Minimum Dietary Diversity -Women

Transcription

1 Minimum Dietary Diversity -Women Estefanía Custodio Scientific Officer Joint Research Centre the European Commission's in-house science service JRC Science Hub: ec.europa.eu/jrc

2 JRC Role - Facts & Figures One Directorate-General of the EC In-house science service of the European Commission Independent, evidence-based scientific and technical support for many EU policies Established institutes in 6 locations Around 3000 staff, including PhDs and visiting scientists 1370 publications in

3 Minimum Dietary Diversity -Women Estefanía Custodio Scientific Officer Joint Research Centre the European Commission's in-house science service JRC Science Hub: ec.europa.eu/jrc

4 Exploring the new indicator - Background and basic concepts - Constructing MDD-W-- how do me measure it? - Analysis with MDD-W-- results from Burkina Faso - MDD-W and programmatic action --OPEN DISCUSSION-- 4

5 Background and basic concepts Constructing the indicator What next? Results from BF MDD-W is a simple proxy indicator for global use in assessing the micronutrient adequacy of women s diets. It is defined as the percentage of women, years, who consume at least 5 out of 10 defined food groups., 5

6 Background and basic concepts Constructing the indicator Analyzing Results from BF? Nutrition Food Security 6

7 Nutrition versus Food 7

8 Nutrition versus Food FOOD 8

9 Nutrition versus Food Food production Food Access Food availability Food utilization Food consumption 9

10 Nutrition versus Food NUTRITION FOOD SECURITY 10

11 Nutrition versus Food Food intake Life cycle Physiological status FOOD SECURITY Health/Disease Drug/Food interactions 11

12 How do you measure 12 12

13 How do you measure Food Security? 13 13

14 Nutrition versus Food Undernourishment Food expenditure Food consumption score Household diet diversity score Composite Food sec indicators Ind dietary div score 14

15 Nutrition versus Food U5Y undernutrition Adult undernutrition Overweight/obesity Micronutrient defs: Anemia, Fe def Dietary intake 15

16 Wasting and Stunting SDGs indicators? Low weight for height Wasting acute malnutrition Low height for age Stunting chronic malnutrition 16

17 Stunting also called hidden hunger Age Height Weight BM I Girl X 2 yr 2 mo 86 cm 12 kg 16.2 Girl 4 yr 4 mo 86 cm 12 kg

18 The face of stunting Million aprox. affected - Is irreversible after 2-3 years of age and is associated with impaired cognitive ability, and reduced school and work capacity - Initiatives like SUN and others to tackle it - EU commitment of reducing stunting by 7 Million by

19 19

20 20

21 21

22 22

23 1000 DAYS 23

24 2x 365 = 730 DAYS DAYS 1000 DAYS 24

25 2x 365 = 730 DAYS DAYS =1000 DAYS 25

26 1000 DAYS MDD-W MDD-W MDD-W 26

27 Nutrition versus Food NUTRITION FOOD SECURITY 27

28 Nutrition versus Food Food intake NUTRITION FOOD security Indv Food consumption 28

29 Exploring the new indicator - Background and basic concepts - Constructing MDD-W-- how do me measure it? - Analysis with MDD-W-- results from Burkina Faso - MDD-W and programmatic action 29

30 Background and basic concepts Constructing the indicator Analyzing Results from BF Measuring dietary intake 30

31 Background and basic concepts Constructing the indicator Analyzing Results from BF Measuring dietary intake Food records gold standard Very costly and burdersome! Unfeasible in certain contexts Late 90s--- dietary diversity indicators 31

32 Dietary Diversity Indicators (Individual) - Measured by count of food groups consumed over limited time period (24 hours) Based on 24 h recall-quantitatively Based on extended food lists-qualitatively - Meant to be proxies for micronutrient adequacy - Validated against gold standard (MPA) - Defined food groups for specific population groups - Restricted versions - Good for monitoring and evaluation at pop level - Not for screening - Not reflect all dimensions of dietary quality 32

33 Background and basic concepts Constructing the indicator Analyzing Results from BF Women Dietary Diversity Project I FANTA/USAID initiative coordinated by IFPRI Asia (Bangladesh and Philippines) Africa (Mali, Burkina Faso and Mozambique) 33

34 Background and basic concepts Constructing the indicator Analyzing Results from BF Women Dietary Diversity Project-I 34

35 Background and basic concepts Constructing the indicator Analyzing Results from BF Women Dietary Diversity Project-I Not dicothomous Mean population score Context specific 35

36 Women Dietary Diversity Project-II Eu funded Programme on Impoved Global Governance for Hunger Reduction FAO Nutrition Division Institut de Recherche pour le Développement 4 more datasets: Bangladesh, BF, Uganda (2) 36

