95754 SCALING UP NUTRITION FOR A MORE RESILIENT MALI: NUTRITION DIAGNOSTICS AND COSTED PLAN FOR SCALING UP DISCUSSION PAPER FEBRUARY 2015 Meera Shekar Max Mattern Patrick Eozenou Julia Dayton Eberwein Jonathan Kweku Akuoku Emanuela Di Gropello Wendy Karamba SCALING UP NUTRITION FOR A MORE RESILIENT MALI: Nutrition Diagnostics and Costed Plan for Scaling Up Meera Shekar, Max Mattern, Patrick Eozenou, Julia Dayton Eberwein, Jonathan Kweku Akuoku, Emanuela Di Gropello and Wendy Karamba February 2015 Health, Nutrition and Population (HNP) Discussion Paper This series is produced by the Health, Nutrition, and Population Global Practice. The papers in this series aim to provide a vehicle for publishing preliminary results on HNP topics to encourage discussion and debate. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations or to members of its Board of Executive Directors or the countries they represent. Citation and the use of material presented in this series should take into account this provisional character. For information regarding the HNP Discussion Paper Series, please contact the Editor, Martin Lutalo at mlutalo@worldbank.org or Erika Yanick at Eyanick@worldbank.org. © 2015 The International Bank for Reconstruction and Development / The World Bank 1818 H Street, NW Washington, DC 20433 All rights reserved. ii Health, Nutrition and Population (HNP) Discussion Paper Scaling Up Nutrition for a More Resilient Mali: Nutrition Diagnostics and Costed Plan for Scaling Up Meera Shekar,a Max Matternb, Patrick Eozenoua, Julia Dayton Eberwein b, Jonathan Kweku Akuokuthe c, Emanuela Di Gropello d, and Wendy Karamba b a Health, Nutrition and Population Global Practice, World Bank, Washington, DC, USA b Consultant, HNP Global Practice, World Bank, Washington, DC, USA c Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA d Mali Country Management Unit AFCW3, World Bank, Washington DC, USA The authors are grateful for support from the Bill & Melinda Gates Foundation Abstract: This paper builds on the global experience and Mali’s context to identify an effective nutrition approach as well as costs and benefits of key nutrition programs, as part of a resilience agenda after the crisis. It is intended to help guide the selection of the most cost-effective interventions as well as strategies for scaling these up. The paper looks at both relevant “nutrition-specific” interventions, largely delivered through the health sector, and at multisectoral “nutrition-sensitive” interventions delivered through other sectors such as agriculture, social protection, and water and sanitation that have the potential to strengthen nutritional outcomes in Mali. We first estimate that the costs and benefits of implementing 10 nutrition-specific interventions in all regions of Mali would require a yearly public investment of $64 million. The expected benefits are large: annually about 480,000 DALYs and more than 14,000 lives would be saved and over 260,000 cases of stunting among children under five would be averted. However, because it is unlikely that the Government of Mali or its partners will find the $64 million necessary to reach full national coverage, we also consider three potential scale-up scenarios based on considerations of their potential for impact, the burden of stunting, resource requirements, and implementation capacity. Using cost-benefit analyses, we propose scale-up scenarios that represent a compromise between the need to move to full coverage and the constraints imposed by limited resources. We identify and cost six nutrition-sensitive interventions that are relevant to Mali’s context and for which there are both evidence of positive impact on nutrition outcomes and some cost information. These findings point to a powerful set of nutrition-specific interventions and a candidate list of nutrition-sensitive approaches that represent a highly cost-effective approach to reducing child malnutrition in Mali. Keywords: nutrition-specific interventions, nutrition-sensitive interventions, cost-effectiveness of nutrition interventions, cost-benefit analysis, nutrition financing. Disclaimer: The findings, interpretations and conclusions expressed in the paper are entirely those of the authors and do not represent the views of the World Bank, its Executive Directors, or the countries they represent. Correspondence Details: Meera Shekar, World Bank, 1818 H Street NW, Washington DC, 20433 USA; Tel: 202-473-6029; mshekar@worldbank.org iii iv Table of Contents ACKNOWLEDGMENTS .......................................................................................................... IX ABBREVIATIONS AND ACRONYMS ................................................................................... XI GLOSSARY OF TECHNICAL TERMS............................................................................... XIII EXECUTIVE SUMMARY ................................................................................................... XVII PART I – BACKGROUND .......................................................................................................... 1 COUNTRY CONTEXT AND NUTRITION DIAGNOSTICS ..................................................................... 1 HEALTH AND NUTRITIONAL STATUS 2001–2013.......................................................................... 2 Levels and Trends in Malnutrition .......................................................................................... 3 Determinants of Malnutrition .................................................................................................. 8 THE IMPORTANCE OF INVESTING IN NUTRITION .......................................................................... 16 A MULTISECTORAL APPROACH FOR IMPROVING NUTRITION ...................................................... 18 NATIONAL AND PARTNER EFFORTS TO ADDRESS MALNUTRITION IN MALI ................................ 21 PART II – COSTED SCALE-UP SCENARIOS: RATIONALE, OBJECTIVES, AND METHODOLOGY ..................................................................................................................... 25 RATIONALE AND OBJECTIVES OF THE ANALYSIS ........................................................................ 25 SCOPE OF THE ANALYSIS AND DESCRIPTION OF THE INTERVENTIONS ......................................... 26 ESTIMATION OF TARGET POPULATION SIZES, CURRENT COVERAGE LEVELS, AND UNIT COSTS . 29 ESTIMATION OF COSTS AND BENEFITS ........................................................................................ 32 SCENARIOS FOR SCALING UP NUTRITION INTERVENTIONS ......................................................... 34 PART III – RESULTS FOR NUTRITION-SPECIFIC INTERVENTIONS ........................ 35 TOTAL COST, EXPECTED BENEFITS, AND COST-EFFECTIVENESS ................................................ 35 THREE POTENTIAL SCALE-UP SCENARIOS .................................................................................. 38 Scenario 1: Scale Up by Region .............................................................................................. 38 Scenario 2: Scale Up by Intervention .................................................................................... 40 Scenario 3: Scaling Up by Intervention and by Region .......................................................... 43 COST-BENEFIT ANALYSIS OF THE SCALE-UP SCENARIOS ........................................................... 45 FINANCING NUTRITION IN MALI ................................................................................................. 47 UNCERTAINTIES AND SENSITIVITY ANALYSES ............................................................................ 49 PART IV – RESULTS FOR NUTRITION-SENSITIVE INTERVENTIONS ..................... 50 INCORPORATING NUTRITION INTERVENTIONS INTO SOCIAL PROTECTION PROGRAMS ................ 51 NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE AGRICULTURE SECTOR ....... 51 NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE EDUCATION SECTOR ........... 52 IMPROVING NUTRITION THROUGH INVESTMENTS IN THE WASH INFRASTRUCTURE ................... 53 CONCLUSIONS AND POLICY IMPLICATIONS ................................................................ 54 APPENDIXES ............................................................................................................................. 57 APPENDIX 1: DEFINITIONS OF ADEQUACY VARIABLES ............................................................... 57 v APPENDIX 2: PARTNER LANDSCAPE IN NUTRITION INTERVENTIONS IN MALI, 2013 ................... 58 APPENDIX 3: TARGET POPULATION SIZE ................................................................................... 61 APPENDIX 4: DATA SOURCES AND RELEVANT ASSUMPTIONS .................................................... 62 APPENDIX 5: METHODOLOGY FOR ESTIMATING COSTS FOR MALI .............................................. 65 APPENDIX 6: METHODOLOGY FOR ESTIMATING DALYS FOR MALI............................................ 67 APPENDIX 7: METHODOLOGY FOR MALI LIST ESTIMATES ......................................................... 69 APPENDIX 8: METHODOLOGY FOR ESTIMATING ECONOMIC BENEFITS ....................................... 72 APPENDIX 9: SENSITIVITY ANALYSIS ......................................................................................... 