# Twelve-month trajectories of depressive symptoms and associated socio-structural factors among adolescents and young adults in Soweto and Durban, South Africa

T.E. Pakhomova, M. Beksinska, J.J. Dietrich, T.J. Salway, A. Gadermann, K. Closson, A. Rowlands, Julie Jesson, T. Ndung’U, M. Brockman, et al.

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T.E. Pakhomova, M. Beksinska, J.J. Dietrich, T.J. Salway, A. Gadermann, et al. Twelve-month trajectories of depressive symptoms and associated socio-structural factors among adolescents and young adults in Soweto and Durban, South Africa. Journal of Affective Disorders, 2026, 393, pp.120409. 10.1016/j.jad.2025.120409. hal-05392499

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Research paper

# Twelve-month trajectories of depressive symptoms and associated socio-structural factors among adolescents and young adults in Soweto and Durban, South Africa

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T.E. Pakhomova<sup>a</sup>, M. Beksinska<sup>b</sup>, J.J. Dietrich<sup>c,d,e</sup>, T.J. Salway<sup>a</sup>, A. Gadermann<sup>f</sup>, K. Closson<sup>a</sup>, A. Rowlands<sup>a</sup>, J. Jesson<sup>g</sup>, T. Ndung'u<sup>h,i,j,k</sup>, M. Brockman<sup>a</sup>, M. Nduna<sup>l</sup>, S. Vermaak<sup>c</sup>, J. Smit<sup>b</sup>, A. Kaida<sup>a,*</sup>

ARTICLE INFO

Keywords:

Longitudinal

Adolescents

Young adults

Depression

Depressive symptoms

Social determinants of health

South Africa

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.

<footnote><sup>a</sup> Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada</footnote>

<footnote><sup>b</sup> Wits MRU (MatCH Research Unit), Department of Obstetrics and Gynaecology, Faculty of Health Sciences, University of the Witwatersrand, Durban, South Africa</footnote>

<footnote><sup>c</sup> Perinatal HIV Research Unit (PHRU), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa</footnote>

<footnote><sup>d</sup> African Social Sciences Unit of Research and Evaluation (ASSURE), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa</footnote>

<footnote><sup>e</sup> Health Systems Research Unit, South African Medical Research Council, Bellville, South Africa</footnote>

<footnote><sup>f</sup> School of Population and Public Health, University of British Columbia, Vancouver, Canada</footnote>

<footnote><sup>g</sup> Center for Epidemiology and Research in POPulation Health (CERPOP), Inserm, Université de Toulouse, Université Paul Sabatier, Toulouse, France</footnote>

<footnote><sup>h</sup> HIV Pathogenesis Programme, The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa</footnote>

<footnote><sup>i</sup> Africa Health Research Institute, Durban, South Africa</footnote>

<footnote><sup>j</sup> Ragon Institute of Mass General Brigham, MIT and Harvard University, Cambridge, MA, United States of America</footnote>

<footnote><sup>k</sup> Division of Infection and Immunity, University College London, London, United Kingdom</footnote>

<footnote><sup>l</sup> AVReQ, University of Stellenbosch and the Human Sciences Research Council (HSRC), South Africa</footnote>

<footnote>* Corresponding author at: Faculty of Health Sciences, Simon Fraser University, Blusson Hall 8888 University Dr., Burnaby, B.C. V5A 1S6, Canada.
E-mail address: angela_kaida@sfu.ca (A. Kaida).</footnote>
# 1. Introduction

Depression is a common, often chronic, and debilitating multicausal mental disorder characterized by persistent feelings of sadness, loss of interest, low self-worth, disrupted sleep, changes in weight, and fatigue (World Health Organization, 2017). Globally, an estimated 14% adolescents and young adults (AYA), referring to those aged 10–24, live with mental health disorders including depression (Global Burden of Disease Collaborative Network, 2019), with an additional, and substantially larger proportion, living with sub-clinical depressive symptoms (Hankin, 2006; Pedersen et al., 2023; Shorey et al., 2022; Villarroel and Terlizzi, 2020). An estimated half of mental disorders develop during adolescence (Pedersen et al., 2023), though many are likely under or misdiagnosed (World Health Organization, 2021), with a substantially greater risk of earlier depression onset among young women during mid-adolescence compared to young men (Pedersen et al., 2023). Earlier onset of depression is associated with more severe outcomes in adulthood, including greater depression recurrence, longer periods of depression, and a greater risk of lingering or unresolved symptoms (Shorey et al., 2022). Systematic reviews and meta-analysis examining global data have found an increasing prevalence of depression over the past two decades among those aged 10–29 years (Shorey et al., 2022; Yang et al., 2024).

The causes of depression are multi-faceted and hypothesized to involve numerous interacting socio-structural and biological factors which together play a role in its etiology (Beurel et al., 2020; Liu et al., 2020; Slavich and Sacher, 2019). For youth these pathways include biological and psycho-social changes which occur during important periods of development (Thapar et al., 2012), in addition to the influence of socio-structural determinants of health (SDOH) and psychological and physiological stress-responses and processes that are implicated in the onset of depression (Pedersen et al., 2023). Depression and depressive symptoms appear to vary across social contexts (Karasz, 2005), with cross-cultural and social-group variability reported in symptom presentation and experience (Chang et al., 2017; James et al., 2020). Differences are also reported for those living in low-and-middle-income-countries (LMICs) versus high-income-countries (HICs) (Haroz et al., 2017; James et al., 2020), with scholars noting that Western medical models of diagnosis may not be sufficient or appropriate given the African sociocultural contexts and colonial legacies (Alemu et al., 2023; Asongu and Moola, 2024).

While there is evidence of substantial variability in depression rates across age groups, higher rates of depression are reported for adolescents and young adults, compared with other age groups (Villarroel and Terlizzi, 2020). Studies examining trends in depressive symptoms over time indicate an increase in depressive symptoms in adolescence, with an observed peak around the age of 17 (Schubert et al., 2017). Adolescent girls and young women report greater occurrence and severity of symptoms, with a larger increase in depressive symptoms beginning in adolescence, from around 12 years of age, reported among girls compared with boys. Despite a narrowing risk gap in young adulthood by around 25 years of age, with declines in symptoms reported for both men and women post adolescence (Schubert et al., 2017), gender disparities continue across the life course (Kuehner, 2017; Salk et al., 2017). Sex and gender differences in depression outcomes are attributed to sex-specific physiological factors such as hormonal differences and genetic variations (Labaka et al., 2018), and gender-related psychosocial and socio-structural factors such as coping styles, internalizing behaviours, differential experiences of social stressors such as gender-based violence, gender role stress, and differences in symptom presentation (Boyce et al., 2021; Ernst et al., 2021; Gough and Godde, 2018; Kuehner, 2017; Niles et al., 2018).

