Household food insecurity is associated with depressive symptoms: results from a Mexican population-based survey
Bibliographic record
Abstract
Abstract The objective of this cross-sectional study was to assess the relationship between food insecurity and depression in the Mexican population. We used data from the 2012 health and nutrition survey (ENSANUT), which is representative of the Mexican population. Food insecurity was determined by the Latin American and Caribbean Food Security Scale (ELCSA). Depressive symptoms were evaluated using the Center for Epidemiological Studies Depression Scale Short-Form (CES-D-SF). Adjusted logistic regression analyses and ANCOVA were used. Out of 33,011 participants, 5788 (18%) had high depressive symptoms and 24,098 (73%) experienced food insecurity. The adjusted logistic regression analysis showed that, participants with mild food insecurity, (OR = 1.47,95% CI = 1.27 to 1.71), moderate food insecurity (OR = 2.14,95% CI = 1.85 to 2.47) and severe food insecurity (OR = 3.01,95% CI = 2.51 to 3.60,) were more likely to have high depressive symptoms than food secure participants. Participants with moderate food insecurity (OR =1.45, 95% CI = 1.28 to 1.64) and severe food insecurity (OR =2.04, 95% CI = 1.76 to 2.37) were more likely to suffer from depression as compared to participants with mild food insecurity. Participants with severe food insecurity were more likely (OR=1.41, 95% CI = 1.21 to 1.65) to suffer from depression compared to participants with moderate food insecurity. This paper provides an overview of the complex problem of food insecurity and mental health. Despite the unknown causality, the analysis suggests a strong association between depression and food insecurity. This problem calls for much more attention from the scientific community. Given the high prevalence of depression and the high prevalence of household food insecurity in Mexico, the implementation of successful public health programs to improve food security is necessary.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".