75 REFERENCES ............................................................................................................................ 76 vi List of Figures Figure 1. Map of Mali ..................................................................................................................... 2 Figure 2. Changes in Child Mortality, Selected Countries in Sub-Saharan Africa, 2005–2010 ..... 3 Figure 3. Changes over Time in the Prevalence of Stunting, Wasting, and Underweight, percent of children under five, 2001–2013........................................................................................... 4 Figure 4. Stunting Prevalence in Mali Before and After the Crisis ................................................ 5 Figure 5. Height-for-Age Z-Score Curves by Age, 2001–2013 ...................................................... 6 Figure 6. Regional Distribution of Multiple Nutritional Deficits, 2001–2013 ............................... 7 Figure 7. Prevalence of the Stunting Gap in the North and South .................................................. 8 Figure 8. Population, Poverty, and Child Stunting by Region, 2010 .............................................. 9 Figure 9. Acute Malnutrition (Wasting), by Year and Region...................................................... 10 Figure 10. Child Stunting by Wealth Quintile: 2001, 2006, and 2010 ......................................... 11 Figure 11. Distribution of Vulnerability and Observed Stunting Rates by Region, 2010 ............ 12 Figure 12. Anemia in Mali ............................................................................................................ 15 Figure 13. Rates of Return to Investment in Human Capital ........................................................ 17 Figure 14. Proposed Scenario 2: Stepwise Scale-Up by Intervention........................................... 41 Figure 15. National Budget Allocation to Nutrition ..................................................................... 48 Figure 16. Aid Flows to Nutrition and Health, 2004–2012 .......................................................... 49 List of Tables Table 1. Vulnerability Profile for Mali, 2010 ............................................................................... 13 Table 2. Nutrition-Specific Interventions Delivered Primarily Through the Health Sector ......... 26 Table 3. Multisectoral, Nutrition-Sensitive Interventions: An Exploratory Process .................... 28 Table 4. Unit Costs and Delivery Platforms Used in the Calculations for ................................... 30 Table 5. Unit Costs and Delivery Platforms Used in the Estimations for Selected Nutrition- Sensitive Interventions........................................................................................................... 31 Table 6. Estimated Cost of Scaling Up 10 Nutrition-Specific Interventions to Full Coverage .... 35 Table 7. Estimated Annual Benefits for Scaling Up 10 Nutrition Interventions to Full Coverage ............................................................................................................................................... 37 Table 8. Cost-Effectiveness of Scaling Up 10 Nutrition Interventions to Full Coverage (US$) .. 38 Table 9. Scenario 1: Costs and Benefits of Scaling Up 10 Nutrition Interventions by Region .... 39 Table 10. Scenario 2: Costs and Benefits for Scaling Up Nutrition-Specific Interventions, by Intervention ............................................................................................................................ 43 Table 11. Scenario 3: Cost of Scaling Up Selected Nutrition Interventions, by Intervention and Region (US$, millions) .......................................................................................................... 44 Table 12. Summary of Costs and Benefits by Scenario ................................................................ 45 Table 13. Cost for Scale-Up of All Scenarios (US$, millions) ..................................................... 46 vii Table 14. Economic Analysis of the Full Coverage Investment in Nutrition, Five-Year Scale-Up ............................................................................................................................................... 47 Table 15. Preliminary Results for Costing Nutrition-Sensitive Interventions .............................. 50 Table 16. Estimated Cost-Effectiveness of Aflatoxin Control Methods for Groundnuts ............. 52 viii ACKNOWLEDGMENTS First and foremost we would like to thank our partners in the Government of Mali for their collaboration in this effort. We want to acknowledge the strong collaboration with Dr. Modibo Diarra, Nutrition Advisor at the Ministry of Health and SUN Focal Point in Mali, and Dr. Modibo Traoré, Head of the Nutrition Division (DN) at the National Health Directorate (DNS). The report benefitted tremendously from advice and consultations with several other colleagues from the DNS/DN, including the Head of Nutrition Statistics, Bakary Koné, and Dr. Aissatou Pléah. Additionally, we would like to acknowledge the comments and suggestions provided by participants at the Micronutrient Forum Global Conference sessions on nutrition costing in June 2014 in Addis Ababa, Ethiopia. The Bill & Melinda Gates Foundation (BMGF) was a strong partner with the World Bank in advancing this work, and provided financial support. Ellen Piwoz from the BMGF provided valuable technical inputs. Our partners at REACH, including Sian Evans and Amadou Fofana, were invaluable in coordinating this costing work with the broader costing of the government’s multisectoral strategic plan for nutrition. Jakub Katietek and Helen Connolly from the Inner City Fund International (ICF), who carried out the costing of the strategic plan, provided several local unit costs used in this study. Anna Horner, Head of Nutrition at UNICEF, and her colleague Anne Marie Dembele facilitated access to necessary local data. Mahamadou Tanimoune from the World Food Program (WFP), and Dr. Lazare Coulibaly and Zana Berthe from Helen Keller International (HKI), also provided valuable information on local costs and coverage. Dao Boubacar, National Consultant, and Alice Diarra Sangare, Programme Assistant for the World Bank in Mali, provided help with mission logistics. This report is also part of the broader programmatic economic and sector work on the impact of crisis on social sectors in Mali. As such it has also been informed by inputs and suggestions from the Mali Country Management Unit, Aissatou Diack (Senior Health Specialist, Mali), and the multisectoral Government Steering Committee for the broader study. Johannes Hoogeveen, Luc Laviolette, and Nkosinathi Mbuya from the World Bank gave useful comments during the peer review process. The authors are grateful for the skilled editing provided by Hope Steele. Finally, the team is grateful for the support and guidance from Paul Noumba Um, Mali Country Director at the World Bank, and Trina Haque, HNP Practice Manager, Health, Nutrition and Population Global Practice, World Bank. The authors are grateful to the World Bank for publishing this report as an HNP Discussion Paper. ix x ABBREVIATIONS AND ACRONYMS AGIR Global Alliance for Resilience Initiative BCG Bacille de Calmettet et Guérin (vaccine against tuberculosis) BMGF Bill & Melinda Gates Foundation CFAF CFA francs CMAM Community-based Management of Acute Malnutrition CNP Community Nutrition Program DALYs Disability-adjusted Life Years DHS Demographic and Health Survey DN Division Nutrition (Nutrition Division of the DNS) DNS Direction Nationale de la Santé (Ministry of Health’s National Health Directorate) ECHO European Commission for Humanitarian Aid and Civil Protection EU European Union FAO United Nations Food and Agriculture Organization GBD Global Burden of Disease GDP Gross Domestic Product HAZ Height-for-Age Z-scores HKI Helen Keller International ICF Inner City Fund International IFPRI International Food Policy Research Institute IRRIGAR Initiative pour le renforcement de la Résilience par l’Irrigation et la Gestion appropriée des Ressources (Initiative for Strengthening the Resilience of Irrigation and Proper Management of Resources) LDF Lifetime Discount Factor LiST Lives Saved Tool M&E Monitoring and Evaluation MDGs Millennium Development Goals MICS/ELIM Multiple Indicator Cluster Survey/Enquête Légère Intégrée auprès des Ménages (household income and expenditure survey) MOH Ministry of Health MNP Micronutrient Programs NIH National Institutes of Health NPV Net Present Value OCHA United Nations Office for the Coordination of Humanitarian Affairs ODA Official Development Assistance ORS Oral Rehydration Salts PAF Population Attributable Fractions PASA Programmes d’appui à la sécurité alimentaire (Program to Support Food Security) REACH Renewed Efforts Against Child Hunger and undernutrition (United Nations interagency partnership to accelerate the scale-up of food and nutrition actions) SIAN Semaine d'Intensification des Activités de Nutrition (National Nutrition Week) SMART Standardized Monitoring and Assessment of Relief and Transitions xi SUN Scaling Up Nutrition UNICEF United Nations Children’s Fund USAID United States Agency for International Development WASH Water, Sanitation and Hygiene WAZ Weight-for-age Z-score WFP World Food Program WHO World Health Organization WHO-CHOICE Choosing