Studies with both youth and adult populations have consistently found strong associations between exposure to adverse childhood events (ACEs)/stressors in early life, such as traumatic or negative life events, violence, and maltreatment, with adverse mental health outcomes in

adulthood such as the development of depression (Garami et al., 2019; Grant et al., 2003; Heim et al., 2010; LeMoult et al., 2020; Patel et al., 2007; Pedersen et al., 2023). Trajectories in youth depression are also strongly associated with socio-economic inequities (Schubert et al., 2017), including food insecurity (Cotton and Shim, 2022), changes in weight, including obesity (Rao et al., 2020), substance use (Cairns et al., 2014; Chaiton et al., 2009; Pozuelo et al., 2022), as well as educational inequities and experiences of discrimination (Cotton and Shim, 2022). The effects of some stressors appear to have gendered dimensions, with longitudinal studies reporting that socioeconomic disadvantages as well as adverse childhood experiences have more pronounced and long-term effects on women compared to men (Schubert et al., 2017). These effects are likely amplified by psychological traits such as coping styles and resiliency factors, which can modify and mediate depression risk over time (Evans et al., 2015; Lau, 2022). Youth who utilize maladaptive coping strategies (e.g. avoidance behaviours, substance use, rumination, withdrawal, etc) report higher rates of depression and vice versa (Cairns et al., 2014). The relationship between psychosocial stressors and depression appears to be bidirectional, increasing sensitivity to subsequent later life stress and depression (Bifulco et al., 2000; Cohen et al., 2007; Hammen, 2005), and thus supporting the ‘stress-vulnerability’ model for depressive illness trajectories in youth (Schubert et al., 2017). Likewise, psychosocial resiliency – referring to one’s ability to adapt to life circumstances in the context of adversity (Lau, 2022) – can dynamically influence depression trajectories through social and biological processes which moderate responses to stress and adversity (Silk et al., 2007).

Understanding depression trajectories in youth can help provide crucial insights into the emergence, development, and changes associated with depression, as well as contributing factors, over the life course. While the majority of adolescents and young adults live in LMICs (Nagata et al., 2018; Ritchie and Roser, 2024), majority of depression studies with youth populations are from Western Europe and North America (Henrich et al., 2010; Kirkbride et al., 2024; Pedersen et al., 2023; Pozuelo et al., 2022; Schubert et al., 2017). Differences in social, environmental, and health resource challenges existing in LMICs, including higher socio-economic disparities, under-diagnosis and under treatment of mental health conditions (Fekadu et al., 2022), food scarcity, higher rates of violence, and ACEs, thus present an important challenge to the current the body of evidence. In South Africa, a demographically young country with just under half of the population below the age of 25 (Statistics South Africa, 2020), socio-structural inequities such as poverty, food insecurity, and violence, are major drivers of poor mental health conditions prevalent among youth (Buckley et al., 2020). Such inequities are prevalent, multidimensional, and racialized (Coovadia et al., 2009; Mayosi et al., 2012). Black adolescent girls and young women in South Africa experience numerous and intersecting social and structural disparities that impact their health, driven by gender-based inequities, violence, and persisting patriarchal norms (George et al., 2020; Heise et al., 2019). This includes high rates of poverty discrimination, food insecurity, and physical and sexual violence (SANAC, 2017; Health Systems Research Unit, 2020).

Given that a large proportion of mental health disorders first develop in adolescence, and youth living in LMICs may face a disproportionately higher risk of early depression onset, we investigated how socio-structural determinants of health are associated with depressive symptoms among South African adolescents and young adults over time. Our primary aims were to examine longitudinal trends in depression and characterize participants with and without probable depression at baseline and examine group-based trajectories of changes in depressive symptoms over a 1-year follow-up period, to identify covariate predictors of trajectories for the overall sample.
## 2. Methods

### 2.1. Study overview

The AYAZAZI study (meaning “adolescents and young adults” (AYA) and “knowing themselves” (ZAZI) in Zulu, one of the 12 official South African languages) is a longitudinal cohort study which assesses intersecting socio-behavioural, structural, and biomedical HIV risk factors among 425 HIV-negative or HIV-status unknown youth, aged 16–24, in two communities — Durban and Soweto, South Africa. Guided by a youth-engagement framework, the AYAZAZI study supported the meaningful inclusion of AYA as valued partners in the research process (Dietrich et al., 2014; Kaida et al., 2018). The study prioritized youth-friendly, safe spaces, allowing participants to come in between visits and use the study lounge to socialize, use computers, and access additional services as needed. The study was run, and data collected at two study sites — the Perinatal HIV Research Unit (PHRU) located at the Chris Hani Baragwanath Hospital (2014–2017) in Soweto, Gauteng Province, and the Maternal, Adolescent and Child Health Research Unit (MRU) located in Durban's central business district, in the eThekwini Municipality in the KwaZulu-Natal Province.

Recruitment was conducted via community outreach using posters, pamphlets, and word of mouth, as well as through the PHRU HIV Counselling and Testing clinic and a public sector reproductive health clinic located near MRU. An age and gender stratified sampling approach was used for recruitment, targeting more young women (60 %), to reflect gendered HIV risk in South Africa. Participants were followed for 12 months in Durban and 18 months in Soweto, with regular follow-up visits every 6-months at both sites; Durban had an additional check-in follow-up visit at 3-months (Closson et al., 2019). For each completed scheduled study visit, participants received ZAR150 (approximately $12 USD) to compensate for travel costs and time spent at the study site.

### 2.2. Data collection

Data were collected via surveys, administered by trained young multilingual peer-interviewers in the preferred language of the participant (English, isiZulu, Sesotho), and completed online using DataFAX™ software. Study participants completed detailed socio-behavioural survey questionnaires every 6 months [baseline, 6 months, 12 months (Soweto)/18 months (Durban)]. Survey questionnaires included questions assessing socio-demographics, sexual and reproductive health, and mental health, and were reviewed by both the PHRU's adult and adolescent prevention community advisory boards (CABs).

