Conerly, K. E., Wasil, A. R., Schleider, J. L., & Weisz, J. R. (2020). Depression and Anxiety Symptoms, Social Support, and Demographic Factors Among Kenyan High School Students. Journal of Child and Family Studies , 29 (5), 1432–1443. https://doi.org/10.1007/s10826-019-01646-8 Panagou, C., & MacBeth, A. (2022). Deconstructing pathways to resilience: A systematic review of associations between psychosocial mechanisms and transdiagnostic adult mental health outcomes in the context of adverse childhood experiences. Clinical Psychology & Psychotherapy , 29 (5), 1626–1654. https://doi.org/10.1002/cpp.2732 Pattullo, A. L. S., Malonza, M., Kimani, G. G., Muthee, A., Otieno, P. A. O., Odhiambo, K., Moses, S., & Plummer, F. A. (1994). Survey of knowledge, behaviour and attitudes relating to HIV infection and AIDS among kenyan secondary school students. Aids Care , 6 (2), 173–181. https://doi.org/10.1080/09540129408258628 Petersen, K. J., Qualter, P., Humphrey, N., Damsgaard, M. T., & Madsen, K. R. (2023). With a Little Help from My Friends: Profiles of Perceived Social Support and Their Associations with Adolescent Mental Health. Journal of Child and Family Studies , 32 (11), 3430–3446. https://doi.org/10.1007/s10826-023-02677-y Pigatto, F., Grant, C. C., Marks, E., Walker, C., & Waldie, K. E. (2025). Cumulative risk for comorbidity of depression and anxiety in young people. Journal of Affective Disorders , 382 , 611–618. https://doi.org/10.1016/j.jad.2025.04.150 Pina, D., Pérez-Albéniz, A., Díez-Gómez, A., Pérez-Esteban, A., & Fonseca-Pedrero, E. (2025). Validation of the Multidimensional Scale of Perceived Social Support (MSPSS) in a Representative Sample of Adolescents: Links with Well-being, Mental Health, and Suicidal Behavior. Psychosocial Intervention , 34 (2), 79–87. https://doi.org/10.5093/pi2025a7 Plamondon, A., Racine, N., McDonald, S., Tough, S., & Madigan, S. (2022). Disentangling adversity timing and type: Contrasting theories in the context of maternal prenatal physical and mental health using latent formative models. Development and Psychopathology , 34 (5), 1961–1973. https://doi.org/10.1017/S0954579421000353 Randhawa, A., Wood, G., Michail, M., Pallan, M., Patterson, P., & Goodyear, V. (2024). Safeguarding in adolescent mental health research: Navigating dilemmas and developing procedures. British Medical Journal Open , 14 (2), e076700. https://doi.org/10.1136/bmjopen-2023-076700 Rueger, S. Y., Malecki, C. K., & Demaray, M. K. (2010). Relationship Between Multiple Sources of Perceived Social Support and Psychological and Academic Adjustment in Early Adolescence: Comparisons Across Gender. Journal of Youth and Adolescence , 39 (1), 47–61. https://doi.org/10.1007/s10964-008-9368-6 Rueger, S. Y., Malecki, C. K., Pyun, Y., Aycock, C., & Coyle, S. (2016). A meta-analytic review of the association between perceived social support and depression in childhood and adolescence. Psychological Bulletin , 142 (10), 1017–1067. https://doi.org/10.1037/bul0000058 Samia, P., Premji, S., Tavangar, F., Yim, I. S., Wanyonyi, S., Merali, M., Musana, W., Omuse, G., Forcheh, N., Dosani, A., Letourneau, N., & MIGHT Consortium. (2020). Adverse Childhood Experiences and Changing Levels of Psychosocial Distress Scores across Pregnancy in Kenyan Women. International Journal of Environmental Research and Public Health , 17 (10), 3401. https://doi.org/10.3390/ijerph17103401 Scheier, L. M., Griffin, K. W., Doyle, M. M., & Botvin, G. J. (2002). Estimates of Intragroup Dependence for Drug Use and Skill Measures in School-Based Drug Abuse Prevention Trials: An Empirical Study of Three Independent Samples. Health Education & Behavior , 29 (1), 85–103. https://doi.org/10.1177/109019810202900109 Sevenoaks, T., Fouche, J. P., Phillips, N., Heany, S., Myer, L., Zar, H. J., Stein, D. J., & Hoare, J. (2022). Childhood Trauma and Mental Health in the Cape Town Adolescent Antiretroviral Cohort. Journal of Child & Adolescent Trauma , 15 (2), 353–363. https://doi.org/10.1007/s40653-021-00362-0 Shackleton, N., Hale, D., Bonell, C., & Viner, R. M. (2016). Intraclass correlation values for adolescent health outcomes in secondary schools in 21 European countries. SSM - Population Health , 2 , 217–225. https://doi.org/10.1016/j.ssmph.2016.03.005 Sloss, I. M., Smith, J., Sebben, S., Wade, M., Prime, H., & Browne, D. T. (2025). Family functioning in the context of current and historical stressors: Exploring the buffering role of social support. Child Abuse & Neglect , 168 , 106711. https://doi.org/10.1016/j.chiabu.2024.106711 Smolkowski, K., Biglan, A., Dent, C., & Seeley, J. (2006). The Multilevel Structure of Four Adolescent Problems. Prevention Science , 7 (3), 239–256. https://doi.org/10.1007/s11121-006-0034-5 Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Archives of Internal Medicine , 166 (10), 1092. https://doi.org/10.1001/archinte.166.10.1092 Stochl, J., Fried, E. I., Fritz, J., Croudace, T. J., Russo, D. A., Knight, C., Jones, P. B., & Perez, J. (2022). On Dimensionality, Measurement Invariance, and Suitability of Sum Scores for the PHQ-9 and the GAD-7. Assessment , 29 (3), 355–366. https://doi.org/10.1177/1073191120976863 Strugnell, C., Orellana, L., Hayward, J., Millar, L., Swinburn, B., & Allender, S. (2018). Active (Opt-In) Consent Underestimates Mean BMI-z and the Prevalence of Overweight and Obesity Compared to Passive (Opt-Out) Consent. Evidence from the Healthy Together Victoria and Childhood Obesity Study. International Journal of Environmental Research and Public Health , 15 (4), 747. https://doi.org/10.3390/ijerph15040747 Suh, Y., Weikert, M., Dlugonski, D., Sandroff, B., & Motl, R. W. (2012). Physical activity, social support, and depression: Possible independent and indirect associations in persons with multiple sclerosis. Psychology Health & Medicine , 17 (2), 196–206. https://doi.org/10.1080/13548506.2011.601747 Suliman, S., Mkabile, S. G., Fincham, D. S., Ahmed, R., Stein, D. J., & Seedat, S. (2009). Cumulative effect of multiple trauma on symptoms of posttraumatic stress disorder, anxiety, and depression in adolescents. Comprehensive Psychiatry , 50 (2), 121–127. https://doi.org/10.1016/j.comppsych.2008.06.006 Tang, X., Tang, S., Ren, Z., & Wong, D. F. K. (2019). Prevalence of depressive symptoms among adolescents in secondary school in mainland China: A systematic review and meta-analysis. Journal of Affective Disorders , 245 , 498–507. https://doi.org/10.1016/j.jad.2018.11.043 Tele, A., Nyamai, D., Shawar, Y. R., Nyongesa, V., Kiogora, S., Peterson, S. S., Obonyo, G., Cuijpers, P., & Kumar, M. (2025). The political economy of adolescent mental health in Kenya. Health Policy and Planning , 40 (10), 1017–1026. https://doi.org/10.1093/heapol/czaf057 Templeton, J. M., Dixon, W. E., Williams, S., Morelen, D., Driggers-Jones, L., & Robertson, C. (2025). The mediating role of social support on the link between adverse childhood experiences and adult mental health. Journal of Experimental Child Psychology , 252 , 106148. https://doi.org/10.1016/j.jecp.2024.106148 Tsehay, M., Necho, M., & Mekonnen, W. (2020). The Role of Adverse Childhood Experience on Depression Symptom, Prevalence, and Severity among School Going Adolescents. Depression Research and Treatment , 2020 , 1–9. https://doi.org/10.1155/2020/5951792 Von Simson, K., Brekke, I., & Hardoy, I. (2022). The Impact of Mental Health Problems in Adolescence on Educational Attainment. Scandinavian Journal of Educational Research , 66 (2), 306–320. https://doi.org/10.1080/00313831.2020.1869077 Wado, Y. D., Njeri, A., Odunga, S. A., Akuku, I., Wahdi, A. E., Fine, S. L., Ramaiya, A., Li, M., Loi, V. M., Maravilla, J. C., Scott, J. G., Erskine, H. E., & Kabiru, C. W. (2025). The association between adverse childhood experiences and mental disorders among adolescents in Kenya, Indonesia, and Vietnam: Evidence from the National Adolescent Mental Health Surveys. Child and Adolescent Psychiatry and Mental Health , 19 (S1), 86. https://doi.org/10.1186/s13034-025-00919-z Wahdi, A. E., Astrini, Y. P., Setyawan, A., Fine, S. L., Ramaiya, A., Li, M., Wado, Y. D., Loi, V. M., Maravilla, J. C., Scott, J. G., Wilopo, S. A., & Erskine, H. E. (2025). Mental health service use among adolescents in three low- and middle-income countries: An analysis of the National Adolescent Mental Health Surveys. Child and Adolescent Psychiatry and Mental Health , 19 (S1), 84. https://doi.org/10.1186/s13034-025-00924-2 Wahlbeck, K., Cresswell-Smith, J., Haaramo, P., & Parkkonen, J. (2017). Interventions to mitigate the effects of poverty and inequality on mental health. Social Psychiatry and Psychiatric Epidemiology , 52 (5), 505–514. https://doi.org/10.1007/s00127-017-1370-4 Wickrama, T., & Vazsonyi, A. T. (2011). School contextual experiences and longitudinal changes in depressive symptoms from adolescence to young adulthood. Journal of Community Psychology , 39 (5), 566–575. https://doi.org/10.1002/jcop.20453 Wills, T. A., & Cleary, S. D. (1996). How are social support effects mediated? A test with parental support and adolescent substance use. Journal of Personality and Social Psychology , 71 (5), 937–952. https://doi.org/10.1037/0022-3514.71.5.937 Wuthrich, V. M., Belcher, J., Kilby, C., Jagiello, T., & Lowe, C. (2021). Tracking stress, depression, and anxiety across the final year of secondary school: A longitudinal study. Journal of School Psychology , 88 , 18–30. https://doi.org/10.1016/j.jsp.2021.07.004 Youssef, N. A., Belew, D., Hao, G., Wang, X., Treiber, F. A., Stefanek, M., Yassa, M., Boswell, E., McCall, W. V., & Su, S. (2017). Racial/ethnic differences in the association of childhood adversities with depression and the role of resilience. Journal of Affective Disorders , 208 , 577–581. https://doi.org/10.1016/j.jad.2016.10.024 Yuan, G., Xu, W., Liu, Z., & An, Y. (2018). Resilience, Posttraumatic Stress Symptoms, and Posttraumatic Growth in Chinese Adolescents After a Tornado: The Role of Mediation Through Perceived Social Support. Journal of Nervous & Mental Disease , 206 (2), 130–135. https://doi.org/10.1097/NMD.0000000000000778 Zhao, M., Miao, H., Zhu, L. L., Zhang, X. H., & Zang, L. W. (2025). Social support and adolescent mental health in Kenya: A parallel mediation analysis of perceived control and gratitude. Frontiers in Pediatrics , 13 , 1626249. https://doi.org/10.3389/fped.2025.1626249 Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment , 52 (1), 30–41. https://doi.org/10.1207/s15327752jpa5201_2 Additional Declarations No competing interests reported. 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. 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17:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9327795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9327795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106345372,"identity":"bb79aad4-a463-4eff-9df8-fa69bcf58bb4","added_by":"auto","created_at":"2026-04-07 16:21:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of depressive and anxiety symptom scores (PHQ and GAD) by gender among Kenyan secondary school students\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9327795/v1/d15d00d6cdfbadf745b231a8.png"},{"id":106404161,"identity":"0478afa7-d31b-4d20-a9ca-539abd7317d4","added_by":"auto","created_at":"2026-04-08 09:15:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted depressive symptom scores (PHQtotal) across the observed range of adverse childhood experiences (ACEStotal), by gender, from the multilevel model adjusted for age, school form, perceived social support, and school clustering\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9327795/v1/6e4f5927bfb8640f23d528fa.png"},{"id":106345373,"identity":"3b665cea-0288-4e90-8625-61415fc1bd3b","added_by":"auto","created_at":"2026-04-07 16:21:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted anxiety symptom scores (GAD_total) across the observed range of adverse childhood experiences (ACEStotal), by gender, from the multilevel model adjusted for age, school form, perceived social support, and school clustering\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9327795/v1/11ca095e0a51dd70dbf71703.png"},{"id":106406047,"identity":"4e3de8d9-aaaf-4516-b083-69b783d85273","added_by":"auto","created_at":"2026-04-08 09:29:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1229287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327795/v1/668d5f74-a748-4bb4-b329-366dba57d516.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cumulative childhood trauma exposure and depressive and anxiety symptoms among Kenyan adolescents: A multilevel school-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescence is a developmental period marked by heightened vulnerability to the psychological effects of childhood trauma, including the emergence of depression and anxiety, which together account for a substantial proportion of disease burden worldwide (Kieling et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In many countries, these conditions emerge during secondary school years and can have lasting consequences for educational attainment, social functioning, and risk of chronic mental disorders in adulthood (Von Simson et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In low- and middle-income countries such as Kenya, this burden is compounded by limited access to mental health services, high rates of exposure to violence, neglect, and household dysfunction, and ongoing educational pressures within school systems (Fazel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kisito et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Tele et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent school-based surveys in Kenya report that between roughly one fifth and over half of adolescents meet criteria for probable depression, depending on region and methodology, underscoring the urgency of understanding modifiable risk and protective factors in school settings where young people can be routinely reached (Khasakhala et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Magige et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Osborn, Venturo-Conerly, Wasil, Schleider, et al., 2020).\u003c/p\u003e \u003cp\u003eAdverse childhood experiences (ACEs) \u0026mdash; encompassing childhood traumas such as abuse, neglect, and household dysfunction \u0026mdash; are consistently linked to elevated depressive and anxiety symptoms and other negative outcomes across the life course (Dube et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Felitti et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Cumulative ACE exposure shows a dose-response association with depressive and anxiety symptoms, substance use, and suicidality in diverse adolescent samples (Matthews et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Emerging evidence from sub-Saharan Africa suggests that Kenyan adolescents experience high rates of adversity, with higher ACE counts associated with more severe depression, anxiety, and bullying victimization (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; P. Ferraj\u0026atilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A recent study in Nairobi\u0026rsquo;s informal settlements, for example, found that many youths had experienced multiple forms of violence and deprivation, and that cumulative ACEs were strongly associated with symptoms of depression, anxiety, and stress (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Taken together, these findings highlight childhood trauma as a central risk factor for adolescent mental health in Kenya and raise the question of which resources within and around schools might support resilience and recovery among trauma-exposed youth.