Interventions that are Cost-Effective YLD Years of Life spent with Disability (from a disease) YLL Years of Life Lost (from a disease) All dollar amounts are U.S. dollars unless otherwise indicated. xii GLOSSARY OF TECHNICAL TERMS Aflatoxins are a group of toxic compounds produced by certain molds, especially Aspergillus flavus, which contaminate stored food supplies such as animal feed, maize, and peanuts. Research shows that human consumption of high levels of aflatoxins can lead to liver cirrhosis (Kuniholm et al. 2008) and liver cancer in adults (Abt Associates 2014). It is widely understood that there is a relationship between aflatoxin exposure and child stunting, but this relationship has not yet been adequately quantified in the published literature (Unnevehr and Grace 2013; Abt Associates 2014). A benefit-cost ratio summarizes the overall value of a project or proposal. It is the ratio of the benefits of a project or proposal, expressed in monetary terms, relative to its costs, also expressed in monetary terms. The benefit-cost ratio takes into account the amount of monetary gain realized by implementing a project versus the amount it costs to execute the project. The higher the ratio, the better the investment. A general rule is that if the benefit from a project is greater than its cost, the project is a good investment. Biocontrol (also called biological control) is the use of an invasive agent to reduce pest or mold population below a desired level. Aflatoxins can be reduced through biocontrol; the most effective method involves a single application of a product (such as aflasafe™) that contains strains unique to the specific country or location. Biofortification is the breeding of crops to increase their nutritional value. This can be done either through conventional selective breeding or through genetic engineering. Capacity development for program delivery is a process that involves increasing in-country human capacity and systems to design, deliver, manage, and evaluate large-scale interventions (World Bank 2010). This includes developing skills by training public health personnel and community volunteers to improve the delivery of services. These efforts typically accompany program implementation or, when possible, precede program implementation. In this costing analysis we allocate 9 percent of total programmatic costs to capacity development for program delivery. Cost-benefit analysis is an approach to economic analysis that weighs the cost of an intervention against its benefits. The approach involves assigning a monetary value to the benefits of an intervention and estimating the expected present value of the net benefits, known as the net present value. Net benefits are the difference between the cost and monetary value of benefits of the intervention. The net present value is defined mathematically as: xiii where is net cash inflows, is the initial investment, the index is the time period, and is the discount rate. A positive net present value, when discounted at appropriate rates, indicates that the present value of cash inflows (benefits) exceeds the present value of cash outflows (cost of financing). Interventions with net present values that are at least as high as alternative interventions provide greater benefits than interventions with net present values equal to or lower than alternatives. The results of cost-benefit analysis can also be expressed in terms of the benefit-cost ratio. Cost-effectiveness analysis is an approach to economic analysis that is intended to identify interventions that produce the desired results at the lowest cost. Cost-effectiveness analysis requires two components: the total cost of the intervention and an estimate of the intervention’s impact, such as the number of lives saved. The cost-effectiveness ratio can be defined as: The analysis involves comparing the cost-effectiveness ratios among alternative interventions with the same outcomes. The intervention with the lowest cost per benefit is considered to be the most cost-effective intervention among the alternatives. A DALY is a disability-adjusted life year, which is equivalent to a year of healthy life lost due to a health condition. The DALY, developed in 1993 by the World Bank, combines the years of life lost from a disease (YLL) and the years of life spent with disability from the disease (YLD). DALYs count the gains from both mortality (how many more years of life lost due to premature death are prevented) and morbidity (how many years or parts of years of life lost due to disability are prevented). An advantage of the DALY is that it is a metric that is recognized and understood by external audiences such as the World Health Organization (WHO) and the National Institutes of Health (NIH). It helps to gauge the contribution of individual diseases relative to the overall burden of disease by geographic region or health area. Combined with cost data, DALYs allow for estimating and comparing the cost-effectiveness of scaling up nutrition interventions in different countries. A discount rate refers to a rate of interest used to determine the current value of future cash flows. The concept of the time value of money suggests that income earned in the present is worth more than the same amount of income earned in the future because of its earning potential. A higher discount rate reflects higher losses to potential benefits from alternative investments in capital. A higher discount rate may also reflect a greater risk premium of the intervention. The internal rate of return is the discount rate that produces a net present value of cash flows equal to zero. An intervention has a non-negative net present value when the internal rate of return equals or exceeds the appropriate discount rate. Interventions yielding higher internal rates of return than alternatives tend to be considered more desirable than the alternatives. xiv The Lives Saved Tool (LiST) is an estimation tool that translates measured coverage changes into estimates of mortality reduction and cases of childhood stunting averted. LiST is used to project how increasing intervention coverage would impact child and maternal survival. It is part of an integrated set of tools that comprise the Spectrum policy modeling system. Monitoring and evaluation, operations research, and technical support for program delivery are all elements of cost-effective and efficient program implementation. Monitoring involves checking progress against plans through the systematic and routine collection of information from projects and programs in order to learn from experience to improve practices and activities in the future, to ensure internal and external accountability of the resources used and the results obtained, and to make informed decisions on the future of the intervention. Monitoring is a periodically recurring task. Evaluation is the assessing, as systematically and objectively as possible, of a completed project or intervention (or a phase of an ongoing project). Operations research aims to inform the program designers about ways to deliver interventions more effectively and efficiently. Technical support entails ensuring that training, support, and maintenance for the physical elements of the intervention are available. In this costing exercise we allocate 2 percent of total intervention costs for monitoring and evaluation, operations research, and technical support. Nutrition-sensitive interventions are those that have an indirect impact on nutrition and are delivered through sectors other than health such as the agriculture, education, and water, sanitation, and hygiene sectors. Examples include biofortification of food crops, conditional cash transfers, and water and sanitation infrastructure improvements. Nutrition-specific interventions are those that address the immediate determinants of child nutrition, such as adequate food and nutrition intake, feeding and caregiving practices, and treating disease. Examples include community nutrition programs, micronutrient supplementation, and deworming. Sensitivity analysis is a technique that evaluates the robustness of findings when key variables change. It helps to identify the variables with the greatest and least influence on the outcomes of the intervention, and it may involve adjusting the values of a variable to observe the impact of the variable on the outcome. SMART (Standardized Monitoring and Assessment of Relief and Transitions) is a standardized, simplified field survey methodology that produces a snapshot of the current situation on the ground. It is used to measure responses to emergencies such as famine, war, and natural disaster and for surveillance. The methodology is based on the two most vital and basic public health indicators for the assessment of the magnitude and severity of a humanitarian crisis: the nutritional status of children under five and the mortality rate of the population. These indicators are useful for prioritizing resources as well as for monitoring the extent to which the relief system is meeting the needs of the population, and therefore the overall impact of relief response. xv Stunting is an anthropometric measure of low height-for-age. It is an indicator of chronic undernutrition and is the result of prolonged food deprivation and/or disease or illness. It is measured in terms of