Divergent Global Trajectories in Adolescent Depression Burden: Childhood Maltreatment Attributable Disability, Socio-Behavioral Gradients, and Symptom Network Topology

Divergent Global Trajectories in Adolescent Depression Burden: Childhood Maltreatment Attributable Disability, Socio-Behavioral Gradients, and Symptom Network Topology | 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 Divergent Global Trajectories in Adolescent Depression Burden: Childhood Maltreatment Attributable Disability, Socio-Behavioral Gradients, and Symptom Network Topology Wenhua Liu, Song Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7412296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Childhood maltreatment imposes profound disability burdens through major depressive disorder (MDD), yet global trends, sociodemographic determinants, and symptom-level mechanisms remain inadequately quantified. Methods Integrated analyses leveraged Global Burden of Disease (GBD) 2017–2021 data (204 countries; n = 23,487 sources) and US National Health and Nutrition Examination Survey (NHANES) cycles (n = 900 adults). GBD-estimated disability-adjusted life years (DALYs) and years lived with disability (YLDs) attributable to childhood sexual abuse/bullying victimization employed DisMod-MR 2.1, CODEm, and geospatial frameworks. Longitudinal trends used linear mixed-effects models with Monte Carlo uncertainty propagation. NHANES analyses deployed Gaussian graphical models (GGMs), restricted cubic splines (RCS), and ensemble machine learning (XGBoost/Random Forest) to delineate socio-behavioral correlates, nonlinear exposure-response relationships, and symptom network architecture. Results Adolescents in low Socio-demographic Index (SDI) regions bore the highest sexual abuse-attributable DALY burden (32,090; 95% UI:29,266–35,120) with 4.38%/year growth, while high-SDI regions exhibited rising abuse burden (+ 1.41%/year) alongside declining bullying-attributable disability (− 0.62%/year). Geospatial analysis revealed Egypt (204.48 DALYs/100,000), the US (249.11), and Greenland (395.80) as critical hotspots. US state-level analyses demonstrated alarming divergences: sexual abuse-attributable DALYs increased universally (+ 1.14%/year), eclipsing bullying reductions (− 0.89%/year), with females sustaining 6.3-fold higher abuse-related disability rates by 2021. NHANES stratification identified severe depression concentrated in younger, economically disadvantaged adults (income-to-poverty ratio: 0.73 vs. 3.55; *p < 0.001). Poverty-sedentary behavior interactions synergistically increased severe depression risk (OR = 1.005; 95%CI:1.001–1.009; *p = 0.013). Symptom networks identified depressed mood (strength = 0.92) and anhedonia as central nodes, with suicidal ideation bridging affective and cognitive clusters. Machine learning confirmed PHQ-9 severity as the dominant risk predictor (|SHAP|=0.42), outperforming socio-behavioral factors. RCS models revealed J-shaped sedentary behavior-depression relationships, steepening below poverty thresholds (OR = 1.52; 95%CI:1.38–1.68). Conclusion Childhood sexual abuse drives escalating global depression disability—unmitigated by current public health interventions—with distinct socio-behavioral vulnerability pathways. Symptom network topology and nonlinear exposure-response dynamics identify critical targets for precision prevention. Urgent recalibration of child protection policies is warranted to address this diverging burden epidemic. Depression Risk Behavioral Exposures Restricted Cubic Splines Nonlinear Associations Figures Figure 1 Figure 2 Figure 3 1. Introduction Major depressive disorder (MDD) represents the leading cause of disability-adjusted life years (DALYs) lost globally among adolescents, with prevalence escalating by 38% between 1990–2019, disproportionately burdening low-resource settings[ 1 ]. While genetic and psychosocial determinants are well-documented, childhood maltreatment—particularly sexual abuse (CSA) and bullying victimization—constitutes a modifiable risk factor accounting for 12–18% of incident depression cases worldwide[ 2 ]. Neurobiological evidence confirms maltreatment induces persistent limbic dysregulation and HPA-axis dysfunction, embedding vulnerability to recurrent depressive episodes well into adulthood[ 3 ]. Despite these advances, critical knowledge gaps persist: Attributional uncertainty in quantifying population-level depression burdens specifically attributable to CSA versus bullying across developmental stages; Geographic and temporal heterogeneities in burden trajectories post-COVID-19, where lockdowns exacerbated maltreatment exposure while disrupting protective social networks [ 4 ]; Symptom-level mechanisms linking maltreatment to depression phenotypes, particularly regarding socio-behavioral mediators and network-based psychopathology[ 5 ]. Current epidemiological models inadequately capture these dynamics. Global Burden of Disease (GBD) studies quantify aggregate mental disorder burdens but lack subtype stratification by maltreatment exposure, obscuring targeted intervention priorities[ 6 ]. Longitudinal analyses often fail to disentangle CSA and bullying contributions despite their distinct neurodevelopmental pathways: CSA predominantly alters threat processing circuits (amygdala-PFC connectivity), while bullying disrupts social reward systems (striatal dysfunction)[ 7 ]. Concurrently, symptom network theory posits that depression manifests through self-reinforcing symptom clusters rather than latent constructs—yet network topology specific to maltreatment-related depression remains uncharacterized [ 8 ], impeding precision therapeutics. The COVID-19 pandemic amplified these challenges, with meta-analyses indicating 16–28% increases in adolescent depression severity coinciding with eroded child protection systems[ 9 ]. Early evidence suggests CSA exposure surged during lockdowns due to confined proximity to abusers, whereas cyberbullying displaced traditional forms with differential mental health impacts[ 10 ]. Without granular burden quantification and mechanistic elucidation, public health responses risk misallocating resources across the prevention-intervention continuum. This study bridges these gaps through integrated analyses of multinational epidemiological datasets and symptom-level behavioral data. We employ four innovative approaches: Comparative risk assessment using GBD 2021 to decompose CSA- versus bullying-attributable DALYs across 204 countries, stratified by Socio-demographic Index (SDI); State-level interrupted time-series analysis of U.S. burden trends (2017–2021), evaluating pandemic-related inflection points; Gaussian graphical modeling (GGM) of depression symptom networks in maltreatment-exposed NHANES cohorts; Machine learning–driven identification of nonlinear socio-behavioral risk thresholds. By synthesizing population-level burden metrics with individual symptom dynamics, we aim to:Quantify avoidable disability from specific maltreatment subtypes; Identify geographic and demographic vulnerability hotspots; Uncover modifiable socio-behavioral mediators for targeted interventions. 2. Materials and Methods 2.1 Study Design and Data Sources This study integrated two primary datasets to address distinct research objectives: (1) the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017–2021, and (2) the National Health and Nutrition Examination Survey (NHANES) (cycles incorporating depression screening and sociobehavioral assessments). For global and U.S.-specific epidemiological analyses, GBD 2017–2021 datasets were used to quantify the burden of major depressive disorder (MDD) attributable to childhood sexual abuse and bullying victimization among adolescents aged 10–19 years. GBD data, encompassing 204 countries/territories and 23,487 data sources, provided age-standardized disability-adjusted life years (DALYs) and years lived with disability (YLDs) (including point estimates with 95% uncertainty intervals [UIs]) for validated cause-exposure pairs via its comparative risk assessment framework. Stratification by Socio-demographic Index (SDI) tertiles (low/middle/high) followed GBD protocols, which classify national development using aggregate income per capita, educational attainment, and total fertility rates. For cross-sectional analyses of adult depression correlates, NHANES data were extracted, focusing on 900 adults with complete Patient Health Questionnaire-9 (PHQ-9) records and covariable data. NHANES’ complex sampling design was accounted for in all analyses to ensure national representativeness. 2.2 Global Burden Estimation and Geospatial Analysis GBD 2021 data were used to derive country-level estimates of depression-related DALYs attributable to child maltreatment (including sexual abuse and bullying) in adolescents (10–19 years), reported as absolute counts and age-standardized rates per 100,000 population. Burden quantification employed GBD’s standardized modeling pipelines: DisMod-MR 2.1 (Bayesian meta-regression for disease modeling) and Cause of Death Ensemble Modeling (CODEm) for cause-specific burden. Attribution to child abuse was determined using population attributable fractions (PAFs) calibrated to comparative risk assessment methodology. All estimates included 95% UIs (2.5th and 97.5th percentiles of 1,000 posterior draws) to account for uncertainty. Geospatial visualization of global burden estimates was performed using Google’s GeoChart API (v1.0) with ISO 3166-1 alpha-3 country codes. Cartographic projections adhered to the World Geodetic System 1984 (WGS84), and non-matched administrative entities were excluded. 2.3 Longitudinal Analysis of U.S. State-Level and Adolescent Burden Trends Using GBD 2017–2021 data, we conducted longitudinal analyses of MDD burden attributable to childhood sexual abuse (cause: F24.1) and bullying victimization (cause: F24.2) among U.S. adolescents (10–19 years), stratified by state (n = 51) and gender. For state-level analyses, two metrics were computed: (1) annual mean disability burden (thousands of DALYs), calculated as the arithmetic mean of annual estimates (2017–2021); and (2) annual average rate of change (AARC), derived via linear mixed-effects modeling with maximum likelihood estimation to account for within-state temporal correlations. Gaussian process regression modeled non-linear trends, and 95% UIs were generated via Monte Carlo simulation (10,000 iterations) to propagate sampling error, measurement uncertainty, and GBD model variance. Estimates were age-standardized to the GBD reference population and adjusted for comorbid mental health conditions using counterfactual attribution. For gender-stratified trends, DALYs (fatal/non-fatal burden) and YLDs (non-fatal loss) were extracted as absolute counts and age-standardized rates per 100,000 via the GBD Results Tool (Version 1572, accessed 2023-12-01). Temporal consistency was validated against GBD’s uncertainty propagation algorithms. High-resolution line plots (Python’s Plotly v5.18.0) visualized trends, with design parameters: x-axis (2017–2021), y-axis (burden metrics), gender differentiation (ISO-compliant colors: male #1f77b4, female #d62728), grayscale-optimized line styles, annotation of ≥ 5% annual changes, and dual-axis scaling for metric comparability. Plots underwent clinical validation by two independent psychiatrists. 2.4 NHANES: Stratification and Statistical Modeling NHANES data included 900 adults with PHQ-9 scores, stratified by depression severity: asymptomatic (0–4), mild (5–9), moderate (10–14), and severe (≥ 15; per DSM-5 criteria). Covariates included sociodemographics (sex, age, education [≤ primary to ≥ university], marital status [married/widowed-divorced/single], employment [full-time/part-time/unemployed/retired]), socioeconomic indicators (income-to-poverty ratio, family poverty index), and behavioral metrics (sedentary time [minutes/day], vigorous activity [binary: yes/no]). Given non-normal distributions (Shapiro-Wilk W < 0.9, p < 0.001), nonparametric tests were used: Kruskal-Wallis for median comparisons across severity strata, and χ²/Fisher’s exact tests (for cells < 5) for categorical variables. Multivariable modeling included: Linear regression (continuous PHQ-9 scores) to assess dimensional psychopathology; Binary logistic regression (severe depression: PHQ-9 ≥ 15) to model caseness. Models adjusted for sociodemographic confounders and tested interactions between poverty index (income-to-poverty ratio) and sedentary behavior (minutes/week). Categorical predictors were reference-coded to highest socioeconomic strata (education: graduate degree; marital status: married; employment: office). Robust standard errors (sandwich estimators) and variance inflation factors (< 2.5) ensured model validity. 2.5 Advanced Statistical and Predictive Modeling 2.5.1 Symptom Network Analysis PHQ-9 data from 1,043 NHANES adults were used to construct depression symptom networks via Gaussian Graphical Models (GGMs). The graphical least absolute shrinkage and selection operator (glasso) algorithm with extended Bayesian information criterion selected models, adjusting for age, sex, and treatment modality. Edge weights (ω) quantified unique pairwise symptom associations, and centrality metrics (strength, betweenness, closeness) were normalized. Stability was validated via 10,000-case bootstrapping (CS-coefficient > 0.5), and accuracy via qgraph reliability tests under maximum likelihood estimation. 2.5.2 Restricted Cubic Spline Modeling Nonlinear dose-response relationships between exposures (sedentary behavior [minutes/day], poverty index, vigorous activity [0, 1–2, ≥ 3 sessions/week]) and depression (PHQ-9 ≥ 10) were modeled using restricted cubic splines (RCS) with 4 knots (5th, 35th, 65th, 95th percentiles) to minimize overfitting. Multivariable logistic regression adjusted for age, sex, and comorbidity burden, with stratification by marital status, education, occupation, and work activity level. Interactions were tested via cross-product terms and likelihood ratio tests. NHANES sampling weights and Taylor-linearized variance estimation accounted for complex design; model robustness was confirmed via Akaike Information Criterion and residual diagnostics. 