Columbia University Analysis for the Scaling Up Nutrition (SUN) Secretariat

1. Columbia University Analysis for the Scaling Up Nutrition (SUN) Secretariat Simulating Potential of Nutrition Sensitive Investments Slide Deck to Accompany the Technical Report January 2014

2. The Lives Saved Tool (LiST) Visualizer Five outcome areas, intermediary to stunting, served as key starting point for our study Lancet, 2013 FAMILY PLANNING MATERNAL NUTRITION Source: http://list.cherg.org/

3. Theoretical Framework Nutrition Sensitive Sector Literature Review And Modeling Outcome Areas Predictors Health Exclusive Breastfeeding [EBF for 0-6 months} Selected outcomes delivered through nutrition sensitive channels for literature search. Used data available at the national level to model associations of outcomes with contextual factors. Used data available at the national level and in the literature review to model associations of interventions with outcomes. Environment and Water Complementary Feeding [minimum acceptable diet] Strong or Weak Associations for decision making Education Maternal Nutrition [low birth weight and dietary patterns] Social Protection Family Planning [Contraceptive use] Agriculture Diarrhea incidence [diarrhea rate]

4. Public Health Model: Theoretical Framework Interventions/ Delivery Channels Nutrition Sensitive Sector Outcome Areas Predictors Contextual Factors Exclusive Breastfeeding [EBF for 0-6 months} GNI per capita Adult literacy rate Adolescent birth rate Female labor participation Sec. School Enrollment Maternity leave Peer counseling Commercial packets Facility based education Complementary Feeding [minimum acceptable diet] Maternal education Health professional training Supplement provision Strong or Weak Associations for decision making Public Health Family Planning [Contraceptive use] Community Health Workers School promotion Media campaign Done by LIST: Iron/ Folic Acid Supplementation Multiple Micronutrient Supp Calcium Supplementation Balanced Energy Protein Supp Maternal Nutrition [Low-birth weight]

5. Environment Model: Theoretical Framework Interventions/ Delivery Channels Nutrition Sensitive Sector Outcome Areas Predictors Contextual Factors Strong or Weak Associations for decision making Promotion of access to water and sanitation Safety of complementary foods % Rural Population Adult Literacy Rates Vaccination Rates Reduced diarrhea [diarrhea rate] Environment and Water

6. Agriculture Model: Theoretical framework Nutrition Sensitive Sector Contextual factors Agricultural Investments Outcome areas Predictors • Inputs • Ag biodiversity • Fertilizer • Land available for agriculture • Water available for agriculture • Mechanization • Rural Infrastructure • Irrigation • Crop storage facilities • Road infrastructure • Port infrastructure • Mobile network • Institutions and governance • Access to finance • Policy and legal framework • Accountability and transparancy • Allocation of pub resources • Markets • Ag imports and exports • Ag import tarifs • Research • Ag R&D • Economic setting and agricultural role in society • GNI per capita • GINI index • % rural population • % Ag value added • % Ag employment • Health setting • Life expectancy • # physicians per 1000 • Gender • Girls/boys ratio in secondary school • Education • Literacy Maternal nutrition [low birth weight; dietary patterns: % energy from non staples, calories per capita, micronutrient availability] Agriculture Strong or Weak Associations for decision making Complementary feeding (minimum acceptable diet)

7. Public Health and Environment: Methodology We investigate the estimated effect of interventions in these two sectors on the following key outcomes: exclusive breastfeeding, complementary feeding, maternal diet and family planning (public health sector), and promotion of access to improved water and sanitation (environment and water sector). Contextual models • we used country level data from The State of World Children (SOWC) 2013 report, and most recent data available from International Labor Organization, World Bank or other sources . The available data ranges from period 2006-2010 for adolescent birth rate to year 2011 for vaccination data.   • we ran cross country multiple regression analysis with 1000 simulations to investigate the predictors of the outcome measures. We estimated the 95% confidence interval Intervention models: Interventions in these two sectors were assessed via meta-analysis, as following: • we conducted an extensive literature review on intervention impacts on the following outcome measures: exclusive breastfeeding, minimum acceptable diet, contraceptive use, percent of low births and diarrhea rate. • we selected relevant studies and formatted the results as needed for meta regressions • we estimated the pooled relative risks (RR) and their respective confidence intervals (CI) using a restricted maximum likelihood estimator (REML) on a random effect model. The pooled estimates were calculated using the natural logarithms of the RRs and their standard errors from the individual studies. • we explored sources of heterogeneity using sub-group analysis. The sub-groups (moderators) were identified based on participant or study characteristics. For moderators that were not systematically available at the study level, we used country level data that was matched to the country of the study. Significant moderator were identified using contextual models – described below.

