Self-Reported Food Insecurity and Depression among the Older Population in South Africa
Bibliographic record
Abstract
South Africa represents one of the most rapidly aging countries in sub-Saharan Africa with a rising burden of age-related psychological morbidities. Despite having one of the highest human development scores in the region, the country faces serious poverty and food insecurity related challenges. Previous studies have shown a positive association between food insecurity and poor mental health among the adult population, however there is no systematic evidence on this association among the elderly population in an African setting. In the present study, we aimed to address this research gap by analyzing cross-sectional data (n = 931) on the over-50 population (>50 years) from the SAGE (Study on global AGEing and adult health) Well-Being of Older People Study (WOPS) of the World Health Organization, conducted between 2010 and 2013. The outcome variable was perceived depression and the explanatory variables included several sociodemographic factors including self-reported food insecurity. The independent associations between the outcome and explanatory variables were measured using multivariable regression analysis. Results showed that close to a quarter of the population (22.6%, 95% CI = 21.4, 24.7) reported having depression in the last 12 months, with the percentage being markedly higher among women (71.4%). In the multivariable regression analysis, self-reported food insecurity was found to be the strongest predictor of depression among both sexes. For instance, severe food insecurity increased the odds of depression by 4.805 [3.325, 7.911] times among men and by 4.115 [2.030, 8.341] times among women. Based on the present findings, it is suggested that national food security programs focus on promoting food security among the elderly population in an effort to improve their mental health status. Nonetheless, the data were cross-sectional and the associations can’t imply causality.
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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 | 0.002 |
| 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 | 0.000 |
| 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".