Z-score (or standard deviation score; see definition below); a child is considered stunted with a height-for-age Z-score of −2 or lower. Underweight is an anthropometric measure of low weight-for-age. It is used as a composite indicator to reflect both acute and chronic undernutrition, although it cannot distinguish between them. It is measured in terms of Z-score (or standard deviation score; see definition below); a child is considered underweight with a weight-for-age Z-score of −2 or lower. Wasting is an anthropometric indicator of low weight-for-height. It is an indicator of acute undernutrition and the result of more recent food deprivation or illness. It is measured in terms of Z-score (or standard deviation score; see definition below). A child with a weight-for-height Z- score of −2 or lower is considered wasted. A Z-score or standard deviation score is a calculation used to explain deviations from an established norm. It is calculated with the following formula: xvi EXECUTIVE SUMMARY The overall objective of this paper is to support the Government of Mali in developing a strategic approach to addressing its malnutrition challenge and to cost a scale-up plan for nutrition. The executive summary is written for policy makers; it highlights the study’s main findings and discusses their implications for nutrition policy and programming in Mali. The paper itself is more technical in nature and is written for planners and programmers. The analysis is expected to bring evidence of potential for impact and allocative efficiency into Mali’s nutrition programming. Between 2001 and 2010 chronic malnutrition in Mali declined dramatically, with an annual reduction rate in stunting of 4.4 percent per year. Between 2010 and 2012 there was an increase in stunting as a result of the 2011–2012 crisis, although by 2013 stunting rates had begun to decline again. The prevalence and severity of stunting (stunting gap) worsened across the South and most likely the North (as documented by the case of Gao) following the crisis. Children aged 0–12 months in 2011–2012 were particularly affected by the crisis and will likely suffer life-long consequences. In 2013, the most recent year for which statistics are available, 28 percent of children under five were stunted, 17 percent were wasted, and 9 percent were underweight. These very serious levels of malnutrition require action. Chronic and acute malnutrition affect, to different extents, the whole country—North and South alike. In addition, many segments of the population are highly vulnerable to malnutrition; in times of crises, this vulnerability translates into increases in malnutrition rates, as seen in 2011–2012. At the same time, micronutrient deficiencies (hidden hunger) are also prevalent in Mali, with vitamin A and anemia rates particularly high. In light of the above, Mali needs a two-pronged strategy to address malnutrition. This strategy must address the immediate effects of the crisis on nutrition in the North and South, with emphasis on very young children; and it must also invest in nutrition interventions that increase the resilience of vulnerable populations (including the poor, farmers, women, children, populations in vulnerable regions, etc.) while fostering long-term benefits. Prompt action is warranted because malnutrition, particularly in very young children, leads to increased mortality rates, increased illness, and longer-term effects on cognitive abilities, thereby producing irreversible losses to human capital that contribute to later losses in economic productivity. Undernutrition is responsible for about one-half of under-five mortality and one- fifth of maternal mortality in developing countries. Children who have been malnourished early in life are more likely to experience cognitive deficiencies and poor schooling outcomes. Longer- term stunting results in a 10 to 17 percent loss of wages. In addition, a World Bank study estimated that Mali loses over $235 million in gross domestic product (GDP) annually to vitamin and mineral deficiencies alone (World Bank 2013b). At the same time, nutrition interventions are consistently identified as among the most cost- effective development actions, with huge potential to contribute to the World Bank Group’s twin goals of reducing poverty and boosting shared prosperity. Cost-benefit analysis shows that xvii nutrition interventions are highly effective (World Bank 2010; Hoddinott et al. 2013). It is estimated that investing in nutrition can increase a country’s GDP by at least 3 percent annually (Horton and Steckel 2013). The global cost to scaling up key nutrition interventions is estimated at $10.3 billion per annum (World Bank 2010). These investments would provide preventive nutrition services to about 356 million children, save at least 1.1 million lives and 30 million disability-adjusted life years (DALYs), and reduce the number of stunted children by about 30 million worldwide. This report builds on the global experience and Mali’s context to identify an effective nutrition approach as well as costs and benefits of key nutrition programs in Mali. It is intended to help guide the selection of the most cost-effective interventions and strategies for scaling these up. The report looks at both relevant “nutrition-specific” interventions, largely delivered through the health sector, and at multisectoral “nutrition-sensitive” interventions, delivered through the agriculture sector and through social protection, water and sanitation, and poverty reduction programs. It uses the costing framework established by Scaling Up Nutrition: What Will It Cost? (World Bank 2010) and applies it to the country-specific context of Mali. Combining costing with estimates of impact (in terms of lives saved, DALYs saved, and cases of stunting averted), and cost-effectiveness analysis will make the case for nutrition stronger and aid in priority-setting by identifying the most cost-effective packages of interventions in situations where financial and human resources are constrained. We first estimate the costs and benefits of implementing 10 nutrition-specific interventions in all regions of Mali. We refer to this as the full-coverage scenario and estimate that it would require an annual public investment of $64 million. The expected benefits are large: annually about 480,000 DALYs and more than 14,000 lives would be saved, while over 260,000 cases of stunting among children under five would be averted. Given resource and capacity constraints, few countries are able to effectively scale up all 10 nutrition-specific interventions to full national coverage immediately. We therefore consider three potential scale-up scenarios, based on considerations of burden of stunting and alignment with the most pressing Mali’s structural and crisis-induced needs, potential for impact, costs, and capacity for implementation:  Scenario 1: Scale up by region  Scenario 2: Scale up by intervention  Scenario 3: Scale up by intervention and region Our analysis reveals significant differences in the cost-effectiveness of the various scenarios, with Scenarios 2 and 3 being the most attractive. Scenario 2 offers the most cost-effective solution, with a cost per DALY saved of just $51 (as compared with $197 for Scenario 1 and $57 for Scenario 3). While all scenarios offer significant benefits, a combination of cost-effectiveness considerations and resource limitations make Scenarios 2 and 3 the most attractive options (see Box 1 for a summary of key findings). xviii Recognizing the challenges of scaling up Box 1: Key Findings to reach full coverage in one year, we estimate the investment required to scale The full scale-up of 10 interventions nationwide would up over five years to be $67 million for require $64 million in public investment and generate Scenario 2 and $50 million for Scenario these benefits annually: 3.1 These total costs for five years are significantly lower than the $174 million  480,000 DALYs saved needed for the full coverage scenario, but  14,000 lives saved  260,000 cases stunting averted still represent a significant increase over  $194 million added to the economy current spending on nutrition in Mali.  cost per DALY saved = $188 An investment in nutrition is also an Most of the 10 interventions are very cost-effective, investment in Mali’s economic future and although the public provision of complimentary food is a sound economic investment. When for the prevention of moderate acute malnutrition is scaled up gradually over five years, the not cost-effective. full investment of US$64 million has the potential to add at least US$194 million In the event that scale-up to full coverage is not annually to the economy of Mali over the immediately feasible, the two most cost-effective productive lives of children who would gradual scale-up scenarios are: otherwise have died or become stunted. It  Implementing all interventions except the would also yield impressive returns on public provision of complimentary food investments in terms of highly positive net nationwide (Scenario 2) would require $24 present values and internal rates of return million and save almost 470,000 DALYs and of almost 18 percent. over 11,000 lives: cost per DALY saved = $51. Although every attempt has been made to  Implementing all interventions except the use real programming costs