### 2.3. Analytic sample

Our analytic sample consisted of all AYAZAZI study participants who were enrolled at baseline who had complete data for covariates of

interest (n = 421). For multinomial regression, three individuals were excluded from the baseline analytic sample due to missing data for covariates of interest at baseline (Fig. 1).

### 2.4. Primary outcome: depressive symptoms

The primary outcome of interest, depressive symptoms, was measured via the 10-item Centre for Epidemiologic Studies Short Depression Scale (CES-D-10), a shortened version of the original and widely validated CES-D 20 item scale (Radloff, 1977). The CES-D-10 assesses depressive symptom frequency in the past 7 days (Zhang et al., 2012); the scale uses a 4-point response format (“less than a day” = 0, “1–2 days” = 1, “3–4 days” = 2, “5–7 days” = 3), with scores ranging from 0 to 30. The scale has been validated to differentiate between having an indication of significant depressive symptomology or probable depression and those who have no depressive symptoms or who have less significant or less clinically relevant depressive symptoms (Andresen et al., 1994; Zhang et al., 2012). Study participants who scored high (above the threshold of 10) on the CES-D-10, indicating probable depression, were offered additional services and referrals, including to the onsite study social worker for additional support.

Previous research has demonstrated evidence for the validity of the CES-D-10 in South Africa, among multilingual populations, performing well in relation to other screening tools, including the CES-D-20, with results indicating adequate construct validity and reliability (Baron et al., 2017). The CES-D-10 has also been validated with youth living in rural settings in sub-Saharan African countries, with authors reporting the CES-D-10 to be a reliable measure of significant depressive symptoms, with strong psychometric properties and good internal consistency as a measure of reliability (Cronbach alphas = 0.70–0.76) (Kilburn et al., 2018).

In our analyses, the CES-D-10 was used as a continuous measure, aimed at providing nuanced information on changes associated with incremental changes in depressive symptoms, and identifying clusters of individuals who may be at higher risk of worsening symptoms. We calculated gender-specific Cronbach's alpha scores for baseline, 6-month follow-up, and 12-month follow-up full scales, all of which were in the “good” or “acceptable” range (0.72–0.79, baseline — 12 months) (Tavakol and Dennick, 2011).

### 2.5. Covariates

#### 2.5.1. Model selection

Covariate selection was informed by empirical evidence, and a priori hypothesized factors, based on a review of the literature (Arenas et al., 2019; Blair et al., 2014; Cotton and Shim, 2022; Heath et al., 2013; Myers, 2020; Rao et al., 2020; Schubert et al., 2017).

<figure><img src="image_4.png" /><figcaption>Fig. 1. Baseline analytic sample flow-chart.</figcaption></figure>
## 2.6. Covariate overview

### 2.6.1. Socio-demographic

Demographic covariates included in this analysis were age in years, categorized as 'adolescents' (16–18 years), and 'young adults' (19–24 years), current student status (no[reference]; yes).

Our study included measures for both sex (female, male, other, don't know/prefer not to answer), and gender (women, men, transgender, "do not identify with any gender", other, don't know/prefer not to answer). All participants identified as cisgender (man/male or woman/female), with assigned sex at birth matching their reported gender identity; we use the terminology of 'gender' (woman [reference]; man) in our analyses.

We included a measure for student status, which asked whether participants were currently enrolled in primary school, high school, or post-high school (current student: no[reference]; yes), monthly income (≤R400 [reference]; >R400), and having financial dependents (no; yes). We also included housing status, defined as living in formal housing (reference), versus informal housing [RDP (Reconstruction and Development Programme housing), shack, other]).

Food insecurity was measured using the Household Hunger Scale (HHS) (Ballard et al., 2011). The scale consists of three questions which assess food security in the household over the past 30 days: (a) "Was there ever no food to eat in your house because there was no money to get food?"; (b) "Did you or any household member go to sleep at night hungry because there was not enough food?"; and (c) "Did you or any household member go a whole day and night without eating anything at all because there was not enough food?". HHS scores range from 0 to 6. For this analysis, the HHS was dichotomised, with 'moderate or severe hunger' (scores 2–6) indicating 'yes' and 'little to no hunger' (scores 0–1), indicating 'no'. It has previously been validated in different settings and countries, including Sub-Saharan Africa (Deitchler et al., 2010).

### 2.7. Psychosocial & behavioural

#### 2.7.1. Substance use

We created a combined variable consisting of alcohol and drug use to indicate substance use in the past 30 days (no [reference]; yes). Alcohol consumption was measured using three standalone survey questions assessing use and frequency in the past month; those who drank 5 or more drinks a day, within the past month were categorized as "binge drinkers" (versus less than 5 drinks).

Individuals who answered "yes" to ever having used drugs, were further asked about illicit [i.e. heroin, cocaine, crack cocaine, LSD, ecstasy, methamphetamines ("tik"), Mandrax (methaqualone), nyoape (Mthembi et al., 2018), marijuana (decriminalised in 2018 after study was completed)] and prescription (non-medical) drug use in the past 30 days, with the use of each substance specified in a separate question (n = 16). The combined variable "substance use over the past 30 days" consisted of individuals who had answered 'yes' to either binge drinking or reported any illicit drug use over the past 30 days. We additionally included a measure for whether participants had smoked cigarettes (no [reference]; yes) over the past 30 days.

#### 2.7.2. Coping/resiliency

The Connor-Davidson Resilience Scale (CD-RISC) (Connor and Davidson, 2003) assesses psychological resilience using a 25 item scale (Connor and Davidson, 2003). The shortened, two items from the CD-RISC scale (CD-RISC2), which has been validated in a number of multi-cultural contexts (Davidson and Connor, 2003), includes only the items 1: "Able to adapt to change", and 8: "Tend to bounce back after illness or hardship", which measure the 'ability to spring back and successfully adapt to change' (Davidson and Connor, 2003; Vaishnavi et al., 2007); scores range from 0 to 8 (Davidson and Connor, 2003). While the full Connor-Davidson scale has been validated with South African youth (Jorgensen and Seedat, 2008), as well as globally with other Sub-

Saharan youth populations (Asante and Meyer-Weitz, 2014; Scorza et al., 2017), the 2-item CD-RISC scale has not yet been validated in the South African youth context, but has been utilized with other youth populations globally. The CD-RISC 2-item scale has been used as a continuous scale, with higher scores indicating greater resiliency.