\u003c/p\u003e \u003cp\u003ePerceived social support is widely recognized as a key protective factor in adolescent mental health, reflecting the extent to which young people feel valued, cared for, and embedded in supportive relationships with family, friends, teachers, and other significant figures (Jakobsen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Higher perceived support is typically associated with lower depressive and anxiety symptoms, greater wellbeing, and better academic outcomes in school-based samples (Demaray \u0026amp; Malecki, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rueger et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In Kenya, recent work using the Multidimensional Scale of Perceived Social Support (MSPSS) has shown that social support is both directly associated with fewer internalizing symptoms and indirectly associated with better mental health via mediators such as perceived control and gratitude (Zhao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, less is known about whether social support independently predicts mental health once cumulative trauma exposure is considered, and whether its role among trauma-exposed adolescents is best conceptualized as a direct buffer or as part of a broader resilience process that facilitates recovery.\u003c/p\u003e \u003cp\u003eDespite the growing literature on adolescent mental health in Kenya, several important gaps remain. First, many studies focus on small or geographically restricted samples, limiting the generalizability of findings to the diverse population of Kenyan secondary school students (Muhati, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pattullo et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Notably, the Journal of Child \u0026amp; Adolescent Trauma has published latent class analyses of ACEs among Kenyan adolescents (Ferraj\u0026atilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but no study in this journal has yet examined cumulative ACE dose-response associations with both depression and anxiety in a large Kenyan school sample. Third, school-based surveys often rely on single-level analyses that do not explicitly account for the clustering of students within schools, which can bias standard errors and yield misleading inferences when intraclass correlations are non-trivial (Cheong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; McCoach \u0026amp; Adelson, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Finally, there is limited evidence on how these risk and protective processes operate in ways that are directly informative for early identification and mental health care for adolescents in routine service and school-linked settings.\u003c/p\u003e \u003cp\u003eThe present study addresses these gaps by conducting a secondary analysis of a large, school-based dataset on the mental health and wellbeing of Kenyan adolescents collected by the Shamiri Institute and collaborators (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The dataset includes more than 17,000 secondary school students from multiple counties, providing one of the most comprehensive sources of information on adolescent internalizing symptoms and psychosocial adversity in a low- and middle-income context to date (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Using validated measures of depressive symptoms (PHQ-8), anxiety symptoms (GAD-7), adverse childhood experiences, and perceived social support (MSPSS), this study examines how cumulative ACE exposure and perceived social support relate to depressive and anxiety symptoms, while accounting for age, gender, school form, and the clustering of students within schools (Dube et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Marks, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study is guided by a cumulative trauma and resilience framework. From a cumulative trauma perspective, each additional traumatic experience is expected to incrementally increase the likelihood of depressive and anxiety symptoms, in line with prior ACE research in both high-income and Kenyan settings (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wado et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). From a resilience perspective, perceived social support is conceptualized as a potentially protective factor that may attenuate the association between trauma exposure and symptoms, either directly or through mediators such as perceived control and gratitude, thereby facilitating recovery and positive adaptation in the face of childhood trauma (Lee, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Clarifying how cumulative trauma and perceived support jointly relate to depressive and anxiety symptoms in adolescents is therefore directly relevant for trauma-informed mental health services seeking to identify high-risk youth and to design contextually appropriate interventions in low- and middle-income settings (Pigatto et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Suliman et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccordingly, the primary aims of this study are threefold. First, to describe levels of depressive symptoms, anxiety symptoms, cumulative ACE exposure, and perceived social support in a large sample of Kenyan secondary school students. Second, to estimate the associations between ACEStotal and both PHQtotal and GADtotal, after adjusting for sociodemographic covariates and school clustering. Third, to assess whether MSPSStotal shows an independent association with depressive and anxiety symptoms when modeled alongside cumulative ACEs and demographic factors. By addressing these aims, the study seeks to clarify the relative contributions of adversity and perceived social support to Kenyan adolescents\u0026rsquo; mental health and to inform ACE-informed approaches to risk stratification, early identification, and referral within adolescent mental health and school-linked services.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and Data Source\u003c/h2\u003e \u003cp\u003eThis study is a secondary analysis of anonymised survey data drawn from a large, school-based study of adolescent mental health and wellbeing conducted by the Shamiri Institute and collaborators in Kenya (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The original project employed a cross-sectional design to characterize mental health symptoms, psychosocial risk and protective factors, and school context among Kenyan secondary school students in multiple counties (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Secondary analyses of existing datasets are increasingly used in adolescent mental health research to maximize the scientific value of large surveys and to address new questions without imposing additional burden on participants (Kelly et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kievit et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the present study, the existing dataset was restricted to variables relevant to depressive symptoms, anxiety symptoms; adverse childhood experiences (ACEs), perceived social support, sociodemographic characteristics, and school identifiers. No new data were collected; all procedures described below for sampling, recruitment, and data collection summarize the methods of the original investigators as reported in their study documentation.\u003c/p\u003e \u003cp\u003eThe data analyzed in this study were drawn from the open, de-identified project \u0026ldquo;A Dataset on Adolescent Mental Health in Kenya,\u0026rdquo; hosted on the Open Science Framework (OSF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/k3xtd/files/8zh4g\u003c/span\u003e\u003cspan address=\"https://osf.io/k3xtd/files/8zh4g\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The original dataset (MHS_merged 1.csv) includes survey responses from 17,089 Kenyan adolescents (ages 12\u0026ndash;19) across four counties, with measures of depressive and anxiety symptoms (PHQ-8, GAD-7), psychosocial stressors (including adverse childhood experiences), social support, and sociodemographic characteristics. For the present secondary analysis, we used a derived restricted file (MHS_merged-1.csv) that focused on variables relevant to depressive and anxiety symptoms, cumulative ACE exposure, perceived social support, and core sociodemographic indicators.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling and Participants\u003c/h3\u003e\n\u003cp\u003eIn the parent study, schools were recruited in collaboration with county education officials to ensure representation of different school types (e.g., day versus boarding, county versus extra-county), single-sex and mixed-sex schools, and diverse geographic regions (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Within participating schools, entire classes or grade streams were invited to participate to minimize selection bias, a common approach in school-based epidemiological surveys (Booth et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Strugnell et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe full dataset includes responses from 17,089 adolescents enrolled in Forms 1\u0026ndash;4. For the current secondary analysis, cases with missing data on key study variables were removed via listwise deletion, resulting in an analytic sample of 15,177 adolescents nested within 23 school clusters. The mean age of adolescents in the analytic sample was approximately 16 years, and students were distributed across Forms 1\u0026ndash;4, consistent with the typical age-grade structure of Kenyan secondary education (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Descriptive statistics for the sample and key study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Gender was coded as male (1) and female (2) based on school administrative records. This binary classification reflects the categories available in the original dataset and does not capture the full spectrum of gender identities. A small number of cases with missing or other codes were excluded from models involving gender.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThe original data collection was approved by Kenyan institutional review boards and partnering universities and conducted in accordance with national guidelines for research in schools (Onyango-Ouma et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Head teachers provided school-level consent, parents or guardians provided consent according to school policy, and adolescents gave written assent after being informed about the voluntary and confidential nature of participation (Bonell et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The original study implemented a safety protocol whereby students reporting severe distress or suicidality were referred to school counselors or local services, which is consistent with best practice for school-based mental health research (Randhawa et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the present secondary analysis, only de-identified data were used. No attempt was made to link responses back to individual students or schools beyond the anonymous school_id variable required for multilevel modeling. The secondary analysis was reviewed and judged exempt from additional full ethics review by the authors\u0026rsquo; institution because it involved analysis of pre-existing, anonymised data collected under prior ethical approvals (Herington et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eDepressive symptoms\u003c/b\u003e. Depressive symptoms were assessed in the original survey using the eight-item Patient Health Questionnaire (PHQ-8), which asks how often core depressive symptoms were experienced over the past two weeks (Arias De La Torre et al., 2023). Items are rated from 0 (\u0026ldquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every day\u0026rdquo;) and summed to yield a total score, with higher scores indicating more severe depressive symptoms (Kroenke et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The PHQ-8 has demonstrated good reliability and validity in adolescent populations and has been used extensively in Kenyan youth in prior Shamiri trials (Osborn, Venturo-Conerly, Arango G., et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osborn, Venturo-Conerly, Wasil, Rodriguez, et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For this secondary analysis, PHQtotal was calculated as the sum of PHQ-8 items for each participant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnxiety symptoms\u003c/b\u003e. Anxiety symptoms were measured using the seven-item Generalized Anxiety Disorder scale (GAD-7), which assesses the frequency of common anxiety symptoms over the past two weeks (Spitzer et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Items are rated from 0 to 3 and summed to produce a total score, with higher scores reflecting greater anxiety (Spitzer et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The GAD-7 has shown strong psychometric properties in adolescent and young adult samples and has been used in Kenyan school-based intervention studies (Osborn, Roe, et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). GADtotal was computed as the sum of GAD-7 items in the existing dataset.