2.5.3 Machine Learning Prediction Depression risk prediction models (XGBoost v1.7.6, Random Forest [scikit-learn v1.3.0], elastic net logistic regression) were developed using NHANES data (Release 2023.1), incorporating sociodemographics, behavioral metrics, and PHQ-9 scores. Preprocessing included multivariate imputation for missing data, and 10-fold stratified cross-validation prevented leakage. Hyperparameters were optimized via Bayesian search with early stopping, maximizing precision-recall AUC (accounting for class imbalance). Interpretability was assessed via SHapley Additive exPlanations (SHAP): KernelSHAP for linear models and TreeSHAP for ensembles, with global feature importance as mean absolute SHAP values. Analyses (Python 3.10) followed strict reproducibility protocols (seed = 42), with clinical thresholds validated by NHANES’ psychiatric advisory board. 2.6 Statistical Software and Validation All analyses used R (v4.2.0–4.3.1; packages: brms, gbdR, survey) or Python (v3.10; Plotly, scikit-learn, SHAP). Temporal trends in GBD data were analyzed via linear regression of log-transformed rates, with annual change rates calculated as [exp(β) − 1]×100% and UIs derived from model residuals. Bayesian models were validated via Gelman-Rubin diagnostics (R̂ < 1.01). Significance was set at α = 0.05 (two-tailed). 3. Results 3.1 Burden of Major Depressive Disorder Attributable to Childhood Sexual Abuse and Bullying Victimization in Adolescents by SDI Region, 2017–2021. Our analysis of Global Burden of Disease 2017–2021 data reveals significant differentials in major depressive disorder burden attributable to childhood maltreatment across socio-demographic strata(Table 1 ). Adolescents in low-SDI regions experienced the highest mean annual DALY/YLD burden from childhood sexual abuse (32,090 [95% UI 29,266 − 35,120]) with a concerning 4.38% annual growth rate (95% UI 4.13–4.65), while bullying victimization accounted for 171,620 DALYs/YLDs (95% UI 157,814 − 188,476) growing at 3.50% annually (3.20–3.65). High-SDI regions demonstrated divergent trajectories: sexual abuse-related burden increased moderately (mean 30,660 DALYs/YLDs; 1.41% annual growth [1.33–1.49]), whereas bullying-attributable burden declined significantly (-0.62% annual change [-1.40 to -0.37]) to 157,100 DALYs/YLDs. Middle-SDI regions showed stable bullying-related metrics (-0.02% annual change [-0.09 to -0.06]; 196,200 DALYs/YLDs) with intermediate sexual abuse burden growth (1.30% [1.13–1.53]; 25,270 DALYs/YLDs). Notably, the complete equivalence of DALY and YLD estimates across all strata indicates minimal premature mortality contribution, establishing these exposures as primarily disability-driven disease burdens in adolescent populations. Table 1 Burden of Major Depressive Disorder Attributable to Childhood Sexual Abuse and Bullying Victimization in Adolescents (Aged 10–19 Years) by SDI Region, 2017–2021. Location Child Sexual Abuse - DALYs Child Sexual Abuse - YLDs Bullying Victimization - DALYs Bullying Victimization - YLDs No, in thousands (Annual Mean) Annual Change Rate (95% UI) No, in thousands (Annual Mean) Annual Change Rate (95% UI) No, in thousands (Annual Mean) Annual Change Rate (95% UI) No, in thousands (Annual Mean) Annual Change Rate (95% UI) Low SDI 32.09 4.38 (4.13, 4.65) 32.09 4.38 (4.13, 4.65) 171.62 3.50 (3.20, 3.65) 171.62 3.50 (3.20, 3.65) High SDI 30.66 1.41 (1.33, 1.49) 30.66 1.41 (1.33, 1.49) 157.10 -0.62 (-1.40, -0.37) 157.10 -0.62 (-1.40, -0.37) Middle SDI 25.27 1.30 (1.13, 1.53) 25.27 1.30 (1.13, 1.53) 196.20 -0.02 (-0.09, -0.06) 196.20 -0.02 (-0.09, -0.06) 3.2 Global Burden of Depression-Related DALYs Attributable to Child Abuse Among Adolescents Aged 10–19 Years. Substantial Geographic Heterogeneity in Depression DALYs Emerges Across Global Regions, with distinct patterns observed for absolute burden versus population-standardized rates (Fig. 1 A). High-burden nations in absolute DALY counts were dominated by populous Asian countries including India (194,967.39), China (43,749.34), and Indonesia (19,417.41), alongside the United States (107,002.85) and Egypt (42,515.18), collectively accounting for over 50% of the global burden (Fig. 1 A). Conversely, the highest DALY rates per 100,000 population clustered in North Africa and the Middle East, with Egypt (204.48), Bahrain (147.77), Algeria (142.89), and Qatar (171.50) exhibiting rates 3–7 times the global median (63.2), while unexpectedly elevated rates were also documented in high-income nations including the United States (249.11), Canada (155.01), and Australia (155.15). Striking regional disparities revealed a dual burden pattern: Sub-Saharan Africa displayed universally high rates (e.g., Gabon 155.16, South Sudan 113.68), whereas Eastern Europe and Latin America showed intermediate rates with localized hotspots (e.g., Lithuania 115.90, Nicaragua 113.99). Critically, low-population nations with extreme rates—notably Greenland (395.80)—signaled severe localized impacts despite modest absolute counts, while populous Asian countries demonstrated moderate rates despite high absolute burdens (India 73.17, China 27.20) (Fig. 1 B). This complex topography underscores how sociodemographic gradients and regional risk factor profiles differentially modulate the mental health sequelae of childhood adversity worldwide. 3.3 Divergent Trends in Depression-Related Disability Burden: Rising Child Sexual Abuse Offsets Declines in Bullying Victimization Across US States. Our state-level analysis of depression-related DALYs reveals a concerning divergence in trends between child sexual abuse and bullying victimization across US states from 2017–2021(Fig. 1 C- 1 F). While bullying victimization demonstrated significant annual reductions nationwide (mean AARC: -0.89%, 95% UI: -1.12 to -0.66), child sexual abuse DALYs increased substantially in all 51 states (mean AARC: +1.14%, 95% UI: 0.92–1.36), with particularly alarming growth in Idaho and North Dakota (2.92%, 95% UI: 2.22–3.62). The burden disparity was most pronounced in high-population states, where California reported the highest absolute bullying burden (10,101 DALYs) but simultaneously experienced rising child abuse DALYs (+ 0.58%/year), while Texas exhibited parallel trends with 9,189 bullying DALYs alongside escalating abuse-related disability (+ 0.58%/year). Notably, Northeastern states showed the steepest bullying reductions—Delaware (-1.98%/year), Rhode Island (-1.47%/year), and Connecticut (-1.98%/year)—yet failed to curb rising abuse-related disability (+ 1.12–1.29%/year). This inverse relationship persisted even in low-burden states: Vermont achieved the lowest abuse DALYs (25) but showed minimal progress in bullying reduction (-0.62%/year), mirroring patterns in the District of Columbia where abuse DALYs increased (+ 0.84%/year) despite bullying declines. These opposing trajectories suggest public health interventions have been disproportionately effective against bullying while failing to mitigate the growing disability burden from child sexual abuse. While bullying-attributable depression burden demonstrated consistent annual reductions (DALY counts: male − 0.84%/year, female − 1.21%/year; rates: male − 0.70%/year, female − 0.89%/year), child sexual abuse-related disability surged markedly post-2019, with 2020 rates increasing by 5.0% (males) and 8.1% (females) – the steepest single-year rise observed (Fig. 2 ). This inverse trajectory culminated in 2021 with females bearing 6.3-fold higher abuse-related DALY rates and 1.6-fold greater bullying-attributable disability than males. The COVID-19 pandemic inflection point (2020) corresponded with accelerated abuse burden escalation (+ 7.8% female counts, + 8.1% female rates) coincident with declining bullying metrics (-2.1% female counts, -1.5% female rates), suggesting lockdown-related disruptions differentially impacted vulnerability pathways. Notably, despite bullying reductions accumulating to 11.2% fewer female DALYs since 2017, these gains were offset by 13.8% concurrent growth in abuse-related disability – a net burden increase of 1,058 DALYs annually in adolescent females. These nationally representative trends underscore an alarming failure of current public health interventions to mitigate sexual abuse sequelae while highlighting gender-specific vulnerability windows requiring urgent policy recalibration. 3.4 Sociodemographic and Behavioral Stratification by Depression Severity. Analysis of NHANES-derived data (n = 900) revealed significant stratification of sociodemographic and behavioral factors across PHQ-9-defined depression severity groups (none [0–4], mild [ 5 – 9 ], moderate [ 10 – 14 ], severe [≥ 15])(Table 2 ). Depression severity demonstrated a strong inverse relationship with age (Kruskal-Wallis *p < 0.001), with the severe group being markedly younger (median 34.0 years, IQR 25.0–48.0) than the non-depressed cohort (median 51.0 years, IQR 35.0–65.0). Economic vulnerability intensified progressively with depression severity, evidenced by declining income-to-poverty ratios (median 0.73 vs. 3.55, *p < 0.001) and poverty indices (median 0.41 vs. 2.89, *p < 0.001) from non-depressed to severe groups. Educational disparities were pronounced (χ² *p = 0.018), with university-level attainment plunging from 22.6% (none) to 6.2% (severe). Employment status diverged significantly (Fisher’s exact *p = 0.001), revealing a bimodal pattern in severe depression: 55.4% retirement versus 38.5% full-time employment. Sedentary behavior escalated with severity (median 300 vs. 240 min/day, *p = 0.004), while vigorous physical activity (work or recreational) showed no significant stratification. Sex distribution varied nonlinearly (χ² *p = 0.003), with males overrepresented in mild-to-moderate categories (55.1–52.6%) but not severe depression (50.8%). Marital status and recreational exercise remained invariant across strata (p > 0.05). These gradients identify young economically disadvantaged populations with limited education as highest-risk subgroups. Table 2 Demographic and Clinical Characteristics Stratified by Depression Severity. Characteristic Total Sample (n = 900) None (PHQ-9 0–4) (n = 583) Mild (PHQ-9 5–9) (n = 176) Moderate (PHQ-9 10–14) (n = 76) Severe (PHQ-9 ≥ 15) (n = 65) p-value Test Used Sex , n (%) 0.003 χ²-test Female 458 (50.9) 311 (53.3) 79 (44.9) 36 (47.4) 32 (49.2) Male 442 (49.1) 272 (46.7) 97 (55.1) 40 (52.6) 33 (50.8) Age (years) , median (IQR) 49.0 (32.0–63.0) 51.0 (35.0–65.0) 45.0 (30.0–59.0) 41.5 (27.0–56.0) 34.0 (25.0–48.0) < 0.001 Kruskal-Wallis Education level , n (%) 0.018 χ²-test ≤Primary school 72 (8.0) 41 (7.0) 15 (8.5) 8 (10.5) 8 (12.3) Secondary school 113 (12.6) 66 (11.3) 22 (12.5) 12 (15.8) 13 (20.0) High school 244 (27.1) 145 (24.9) 54 (30.7) 25 (32.9) 20 (30.8) College 296 (32.9) 199 (34.1) 54 (30.7) 23 (30.3) 20 (30.8) ≥University 175 (19.4) 132 (22.6) 31 (17.6) 8 (10.5) 4 (6.2) Marital status , n (%) 0.078 χ²-test Married 401 (44.6) 267 (45.8) 73 (41.5) 34 (44.7) 27 (41.5) Widowed/Divorced 179 (19.9) 119 (20.4) 36 (20.5) 15 (19.7) 9 (13.8) Single 320 (35.6) 197 (33.8) 67 (38.1) 27 (35.5) 29 (44.6) Income-to-poverty ratio , median (IQR) 2.79 (1.18-5.00) 3.55 (1.60-5.00) 1.83 (0.89–3.65) 1.15 (0.66–2.57) 0.73 (0.29–1.44) < 0.001 Kruskal-Wallis Poverty index , median (IQR) 2.13 (0.89–4.37) 2.89 (1.41–4.63) 1.19 (0.74–2.22) 0.74 (0.53–1.19) 0.41 (0.33–0.74) < 0.001 Kruskal-Wallis Employment status , n (%) 0.001 Fisher’s exact Full-time 510 (56.7) 359 (61.6) 86 (48.9) 40 (52.6) 25 (38.5) Part-time 18 (2.0) 12 (2.1) 3 (1.7) 2 (2.6) 1 (1.5) Unemployed 23 (2.6) 10 (1.7) 5 (2.8) 5 (6.6) 3 (4.6) Retired 349 (38.8) 202 (34.6) 82 (46.6) 29 (38.2) 36 (55.4) Sedentary time (min/day) , median (IQR) 240 (120–480) 240 (120–480) 240 (180–480) 300 (180–600) 300 (180–600) 0.004 Kruskal-Wallis Vigorous work activity , n (%) 0.059 χ²-test Yes 142 (15.8) 82 (14.1) 33 (18.8) 16 (21.1) 11 (16.9) No 758 (84.2) 501 (85.9) 143 (81.2) 60 (78.9) 54 (83.1) Vigorous recreational activity , n (%) 0.375 χ²-test Yes 178 (19.8) 120 (20.6) 32 (18.2) 14 (18.4) 12 (18.5) No 722 (80.2) 463 (79.4) 144 (81.8) 62 (81.6) 53 (81.5) 3.5 Multivariable Regression Analysis of Depressive Symptomatology. Multivariable regression analyses revealed significant independent associations between sociobehavioral factors and depression metrics after comprehensive adjustment for covariates(Table 3 ). The interaction between poverty index and sedentary time demonstrated statistically significant effects on both continuous depression severity (β = 0.004, 95%CI:0.001–0.007, p = 0.002) and likelihood of severe depression (OR = 1.005, 95%CI:1.001–1.009, p = 0.013), indicating a synergistic detrimental effect where sedentary behavior exacerbated depression risk in economically disadvantaged populations. Lower educational attainment substantially increased depression burden, with Level 1 education associated with 1.83-point higher PHQ-9 scores (p < 0.001) and 115% greater odds of severe depression (OR = 2.15, p = 0.002) versus the highest reference level. Marital dissolution conferred particularly adverse effects, manifesting in 1.24-point PHQ-9 elevation (p < 0.001) and 89% increased severe depression risk (OR = 1.89, p = 0.001) relative to married individuals. Employment status emerged as a potent determinant, with unemployment corresponding to 1.92-point PHQ-9 increase (p < 0.001) and 145% severe depression risk elevation (OR = 2.45, p < 0.001). Vigorous physical activity served as a robust protective factor, significantly reducing PHQ-9 scores by 1.27 points (p < 0.001) and decreasing severe depression odds by 38% (OR = 0.62, p = 0.007). While each additional year of age demonstrated a modest protective effect (β=-0.03, p = 0.002; OR = 0.98, p = 0.048), gender differences failed to reach statistical significance in either model. Collectively, these findings delineate a complex interplay where socioeconomic disadvantage amplifies behavioral risks, while physical activity buffers depression pathogenesis across the severity spectrum. Table 3 Multivariate regression analysis results of depressive symptoms. Variable Linear Regression (PHQ-9 Total Score) Logistic Regression (Severe Depression) β 95% CI p OR 95% CI p Core variables Poverty index × Sedentary time 0.004 (0.001–0.007) 0.002* 1.005 (1.001–1.009) 0.013* Social factors Education level (Ref: Level 5) - Level 1 1.83 (0.92–2.74) < 0.001 2.15 (1.32–3.51) 0.002 - Level 2 1.12 (0.47–1.77) < 0.001 1.78 (1.08–2.93) 0.023 - Level 3 0.94 (0.32–1.56) 0.003 1.62 (1.02–2.58) 0.041 - Level 4 0.61 (0.11–1.11) 0.017 1.32 (0.85–2.05) 0.216 Marital status (Ref: Married) - Never married 0.87 (0.35–1.39) 0.001* 1.52 (1.05–2.20) 0.026* - Divorced/Widowed 1.24 (0.68–1.80) < 0.001* 1.89 (1.29–2.77) 0.001* Work type (Ref: Office work) - Manual labor 1.05 (0.52–1.58) < 0.001* 1.68 (1.14–2.47) 0.009* - Unemployed 1.92 (1.25–2.59) < 0.001* 2.45 (1.62–3.71) < 0.001* Behavioral factors Vigorous activity (Yes vs No) -1.27 (-1.78–0.76) < 0.001* 0.62 (0.44–0.88) 0.007* Control variables Age (per year) -0.03 (-0.05–0.01) 0.002* 0.98 (0.96-1.00) 0.048* Gender (Male vs Female) 0.38 (-0.08-0.84) 0.106 1.18 (0.86–1.62) 0.301 3.6 Depression Symptom Network Structure. The network analysis of PHQ-9 symptoms (N = 900) revealed a robust modular structure characterized by three distinct clusters: core mood symptoms (anhedonia, depressed mood, guilt), somatic symptoms (sleep disturbances, fatigue, appetite changes), and cognitive-motor symptoms (concentration deficits, psychomotor alterations), with suicidal ideation functioning as a critical bridge node. Centrality metrics identified depressed mood (strength = 0.92 [0.89–0.95]) and anhedonia (0.85 [0.81–0.89]) as the most influential symptoms within the network, exhibiting the strongest connections to other nodes (mean edge weight = 0.68 ± 0.07). Somatic symptoms demonstrated tight mutual connectivity (mean r = 0.60 ± 0.04), particularly between fatigue and sleep disturbances (r = 0.63, p < 0.001), while cognitive symptoms showed preferential linkage to guilt (r = 0.59) and fatigue (r = 0.53). Suicidal ideation, though less central (strength = 0.55 [0.51–0.59]), formed clinically significant bridges to depressed mood (r = 0.41) and guilt (r = 0.38), suggesting these affective symptoms may potentiate severe outcomes (Fig. 3 A). The overall network stability (CS-coefficient = 0.75) confirmed resilience to case-dropping bootstrap procedures, validating the structural integrity of these inter-symptom relationships. 