37 Women Dietary Diversity Project-II - 2 candidate dichotomous indicators (R) FGI-9 FGI-10 1 All starchy staples 1 All starchy satples 2 All legumes and nuts 2 Beans and peas 3 Nuts and seeds 3 All dairy 4 All Dairy 4 Organ meat 5 Flesh food and miscellaneious small animal protein 5 Flesh foods (including organ meat an dmiscellaneous small animal protein) 6 Eggs 6 Eggs 7 Vitamin A-rich dark green leafy vegetables 7 Vitamin A-rich dark green leafy vegetables 8 Other Vitamin A-rich vegetables and fruits 8 Other Vitamin A-rich vegetables and fruits 9 Other vegetables 9 Other fruits and vegetables 10 Other fruits More than 4 from any of the 2 FGI 37

38 Background and basic concepts Constructing the indicator Analyzing Results from BF Internationl meeting in August Minimum Dietary Diversity- Women endorsed! as the percentage of women, years, who consume at least 5 out of 10 defined food groups. 38

39 Background and basic concepts Constructing the indicator Analyzing Results from BF What next? In collaboration with the Nutrition Advisory Service of the EC Yves Martin Prevel Kate Sadler 39

40 Background and basic concepts Constructing the indicator Analyzing Results from BF Objectives 1) How sensitive is to external changes 2) How did it relate to other dimensions 3) How it relates to other composite food security indicators 40

41 Background and basic concepts Constructing the indicator Analyzing Results from BF Looking for data! DHS MICS Feed the Future Laboratories IFPRI Literature review Hellen Keller International WHO Institute de Recherche pour le Developpement 41

42 Background and basic concepts Constructing the indicator Analyzing Results from BF Looking for data! DHS MICS Feed the Future Laboratories IFPRI Literature review Hellen Keller International WHO Institute de Recherche pour le Développement Challenges: Focus in women recent Surveys collecting information based in the DDW so, Legumes & nuts together Other fruits & Vegs 24 H recall not available 42

43 Background and basic concepts Constructing the indicator Analyzing Results from BF Looking for data! DHS MICS Feed the Future Laboratories IFPRI Literature review Hellen Keller International WHO Institute de Recherche pour le Développement Projects leaded by Yves Martin Prevel Challenges: Focus in women recent Surveys collecting information based in the DDW so, Legumes & nuts together Other fruits & Vegs 24 H recall not available 43

44 Description of samples Urban datasets Ouagadougou and Bobo Dioulasso Ouagadougou=1.5 Mill Bobo-Dioulasso= 0.5 Mill Diet data collected through qualitative based in 21 food groups Table 2: Urban sampling sizes used in the analysis Year Ouagadougou (N*) Bobo-Dioulasso (N*) *N=Number of women interviewed Dataset from project: Mesure de la Vulnérabilité Alimentaire et Nutritionnelle en Milieu Urbain Sahélien», UNICEF and IRD. 44

45 Background and basic concepts Constructing the indicator Analyzing Results from BF Description of samples Rural samples- Sanguie and Sourou provinces SOROU province 240 women SANGUIE province 240 women Measures in lean and post harvest-2010 Diet data collected through quantitative 24 hour recall Dataset from project: Food consumption and iron status survey in the rural sourou and sanguie provinces of burkina faso. IRD, IRSS, HarvestPlus Challenge Program Phase II Agreement #

46 Background and basic concepts Constructing the indicator Analyzing Results from BF Methods 1)Descriptive statistics and Х square for comparisons within rural and urban populations (not between rural-urban!) 2) Logistic regressions, dependent variable MDD-W 3)Ordinal least squares regressions, with the different FS composite indicators as dependent variables 46

47 Results from BF 1)Descriptive--Urban 47

48 1) Descriptive--Urban Background and basic concepts Constructing the indicator Analyzing Results from BF 4.0(1.3) 3.8(1.2) 4.1(1.3) 3.9(1.4) 3.7(1.3) 3.8(1.2) 48

49 1) Descriptive--Urban 49

50 Background and basic concepts Constructing the indicator Analyzing Results from BF 2)Factors associated with MDD-W=0 Ouagadougou Low SES Women Young Age High HH youth ratio Low HH food stocks Bobo-Dioulasso 50

51 Background and basic concepts Constructing the indicator Analyzing Results from BF 2)Factors associated with MDD-W=0 Ouagadougou Low SES Women Young Age High HH youth ratio Low HH food stocks Bobo-Dioulasso Female headed HH Chicken/garden HHH no education Education of the women not collected! 51

52 Background and basic concepts Constructing the indicator Analyzing Results from BF 3)MDD-W and other composite FSI Experienced based indicators HFIAS(Household Food Insecurity Assessment Scale) Captures Food Insecurity (FI) severity. Maximum score 27 indicating most FI households HHS (Household Hunger Score): Food deprivation scale based in HFIAS. Range 0-6. CSI (Coping Strategy Index):the reduced version is not context specific. Range

53 Background and basic concepts Constructing the indicator Analyzing Results from BF 3)MDD-W and FI Indicators--Urban Coef:-4.38, p<0.001 Coef:-0.72 p< Coef:-6.07 p<

