Twelve-month trajectories of depressive symptoms and associated socio-structural factors among adolescents and young adults in Soweto and Durban, South Africa
Bibliographic record
Abstract
INTRODUCTION: Globally, approximately 14 % of adolescents and young adults live with health disorders such as depression. We investigated longitudinal (12-months) trajectories of depressive symptoms and associated socio-structural correlates, among South African youth aged 16-24. METHODS: 425 youth were enrolled in AYAZAZI, a prospective cohort study (2014-2019) assessing intersecting socio-behavioural, structural, and biomedical HIV risk-factors among youth. Median age was 19 [IQR 18-21]; 60 % were women. The Centre for Epidemiological Studies Depression (CES-D-10) scale measured depressive symptoms; a CES-D-10 score of ≥10 indicating probable depression. A group-based trajectory modeling (GBMT) approach was used to identify trajectory classes of depressive symptoms over 12-months. We used prediction modeling, utilizing multinomial regression, to estimate baseline candidate predictors (n = 9) of depression trajectory class. RESULTS: At baseline, 43 % (n = 181) had probable depression (49 % women, 35 % men), decreasing to 35 % (p < 0.01) at 12-months (39 % women, 30 % men). GBTM modeling indicated four distinct probable depression trajectories comprising 'high-stable' (n = 39), 'moderate-stable' (n = 182), 'low-declining' (n = 165), and 'lowest-declining' (n = 35). In multinomial regression, being a woman was associated with higher, persistent symptoms and lower likelihood of symptom decline; being a student and higher resiliency were associated with a lower likelihood of higher or moderate symptoms trajectories; food insecurity predicted high persistent depressive symptoms across follow-up. DISCUSSION: The prevalence of probable depression decreased over the 12-month among both genders. Given evident longitudinal associations between depression trajectories and markers of sociostructural inequity, including gender, food insecurity, our results emphasize the need for holistic approaches to address fundamental health determinants for South African youth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".