Analysis of predictors of malnutrition in adult hospitalized patients: social determinants and food security
Notice bibliographique
Résumé
Background: Malnutrition in hospitalized patients is becoming a priority during the patient care process due to its implications for worsening health outcomes. It can be the result of numerous social factors beyond clinical ones. This study aimed to evaluate the link between these various risk factors considered social determinants of health, food security levels, and malnutrition and to identify potential predictors. Methods: A cross-sectional observational study was conducted on a random sample of adult patients in five different hospitals in Lebanon. Malnutrition was assessed using the Global Leadership Initiative on Malnutrition (GLIM) criteria. Patients were interviewed to collect social and economic characteristics and were categorized into four criteria: (1) area of residence (urbanization level), (2) level of education, (3) employment status, and (4) source of health coverage. The food security level was screened by a validated two-question tool, adapted from the US Department of Agriculture Household Food Security Survey, targeting both quantity and quality. Results: In a random sample of 343 patients, the prevalence of malnutrition according to the GLIM criteria was 35.6%. Patients with low levels of food security, mainly low quality of food, had higher odds of being malnourished (OR = 2.93). Unemployed or retired patients and those who have only completed only elementary school had higher odds of being diagnosed with malnutrition as compared to those who were employed or had university degrees, respectively (OR = 4.11 and OR = 2.33, respectively). Employment status, education level, and type of health coverage were identified as predictors of malnutrition in the multiple regression model. Household location (urban vs. rural) was not associated with malnutrition. Conclusion: The social determinants of health identified in our study, mainly the level of education and income level, in addition to food security, were identified as predictors of malnutrition in hospitalized patients. These findings should guide healthcare professionals and national policies to adopt a broader perspective in targeting malnutrition by including social determinants in their nutrition care.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».