Population-specific Mini Nutritional Assessment effectively predicts the nutritional state and follow-up mortality of institutionalized elderly Taiwanese regardless of cognitive status.
The study was conducted to determine the effectiveness of a modified Mini Nutritional Assessment (MNA) for assessing the nutritional status and predicting follow-up mortality of institutionalized elderly Taiwanese. The study was conducted in a large long-term care centre in central Taiwan. Trained interviewers assisted by the caregivers elicited sociodemographic data, healthcare and disease history, and answers to the MNA screen from each subject. One researcher performed all subjects' anthropometric measurements. Plasma albumin and cholesterol concentrations were determined. Results showed that the MNA without BMI, modified according to population-specific mid-arm circumference and calf circumference cut-points, effectively predicted the nutritional risk status of the elderly regardless of cognitive status. Substituting caregiver's assessments for self-viewed nutrition and health status (questions O and P of MNA) improved the predicting power of the tool in cognition-normal subjects. Results showed that 21.9% of the elderly were malnourished, 59.2% were at risk of malnutrition and 18.9% were normal according to self-assessment whereas 14.2% were malnourished, 59.2% were at risk of malnutrition, and 26.6% were normal according to caregiver's evaluation. The tool was also effective in predicting 12- and 6-month follow-up mortality in cognition-normal and cognition-impaired elderly, respectively. Results indicate that a population-specific MNA can effectively predict the nutritional status and 6-month follow-up mortality of elderly Taiwanese regardless of cognitive condition. Easier and wider application of the tool will enable early detection of emerging nutritional problems and timely intervention to prevent the development of severe malnutrition in the elderly.
Tsai
Alan C
AC
Human Nutrition Program, Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
Ku
Pei-Yu
PY
D000368:Aged; D000369:Aged, 80 and over; D000886:Anthropometry / Q000379:methods; D015992:Body Mass Index; D003071:Cognition; D005260:Female; D005500:Follow-Up Studies; D016330:Frail Elderly; D015577:Geriatric Assessment / Q000379:methods*; D006304:Health Status; D006707:Homes for the Aged; D006801:Humans; D007326:Institutionalization; D008297:Male; D015596:Nutrition Assessment; D009752:Nutritional Status; D011379:Prognosis; D016019:Survival Analysis