Keakuratan Ukuran Lingkar Lengan atas Dibandingkan Berat Badan/ Panjang – Tinggi Badan Berdasarkan Z Score dalam Menilai Gizi Buruk pada Anak Usia 6 – 60 Bulan di Rumah Sakit Umum Pusat Haji Adam Malik Medan

Abstract

Objective: Malnutrition in children under 5 years is a global health problem due to higher morbidity and mortality. It also occur in Indonesia with prevalence of malnutrition in 13,8%. Malnutrition has been assessed by several methods, including mid-upper arm circumference (MUAC) and weight-for-height z-score (WHZ). However, WHZ cannot be applied in patient with oedema or organ enlargement. In fact, MUAC is more convenient to perform in primary healthcare. We compared WHZ and MUAC cut off to identify wasting among children under 5 years, using WHZ as the reference standard. Methods: A cross-sectional study was conducted in pediatric inpatient and outpatient, Adam Malik General Hospital, North Sumatera, Indonesia. A total 99 children ages 6-59 months were recruited. Anthropometric measurements were performed by a trained doctor. Statistical analysis was done by SPSS; results with p values <0,05 is significant. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined by 2x2 tables. We also used receiver operator curve. Results: The mean age of subjects was 2.0 ± 1.2 years. Majority of subjects were born by section caesarea (78,8%). Subjects were diagnosed by cardiovascular disease (29,3%), infection (20,2%), endocrine disturbance (18,2%), operative (9,1%), and other (6,1%). The prevalence of severe malnutrition based on MUAC <11.5 cm was 49,5% and 34.3% based on WHZ <−3. While moderate malnutrition based on MUAC 11.5 – 12.5 cm was 27,3% and 24.4% based on WHZ -2 - -3 SD. Sensitivity, specificity, PPV, and NPV of MUAC <11,5 cm were 59%, 90%, 85%, 69%, respectively. The total area of the ROC curve was 0.765 with accuracy 74%. Conclusion: The MUAC compared to WHZ has good accuracy to identify severe malnutrition in children under five years. A higher cut-off could improve case identification