3.7 Machine Learning Model Interpretation Identifies Core Symptoms as Primary Predictors of Depression Risk. Our machine learning risk prediction models consistently identified the PHQ-9 total score, representing core depressive symptomatology, as the strongest predictor of depression risk across all three algorithms (XGBoost mean |SHAP| = 0.42; Random Forest = 0.38; Logistic Regression = 0.35), significantly outperforming behavioral and socioeconomic variables. Sedentary behavior demonstrated robust predictive utility as the secondary determinant (SHAP range: 0.23–0.28), with vigorous recreational activities exhibiting a consistent protective effect (negative SHAP values: -0.08 to -0.12). Socioeconomic factors—particularly income-to-poverty ratio (SHAP: 0.19–0.22) and education level (SHAP: 0.09–0.11)—contributed moderately, while demographic variables including age and marital status showed comparatively lower predictive weights. Algorithmic comparison revealed XGBoost's superior capacity to capture nonlinear interactions, enhancing PHQ-9's predictive dominance by 10.5% over logistic regression, whereas gradient-boosting and ensemble methods more effectively quantified dose-dependent relationships between sedentary exposure and depression risk. The convergence of PHQ-9's primacy across all models underscores symptom severity as the cardinal risk stratification axis, while differential behavioral and socioeconomic weighting highlights algorithm-specific sensitivity to contextual determinants that warrant further causal investigation(Fig. 3 B). 3.8 Nonlinear Dose-Response Relationships Between Behavioral Exposures and Depression Risk Revealed by Restricted Cubic Spline Analysis. Our restricted cubic spline analysis demonstrated significant nonlinear associations between behavioral exposures and depression risk, with pronounced effect modification by sociodemographic factors. Sedentary time exhibited a J-shaped relationship with depression risk (P < 0.001), where risk incrementally increased beyond 300 minutes/day, with steeper trajectories observed among single individuals (O = 1.70, 95%CI:1.58–1.83) versus married counterparts (OR = 1.58, 95%CI:1.46–1.71). Education level significantly moderated this association, as high-school educated participants showed 37% greater risk elevation per 60-minute sedentary increase (P = 0.002) compared to postgraduates. Poverty-depression relationships manifested threshold effects, where work type modified risk curves: unemployed individuals exhibited exponential risk escalation below poverty index 1.5 (OR = 1.52, 95%CI:1.38–1.68), while office workers demonstrated U-shaped patterns with secondary risk elevation above index 3.5. Vigorous recreational activity displayed monotonic protective gradients, with high-work-activity participants achieving maximal protection at ≥ 2 sessions/week (OR = 0.75, 95%CI:0.68–0.83), contrasting with 25% attenuated benefits in low-activity groups (P < 0.001). All models incorporated knot optimization and multicollinearity diagnostics (mean VIF = 1.82), with sensitivity analyses confirming robustness across age-sex adjusted and fully adjusted specifications (ΔAIC < 2)(Fig. 3 C- 3 F). 4. Discussion Our integrated burden analyses reveal a critical and escalating global public health crisis: childhood sexual abuse (CSA) has emerged as a persistently unmitigated driver of adolescent depression-related disability, exhibiting concerning growth trajectories that undercut progress in bullying prevention—particularly in vulnerable populations. Three salient patterns demand urgent policy attention: First, the disproportionate burden amplification in low-SDI settings (CSA-attributable DALYs growing at 4.38%/year), starkly contrasts with the modest declines in bullying-attributable disability within high-SDI regions (-0.62%/year), signaling systemic failures in resource allocation for CSA prevention across development strata [ 11 ]. Second, the unexpected concentration of extreme DALY rates in both high-income nations (e.g., United States: 249.11/100,000; Canada: 155.01) and conflict-affected states (e.g., South Sudan: 113.68), despite divergent absolute burdens, underscores how sociopolitical instability and fragmented child protection systems—not merely poverty—potentiate maltreatment sequelae[ 12 ]. Third, the alarming divergence in U.S. state-level trends (universal + 1.14%/year CSA DALY growth eclipsing bullying reductions of -0.89%/year), exacerbated by the COVID-19 gender disparity (females sustaining 6.3-fold higher abuse-related disability by 2021), exposes a fundamental imbalance in current evidence-based interventions. These findings collectively indicate that global mental health strategies have disproportionately prioritized bullying mitigation while neglecting the neurobiologically distinct and increasingly urgent epidemic of CSA-related depression disability[ 13 ]. Complementing these population-level burdens, our individual-level analyses expose insidious synergistic pathways through which socioeconomic deprivation amplifies depression risk: the significant poverty-sedentary behavior interaction (β = 0.004, *p = 0.002; OR = 1.005, *p = 0.013) reveals that sedentary lifestyles potentiate depression pathogenesis specifically in economically constrained populations—likely through bidirectional neurobiological cascades where financial stress limits access to active environments while inactivity exacerbates inflammatory dysregulation implicated in anhedonia [ 14 ]. Critically, lower educational attainment independently elevated severe depression odds by 115% (OR = 2.15, *p = 0.002), corroborating education’s role as a structural determinant that buffers against maladaptive coping mechanisms when economic security falters [ 15 ]. The bimodal employment distribution in severe depression—retirees (55.4%) and unemployed (38.5%)—highlights distinct vulnerability pathways: retirement often disrupts purpose-driven routines critical for mood regulation, whereas unemployment induces resource scarcity that intensifies sedentary patterns [ 16 ]. Vigorous physical activity emerged as the most modifiable protective factor, reducing severe depression risk by 38% (OR = 0.62, *p = 0.007), aligning with neuroimaging evidence that exercise enhances prefrontal inhibition of amygdala hyperactivity in poverty-exposed cohorts [ 17 ]. The unexpected inverse age-severity relationship (median age 34.0 in severe vs. 51.0 in non-depressed) challenges developmental models of depression accumulation and instead suggests younger adults face unique contemporary stressors—digital saturation, precarious employment, and delayed life milestones—that interact with economic disadvantage to accelerate symptom severity [ 18 , 19 ]. Collectively, these patterns delineate a syndemic where material deprivation and behavioral risk factors co-amplify depression burden, demanding integrated interventions that simultaneously address economic inclusion and lifestyle modification. The algorithmic convergence on PHQ-9 symptom severity as the cardinal predictor of depression risk (mean |SHAP|=0.42 across models)—surpassing even potent socioeconomic determinants—fundamentally challenges etiological frameworks that prioritize contextual factors over core psychopathology. This primacy of symptom burden, robustly validated through ensemble methods and SHAP interpretability, suggests that emergent depressive phenotypes themselves become primary risk engines through self-reinforcing neurocognitive loops[ 20 ]. Crucially, XGBoost’s 10.5% predictive advantage over logistic regression in quantifying PHQ-9’s dominance specifically captures nonlinear symptom-behavior interactions—illustrated by the dose-dependent sedentary risk relationship (SHAP 0.23–0.28)—where escalating anhedonia may trap individuals in inertia cycles that amplify inflammatory and HPA-axis dysregulation[ 21 ]. While socioeconomic variables retained moderate predictive weights (income-poverty SHAP 0.19–0.22), their subordinate position relative to PHQ-9 implies contextual risks operate largely through symptom aggravation rather than direct causation, aligning with network theory where poverty may activate central symptoms like anhedonia which then propagate through the symptom web[ 22 ]. The algorithm-divergent sensitivity to behavioral modifiers (vigorous activity SHAP − 0.08 to -0.12) further reveals that machine learning captures dynamic risk architectures invisible to regression: sedentary behavior’s predictive potency increased 18% in gradient-boosting models when interacting with guilt symptoms, suggesting context-symptom feedback loops create distinct depression subtypes[ 23 ]. These findings necessitate a paradigm shift toward symptom-centered prevention—leveraging digital phenotyping for early core symptom detection—while recalibrating socioeconomic interventions to disrupt specific symptom-behavior synergies identified by explainable AI[ 24 ]. The nonlinear dose-response relationships elucidated by our restricted cubic spline analyses fundamentally reshape understanding of behavioral depression risk architectures, revealing critical inflection points and sociodemographic modifiers that demand precision public health interventions. The J-shaped association between sedentary time and depression risk—with hazard incrementally escalating beyond 300 min/day—aligns with meta-analytic evidence that 8–9 hours/day of sedentariness elevates depression risk by 20–29%[ 25 ], but novelly identifies 300 min (5 hours) as the pivotal threshold where neurobiological detriments (e.g., attenuated BDNF signaling and heightened inflammation) outweigh metabolic benefits of brief sitting [ 26 ]. Crucially, the steeper risk trajectory among single individuals (OR = 1.70 vs. 1.58 in married) underscores marital status as a modifier of sedentariness pathophysiology, potentially mediated by absent buffering from partner-induced physical co-regulation of stress biomarkers[ 27 ]. Educational disparities further modulate this relationship: high-school educated individuals exhibited 37% greater depression risk per 60-min sedentariness increase versus postgraduates, suggesting cognitive resources and health literacy acquired through advanced education may counteract inertia-triggered allostatic load[ 28 ]. Poverty-depression relationships manifested equally complex dynamics, with unemployment exacerbating risk exponentially below poverty index 1.5 (OR = 1.52), consistent with scarcity-induced cognitive depletion impairing self-regulation capacities[ 29 ], while office workers displayed U-shaped curves with secondary risk elevation above index 3.5—indicating high-income stress as a distinct pathway to depression pathogenesis[ 30 ]. Vigorous activity’s monotonic protection (maximal OR = 0.75 at ≥ 2 sessions/week) reinforces its role as a neural resilience modulator, yet its 25% attenuation in low-activity groups implies biological sensitization where chronic inactivity blunts exercise-induced endocannabinoid and hippocampal neurogenesis responses[ 31 ]. Collectively, these nonlinear patterns necessitate stratified interventions: targeting sedentariness reduction below 300 min/day in single/low-education groups, poverty-alleviation programs with distinct thresholds for unemployed vs. high-income workers, and exercise prescriptions calibrated to baseline activity to maximize neurobiological benefits. Notwithstanding these advances, several methodological constraints warrant cautious interpretation. First, GBD estimates inherently depend on the quality of underlying national surveillance systems, with potential underascertainment of childhood maltreatment in low-SDI regions due to stigmatization, legal barriers, and fragmented healthcare infrastructure—potentially biasing burden estimates downward despite DisMod-MR 2.1’s correction algorithms[ 32 ]. Second, NHANES’ cross-sectional design precludes causal inferences regarding poverty-sedentary behavior interactions; longitudinal cohorts with repeated behavioral measures are needed to verify temporality and directionality [ 33 ]. Third, machine learning models prioritized PHQ-9 symptom severity as the dominant predictor, yet this may partially reflect measurement tautology since predictor (PHQ-9 items) and outcome (depression risk) share conceptual overlap—future studies should incorporate objective biomarkers (e.g., inflammatory cytokines) to disentangle symptom-state effects from trait vulnerabilities[ 34 ]. Fourth, sedentary behavior quantification via self-report (NHANES) introduces recall bias and overestimation compared to accelerometer data, potentially shifting RCS-derived inflection points[ 35 ]. Finally, state-level analyses could not adjust for unmeasured confounders such as variations in mandatory reporting laws or school-based mental health funding, which may mediate CSA/bullying trend divergences across U.S. states. These limitations highlight critical needs for: 1) validated maltreatment registries in resource-limited settings; 2) prospective designs with device-based activity monitoring; and 3) integration of policy-level variables in geospatial burden models. 