for these public provision of complimentary foods in estimates, the costs presented here are the four highest burden regions (Scenario 3) likely to be slight overestimates, while the would require $18 million and save 320,000 benefits are likely to be underestimated. In DALYs and 11,000 lives: cost per DALY many cases, actual program costs will be saved = $57. lower than estimated because these Preliminary evidence suggests that some interventions programs can be added to existing outside the health sector (nutrition-sensitive programs. Program experience shows that interventions) would be cost-effective in improving the incremental costs of adding to an nutritional outcomes. In Mali, these include aflatoxin existing program are lower than the cost control with improved granaries for groundnuts and of starting an entirely new program school-based deworming. More robust data are needed because existing implementation to build on these finding and identify other effective arrangements can be used, thereby nutrition-sensitive interventions. minimizing costs for staffing, operations, and training. The estimate of costs presented here is therefore high because it does not account for expected economies of scale. With respect to the benefits of these programs, estimates are 1 Interventions are assumed to scale from current coverage as follows: 20 percent of coverage in Year 1, 40 percent in Year 2, 60 percent in Year 3, 80 percent in Year 4, and 100 percent in Year 5. xix likely to be underestimates of the true benefits because the LiST tool we use has limitations, making it possible to estimate the benefits of only some of the interventions that are proposed to be scaled up. As mentioned above, this analysis takes an innovative approach to nutrition costing not only by estimating the costs and benefits of nutrition-specific interventions, but also by exploring costs for a selected number of nutrition-sensitive interventions implemented outside of the health sector. We identify and cost six nutrition-sensitive interventions highly relevant to the Mali’s context, for which there is some evidence of positive impact on nutrition outcomes and for which there is some cost information. First, we consider a nutritional package delivered as part of a conditional cash transfer program. We also consider two potential interventions delivered through the agriculture sector—aflatoxin reduction in groundnuts and nutrition education via agricultural extension workers—and two delivered through the education sector—school-based deworming and school-based promotion of good hygiene. Finally, we report the costs and benefits identified by Hutton (2012) of reaching the Millennium Development Goals (MDGs) for improved access to water and sanitation. The estimated annual costs are relatively modest: $49.4 million for the nutrition package in the conditional cash transfer program; $1.3 million for aflatoxin control via improved granaries; $19.0 million for nutrition education delivered via agricultural extension workers; $0.3 million for school-based deworming, and $6.2 million for school-based promotion of good hygiene. DALY estimates from global sources suggest that aflatoxin control via improved granaries would cost $272 per DALY saved and school-based deworming $4.55 per DALY saved. These results must be considered rough approximations, as there are significant limitations in the available data and in the methodological approaches. Despite the resumption of bilateral development assistance and the mobilization of resources for the emergency response, additional financing for nutrition in Mali will be needed to scale up even the most modest scenarios (Scenarios 2 and 3) presented here. At the central government level, there is currently no dedicated budget line item for nutrition. Thus resources will need to be mobilized from national budgets, with additional support from official development assistance (ODA) from donors. Within the national budgets, it will be important to prioritize health sector funds for nutrition-specific interventions. It will also be important for other sectors—such as water and sanitation and social protection—to engage in the cost-effective nutrition-sensitive interventions the report has identified. Overall, these findings point to a powerful set of nutrition-specific interventions and a candidate list of nutrition-sensitive approaches that represent a highly cost-effective approach to reducing the high levels of child malnutrition in Mali. An important next step will be to leverage the additional financing needed to implement the scale-up. Most of the malnutrition that occurs in the first 1,000 days of a child’s life is essentially irreversible. Investing in early childhood nutrition interventions offers a window of opportunity to permanently lock in human capital. xx PART I – BACKGROUND COUNTRY CONTEXT AND NUTRITION DIAGNOSTICS Mali is a vast, landlocked, and sparsely populated country in the Sahel of West Africa. It shares borders with seven countries and has an area of about 475,000 square miles and a population of 15 million, 10 percent of whom are living in the northern regions that represent two-thirds of the country’s area (Figure 1). Delivery of services to the large, sparsely populated territory poses severe challenges; the scarcity of services has a negative impact on geographic equity and social cohesion. In 2012, Mali experienced institutional and political turmoil as well as conflict and insecurity in the North.2 In combination with the 2011 drought, this unrest resulted in a humanitarian crisis and food insecurity across the country. As a result, over 400,000 Malians have been displaced and 3 million are now at risk of food insecurity, of which 800,000 are in need of immediate food assistance (ECHO 2014). Following a good rainfall in 2012, the Malian economy has held up fairly well against the 2012 crisis: agricultural production increased by 14 percent in 2012 (but was down again in 2014 as a result of poor rainfall), gold production increased by 9 percent in 2012, and the economic growth rate was expected to reach almost 5 percent in 2013. But the effects of the crisis on food insecurity and malnutrition have persisted (detailed in the next section). Prior to the 2012 political crisis, Mali was ranked 175 on the UN Human Development Index. The country’s poverty rate declined from 49 percent to 36 percent between 2001 and 2010, yet the high population growth rate has kept the absolute number of poor unchanged. Since 2010, poverty has almost certainly increased. 2 In January 2012, an armed conflict broke out in the North of the country after a group of insurgents began fighting a campaign against the Malian army. In March 2012, these events were further complicated by the fact that mutinous soldiers took control of the capital, Bamako, and suspended the constitution of Mali after a coup d’état. This led to increased instability and to the control by the rebels of the three largest northern cities of Kidal, Gao, and Tombouctou. The National Movement for the Liberation of Azawad and the Islamist armed forces of Ansar Dine then battled over the control of the North. The Government of Mali asked for foreign military assistance to re-conquer the North of the country; military operations began in January 2013. By early February 2013, the Malian military and the international coalition had regained control of the northern territory, and a peace deal was signed between the government and the Tuareg rebels in June 2013. However, the rebels pulled out from the peace agreement in September 2013 and fighting is still ongoing. 1 Figure 1. Map of Mali Source: World Bank Group, internal map, 2009. HEALTH AND NUTRITIONAL STATUS 2001–2013 Until the 2012 political crisis, health indicators—although still serious—were improving in Mali. Life expectancy increased from 49 years in 2000 to 55 in 2012 (World Bank 2014a). Child mortality rates in Mali declined by 3 percent between 2005 and 2010 (Figure 2), although they remain high—at 128 per 1,000 live births in 2012 (World Bank 2014a); the figure also shows that many other countries in Sub-Saharan Africa improved more than Mali did. Furthermore, infant mortality in Mali improved from 111 per 1,000 live births in 2000 to 80 in 2012. Nevertheless, the country still has the 3rd highest infant mortality rate in the world (ECHO 2014). 2 Figure 2. Changes in Child Mortality, Selected Countries in Sub-Saharan Africa, 2005–2010 Source: World Bank analysis, based on DHS datasets. Levels and Trends in Malnutrition Starting from very high levels, Mali’s malnutrition rates dropped dramatically between 2001 and 2010, though they increased between 2001 and 2012 as a result of the food, political, and security crisis. The situation began to improve again by 2013, at least in the South (Figure 3).3 These levels—28 percent of children stunted, 17 percent underweight, and 9 percent wasted—indicate a serious problem of malnutrition according to the WHO classification. 