### 2.8. Statistical analyses

Descriptive statistics were used to present summary descriptive characteristics of study participants overall and by gender; analyses for the group-based trajectory modeling and multinomial regression were conducted for the overall sample, to improve the statistical power of the models.

We used a latent class growth modeling approach (lcmml package in R, version 4.1.3.) (Proust-Lima et al., 2017), to conduct group-based trajectory modeling (GBTM) (Nagin and Odgers, 2010; Nguena Nguefack et al., 2020) of depressive symptoms over a one-year follow-up period, consisting of three visit dates (baseline, 6-months, and 12-months), aimed at identifying participant subgroups following similar trajectories. We began with a single class model to establish initial values and parameters, adding an additional trajectory class at each next step (total of 5). Model specification choices were guided by both statistical fit indices (with lower AIC and BIC values indicative of better fit) as well as interpretability of findings (complexity of the data, parsimonious models with interpretable latent classes). Maximum likelihood estimation was used to estimate model parameters (Nagin and Odgers, 2010; Nguena Nguefack et al., 2020). The models used finite mixture modeling to differentiate between distinct subgroups, with non-linear link functions allowing for flexibility in capturing complex, non-linear patterns over time. We selected a four-class trajectory model, as we felt that it provided richer information on the depression symptom trajectories of the study participants, in addition to improved fit compared to the two-class model (AIC = 7264.84 vs 7279.81); the three-class and five-class models did not converge, further supporting the four-class model as the best approach for capturing heterogeneity in the data supported by model fit.

Multinomial logistic regression was used to estimate the predicted probability of depression trajectory grouping by the candidate predictors at baseline. Model performance was assessed using McFadden's R-squared and the likelihood ratio tests, which both indicated reasonable fit, providing a modest improvement in fit over the null model. All statistical analyses were carried out in R (version 4.1.3).

### 2.9. Single Imputation for the CES-D-10 (GBTM)

For the group-based trajectory modeling, which utilized longitudinal data, 15 participants had partial missing data for CES-D-10 items (n = 6 missing at baseline, n = 3 at 6-months, n = 6 at 12-months), with one individual missing 6-month data for the CESD-10. For those missing 1–2 items on the CES-D-10, we used a single imputation method, imputing the median of available CES-D-10 score responses for each participant for the missing CES-D-10 item to calculate the total score, as per recommended practice for scale construction (Graham, 2009). We did not impute scores for the individual who was missing 6-month data for the full scale, given that GBTM modeling can handle missing data through maximum likelihood estimation, allowing them to still generate relatively unbiased parameter estimates with incomplete data (Nagin and Odgers, 2010; Nguena Nguefack et al., 2020). The item median for each time-point was used for each participant, thus imputing based on within-person consistency over the follow-up period.

### 2.10. Ethical approval

Prior to participation all participants 18 years or older provided voluntary written informed consent. Written assent together with informed consent from their parental or legal guardian was obtained for
minors (<18 years). Ethical approval was granted by the Research Ethics Boards of [ANONYMIZED], the [ANONYMIZED], and the [ANONYMIZED] Ethics Committee.

# 3. Results

## 3.1. Descriptive statistics

Of the 421 participants included at baseline (Table 1), the median age was 19 [IQR = 18, 21], 60 % (n = 251) were women, and 52 % were from Durban. Young men, compared with young women, were more likely to live in formal housing (79 % vs. 65 %, *p* = 0.003) and report substance use within the past 30 days (75 % vs. 44 %, *p* < 0.001), while young women were more likely to report higher median CD-RISC2 scores (*p* = 0.03).

## 3.2. Probable depression and CES-D-10 score trends over the follow-up period

At baseline (n = 421), for the analytic sample, the prevalence of probable depression was 43 % (n = 182); 49 % among women and 34.7 % among men (*p* = 0.005). The prevalence of probable depression decreased at 6 months (34.9 %), remaining similarly low at 12 months follow-up (34.5 %, *p* < 0.01). Among young women, prevalence decreased (*p* = 0.05) from baseline (49 %) to 6-month (43.7 %), and further decreased at 12-months (38 %). Among young men (*p* = 0.023), we observed a large decline from baseline (34.7 %) to 6-months (20.9 %), with an increase again at 12-months (29.1 %). Mean CES-D-10 scores also decreased from baseline to follow up, with decreases significant for men (*p* = 0.004), though not women (*p* = 0.08) (Table 2).

## 3.3. Primary analysis results: group-based trajectories of depressive symptoms

Using a latent class analysis approach, our GBTM model identified four distinct trajectories comprising of 1) “high stable”, 2) “moderate,

stable”, 3) “low, declining”, and 4) “lowest, declining” (Fig. 2). Of the four trajectory classes identified, a majority of participants (n = 182, 43.2 %) were in the “moderate stable” group, with CES-D-10 median scores near the depression threshold at baseline of 11 (Q1 = 8, Q3 = 14), declining slightly at 12 months to 10 (7, 13). The “low, declining” group (n = 165, 39.2 %) started below the threshold for probable depression (<10) with a baseline median(Q1, Q3) of 6 (4, 8) declining to 4 (3, 7) by 12-month follow-up. The “high, stable” group (n = 39; 10 %) had persistently high scores above the threshold, with a baseline median 17 (14, 20), peaking at 6-months 18 (15.5, 21), and ending at 16 (14, 20) by 12 months. Those remaining (n = 35, 8.3 %) were classed in the “lowest, declining” group; individuals classed in this trajectory has the lowest CES-D-10 scores of the analytic sample, and saw a large decrease in CES-D-10 symptoms, with a baseline of 4 (2, 7.5), declining to 1 (0, 1.5) at 12-months.

