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Skin tears cause pain, increased length of stay, increased costs, and reduced quality of life. Minimal research reports the association between skin tears, and malnutrition using robust measures of nutritional status. This study aimed to articulate the association between malnutrition and skin tears in hospital inpatients using a yearly point prevalence of inpatients included in the Queensland Patient Safety Bedside Audit, malnutrition audits and skin tear audits conducted at a metropolitan tertiary hospital between 2010 and 2015. Patients were excluded if admitted to mental health wards or were <18 years. A total of 2197 inpatients were included, with a median age of 71 years. The overall prevalence of skin tears was 8.1%. Malnutrition prevalence was 33.5%. Univariate analysis demonstrated associations between age (P ˂ .001), body mass index (BMI) (P < .001) and malnutrition (P ˂ .001) but not gender (P = .319). Binomial logistic regression analysis modelling demonstrated that malnutrition diagnosed using the Subjective Global Assessment was independently associated with skin tear incidence (odds ratio, OR: 1.63; 95% confidence interval, CI: 1.13-2.36) and multiple skin tears (OR 2.48 [95% CI 1.37-4.50]). BMI was not independently associated with skin tears or multiple skin tears. This study demonstrated independent associations between malnutrition and skin tear prevalence and multiple skin tears. It also demonstrated the limitations of BMI as a nutritional assessment measure.

Background: Nutrition screening and assessment tools often include body mass index (BMI) as a component in identifying malnutrition risk. However, rising obesity levels will impact on the relevancy and applicability of BMI cutoff points which may require re-evaluation. This study aimed to explore the relationship between commonly applied BMI cutoffs and diagnosed malnutrition. Methods: Data (age, gender, BMI and Subjective Global Assessment (SGA) ratings) were analysed for 1152 inpatients aged !65 years across annual malnutrition audits (2011e2015). The receiver operation characteristic (ROC) curve analysed the optimal BMI cutoff for malnutrition and concurrent validity of commonly applied BMI cutoffs in nutritional screening and assessment tools. Results: Malnutrition prevalence was 36.0% (n ¼ 372) using SGA criteria (not malnourished, moderate or severe malnutrition). Median age was 78.7 (IQR 72e85) years, median BMI 25.4 (IQR 21.8e29.7) kg/m 2 ; 52.1% male and 51.2% overweight/obese. ROC analysis identified an optimal BMI cutoff of <26 kg/m 2 , 80.8% sensitivity and 61.5% specificity (AUC 0.802, 95% CI 0.773, 0.830; p < 0.0001). Commonly applied BMI cutoffs (between 18.5 and 23 kg/m 2) failed to meet the alpha-priori requirement of 80% sensitivity and 60% specificity. However, BMI <23 kg/m 2 had the highest agreement (k ¼ 0.458) with malnutrition diagnosed using the SGA. Conclusions: Both malnutrition and overweight/obesity are common in older inpatients. Continuing increases in the prevalence of overweight and obesity will impact on the sensitivity of BMI as a screening component for malnutrition risk. The current study suggests tools developed over a decade ago may need to be revisited in future.

for her support in obtaining the falls related data for this study.

Despite its high prevalence, there is no systematic approach to documenting and coding obesity in hospitals. This study aimed to determine the prevalence of obesity among inpatients, the proportion of obese patients recognised as obese by hospital administration, and the cost associated with their admission. A cross-sectional study was undertaken in three hospitals in Queensland, Australia. Inpatients present on three audit days were included in this study. Data collected were age, sex, height, and weight. Body mass index (BMI) was calculated in accordance with the World Health Organization’s definition. Administrative data were sourced from hospital records departments to determine the number of patients officially documented as obese. Total actual costing data were sourced from hospital finance departments. From a combined cohort of n = 1327 inpatients (57% male, mean (SD) age: 61 (19) years, BMI: 28 (9) kg/m2), the prevalence of obesity was 32% (n = 421). Only half of obese patients were recognised as obese by hospital administration. A large variation in the cost of admission across BMI categories prohibited any statistical determination of difference. Obesity is highly prevalent among hospital inpatients in Queensland, Australia. Current methods of identifying obesity for administrative/funding purposes are not accurate and would benefit from reforms to measure the true impact of healthcare costs from obesity.