Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 The article has been published on 10.3390/nu12010168.
Chisa Shinsugi
 + 639 word(s) 639 2020-01-15 03:18:27 |
2 layout
Dean Liu
 -2 word(s) 637 2020-10-30 04:18:00 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Shinsugi, C.; Gunasekara, D.; Takimoto, H. Mid-Upper Arm Circumference to Predict Malnutrition. Encyclopedia. Available online: https://encyclopedia.pub/entry/263 (accessed on 16 November 2024).
Shinsugi C, Gunasekara D, Takimoto H. Mid-Upper Arm Circumference to Predict Malnutrition. Encyclopedia. Available at: https://encyclopedia.pub/entry/263. Accessed November 16, 2024.
Shinsugi, Chisa, Deepa Gunasekara, Hidemi Takimoto. "Mid-Upper Arm Circumference to Predict Malnutrition" Encyclopedia, https://encyclopedia.pub/entry/263 (accessed November 16, 2024).
Shinsugi, C., Gunasekara, D., & Takimoto, H. (2020, January 28). Mid-Upper Arm Circumference to Predict Malnutrition. In Encyclopedia. https://encyclopedia.pub/entry/263
Shinsugi, Chisa, et al. "Mid-Upper Arm Circumference to Predict Malnutrition." Encyclopedia. Web. 28 January, 2020.
Mid-Upper Arm Circumference to Predict Malnutrition
Edit
This entry is adapted from the peer-reviewed paper 10.3390/nu12010168

The double burden of malnutrition (under- and overnutrition) is a serious public health issue in childhood. The mid-upper arm circumference (MUAC) is a simple tool for screening nutritional status, but studies of the optimal cutoff to define malnutrition are limited. This study aimed to explore the prediction of malnutrition by MUAC in Sri Lankan schoolchildren. The participants were 538 students (202 boys, 336 girls) aged 5–10 years. Spearman’s rank correlation was calculated for MUAC and both body-mass-index-for-age z-score (BAZ) and height-for-age z-score (HAZ). Receiver operating characteristic (ROC) analysis was conducted to assess the ability of MUAC to correctly classify malnutrition, after stratifying for age and birth weight. MUAC correlated significantly with BAZ (r = 0.84) and HAZ (r = 0.35). The areas under the ROC curve for thinness, overweight, obesity, and stunting were 0.88, 0.97, 0.97, and 0.77, respectively. The optimal MUAC cutoff values for predicting thinness and stunting were 167.5 mm and 162.5 mm, respectively; the optimal cutoffs for predicting overweight and obesity were 190.5 mm and 218.0 mm, respectively. These cutoffs differed after stratification by age group and birth weight. Our results confirm MUAC to be a useful tool for monitoring growth in schoolchildren.

child malnutrition anthropometry mid-upper arm circumference BMI-for-age z-score height-for-age z-score thinness and stunting overweight and obesity cutoffs schoolchildren Sri Lanka

1. Introduction

Child malnutrition is a serious public health concern worldwide [1]. In urban Sri Lanka, approximately one in three primary-school children suffer the double burden of malnutrition (thinness or overweight/obesity), defined by the World Health Organization (WHO) Child Growth Standards as body mass index (BMI)-for-age z-score (BAZ) that is <−2 standard deviation (SD) and >1 SD, respectively [2]. Stunting (low height-for-age) is also recognized as a critical indicator of chronic undernutrition in assessing child growth and development, and the proportion of stunting among children aged 5–6 years old is estimated at 8.7% in Sri Lanka [3]. Early detection of and intervention against childhood malnutrition are important, given the lifelong adverse impacts of thinness, stunting, and overweight on academic performance and economic productivity [4], health-related quality of life [5], metabolic syndrome [6], and adult mortality [7].

2. Role of MUAC

Measurement of mid-upper arm circumference (MUAC) provides a simple and reliable tool for screening nutritional status and also enables rapid assessment of large populations in epidemiological field study. Traditionally, MUAC has served as a practical proxy measure of undernutrition and in particular, of severe acute malnutrition among infants, children under 5 years [8], and pregnant women [9]. A study of Cambodian infants under age 30 months showed the probability of acute malnutrition as defined by MUAC, varies with height-for-age z-score (HAZ) [10]. Further, repeated cohort studies have shown MUAC to be a good predictor of mortality risk in Gambian infants [11], Southeast African children and adolescents [12], and in Taiwanese older adults [13]. Despite this, there are no universally established age- and sex-specific MUAC cutoff values for identification of undernutrition in children over 5 years.

Recently, MUAC was found to be highly accurate for detecting overweight in schoolchildren in South Africa [14] and the Netherlands [15], but the cutoffs for overweight in schoolchildren established in both studies were inconsistent. There have been few studies examining MUAC cutoffs for overweight in Asian children over 5 years. Furthermore, one study in Delhi showed that birth weight affects nutritional status in schoolchildren [16]; based on this finding, a birth weight–stratified MUAC cutoff will more accurately identify vulnerable children.

In this study, we aimed to identify the MUAC cutoff values that best predict malnutrition (under- and overnutrition) in primary school children aged 5–10 years. The second aim was to obtain more accurate MUAC cutoff values for school children in Sri Lanka, where low birth weight is prevalent.

3. Conclusions

Our findings showed that MUAC is a good predictor of malnutrition (under- and overnutrition) in Sri Lankan schoolchildren and that MUAC cutoff values for malnutrition differ according to age group and birth weight. Easily performed, regular growth monitoring with the MUAC should be undertaken for ongoing assessment of nutritional status in schoolchildren.