3 Because the 2012 and 2013 Standardized Monitoring and Assessment of Relief and Transitions (SMART) surveys did not cover the whole country but focused mainly on the southern regions (where 90 percent of the population lives), we needed to verify how different the trends in nutritional outcomes between the North and the South were before these dates. The analysis shows that, most of the time, the level and trend of undernutrition are not significantly different between the North and the South, validating the interpretation of the latest trends. It is likely, however, that the 2013 improvement would not be reflected in the data on the North, as also later illustrated by the case of Gao. 3 Figure 3. Changes over Time in the Prevalence of Stunting, Wasting, and Underweight, percent of children under five, 2001–2013 Sources: Mali DHS for 2001, 2006; MICS/ELIM for 2010; SMART surveys for 2011 –2013. A closer examination of trends in stunting prevalence shows that stunting declined steadily in the years leading up to the recent crisis (Figure 4). Evidence since the crisis suggests a negative impact on child stunting. Without more recent survey data on the North, it is impossible to know whether or not the ongoing conflict in the North caused stunting prevalence to exceed that of the southern regions, but this is likely. 4 Figure 4. Stunting Prevalence in Mali Before and After the Crisis Sources: Mali DHS for 2001 and 2006; MICS/ELIM for 2010; and SMART surveys for 2011–2013. Note: HAZ = height-for-age Z-scores. A detailed analysis of recent survey data found an increase in stunting in the first months of life in 2013, both overall and for the region of Gao (World Bank 2014b). This is illustrated by the evolution in age distributions of height-for-age Z-scores (Figure 5):4 up until 2011, the overall age distribution of height-for-age Z-scores improves from one survey to another. However, between 2011 and 2012, we observe lower height-for-age Z-scores for children aged 18 months and older. A similar important change can be observed between 2012 and 2013 among children aged 0–12 months. This drop in height-for-age Z-score during the first months of life in 2013 probably reflects deterioration in maternal health during the 2011–2012 crisis, in turn at least partly related to disruptions in the delivery of health services in both North and South. Along the same lines, using 2013 SMART data, the cohort of children born after the 2012 crisis in Gao had significantly worse nutrition outcomes than the same cohort born before the crisis.5 This is especially noticeable for chronic undernutrition, since postcrisis height-for-age Z-scores are, on average, 21 percent lower than precrisis scores. In absolute value, the difference is about 0.15 4 Figure 5 shows fractional polynomial curves of height-for-age Z-scores across ages, which from 2001 to 2012 follow a familiar shape wherein children begin life with a height-for-age Z-score near 0 that declines rapidly before stabilizing around 24 months. 5 Since the SMART survey in Gao was conducted in May 2013, the focus was on the cohort of children aged 6 –14 months, comparing their nutritional outcomes with the cohort of children aged 6–14 months in the surveys conducted before the crisis. 5 standard deviations in height for age, which translates into a difference of 0.7 centimeters on average for this cohort of children aged 6–14 months old. This is a significant difference for such a young age and is likely the result of poor maternal health and nutrition following the significant disruptions at all levels in the North. It will probably contribute to significant differences later in life in terms of mortality, morbidity, and cognitive development. Overall, in the absence of interventions to improve their nutrition status, children younger than 24 months are at high risk of negative long-term consequences in the form of increased mortality, increased morbidity, lower cognitive ability, and lower adult productivity in the future. Figure 5. Height-for-Age Z-Score Curves by Age, 2001–2013 Source: World Bank 2014b. Note: The arrow indicates the substantial shift in the HAZ/age relation for the youngest children, aged 0 –12 months. This relation is usually very similar to the other years in many countries: that is, the difference in HAZ is usually close to zero after birth, but height then falters after inadequate breastfeeding and complementary feeding and multiple fever/diarrhea episodes during young infancy. What is noticeable in this figure is that even newborn babies are shorter than the reference, indicating serious nutritional challenges during pregnancy. Figure 6 shows the regional distribution of children exposed to multiple nutritional deficits. Between 2001 and 2012, more than half of the children with at least two nutritional deficits were concentrated in Sikasso, Segou, and Mopti. Interestingly, in 2013 we also observe a noticeable increase in the share of children living in Gao exposed to multiple nutritional deficits—further evidence of negative effects of the crisis in Gao. Also notable is the decline in the share of these children in Kayes in 2013, the most recent year. 6 Figure 6. Regional Distribution of Multiple Nutritional Deficits, 2001–2013 2001 2006 2010 2011 2012 2013 0 20 40 60 80 100 % children under 5 with at least 2 nutritional deficits Kayes Koulikoro Sikasso Segou Mopti Tombouctou Gao Kidal Bamako Source: World Bank 2014b. The impact of the 2011–2012 crisis on nutrition outcomes becomes even clearer when examining changes in the “stunting gap,” which measures the severity rather than the prevalence of stunting. Similar to the poverty gap, the stunting gap is defined as the average distance below a given reference line (in this case, a −2 height-for-age Z-score). Figure 7 presents the spatial distribution in the stunting gap, which continued to improve until 2011 before reversing course in the southern regions beginning in 2012. Some partial recovery was made in 2013, although stunting is not yet back to 2011 levels in Koulikoro and Ségou. Between 2011 and 2012, deterioration in nutritional status was most pronounced in the region of Ségou; the decline in that region continued even further between 2012 and 2013 (World Bank 2014b). 7 Figure 7. Prevalence of the Stunting Gap in the North and South Source: World Bank 2014b. Note: Surveys covered only part of the country in 2012 and 2013. The data shown in the key refer to the range of percentages below the stunting threshold: dark orange = (15–20]; medium orange = 10–15]; pale orange = (5–10]; palest orange = [0–5]. HAZ = height-for-age Z-score. Determinants of Malnutrition The determinants of malnutrition in Mali show geographic disparities and an association with poverty that both vary according to the definition of malnutrition being used. Figure 8 depicts the regional variation in population, poverty, and stunting in 2010; it is important to keep in mind that the population of Mali is concentrated in the south of the country. Rates of stunting are highest in the south and southwest provinces of Sikasso, Ségou, and Mopti. Stunting rates are also very high in the northern region of Tombouctou, although this accounts for a very small proportion of the population. It is expected that the crisis may have exacerbated the North-South divide. It is important to recognize that chronic undernutrition (stunting) in Mali is strongly correlated with household income, while the correlation is much weaker with acute malnutrition (wasting). Along the same lines, overall, acute malnutrition rates (Figure 9), with the possible exception of Ségou, seem consistently higher in the northern (relatively less poor) regions, especially in Tombouctou and Gao, than in the South. 8 Figure 8. Population, Poverty, and Child Stunting by Region, 2010 Source: World Bank 2014b. Note: The data shown in the key refer to the range of percentages below the stunting threshold: Population: dark green = (2.3–2.6]; medium green = (2.0–2.3]; pale green = (0.7–2.0]; palest green = [0.1–0.7]; Poverty headcount: dark blue = (47–56]; medium blue = (29–47]; pale blue = (27–29]; palest blue = [10–27]; Stunting rate: dark orange = (33–37]; medium orange = (26–33]; pale orange = (25–26]; palest orange = [16–25]. 9 Figure 9. Acute Malnutrition (Wasting), by Year and Region Source: World Bank 2014b. Note: Surveys covered only part of the country in 2012 and 2013. The data shown in the key refer to the range of percentages below the stunting threshold: Darkest green = (20–25]; dark green = (15–20]; medium green = (10–15]; pale green = (5–10]; palest green = [0–5]. Despite an association between poverty and stunting, undernutrition is also exacerbated by improper infant and young child feeding practices, poor hygiene, and inadequate prevention and treatment of childhood illnesses. This is evidenced by the continued prevalence of child stunting in Mali’s wealthiest households (Figure 10). In 2010, 13 percent of children in the wealthiest income quintile were stunted, suggesting that undernutrition is not simply the result of limited access to food but also of poor feeding practices and exposure to disease. This underscores the continued need for effective communication on optimal child feeding and caregiving practices, as well as disease prevention and treatment. Nevertheless, Figure 10 reveals the progress made by Mali over the past decade in reducing stunting prevalence across all income quintiles. The 2011– 2012 crisis affected this progression not only through reduced access to food but also and importantly through disruption in health care facilities. 