Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status.

OBJECTIVE: To validate a revision of the Mini Nutritional Assessment short-form (MNA(R)-SF) against the full MNA, a standard tool for nutritional evaluation. METHODS: A literature search identified studies that used the MNA for nutritional screening in geriatric patients. The contacted authors submitted original datasets that were merged into a single database. Various combinations of the questions on the current MNA-SF were tested using this database through combination analysis and ROC based derivation of classification thresholds. RESULTS: Twenty-seven datasets (n=6257 participants) were initially processed from which twelve were used in the current analysis on a sample of 2032 study participants (mean age 82.3y) with complete information on all MNA items. The original MNA-SF was a combination of six questions from the full MNA. A revised MNA-SF included calf circumference (CC) substituted for BMI performed equally well. A revised three-category scoring classification for this revised MNA-SF, using BMI and/or CC, had good sensitivity compared to the full MNA. CONCLUSION: The newly revised MNA-SF is a valid nutritional screening tool applicable to geriatric health care professionals with the option of using CC when BMI cannot be calculated. This revised MNA-SF increases the applicability of this rapid screening tool in clinical practice through the inclusion of a "malnourished" category.

Point and interval estimation of partial attributable risks from case-control data using the R-package 'pARccs'.

The attributable risk (AR) is an epidemiologic measure quantifying the relationship between an exposure factor and a disease at the population level. In addition to its original use as a one-dimensional parameter the AR is increasingly applied in multifactorial epidemiologic situations when the combined impact of multiple exposure factors has to be partitioned into factor-specific components. We discuss the point and interval estimation of the resulting multidimensional parameter termed partial attributable risk (PAR) and introduce the R-package 'pARccs', a comprehensive software enabling the application of the methods. 'pARccs' allows for point and interval estimation of PAR from case-control data utilizing the non-parametric bootstrap with stratified resampling in combination with the percentile or BC(a) method to compute confidence intervals. We illustrate the concept of partial attributable risks and the application of the software by an example from a recent case-control study on risk factors for melanoma. We also discuss practical aspects of the software application for epidemiologic purposes and its limitations.