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdverse childhood experiences\u003c/b\u003e. ACEs were captured using a set of items adapted from standard ACE checklists to reflect experiences relevant in the Kenyan context, including household dysfunction, abuse, neglect, and exposure to violence (Mutiso et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Samia et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Each item was coded to indicate the presence or absence of the adverse experience, and ACEStotal was constructed as a simple count of endorsed ACEs, consistent with the cumulative risk approach commonly used in ACE research (Anda et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bethell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Higher ACEStotal scores therefore indicate exposure to a greater number of distinct adversities. Previous analyses of this dataset have shown that the ACE index exhibits acceptable internal consistency and strong predictive validity for internalizing symptoms among Kenyan adolescents (Wado et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePerceived social support\u003c/b\u003e. Perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS), a 12-item instrument that assesses support from family, friends, and a significant other (Koğar \u0026amp; Yılmaz Koğar, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Items are rated on a 7-point Likert scale from 1 (\u0026ldquo;very strongly disagree\u0026rdquo;) to 7 (\u0026ldquo;very strongly agree\u0026rdquo;) and summed or averaged to yield a global support score, with higher scores indicating greater perceived social support (Zimet et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The MSPSS has been widely validated, including in adolescent school samples, and typically demonstrates high internal consistency (Pina et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this secondary analysis, MSPSStotal was calculated as the sum of all MSPSS items; prior work using the same dataset has reported good reliability for this total score (Cartwright et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSociodemographic and school variables\u003c/b\u003e. Adolescents reported their age (in years), gender, and school form (1\u0026ndash;4), which were treated as covariates because mental health symptoms vary, systematically across these dimensions during adolescence (Barbieri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barene et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). School-level information (e.g., boarding versus day, school type, demographic composition, county) was derived from administrative records in the original study and combined to create an anonymous school_id variable representing unique school clusters. This variable was used to specify clustering in multilevel and cluster-robust models.\u003c/p\u003e\n\u003ch3\u003eData Preparation\u003c/h3\u003e\n\u003cp\u003eFor the current secondary analysis, data were imported into R (version 4.5.2) from the original CSV file. After preliminary checks, cases with missing values on PHQtotal, GADtotal, ACEStotal, MSPSStotal, or core covariates (age, gender, form, school_id) were excluded using listwise deletion, yielding a final analytic sample of 15,177 adolescents. Listwise deletion is commonly used in secondary analyses of large surveys when missingness is low and when more complex imputation procedures are not feasible without access to the full original study documentation (Langkamp et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). All scale scores (PHQtotal, GADtotal, ACEStotal, MSPSStotal) were recalculated from item-level data to ensure consistency and to allow sensitivity checks if needed, following standard scoring procedures (Chae et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stochl et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalytic Strategy\u003c/h2\u003e \u003cp\u003eThe analytic approach for this secondary analysis mirrored and extended the methods used in the original study, with a specific focus on modeling the associations of ACEStotal and MSPSStotal with depressive and anxiety symptoms while accounting for school clustering (Giri et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). First, descriptive statistics and distribution plots were generated to summarize symptom levels and key predictors, including Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showing PHQtotal and GADtotal distributions by gender. Second, null random-intercept multilevel models were estimated for PHQtotal and GADtotal to compute intraclass correlation coefficients (ICCs) and quantify the proportion of variance attributable to between-school differences (Hale et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, main multilevel models were fitted for each outcome including age, gender, school form, ACEStotal, and MSPSStotal as fixed effects and random intercepts for school_id. Models were estimated using restricted maximum likelihood in the lme4 package, which is recommended for continuous outcomes in multilevel designs (Bates et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Fourth, single-level ordinary least squares models with cluster-robust standard errors were estimated as sensitivity analyses to evaluate the robustness of fixed-effect estimates to modeling choices (Abe \u0026amp; Gee, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hazlett \u0026amp; Wainstein, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis secondary-analysis design allowed the leveraging of a rich, existing dataset to address new questions about the joint roles of ACEs and perceived social support in Kenyan adolescents\u0026rsquo; mental health while avoiding additional data-collection burden and adhering to ethical standards for the use of de-identified data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eA total of 15,177 adolescents from 23 school clusters were included in the analytic sample after listwise deletion of incomplete cases. Participants\u0026rsquo; mean age was 15.90 years (SD\u0026thinsp;=\u0026thinsp;1.42, range 11\u0026ndash;25), and students were drawn from Forms 1\u0026ndash;4 of Kenyan secondary schools. The mean PHQtotal score was 7.77 (SD\u0026thinsp;=\u0026thinsp;4.83, range 0\u0026ndash;27), indicating mild but heterogeneous depressive symptoms. The mean GAD_total score was 7.15 (SD\u0026thinsp;=\u0026thinsp;4.94, range 0\u0026ndash;27). Exposure to adverse childhood experiences was generally low but skewed, with ACEStotal showing a mean of 0.33 (SD\u0026thinsp;=\u0026thinsp;1.06, range 0\u0026ndash;10). Perceived social support (MSPSS_total) had a mean of 3.85 (SD\u0026thinsp;=\u0026thinsp;8.78, range 0\u0026ndash;28). This low mean and wide range suggest that many participants endorsed minimal social support on the items available in the dataset. The scoring reflects the sum of recoded or selected MSPSS items as provided in the original dataset; because the standard 12-item MSPSS (scored 1\u0026ndash;7 per item) yields summed totals of 12\u0026ndash;84, the observed range indicates that the original investigators may have used a subset of items or an alternative scoring scheme. Readers should interpret absolute MSPSS_total values with caution, though relative associations remain valid. Gender distribution was 54.2% male (n\u0026thinsp;=\u0026thinsp;8,223) and 45.8% female (n\u0026thinsp;=\u0026thinsp;6,954), and students were distributed across Forms 1\u0026ndash;4 as follows: 36.6% (n\u0026thinsp;=\u0026thinsp;5,555), 32.6% (n\u0026thinsp;=\u0026thinsp;4,952), 19.3% (n\u0026thinsp;=\u0026thinsp;2,932), and 11.5% (n\u0026thinsp;=\u0026thinsp;1,738), respectively.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive statistics for key study variables (N\u0026thinsp;=\u0026thinsp;15,177)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQtotal (depression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD_total (anxiety)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEStotal (adversity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS_total (social support)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e PHQ\u0026thinsp;=\u0026thinsp;Patient Health Questionnaire-8; GAD\u0026thinsp;=\u0026thinsp;Generalized Anxiety Disorder-7; ACES\u0026thinsp;=\u0026thinsp;Adverse Childhood Experiences Scale; MSPSS\u0026thinsp;=\u0026thinsp;Multidimensional Scale of Perceived Social Support. Data were drawn from the Shamiri Institute dataset (Baseke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026lt;\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/td\u003e\u003c/tr\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntraclass Correlations\u003c/h2\u003e \u003cp\u003ePrior to fitting the substantive multilevel models, null (intercept-only) random-intercept models were estimated for each outcome to quantify the proportion of variance attributable to between-school differences. For PHQtotal, the intraclass correlation coefficient (ICC) was .024, indicating that approximately 2.4% of the variance in depressive symptoms lay between schools and 97.6% within schools. For GAD_total, the ICC was .015, indicating that about 1.5% of the variance in anxiety symptoms was between schools. Although these values are modest, they are consistent with ICCs reported in school-based mental health studies internationally, where values in the .01-.05 range are common (Pina et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). ICCs of this magnitude can meaningfully inflate Type I error rates when clustering is ignored (McCoach \u0026amp; Adelson, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thereby justifying the use of multilevel modeling or, at minimum, cluster-robust standard errors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultilevel Models for Depressive Symptoms (PHQtotal)\u003c/h2\u003e \u003cp\u003eThe main multilevel model for PHQtotal included Age, Gender, Form, ACEStotal, and MSPSS_total as fixed effects with a random intercept for school cluster. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMultilevel model predicting PHQtotal (Depression)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female vs. male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;10.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEStotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS_total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool-level variance (τ₀₀)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual variance (σ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Gender reference category\u0026thinsp;=\u0026thinsp;male; Form reference category\u0026thinsp;=\u0026thinsp;Form 1. Model estimated with REML using lme4 (Bates et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). N\u0026thinsp;=\u0026thinsp;15,177 students in 23 school clusters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOlder age was positively associated with depressive symptoms (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08), indicating that each additional year of age corresponded to higher PHQtotal scores. This age effect is consistent with evidence that older adolescents in Kenya report higher levels of depression (Osborn, Venturo-Conerly, Wasil, Schleider, et al., 2020). Gender was also significantly associated with PHQtotal: female participants reported lower depressive symptoms than males (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.94, SE\u0026thinsp;=\u0026thinsp;0.19), consistent with prior findings of gender differences in adolescent depression (Cindik-Herbrueggen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA monotonic increase in depressive symptoms was observed across school forms: relative to Form 1, Form 2 (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29), Form 3 (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.53), and Form 4 (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.45) were each associated with progressively higher PHQtotal scores. This gradient may reflect increasing academic and psychosocial demands in higher grades (Tang et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCritically, ACEStotal was strongly and positively associated with PHQtotal (\u003cem\u003eb\u0026thinsp;=\u0026thinsp;0.40, SE\u0026thinsp;=\u0026thinsp;0.04\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.88), indicating that each additional childhood trauma exposure was associated with a 0.40-point increase in depressive symptoms, even after adjusting for demographics and school clustering. This finding is consistent with the cumulative risk model of ACEs and aligns with evidence from both Kenyan (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tsehay et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and international (Carbone et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Templeton et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) studies demonstrating dose-response relationships between ACE exposure and internalizing symptoms. In contrast, MSPSS_total showed no clear independent association with PHQtotal (\u003cem\u003eb\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.00, SE\u0026thinsp;=\u0026thinsp;0.00\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.32), suggesting that perceived social support did not independently predict depressive symptoms once adversity and demographics were included in the model. This null finding diverges from bivariate associations reported elsewhere (Acoba, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Suh et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and raises important questions about the conditions under which social support functions as a resilience factor for trauma-exposed adolescents. The protective role of MSPSS may operate through indirect or mediated pathways \u0026mdash; such as perceived control, gratitude, or cognitive reappraisal \u0026mdash; rather than as a direct buffer against trauma-related symptoms.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the predicted depressive symptom scores across the observed range of ACEs, separately for male and female participants, based on the multilevel model.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultilevel Models for Anxiety Symptoms (GAD_total)\u003c/h2\u003e \u003cp\u003eResults for GAD_total are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The pattern of associations was broadly similar to that observed for PHQtotal, with some differences in magnitude.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMultilevel model predicting GAD_total (Anxiety)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female vs. male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;14.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEStotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS_total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool-level variance (τ₀₀)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual variance (σ\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Gender reference category\u0026thinsp;=\u0026thinsp;male; Form reference category\u0026thinsp;=\u0026thinsp;Form 1. Model estimated with REML using lme4. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15,177 students in 23 school clusters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAge was positively associated with anxiety symptoms (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08), with a larger effect than that observed for depression. Gender differences were more pronounced for GAD_total than for PHQtotal: female participants had lower anxiety scores (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.70, SE\u0026thinsp;=\u0026thinsp;0.19). The Form gradient was less consistent for anxiety than for depression; while Form 4 showed a positive association with GAD_total (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37), Forms 2 and 3 had weaker or non-significant effects, suggesting that grade-related differences in anxiety may be less linear.