10 Figure 10. Child Stunting by Wealth Quintile: 2001, 2006, and 2010 Sources: DHS 2001, 2006; MICS/ELIM 2010. These findings on key determinants of malnutrition are confirmed by an analysis of vulnerability to stunting, which points to variations in vulnerability across regions and household characteristics.6 Unlike previously mentioned descriptions of chronic malnutrition, vulnerability measures the risk of a child becoming stunted—an important factor when considering interventions designed to increase resiliency to crisis and prevent chronic undernutrition. To measure vulnerability to stunting, we adapt the methodology in Chaudhuri (2003), which estimates household vulnerability to poverty based on cross-sectional data.7 Comparing mean vulnerability across different selected groups allows us to draw a vulnerability profile for Mali. We show this in Table 1 below by simply testing for the mean difference in vulnerability between selected groups and the rest of the population. We also present the results of the vulnerability by region in Figure 11. We start by comparing the northern regions (Tombouctou, Gao, Kidal) with the rest of the country. Although the northern regions comprise less than 10 6 Because of data and analytical limitations, it was not feasible to perform a rigorous causal analysis of the determinants of changes in malnutrition. First, most if not all of the traditional determinants of nutrition are endogenous to nutrition outcomes, making the estimation of the causal effects challenging when using traditional statistical techniques (such as ordinary least squares regression). It is possible to overcome those challenges using simultaneous equation modeling with instrumental variables, but the available data do not include data points that could be convincingly used as instrumental variables. Furthermore, the time span (the number of time points) of the data is short and does not allow for the consideration of dynamic panel estimators. 7 We transpose this method to study children’s vulnerability to stunting, and we measure vulnerability as the conditional probability of a child’s height-for-age Z-score falling below the −2 standard deviation reference line in the next period. 11 percent of all Mali’s children under the age of five, the average vulnerability is about 10 percentage points higher than in the rest of the country. This difference is statistically significant, and we also reject equality in distribution. Children of farmers (especially those of cotton growers who are concentrated in the region of Sikasso) are more at risk of falling into chronic undernutrition. Children living in female-headed households are also more at risk, although the magnitude of the difference is smaller. Household head’s and mother’s education influence the risk of children falling below the HAZ reference line. Old household heads and poverty significantly increases children’s vulnerability to undernutrition. Being born during a lean month (July–October) is associated with a higher risk, as does being born a boy. Households with more than two children under the age of two are more likely to face risks of chronic undernutrition than households with fewer than two children under the same age range. Figure 11. Distribution of Vulnerability and Observed Stunting Rates by Region, 2010 Source: World Bank 2014b. 12 Table 1. Vulnerability Profile for Mali, 2010 p-value for Mean Mean p-value for t- Kolmogorov- vulnerability to difference with test of mean Smirnov test chronic the rest of the equality (H0 = (H0 = equal Characteristic Average undernutrition population equal mean) distribution) North 8.8 59.6 −10.5 0.00 0.00 Sikasso 17.8 56.5 −6.9 0.00 0.00 Farmer cotton 25.2 57.0 −8.5 0.00 0.00 Farmer non-cotton 39.6 54.7 −7.1 0.00 0.00 Non-farmer 35.1 41.1 14.3 0.00 0.00 Livestock owner 71.1 54.0 −12.0 0.00 0.00 Rural 76.1 54.9 −18.1 0.00 0.00 Head of household: female 5.5 51.3 −1.6 0.02 0.23 Head of household: < primary education 80.2 53.8 −16.9 0.00 0.00 Mother: < primary education 79.7 53.6 −15.8 0.00 0.00 Head young [< 25 years] 1.6 50.2 −0.1 0.94 0.81 Head middle-aged [25–65 years] 77.1 49.5 3.7 0.00 0.00 Head old [> 65 years] 21.3 54.0 −3.9 0.00 0.00 Poor 45.5 57.6 −12.5 0.00 0.00 Born in lean month 33.5 49.9 −0.9 0.01 0.00 Boy 51.8 54.6 −8.5 0.00 0.00 Two or more children under 5 57.0 51.9 −3.0 0.00 0.00 Adequate food 35.0 48.9 2.6 0.00 0.00 Adequate health care 43.2 51.2 1.2 0.00 0.00 Adequate environment 6.6 35.5 16.0 0.00 0.00 Source: World Bank 2014b. We defined adequacy variables for children’s diets, health, and environment. Food and health adequacy are defined according to standard recommendations and depend on the age of the child. Environment adequacy is related to household level characteristics in terms of access to improved sanitation, improved sources of water, and handwashing stations. The exact definition 13 of these variables is given in Appendix 1. Children with adequate diets, and those with adequate health care, are less likely to be exposed to chronic undernutrition risk. This is also the case for children living in households with an adequate environment, and the effect is larger for this dimension (an adequate environment yields a difference of 16 percentage points in mean expected vulnerability). On the one hand, the importance of these factors confirms the relation between poverty and malnutrition. The difficulty some farmers have in providing sufficient food for their families and the high vulnerability of the largely pastoralist North is likely to have translated into higher malnutrition than the national average after the 2011–2012 crisis (in particular in Tombouctou).8 On the other hand, these findings also point to the importance of other factors—such as poor knowledge of feeding and hygienic practices—in determining malnutrition. The significance of knowledge is illustrated by the role that education can play in improving outcomes and the fact that malnutrition is higher in rural areas where, in principle, there should be more access to food. The results of this vulnerability analysis demonstrate the need to invest in interventions that increase the resilience of the poor, farmers, women, and populations living in the North and other vulnerable regions in the face of ongoing crises. Such interventions should include efforts to tackle the issues of lack of knowledge of feeding and hygienic practices. Finally, beyond access to food, dietary quality is also an issue: vitamin and mineral deficiencies (hidden hunger) are pervasive in Mali. About 60 percent of preschool-aged children and almost one in five pregnant women are deficient in vitamin A (World Bank 2013b). Of children under five, 42 percent suffered from anemia in 2010, down from 53 percent in 2001 (Figure 12a); anemia is a widespread problem and not limited to one income or geographic group (Figure 12b). Although over 45 percent of children in the poorest quintile were anemic, over 33 percent of children in the richest quintile were also anemic. Similarly, both rural and urban children suffer from high rates of anemia, although it is worse in rural areas. Coverage of salt iodization is also low: one-fifth of households do not consume iodized salt (World Bank 2013b). 8 This supposition is confirmed by the recently published AGIR Regional Roadmap (see AGIR/OECD 2013). 14 Figure 12. Anemia in Mali 12a. Change Over Time, 2001–2010 12b. By Wealth Quintile and Rural vs. Urban, 2010 Sources: Data from DHS 2001, 2006; MICS/ELIM 2010. Another health burden in Mali is the high levels of aflatoxins found in groundnuts. Aflatoxins are naturally occurring carcinogenic byproducts of common fungi on crops, especially groundnuts and maize. A study by the International Food Policy Research Institute (IFPRI) in Mali in 2009– 2010 found that the proportion of groundnut samples taken from farmers’ fields with aflatoxin levels greater than 20 parts per billion was 33 to 59 percent. Analysis of more than 2,500 groundnut samples collected at regular intervals from traders, processors, wholesalers, and retail markets revealed no exception in the prevalence of unacceptably high levels of aflatoxins, including in markets in Bamako. The study also found great variation in levels across time (IFPRI 2012). The prevalence of aflatoxins in maize in Mali is not known but thought to also be high. Consumption of high levels of aflatoxin can lead to growth impairment in children (Khlangwiset 2011) and liver cirrhosis (Kuniholm et al. 2008) and liver cancer in adults (Abt Associates 2014). It is widely understood that there is a relationship between aflatoxin exposure and stunting, but this burden has not yet been adequately quantified in the published literature (Unnevehr and Grace 2013; Abt Associates 2014). Infection with parasitic intestinal worms is concentrated in the south of Mali, as the northern desert is too dry and hot for these worms to survive (Global Atlas of Helminth Infections 2014). In the short term, parasitic intestinal worm infections potentially cause anemia, increase morbidity and undernutrition, and impair mental and physical development (Hotez et al. 2008). In the long term, infected children are estimated to have an average IQ loss of 3.75 points per child, and they earn significantly less (43 percent) as adults than those who grow up free of worms (Bleakley 2007). To summarize: Mali’s high levels of chronic and acute malnutrition