In our study, the prevalence of malnutrition was exceedingly concerning, as 85% of the participants were categorized as malnourished; this aligns with previous research that has shown similarly high rates of malnutrition among older adult populations in the Middle East/North Africa region [16, 20, 21]. Additionally, the high prevalence in this study may be attributed to the participants’ complex co-morbidities and disabilities, despite receiving regular input from a multidisciplinary team [1].

Despite previous studies linking a few co-morbidities to malnutrition, our study did not identify any specific comorbid predictors of malnutrition [9]. Our study found that hip fractures trended toward statistical significance as predictors of malnutrition, but the association did not reach the threshold for significance (p = 0.07). Previous research has shown that hip fractures can negatively affect nutritional status, leading to greater dependence on others and higher mortality rates [22]. Hip fractures frequently cause reduced mobility and functional limitations, resulting in decreased appetite, increased energy needs, and impaired nutrient intake. Conducting early nutritional assessments and interventions, as well as rehabilitative measures, is critical to promote optimal nutrition and recovery in this population.

BMI has become an efficient tool to quickly and conveniently detect malnourishment and is widely regarded as a more favorable assessment technique than other approaches [23, 24]. However, some disadvantages are associated with BMI. For example, values may take longer to reflect reduced food intake and biochemical or inflammatory markers are not considered during the assessment [25]. The importance of using nutritional risk assessment tools in long-term care is well established. GNRI is a useful tool for identifying the risk of malnutrition and has been successfully applied in various settings, including long-term care. It is particularly effective in predicting functional status and mortality [26]. In our study, a variance in the GNRI risk was identified among malnourished and non-malnourished individuals. Although such differences indicated a tendency toward statistical significance, no correlation between the GNRI and mortality was found in our study.

The link between malnutrition and the percentage of weight change within 3 months was found to be strong in our study, identifying such as an important predictor. Similar observations have been made in previous studies [27, 28]. Rapid weight loss may indicate underlying health problems, insufficient intake of nutrients owing to loss of appetite [11], or alterations in metabolic processes. Regularly monitoring weight and implementing timely intervention measures, such as individualized nutritional plans and supplementation, is essential to prevent and manage malnutrition [29].

The identification of mortality indicators is an ongoing pursuit aimed at decreasing mortality rates. A strong association has been established between malnutrition and an increased mortality risk [28, 30]. Individuals who are malnourished have impaired immune function, wound healing, and muscle strength. As a result, the underlying diseases and complications can become more severe, which can make this population more vulnerable to infections, lengthen their recovery period, and increase their chances of treatment failure. Ultimately, this increases the mortality risk. Moreover, malnutrition often occurs alongside chronic conditions such as cancer, cardiovascular disease, and respiratory disorders. This worsens the negative consequences of these conditions and creates a cycle in which poor nutritional status leads to the further deterioration of health outcomes caused by the underlying disease. Our study indicated a strong association between malnutrition and mortality. Patients with malnutrition were approximately 25 times more susceptible to mortality, as indicated by an OR of 24.84; this significant increase in mortality risk highlights the crucial effect of malnutrition on patient outcomes. Of note, the confidence interval has a broad range owing to the sample size and possible variations within the patient group, indicating that our estimate may be imprecise. Nonetheless, our study implies that efficiently identifying and addressing malnutrition is crucial for minimizing the adverse consequences in this susceptible population.

Our study, with the discovery of a high prevalence of malnutrition despite rigorous nutritional assessment and management, emphasizes the necessity for a multidimensional approach in Qatar’s long-term care settings. Combining nutrition, physical therapy, medication management, swallowing therapy, and medical care is likely necessary to enhance outcomes. The percentage of weight change within 3 months was identified as the only statistically significant predictor of malnutrition risk in older Qatar LTCF residents. This underscores the significance of vigilant monitoring for body weight fluctuations, serving as an early warning system for potential malnutrition risk. LTCFs could adopt weekly or biweekly weight assessments and establish thresholds to initiate nutrition consultations and interventions in the event of abrupt weight loss. Offering high-protein and calorie-dense foods, coupled with daily activity assistance, can address heightened nutritional requirements during rehabilitation. Moreover, any major health changes, such as acute illnesses, should prompt an evaluation of nutritional status. If malnutrition is identified, individualized nutrition intervention plans should be immediately implemented, focusing on altering modifiable risk factors. This may involve diet liberalization to allow favorite foods, nutrient-dense meal and snack options, oral nutrition supplements, and feeding assistance as needed. Although no specific co-morbidities were predictive in this study, prior research suggests that conditions such as dementia and chronic kidney disease elevate the risk of malnutrition. Therefore, conducting screenings to assess the overall co-morbidity burden and associated nutritional risks is advisable.

This study has several limitations. First, it had a cross-sectional design, which prevented the establishment of causality between the variables. Second, we used self-reported data, which may not be entirely accurate and could have introduced bias. Additionally, the sample size was small, which limited the generalizability of the findings to a larger population. There is also the possibility of selection bias, as the sample may not represent the target population. Finally, all the limitations and potential biases associated with this research design apply to this study as well.