54 Background and basic concepts Constructing the indicator Analyzing Results from BF 3)MDD-W and FI Indicators--Urban Coef:-2.67 p< Coef:-0.56 p< Coef:-2.67 p<

55 Background and basic concepts Constructing the indicator Analyzing Results from BF 1) Descriptive-- Rural 55

56 1) Descriptive--Rural Background and basic concepts Constructing the indicator Analyzing Results from BF 56

57 Background and basic concepts Constructing the indicator Analyzing Results from BF 1) Descriptive Rural 57

58 Background and basic concepts Constructing the indicator Analyzing Results from BF 2)Factors associated with MDD-W=0 Sanguie Province LowSES Women not receiving post natal care Low HH Education Sourou Province Earthen floor HHH old age HH not owning cart LEAN SEASON 58

59 Background and basic concepts Constructing the indicator Analyzing Results from BF 2)Factors associated with MDD-W=0 Sanguie Province Sourou Province HH not owning sheep Women not having received vitamin A supplementation HH muslim HH not owning a plough POST HARVEST SEASON 59

60 Background and basic concepts Constructing the indicator Analyzing Results from BF 3)MDDw and other FS indicators RURAL Coef: p=0.003 Coef: p=0.311 Coef: p=0.010 Coef: p=0.273 Coef: p=0.616 Coef: 0.15 p=0.718 Coef: p=0.755 Coef: p=

61 Background and basic concepts Constructing the indicator Analyzing Results from BF Report key points Scarcity of public datasets with indicator available Sensitive to changes Season specific indicator Food groups related to change 4 to 5 mainly animal source foods Urban factors: SES, Education and food stocks Rural factors: HH assets and access to health services Food security composite indicators strongly related to MDDW, modifiable by SES and context specific!? 61

62 Exploring the new indicator - Background and basic concepts - Constructing MDDW-- how do me measure it? - Analysis with MDDW-- results from Burkina Faso - MDDW and programmatic action 62

63 Background and basic concepts Constructing the indicator Analyzing Results from BF To include MDD-W in national-regular surveys! Health oriented surveys: DHS, MICS others? Already at individual level! Easy to incorporate. ADVOCACY 63

64 Background and basic concepts Constructing the indicator Analyzing Results from BF To include MDD-W in national-regular surveys! Agriculture/Food security oriented surveys: Challenges: Indv. And HH Quest-longer More training Higher sample size Data management more complex Higher level of expertise analysis 64

65 Background and basic concepts Constructing the indicator Analyzing Results from BF To include MDD-W in national-regular surveys! Agriculture/Food security oriented surveys: Challenges: Indv. And HH Quest-longer More training Higher sample size Data management more complex Higher level of expertise analysis Advantages: Cost savings Training-bridging FS&N FS & N data on same people, same time-rgion Greater understanding Powerful advocacy tools Joint actions 65

66 Background and basic concepts Constructing the indicator Analyzing Results from BF To include MDD-W in national-regular surveys! Agriculture/Food security oriented surveys: Challenges: Indv. And HH Quest-longer More training Higher sample size Data management more complex Higher level of expertise analysis Needs to be piloted! Advantages: Cost savings Training-bridging FS&N FS & N data on same people, same time-rgion Greater understanding Powerful advocacy tools Joint actions 66

67 Background and basic concepts Constructing the indicator Analyzing Results from BF Joint Approach in Nutrition and Food Security Assessment (TOF/JANFSA) Available at: 67

68 Background and basic concepts Constructing the indicator Analyzing Results from BF EU-FAO project framework: Improved Global Governance for Hunger Reduction Nutrition Assessment and Scientific Advice team (ESNA) in FAO Conclusion: The nutrition module by means of MDD-W was successfully adaptaed to the HBS in Tajikistan Available at: 68

69 Background and basic concepts Constructing the indicator Analyzing Results from BF Other initiatives: Included as indicator of the One World-No Hunger initiative Proposed as indicator for Goal 2 End hunger, achieve food security and improved nutrition, and promote sustainable agriculture For Target 2.1 by Nutrition related agents Target 2.1. By 2030, end hunger and ensure access by all people, and particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round. For Target 2.2 by Food Security related agents Target 2.2. By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting & wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older persons. 69

70 Key points - More research is needed to understand the potential of the indicator - Needs to be integrated in national, periodical surveys - The inclusion of the indicator in health and agriculture related surveys is feasible needs advocacy - It can be a good indicator of agriculuture nutrition sensitive programs - It is a key indicator for interventions in the 1000 days framework 70

71 Stay in touch JRC Science Hub: ec.europa.eu/jrc Twitter and YouTube: JRC Audiovisuals Vimeo: LinkedIn: european-commission-joint-research-centre 71

72 Minimum Dietary Diversity -Women Lets discuss Estefanía Custodio Scientific Officer Joint Research Centre the European Commission's in-house science service JRC Science Hub: ec.europa.eu/jrc