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Year : 2018  |  Volume : 43  |  Issue : 2  |  Page : 132-134

Screening maternal acute malnutrition using adult mid-upper arm circumference in resource-poor settings

1 Department of Pediatrics, Lady Hardinge Medical College, Kalawati Saran Children's Hospital, New Delhi, India
2 Independent Consultant, UNICEF India Country Office, 73 Lodhi Estate, New Delhi, India
3 Public Health Foundation of India, Gurgaon, Haryana, India
4 Nutrition Section, UNICEF India Country Office, 73 Lodhi Estate, New Delhi, India

Date of Submission20-Sep-2017
Date of Acceptance05-Feb-2018
Date of Web Publication18-May-2018

Correspondence Address:
Dr. Vani Sethi
Nutrition Section, UNICEF India Country Office, 73 Lodhi Estate, New Delhi – 110003
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijcm.IJCM_248_17

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How to cite this article:
Kumar P, Sareen N, Agrawal S, Kathuria N, Yadav S, Sethi V. Screening maternal acute malnutrition using adult mid-upper arm circumference in resource-poor settings. Indian J Community Med 2018;43:132-4

How to cite this URL:
Kumar P, Sareen N, Agrawal S, Kathuria N, Yadav S, Sethi V. Screening maternal acute malnutrition using adult mid-upper arm circumference in resource-poor settings. Indian J Community Med [serial online] 2018 [cited 2021 Sep 17];43:132-4. Available from: https://www.ijcm.org.in/text.asp?2018/43/2/132/232763


In India, one-third adult women are chronically energy deficient (body mass index [BMI] <18.5 kg/m2).[1] BMI is commonly used to assess nutritional status of non pregnant adult women. In resource poor field settings, availability of standardized well calibrated equipment to measure weight and height and calculate BMI by field workers requires is often challenging. In such situations, mid-upper arm circumference (MUAC) measurement can be useful as an indicator of protein–energy malnutrition. Since the arm contains both subcutaneous fat and muscle, changes in MUAC can reflect a change in muscle mass, a change in subcutaneous fat, or both. In resource-poor settings, where individuals tend to have smaller amounts of subcutaneous fat, changes in MUAC are more likely to reflect changes in muscle mass.[2] Adult MUAC is increasingly being used as a surrogate to assess nutritional status and determine eligibility for services among adults, especially in pregnant women and people living with HIV and/or tuberculosis.[3],[4] While child MUAC is being used for Indian children in public health settings as screening criteria for identifying and managing severe acute malnutrition,[5] however it is not being used in preconception and antenatal programs where routine assessment of nutritional status of women is carried out. A large proportion of adult preconception Indian women enter pregnancy with poor nutritional status – either thin (35.5%, BMI <18.5) or anemic (55.2%) and a sizeable proportion of women (8 million) are married in adolescent phase (15–19 years).[1] Given that preconception acute malnutrition is a leading cause of fetal stunting (35%).[6] In addition to fetal outcomes, low MUAC has also been associated with poor maternal outcomes including anemia, postpartum uterine infection, and mortality.[7],[8] Hence, we hypothesize that if programs, which have nutrition screening of adult females, can also include “adult MUAC” as one of the screening criteria of acute malnutrition (often also called wasting or thinness) and thereafter institute measures for nutritional care and support, poor fetal stunting outcomes may be averted. Overall, Indian studies/surveys report the prevalence of maternal acute malnutrition using adult MUAC [9],[10],[11],[12],[13] and even fewer Indian studies report its diagnostic accuracy compared to BMI.[13],[14] With this in mind, we tested diagnostic accuracy of MUAC as screening tool to identify maternal acute malnutrition in adult nonpregnant women of reproductive age and compared it to the gold standard (BMI).