References

  1. Robert E Black; Cesar G Victora; Susan P Walker; Zulfiqar A Bhutta; Parul Christian; Mercedes De Onis; Majid Ezzati; Sally Grantham-McGregor; Joanne Katz; Reynaldo Martorell; et al.Ricardo Uauy Maternal and child undernutrition and overweight in low-income and middle-income countries. The Lancet 2013, 382, 427-451, 10.1016/s0140-6736(13)60937-x.
  2. Chisa Shinsugi; Deepa Gunasekara; N. K. Gunawardena; Wasanthi Subasinghe; Miki Miyoshi; Satoshi Kaneko; Hidemi Takimoto; Double burden of maternal and child malnutrition and socioeconomic status in urban Sri Lanka.. PLOS ONE 2019, 14, e0224222, 10.1371/journal.pone.0224222.
  3. Ministry of Health. Nutrition and Indigenous Medicine. Annual Health Bulletin 2015. Available online: http://www.health.gov.lk/moh_final/english/public/elfinder/files/publications/AHB/2017/AHB%202015.pdf
  4. Cesar G Victora; Linda Adair; Caroline Fall; Pedro C Hallal; Reynaldo Martorell; Linda Richter; Harshpal Singh Sachdev; Maternal and Child Undernutrition Study Group; Maternal and child undernutrition: consequences for adult health and human capital.. The Lancet 2008, 371, 340-57, 10.1016/S0140-6736(07)61692-4.
  5. Joanne Williams; Melissa Wake; Kylie Hesketh; Elise Maher; Elizabeth Waters; Health-Related Quality of Life of Overweight and Obese Children. JAMA 2005, 293, 70-76, 10.1001/jama.293.1.70.
  6. Ram Weiss; James Dziura; Tania S. Burgert; William V. Tamborlane; Sara E. Taksali; Catherine W. Yeckel; Karin Allen; Melinda Lopes; Mary Savoye; John Morrison; et al.Robert S. SherwinSonia Caprio Obesity and the Metabolic Syndrome in Children and Adolescents. New England Journal of Medicine 2004, 350, 2362-2374, 10.1056/nejmoa031049.
  7. J J Reilly; J Kelly; Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. International Journal of Obesity 2011, 35, 891-898, 10.1038/ijo.2010.222.
  8. Sylvie Goossens; Yodit Bekele; Oliver Yun; Géza Harczi; Marie Ouannes; Susan Shepherd; Mid-Upper Arm Circumference Based Nutrition Programming: Evidence for a New Approach in Regions with High Burden of Acute Malnutrition. PLOS ONE 2012, 7, e49320, 10.1371/journal.pone.0049320.
  9. Tang, A.M.; Dong, K.; Deitchler, M.; Chung, M.; Maalouf-Manasseh, Z.; Tumilowicz, A.; Wanke, C. Use of Cutoffs for Mid-Upper Arm Circumference (MUAC) as an Indicator or Predictor of Nutritional and Health-Related Outcomes in Adolescents and Adults: A Systematic Review; Food and Nutrition Technical Assistance III Project (FANTA III)/FHI 360: Washington, DC, USA, 2013.
  10. Frank Tammo Wieringa; Ludovic Gauthier; Valérie Greffeuille; Somphos Vicheth Som; Marjoleine Amma Dijkhuizen; Arnaud Laillou; Chhoun Chamnan; Jacques Berger; Etienne Poirot; Identification of Acute Malnutrition in Children in Cambodia Requires Both Mid Upper Arm Circumference and Weight-For-Height to Offset Gender Bias of Each Indicator.. Nutrients 2018, 10, 786, 10.3390/nu10060786.
  11. Martha K Mwangome; Greg Fegan; Tony Fulford; Andrew M Prentice; James A Berkley; Mid-upper arm circumference at age of routine infant vaccination to identify infants at elevated risk of death: a retrospective cohort study in the Gambia. Bulletin of the World Health Organization 2012, 90, 887-894, 10.2471/BLT.12.109009.
  12. Lazarus Mramba; Moses Ngari; Martha Mwangome; Lilian Muchai; Evasius Bauni; A Sarah Walker; Diana M Gibb; Gregory Fegan; James A Berkley; A growth reference for mid upper arm circumference for age among school age children and adolescents, and validation for mortality: growth curve construction and longitudinal cohort study. BMJ 2017, 358, j3423, 10.1136/bmj.j3423.
  13. Chien-Hsiang Weng; Chia-Ping Tien; Chia-Ing Li; Abby L’Heureux; Chiu-Shong Liu; Chih-Hsueh Lin; Cheng-Chieh Lin; Shih-Wei Lai; Ming-May Lai; Wen-Yuan Lin; et al. Mid-upper arm circumference, calf circumference and mortality in Chinese long-term care facility residents: a prospective cohort study. BMJ Open 2018, 8, e020485, 10.1136/bmjopen-2017-020485.
  14. E. Craig; R. Bland; J. Ndirangu; J. J. Reilly; Use of mid-upper arm circumference for determining overweight and overfatness in children and adolescents. Archives of Disease in Childhood 2014, 99, 763-766, 10.1136/archdischild-2013-305137.
  15. Henk Talma; Paula Van Dommelen; Joachim J Schweizer; Boudewijn Bakker; Joana E Kist-Van Holthe; J Mai M Chinapaw; Remy A HiraSing; Is mid-upper arm circumference in Dutch children useful in identifying obesity?. Archives of Disease in Childhood 2019, 104, 159-165, 10.1136/archdischild-2017-313528.
  16. A Sharma; K Sharma; Kp Mathur; Growth pattern and prevalence of obesity in affluent schoolchildren of Delhi. Public Health Nutrition 2007, 10, 485-491, 10.1017/s1368980007223894.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Nutrition & Dietetics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Chisa Shinsugi
,
Deepa Gunasekara
,
Hidemi Takimoto
View Times: 1.3K
Revisions: 2 times (View History)
Update Date: 30 Oct 2020
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback