Food insecurity and depression: a cross‐sectional study of a multi‐site urban youth cohort in Durban and Soweto, South Africa
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of food insecurity and the independent association between depression and food insecurity among youth living in two urban settings in South Africa. METHODS: Baseline cross-sectional survey data was analysed from a prospective cohort study conducted between 2014 and 2016 among youth (aged 16-24 years) in Soweto and Durban. Interviewer-administered questionnaires collecting socio-demographic, sexual and reproductive health and mental health data were conducted. Household food insecurity was measured using the 3-item Household Hunger Scale, with food insecure participants defined as having 'moderate' or 'severe hunger' compared to 'no hunger'. Depression was assessed using the 10-item Center for Epidemiological Studies Depression (CES-D 10) Scale (range 0-30, probable depression ≥ 10). Multivariable logistic regression models were used to estimate the association between depression and food insecurity. RESULTS: There were 422 participants. Median age was 19 years (interquartile range [IQR] 18-21) and 60% were women. Overall, 18% were food insecure and 42% had probable depression. After adjustment for socio-demographic variables (age, gender, female-headed household, household size and school enrolment), participants with probable depression had higher odds of being food insecure than non-depressed participants (2.79, 95%CI 1.57-4.94). CONCLUSION: Nearly one-fifth of youth in this study were food insecure. Those with probable depression had increased odds of food insecurity. Interventions are needed to address food insecurity among urban youth in South Africa, combining nutritional support and better access to quality food with mental health support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".