## 3.4. Baseline predictors of depression-score trajectory cluster

In a multinomial regression to predict class membership, we compared each of the other three trajectory classes (“lowest, declining”, “moderate, stable”, and “high-stable”) against the “low, declining” CES-D-10 trajectory group. The low-declining group was chosen as the reference category as it included a substantial proportion of the participant sample (39.2 %), and presented an improving trajectory over-time, providing a meaningful comparison for investigating differences related to higher symptom (high-stable, moderate classes) as well as the lower symptom (lowest-declining class) trajectories. In adjusted models, we found that young women were significantly less likely to belong to the “lowest, declining” group (aOR 0.11; 95 % CI 0.04–0.31), while more likely to be classed in the “high, stable” (aOR 2.92; 95 % CI 1.19–7.18) (Table 3). Having an income of more than R400 decreased the likelihood of being in the “lowest, declining” group (aOR 0.20; 95 % CI 0.08–0.49), while being a student at the time of the study decreased the likelihood of being in the “moderate, stable” (aOR 0.49; 95 % CI 0.28–0.87) and the “high, stable” classes (aOR 0.30; 95 % CI 0.12–0.70). Reporting an experience of food insecurity in the past month was associated with

<table><caption>**Table 1**
Socio-demographic characteristics of AYAZAZI participants at baseline, by gender (n = 421).</caption><thead><tr><th></th><th></th><th>Overall (n = 421)<br>n (%)</th><th>Men (n = 170)<br>n (%)</th><th>Women (n = 251)<br>n (%)</th><th>p-Value</th></tr></thead><tbody><tr><td rowspan="2">Age category</td><td>16 to 18</td><td>206 (48.9)</td><td>82 (48.2)</td><td>124 (49.4)</td><td rowspan="2">0.892</td></tr><tr><td>19 to 24</td><td>215 (51.1)</td><td>88 (51.8)</td><td>127 (50.6)</td></tr><tr><td>Age (continuous)</td><td>Median (Q1, Q3)</td><td>9 (18, 21)</td><td>19 (18, 21)</td><td>19 (17, 21)</td><td>0.795</td></tr><tr><td rowspan="2">Study site</td><td>Durban</td><td>219 (52.0)</td><td>88 (51.8)</td><td>131 (52.2)</td><td rowspan="2">1</td></tr><tr><td>Soweto</td><td>202 (48.0)</td><td>82 (48.2)</td><td>120 (47.8)</td></tr><tr><td rowspan="2">Sexual orientation</td><td>Heterosexual</td><td>391 (92.9)</td><td>158 (92.9)</td><td>233 (92.8)</td><td rowspan="2">0.461</td></tr><tr><td>LGB</td><td>29 (6.9)</td><td>11 (6.5)</td><td>18 (7.2)</td></tr><tr><td rowspan="2">Current student</td><td>No</td><td>120 (28.5)</td><td>49 (28.8)</td><td>71 (28.3)</td><td rowspan="2">0.992</td></tr><tr><td>Yes</td><td>301 (71.5)</td><td>121 (71.2)</td><td>180 (71.7)</td></tr><tr><td rowspan="2">Housing status</td><td>Formal housing</td><td>299 (71.0)</td><td>135 (79.4)</td><td>164 (65.3)</td><td rowspan="2">0.003</td></tr><tr><td>Informal housing</td><td>122 (29.0)</td><td>35 (20.6)</td><td>87 (34.7)</td></tr><tr><td rowspan="2">Financial dependents</td><td>None</td><td>92 (21.9)</td><td>42 (24.7)</td><td>50 (19.9)</td><td rowspan="2">0.296</td></tr><tr><td>One or more</td><td>329 (78.1)</td><td>128 (75.3)</td><td>201 (80.1)</td></tr><tr><td rowspan="2">Income</td><td>≤R400</td><td>301 (71.5)</td><td>129 (75.9)</td><td>172 (68.5)</td><td rowspan="2">0.126</td></tr><tr><td>&gt;R400</td><td>120 (28.5)</td><td>41 (24.1)</td><td>79 (31.5)</td></tr><tr><td rowspan="2">Food insecurity</td><td>Not present</td><td>327 (77.7)</td><td>132 (77.6)</td><td>195 (77.7)</td><td rowspan="2">1</td></tr><tr><td>Present</td><td>94 (22.3)</td><td>38 (22.4)</td><td>56 (22.3)</td></tr><tr><td rowspan="2">Substance use (30/d)</td><td>No</td><td>184 (43.7)</td><td>43 (25.3)</td><td>141 (56.2)</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Yes</td><td>237 (56.3)</td><td>127 (74.7)</td><td>110 (43.8)</td></tr><tr><td rowspan="2">CD-RISC2</td><td>Median (Q1, Q3)</td><td>5 (3,6)</td><td>5 (3,6)</td><td>4 (3,6)</td><td rowspan="2">0.03</td></tr><tr><td>Mean (SD)</td><td>4.64 (2.24)</td><td>4.35 (2.34)</td><td>4.83 (2.16)</td></tr><tr><td rowspan="2">CES-D-10</td><td>Median (Q1, Q3)</td><td>8 (5, 13)</td><td>9 (6, 14)</td><td>8 (5, 11)</td><td>0.006</td></tr><tr><td>Mean (SD)</td><td>9.30 (5.41)</td><td>8.44 (4.93)</td><td>9.89 (5.66)</td><td>0.007</td></tr><tr><td rowspan="2">Probable depression</td><td>No</td><td>182 (43.2)</td><td>59 (34.7)</td><td>123 (49.0)</td><td rowspan="2">0.005</td></tr><tr><td>Yes</td><td>239 (56.8)</td><td>111 (65.3)</td><td>128 (51.0)</td></tr></tbody></table>

LGB: lesbian, gay, bisexual; 30/d: past 30 days; SD: standard deviation; Food insecurity: measured using the Household Hunger Scale (HHS); CES-D-10: 10-item Centre for Epidemiological Studies Depression Scale; Probable Depression: CES-D-10 Score ≥ 10; CD-RISC2: Connor-Davidson Resilience (2-item) Scale; *p*-values were derived using chi-square test of independence for categorical variables, and a *t*-test for continuous variables.
<caption>**Table 2**</caption>

Longitudinal CES-D-10 scores, and probable depression, among AYAZAZI participants, across the 12-month follow up period [baseline (n = 421), 6-months (n = 398), 12-months (n = 388)].