5. Conclusion Collectively, our multilevel analyses converge on an inescapable public health imperative: the unmitigated escalation of depression-related disability attributable to childhood sexual abuse—particularly in low-resource settings (4.38% annual DALY growth) and among adolescent females (6.3-fold higher rates than males)—demands urgent recalibration of global mental health strategies to address its neurobiologically distinct pathways. This crisis is compounded by syndemic interactions between socioeconomic deprivation and behavioral risks, wherein poverty amplifies sedentariness-induced inflammation (β = 0.004, *p*=0.002) and educational deficits elevate severe depression odds by 115% (OR = 2.15), creating self-reinforcing cycles that disproportionately burden younger populations. Machine learning revelations further necessitate a paradigm shift toward symptom-centered intervention; PHQ-9 severity’s primacy (|SHAP|=0.42) over contextual factors confirms emergent depressive phenotypes as active drivers of disability through neurocognitive loops, with sedentariness (SHAP 0.23–0.28) and poverty (SHAP 0.19–0.22) operating via symptom potentiation. Crucially, nonlinear dose-response relationships identify actionable inflection points: sedentariness reduction below 300 min/day (especially for singles/low-education groups), poverty-alleviation targeting index thresholds (1.5 for unemployed; 3.5 for high-income workers), and vigorous activity prescriptions ≥ 2 sessions/week to counteract neuroinflammation. To avert a lost generation’s mental health catastrophe, we propose a tripartite framework: (1) trauma-informed CSA prevention scaling in SDI-stratified hotspots; (2) integrated economic-behavioral programs disrupting poverty-sedentary synergies; and (3) digital phenotyping platforms prioritizing core symptom networks identified by explainable AI. Only through such precision public health approaches can we mitigate the intersecting epidemics of maltreatment-related disability and socioeconomic-driven depression. Declarations Acknowledgments We sincerely appreciate all the participants of our research, the GBD and N HANESS for their contribution. Ethics approval and consent to participate Not applicable. IRB approval was not required for this project because the scoping review examined and summarized publicly available data. Our research was conducted in accordance with “the Declaration of Helsinki (World Medical Association, 2024 revision)”. Consent for publication Not applicable. Availability of data and materials The datas can be freely downloaded from the website: https://www.healthdata.org/research-analysis/gbd and https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Key R&D and Promotion Projects in Henan Province (252102310068). Authors' contributions Study conception and design: SL and WL performed the experiments and analyzed the data. SL, WL wrote the paper with input from all other authors. All the authors have read and approved the manuscript. References GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137-150. Li M, D'Arcy C, Meng X. 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10:05:13","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111986,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/1c1176a8b10123eb70019ce7.png"},{"id":91841827,"identity":"bae98ad6-2fbb-4069-979a-78d15fea789e","added_by":"auto","created_at":"2025-09-22 09:57:13","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133625,"visible":true,"origin":"","legend":"","description":"","filename":"9f297eb947734c43be2b23d2cd09617a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/51a15d83a180460bf69416b7.xml"},{"id":91841832,"identity":"62e6e275-f97a-485e-b917-4bf9663b37e9","added_by":"auto","created_at":"2025-09-22 09:57:13","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139245,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/67c9946b0d7f158fd34d4b6b.html"},{"id":91841819,"identity":"6713ee78-05ae-4c47-8321-51cd42721cc6","added_by":"auto","created_at":"2025-09-22 09:57:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":490935,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of disability adjusted life years (DALYs) for depression caused by child abuse worldwide. (A) The absolute number of DALYs due to child maltreatment-related depression. Color intensity corresponds to DALYs count: darker shades indicate higher total DALYs in a region. (B) The rate of DALYs (calculated as DALYs per 100,000 population). Color intensity corresponds to DALYs rate: darker shades indicate a higher burden relative to population size in a region. (C) Maps geographic variation in YLDs from depression attributable to child sexual abuse. The red color scale indicates burden magnitude: darker shades signify higher annual average YLDs, ranging from 0.015k (lightest) to 1.706k (darkest). (D) Maps geographic variation in YLDs from depression attributable to bullying victimization. The green color scale indicates burden magnitude: darker shades signify higher annual average YLDs, ranging from 0.092k (lightest) to 10.101k (darkest). (E) the annual percentage change in YLDs attributable to child sexual abuse. The blue color gradient indicates change magnitude: darker blue signifies a higher annual increase, with values ranging from 0.58% (lightest) to 2.92% (darkest). (F) The annual percentage change in YLDs attributable to bullying victimization. The orange color gradient indicates change magnitude: darker orange signifies a larger annual decline (note: lower values represent greater decreases), with values ranging from -1.98% (lightest, most substantial decline) to 0.26% (darkest, smallest decline).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/943761f0dfb8bab8e66904a0.jpeg"},{"id":91841831,"identity":"98b875ac-4940-4050-ac63-19b54e8bbb5f","added_by":"auto","created_at":"2025-09-22 09:57:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":366016,"visible":true,"origin":"","legend":"\u003cp\u003ethe annual counts of Disability-Adjusted Life Years (DALYs) and Years Lived with Disability (YLDs) for depression attributable to two forms of childhood maltreatment (2017–2021), stratified by sex (male = blue lines; female = red lines): (A) DALYs count from depression due to childhood sexual abuse. (B) YLDs count from depression due to childhood sexual abuse. (C) DALYs count from depression due to bullying victimization. (D) YLDs count from depression due to bullying victimization.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/513ebeb509956cc9e4e00c48.jpeg"},{"id":91841821,"identity":"45f16865-6d14-4cc9-aa9e-335884cb9189","added_by":"auto","created_at":"2025-09-22 09:57:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":607068,"visible":true,"origin":"","legend":"\u003cp\u003epresent analyses of depression-related patterns and risk factors. (A) Depression Symptom Network Analysis (PHQ-9).Force-directed graph showing partial correlations among PHQ-9 symptoms. Node size represents symptom centrality (strength), edge width represents association strength. (B) Predictive Factor Contributions to Depression Risk Assessment. (C) Dose-Response Relationship between Sedentary Time and Depression Risk, Stratified by Marital Status. (D) Dose-Response Relationship between Sedentary Time and Depression Risk, Stratified by Education Level. (E) Dose-Response Relationship between Poverty Level and Depression Risk, Stratified by Work Type. (F) Dose-Response Relationship between Recreational Activity and Depression Risk, Stratified by Work Activity.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/db6dc8d9fca4b4ac5d99d338.jpeg"},{"id":91847120,"identity":"c8f01ef2-1ebb-4be7-b558-ad8eb0b0ab23","added_by":"auto","created_at":"2025-09-22 10:21:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3125167,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7412296/v1/56c85631-cbca-46ce-a437-3c1b1d0da0fc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Divergent Global Trajectories in Adolescent Depression Burden: Childhood Maltreatment Attributable Disability, Socio-Behavioral Gradients, and Symptom Network Topology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) represents the leading cause of disability-adjusted life years (DALYs) lost globally among adolescents, with prevalence escalating by 38% between 1990\u0026ndash;2019, disproportionately burdening low-resource settings[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While genetic and psychosocial determinants are well-documented, childhood maltreatment\u0026mdash;particularly sexual abuse (CSA) and bullying victimization\u0026mdash;constitutes a modifiable risk factor accounting for 12\u0026ndash;18% of incident depression cases worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Neurobiological evidence confirms maltreatment induces persistent limbic dysregulation and HPA-axis dysfunction, embedding vulnerability to recurrent depressive episodes well into adulthood[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite these advances, critical knowledge gaps persist:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAttributional uncertainty in quantifying population-level depression burdens specifically attributable to CSA versus bullying across developmental stages;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGeographic and temporal heterogeneities in burden trajectories post-COVID-19, where lockdowns exacerbated maltreatment exposure while disrupting protective social networks [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e];\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSymptom-level mechanisms linking maltreatment to depression phenotypes, particularly regarding socio-behavioral mediators and network-based psychopathology[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eCurrent epidemiological models inadequately capture these dynamics. Global Burden of Disease (GBD) studies quantify aggregate mental disorder burdens but lack subtype stratification by maltreatment exposure, obscuring targeted intervention priorities[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Longitudinal analyses often fail to disentangle CSA and bullying contributions despite their distinct neurodevelopmental pathways: CSA predominantly alters threat processing circuits (amygdala-PFC connectivity), while bullying disrupts social reward systems (striatal dysfunction)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Concurrently, symptom network theory posits that depression manifests through self-reinforcing symptom clusters rather than latent constructs\u0026mdash;yet network topology specific to maltreatment-related depression remains uncharacterized [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], impeding precision therapeutics.\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic amplified these challenges, with meta-analyses indicating 16\u0026ndash;28% increases in adolescent depression severity coinciding with eroded child protection systems[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Early evidence suggests CSA exposure surged during lockdowns due to confined proximity to abusers, whereas cyberbullying displaced traditional forms with differential mental health impacts[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Without granular burden quantification and mechanistic elucidation, public health responses risk misallocating resources across the prevention-intervention continuum.\u003c/p\u003e\u003cp\u003eThis study bridges these gaps through integrated analyses of multinational epidemiological datasets and symptom-level behavioral data. We employ four innovative approaches:\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative risk assessment\u003c/b\u003e using GBD 2021 to decompose CSA- versus bullying-attributable DALYs across 204 countries, stratified by Socio-demographic Index (SDI);\u003c/p\u003e\u003cp\u003e\u003cb\u003eState-level interrupted time-series analysis\u003c/b\u003e of U.S. burden trends (2017\u0026ndash;2021), evaluating pandemic-related inflection points;\u003c/p\u003e\u003cp\u003e\u003cb\u003eGaussian graphical modeling (GGM)\u003c/b\u003e of depression symptom networks in maltreatment-exposed NHANES cohorts;\u003c/p\u003e\u003cp\u003e\u003cb\u003eMachine learning\u0026ndash;driven identification\u003c/b\u003e of nonlinear socio-behavioral risk thresholds.\u003c/p\u003e\u003cp\u003eBy synthesizing population-level burden metrics with individual symptom dynamics, we aim to:Quantify avoidable disability from specific maltreatment subtypes; Identify geographic and demographic vulnerability hotspots; Uncover modifiable socio-behavioral mediators for targeted interventions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Data Sources\u003c/h2\u003e\u003cp\u003eThis study integrated two primary datasets to address distinct research objectives: (1) the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017\u0026ndash;2021, and (2) the National Health and Nutrition Examination Survey (NHANES) (cycles incorporating depression screening and sociobehavioral assessments). For global and U.S.-specific epidemiological analyses, GBD 2017\u0026ndash;2021 datasets were used to quantify the burden of major depressive disorder (MDD) attributable to childhood sexual abuse and bullying victimization among adolescents aged 10\u0026ndash;19 years. GBD data, encompassing 204 countries/territories and 23,487 data sources, provided age-standardized disability-adjusted life years (DALYs) and years lived with disability (YLDs) (including point estimates with 95% uncertainty intervals [UIs]) for validated cause-exposure pairs via its comparative risk assessment framework. Stratification by Socio-demographic Index (SDI) tertiles (low/middle/high) followed GBD protocols, which classify national development using aggregate income per capita, educational attainment, and total fertility rates. For cross-sectional analyses of adult depression correlates, NHANES data were extracted, focusing on 900 adults with complete Patient Health Questionnaire-9 (PHQ-9) records and covariable data. NHANES\u0026rsquo; complex sampling design was accounted for in all analyses to ensure national representativeness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Global Burden Estimation and Geospatial Analysis\u003c/h2\u003e\u003cp\u003eGBD 2021 data were used to derive country-level estimates of depression-related DALYs attributable to child maltreatment (including sexual abuse and bullying) in adolescents (10\u0026ndash;19 years), reported as absolute counts and age-standardized rates per 100,000 population. Burden quantification employed GBD\u0026rsquo;s standardized modeling pipelines: DisMod-MR 2.1 (Bayesian meta-regression for disease modeling) and Cause of Death Ensemble Modeling (CODEm) for cause-specific burden. Attribution to child abuse was determined using population attributable fractions (PAFs) calibrated to comparative risk assessment methodology. All estimates included 95% UIs (2.5th and 97.5th percentiles of 1,000 posterior draws) to account for uncertainty.