\u003c/p\u003e \u003cp\u003eAs with depression, ACEStotal showed a robust positive association with GAD_total (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.25), indicating that cumulative adversity was strongly related to anxiety symptoms. This is consistent with prior evidence of differential but significant ACE-anxiety associations in both Kenyan (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and US (Matthews et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) adolescent samples. The magnitude of the ACEStotal coefficient was slightly larger for anxiety than for depression, mirroring patterns reported by Ferraj\u0026atilde;o et al. (P. Ferraj\u0026atilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who found that indirect ACE exposure was more strongly related to anxiety symptoms. MSPSS_total again showed no independent association with GAD_total (\u003cem\u003eb\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.00, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), replicating the null finding observed for depression.\u003c/p\u003e \u003cp\u003eRandom effects for the GAD model showed school-level variance of 1.69 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.30) and residual variance of 93.35 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.66), implying slightly lower between-school variability for anxiety than for depression. These patterns are consistent with evidence that individual-level adversity is a more potent correlate of internalizing symptoms than school context in Kenyan samples (P. Ferraj\u0026atilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the predicted anxiety symptom scores across the range of ACEs by gender from the multilevel model.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses: Single-Level Models with Cluster-Robust Standard Errors\u003c/h2\u003e \u003cp\u003eTo assess robustness, single-level ordinary least squares (OLS) regression models were fitted for PHQtotal and GAD_total with the same fixed-effect structure, and cluster-robust standard errors were computed using the sandwich estimator clustered by school_id (Colin Cameron \u0026amp; Miller, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSingle-level OLS models with cluster-robust standard errors\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHQtotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGAD_total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSECR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSECR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female vs. male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEStotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS_total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e SECR\u0026thinsp;=\u0026thinsp;cluster-robust standard error (sandwich estimator, clustered by school_id). Gender reference category\u0026thinsp;=\u0026thinsp;male; Form reference category\u0026thinsp;=\u0026thinsp;Form 1. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15,177.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe pattern of estimates closely matched the multilevel results. For PHQtotal, ACEStotal remained strongly associated with depressive symptoms (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003eSECR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and Gender, Age, and Form retained similar directions and magnitudes. For GAD_total, ACEStotal again showed a strong positive association (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003eSECR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and gender differences were pronounced. MSPSS_total remained non-significant in both models (\u003cem\u003ep\u003c/em\u003e = .441 and \u003cem\u003ep\u003c/em\u003e = .911, respectively). These sensitivity analyses confirm that the observed associations between ACEStotal and both depressive and anxiety symptoms are not artifacts of the specific modeling approach used to handle clustering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Key Findings\u003c/h2\u003e \u003cp\u003eAcross all models and both outcomes, ACEStotal emerged as the strongest and most consistent predictor of depressive and anxiety symptoms among Kenyan adolescents. Each additional adverse childhood experience was associated with 0.40-0.46-point increases in symptom scores, after adjustment for age, gender, school form, perceived social support, and school-level clustering. This dose-response pattern aligns with the cumulative risk framework and with growing evidence from sub-Saharan African adolescent samples (L. Cluver et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tsehay et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MSPSS_total did not independently predict either outcome in adjusted models, suggesting that its protective effects may operate through mediating mechanisms such as perceived control or gratitude rather than as a direct buffer (Zhao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Gender differences were consistently observed, with female participants reporting lower symptoms on both measures. School-level variability was modest (ICCs of .015-.024) but was appropriately accounted for through multilevel modeling and confirmed via cluster-robust sensitivity analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined how adverse childhood experiences and perceived social support relate to depressive and anxiety symptoms among Kenyan secondary school students using a large, school-based sample and a multilevel analytic approach. Overall, the findings highlight a robust and graded association between cumulative adversity and internalizing symptoms, whereas perceived social support showed no independent association once adversity and demographics were controlled. These patterns have important implications for theory, prevention, and the organization of child and adolescent mental health care, including school-linked services, in low- and middle-income contexts such as Kenya (Fazel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wahdi et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of Main Findings\u003c/h2\u003e \u003cp\u003eAcross all models and both outcomes, ACEStotal emerged as the strongest and most consistent correlate of adolescents\u0026rsquo; mental health. Each additional adverse childhood experience was associated with approximately 0.40-0.46-point increases in PHQtotal and GADtotal scores, even after adjustment for age, gender, school form, perceived social support, and school-level clustering. This pattern is consistent with the cumulative risk framework, which posits that the accumulation of multiple adversities exerts a more detrimental effect on mental health than any single stressor in isolation (Atkinson et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Plamondon et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similar dose-response relationships between ACE exposure and internalizing symptoms have been documented among adolescents in both high-income and sub-Saharan African settings, including recent Kenyan samples using the same or closely related datasets (Brown et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAge and school form showed a coherent pattern, particularly for depressive symptoms. Older adolescents and those in higher forms reported more severe depressive symptoms, which may reflect increased academic pressure, high-stakes examinations, and developmental transitions during mid- to late adolescence (Lazaratou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lipps et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For anxiety, age effects were larger than for depression, and form effects were strongest in Form 4, suggesting that anxiety may be especially sensitive to the demands associated with terminal examination years (Wuthrich et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gender differences were pronounced for both outcomes: students coded as Gender 2 reported significantly lower PHQtotal and GADtotal scores than those in the reference category, mirroring gender-patterned internalizing symptom profiles in other Kenyan and global adolescent studies (Osborn, Venturo-Conerly, Gan, et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osborn, Venturo-Conerly, Wasil, Schleider, et al., 2020).\u003c/p\u003e \u003cp\u003eIn contrast to expectations, MSPSStotal did not show a statistically significant independent association with either depressive or anxiety symptoms once ACEs and demographics were included in the models. This finding diverges from bivariate evidence and mediation work suggesting that higher perceived social support is associated with better mental health among Kenyan adolescents, partly through increased perceived control and gratitude (Zhao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, it is consistent with emerging evidence that social support may function as a resilience factor primarily through indirect pathways rather than as a simple additive predictor. For instance, among HIV-affected adolescents in Uganda, Ashaba et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that social support moderated the trauma-depression association only among those with low support, suggesting a threshold effect rather than a linear protective gradient. In the present sample, the magnitude of cumulative trauma-related risk may be sufficiently large that it attenuates or overwhelms the unique contribution of perceived support when both are entered simultaneously (Sloss et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This raises the possibility that resilience among trauma-exposed Kenyan adolescents depends less on perceived support per se and more on the interplay between support, cognitive appraisals, and agentic coping \u0026mdash; a question that warrants investigation using mediation and moderation designs (Batthyany \u0026amp; Mokoena, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Panagou \u0026amp; MacBeth, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSchool Context and Clustering\u003c/h2\u003e \u003cp\u003eThe intraclass correlation coefficients indicated that 2.4% of the variance in depressive symptoms and 1.5% of the variance in anxiety symptoms were attributable to differences between schools. Although modest, these ICC values fall within the range reported for adolescent psychosocial outcomes in school-based studies, where ICCs between .01 and .05 are typical (Scheier et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Shackleton et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Even small ICCs can meaningfully inflate Type I error rates when clustering is ignored, particularly in large samples (Shackleton et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The use of random-intercept multilevel models therefore follows best-practice recommendations for analyzing clustered adolescent health data and allows more accurate estimation of standard errors and confidence intervals (Krull \u0026amp; MacKinnon, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The parallel single-level models with cluster-robust standard errors yielded highly similar estimates, further reinforcing that the observed ACE-symptom associations are not artifacts of model specification.\u003c/p\u003e \u003cp\u003eAt the same time, the low ICCs and modest school-level variance components suggest that most of the variability in symptoms lies within schools rather than between them. This pattern implies that, although school context matters, individual-level experiences of adversity remain the dominant correlate of internalizing symptoms in this sample (Smolkowski et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wickrama \u0026amp; Vazsonyi, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Nevertheless, schools remain a strategic and efficient delivery platform for ACE-informed identification and early support, given the feasibility of reaching large numbers of adolescents through existing educational structures and student support services (Blodgett \u0026amp; Lanigan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComparison with Previous Research\u003c/p\u003e \u003cp\u003eThe strong associations between cumulative ACEs and both depression and anxiety are consistent with a growing body of literature from Kenya and other low- and middle-income countries. Recent work in informal settlements and rural Kenyan counties has shown that adolescents exposed to multiple adversities such as parental death, violence, poverty, and neglect are more likely to meet criteria for clinically significant internalizing symptoms (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; P. Ferraj\u0026atilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wado et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similar patterns have been reported for young people in urban slums in other African countries, as well as among marginalized youth in high-income contexts, supporting the cross-cultural relevance of trauma-focused ACE frameworks (Byansi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Swahn et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within the Journal of Child \u0026amp; Adolescent Trauma specifically, latent class analyses have identified distinct victimization profiles among Kenyan and Ugandan adolescents, with high-risk classes showing markedly elevated PTSD rates (Ferraj\u0026atilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and studies of South African adolescents have demonstrated associations between childhood trauma and multiple domains of psychopathology (Sevenoaks et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The present findings extend this evidence by showing that these dose-response relationships persist in a large, multi-county school sample and remain robust after accounting for age, gender, school form, perceived social support, and clustering by school.