8. Agriculture model: Methodology We focused on two intermediary outcome areas for the agriculture model including: maternal nutrition and complementary feeding. For both outcome areas, we developed a national-level contextual model that allows us to identify associations rather than causal relationships between agricultural and nutrition variables. Quantitative model: • National level data from seven publicly available databases were collated and organized to populate the modelintegrating agriculture (FAO, IFAD, EIU), socio-economic (World Bank), human nutrition and health (WHO NILS, WHO IYCF) data using the country as the unit • we first identified model indicators for maternal nutrition and complementary feeding that are significantly related to stunting, while controlling for income level • Starting from these results we fit the agricultural factors into multivariate regressions against each of these indicators while taking into account a set of contextual factors Additional literature review: • Starting from the results of the quantitative model, we revisited the literature to identify specific programs/ interventions and delivery channels that contribute to the implementation of the agricultural investments that were identified as significantly related to nutrition specific indicators.

9. Public Health Model: ResultsContextual model GNI per capita Adolescent birth rate % urban Secondary school enrollment female/male ratio 0.28*** [0.07,0.71] -0.10 [-0.21,0.03] 0.47*** [0.26,0.69] -0.18* [0.02,0.33] 8.60*** [0.022,3295] 0.01*** [0.0001, 1.84] -0.56** [-0.90,-0.19] Maternal Nutrition [Low-birth weight] Family Planning [Contraceptive use] Exclusive Breastfeeding [EBF for 0-6 months} Complementary Feeding [Minimum acceptable diet] 0.24* [0.004, 0.48] -64.31*** [-95.5,-35.0] African -17.90* [-32.16, -3.39] Asian -14.41* [-28.89, 1.09] Asian 19.10*, [0.64, 37.66] Mixed 24.59* [4.98, 44.50] 0.21** [0.09,0.34] -6.13** [-10.79,-1.60] Female to male labor participation Access to improved rural sanitation Adult literacy: females as a % of males Female to male labor participation * maternity leave Ethnicity Latino/Hispanic 12.46* ,95% CI [1.48, 23.75]

10. Public Health Model: ResultsIntervention model EBF Exclusive Breastfeeding [EBF for 0-6 months} RR 2.46*** RR 1.33*** RR 1.55*** Facility based education Non provision of commercial packets Peer counseling Contextual factors influencing the intervention effect Duration of BF % rural population Adult literacy rate Maternity leave Female labor participation Adolescent birth rate For interpretation: Relative Risk (RR) = 1 indicates that the outcome in intervention and control groups are equally likely to occur; RR<1 outcome in intervention is less likely to occur compared with control; RR>1 outcome in intervention is more likely to occur compared with RR>control. E.g. RR 0.6 is usually interpreted in the following way (exp for RR=0.6). (1-0.6)*100=40%, the outcome in intervention is 40% less likely to occur. If RR is 1.5, the outcome is 1.5 times more likely to occur in the intervention compared with control (or 50% increased risk.