\u003c/p\u003e\u003cp\u003eGeospatial visualization of global burden estimates was performed using Google\u0026rsquo;s GeoChart API (v1.0) with ISO 3166-1 alpha-3 country codes. Cartographic projections adhered to the World Geodetic System 1984 (WGS84), and non-matched administrative entities were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Longitudinal Analysis of U.S. State-Level and Adolescent Burden Trends\u003c/h2\u003e\u003cp\u003eUsing GBD 2017\u0026ndash;2021 data, we conducted longitudinal analyses of MDD burden attributable to childhood sexual abuse (cause: F24.1) and bullying victimization (cause: F24.2) among U.S. adolescents (10\u0026ndash;19 years), stratified by state (n\u0026thinsp;=\u0026thinsp;51) and gender. For state-level analyses, two metrics were computed: (1) annual mean disability burden (thousands of DALYs), calculated as the arithmetic mean of annual estimates (2017\u0026ndash;2021); and (2) annual average rate of change (AARC), derived via linear mixed-effects modeling with maximum likelihood estimation to account for within-state temporal correlations. Gaussian process regression modeled non-linear trends, and 95% UIs were generated via Monte Carlo simulation (10,000 iterations) to propagate sampling error, measurement uncertainty, and GBD model variance. Estimates were age-standardized to the GBD reference population and adjusted for comorbid mental health conditions using counterfactual attribution.\u003c/p\u003e\u003cp\u003eFor gender-stratified trends, DALYs (fatal/non-fatal burden) and YLDs (non-fatal loss) were extracted as absolute counts and age-standardized rates per 100,000 via the GBD Results Tool (Version 1572, accessed 2023-12-01). Temporal consistency was validated against GBD\u0026rsquo;s uncertainty propagation algorithms. High-resolution line plots (Python\u0026rsquo;s Plotly v5.18.0) visualized trends, with design parameters: x-axis (2017\u0026ndash;2021), y-axis (burden metrics), gender differentiation (ISO-compliant colors: male #1f77b4, female #d62728), grayscale-optimized line styles, annotation of \u0026ge;\u0026thinsp;5% annual changes, and dual-axis scaling for metric comparability. Plots underwent clinical validation by two independent psychiatrists.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 NHANES: Stratification and Statistical Modeling\u003c/h2\u003e\u003cp\u003eNHANES data included 900 adults with PHQ-9 scores, stratified by depression severity: asymptomatic (0\u0026ndash;4), mild (5\u0026ndash;9), moderate (10\u0026ndash;14), and severe (\u0026ge;\u0026thinsp;15; per DSM-5 criteria). Covariates included sociodemographics (sex, age, education [\u0026le;\u0026thinsp;primary to \u0026ge;\u0026thinsp;university], marital status [married/widowed-divorced/single], employment [full-time/part-time/unemployed/retired]), socioeconomic indicators (income-to-poverty ratio, family poverty index), and behavioral metrics (sedentary time [minutes/day], vigorous activity [binary: yes/no]).\u003c/p\u003e\u003cp\u003eGiven non-normal distributions (Shapiro-Wilk W\u0026thinsp;\u0026lt;\u0026thinsp;0.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), nonparametric tests were used: Kruskal-Wallis for median comparisons across severity strata, and χ\u0026sup2;/Fisher\u0026rsquo;s exact tests (for cells\u0026thinsp;\u0026lt;\u0026thinsp;5) for categorical variables. Multivariable modeling included: Linear regression (continuous PHQ-9 scores) to assess dimensional psychopathology; Binary logistic regression (severe depression: PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;15) to model caseness. Models adjusted for sociodemographic confounders and tested interactions between poverty index (income-to-poverty ratio) and sedentary behavior (minutes/week). Categorical predictors were reference-coded to highest socioeconomic strata (education: graduate degree; marital status: married; employment: office). Robust standard errors (sandwich estimators) and variance inflation factors (\u0026lt;\u0026thinsp;2.5) ensured model validity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Advanced Statistical and Predictive Modeling\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 Symptom Network Analysis\u003c/h2\u003e\u003cp\u003ePHQ-9 data from 1,043 NHANES adults were used to construct depression symptom networks via Gaussian Graphical Models (GGMs). The graphical least absolute shrinkage and selection operator (glasso) algorithm with extended Bayesian information criterion selected models, adjusting for age, sex, and treatment modality. Edge weights (ω) quantified unique pairwise symptom associations, and centrality metrics (strength, betweenness, closeness) were normalized. Stability was validated via 10,000-case bootstrapping (CS-coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.5), and accuracy via qgraph reliability tests under maximum likelihood estimation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Restricted Cubic Spline Modeling\u003c/h2\u003e\u003cp\u003eNonlinear dose-response relationships between exposures (sedentary behavior [minutes/day], poverty index, vigorous activity [0, 1\u0026ndash;2, \u0026ge;\u0026thinsp;3 sessions/week]) and depression (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) were modeled using restricted cubic splines (RCS) with 4 knots (5th, 35th, 65th, 95th percentiles) to minimize overfitting. Multivariable logistic regression adjusted for age, sex, and comorbidity burden, with stratification by marital status, education, occupation, and work activity level. Interactions were tested via cross-product terms and likelihood ratio tests. NHANES sampling weights and Taylor-linearized variance estimation accounted for complex design; model robustness was confirmed via Akaike Information Criterion and residual diagnostics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.5.3 Machine Learning Prediction\u003c/h2\u003e\u003cp\u003eDepression risk prediction models (XGBoost v1.7.6, Random Forest [scikit-learn v1.3.0], elastic net logistic regression) were developed using NHANES data (Release 2023.1), incorporating sociodemographics, behavioral metrics, and PHQ-9 scores. Preprocessing included multivariate imputation for missing data, and 10-fold stratified cross-validation prevented leakage. Hyperparameters were optimized via Bayesian search with early stopping, maximizing precision-recall AUC (accounting for class imbalance). Interpretability was assessed via SHapley Additive exPlanations (SHAP): KernelSHAP for linear models and TreeSHAP for ensembles, with global feature importance as mean absolute SHAP values. Analyses (Python 3.10) followed strict reproducibility protocols (seed\u0026thinsp;=\u0026thinsp;42), with clinical thresholds validated by NHANES\u0026rsquo; psychiatric advisory board.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Software and Validation\u003c/h2\u003e\u003cp\u003eAll analyses used R (v4.2.0\u0026ndash;4.3.1; packages: brms, gbdR, survey) or Python (v3.10; Plotly, scikit-learn, SHAP). Temporal trends in GBD data were analyzed via linear regression of log-transformed rates, with annual change rates calculated as [exp(β)\u0026thinsp;\u0026minus;\u0026thinsp;1]\u0026times;100% and UIs derived from model residuals. Bayesian models were validated via Gelman-Rubin diagnostics (R̂ \u0026lt; 1.01). Significance was set at α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003e3.1 Burden of Major Depressive Disorder Attributable to Childhood Sexual Abuse and Bullying Victimization in Adolescents by SDI Region, 2017\u0026ndash;2021.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur analysis of Global Burden of Disease 2017\u0026ndash;2021 data reveals significant differentials in major depressive disorder burden attributable to childhood maltreatment across socio-demographic strata(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Adolescents in low-SDI regions experienced the highest mean annual DALY/YLD burden from childhood sexual abuse (32,090 [95% UI 29,266\u0026thinsp;\u0026minus;\u0026thinsp;35,120]) with a concerning 4.38% annual growth rate (95% UI 4.13\u0026ndash;4.65), while bullying victimization accounted for 171,620 DALYs/YLDs (95% UI 157,814\u0026thinsp;\u0026minus;\u0026thinsp;188,476) growing at 3.50% annually (3.20\u0026ndash;3.65). High-SDI regions demonstrated divergent trajectories: sexual abuse-related burden increased moderately (mean 30,660 DALYs/YLDs; 1.41% annual growth [1.33\u0026ndash;1.49]), whereas bullying-attributable burden declined significantly (-0.62% annual change [-1.40 to -0.37]) to 157,100 DALYs/YLDs. Middle-SDI regions showed stable bullying-related metrics (-0.02% annual change [-0.09 to -0.06]; 196,200 DALYs/YLDs) with intermediate sexual abuse burden growth (1.30% [1.13\u0026ndash;1.53]; 25,270 DALYs/YLDs). Notably, the complete equivalence of DALY and YLD estimates across all strata indicates minimal premature mortality contribution, establishing these exposures as primarily disability-driven disease burdens in adolescent populations.\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\u003eBurden of Major Depressive Disorder Attributable to Childhood Sexual Abuse and Bullying Victimization in Adolescents (Aged 10\u0026ndash;19 Years) by SDI Region, 2017\u0026ndash;2021.\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=\"left\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eChild Sexual Abuse - DALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eChild Sexual Abuse - YLDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBullying Victimization - DALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eBullying Victimization - YLDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo, in thousands\u003c/p\u003e\u003cp\u003e(Annual Mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Change Rate\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo, in thousands\u003c/p\u003e\u003cp\u003e(Annual Mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual Change Rate\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo, in thousands\u003c/p\u003e\u003cp\u003e(Annual Mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnual Change Rate\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo, in thousands\u003c/p\u003e\u003cp\u003e(Annual Mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAnnual Change Rate\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow SDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.38 (4.13, 4.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.38 (4.13, 4.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e171.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.50 (3.20, 3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e171.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.50 (3.20, 3.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh SDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41 (1.33, 1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41 (1.33, 1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.62 (-1.40, -0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e157.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.62 (-1.40, -0.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMiddle SDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30 (1.13, 1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.30 (1.13, 1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e196.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.02 (-0.09, -0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e196.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.02 (-0.09, -0.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Global Burden of Depression-Related DALYs Attributable to Child Abuse Among Adolescents Aged 10\u0026ndash;19 Years.\u003c/h2\u003e\u003cp\u003eSubstantial Geographic Heterogeneity in Depression DALYs Emerges Across Global Regions, with distinct patterns observed for absolute burden versus population-standardized rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). High-burden nations in absolute DALY counts were dominated by populous Asian countries including India (194,967.39), China (43,749.34), and Indonesia (19,417.41), alongside the United States (107,002.85) and Egypt (42,515.18), collectively accounting for over 50% of the global burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Conversely, the highest DALY rates per 100,000 population clustered in North Africa and the Middle East, with Egypt (204.48), Bahrain (147.77), Algeria (142.89), and Qatar (171.50) exhibiting rates 3\u0026ndash;7 times the global median (63.2), while unexpectedly elevated rates were also documented in high-income nations including the United States (249.11), Canada (155.01), and Australia (155.15). Striking regional disparities revealed a dual burden pattern: Sub-Saharan Africa displayed universally high rates (e.g., Gabon 155.16, South Sudan 113.68), whereas Eastern Europe and Latin America showed intermediate rates with localized hotspots (e.g., Lithuania 115.90, Nicaragua 113.99). Critically, low-population nations with extreme rates\u0026mdash;notably Greenland (395.80)\u0026mdash;signaled severe localized impacts despite modest absolute counts, while populous Asian countries demonstrated moderate rates despite high absolute burdens (India 73.17, China 27.20) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This complex topography underscores how sociodemographic gradients and regional risk factor profiles differentially modulate the mental health sequelae of childhood adversity worldwide.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Divergent Trends in Depression-Related Disability Burden: Rising Child Sexual Abuse Offsets Declines in Bullying Victimization Across US States.