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Does stuntingoverweightness change our understanding of the socioeconomic gradient in child malnutrition? Katie Bates (LSHTM), Arjan Gjona (LSE), Tiziana Leone (LSE) IPC2017 Cape Town 31/10/2017 Child Malnutrition Undernutrition

  1. Does stuntingoverweightness change our understanding of the socioeconomic gradient in child malnutrition? Katie Bates (LSHTM), Arjan Gjonça (LSE), Tiziana Leone (LSE) IPC2017 Cape Town 31/10/2017

  2. Child Malnutrition Undernutrition Overnutrition • Leading cause of death and • 41 children overweight disability in children • 36 million in LMICs • Implicated in 45% of child • Short and long term health deaths worldwide consequences • 155 million children (22.9%) Tackling inequalities in UN Sustainable Development Goals child malnutrition crucial to reducing overall levels #2 Zero Hunger #3 Good Health and Well-being (Wagstaff & Wanatabe #10 Reduced Inequality 2000)

  3. Anthropometric Indicators for Malnutrition Status For most LMICs, national levels of child malnutrition(under-five) are created using anthropometric status indicators created from household survey data Weight-for-Height (W/H) Height-for-Age (H/A) Indicates: Indicates: Indicates: • Overweight • Wasting • Stunting Definition: Definition: Definition: • >2SD • <-2SD (moderately) • <-2SD (moderately) • <-3SD (severely) • <-3SD (severely)

  4. Stuntingoverweight, stuntingwasting and the categorisation of continuous z-scores Implicit assumption in the use of anthropometric indices: Children are only presenting with one form of malnutrition Programmes create continuous Stuntedoverweight z-scores • Concurrently stunted and overweight Stata Macros: (stuntedoverweight) • Zscore06 • Found in all of 79 LMICs studied (Bates et al. 2017) • Dm0004_1 (contains zanthro • 0.3% (Senegal) to 11.7% (Guinea-Bissau) and bmicat) • igrowup Stuntingwasting : Other software: • WHO Antho software package • Concurrently stunted and wasted • Study of 84 countries rates from 0 to 8% (Khara et al. No accompanying guidelines for concurrency 2017) UNICEF-WHO- The World Bank Joint Malnutrition Estimates ‘ double count ’ stuntedoverweight children, expect the same for stuntedwasted

  5. Socioeconomic inequality in malnutrition As LMICs progress in their Stunting • Poorer children Nutrition Transitions (NT), • As NT progresses – stunting ↓ socioeconomic gradients in • Inequality doesn’t necessarily malnutrition change change (Wagstaff & Wanatabe 2000; Van de Poel et al. 2008) Overweight • Initially considered a problem of the rich (Popkin & Gordon-Larsen 2004) • Increasingly burden for the poor as NT progresses (Monterio et al. No indication stuntedoverweight 2004) (or stuntedwasted) children are Wasting accounted for in research on • Poor children (some studies have socioeconomic inequality in shown no inequality) malnutrition • Levels decline as NT progresses

  6. Data and Methods Erreygers’ Concentration Index (CI e ) Data: • 100 Demographic and Health Cumulative stunting % Concen- Surveys 80 tration • Albania (2008-09) curve 60 • Azerbaijan (2006) 40 Equality • Benin (2011) line 20 • Egypt (2014) 0 0 20 40 60 80 100 Cumulative % of Children Ranked by Asset Methods: Scores • 6 classifications of malnutrition into all possible combinations • Allows for binary health outcomes (e.g. stunted or not) of binary variables made from • Not dependent on mean continuous z-scores Concentration • Socioeconomic status – wealth • Takes value between -1 and 1 Index= 2*area index between • Ci e =0 -no inequality in malnutrition concentration • Analysed using Erreygers’ curve and 45° Concentration Index (CI e ) • C>0 if richer more affected line of equality • C<0 if poorer more affected

  7. Classification Systems KEY: SO: includes Stunting-overweight SW: includes Stuntingwasting * As in UNICEF-WHO-WB Estimates UR also analysed by urban/rural System 1 System 2 System 3 • Stunting (SO & SW) • Stunting • Stunting ( SO ) • Overweight • Overweight (SO) • Overweight • Wasting • Wasting (SW) • Wasting (SW) System 5 UR System 4 System 6* • Stunting (SW) • Stunting • Stunting (SO & SW) • Overweight (SO) • Overweight • Overweight (SO) • Wasting • Wasting • Wasting (SW) • Stuntingoverweight • Stuntingwasting

