CONCORDANCE BETWEEN THE ESTIMATES OF WASTING MEASURED BY WEIGHT-FOR-HEIGHT AND BY MUAC FOR CLASSIFICATION OF SEVERITY OF NUTRITION CRISIS

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1 CONCORDANCE BETWEEN THE ESTIMATES OF WASTING MEASURED BY WEIGHT-FOR-HEIGHT AND BY MUAC FOR CLASSIFICATION OF SEVERITY OF NUTRITION CRISIS DR. OLEG BILUKHA, CDC MS. EVA LEIDMAN, CDC

2 WEIGHT FOR HEIGHT VS MUAC Both W/H and MUAC can be used to diagnose acute malnutrition (wasting) W/H requires measures of weight, height, sex and age MUAC is a measure of mid-upper arm circumference W/H is used in all national anthropometric surveys and in small scale SMART surveys MUAC is used mostly in mass nutrition screenings where measuring weight and height is not possible

3 MEASURING W/H

4 MEASURING MUAC

5 MUAC VS W/H W/H is age-adjusted indicator MUAC is not age-adjusted, the same cutoff is used for children from 6 mo to 5 years W/H and MUAC identify different children as wasted (overlap is about 30%) MUAC identifies mostly younger children (cutoff is not age adjusted)

6 THRESHOLDS TO DEFINE SEVERITY OF SITUATION Official (WHO) population prevalence thresholds exist only for W/H (5%, 10%, 15%, 30%) These are used in IPC classification to separate Phases There are no officially agreed-on MUAC-based thresholds IPC, Cadre Harmonisee etc. have been using by default the same thresholds for MUAC-based prevalence (5%, 10%, 15%, 30%) This is NOT appropriate, because MUAC- and W/H-based prevalence in the same population have systematic differences

7 WHY THIS ANALYSIS To assess concordance between MUAC- and W/H-based prevalence of wasting in the same population To assess regional variability of this relationship To explore whether it is possible to derive MUAC thresholds corresponding to existing W/H thresholds So that it s possible to use MUAC-based data for IPC purposes if W/H data does not exist

8 METHODS 733 small scale anthropometry surveys in humanitarian settings Conducted by Action Contre la Faim and UNHCR From 41 countries About 550,000 children Both W/H and MUAC measured Exhaustive, simple random or cluster sampling design

9 WASTING PREVALENCE OF W/H AND MUAC BY REGION Number of Surveys Prevalence of wasting by WHZ Median (IQR) Prevalence of wasting by MUAC Median (IQR) Latin America and the Caribbean % ( ) 2 99% ( ) Eastern and Southern Africa % ( ) 4 66% ( ) Democratic Republic of Congo % ( ) 7 97% ( ) West and Central Africa % ( ) 5 89% ( ) South East Asia and Pacific % ( ) 8 00% ( ) Middle East North Africa % ( ) 9 43% ( ) All Surveys % ( ) 6 66% ( )

10 Prevalence of Wasting by WHZ CORRELATION BETWEEN MUAC AND W/H Prevalence of Wasting by MUAC

11 REGRESSION W/H VS MUAC Univariate Multivariate Dependent variables Coefficient 95% CI Coefficient 95% CI Intercept 2 74 ( ) ( ) Prevalence of Wasting, 0 43 ( ) 0 50 ( ) Weight-for-Height <-2Z Prevalence of Stunting, 0 11 ( ) Height-for-Age <-2Z Percent of Children less 0 08 ( ) than 30 Months of Age Percent Females 0 06 ( ) Model R

12 CORRELATION BETWEEN MUAC AND W/H PREVALENCE Spearman correlation WHZ v. MUAC Latin America and Caribbean Eastern and Southern Africa Congo, DRC West and Central Africa Middle East North Africa South East Asia and Pacific All Surveys

13 Difference in Prevalence of Wasting by WHZ and MUAC DIFFERENCE (W/H MUAC) VS W/H Prevalence of Wasting by WHZ

14 Difference in Prevalence of Wasting by WHZ and MUAC DIFFERENCE (W/H MUAC) VS MUAC Prevalence of Wasting by MUAC

15 DIFFERENCE W/H MUAC VS W/H AND VS MUAC (WHZ- MUAC) v MUAC (WHZ-MUAC) v WHZ Latin America and Caribbean Eastern and Southern Africa Congo, DRC West and Central Africa Middle East North Africa South East Asia and Pacific All Surveys

16 Prevalence of Wasting by MUAC EXPLORING THE THRESHOLDS 5% ±2.5% 10% ±2.5% 15% ±2.5% 30% ±2.5% Prevalence of Wasting by WHZ Categories

17 THRESHOLDS BY REGION Prevalence of Wasting by MUAC, [N] Median Poor Serious Emergency Famine 5%± 2 5% 10%± 2 5% 15%± 2 5% 30%± 2 5% Eastern and Southern Africa [61] 4 1% [56] 4 4% [18] 4 1% [7] 9 6% Congo, DRC [60] 5 7% [46] 11 0% [17] 13 9% West and Central Africa [52] 3 4% [42] 6 0% [26] 8 3% [3] 19 4% Middle East North Africa [5] 4 1% [31] 7 1% [31] 7 9% [11] 16 5% South East Asia and Pacific [21] 5 2% [25] 7 0% [20] 8 0% [1] 17 3% All Surveys* [211] 4 5% ( ) [200] 6 6 ( ) [112] 8 2% ( ) [22] 15 7% ( )

18 CONCORDANCE OF PHASE CLASSIFICATION Crisis Categories Based on Prevalence of Wasting by Mid-upper arm Circumference Percent Concordance 0-<5% 5-<15% 15-<30% 30% Acceptable Poor/Serious Emergency Famine (N=733) <4% 4-<7% 7-<17% 17% 42 84% <4% 4-<8% 8-<16% 16% 44 75% <4% 4-<8% 8-<17% 17% 45 70% <4% 4-<9% 9-<16% 16% 46 38% <4% 4-<9% 9-<17% 17% 47 34% <5% 5-<7% 7-<17% 17% 38 88% <5% 5-<8% 8-<16% 16% 40 79% <5% 5-<8% 8-<17% 17% 41 75% <5% 5-<9% 9-<16% 16% 42 43% <5% 5-<9% 9-<17% 17% 43 38%

19 CONCLUSIONS Poor concordance between W/H and MUAC-based prevalence of wasting (R-square <0.4) Prevalence by W/H is usually higher than prevalence by MUAC The higher the prevalence by W/H, the bigger the discrepancy The concordance is too poor to be able to derive good MUAC-based thresholds that would mean the same as existing W/H thresholds Based on MUAC is not possible to distinguish between Phase 2 and 3 Any MUAC thresholds will result in poor (<50%) convergence of Phase classification, even when merging Phase 2 and 3 together

20 ACKNOWLEDGEMENTS - Erin Hulland, CDC - Curtis Blanton, CDC - Rebecca Wee, CDC - Action Contre la Faim (ACF) - United Nations High Commissioner for Human Rights (UNHCR)