\u003c/p\u003e \u003cp\u003eThe absence of a main effect of MSPSStotal contrasts with some Kenyan studies reporting that high-perceived social support is associated with lower depressive symptoms and better wellbeing (Okawa et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, those studies often examine bivariate relationships or mediation models rather than simultaneous prediction in the presence of cumulative adversity, and they frequently conceptualize social support as part of a broader resilience process (Fritz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this context, the current null findings may indicate that support alone is insufficient to offset the mental health burden associated with high ACE exposure, particularly when support is not accompanied by structural changes, targeted psychological interventions, or changes in school climate and safety practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eKey strengths of this study include the large, multi-site sample; the use of validated measures such as the PHQ-8, GAD-7, ACE items, and MSPSS; and the application of multilevel models with appropriate handling of school clustering. The convergence between multilevel and cluster-robust OLS estimates provides additional confidence in the robustness of the main findings (Hazlett \u0026amp; Wainstein, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the focus on a Kenyan adolescent school sample contributes to the limited but rapidly expanding evidence base on ACEs and mental health in sub-Saharan Africa (Mutamba et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral limitations should also be acknowledged. First, the cross-sectional design precludes causal inference; it is not possible to determine the temporal ordering of ACE exposure, social support, and mental health outcomes, and bidirectional relationships are likely (Panagou \u0026amp; MacBeth, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Future longitudinal and intervention studies will be essential to test whether modifying adversity or support meaningfully changes symptom trajectories in this context. Second, all measures were based on self-report, which may be influenced by recall bias, social desirability, or cultural variations in symptom expression. Third, the school identifier was derived from school-level fields rather than an independently verified code, which could introduce minor misclassification of clusters, although the small ICCs make large distortions unlikely. Fourth, the analyses focused on main effects of ACEStotal and MSPSStotal; interactions and mediated pathways involving social support, resilience, or cognitive processes were not examined in depth and may reveal more nuanced protective mechanisms (Kobrinsky \u0026amp; Siedlecki, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, digital stressors, smartphone-related difficulties, and other contemporary risk factors present in the broader dataset were not included in the current models, which may underestimate the full range of influences on adolescent mental health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImplications and Future Directions\u003c/h2\u003e \u003cp\u003eThe consistent and sizeable association between cumulative ACEs and both depressive and anxiety symptoms highlights the importance of prioritizing ACE-informed prevention and intervention strategies for adolescents in Kenyan schools and communities. At the policy level, efforts to reduce exposure to violence, neglect, and other adversities in childhood through social protection programs, parenting support, and community-based violence prevention are likely to yield substantial mental health benefits for adolescents (L. D. Cluver et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wahlbeck et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within schools, integrating trauma-informed screening, establishing clear referral pathways, and offering low-intensity, scalable psychological interventions could help address the needs of students with a history of childhood trauma and reduce the risk of symptom escalation (Fletcher-Janzen \u0026amp; Harrington, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The dose-response pattern observed here \u0026mdash; each additional ACE adding approximately 0.40\u0026ndash;0.46 points to symptom scores \u0026mdash; could inform stepped-care models in which adolescents endorsing higher ACE counts are prioritized for support that is more intensive. Practically, this might involve training teachers and school counselors to recognize trauma-related distress using brief ACE screeners, followed by referral to evidence-based interventions already shown to be effective in Kenyan school settings, such as the Shamiri lay-provider intervention (Osborn, Venturo-Conerly, Arango G., et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Embedding such trauma-informed identification and care pathways within existing school counseling and life-skills programs offers a feasible approach in resource-constrained settings where specialized mental health services remain scarce.\u003c/p\u003e \u003cp\u003eGiven the ambiguous role of perceived social support in the current adjusted models, future research should examine how support interacts with other resilience factors, such as perceived control, gratitude, and school connectedness, rather than assuming a simple direct buffering effect (Zhao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Longitudinal designs would allow a clearer test of whether supportive relationships in families, peer groups, and school communities attenuate the long-term mental health consequences of early adversity, and whether school-based interventions that enhance both support and psychological skills can shift trajectories for high-risk youth.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this large multilevel study of Kenyan secondary school students demonstrates that cumulative childhood trauma exposure is a powerful and consistent predictor of both depressive and anxiety symptoms; whereas perceived social support does not show an independent main effect once, trauma exposure and demographics are controlled. These findings reinforce the centrality of childhood trauma in adolescent mental health and highlight the need for trauma-informed policies and school-based interventions that both reduce exposure to adversity and support the recovery and resilience of adolescents who have already experienced multiple traumas (Bomysoad \u0026amp; Francis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meeker et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the same time, they point to the importance of moving beyond simple main-effect models of social support to consider more complex, mediated resilience processes \u0026mdash; including the roles of agency, cognitive appraisal, and community connectedness \u0026mdash; in future research and in the design of trauma-informed child and adolescent mental health services, including school-linked care (Rueger et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wills \u0026amp; Cleary, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cb\u003eACEs\u003c/b\u003e Adverse childhood experiences\u003c/p\u003e\u003cp\u003e\u003cb\u003eICC\u003c/b\u003e Intraclass correlation coefficient\u003c/p\u003e\u003cp\u003eLMICs Low‑ and middle‑income countries\u003c/p\u003e\u003cp\u003e\u003cb\u003eMSPSS\u003c/b\u003e Multidimensional Scale of Perceived Social Support\u003c/p\u003e\u003cp\u003e\u003cb\u003ePHQ‑8\u003c/b\u003e Patient Health Questionnaire‑8\u003c/p\u003e\u003cp\u003e\u003cb\u003eGAD‑7\u003c/b\u003e Generalized Anxiety Disorder‑7\u003c/p\u003e\u003cp\u003e\u003cb\u003eOSF\u003c/b\u003e Open Science Framework\u003c/p\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e Standard deviation\u003c/p\u003e\u003cp\u003e\u003cb\u003eCI\u003c/b\u003e Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.N.A. conceived and designed the secondary analysis, performed the statistical analyses, and drafted the main manuscript text. N.A. contributed to data curation, investigation, and critical revision of the manuscript. D.K.S. supervised the project, validated the findings, and reviewed and edited the manuscript. E.E.N. contributed to methodological decisions and software support, and reviewed and edited the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors wish to thank the Shamiri Institute and collaborating schools for providing access to the dataset. We also acknowledge the adolescents who participated in the original survey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original de-identified dataset \u0026ldquo;A Dataset on Adolescent Mental Health in Kenya\u0026rdquo; (file MHS_merged (1).csv) is openly available on OSF at https://osf.io/k3xtd/files/8zh4g. For this secondary analysis, a derived restricted file (MHS_merged-1.csv) and all R code (R/01_analysis_PHQ_GAD.R) are available at https://github.com/nyantakyiappiah-eng/kenya-adolescent-mental-health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbe, Y., \u0026amp; Gee, K. A. (2014). Sensitivity analyses for clustered data: An illustration from a large-scale clustered randomized controlled trial in education. \u003cem\u003eEvaluation and Program Planning\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, 26\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.evalprogplan.2014.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.evalprogplan.2014.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcoba, E. F. (2024). Social support and mental health: The mediating role of perceived stress. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1330720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2024.1330720\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2024.1330720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., Dube, S. R., \u0026amp; Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. \u003cem\u003eEuropean Archives of Psychiatry and Clinical Neuroscience\u003c/em\u003e, \u003cem\u003e256\u003c/em\u003e(3), 174\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00406-005-0624-4\u003c/span\u003e\u003cspan address=\"10.1007/s00406-005-0624-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe La Arias, J., Vilagut, G., Ronaldson, A., Valderas, J. M., Bakolis, I., Dregan, A., Molina, A. J., Navarro-Mateu, F., P\u0026eacute;rez, K., Bartoll-Roca, X., Elices, M., P\u0026eacute;rez-Sola, V., Serrano-Blanco, A., Mart\u0026iacute;n, V., \u0026amp; Alonso, J. (2023). Reliability and cross-country equivalence of the 8-item version of the Patient Health Questionnaire (PHQ-8) for the assessment of depression: Results from 27 countries in Europe. \u003cem\u003eThe Lancet Regional Health - Europe\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 100659. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lanepe.2023.100659\u003c/span\u003e\u003cspan address=\"10.1016/j.lanepe.2023.100659\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshaba, S., Cooper-Vince, C. E., Maling, S., Satinsky, E. N., Baguma, C., Akena, D., Nansera, D., Bajunirwe, F., \u0026amp; Tsai, A. C. (2021). Childhood trauma, major depressive disorder, suicidality, and the modifying role of social support among adolescents living with HIV in rural Uganda. \u003cem\u003eJournal of Affective Disorders Reports\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 100094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jadr.2021.100094\u003c/span\u003e\u003cspan address=\"10.1016/j.jadr.2021.100094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkinson, L., Beitchman, J., Gonzalez, A., Young, A., Wilson, B., Escobar, M., Chisholm, V., Brownlie, E., Khoury, J. E., Ludmer, J., \u0026amp; Villani, V. (2015). Cumulative Risk, Cumulative Outcome: A 20-Year Longitudinal Study. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(6), e0127650. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0127650\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0127650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbieri, V., Piccoliori, G., Mahlknecht, A., Plagg, B., Ausserhofer, D., Engl, A., \u0026amp; Wiedermann, C. J. (2023). Adolescent Mental Health during the COVID-19 Pandemic: The Interplay of Age, Gender, and Mental Health Outcomes in Two Consecutive Cross-Sectional Surveys in Northern Italy. \u003cem\u003eBehavioral Sciences\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(8), 643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bs13080643\u003c/span\u003e\u003cspan address=\"10.3390/bs13080643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarene, S., Ruud-Tronsmoen, A., \u0026amp; Johansen, P. F. (2022). Associations between Demographic Characteristics, Lifestyle Factors and School-Related Conditions and Symptoms of Mental Health Problems in Norwegian Upper Secondary School Students. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(15), 9575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph19159575\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19159575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaseke, R., Kilonzo, R., Ngesa, M., Mwende, P., \u0026amp; Osborn, T. (2026). A dataset on adolescent mental health in Kenya. \u003cem\u003eData in Brief\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e, 112513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dib.2026.112513\u003c/span\u003e\u003cspan address=\"10.1016/j.dib.2026.112513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatthyany, K., \u0026amp; Mokoena, T. (2025). The Impact of Social Support on Academic Resilience Mediated by Positive Affect in University Students. \u003cem\u003eJournal of Adolescent and Youth Psychological Studies\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(7), 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.61838/kman.jayps.4358\u003c/span\u003e\u003cspan address=\"10.61838/kman.jayps.4358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBethell, C. D., Carle, A., Hudziak, J., Gombojav, N., Powers, K., Wade, R., \u0026amp; Braveman, P. (2017). Methods to Assess Adverse Childhood Experiences of Children and Families: Toward Approaches to Promote Child Well-being in Policy and Practice. \u003cem\u003eAcademic Pediatrics\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(7), S51\u0026ndash;S69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.acap.2017.04.161\u003c/span\u003e\u003cspan address=\"10.1016/j.acap.2017.04.161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlodgett, C., \u0026amp; Lanigan, J. D. (2018). The association between adverse childhood experience (ACE) and school success in elementary school children. \u003cem\u003eSchool Psychology Quarterly\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 137\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/spq0000256\u003c/span\u003e\u003cspan address=\"10.1037/spq0000256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBomysoad, R. N., \u0026amp; Francis, L. A. (2020). Adverse Childhood Experiences and Mental Health Conditions Among Adolescents. \u003cem\u003eJournal of Adolescent Health\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(6), 868\u0026ndash;870. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jadohealth.2020.04.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jadohealth.2020.04.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonell, C., Humphrey, N., Singh, I., Viner, R. M., \u0026amp; Ford, T. (2023). Approaches to consent in public health research in secondary schools. \u003cem\u003eBritish Medical Journal Open\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), e070277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2022-070277\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2022-070277\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBooth, M. L., Okely, A. D., \u0026amp; Denney-Wilson, E. (2011). Validation and application of a novel method of measuring non-response bias in school-based surveys of paediatric overweight and obesity. \u003cem\u003eInternational Journal of Pediatric Obesity\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2\u0026ndash;2), e87\u0026ndash;e93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/17477166.2010.498917\u003c/span\u003e\u003cspan address=\"10.3109/17477166.2010.498917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, C., Nkemjika, S., Ratto, J., Dube, S. R., Gilbert, L., Chiang, L., Picchetti, V., Coomer, R., Kambona, C., McOwen, J., Akani, B., Kamagate, M. F., Low, A., Manuel, P., Agusto, A., \u0026amp; Annor, F. B. (2024). Adverse childhood experiences and associations with mental health, substance use, and violence perpetration among young adults in sub-Saharan Africa. \u003cem\u003eChild Abuse \u0026amp; Neglect\u003c/em\u003e, \u003cem\u003e150\u003c/em\u003e, 106524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chiabu.2023.106524\u003c/span\u003e\u003cspan address=\"10.1016/j.chiabu.2023.106524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByansi, W., Musyoka, C. M., Baidoo, C. E., Okumu, M., Mutavi, T., Mbwayo, A., Mulwa, A. S., Kyalo, D. N., \u0026amp; Mathai, M. (2025). Cumulative adverse childhood experiences and their association with depression and anxiety: A cross-sectional study of youth living in informal urban settings in Kenya. