Multimorbidity and food insecurity in adults: A systematic review and meta-analysis
Bibliographic record
Abstract
Food insecurity is one of the main factors affecting multimorbidity. Previous studies have shown that food insecurity may lead to multimorbidity due to person's inability to consume nutritious diet. However, considering that multimorbidity may cause work-related disabilities and an unstable income, others support the possible effect that multimorbidity has on food insecurity. The purpose of this systematic review and meta-analysis is to examine the relationship between food insecurity and multimorbidity in adults. A systematic literature review of studies was performed using the PubMed, EBSCO and SCOPUS for all articles including adults ≥ 18-year-old with multimorbidity living in developed countries published from August 5th until December 7th 2022. Meta-analysis was performed considering results from the fully adjusted model. The methodological quality was assessed using the Newcastle-Ottawa Scale adapted for cross-sectional studies. This systematic review was not registered. This research received no specific grant from any funding agency. Four cross-sectional studies involving 45,404 participants were included in order to investigate the possible impact that food insecurity has on multimorbidity. The study findings showed an increased probability of multimorbidity 1.55 (95% CI:1.31-1.79, p<0.001, I2 = 44.1%) among people with food insecurity. Conversely, three of the included studies, involving 81,080 participants concluded that people with multimorbidity, have 2.58 (95% CI: 1.66-3.49, p<0.001, I2 = 89.7%) times higher odds to present food insecurity. This systematic review and meta-analysis provide evidence of a reverse association between food insecurity and multimorbidity. Further cross-sectional studies must be conducted in order to elucidate the association between multimorbidity and food insecurity across age groups and between the two genders.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".