Risk of food insecurity and its association with social determinants of health among hospitalized patients in Lebanon
Bibliographic record
Abstract
BACKGROUND: Food insecurity is a growing concern globally, particularly in conflict-affected settings. In these contexts, hospitalized patients face heightened risks of poor health outcomes. The present study aims to assess the risk of food insecurity among hospitalized patients in Lebanon and investigate its association with social determinants of health (SDH) amidst multiple crises. METHODS: A cross-sectional observational study was conducted from May to October 2021 on a random sample of adult hospitalized patients in five large hospitals across different districts inLebanon. A structured survey was used to collect socio-demographic characteristics, sources of health coverage, and medical history among study participants. In addition, survey included analysis of four indicators considered as integral part of SDH criteria: (1) area of residence and household size, (2) level of education, (3) employment status and type of employment, (4) healthcare access and coverage. Risk of food insecurity among praticipants was screened by a validated two-question tool, adapted from the US Department of Agriculture Household Food Security Survey. Associations between the SDH and risk of food insecurity were explored using logistic regression analysis using STATA V13.1. RESULTS: Among the 343 participants, the majority (79.5%) were identified as being at risk of food insecurity with 62.4% classified as experiencing mild food insecurity, 15% as moderate, and 2.1% living with severe food insecurity. Higher odds of food insecurity were observed among residents of of predominantly rural areas mainly in the North of Lebanon (OR = 6.59, CI [1.79; 24.32], p = 0.005) and Bekaa (OR = 2.55, CI [0.92; 7.05], p = 0.071) districts. Additionally, participants with higher levels of education, particularly those with high school degree or higher, had lower odds of food insecurity (p < 0.05). Employment status, household size, and healthcare coverage were not found to be significant predictors of food insecurity among hospitalized patients in the multiple logistic regression analysis in the study sample. CONCLUSION: The study highlights the critical role of SDH, including educational level and geographical residence on experience of food insecurity among hospitalized patients. Screening for risk of food insecurity and associated determinants in health care settings are critical to design adequate programs and interventions to mitigate the risk of food and nutrition insecurity among vulnerable groups, particularly in conflict-affected settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".