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 1641321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyt.2025.1641321\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2025.1641321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarbone, G. A., Adenzato, M., Dell\u0026rsquo;Acqua, C., Imperatori, C., Lo Presti, A., De Rossi, E., Allegrini, G., Messerotti Benvenuti, S., Farina, B., \u0026amp; Ardito, R. B. (2026). Breaking the cycle: The protective role of perceived social support in the relationship between adverse childhood experiences, emotional dysregulation and depression in university students. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e395\u003c/em\u003e, 120669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2025.120669\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2025.120669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCartwright, A. V., Pione, R. D., Stoner, C. R., \u0026amp; Spector, A. (2022). Validation of the multidimensional scale of perceived social support (MSPSS) for family caregivers of people with dementia. \u003cem\u003eAging \u0026amp; Mental Health\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 286\u0026ndash;293. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13607863.2020.1857699\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2020.1857699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChae, D., Lee, J., \u0026amp; Lee, E. H. (2025). Internal Structure of the Patient Health Questionnaire-9: A Systematic Review and Meta-analysis. \u003cem\u003eAsian Nursing Research\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.anr.2024.12.005\u003c/span\u003e\u003cspan address=\"10.1016/j.anr.2024.12.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheong, Y. F., Fotiu, R. P., \u0026amp; Raudenbush, S. W. (2001). Efficiency and Robustness of Alternative Estimators for Two- and Three-level Models: The Case of NAEP. \u003cem\u003eJournal of Educational and Behavioral Statistics\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 411\u0026ndash;429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3102/10769986026004411\u003c/span\u003e\u003cspan address=\"10.3102/10769986026004411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCindik-Herbrueggen, E. D. (2025). Gender Differences in Depressive Symptomatology Among Turkish Immigrants: An Analysis of PHQ9 Scores. \u003cem\u003eEuropean Psychiatry\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(S1), S645\u0026ndash;S645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/j.eurpsy.2025.1312\u003c/span\u003e\u003cspan address=\"10.1192/j.eurpsy.2025.1312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCluver, L. D., Orkin, F. M., Meinck, F., Boyes, M. E., Yakubovich, A. R., \u0026amp; Sherr, L. (2016). Can Social Protection Improve Sustainable Development Goals for Adolescent Health? \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(10), e0164808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0164808\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0164808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCluver, L., Orkin, M., Boyes, M. E., \u0026amp; Sherr, L. (2015). Child and Adolescent Suicide Attempts, Suicidal Behavior, and Adverse Childhood Experiences in South Africa: A Prospective Study. \u003cem\u003eJournal of Adolescent Health\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(1), 52\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jadohealth.2015.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jadohealth.2015.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColin Cameron, A., \u0026amp; Miller, D. L. (2015). A Practitioner\u0026rsquo;s Guide to Cluster-Robust Inference. \u003cem\u003eJournal of Human Resources\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 317\u0026ndash;372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3368/jhr.50.2.317\u003c/span\u003e\u003cspan address=\"10.3368/jhr.50.2.317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemaray, M. K., \u0026amp; Malecki, C. K. (2002). Critical levels of perceived social support associated with student adjustment. \u003cem\u003eSchool Psychology Quarterly\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 213\u0026ndash;241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1521/scpq.17.3.213.20883\u003c/span\u003e\u003cspan address=\"10.1521/scpq.17.3.213.20883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDube, S. R., Anda, R. F., Felitti, V. J., Chapman, D. P., Williamson, D. F., \u0026amp; Giles, W. H. (2001). Childhood Abuse, Household Dysfunction, and the Risk of Attempted Suicide Throughout the Life Span: Findings From the Adverse Childhood Experiences Study. \u003cem\u003eJournal Of The American Medical Association\u003c/em\u003e, \u003cem\u003e286\u003c/em\u003e(24), 3089. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.286.24.3089\u003c/span\u003e\u003cspan address=\"10.1001/jama.286.24.3089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFazel, M., Patel, V., Thomas, S., \u0026amp; Tol, W. (2014). Mental health interventions in schools in low-income and middle-income countries. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(5), 388\u0026ndash;398. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(14)70357-8\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(14)70357-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., Koss, M. P., \u0026amp; Marks, J. S. (1998). Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults. \u003cem\u003eAmerican Journal of Preventive Medicine\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4), 245\u0026ndash;258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0749-3797(98)00017-8\u003c/span\u003e\u003cspan address=\"10.1016/S0749-3797(98)00017-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerraj\u0026atilde;o, P. C., Tourais, B., Faria, I., Dias, J., \u0026amp; Elklit, A. (2024). A Latent Class Analysis of Adverse Life Events for Kenyan Adolescents. \u003cem\u003eJournal of Child \u0026amp; Adolescent Trauma\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(2), 261\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40653-023-00603-4\u003c/span\u003e\u003cspan address=\"10.1007/s40653-023-00603-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerraj\u0026atilde;o, P., Dias, J., \u0026amp; Elklit, A. (2024). Defense mechanisms mediate associations between exposure to adverse childhood experiences and anxiety and depression in Kenyan adolescents. \u003cem\u003eTraumatology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(3), 238\u0026ndash;250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/trm0000426\u003c/span\u003e\u003cspan address=\"10.1037/trm0000426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFletcher-Janzen, E., \u0026amp; Harrington, E. (2021). Translating ACE research into multi-tiered systems of supports for at-risk high-school students. \u003cem\u003eJournal of Pediatric Neuropsychology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 89\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/h0101953\u003c/span\u003e\u003cspan address=\"10.1037/h0101953\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritz, J., De Graaff, A. M., Caisley, H., Van Harmelen, A. L., \u0026amp; Wilkinson, P. O. (2018). A Systematic Review of Amenable Resilience Factors That Moderate and/or Mediate the Relationship Between Childhood Adversity and Mental Health in Young People. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyt.2018.00230\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2018.00230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiri, S., Ross, N., Kornhaber, R., Ahmed, K. Y., \u0026amp; Thapa, S. (2025). The effects of parental adverse childhood experiences (ACEs) and childhood threat and deprivation on adolescent depression and anxiety: An analysis of the longitudinal study of Australian children. \u003cem\u003eEpidemiology and Psychiatric Sciences\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, e49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S2045796025100255\u003c/span\u003e\u003cspan address=\"10.1017/S2045796025100255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwahn, H., Babihuga, M., Dube, N. K., S. R., \u0026amp; Kasirye, R. (2020). Adverse Childhood Experiences and Alcohol Use Consumption Patterns among Vulnerable Urban Youth in Uganda. \u003cem\u003eAddiction Research and Adolescent Behaviour\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 01\u0026ndash;08. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31579/2688-7517/017\u003c/span\u003e\u003cspan address=\"10.31579/2688-7517/017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHale, D. R., Patalay, P., Fitzgerald-Yau, N., Hargreaves, D. S., Bond, L., G\u0026ouml;rzig, A., Wolpert, M., Stansfeld, S. A., \u0026amp; Viner, R. M. (2014). School-Level Variation in Health Outcomes in Adolescence: Analysis of Three Longitudinal Studies in England. \u003cem\u003ePrevention Science\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 600\u0026ndash;610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11121-013-0414-6\u003c/span\u003e\u003cspan address=\"10.1007/s11121-013-0414-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHazlett, C., \u0026amp; Wainstein, L. (2022). Understanding, Choosing, and Unifying Multilevel and Fixed Effect Approaches. \u003cem\u003ePolitical Analysis\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), 46\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/pan.2020.41\u003c/span\u003e\u003cspan address=\"10.1017/pan.2020.41\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerington, J., Li, K., \u0026amp; Pisani, A. R. (2024). Expanding the role of justice in secondary research using digital psychological data. \u003cem\u003eAmerican Psychologist\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(1), 123\u0026ndash;136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/amp0001190\u003c/span\u003e\u003cspan address=\"10.1037/amp0001190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJakobsen, A. L., Hansen, C. D., \u0026amp; Andersen, J. H. (2022). The association between perceived social support in adolescence and positive mental health outcomes in early adulthood: A prospective cohort study. \u003cem\u003eScandinavian Journal of Public Health\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(3), 404\u0026ndash;411. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1403494821993718\u003c/span\u003e\u003cspan address=\"10.1177/1403494821993718\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly, M. M., Martin-Peters, T., \u0026amp; Farber, J. S. (2024). Secondary Data Analysis: Using existing data to answer new questions. \u003cem\u003eJournal of Pediatric Health Care\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(4), 615\u0026ndash;618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pedhc.2024.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.pedhc.2024.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhasakhala, L., Ndetei, D., Mutiso, V., Mbwayo, A., \u0026amp; Mathai, M. (2012). The prevalence of depressive symptoms among adolescents in Nairobi public secondary schools: Association with perceived maladaptive parental behaviour. \u003cem\u003eAfrican Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 106\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ajpsy.v15i2.14\u003c/span\u003e\u003cspan address=\"10.4314/ajpsy.v15i2.14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKieling, C., Baker-Henningham, H., Belfer, M., Conti, G., Ertem, I., Omigbodun, O., Rohde, L. A., Srinath, S., Ulkuer, N., \u0026amp; Rahman, A. (2011). Child and adolescent mental health worldwide: Evidence for action. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e378\u003c/em\u003e(9801), 1515\u0026ndash;1525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(11)60827-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(11)60827-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKievit, R. A., McCormick, E. M., Fuhrmann, D., Deserno, M. K., \u0026amp; Orben, A. (2022). Using large, publicly available data sets to study adolescent development: Opportunities and challenges. \u003cem\u003eCurrent Opinion in Psychology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e, 303\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.copsyc.2021.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.copsyc.2021.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKisito, J. M., Ogodo, F. M., \u0026amp; Gatumu, E. P. W. (2026). Economic Precariousness, Racialized Gender Inequality, and Child Mental Health Outcomes in Kenya: A systematic Literature Review. \u003cem\u003eInternational Journal of Research and Innovation in Social Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 5637\u0026ndash;5652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47772/IJRISS.2026.10100434\u003c/span\u003e\u003cspan address=\"10.47772/IJRISS.2026.10100434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobrinsky, V., \u0026amp; Siedlecki, K. L. (2023). Mediators of the Relationship Between Adverse Childhood Experiences (ACEs) and Symptoms of Anxiety, Depression, and Suicidality among Adults. \u003cem\u003eJournal of Child \u0026amp; Adolescent Trauma\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 233\u0026ndash;246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40653-022-00510-0\u003c/span\u003e\u003cspan address=\"10.1007/s40653-022-00510-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoğar, H., \u0026amp; Yılmaz Koğar, E. (2024). The structure of the multidimensional scale of perceived social support: A meta-analytic confirmatory factor analysis. \u003cem\u003eCambridge Prisms: Global Mental Health\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, e126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/gmh.2024.118\u003c/span\u003e\u003cspan address=\"10.1017/gmh.2024.118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke, K., Spitzer, R. L., \u0026amp; Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. \u003cem\u003eJournal of General Internal Medicine\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(9), 606\u0026ndash;613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1525-1497.2001.016009606.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1525-1497.2001.016009606.