Mid-upper arm circumference as a screening tool for identifying adolescents with thinness

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Research snapshot¹

Adolescence is a unique period of rapid growth and, when combined with household food insecurity, is also a period of an increased risk of undernutrition. Failure to achieve optimal nutrition during adolescence may lead to delayed physical growth and sexual maturation and, for adolescent girls, increases the risk of giving birth to undernourished infants. There is a need for a simple, reliable and accurate screening tool to identify undernutrition in this age group to underpin effective interventions. This cross-sectional study aimed to evaluate the performance of mid-upper-arm circumference (MUAC) as an alternative to the World Health Organization-recommended body mass index (BMI) z-score to identify thinness in the late adolescence period.

In a sample of 851 adolescents aged 15 to 19 years from 15 schools in Addis Ababa, Ethiopia, the study found that MUAC had a significant strong positive correlation with BMI z-score, r = 0.81 (95% CI 0.79, 0.84) but was poorly correlated with age, r = 0.15 (95% CI 0.08, 0.21). MUAC could identify thinness among adolescents with excellent discriminatory performance (area under the curve (AUC) 0.91). The optimum MUAC cut-off point to identify thinness was ≤23.3 cm for males (sensitivity: 87.9%; specificity: 75.9%), whereas the optimum MUAC cut-off point for females was ≤22.6 cm (sensitivity: 100%; specificity: 88.2%). Based on the proposed cut-off point for the total sample (≤23.3 cm), MUAC had a high negative predictive value (NPV) – among those adolescents who identified as non-thin by MUAC, 98.0% were non-thin by BMI Z-score – and lower positive predictive value (PPV) – among those adolescents identified as thin by MUAC, only 32.4% were thin by BMI Z-score.

This indicates that MUAC is a good indicator for ruling in and ruling out thinness among adolescents but that, due to the low PPV, a large number of adolescents would be incorrectly considered to be thin leading to unnecessary nutritional support, poor use of resources and an overburdened healthcare system. When used in a relatively well-nourished population, it would be necessary to choose a cut-off with a greater positive predictive value.

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