<table><thead><tr><th rowspan="2">Measure</th><th rowspan="2">Level</th><th colspan="4">Overall</th></tr><tr><th>Baseline</th><th>FU1 (6 m)</th><th>FU2 (12 m)</th><th>p-Value<sup>^</sup></th></tr></thead><tbody><tr><td>n</td><td></td><td>421</td><td>398</td><td>388</td><td></td></tr><tr><td>CES-D-10 (continuous)</td><td>Mean (SD)</td><td>9.3 (5.4)</td><td>8.5 (5.7)</td><td>8.0 (5.6)</td><td>&lt;0.002*</td></tr><tr><td rowspan="3">Probable depression (CES-D-10 ≥ 10)</td><td>median (Q1, Q3)</td><td>8 (5,13)</td><td>8 (4,12)</td><td>7 (4,11)</td><td></td></tr><tr><td>No</td><td>239 (56.8)</td><td>259 (65.1)</td><td>254 (65.5)</td><td>&lt;0.015*</td></tr><tr><td>Yes</td><td>182 (43.2)</td><td>139 (34.9)</td><td>134 (34.5)</td><td></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Measure</th><th rowspan="2">Level</th><th colspan="4">Women</th><th colspan="4">Men</th></tr><tr><th>Baseline</th><th>FU1 (6 m)</th><th>FU2 (12 m)</th><th>p-Value<sup>^</sup></th><th>Baseline</th><th>FU1 (6 m)</th><th>FU2 (12 m)</th><th>p-Value<sup>^</sup></th></tr></thead><tbody><tr><td>n</td><td></td><td>251</td><td>245</td><td>237</td><td></td><td>170</td><td>153</td><td>151</td><td></td></tr><tr><td>CES-D-10<br>(continuous)</td><td>Mean (SD)</td><td>9.9 (5.6)</td><td>9.5 (5.8)</td><td>8.8 (5.7)</td><td>0.08</td><td>8.4 (4.9)</td><td>6.9 (5.2)</td><td>6.7 (5.4)</td><td>0.004*</td></tr><tr><td></td><td>Median (Q1,<br>Q3)</td><td>9 (6,14)</td><td>8 (5,13)</td><td>7 (4,12)</td><td></td><td>8 (5,11)</td><td>7 (3,9)</td><td>6 (2,10)</td><td></td></tr><tr><td>Probable depression<br>(CES-D-10 ≥ 10)</td><td>No</td><td>128 (51.0)</td><td>138 (56.3)</td><td>147 (62)</td><td>0.05*</td><td>111 (65.3)</td><td>121 (79.1)</td><td>107 (70.9)</td><td>0.023*</td></tr><tr><td></td><td>Yes</td><td>123 (49.0)</td><td>107 (43.7)</td><td>90 (38)</td><td></td><td>59 (34.7)</td><td>32 (20.9)</td><td>44 (29.1)</td><td></td></tr></tbody></table>

FU: follow-up period; 6 m: 6-months; 12 m: 12-months; SD: standard deviation; Q1/Q3: first quartile, third quartile; CES-D-10: 10-item Centre for Epidemiological Studies Depression Scale; Probable Depression: CES-D-10 Score ≥ 10.

Changes over time in probable depression and CES-D-10 scores were assessed using a mixed-effects models to test for significant changes; \*p < 0.05.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Participant trajectories of CES-D-10 scores, over the follow-up period, by trajectory class cluster (with median, IQR), n = 421.</figcaption></figure>

higher odds of being in the “high, stable” group (aOR 2.29; 95 % CI 1.00–5.23). A higher, per unit-increase (1) on the Connor-Davidson resiliency scale was associated with lower odds of being grouped in the “moderate, stable” class (aOR 0.83; 95 % CI 0.75–0.93).

## 4. Discussion

In our study, we found that the prevalence of probable depression decreased over a 12-month period, among both young South African

men and women living in Soweto and Durban, South Africa. Based on longitudinal follow-up data, we identified four distinct depressive symptom trajectories over the course of one-year, inclusive of ‘lowest, declining’, ‘low, declining’, ‘high stable’, and ‘moderate, stable’ probable depression patterns. Our analyses also indicated that markers of socio-structural vulnerability, including food insecurity and gender, were longitudinally associated with higher, persistent depression symptom trajectories and lower likelihood of symptom decline among participants in our study, while higher resiliency and being a student at
<table><caption>**Table 3**
Depression trajectory grouping membership (ref. category “low, declining”, n = 165).*</caption><thead><tr><th rowspan="2">Predictors</th><th rowspan="2"></th><th colspan="2">Lowest declining (n = 35)</th><th colspan="2">Moderate stable (n = 182)</th><th colspan="2">High stable (n = 39)</th></tr><tr><th>aOR</th><th>95 % CI</th><th>aOR</th><th>95 % CI</th><th>aOR</th><th>95 % CI</th></tr></thead><tbody><tr><td rowspan="2">Age category</td><td>16-18</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>19-24</td><td>0.80</td><td>0.33-1.98</td><td>1.50</td><td>0.92-2.45</td><td>1.32</td><td>0.57-3.05</td></tr><tr><td rowspan="2">Gender</td><td>Man</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>Woman</td><td>0.11</td><td>0.04-0.31*</td><td>1.25</td><td>0.77-2.04</td><td>2.92</td><td>1.19-7.18*</td></tr><tr><td rowspan="2">Income</td><td>≤R400</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>>R400</td><td>0.20</td><td>0.08-0.49*</td><td>1.43</td><td>0.79-2.58</td><td>2.04</td><td>0.68-5.98</td></tr><tr><td rowspan="2">Current student</td><td>No</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>Yes</td><td>0.58</td><td>0.21-1.61</td><td>0.49</td><td>0.28-0.87*</td><td>0.30</td><td>0.12 - -0.70*</td></tr><tr><td rowspan="2">Housing status</td><td>Formal housing</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>Informal housing</td><td>0.61</td><td>0.23-1.66</td><td>0.70</td><td>0.42-1.17</td><td>0.67</td><td>0.29-1.52</td></tr><tr><td rowspan="2">Financial dependents</td><td>None</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>One or more</td><td>1.33</td><td>0.41-4.32</td><td>1.29</td><td>0.75-2.22</td><td>1.08</td><td>0.46-2.55</td></tr><tr><td rowspan="2">Food insecurity</td><td>No</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>Yes</td><td>0.71</td><td>0.23-2.19</td><td>1.71</td><td>0.98-2.99</td><td>2.29</td><td>1.00-5.23*</td></tr><tr><td rowspan="2">Substance use (30/d)</td><td>No</td><td>REF</td><td></td><td>REF</td><td></td><td>REF</td><td></td></tr><tr><td>Yes</td><td>0.49</td><td>0.20-1.19</td><td>0.77</td><td>0.48-1.24</td><td>0.88</td><td>0.41-1.90</td></tr><tr><td>CD-RICS2 score</td><td></td><td>0.89</td><td>0.75-1.06</td><td>0.83</td><td>0.75-0.93*</td><td>0.91</td><td>0.77-1.08</td></tr></tbody></table>

30/d: past 30 days; Food insecurity: measured using Household Hunger Scale (HHS); CD-RISC2: Connor-Davidson Resilience (2-item) Scale.

the time of the study were associated with a lower likelihood of higher or moderate symptoms over follow-up.

Group-based trajectories of CES-D-10 scores indicated that most participants fit into the ‘low, declining’, and ‘moderate, stable’ trajectory classes. While the ‘high, stable’ group remained consistently above the CES-D-10 threshold for probable depression, the median scores for the ‘moderate, stable’ group decreased slightly at 12-months follow-up to settle at the probable depression threshold (CES-D score of 10). These findings are similar to other studies with youth which indicate more stable patterns of depressive symptoms among the majority of youth, with a slight decline over time (Lewis et al., 2020). While the prevalence of probable depression (CES-D-10 scores ≥10) decreased over the 12-month follow-up period for the full group, with declines in CES-D-10 scores seen across both genders, the group based trajectory models indicated that declines were seen largely among those with low CES-D-10 scores with no improvements in the high-stable and moderate symptom trajectory groups. These results are consistent with other research which indicates that youth who follow low depressive symptom trajectories tend to demonstrate greater adaptive qualities, such as better psychosocial adjustment, and lower stress responsiveness, and tend to experience overall lower mental health risk (Shore et al., 2018). The lack of improvement in those with significant or even moderate symptoms may be indicative of the barriers to mental health care and assessment present in South Africa (Sorsdahl et al., 2023), and the need for more systemic, youth-specific interventions to target adolescents and young adults experiencing mental health challenges.

Consistent with other research on depression trajectories among youth (Lewis et al., 2020; Shore et al., 2018) we found that being a woman was associated with a higher likelihood of being in the ‘high, stable’ category, and lower likelihood of being classed in the ‘lowest, declining’ category. Gender differences in probable depression, with higher rates among women compared with men at baseline and follow-up, align with other studies reporting higher rates of depression among women during adolescence and young adulthood (Schubert et al., 2017), underscoring the critical importance of gender-specific examinations to uncover distinct patterns in the prevalence and determinants of depressive symptoms. We also found that being a student at the time of study enrollment was associated with a lower odds of being in the ‘moderate, stable’ trajectory, compared with the ‘low, declining’ trajectory, which may be suggestive of the protective effects of schooling and peer environments (Adedeji et al., 2022; Young et al., 2005). Though we have no definitive conclusion as to the reduction in significant depressive symptoms over time, we would like to note that the

AYAZAZI study was designed to take a transformative approach and engage youth in a meaningful way. In addition to being guided by a youth engagement framework (Asuquo et al., 2021) and prioritizing youth-friendly spaces, the study provided opportunities for young people to be meaningfully involved throughout the research process, including in the planning, collection, analysis and interpretation of data (Closson et al., 2019). Participants also had access to additional supports such as a referral to a social worker and health services as part of their enrollment, if they had an indication of significant depressive symptoms based on the CES-D-10.

Higher resiliency scores predicted a lower likelihood of ‘moderate, stable’ trajectories compared with a ‘low, declining’ trajectory, indicating resilience may be beneficial for avoiding long-term depressive states, and potentially suggests that higher resilience may help individuals maintain a stable non-depressed state, rather than transitioning into other depression states. These results build on other South African studies examining the protective role of resiliency factors on mental health outcomes (Pretorius, 2021). Resiliency, a measure of one's ability to cope with stress, references psychological characteristics which foster effective coping and adaptation to adversity (Connor and Davidson, 2003; Schoon and Bynner, 2003). Psychosocial factors such as coping and resiliency have been implicated in the onset, trajectory, and severity of depression outcomes (Irwin et al., 2019; Slavich et al., 2020; Slavich and Irwin, 2014; Slavich and Sacher, 2019), with adaptive or maladaptive coping strategies (e.g. internalizing behaviours, risk or help seeking behaviours) often cited as important factors influencing depression trends (Guan et al., 2016; Kinderman et al., 2015). These are often described as having gendered dimensions with higher depressive rates among women often attributed to gendered differences in coping-related factors (Kuehner, 2017; Piccinelli and Wilkinson, 2000).

Experiencing food insecurity was associated with membership in the ‘high, stable’ category. Food insecurity is strongly associated with poor mental health outcomes, including depression, among adolescent and young adults, both globally (Bruening et al., 2017; Jones, 2017) and in South African studies (Carries et al., 2024; Dlamini et al., 2024; Harper et al., 2022; Tomita et al., 2020). Despite access to social grants and food programs for poorer households, many are not able to adequately meet nutritional needs (Berejena et al., 2025; Chakona and Shackleton, 2019). Our current study also builds on earlier, cross-sectional, work from the AYAZAZI study (Jesson et al., 2021) on the relationship between food insecurity and probable depression among participants, demonstrating a significant longitudinal trend of association. Residing in food-insecure households, as well as malnutrition resulting from

<footnote>* p < 0.05.</footnote>
insufficient or low-quality/low-nutrition food, has been strongly asso-
ciated with numerous physical and mental health detriments, including
impacts on cognitive and emotional development (Bhattacharya et al.,
2004; Bruening et al., 2017; Jyoti et al., 2005; Weaver and Hadley,
2009). Though we are not able to infer a causal relationship, given the
short follow-up period and insufficient temporal information, findings
from other studies suggest that there may be a bi-directional association
between poor mental health and food insecurity (Bruening et al., 2017).
Our findings for depressive symptom trajectory and income indicated
that having an income of more than R400 decreased the likelihood of
being in the “lowest, declining” group, though we did not see a rela-
tionship for other depression category groups. It should be noted that
although we did observe differences by income level, for the study
participants as a whole the majority earned below R3,200/month
(approximately 240 USD in 2017). Thus interpretation is cautioned, and
we hypothesize that additional unmeasured factors not included in this
analysis may influence the results. In South Africa, which has dis-
proportionally high socio-economic inequities (Umuhoza and Ataguba,
2018), socio-structural determinants, including SES-related factors such
as income inequality, food insecurity, and experiences of violence,
contribute to the development and sustained presence of health in-
equities including lower mental health for youth (Buckley et al., 2020;
Link and Phelan, 1995; World Health Organization Commission on So-
cial Determinants of Health, 2008). Such factors can worsen mental
health symptoms, and can in turn interact with biological processes,
which can impact the risk of depression over time.

