Associations Between Food Insecurity and Depression, Anxiety, and Psychological Distress in Adulthood Across High-Income and Low- to Middle-Income Countries: A Systematic Review of Observational Studies
Bibliographic record
Abstract
Background: Food insecurity is the lack of consistent access to enough food for an active, healthy life, affects millions globally, including high-income countries. It is linked to adverse mental health outcomes like depression, anxiety, and stress. Objectives: To synthesize literature on the impact of food insecurity on depression, anxiety, and stress across various populations and regions. Methods: This mini systematic review follows PRISMA guidelines. Observational studies published from 2014 onwards, involving participants aged 18 and older exposed to food insecurity were included. Studies involving COVID-19, pregnant individuals, and cancer patients were excluded. A search was conducted in PubMed, Cochrane, and Google Scholar between April and June 2024. Covidence was used for screening, data extraction, and quality assessment. Risk of bias was evaluated using the Newcastle–Ottawa Scale. Results: 871 papers identified, 11 met the inclusion criteria. Six studies were conducted in the USA, while others included Canada, India, Panama, China, Mexico, and Russia. Food insecurity was assessed using validated scales like the USDA 10-Item Adult Food Security Module. The review found that individuals with food insecurity had significantly higher odds of experiencing depression (adjusted ORs 2.5-3.4), anxiety (adjusted ORs 2.3-3.1), and stress (adjusted ORs 2.0-2.8). These associations were consistent across different demographic groups and regions. Discussion: Food insecurity significantly increases risks of depression, anxiety, and stress, persisting after adjustments for sociodemographic and health factors. Conclusions: Food insecurity profoundly impacts mental health, highlighting the need for targeted interventions and policies to improve food security and mental health, especially for vulnerable populations.
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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 | 0.044 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".