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrull, J. L., \u0026amp; MacKinnon, D. P. (2001). Multilevel Modeling of Individual and Group Level Mediated Effects. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(2), 249\u0026ndash;277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/S15327906MBR3602_06\u003c/span\u003e\u003cspan address=\"10.1207/S15327906MBR3602_06\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangkamp, D. L., Lehman, A., \u0026amp; Lemeshow, S. (2010). Techniques for Handling Missing Data in Secondary Analyses of Large Surveys. \u003cem\u003eAcademic Pediatrics\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 205\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.acap.2010.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.acap.2010.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazaratou, H., Dikeos, D. G., Anagnostopoulos, D. C., \u0026amp; Soldatos, C. R. (2010). Depressive Symptomatology in High School Students: The Role of Age, Gender and Academic Pressure. \u003cem\u003eCommunity Mental Health Journal\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 289\u0026ndash;295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10597-010-9296-2\u003c/span\u003e\u003cspan address=\"10.1007/s10597-010-9296-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J. E. C., Phinney, B., Watkins, K., \u0026amp; Zamorski, M. A. (2016). Psychosocial Pathways Linking Adverse Childhood Experiences to Mental Health in Recently Deployed Canadian Military Service Members. \u003cem\u003eJournal of Traumatic Stress\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 124\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jts.22085\u003c/span\u003e\u003cspan address=\"10.1002/jts.22085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J. S. (2019). Perceived social support functions as a resilience in buffering the impact of trauma exposure on PTSD symptoms via intrusive rumination and entrapment in firefighters. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(8), e0220454. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0220454\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0220454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipps, G. E., Lowe, G. A., Halliday, S., Morris-Patterson, A., Clarke, N., \u0026amp; Wilson, R. N. (2010). The association of academic tracking to depressive symptoms among adolescents in three Caribbean countries. \u003cem\u003eChild and Adolescent Psychiatry and Mental Health\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1753-2000-4-16\u003c/span\u003e\u003cspan address=\"10.1186/1753-2000-4-16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagige, L., Bukania, Z., Mwangi, M., Chuchu, V., Wanjihia, V., Karanja, S., Onteri, S., Macharia, A., \u0026amp; Ongeri, L. (2025). Prevalence and predictors of depressive symptoms among school-going adolescents in Kenya. \u003cem\u003ePLOS Mental Health\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(11), e0000474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmen.0000474\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmen.0000474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarks, G. N. (2008). Accounting for the gender gaps in student performance in reading and mathematics: Evidence from 31 countries. \u003cem\u003eOxford Review of Education\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 89\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03054980701565279\u003c/span\u003e\u003cspan address=\"10.1080/03054980701565279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews, T. A., Shao, H., Forster, M., \u0026amp; Kim, I. (2024). Associations of adverse childhood experiences with depression and anxiety among children in the United States: Racial and ethnic disparities in mental health. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e362\u003c/em\u003e, 645\u0026ndash;651. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2024.07.121\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2024.07.121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCoach, D. B., \u0026amp; Adelson, J. L. (2010). Dealing With Dependence (Part I): Understanding the Effects of Clustered Data. \u003cem\u003eGifted Child Quarterly\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(2), 152\u0026ndash;155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0016986210363076\u003c/span\u003e\u003cspan address=\"10.1177/0016986210363076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeeker, E. C., O\u0026rsquo;Connor, B. C., Kelly, L. M., Hodgeman, D. D., Scheel-Jones, A. H., \u0026amp; Berbary, C. (2021). The impact of adverse childhood experiences on adolescent health risk indicators in a community sample. \u003cem\u003ePsychological Trauma: Theory Research Practice and Policy\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 302\u0026ndash;312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/tra0001004\u003c/span\u003e\u003cspan address=\"10.1037/tra0001004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhati, L. I. (2012). \u003cem\u003eAn investigation into the strategies used in addressing drug abuse problems\u0026rsquo;\u0026rsquo; a case study of Nairobi provincial boys secondary schools\u003c/em\u003e [Thesis, Kenyatta University]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ir-library.ku.ac.ke/handle/123456789/2492\u003c/span\u003e\u003cspan address=\"http://ir-library.ku.ac.ke/handle/123456789/2492\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutamba, E. M., Mbwayo, A., \u0026amp; Njiru, L. (2026). Adverse Childhood Experiences and Associated Correlates among Adolescents at a Rural Private High School in Kenya. \u003cem\u003eInternational Journal of Research and Innovation in Social Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1538\u0026ndash;1551. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47772/IJRISS.2026.10100125\u003c/span\u003e\u003cspan address=\"10.47772/IJRISS.2026.10100125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutiso, V., Ndetei, D., Jeremiah, E., Nyamai, P., Walusaka, S., Onyango, V., Musyimi, C., Bhui, K., \u0026amp; Mamah, D. (2025). The moderating and mediating role of resilience in the relationship between adverse childhood experiences and depression, PTSD, and suicidality in Kenyan youth. \u003cem\u003eCambridge Prisms: Global Mental Health\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, e88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/gmh.2025.10041\u003c/span\u003e\u003cspan address=\"10.1017/gmh.2025.10041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkawa, S., Yasuoka, J., Ishikawa, N., Poudel, K. C., Ragi, A., \u0026amp; Jimba, M. (2011). Perceived social support and the psychological well-being of AIDS orphans in urban Kenya. \u003cem\u003eAids Care\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(9), 1177\u0026ndash;1185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09540121.2011.554530\u003c/span\u003e\u003cspan address=\"10.1080/09540121.2011.554530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyango-Ouma, W., Birungi, H., \u0026amp; Geibel, S. (2005). Understanding the HIV/STI risks and prevention needs of men who have sex with men in Nairobi, Kenya. \u003cem\u003ePopulation Council\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31899/HIV2.1031\u003c/span\u003e\u003cspan address=\"10.31899/HIV2.1031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn, T. L., Roe, E., Alemu, R., Venturo-Conerly, K., Gan, J., Arango, S., Johnson, N., Campbell, S., \u0026amp; Rodriguez, M. (2020). \u003cem\u003eDepression and Anxiety Symptoms in Kenyan Adolescents (2020)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/KSX6Y\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/KSX6Y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn, T. L., Venturo-Conerly, K. E., Arango, G., Roe, S., Rodriguez, E., Alemu, M., Gan, R. G., Wasil, J., Otieno, A. R., Rusch, B. H., Ndetei, T., Wasanga, D. M., Schleider, C., J. L., \u0026amp; Weisz, J. R. (2021). Effect of Shamiri Layperson-Provided Intervention vs Study Skills Control Intervention for Depression and Anxiety Symptoms in Adolescents in Kenya: A Randomized Clinical Trial. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(8), 829. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamapsychiatry.2021.1129\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2021.1129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn, T. L., Venturo-Conerly, K. E., Gan, J., Rodriguez, M., Alemu, R. G., Roe, E., Arango, S., Wasil, A. R., Campbell, S., Weisz, J. R., \u0026amp; Wasanga, C. (2021). \u003cem\u003eDepression and Anxiety Symptoms Amongst Kenyan Adolescents: Psychometric Properties, Prevalence, Sociodemographic Factors, and Psychological Wellbeing\u003c/em\u003e. PsyArXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31234/osf.io/ze8tf\u003c/span\u003e\u003cspan address=\"10.31234/osf.io/ze8tf\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn, T. L., Venturo-Conerly, K. E., Wasil, A. R., Rodriguez, M., Roe, E., Alemu, R., Arango, G., Gan, S., Wasanga, J., Schleider, C., J. L., \u0026amp; Weisz, J. R. (2020). The Shamiri group intervention for adolescent anxiety and depression: Study protocol for a randomized controlled trial of a lay-provider-delivered, school-based intervention in Kenya. \u003cem\u003eTrials\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 938. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13063-020-04732-1\u003c/span\u003e\u003cspan address=\"10.1186/s13063-020-04732-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn, T. L., Venturo-Conerly, K. E., Wasil, A. R., Schleider, J. L., \u0026amp; Weisz, J. R. (2020). Depression and Anxiety Symptoms, Social Support, and Demographic Factors Among Kenyan High School Students. \u003cem\u003eJournal of Child and Family Studies\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 1432\u0026ndash;1443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10826-019-01646-8\u003c/span\u003e\u003cspan address=\"10.1007/s10826-019-01646-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagou, C., \u0026amp; MacBeth, A. (2022). Deconstructing pathways to resilience: A systematic review of associations between psychosocial mechanisms and transdiagnostic adult mental health outcomes in the context of adverse childhood experiences. \u003cem\u003eClinical Psychology \u0026amp; Psychotherapy\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 1626\u0026ndash;1654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cpp.2732\u003c/span\u003e\u003cspan address=\"10.1002/cpp.2732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePattullo, A. L. S., Malonza, M., Kimani, G. G., Muthee, A., Otieno, P. A. O., Odhiambo, K., Moses, S., \u0026amp; Plummer, F. A. (1994). Survey of knowledge, behaviour and attitudes relating to HIV infection and AIDS among kenyan secondary school students. \u003cem\u003eAids Care\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 173\u0026ndash;181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09540129408258628\u003c/span\u003e\u003cspan address=\"10.1080/09540129408258628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen, K. J., Qualter, P., Humphrey, N., Damsgaard, M. T., \u0026amp; Madsen, K. R. (2023). With a Little Help from My Friends: Profiles of Perceived Social Support and Their Associations with Adolescent Mental Health. \u003cem\u003eJournal of Child and Family Studies\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(11), 3430\u0026ndash;3446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10826-023-02677-y\u003c/span\u003e\u003cspan address=\"10.1007/s10826-023-02677-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePigatto, F., Grant, C. C., Marks, E., Walker, C., \u0026amp; Waldie, K. E. (2025). Cumulative risk for comorbidity of depression and anxiety in young people. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e382\u003c/em\u003e, 611\u0026ndash;618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2025.04.150\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2025.04.150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePina, D., P\u0026eacute;rez-Alb\u0026eacute;niz, A., D\u0026iacute;ez-G\u0026oacute;mez, A., P\u0026eacute;rez-Esteban, A., \u0026amp; Fonseca-Pedrero, E. (2025). Validation of the Multidimensional Scale of Perceived Social Support (MSPSS) in a Representative Sample of Adolescents: Links with Well-being, Mental Health, and Suicidal Behavior. \u003cem\u003ePsychosocial Intervention\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(2), 79\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5093/pi2025a7\u003c/span\u003e\u003cspan address=\"10.5093/pi2025a7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlamondon, A., Racine, N., McDonald, S., Tough, S., \u0026amp; Madigan, S. (2022). Disentangling adversity timing and type: Contrasting theories in the context of maternal prenatal physical and mental health using latent formative models. \u003cem\u003eDevelopment and Psychopathology\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(5), 1961\u0026ndash;1973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0954579421000353\u003c/span\u003e\u003cspan address=\"10.1017/S0954579421000353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandhawa, A., Wood, G., Michail, M., Pallan, M., Patterson, P., \u0026amp; Goodyear, V. (2024). Safeguarding in adolescent mental health research: Navigating dilemmas and developing procedures. \u003cem\u003eBritish Medical Journal Open\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), e076700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2023-076700\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2023-076700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRueger, S. Y., Malecki, C. K., \u0026amp; Demaray, M. K. (2010). Relationship Between Multiple Sources of Perceived Social Support and Psychological and Academic Adjustment in Early Adolescence: Comparisons Across Gender. \u003cem\u003eJournal of Youth and Adolescence\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(1), 47\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10964-008-9368-6\u003c/span\u003e\u003cspan address=\"10.1007/s10964-008-9368-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRueger, S. Y., Malecki, C. K., Pyun, Y., Aycock, C., \u0026amp; Coyle, S. (2016). A meta-analytic review of the association between perceived social support and depression in childhood and adolescence. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e(10), 1017\u0026ndash;1067. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/bul0000058\u003c/span\u003e\u003cspan address=\"10.1037/bul0000058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamia, P., Premji, S., Tavangar, F., Yim, I. S., Wanyonyi, S., Merali, M., Musana, W., Omuse, G., Forcheh, N., Dosani, A., Letourneau, N., \u0026amp; MIGHT Consortium. (2020). Adverse Childhood Experiences and Changing Levels of Psychosocial Distress Scores across Pregnancy in Kenyan Women. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(10), 3401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17103401\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17103401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheier, L. M., Griffin, K. W., Doyle, M. M., \u0026amp; Botvin, G. J. (2002). Estimates of Intragroup Dependence for Drug Use and Skill Measures in School-Based Drug Abuse Prevention Trials: An Empirical Study of Three Independent Samples. \u003cem\u003eHealth Education \u0026amp; Behavior\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(1), 85\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/109019810202900109\u003c/span\u003e\u003cspan address=\"10.1177/109019810202900109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSevenoaks, T., Fouche, J. P., Phillips, N., Heany, S., Myer, L., Zar, H. J., Stein, D. J., \u0026amp; Hoare, J. (2022). Childhood Trauma and Mental Health in the Cape Town Adolescent Antiretroviral Cohort. \u003cem\u003eJournal of Child \u0026amp; Adolescent Trauma\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 353\u0026ndash;363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40653-021-00362-0\u003c/span\u003e\u003cspan address=\"10.1007/s40653-021-00362-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShackleton, N., Hale, D., Bonell, C., \u0026amp; Viner, R. M. (2016). Intraclass correlation values for adolescent health outcomes in secondary schools in 21 European countries. \u003cem\u003eSSM - Population Health\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 217\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ssmph.2016.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ssmph.2016.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSloss, I. M., Smith, J., Sebben, S., Wade, M., Prime, H., \u0026amp; Browne, D. T. (2025). Family functioning in the context of current and historical stressors: Exploring the buffering role of social support. \u003cem\u003eChild Abuse \u0026amp; Neglect\u003c/em\u003e, \u003cem\u003e168\u003c/em\u003e, 106711. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chiabu.2024.106711\u003c/span\u003e\u003cspan address=\"10.1016/j.chiabu.2024.106711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolkowski, K., Biglan, A., Dent, C., \u0026amp; Seeley, J. (2006). The Multilevel Structure of Four Adolescent Problems. \u003cem\u003ePrevention Science\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 239\u0026ndash;256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11121-006-0034-5\u003c/span\u003e\u003cspan address=\"10.1007/s11121-006-0034-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpitzer, R. L., Kroenke, K., Williams, J. B. W., \u0026amp; L\u0026ouml;we, B. (2006). A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. \u003cem\u003eArchives of Internal Medicine\u003c/em\u003e, \u003cem\u003e166\u003c/em\u003e(10), 1092. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/archinte.166.10.1092\u003c/span\u003e\u003cspan address=\"10.1001/archinte.166.10.1092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStochl, J., Fried, E. I., Fritz, J., Croudace, T. J., Russo, D. A., Knight, C., Jones, P. B., \u0026amp; Perez, J. (2022). On Dimensionality, Measurement Invariance, and Suitability of Sum Scores for the PHQ-9 and the GAD-7. \u003cem\u003eAssessment\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 355\u0026ndash;366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1073191120976863\u003c/span\u003e\u003cspan address=\"10.1177/1073191120976863\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrugnell, C., Orellana, L., Hayward, J., Millar, L., Swinburn, B., \u0026amp; Allender, S. (2018). Active (Opt-In) Consent Underestimates Mean BMI-z and the Prevalence of Overweight and Obesity Compared to Passive (Opt-Out) Consent. Evidence from the Healthy Together Victoria and Childhood Obesity Study. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 747. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph15040747\u003c/span\u003e\u003cspan address=\"10.3390/ijerph15040747\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuh, Y., Weikert, M., Dlugonski, D., Sandroff, B., \u0026amp; Motl, R. W. (2012). Physical activity, social support, and depression: Possible independent and indirect associations in persons with multiple sclerosis. \u003cem\u003ePsychology Health \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(2), 196\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13548506.2011.601747\u003c/span\u003e\u003cspan address=\"10.1080/13548506.2011.601747\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuliman, S., Mkabile, S. G., Fincham, D. S., Ahmed, R., Stein, D. J., \u0026amp; Seedat, S. (2009). Cumulative effect of multiple trauma on symptoms of posttraumatic stress disorder, anxiety, and depression in adolescents. \u003cem\u003eComprehensive Psychiatry\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 121\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.comppsych.2008.06.006\u003c/span\u003e\u003cspan address=\"10.1016/j.comppsych.2008.06.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, X., Tang, S., Ren, Z., \u0026amp; Wong, D. F. K. (2019). Prevalence of depressive symptoms among adolescents in secondary school in mainland China: A systematic review and meta-analysis. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e245\u003c/em\u003e, 498\u0026ndash;507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2018.11.043\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2018.11.043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTele, A., Nyamai, D., Shawar, Y. R., Nyongesa, V., Kiogora, S., Peterson, S. S., Obonyo, G., Cuijpers, P., \u0026amp; Kumar, M. (2025). The political economy of adolescent mental health in Kenya. \u003cem\u003eHealth Policy and Planning\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(10), 1017\u0026ndash;1026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/heapol/czaf057\u003c/span\u003e\u003cspan address=\"10.1093/heapol/czaf057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTempleton, J. M., Dixon, W. E., Williams, S., Morelen, D., Driggers-Jones, L., \u0026amp; Robertson, C. (2025). The mediating role of social support on the link between adverse childhood experiences and adult mental health. \u003cem\u003eJournal of Experimental Child Psychology\u003c/em\u003e, \u003cem\u003e252\u003c/em\u003e, 106148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jecp.2024.106148\u003c/span\u003e\u003cspan address=\"10.1016/j.jecp.2024.106148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsehay, M., Necho, M., \u0026amp; Mekonnen, W. (2020). The Role of Adverse Childhood Experience on Depression Symptom, Prevalence, and Severity among School Going Adolescents. \u003cem\u003eDepression Research and Treatment\u003c/em\u003e, \u003cem\u003e2020\u003c/em\u003e, 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2020/5951792\u003c/span\u003e\u003cspan address=\"10.1155/2020/5951792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVon Simson, K., Brekke, I., \u0026amp; Hardoy, I. (2022). The Impact of Mental Health Problems in Adolescence on Educational Attainment. \u003cem\u003eScandinavian Journal of Educational Research\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(2), 306\u0026ndash;320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00313831.2020.1869077\u003c/span\u003e\u003cspan address=\"10.1080/00313831.2020.1869077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWado, Y. D., Njeri, A., Odunga, S. A., Akuku, I., Wahdi, A. E., Fine, S. L., Ramaiya, A., Li, M., Loi, V. M., Maravilla, J. C., Scott, J. G., Erskine, H. E., \u0026amp; Kabiru, C. W. (2025). The association between adverse childhood experiences and mental disorders among adolescents in Kenya, Indonesia, and Vietnam: Evidence from the National Adolescent Mental Health Surveys. \u003cem\u003eChild and Adolescent Psychiatry and Mental Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(S1), 86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13034-025-00919-z\u003c/span\u003e\u003cspan address=\"10.1186/s13034-025-00919-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahdi, A. E., Astrini, Y. P., Setyawan, A., Fine, S. L., Ramaiya, A., Li, M., Wado, Y. D., Loi, V. M., Maravilla, J. C., Scott, J. G., Wilopo, S. A., \u0026amp; Erskine, H. E. (2025). Mental health service use among adolescents in three low- and middle-income countries: An analysis of the National Adolescent Mental Health Surveys. \u003cem\u003eChild and Adolescent Psychiatry and Mental Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(S1), 84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13034-025-00924-2\u003c/span\u003e\u003cspan address=\"10.1186/s13034-025-00924-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahlbeck, K., Cresswell-Smith, J., Haaramo, P., \u0026amp; Parkkonen, J. (2017). Interventions to mitigate the effects of poverty and inequality on mental health. \u003cem\u003eSocial Psychiatry and Psychiatric Epidemiology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(5), 505\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00127-017-1370-4\u003c/span\u003e\u003cspan address=\"10.1007/s00127-017-1370-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickrama, T., \u0026amp; Vazsonyi, A. T. (2011). School contextual experiences and longitudinal changes in depressive symptoms from adolescence to young adulthood. \u003cem\u003eJournal of Community Psychology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(5), 566\u0026ndash;575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcop.20453\u003c/span\u003e\u003cspan address=\"10.1002/jcop.20453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWills, T. A., \u0026amp; Cleary, S. D. (1996). How are social support effects mediated? A test with parental support and adolescent substance use. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(5), 937\u0026ndash;952. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0022-3514.71.5.937\u003c/span\u003e\u003cspan address=\"10.1037/0022-3514.71.5.937\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWuthrich, V. M., Belcher, J., Kilby, C., Jagiello, T., \u0026amp; Lowe, C. (2021). Tracking stress, depression, and anxiety across the final year of secondary school: A longitudinal study. \u003cem\u003eJournal of School Psychology\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e, 18\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jsp.2021.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jsp.2021.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoussef, N. A., Belew, D., Hao, G., Wang, X., Treiber, F. A., Stefanek, M., Yassa, M., Boswell, E., McCall, W. V., \u0026amp; Su, S. (2017). Racial/ethnic differences in the association of childhood adversities with depression and the role of resilience. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e208\u003c/em\u003e, 577\u0026ndash;581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2016.10.024\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2016.10.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, G., Xu, W., Liu, Z., \u0026amp; An, Y. (2018). Resilience, Posttraumatic Stress Symptoms, and Posttraumatic Growth in Chinese Adolescents After a Tornado: The Role of Mediation Through Perceived Social Support. \u003cem\u003eJournal of Nervous \u0026amp; Mental Disease\u003c/em\u003e, \u003cem\u003e206\u003c/em\u003e(2), 130\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/NMD.0000000000000778\u003c/span\u003e\u003cspan address=\"10.1097/NMD.0000000000000778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, M., Miao, H., Zhu, L. L., Zhang, X. H., \u0026amp; Zang, L. W. (2025). Social support and adolescent mental health in Kenya: A parallel mediation analysis of perceived control and gratitude. \u003cem\u003eFrontiers in Pediatrics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 1626249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fped.2025.1626249\u003c/span\u003e\u003cspan address=\"10.3389/fped.2025.1626249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimet, G. D., Dahlem, N. W., Zimet, S. G., \u0026amp; Farley, G. K. (1988). The Multidimensional Scale of Perceived Social Support. \u003cem\u003eJournal of Personality Assessment\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 30\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15327752jpa5201_2\u003c/span\u003e\u003cspan address=\"10.1207/s15327752jpa5201_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"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":"adolescents, childhood trauma, adverse childhood experiences, depression, anxiety, social support, resilience","lastPublishedDoi":"10.21203/rs.3.rs-9327795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9327795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdolescents in low- and middle-income countries experience a high burden of depression and anxiety, often in the context of exposure to multiple childhood traumas including abuse, neglect, and household dysfunction. This study examined how cumulative childhood trauma exposure (ACEs) and perceived social support relate to depressive and anxiety symptoms among Kenyan secondary school students using a large, school-based dataset. A cross-sectional secondary analysis was conducted using survey data from 15,177 students in 23 secondary schools. Depressive (PHQ-8) and anxiety (GAD-7) symptoms were regressed on a cumulative ACE index (ACEStotal) and perceived social support (MSPSStotal) using multilevel linear models with random school intercepts, adjusting for age, gender, and school form. Single-level ordinary least squares models with cluster-robust standard errors were estimated as sensitivity analyses. Intraclass correlations were modest (ICCPHQ = .024; ICCGAD = .015). Higher ACEStotal predicted higher PHQtotal and GADtotal (b\u0026thinsp;\u0026asymp;\u0026thinsp;0.40\u0026ndash;0.46, p \u0026lt; .001), whereas MSPSStotal showed no independent association with either outcome in adjusted models. Sensitivity analyses yielded a similar pattern of findings. Cumulative childhood trauma is a strong and consistent correlate of depressive and anxiety symptoms among Kenyan secondary school students. These findings suggest that trauma-informed mental health services in low- and middle-income settings should prioritize young people exposed to multiple traumas and consider integrating trauma-informed screening and stepped-care pathways within school-linked services.\u003c/p\u003e","manuscriptTitle":"Cumulative childhood trauma exposure and depressive and anxiety symptoms among Kenyan adolescents: A multilevel school-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:20:47","doi":"10.21203/rs.3.rs-9327795/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":"1d991011-7aa9-4057-a84e-5fbb958789ff","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T02:23:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:20:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9327795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9327795","identity":"rs-9327795","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}