  8. Results: System 5 Malnutrition Rates and GNI per capita 35 4500 4000 30 3500 25 % children under-five 3000 Current US$ 20 2500 2000 15 1500 10 1000 5 500 0 0 Albania Azerbaijan Benin Egypt Stunting % Overweight % Wasting % Stuntedoverweight % Stuntedwasted % GNI per capita (Atlas Method)

  9. Results: CIe by Country and System Pro-poor Pro-rich Not sig Not created Key: - Albania Benin S 1 S 2 S 3 S 4 S 5 S 6 S 1 S 2 S 3 S 4 S 5 S 6 Stunting Stunting -0.096*** -0.071*** -0.090*** -0.078*** -0.071*** -0.096*** -0.074* -0.03 -0.073* -0.03 -0.03 -0.074* Overweight 0.003 -0.016 0.003 0.016 0.003 -0.016 Overweight 0.02 -0.03 0.022 -0.03 0.02 -0.03 Wasting 0.03 0.03 0.027 0.03 0.03 0.027 Wasting -0.032** -0.039** -0.039** 0.032** -0.032** -0.039** Stunting- Stunting- - - - - -0.047* - - - - - -0.019 - overweight overweight Stunting- Stunting- - - - - -0.001 - - - - - -0.007* - wasting wasting Egypt Azerbaijan S 1 S 2 S 3 S 4 S 5 S 6 S 1 S 2 S 3 S 4 S 5 S 6 Stunting Stunting -0.017 -0.021* -0.019 -0.019 -0.021* -0.017 -0.165*** -0.144*** -0.158* -0.147*** -0.144*** -0.165*** Overweight 0.047* 0.03 0.047* 0.032 0.047* 0.03 Overweight 0.016* 0.018 0.016* 0.018 0.016* 0.018 Wasting Wasting -0.046** -0.052** -0.052** -0.046** -0.046** -0.052** -0.008 -0.008 -0.008 0.006 0.006 -0.008 Stunting- Stunting- - - - - - -0.014 - - - - 0.002 - overweight overweight Stunting- Stunting- - - - - - - - - - 0.002 - -0.007 wasting wasting *p<0.05 **p<0.01 ***p<0.001

  10. Results: Cross-country comparison of system 5 Albania Azerbaijan Benin Egypt -0.071*** -0.026 -0.144*** -0.021* Stunting (SE) (0.024) (0.031) (0.010) (0.014) 0.022 0.047* 0.003 0.016* Overweight (SE) (0.018) (0.008) (0.007) (0.027) 0.028 -0.046** -0.032** 0.006 Wasting (SE) (0.017) (0.012) (0.008) (0.031) -0.047* -0.014 -0.019 0.002 Stuntingoverweight (SE) (0.023) (0.012) (0.008) (0.022) -0.001 -0.007 -0.007* 0.002 Stuntingwasting (SE) (0.001) (0.004) (0.003) (0.008) *p<0.05 **p<0.01 ***p<0.001

  11. Results: Rural/Urban inequality for System 5 Albania Azerbaijan Benin Egypt U R U R U R U R Stunting -0.041 -0.061* -0.111** -0.089* -0.080** -0.030 0.005 -0.049*** (SE) (0.029) (0.027) (0.035) (0.044) (0.023) (0.016) (0.014) (0.010) Overweight 0.082* -0.026 0.080** -0.002 0.004 0.002 0.030** 0.010 (SE) (0.034) (0.040) (0.024) (0.011) (0.014) (0.008) (0.010) -0.008 Wasting 0.022 -0.002 0.034 -0.047* -0.012** -0.040** -0.014 0.013 (SE) (0.048) (0.021) (0.018) (0.020) (0.015) (0.013) (0.012) (0.009) Stuntingoverweight -0.081* -0.040 -0.024 0.009 -0.007 -0.024 0.007 0.016 (SE) (0.036) (0.030) (0.029) (0.024) (0.017) (0.013) (0.012) (0.009) Stuntingwasting - -0.000 -0.002 0.001 -0.012* -0.001 -0.002 0.000 (SE) (0.002) (0.002) (0.008) (0.005) (0.004) (0.003) (0.001) N 590 701 892 1050 2808 4828 5488 8194 *p<0.05 **p<0.01 ***p<0.001

  12. Conclusions Malnutrition in LMICs is changing, for efficient targeted malnutrition interventions: 1. Concurrency in malnutrition needs to be accounted for • Stuntingoverweight, in particular, challenges our current understanding of inequality in both stunting and overweight 2. Increased transparency in binary indicator construction is required 3. Spatial differences in inequality exist and need to be accounted for • Results here show rural/urban differentials • Regional differentials also exist (not presented) ‘Evidence that the social gradient in health can be reduced should make us optimistic that reducing health inequalities is a realistic goal for all societies.’ (Marmot & Bell 2016)

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