4.1. *Strengths and limitations*

The longitudinal design of this study provides a unique opportunity to explore interdisciplinary relationships over time. Given there is limited research examining longitudinal depression trends in youth living in LMICs (Hoare et al., 2014; Pozuelo et al., 2022), this study provides significant contributions to the field around changes in depression overtime among youth in South Africa. It is important to note that while a score of ≥10 on the CES-D 10 does not indicate a formal diagnosis of major depressive disorder as outlined in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V) (American Psychiatric Association, 2013), studies examining its diagnostic precision and criterion validity have provided evidence that the CES-D-10 is a reliable tool for identifying clinically significant depressive symptoms in youth (James et al., 2020).

Furthermore, while depression is often referenced as a discreet
condition, with major depressive disorder determined by a set of DSMV5
criteria (American Psychiatric Association, 2013), emerging research
into the heterogeneity of symptoms, severity, and duration have in
recent years expanded knowledge around depression as an episodic
condition/syndrome consisting of many sub-types, with potentially
overlapping and connected causal pathways and etiologies (Schroder
et al., 2023). We also note that participation in the study may have itself
have influenced depression trajectories, given that the study employed a
youth-engagement approach which included youth-friendly spaces
allowing for socialization as well as additional support (e.g. social
worker referral). We recognize that our recruitment approach may
introduce bias, and that the trajectories as well as predictors presented
may be reflective of the sample of participants who took part in the
study, rather than the general population, and thus we caution the
generalizability of the results.

A number of individuals were missing partial data for the outcome of
interest (CES-D-10), and as such their missing data were imputed using
single imputation methods (imputation of the median score), with one
individual missing 6-month data for the CESD-10. GBTM modeling can
however handle missing data through maximum likelihood estimation,
allowing the models to still generate relatively unbiased parameter es-
timates with incomplete datasets (Nagin and Odgers, 2010; Nguena
Nguefack et al., 2020). Furthermore, given the smaller trajectory class

sizes (i.e. high-stable and lowest-declining), there may be limitations to
the statistical power of the models to detect meaningful differences in
trajectory groups. We further acknowledge that while a four-class model
can provide more nuanced information about depressive symptomology
trajectories, the selection of more classes can lead to over-fitting and
could introduce instability and impact the generalizability of the results.

We would also like to acknowledge that although a more expanded
set of covariates were identified through an extensive literature review
and may play significant roles in predicting depression trajectories over
time, we were not able to include additional variables in the current
study due to data constraints, such as low cell count and high missing-
ness. Thus, given that we could not measure all known covariates in our
models, residual confounding is a potential source of bias; future
research should examine additional variables including experiences of
violence as well as gender inequities on depression symptom
trajectories.

While the full Connor-Davidson scale has been validated with South African youth (Jorgensen and Seedat, 2008), the 2-item CD-RISC scale has not, and may lack sufficient validation across all cultural groups in South Africa. Despite a number of studies examining resiliency among South African youth, there is a growing understanding and acknowledgement of the importance of socio-cultural contexts and community-specific interpretations and perspectives on resilience (Theron et al., 2013; Van Rensburg et al., 2015). Thus, further exploration of more contextually and culturally relevant measures of resiliency may be useful in future work.

5. Conclusion

The prevalence of probable depression decreased over the 12-month
follow-up period among both young men and women in our study. Four
distinct trajectories comprising of ‘lowest, declining’, ‘high, stable’
‘moderate, stable’, and ‘low, declining’ were found, with the majority of
individuals included in the ‘moderate, stable’ and ‘low, declining’
depression trajectories. Predictors of depression trajectories included
gender, income level, being a student at the time of the study, food
insecurity, and resiliency scores. Gender differences in probable
depression, at baseline and follow-up, align with other studies reporting
higher rates of depression among women, and underscore the critical
importance of gender-specific examinations to uncover distinct patterns
in the prevalence and determinants of depressive symptoms. Given that
socio-structural inequities are major contributors to poor mental health
for South African youth, these evident longitudinal associations between
gender, income, food insecurity, and depression emphasize the need for
holistic approaches which jointly address fundamental health de-
terminants for adolescents and young adults in South Africa.

CRediT authorship contribution statement

T.E. Pakhomova: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. M. Beksinska: Writing – review & editing, Resources, Investigation, Funding acquisition. J.J. Dietrich: Writing – review & editing, Resources, Investigation, Funding acquisition. T.J. Salway: Writing – review & editing, Supervision. A. Gadermann: Writing – review & editing, Supervision. K. Closson: Writing – review & editing. A. Rowlands: Writing – review & editing. J. Jesson: Writing – review & editing, Data curation. T. Ndung'u: Writing – review & editing, Resources, Investigation, Funding acquisition. M. Brockman: Writing – review & editing, Resources, Investigation, Funding acquisition. M. Nduna: Writing – review & editing. S. Vermaak: Writing – review & editing, Project administration. J. Smit: Writing – review & editing, Resources, Funding acquisition. A. Kaida: Writing – review & editing, Supervision, Resources, Investigation, Funding acquisition.
## Funding

The AYAZAZI was funded through the Canadian HIV Vaccine Initiative (CHVI) and the Canadian Institutes of Health Research (CIHR), with support from the South African Medical Research Council. Research reported in this publication was partially supported for Janan Janine Dietrich by the South African Medical Research Council (SAMRC) through its Division of Research Capacity Development (under the Early Career Investigators Programme) Research Capacity Development Initiative from funding received from the South African National Treasury. The content and findings reported/illustrated are the sole deduction, view and responsibility of the researcher and do not reflect the official position and sentiments of the SAMRC.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could be construed as a potential conflict of interest or influenced the work reported in this paper.

## Acknowledgements

The AYAZAZI Research Team would like to thank our youth participants for their contributions to this study. We would also like to thank the Adolescent Community Advisory Boards at both the PHRU and MRU, as well as our international team of co-investigators, collaborators, and youth representatives.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jad.2025.120409.

## Data availability

Due to the sensitive nature of the questions asked in this study, data cannot be shared publicly because they contain sensitive human subjects' information. Requests to access the data should be directed to Dr. Angela Kaida, angela_kaida@sfu.ca.

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