Our study was a part of larger cross-sectional survey conducted across 96 villages in 8 districts within four Indian states – Bihar, Chhattisgarh, Rajasthan, and Telangana – in early 2016 by state and district authorities (unpublished). The study was designed as the Standardized Monitoring and Assessment of Relief and Transition methodology.[15] In each state, two districts each were purposively identified. In each district, 12 villages were purposively selected by state and local authorities where the assessment was undertaken. Therefore, in each state, data were collected from a total of 24 villages. All households in each selected village were enumerated. If the villages were scattered, then a cluster to be enumerated was selected using probability proportional to size. On completion of enumeration, systematic random sampling was used to select the households with nonpregnant adult women aged 15–49 years. Seven households per village were selected such that an estimated sample of 84 were covered per district. From each state, 168 nonpregnant adult women were selected for the survey. Thus, a total of 672 nonpregnant adult women were to be selected for the study, and we enrolled 716 nonpregnant adult women. Anthropometric measurements (height, weight, and MUAC) were undertaken with consent from the respondents. Weight was measured using UNICEF SECA weighing scale (model 874) with a least count of 100 g. Height was recorded using UNICEF SECA microtoise (model 216) with a least count 0.1 cm and MUAC using MUAC tape (procured from UNICEF supply department) with a least count of 0.1 cm. MUAC cutoff of <23 and <21 cm corresponding to BMI of <18.5 and <16 kg/m2 was used for acute and severe acute malnutrition, respectively.

In the present study, 11% mothers had short stature (height <145 cm). Women with BMI <18.5 and <16 kg/m2 were 44% and 8%, respectively. Those with MUAC <23 and <21 cm were 48% and 14%, respectively. Diagnostic accuracy between MUAC cutoff of <23 cm and BMI of <18.5 kg/m2 as gold standard showed Pearson's correlation of r = 0.86, P = 0.000 and kappa value, κ = 0.655, P = 0.000. The prevalence of test was 44%, sensitivity was 84% (268 being true positive), and specificity was 81% (48 being true negative). Positive and negative predictive values were 78% and 87%, respectively. Positive and negative likelihood ratios were 4.5 (95% confidence interval [CI]: 3.67, 5.58) and 0.19 (95% CI: 0.14, 0.24), respectively. Good agreement (κ = 0.490, P = 0.00) was also observed between MUAC <21 cm and BMI <16.5 kg/m2. The prevalence of test was 7%, sensitivity was 79% (43 being true positive), and specificity was 90% (11 being true negative), respectively. An 8.5-fold likelihood of test being positive (95% CI: 6.5, 11.17) and 0.22-fold likelihood of it being negative (95% CI: 0.13, 0.38) were observed.

We found that MUAC <23 cm can detect 268 of 316 cases – 84.8% (true positive); MUAC <21 cm can detect 43 of 54 cases – 79.6% (true positive). Community-based studies reporting MUAC cutoffs of <23 and <21 cm show prevalence of 15%–69%[9],[10],[11],[12],[13] and 4%–29%,[9],[13] respectively, and those that also checked for diagnostic accuracy and correlation reported good/moderate agreement between MUAC and BMI among nonpregnant women [Table 1].[10],[11],[12],[13],[14]
Table 1: Maternal mid-upper arm circumference (Indian studies)

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Cost of stadiometer is 129.30 USD (INR 8663), cost of weighing scale is 124.40 USD (INR 8,334), and cost of nonstretchable adult MUAC tape is 0.17 USD (INR 12) (UNICEF supply).[16] A study also reported that an average measuring time taken per person for MUAC was 54 s using nonstretchable adult MUAC tape and 59 s for height using a stadiometer.[13] Hence, cost and time taken for measurement are low with MUAC tape compared to stadiometer. The present study findings show that the presence of maternal acute or severe malnutrition in preconception women should be recorded to establish the need for: (a) generating large-scale representative evidence around extent of maternal acute malnutrition in India using MUAC, (b) Generating one MUAC cut off, (c) identifying adult maternal acute malnutrition and ernutritioia using MUAC in settings where BMI is not feasible, (d) operation research devising and testing effectiveness of nutrition support and care packages for those mothers who have very low MUAC on their nutrition as well as fetal stunting.