11. Results of meta-regressions for the effect of peer counseling on exclusive breastfeeding in randomized controlled trials and quasi-experimental studies

12. Results of meta-regressions for the effect of facility based education on exclusive breastfeeding in randomized controlled trials and quasi-experimental studies

13. Public Health Model: ResultsIntervention model Family planning Family Planning [Contraceptive use] RR 1.15* School promotion, media campaign, community-based education

14. Environment model: ResultsContextual model Diarrhea treatment [% treatment with ORS] 0.31* [0.07, 0.56] 0.34* [0.03, 0.68] Adult literacy rate: females as % of males Vaccination rate

15. Environment Model: ResultsIntervention model Diarrhea incidence [Diarrhea rate] RR 0.71** RR 0.76*** Water treatment Hand washing Contextual factors controlled for GNI per capita Adult literacy Vaccination rate % rural population

16. Agriculture ModelIdentification of model indicators for nutrition-related intermediary outcomes Stunting Adj R2 0.84 Adj R2 0.32 Adj R2 0.73 Dietary patterns (proxy for maternal nutrition) Low-birth weight (proxy for maternal nutrition) Complementary feeding % Energy from non staples in supply (-4.75***) Calories per capita (-6.86***) % Minimum acceptable diet (-6.51***) % Low-birth weight (2.82***) Fe availability from animal-products (-4.15*) Adj R2 0.43 % energy from non staples in national food supply significantly related to % low birth weight (-1.91**) Adj R2 0.63 Fe availability from animal based products significantly related to % minimum acceptable diet (9.93*) CONTEXT- SPECIFICITY Log GNI per capita

17. Agriculture ModelAgricultural investments related to dietary patterns Dietary patterns (proxy for maternal nutrition) Calories per capita % Energy from non staples in supply Fe availability from animal-products -0.34 ** -0.39 *** 6.88*** to -1.81 dependent on income level 0.14** -0.48 ** 0.47** 0.48** 0.15** Ag import tariffs Fertilizer use per land unit Ag R&D as % of GDP % land for agriculture % Energy from non staples in production Access to finance for farmers # tractors per land unit Contextual factors influencing outcomes Log GNI per capita Road infrastructure Exports as % of GDP

18. Agriculture ModelSupply diversity as a function of production diversity The relationship between food supply diversity and food production diversity depends on the income level of a country. For low-income countries the diversity of agricultural goods produced by a country is a strong predictor for food supply diversity; for middle- and high-income countries national income and trade are better predictors.

19. Agriculture ModelAgricultural investments related to complementary feeding Complementary feeding % Minimum acceptable diet 9.93* Fe availability from animal-products 9.02* -4.85* -0.48 ** 0.47** Ag import tariffs Ag R&D as % of GDP % land for agriculture % Energy from non staples in production Contextual factors influencing outcomes Log GNI per capita Exports as % of GDP

20. Summary Table of Model Results

21. Summary Table of Model Results

22. Summary Table of Model Results

23. Implications Implications • With limited evidence, the evidence at hand suggests that public health, environment and agriculture investments could support nutrition specific interventions that address undernutrition. • A country’s contextual factors (relating to income, social, education and governance) are important to consider in their impact on nutrition outcomes with nutrition sensitive approaches. • Examining agriculture through large scale investments, rather than nutrition interventions, can provide insight for MoA on impact for nutrition, indirectly. • Alternative delivery channels for public health and environment, through marketing, commercialization, food safety, and social protection, are less clear in their evidence of impact. • Evidence published in the literature remains scant and varied for nutrition sensitive interventions, and more implementation science should be published. • Using a quantitative statistical simulation model can only go so far as with the current literature and data. This has resulted in some interesting insights but unfortunately it is not a tool that is user friendly for countries looking to scale up nutrition. • Costing tools and perhaps game tools could provide an entry point for decision making in which this quantitative modeling could be used as a first step resource.

24. Schematic Summary of Findings SOCIAL PROTECTION Conditional cash transfers • AGRICULTURE • Agriculture diversification • Animal products • Homegardens • Legumes • Agroforestry • Small scale irrigation • Access to Finance • Fertilizer use • Agriculture research and development • Biofortification • Rural infrastructure • Women empowerment PUBLIC HEALTH Peer counseling Facility-based education Non provision of commercial packages Maternal counseling Health worker training School promotion Media campaigns Community-based education Exclusive breastfeeding Complementary feeding Maternal Nutrition (LiST results) Diarrhea incidence Family planning ENVIRONMENT AND WATER Water treatment, Handwashing Food safety measures CONTEXT- SPECIFICITY Income, Education, Urbanization, Geographic Location, Employment Policy,