A Systematic Review of the Literature on the Relationships between Chronic Diseases and Food Insecurity
Bibliographic record
Abstract
Background: The association between food insecurity (FI) and obesity is now so well documented that scholars have dubbed it the “‘new’ food insecurity” and cited it as a leading cause of increased chronic disease (CD) risk. Here, the chain of causality is assumed to be FI → obesity → CD. However, this model overlooks the possible direct associations between FI and CD independent of obesity. Aim: This study assessed the literature on relationships between FI and CD. Methods: We conducted a systematic literature review of electronic databases. Selection criteria were designed to elicit studies that assessed FI and CD using a measure of CD other than obesity. Results: Fifty-one articles met the inclusion criteria. Forty-five studies (87%) reported a statistically significant association between FI and CD, but only 15 adjusted for obesity. The association was less consistent for asthma and dyslipidemia than for diabetes, hypertension, and other dietrelated CDs, and most were conducted in the USA or Canada. Conclusion: There is a body of literature documenting relationships between FI and CD, but it is heavily biased toward Western nations, ecological study designs, and type 2 diabetes as the CD of focus. A small subset of the literature controls for BMI, demonstrating that a portion of the FI-CD relationship cannot be fully explained by obesity. Possible direct pathways linking FI and CD include systematic effects of poverty that accompanies FI, micronutrient deficiencies, and environmental exposure to toxins; however, exploration of these alternative pathways is limited by study designs that fail to include obesity as a control variable.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".