Predictors of malnutrition among older residents in Qatari long-term care facilities: a retrospective study

Abstract

Abstract Background Malnutrition is a prevalent issue among older adults in long-term care facilities and is associated with adverse health outcomes and increased healthcare costs. Identifying the predictors of malnutrition in this population is crucial for developing effective intervention strategies. This study aimed to explore the factors contributing to malnourishment among older individuals living in long-term care facilities in Qatar. Methods This cross-sectional study included 75 older adults from two long-term care facilities (Rumailah Hospital and Enaya Specialized Care Center) in Qatar. Baseline characteristics, including age, sex, length of stay, mortality, weight, body mass index, co-morbidities, and laboratory parameters, were assessed. Data were analyzed using the most recent version of the SPSS software, version 29. Predictors of malnutrition and mortality were identified using logistic regression analysis. Results Of the 75 older individuals included in the study, 85% (64) were malnourished. The average age of the participants was 74.89 years, with a standard deviation of 10.21. Of all participants, approximately 61% (46) were males, and 39% (29) were females. Most malnourished older adults were classified as either at “moderate (29.69%)” or “severe risk (37.50%),” according to the Geriatric Nutritional Risk Index. Malnourished participants experienced a significant percentage of weight change within 3 months (14.01 ± 7.89); the only statistically significant predictor of malnutrition was the percentage of weight change within 3 months with an odds ratio (OR) of 4.8 (confidence interval [CI] 1.56–14.75) and p-value of 0.006. Statistically significant predictors of mortality were malnutrition (OR 24.84, CI 1.09–564) and age (OR 1.07, CI 1.00–1.14). Conclusions A significant predictor of malnutrition in older adults identified in this study was the sudden and recent change in weight, which can be employed to detect individuals at risk early and guide tailored interventions. Malnutrition is a significant predictor of mortality. Employing a multidimensional strategy to tackle malnutrition can improve outcomes for the older individuals.