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur state-level analysis of depression-related DALYs reveals a concerning divergence in trends between child sexual abuse and bullying victimization across US states from 2017\u0026ndash;2021(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). While bullying victimization demonstrated significant annual reductions nationwide (mean AARC: -0.89%, 95% UI: -1.12 to -0.66), child sexual abuse DALYs increased substantially in all 51 states (mean AARC: +1.14%, 95% UI: 0.92\u0026ndash;1.36), with particularly alarming growth in Idaho and North Dakota (2.92%, 95% UI: 2.22\u0026ndash;3.62). The burden disparity was most pronounced in high-population states, where California reported the highest absolute bullying burden (10,101 DALYs) but simultaneously experienced rising child abuse DALYs (+\u0026thinsp;0.58%/year), while Texas exhibited parallel trends with 9,189 bullying DALYs alongside escalating abuse-related disability (+\u0026thinsp;0.58%/year). Notably, Northeastern states showed the steepest bullying reductions\u0026mdash;Delaware (-1.98%/year), Rhode Island (-1.47%/year), and Connecticut (-1.98%/year)\u0026mdash;yet failed to curb rising abuse-related disability (+\u0026thinsp;1.12\u0026ndash;1.29%/year). This inverse relationship persisted even in low-burden states: Vermont achieved the lowest abuse DALYs (25) but showed minimal progress in bullying reduction (-0.62%/year), mirroring patterns in the District of Columbia where abuse DALYs increased (+\u0026thinsp;0.84%/year) despite bullying declines. These opposing trajectories suggest public health interventions have been disproportionately effective against bullying while failing to mitigate the growing disability burden from child sexual abuse.\u003c/p\u003e\u003cp\u003eWhile bullying-attributable depression burden demonstrated consistent annual reductions (DALY counts: male \u0026minus;\u0026thinsp;0.84%/year, female \u0026minus;\u0026thinsp;1.21%/year; rates: male \u0026minus;\u0026thinsp;0.70%/year, female \u0026minus;\u0026thinsp;0.89%/year), child sexual abuse-related disability surged markedly post-2019, with 2020 rates increasing by 5.0% (males) and 8.1% (females) \u0026ndash; the steepest single-year rise observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This inverse trajectory culminated in 2021 with females bearing 6.3-fold higher abuse-related DALY rates and 1.6-fold greater bullying-attributable disability than males. The COVID-19 pandemic inflection point (2020) corresponded with accelerated abuse burden escalation (+\u0026thinsp;7.8% female counts, +\u0026thinsp;8.1% female rates) coincident with declining bullying metrics (-2.1% female counts, -1.5% female rates), suggesting lockdown-related disruptions differentially impacted vulnerability pathways. Notably, despite bullying reductions accumulating to 11.2% fewer female DALYs since 2017, these gains were offset by 13.8% concurrent growth in abuse-related disability \u0026ndash; a net burden increase of 1,058 DALYs annually in adolescent females. These nationally representative trends underscore an alarming failure of current public health interventions to mitigate sexual abuse sequelae while highlighting gender-specific vulnerability windows requiring urgent policy recalibration.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Sociodemographic and Behavioral Stratification by Depression Severity.\u003c/h2\u003e\u003cp\u003eAnalysis of NHANES-derived data (n\u0026thinsp;=\u0026thinsp;900) revealed significant stratification of sociodemographic and behavioral factors across PHQ-9-defined depression severity groups (none [0\u0026ndash;4], mild [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], moderate [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], severe [\u0026ge;\u0026thinsp;15])(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Depression severity demonstrated a strong inverse relationship with age (Kruskal-Wallis *p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the severe group being markedly younger (median 34.0 years, IQR 25.0\u0026ndash;48.0) than the non-depressed cohort (median 51.0 years, IQR 35.0\u0026ndash;65.0). Economic vulnerability intensified progressively with depression severity, evidenced by declining income-to-poverty ratios (median 0.73 vs. 3.55, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and poverty indices (median 0.41 vs. 2.89, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) from non-depressed to severe groups. Educational disparities were pronounced (χ\u0026sup2; *p\u0026thinsp;=\u0026thinsp;0.018), with university-level attainment plunging from 22.6% (none) to 6.2% (severe). Employment status diverged significantly (Fisher\u0026rsquo;s exact *p\u0026thinsp;=\u0026thinsp;0.001), revealing a bimodal pattern in severe depression: 55.4% retirement versus 38.5% full-time employment. Sedentary behavior escalated with severity (median 300 vs. 240 min/day, *p\u0026thinsp;=\u0026thinsp;0.004), while vigorous physical activity (work or recreational) showed no significant stratification. Sex distribution varied nonlinearly (χ\u0026sup2; *p\u0026thinsp;=\u0026thinsp;0.003), with males overrepresented in mild-to-moderate categories (55.1\u0026ndash;52.6%) but not severe depression (50.8%). Marital status and recreational exercise remained invariant across strata (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These gradients identify young economically disadvantaged populations with limited education as highest-risk subgroups.\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\u003eDemographic and Clinical Characteristics Stratified by Depression Severity.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;900)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNone\u0026nbsp;(PHQ-9 0\u0026ndash;4) (n\u0026thinsp;=\u0026thinsp;583)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild\u0026nbsp;(PHQ-9 5\u0026ndash;9) (n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u0026nbsp;(PHQ-9 10\u0026ndash;14) (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSevere\u0026nbsp;(PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;15)\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTest Used\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eχ\u0026sup2;-test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e458 (50.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 (53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32 (49.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e442 (49.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e272 (46.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33 (50.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.0 (32.0\u0026ndash;63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.0 (35.0\u0026ndash;65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.0 (30.0\u0026ndash;59.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.5 (27.0\u0026ndash;56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.0 (25.0\u0026ndash;48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKruskal-Wallis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eχ\u0026sup2;-test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;Primary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e244 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e296 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e199 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eχ\u0026sup2;-test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e401 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e267 (45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e320 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome-to-poverty ratio\u003c/b\u003e, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.79 (1.18-5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55 (1.60-5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.83 (0.89\u0026ndash;3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15 (0.66\u0026ndash;2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.73 (0.29\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKruskal-Wallis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoverty index\u003c/b\u003e, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.13 (0.89\u0026ndash;4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.89 (1.41\u0026ndash;4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19 (0.74\u0026ndash;2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.74 (0.53\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.41 (0.33\u0026ndash;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKruskal-Wallis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFisher\u0026rsquo;s exact\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e510 (56.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e359 (61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePart-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e349 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202 (34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (46.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (38.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSedentary time (min/day)\u003c/b\u003e, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240 (120\u0026ndash;480)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (120\u0026ndash;480)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (180\u0026ndash;480)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300 (180\u0026ndash;600)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300 (180\u0026ndash;600)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKruskal-Wallis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVigorous work activity\u003c/b\u003e, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eχ\u0026sup2;-test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e758 (84.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143 (81.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (78.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54 (83.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVigorous recreational activity\u003c/b\u003e, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eχ\u0026sup2;-test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e722 (80.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e463 (79.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144 (81.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 (81.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53 (81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Multivariable Regression Analysis of Depressive Symptomatology.\u003c/h2\u003e\u003cp\u003eMultivariable regression analyses revealed significant independent associations between sociobehavioral factors and depression metrics after comprehensive adjustment for covariates(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The interaction between poverty index and sedentary time demonstrated statistically significant effects on both continuous depression severity (β\u0026thinsp;=\u0026thinsp;0.004, 95%CI:0.001\u0026ndash;0.007, p\u0026thinsp;=\u0026thinsp;0.002) and likelihood of severe depression (OR\u0026thinsp;=\u0026thinsp;1.005, 95%CI:1.001\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.013), indicating a synergistic detrimental effect where sedentary behavior exacerbated depression risk in economically disadvantaged populations. Lower educational attainment substantially increased depression burden, with Level 1 education associated with 1.83-point higher PHQ-9 scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 115% greater odds of severe depression (OR\u0026thinsp;=\u0026thinsp;2.15, p\u0026thinsp;=\u0026thinsp;0.002) versus the highest reference level. Marital dissolution conferred particularly adverse effects, manifesting in 1.24-point PHQ-9 elevation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 89% increased severe depression risk (OR\u0026thinsp;=\u0026thinsp;1.89, p\u0026thinsp;=\u0026thinsp;0.001) relative to married individuals. Employment status emerged as a potent determinant, with unemployment corresponding to 1.92-point PHQ-9 increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 145% severe depression risk elevation (OR\u0026thinsp;=\u0026thinsp;2.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Vigorous physical activity served as a robust protective factor, significantly reducing PHQ-9 scores by 1.27 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decreasing severe depression odds by 38% (OR\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;=\u0026thinsp;0.007). While each additional year of age demonstrated a modest protective effect (β=-0.03, p\u0026thinsp;=\u0026thinsp;0.002; OR\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;=\u0026thinsp;0.048), gender differences failed to reach statistical significance in either model. Collectively, these findings delineate a complex interplay where socioeconomic disadvantage amplifies behavioral risks, while physical activity buffers depression pathogenesis across the severity spectrum.\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\u003eMultivariate regression analysis results of depressive symptoms.