Analysis of Baseline Data: Ethiopia

1. Analysis of Baseline Data:Ethiopia Nutrition at the Center May 2014

2. Objectives of the Session • Understand findings of the baseline survey • Describe and discuss analyses for programmatically important questions • Consider implications for program design and implementation • Build capability in using data for decision-making

3. What Can We Learn from Baseline Data? • People’s situation at the beginning of the program • Allows program to set targets for indicators • Provides a comparison with endline data • Approach is descriptive • Who is at risk for poor outcomes and who is most likely to have poor health behaviors • Allows the program to target those at higher risk • Identifies what interventions the program should focus on to have the greatest impact • Approach is analytic

4. Description of the Population

5. Participation in Safety Net Programs

6. Defining Poverty Lowest Low middle Middle High middle Highest • Poverty defined by quintiles – each 20% of the population – as used in the DHS • Calculation based on composition of house, WASH facilities and ownership of assets • Often differences between lowest quintiles is small – other categories such as “below poverty line” may be useful to analyze

7. Female Headed HH are More Likely to be Poor Percent Poverty Quintile

8. Poor Families are Less Likely to Participate in PNSP Percent Poverty Quintile

9. Poor Families are Less Likely to Own Land or Animals Percent Poverty Quintile

10. Child Anthropometry Height-for-age compared with WHO standard (boys/girls)

11. Maternal Undernutrition • 2011 Ethiopia DHS reports 17% of women anemic in Amhara • About half of women reported taking iron tablets during pregnancy

12. Description of Feeding Practices Is poor complementary feeding a result of knowledge and behavior or a consequence of food insecurity?

13. Analytic Results Potential Predictor Outcome ? Examples of questions to answer • Who is at risk for poor nutritional outcomes? • Is poor complementary feeding due to poor feeding behaviors or to food insecurity? • Does poor sanitation increase the risk of diarrhea or stunting?

14. Testing for Statistical Significance Assess whether the potential predictor is significantly associatedwith an outcome usinga 2 x 2 table http://www.openepi.com/v37/TwobyTwo/TwobyTwo.htm

15. Statistical Testing: Environmental Enteropathy (EE) Risk Score and Diarrhea Diarrhea in Past 2 Weeks Yes No 130 (30%) 310 High EE Risk Score* 391 (23%) 1301 P <0.01 Med/Low *Score includes animal ownership, keeping animals in the house at night,eating soil or chicken feces, and open defecation

16. What are Risk Factors for HH Hunger? All differences are statistically significant

17. Complementary Feeding Children 6-23 months old

18. Who Eats What Food in the Family? If a child does not eat a food group, it is because of foodinsecurity (not available or not affordable) or because thefamily chooses not to give the child that food (behavior)? *May also represent family choice not to eat a food group or possibly the father eats the food but the mother and child do not

19. Food Group Eaten by Mothers andChildren 6-23 months Mother Eats Food Group Yes No Both eat Child only eats Yes Child Eats Food Group Motheronly eats Neither eats No

20. Food Insecurity or Feeding Behaviors? Families with children 6-23 months old

21. Sanitation Facilities & Behaviors • Sanitation facilities • Improved toilet – 30% • Open defecation – 31% (Intervention 38%, Control 20%) • Child behaviors • Eat soil – 33% (In last 30 days 14%) • Eat chicken feces – 6% (In last 30 days 3%) • Open defecation – 71% (In or outside of house & yard)

22. Environmental Enteropathy Risk? • Risk score = 1 point each for owning animals, keeping animals in the house at night, child eating soil or chicken feces, and open defecation • High score (3 or 4) significantly associated with diarrhea in the past 2 weeks and low maternal BMI • High score not associated with child stunting or anemia

23. Participation in Safety Net Programs

24. Participation in Women’s Empowerment Program and HH Hunger HH Hunger Yes No 24 (5%) 425 Yes Participationin Community WE Program 61 (9%) 610 P =0.02 No

25. Risk Factors for Child Stunting Contractor Report – Multivariate Analysis No association with poverty, head of household, women dietary diversity, PSNP enrollment, household hunger scale, access to unshared improved water, EE risk score, and mother’s or child’s minimum dietary diversity

26. What Are Some Important Things We’ve Learned from the Baseline Survey? • There are some differences between the intervention and control areas that will make comparison difficult • There is a high rate of EBF and continued BF • Female headed HHs are a high risk group • Children’s dietary diversity is very poor • The only foods eaten by a majority of children are grains and legumes • Fewer than 1 child in 10 eats meat, eggs, dairy, vitamin A rich foods and other fruits and vegetables • From comparing with mothers’ diets, most of this is due to food insecurity

27. Triangulation with Other Ethiopia Data • N@C formative research • 2011 Demographic and Health Survey • Alive and Thrive (A & T) baseline survey

28. Are Survey Data Consistent with the Formative Research? (1) • Exclusive BF • Some pre-lacteals; some encouragement to feed at ~4 mo • Complementary feeding & dietary diversity • Some foods not acceptable for children – greens, cabbage, chick peas, possibly mango & papaya (young women are more likely to say these are okay than older women) • Greens, Vit A rich foods, meat & animal products seldom eaten due to seasonal availability and cost • Fruit and eggs are sold to but other foods • Husbands have priority for meat when it is available

29. Are Survey Data Consistent with the Formative Research? (2) • Limitations to HH food production for own consumption • Lack of water, cost of inputs – food often grown to sell • Handwashing • Baseline survey – most respondents reported handwashing at recommended times • Observation in FR – “Handwashing is rare” • Sanitation • Latrines are common but not sure whether they are being used • No open defecation was observed • Animal feces common around houses and animals often kept in the house at night

30. Are Survey Data Consistent with the 2011 DHS? • Stunting in Amhara – 52% • Relatively similar nationally in lowest 4 wealth quintiles (45-49%) and only lower in wealthiest quintile (30%) • Significantly associated with mothers’ low BMI • Exclusive BF – 52% (with predominant BF 75%); and high rates of continued BF (96% at 1 yr) • Complementary feeding is very poor; 6-23 mo diets: • Grains – 66%; Vit A rich – 15%; Other F & V – 3%; Legumes – 20%; Animal foods – 5%; Eggs – 8%; Dairy 13% • Adequate frequency – 49%; adequate diversity – 5% • Anemia in Amhara (children) – 35% • Open defecation – 45%

31. Are Survey Data Consistent with A & T? • A & T in Tigre and SNNPR • Exclusive BF – 70% and continuation “universal” • Problems breastfeeding only 7% • “Half” adequate CF meal frequency but only 6% adequate dietary diversity • CF knowledge poor on when to introduce foods • “Two-thirds” of HH experienced some food insecurity and 15% “extremely food insecure”

32. What Additional Information Would be Useful? What Questions Remain? • Given low consumption of iron rich foods (animals, greens), why aren’t more women and children anemic? • When neither children nor mothers eat a food, is this because the food isn’t available, is too expensive, or is eaten by the man? • When women eat a food but the child doesn’t, why not? What are the barriers? • What dietary factors and other exposures are linked with stunting?

33. What Are the Implications for Program Design and Implementation? • Identification and targeting of those at greatest risk? • Approaches to increase availability of nutritious foods? • Approaches to increase giving nutritious foods to infants and young children? • Importance of maternal nutrition before and during pregnancy (and during lactation)?