Financial support and sponsorship

The study was funded by UNICEF, India Country Office.

Conflicts of interest

There are no conflicts of interest.

   References Top

International Institute for Population Sciences (IIPS) and Macro International. National Family Health Survey (NFHS-4), 2015-2016; India. Vol. I. Mumbai: IIPS; 2016.  Back to cited text no. 1
Tang AM, Chung M, Dong K, Terrin N, Edmonds A, Assefa N, et al. Determining a Global Mid Upper Arm Circumference Cut Off to Assess Malnutrition in Pregnant Women. Washington, DC: FHI 360/Food and Nutrition Technical Assistance III Project (FANTA); 2016.  Back to cited text no. 2
Bahwere P, Deconinck H, Banda T, Mtimuni A, Collins S. Impact of household food insecurity on the nutritional status and the response to therapeutic feeding of people living with human immune deficiency virus. Patient Prefer Adherence 2011;5:619-27.  Back to cited text no. 3
Tumilowicz A. Guide to Screening for Food and Nutrition Services among Adolescents and Adults Living with HIV. Washington, DC: FHI 360/FANTA; 2010.  Back to cited text no. 4
WHO. Child Growth Standards and the Identification of Severe Acute Malnutrition in Infants and Children. A Joint Statement by the World Health Organization and the United Nations Children's Fund; 2009. Available from: http://www.who.int/nutrition/publications/severemalnutrition/9789241598163/en/. [Last accessed on 2017 Aug 23].  Back to cited text no. 5
Christian P, Lee SE, Donahue Angel M, Adair LS, Arifeen SE, Ashorn P, et al. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. Int J Epidemiol 2013;42:1340-55.  Back to cited text no. 6
WHO. The Double Burden of Malnutrition. Policy Brief. Geneva: World Health Organization; 2017. Available from: http://www.who.int/nutrition/publications/doubleburdenmalnutrition-policybrief/en/. [Last accessed on 2017 Aug 25].  Back to cited text no. 7
Christian P, Katz J, Wu L, Kimbrough-Pradhan E, Khatry SK, LeClerq SC, et al. Risk factors for pregnancy-related mortality: A prospective study in rural Nepal. Public Health 2008;122:161-72.  Back to cited text no. 8
United Nations Children's Fund (UNICEF). Nourishing Wombs: Delivering an Integrated Package of Maternal Nutrition Interventions in Andhra Pradesh and Telangana (India). Nutrition Reports, Issue 10, UNICEF, New Delhi, India; 2017.  Back to cited text no. 9
Chakraborty R, Bose K, Bisai S. Mid-upper arm circumference as a measure of nutritional status among adult Bengalee male slum dwellers of Kolkata, India: Relationship with self reported morbidity. Anthropol Anz 2009;67:129-37.  Back to cited text no. 10
Bisai S, Bose K. Undernutrition in the Kora Mudi tribal population, West Bengal, India: A comparison of body mass index and mid-upper-arm circumference. Food Nutr Bull 2009;30:63-7.  Back to cited text no. 11
Banik SD. Nutritional status of adult men from the Oraon tribe in Ranchi district of Jharkhand, India. Malays J Nutr 2008;14:91-9.  Back to cited text no. 12
Sethi V, Parhi RN, Dar S, Agrawal S. Feasibility and diagnostic accuracy of using armband mid-upper arm circumference as a simple screening tool for maternal wasting in rural India. Rural Remote Health 2017;17:4221.  Back to cited text no. 13
Rodrigues VC, Rao RS, Lena A. Utility of arm circumference as a screening instrument to identify women at nutritional risk. Trop Doct 1994;24:164-6.  Back to cited text no. 14
Standardized Monitoring and Assessment of Relief and Transitions (SMART). Measuring Mortality, Nutritional Status, and Food Security in Crisis Situations: SMART Methodology. Available from: http://www.conflict.lshtm.ac.uk/media/SMART_Manual_v1_FINAL.pdf. [Last accessed on 2016 Feb 02].  Back to cited text no. 15


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