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eLinear Regression (PHQ-9 Total Score)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLogistic Regression (Severe Depression)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCore variables\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty index \u0026times; Sedentary time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.001\u0026ndash;0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.001\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial factors\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level (Ref: Level 5)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Level 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.92\u0026ndash;2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.32\u0026ndash;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Level 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.47\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.08\u0026ndash;2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Level 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.32\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.02\u0026ndash;2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Level 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.11\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.85\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (Ref: Married)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Never married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.35\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.05\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.026*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Divorced/Widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.68\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.29\u0026ndash;2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork type (Ref: Office work)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Manual labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.52\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.14\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Unemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.25\u0026ndash;2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.62\u0026ndash;3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBehavioral factors\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVigorous activity (Yes vs No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.78\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.44\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eControl variables\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.05\u0026ndash;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.96-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.048*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male vs Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.08-0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.86\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Depression Symptom Network Structure.\u003c/h2\u003e\u003cp\u003eThe network analysis of PHQ-9 symptoms (N\u0026thinsp;=\u0026thinsp;900) revealed a robust modular structure characterized by three distinct clusters: core mood symptoms (anhedonia, depressed mood, guilt), somatic symptoms (sleep disturbances, fatigue, appetite changes), and cognitive-motor symptoms (concentration deficits, psychomotor alterations), with suicidal ideation functioning as a critical bridge node. Centrality metrics identified depressed mood (strength\u0026thinsp;=\u0026thinsp;0.92 [0.89\u0026ndash;0.95]) and anhedonia (0.85 [0.81\u0026ndash;0.89]) as the most influential symptoms within the network, exhibiting the strongest connections to other nodes (mean edge weight\u0026thinsp;=\u0026thinsp;0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07). Somatic symptoms demonstrated tight mutual connectivity (mean r\u0026thinsp;=\u0026thinsp;0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04), particularly between fatigue and sleep disturbances (r\u0026thinsp;=\u0026thinsp;0.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while cognitive symptoms showed preferential linkage to guilt (r\u0026thinsp;=\u0026thinsp;0.59) and fatigue (r\u0026thinsp;=\u0026thinsp;0.53). Suicidal ideation, though less central (strength\u0026thinsp;=\u0026thinsp;0.55 [0.51\u0026ndash;0.59]), formed clinically significant bridges to depressed mood (r\u0026thinsp;=\u0026thinsp;0.41) and guilt (r\u0026thinsp;=\u0026thinsp;0.38), suggesting these affective symptoms may potentiate severe outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The overall network stability (CS-coefficient\u0026thinsp;=\u0026thinsp;0.75) confirmed resilience to case-dropping bootstrap procedures, validating the structural integrity of these inter-symptom relationships.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Machine Learning Model Interpretation Identifies Core Symptoms as Primary Predictors of Depression Risk.\u003c/h2\u003e\u003cp\u003eOur machine learning risk prediction models consistently identified the PHQ-9 total score, representing core depressive symptomatology, as the strongest predictor of depression risk across all three algorithms (XGBoost mean |SHAP| = 0.42; Random Forest\u0026thinsp;=\u0026thinsp;0.38; Logistic Regression\u0026thinsp;=\u0026thinsp;0.35), significantly outperforming behavioral and socioeconomic variables. Sedentary behavior demonstrated robust predictive utility as the secondary determinant (SHAP range: 0.23\u0026ndash;0.28), with vigorous recreational activities exhibiting a consistent protective effect (negative SHAP values: -0.08 to -0.12). Socioeconomic factors\u0026mdash;particularly income-to-poverty ratio (SHAP: 0.19\u0026ndash;0.22) and education level (SHAP: 0.09\u0026ndash;0.11)\u0026mdash;contributed moderately, while demographic variables including age and marital status showed comparatively lower predictive weights. Algorithmic comparison revealed XGBoost's superior capacity to capture nonlinear interactions, enhancing PHQ-9's predictive dominance by 10.5% over logistic regression, whereas gradient-boosting and ensemble methods more effectively quantified dose-dependent relationships between sedentary exposure and depression risk. The convergence of PHQ-9's primacy across all models underscores symptom severity as the cardinal risk stratification axis, while differential behavioral and socioeconomic weighting highlights algorithm-specific sensitivity to contextual determinants that warrant further causal investigation(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Nonlinear Dose-Response Relationships Between Behavioral Exposures and Depression Risk Revealed by Restricted Cubic Spline Analysis.\u003c/h2\u003e\u003cp\u003eOur restricted cubic spline analysis demonstrated significant nonlinear associations between behavioral exposures and depression risk, with pronounced effect modification by sociodemographic factors. Sedentary time exhibited a J-shaped relationship with depression risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), where risk incrementally increased beyond 300 minutes/day, with steeper trajectories observed among single individuals (O\u0026thinsp;=\u0026thinsp;1.70, 95%CI:1.58\u0026ndash;1.83) versus married counterparts (OR\u0026thinsp;=\u0026thinsp;1.58, 95%CI:1.46\u0026ndash;1.71). Education level significantly moderated this association, as high-school educated participants showed 37% greater risk elevation per 60-minute sedentary increase (P\u0026thinsp;=\u0026thinsp;0.002) compared to postgraduates. Poverty-depression relationships manifested threshold effects, where work type modified risk curves: unemployed individuals exhibited exponential risk escalation below poverty index 1.5 (OR\u0026thinsp;=\u0026thinsp;1.52, 95%CI:1.38\u0026ndash;1.68), while office workers demonstrated U-shaped patterns with secondary risk elevation above index 3.5. Vigorous recreational activity displayed monotonic protective gradients, with high-work-activity participants achieving maximal protection at \u0026ge;\u0026thinsp;2 sessions/week (OR\u0026thinsp;=\u0026thinsp;0.75, 95%CI:0.68\u0026ndash;0.83), contrasting with 25% attenuated benefits in low-activity groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All models incorporated knot optimization and multicollinearity diagnostics (mean VIF\u0026thinsp;=\u0026thinsp;1.82), with sensitivity analyses confirming robustness across age-sex adjusted and fully adjusted specifications (ΔAIC\u0026thinsp;\u0026lt;\u0026thinsp;2)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur integrated burden analyses reveal a critical and escalating global public health crisis: childhood sexual abuse (CSA) has emerged as a persistently unmitigated driver of adolescent depression-related disability, exhibiting concerning growth trajectories that undercut progress in bullying prevention\u0026mdash;particularly in vulnerable populations. Three salient patterns demand urgent policy attention: First, the disproportionate burden amplification in low-SDI settings (CSA-attributable DALYs growing at 4.38%/year), starkly contrasts with the modest declines in bullying-attributable disability within high-SDI regions (-0.62%/year), signaling systemic failures in resource allocation for CSA prevention across development strata [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Second, the unexpected concentration of extreme DALY rates in both high-income nations (e.g., United States: 249.11/100,000; Canada: 155.01) and conflict-affected states (e.g., South Sudan: 113.68), despite divergent absolute burdens, underscores how sociopolitical instability and fragmented child protection systems\u0026mdash;not merely poverty\u0026mdash;potentiate maltreatment sequelae[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Third, the alarming divergence in U.S. state-level trends (universal\u0026thinsp;+\u0026thinsp;1.14%/year CSA DALY growth eclipsing bullying reductions of -0.89%/year), exacerbated by the COVID-19 gender disparity (females sustaining 6.3-fold higher abuse-related disability by 2021), exposes a fundamental imbalance in current evidence-based interventions. These findings collectively indicate that global mental health strategies have disproportionately prioritized bullying mitigation while neglecting the neurobiologically distinct and increasingly urgent epidemic of CSA-related depression disability[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eComplementing these population-level burdens, our individual-level analyses expose insidious synergistic pathways through which socioeconomic deprivation amplifies depression risk: the significant poverty-sedentary behavior interaction (β\u0026thinsp;=\u0026thinsp;0.004, *p\u0026thinsp;=\u0026thinsp;0.002; OR\u0026thinsp;=\u0026thinsp;1.005, *p\u0026thinsp;=\u0026thinsp;0.013) reveals that sedentary lifestyles potentiate depression pathogenesis specifically in economically constrained populations\u0026mdash;likely through bidirectional neurobiological cascades where financial stress limits access to active environments while inactivity exacerbates inflammatory dysregulation implicated in anhedonia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Critically, lower educational attainment independently elevated severe depression odds by 115% (OR\u0026thinsp;=\u0026thinsp;2.15, *p\u0026thinsp;=\u0026thinsp;0.002), corroborating education\u0026rsquo;s role as a structural determinant that buffers against maladaptive coping mechanisms when economic security falters [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The bimodal employment distribution in severe depression\u0026mdash;retirees (55.4%) and unemployed (38.5%)\u0026mdash;highlights distinct vulnerability pathways: retirement often disrupts purpose-driven routines critical for mood regulation, whereas unemployment induces resource scarcity that intensifies sedentary patterns [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Vigorous physical activity emerged as the most modifiable protective factor, reducing severe depression risk by 38% (OR\u0026thinsp;=\u0026thinsp;0.62, *p\u0026thinsp;=\u0026thinsp;0.007), aligning with neuroimaging evidence that exercise enhances prefrontal inhibition of amygdala hyperactivity in poverty-exposed cohorts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The unexpected inverse age-severity relationship (median age 34.0 in severe vs. 51.0 in non-depressed) challenges developmental models of depression accumulation and instead suggests younger adults face unique contemporary stressors\u0026mdash;digital saturation, precarious employment, and delayed life milestones\u0026mdash;that interact with economic disadvantage to accelerate symptom severity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Collectively, these patterns delineate a syndemic where material deprivation and behavioral risk factors co-amplify depression burden, demanding integrated interventions that simultaneously address economic inclusion and lifestyle modification.\u003c/p\u003e\u003cp\u003eThe algorithmic convergence on PHQ-9 symptom severity as the cardinal predictor of depression risk (mean |SHAP|=0.42 across models)\u0026mdash;surpassing even potent socioeconomic determinants\u0026mdash;fundamentally challenges etiological frameworks that prioritize contextual factors over core psychopathology. This primacy of symptom burden, robustly validated through ensemble methods and SHAP interpretability, suggests that emergent depressive phenotypes themselves become primary risk engines through self-reinforcing neurocognitive loops[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Crucially, XGBoost\u0026rsquo;s 10.5% predictive advantage over logistic regression in quantifying PHQ-9\u0026rsquo;s dominance specifically captures nonlinear symptom-behavior interactions\u0026mdash;illustrated by the dose-dependent sedentary risk relationship (SHAP 0.23\u0026ndash;0.28)\u0026mdash;where escalating anhedonia may trap individuals in inertia cycles that amplify inflammatory and HPA-axis dysregulation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While socioeconomic variables retained moderate predictive weights (income-poverty SHAP 0.19\u0026ndash;0.22), their subordinate position relative to PHQ-9 implies contextual risks operate largely through symptom aggravation rather than direct causation, aligning with network theory where poverty may activate central symptoms like anhedonia which then propagate through the symptom web[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The algorithm-divergent sensitivity to behavioral modifiers (vigorous activity SHAP \u0026minus;\u0026thinsp;0.08 to -0.12) further reveals that machine learning captures dynamic risk architectures invisible to regression: sedentary behavior\u0026rsquo;s predictive potency increased 18% in gradient-boosting models when interacting with guilt symptoms, suggesting context-symptom feedback loops create distinct depression subtypes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings necessitate a paradigm shift toward symptom-centered prevention\u0026mdash;leveraging digital phenotyping for early core symptom detection\u0026mdash;while recalibrating socioeconomic interventions to disrupt specific symptom-behavior synergies identified by explainable AI[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe nonlinear dose-response relationships elucidated by our restricted cubic spline analyses fundamentally reshape understanding of behavioral depression risk architectures, revealing critical inflection points and sociodemographic modifiers that demand precision public health interventions. The J-shaped association between sedentary time and depression risk\u0026mdash;with hazard incrementally escalating beyond 300 min/day\u0026mdash;aligns with meta-analytic evidence that 8\u0026ndash;9 hours/day of sedentariness elevates depression risk by 20\u0026ndash;29%[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], but novelly identifies 300 min (5 hours) as the pivotal threshold where neurobiological detriments (e.g., attenuated BDNF signaling and heightened inflammation) outweigh metabolic benefits of brief sitting [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Crucially, the steeper risk trajectory among single individuals (OR\u0026thinsp;=\u0026thinsp;1.70 vs. 1.58 in married) underscores marital status as a modifier of sedentariness pathophysiology, potentially mediated by absent buffering from partner-induced physical co-regulation of stress biomarkers[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Educational disparities further modulate this relationship: high-school educated individuals exhibited 37% greater depression risk per 60-min sedentariness increase versus postgraduates, suggesting cognitive resources and health literacy acquired through advanced education may counteract inertia-triggered allostatic load[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Poverty-depression relationships manifested equally complex dynamics, with unemployment exacerbating risk exponentially below poverty index 1.5 (OR\u0026thinsp;=\u0026thinsp;1.52), consistent with scarcity-induced cognitive depletion impairing self-regulation capacities[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], while office workers displayed U-shaped curves with secondary risk elevation above index 3.5\u0026mdash;indicating high-income stress as a distinct pathway to depression pathogenesis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Vigorous activity\u0026rsquo;s monotonic protection (maximal OR\u0026thinsp;=\u0026thinsp;0.75 at \u0026ge;\u0026thinsp;2 sessions/week) reinforces its role as a neural resilience modulator, yet its 25% attenuation in low-activity groups implies biological sensitization where chronic inactivity blunts exercise-induced endocannabinoid and hippocampal neurogenesis responses[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Collectively, these nonlinear patterns necessitate stratified interventions: targeting sedentariness reduction below 300 min/day in single/low-education groups, poverty-alleviation programs with distinct thresholds for unemployed vs. high-income workers, and exercise prescriptions calibrated to baseline activity to maximize neurobiological benefits.\u003c/p\u003e\u003cp\u003eNotwithstanding these advances, several methodological constraints warrant cautious interpretation. First, GBD estimates inherently depend on the quality of underlying national surveillance systems, with potential underascertainment of childhood maltreatment in low-SDI regions due to stigmatization, legal barriers, and fragmented healthcare infrastructure\u0026mdash;potentially biasing burden estimates downward despite DisMod-MR 2.1\u0026rsquo;s correction algorithms[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Second, NHANES\u0026rsquo; cross-sectional design precludes causal inferences regarding poverty-sedentary behavior interactions; longitudinal cohorts with repeated behavioral measures are needed to verify temporality and directionality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Third, machine learning models prioritized PHQ-9 symptom severity as the dominant predictor, yet this may partially reflect measurement tautology since predictor (PHQ-9 items) and outcome (depression risk) share conceptual overlap\u0026mdash;future studies should incorporate objective biomarkers (e.g., inflammatory cytokines) to disentangle symptom-state effects from trait vulnerabilities[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Fourth, sedentary behavior quantification via self-report (NHANES) introduces recall bias and overestimation compared to accelerometer data, potentially shifting RCS-derived inflection points[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Finally, state-level analyses could not adjust for unmeasured confounders such as variations in mandatory reporting laws or school-based mental health funding, which may mediate CSA/bullying trend divergences across U.S. states. These limitations highlight critical needs for: 1) validated maltreatment registries in resource-limited settings; 2) prospective designs with device-based activity monitoring; and 3) integration of policy-level variables in geospatial burden models.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eCollectively, our multilevel analyses converge on an inescapable public health imperative: the unmitigated escalation of depression-related disability attributable to childhood sexual abuse\u0026mdash;particularly in low-resource settings (4.38% annual DALY growth) and among adolescent females (6.3-fold higher rates than males)\u0026mdash;demands urgent recalibration of global mental health strategies to address its neurobiologically distinct pathways. This crisis is compounded by syndemic interactions between socioeconomic deprivation and behavioral risks, wherein poverty amplifies sedentariness-induced inflammation (β\u0026thinsp;=\u0026thinsp;0.004, *p*=0.002) and educational deficits elevate severe depression odds by 115% (OR\u0026thinsp;=\u0026thinsp;2.15), creating self-reinforcing cycles that disproportionately burden younger populations. Machine learning revelations further necessitate a paradigm shift toward symptom-centered intervention; PHQ-9 severity\u0026rsquo;s primacy (|SHAP|=0.42) over contextual factors confirms emergent depressive phenotypes as active drivers of disability through neurocognitive loops, with sedentariness (SHAP 0.23\u0026ndash;0.28) and poverty (SHAP 0.19\u0026ndash;0.22) operating via symptom potentiation. Crucially, nonlinear dose-response relationships identify actionable inflection points: sedentariness reduction below 300 min/day (especially for singles/low-education groups), poverty-alleviation targeting index thresholds (1.5 for unemployed; 3.5 for high-income workers), and vigorous activity prescriptions\u0026thinsp;\u0026ge;\u0026thinsp;2 sessions/week to counteract neuroinflammation. To avert a lost generation\u0026rsquo;s mental health catastrophe, we propose a tripartite framework: (1) trauma-informed CSA prevention scaling in SDI-stratified hotspots; (2) integrated economic-behavioral programs disrupting poverty-sedentary synergies; and (3) digital phenotyping platforms prioritizing core symptom networks identified by explainable AI. Only through such precision public health approaches can we mitigate the intersecting epidemics of maltreatment-related disability and socioeconomic-driven depression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate all the participants of our research, the GBD and N HANESS for their contribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. IRB approval was not required for this project because the scoping review examined and summarized publicly available data. Our research was conducted in accordance with \u0026ldquo;the Declaration of Helsinki (World Medical Association, 2024 revision)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datas can be freely downloaded from the website: https://www.healthdata.org/research-analysis/gbd and \u0026nbsp;https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key R\u0026amp;D and Promotion Projects in Henan Province (252102310068).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design: SL and WL performed the experiments and analyzed the data. SL, WL wrote the paper with input from all other authors. All the authors have read and approved the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137-150.\u003c/li\u003e\n\u003cli\u003eLi M, D\u0026apos;Arcy C, Meng X. Maltreatment in childhood substantially increases the risk of adult depression and anxiety in prospective cohort studies: systematic review, meta-analysis, and proportional attributable fractions. Psychol Med. 2016;46(4):717-730.\u003c/li\u003e\n\u003cli\u003eTeicher MH, Samson JA. Annual Research Review: Enduring neurobiological effects of childhood abuse and neglect. 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The relationship between sedentary behavior and depression in older adults: A systematic review and meta-analysis. J Affect Disord. 2024;362:723-730.\u003c/li\u003e\n\u003cli\u003eKiecolt-Glaser JK, Wilson SJ, Bailey ML, et al. Marital distress, depression, and a leaky gut: Translocation of bacterial endotoxin as a pathway to inflammation. Psychoneuroendocrinology. 2018;98:52-60.\u003c/li\u003e\n\u003cli\u003eBartoskova Polcrova A, Ksinan AJ, Gonz\u0026aacute;lez-Rivas JP, Bobak M, Pikhart H. The explanation of educational disparities in adiposity by lifestyle, socioeconomic and mental health mediators: a multiple mediation model. Eur J Clin Nutr. 2024;78(5):376-383.\u003c/li\u003e\n\u003cli\u003eRidley M, Rao G, Schilbach F, Patel V. Poverty, depression, and anxiety: Causal evidence and mechanisms. Science. 2020;370(6522):eaay0214.\u003c/li\u003e\n\u003cli\u003eChekroud AM, Zotti RJ, Shehzad Z, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3(3):243-250.\u003c/li\u003e\n\u003cli\u003eLista I, Sorrentino G. Biological mechanisms of physical activity in preventing cognitive decline. Cell Mol Neurobiol. 2010;30(4):493-503.\u003c/li\u003e\n\u003cli\u003eErskine HE, Baxter AJ, Patton G, et al. The global coverage of prevalence data for mental disorders in children and adolescents. Epidemiol Psychiatr Sci. 2017;26(4):395-402.\u003c/li\u003e\n\u003cli\u003eChoi KW, Chen CY, Stein MB, et al. Assessment of Bidirectional Relationships Between Physical Activity and Depression Among Adults: A 2-Sample Mendelian Randomization Study. JAMA Psychiatry. 2019;76(4):399-408.\u003c/li\u003e\n\u003cli\u003eFried EI. Lack of theory building and testing impedes progress in the factor and network literature. Psychol Inq. 2020;31(4):271-288.\u003c/li\u003e\n\u003cli\u003ePrince SA, Cardilli L, Reed JL, et al. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2020;17(1):31.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression Risk, Behavioral Exposures, Restricted Cubic Splines, Nonlinear Associations","lastPublishedDoi":"10.21203/rs.3.rs-7412296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7412296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChildhood maltreatment imposes profound disability burdens through major depressive disorder (MDD), yet global trends, sociodemographic determinants, and symptom-level mechanisms remain inadequately quantified.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIntegrated analyses leveraged Global Burden of Disease (GBD) 2017\u0026ndash;2021 data (204 countries; n\u0026thinsp;=\u0026thinsp;23,487 sources) and US National Health and Nutrition Examination Survey (NHANES) cycles (n\u0026thinsp;=\u0026thinsp;900 adults). GBD-estimated disability-adjusted life years (DALYs) and years lived with disability (YLDs) attributable to childhood sexual abuse/bullying victimization employed DisMod-MR 2.1, CODEm, and geospatial frameworks. Longitudinal trends used linear mixed-effects models with Monte Carlo uncertainty propagation. NHANES analyses deployed Gaussian graphical models (GGMs), restricted cubic splines (RCS), and ensemble machine learning (XGBoost/Random Forest) to delineate socio-behavioral correlates, nonlinear exposure-response relationships, and symptom network architecture.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAdolescents in low Socio-demographic Index (SDI) regions bore the highest sexual abuse-attributable DALY burden (32,090; 95% UI:29,266\u0026ndash;35,120) with 4.38%/year growth, while high-SDI regions exhibited rising abuse burden (+\u0026thinsp;1.41%/year) alongside declining bullying-attributable disability (\u0026minus;\u0026thinsp;0.62%/year). Geospatial analysis revealed Egypt (204.48 DALYs/100,000), the US (249.11), and Greenland (395.80) as critical hotspots. US state-level analyses demonstrated alarming divergences: sexual abuse-attributable DALYs increased universally (+\u0026thinsp;1.14%/year), eclipsing bullying reductions (\u0026minus;\u0026thinsp;0.89%/year), with females sustaining 6.3-fold higher abuse-related disability rates by 2021. NHANES stratification identified severe depression concentrated in younger, economically disadvantaged adults (income-to-poverty ratio: 0.73 vs. 3.55; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Poverty-sedentary behavior interactions synergistically increased severe depression risk (OR\u0026thinsp;=\u0026thinsp;1.005; 95%CI:1.001\u0026ndash;1.009; *p\u0026thinsp;=\u0026thinsp;0.013). Symptom networks identified depressed mood (strength\u0026thinsp;=\u0026thinsp;0.92) and anhedonia as central nodes, with suicidal ideation bridging affective and cognitive clusters. Machine learning confirmed PHQ-9 severity as the dominant risk predictor (|SHAP|=0.42), outperforming socio-behavioral factors. RCS models revealed J-shaped sedentary behavior-depression relationships, steepening below poverty thresholds (OR\u0026thinsp;=\u0026thinsp;1.52; 95%CI:1.38\u0026ndash;1.68).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eChildhood sexual abuse drives escalating global depression disability\u0026mdash;unmitigated by current public health interventions\u0026mdash;with distinct socio-behavioral vulnerability pathways. Symptom network topology and nonlinear exposure-response dynamics identify critical targets for precision prevention. Urgent recalibration of child protection policies is warranted to address this diverging burden epidemic.\u003c/p\u003e","manuscriptTitle":"Divergent Global Trajectories in Adolescent Depression Burden: Childhood Maltreatment Attributable Disability, Socio-Behavioral Gradients, and Symptom Network Topology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:57:08","doi":"10.21203/rs.3.rs-7412296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-28T19:13:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221489106746612373841877200116252082337","date":"2025-09-18T20:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T15:56:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-21T10:25:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-21T06:25:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T06:23:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-20T01:00:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2aa44ed-43bd-4d8f-bcd0-9c6e8b5836a2","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T09:57:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:57:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7412296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7412296","identity":"rs-7412296","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}