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Predicting malnutrition in nursing home residents using the minimum data set.
Neva L Crogan1, Cynthia F Corbett
1College of Nursing at the University of Arizona in Tucson, USA.
Geriatric Nursing (New York, N.Y.)
|August 17, 2002
Summary
Protein/calorie malnutrition is common in new elderly nursing home residents. Early identification using Minimum Data Set (MDS) variables can improve resident health and quality of life.
Area of Science:
- Gerontology
- Nutrition Science
- Healthcare Management
Background:
- Protein/calorie malnutrition is a significant health concern in elderly populations.
- Nursing home residents are particularly vulnerable to malnutrition.
- Early detection and intervention are crucial for managing malnutrition in this demographic.
Purpose of the Study:
- To determine the prevalence of protein/calorie malnutrition in newly admitted elderly nursing home residents.
- To identify key predictors of malnutrition using Minimum Data Set (MDS) variables.
- To highlight the importance of early identification for improved resident outcomes.
Main Methods:
- Cross-sectional study design with random selection of participants.
- Inclusion of 266 residents aged 65 and older from three nursing homes.
- Measurement of malnutrition risk factors, indicators, and prevalence using MDS data upon admission.
Main Results:
- The study assessed the prevalence of malnutrition in elderly nursing home admissions.
- Significant predictors of malnutrition were identified through MDS variable analysis.
- MDS data proved valuable for identifying at-risk residents.
Conclusions:
- Protein/calorie malnutrition is prevalent among newly admitted elderly nursing home residents.
- MDS data facilitates early identification of residents at risk for malnutrition.
- Timely intervention for malnourished or at-risk residents can enhance quality of life and reduce morbidity.