affect, to different extents, the whole country—North as well as South. The situation is made worse by the high vulnerability 15 of certain segments of the population to malnutrition. In times of crisis—as in the 2011–2012 back-to-back food, political, and security crises—this vulnerability translates into higher malnutrition rates. For example, the effects of the crises on stunting prevalence persisted in 2013 in children aged 0–12 months. In the North, this is documented by the poor nutrition outcomes of children in Gao, including those born after the 2012 crisis. The South—Ségou, for example— was affected by severe and multiple nutritional deficits. These findings point to the need for a two-pronged strategy to address malnutrition in Mali that would (1) tackle the immediate effects of the crisis on nutrition in the North and in Ségou, in children 0–24 months old, and in pregnant/lactating women; and (2) invest in nutrition interventions aimed at increasing the resilience of the poor, farmers, women, children, and vulnerable regions in the face of ongoing crises while at the same time fostering long-term benefits. THE IMPORTANCE OF INVESTING IN NUTRITION Undernutrition is an underlying cause of approximately half the deaths in children under five and one-fifth of maternal deaths in developing countries. The joint effect of suboptimum breastfeeding and fetal growth restriction in the neonatal period alone contributes 1.3 million deaths or 19 percent of all deaths of children under five (Black et al. 2013). Undernourished children are more likely to die from common childhood illnesses such as diarrhea, measles, pneumonia, malaria, or HIV/AIDS. Those malnourished children who survive face long-lasting health and schooling consequences, including cognitive deficits and poorer schooling outcomes. Children with impaired cognitive skills have lower school enrollment, attendance, and graduation, which in turn results in lower productivity, earnings, and economic well-being. Stunted children lose 0.7 grades of schooling and are more likely to drop out of school. An adequate intake of micronutrients—particularly iron, vitamin A, iodine, and zinc—is critical for growth and cognitive development. Iodine- deficient children lose on average 13 IQ points, and iron deficiency anemia reduces performance on tests by 8 IQ points, making these children less educable and less productive in the long run (World Bank 2006). Berhman et al. (2009) showed improved schooling and test scores from food supplementation in early childhood. Malnutrition costs developing countries billions of dollars in lost revenue through reduced economic productivity, particularly through lower wages, lower physical capability, and more days away from work as a result of illness. At the individual level, childhood stunting is estimated to reduce a person’s potential lifetime earnings by at least 10 percent (World Bank 2006). Other studies have shown that a 1 percent loss in adult height results in a 2 to 2.4 percent loss in productivity (Strauss and Thomas 1998; Caulfield et al. 2004). In addition, micronutrient deficiencies in childhood and adulthood have tremendous economic cost for both individuals and countries. Childhood anemia alone is associated with a 2.5 percent drop in adult wages. Anemia in adults has been estimated to be equivalent to 0.6 percent of GDP; this estimate goes up to 3.4 percent when including the secondary effects of retarded cognitive development in children (Horton 1999). Horton and Ross (2003) estimate that eliminating iron-deficiency anemia would result in a 5 to 17 percent increase in adult productivity. Annually, Mali loses over $235 million 16 in GDP to vitamin and mineral deficiencies (World Bank 2013b). The economic costs of undernutrition have the greatest effect on the most vulnerable in the developing world. A recent analysis estimates these losses at 11 percent of GDP in Africa and Asia each year (Horton and Steckel 2013)—equivalent to about $149 billion of productivity losses. Because most of the detrimental effects of malnutrition that occur in the first 1,000 days of a child’s life are essentially irreversible, the window of opportunity for preventing these effects is the first 1,000 days, until the child is two years of age. After that age, most actions are too little, too late, and too expensive (World Bank 2006; Black et al. 2008, 2013). Figure 13 shows that the rates of return from nutrition investments are highest for programs targeting the earliest years, since these investments build a foundation for future learning and productivity, prevent irreversible losses, and lock in human capital for life (Heckman and Masterov 2004). Figure 13. Rates of Return to Investment in Human Capital Source: Heckman and Masterov 2004. Note: Age refers to the child’s age from birth, depicted in years for infancy and preschool, then in aggregate for school age and adulthood. Malnutrition and poverty are interrelated and exacerbate each other. A recent study (Hoddinott et al. 2011) concluded that individuals who are not stunted at 36 months are one-third less likely to live in poor households as adults. Poverty increases the risk of malnutrition by lowering poor households’ purchasing power, reducing access to basic health services, and exposing these households to unhealthy environments, thereby compromising food intakes (both quality and quantity) and increasing infections. Poor households are also more likely to have frequent pregnancies, larger family sizes with high dependency ratios, more infections, and increased health care costs. Conversely, malnutrition causes poor health status, poor cognitive 17 development, and less schooling, resulting in in poor human capital and long-term productivity losses. Nutrition interventions are consistently identified as cost-effective development actions, and the costs of scaling up nutrition interventions are modest. Global benefit-cost ratio of micronutrient powders for children is 37 to 1; of deworming it is 6 to 1; of iron fortification of staples it is 8 to 1; and of salt iodization is 30 to 1 (World Bank 2010). A recent World Bank study estimated that investing in nutrition can increase a country’s GDP by at least 3 percent annually (World Bank 2010). The same study estimated these costs at $10.3 billion per annum globally, to be financed through domestic public and private sector and donor resources. These investments would provide preventive nutrition services to about 356 million children, save at least 1.1 million lives and 30 million DALYs, and reduce the number of stunted children by about 30 million worldwide. Bhutta et al. (2013) came up with similar estimates. In another study, Hoddinott, Rosegrant, and Torero (2012) estimate that, for just $100 per child, interventions including micronutrient provision, public provision of complementary food for the prevention of moderate acute malnutrition, treatments for worms and diarrheal diseases, and behavior change programs could reduce chronic undernutrition by 36 percent in developing countries. Clearly there is huge potential pay-off for dedicating more resources to the scale-up of evidence-based, cost-effective nutrition interventions. Investments in improved nutrition outcomes also support the efforts of the World Bank Group and its partners to increase the resilience of Malians to shocks. Ensuring equitable access to evidence-based nutrition interventions can prevent unforeseen crises from pushing vulnerable children into chronic undernutrition, with its severe consequences for their future health and productivity. The recent food and nutrition crisis demonstrated the potential for shocks to threaten the nutritional status of otherwise healthy children, so an investment in nutrition is also an investment in resilience. A MULTISECTORAL APPROACH FOR IMPROVING NUTRITION The determinants of malnutrition are multisectoral. Therefore, to successfully and sustainably improve nutrition outcomes, a multisectoral approach is needed. At a proximate level, access to food, health, hygiene, and adequate child care practices is key to reducing malnutrition. At a more distal level, poverty, women’s status, and other social factors play an important role. It has been demonstrated that direct actions taken to address the proximate determinants of malnutrition can be further enhanced by action on some of the more distal levels. For example, programs supporting improved infant and young child feeding practices will be more effective if they are complemented with programs to address gender issues by reducing women’s workloads, thus allowing women more time for child care. Similarly, conditional cash transfer programs that target the poor, if designed appropriately, have the potential not just to address poverty but also to increase demand for nutrition services and good nutrition behaviors. 18 Although the health care sector is key in Box 2: Nutrition-Specific and Nutrition-Sensitive delivering nutrition-specific interventions to Interventions Distinguished the poor (such as vitamin A supplementation Nutrition-specific interventions address the immediate and deworming), multisectoral nutrition- determinants of child nutrition, such as adequate food sensitive actions through the agriculture and nutrition intake, feeding and caregiving practices, sector and social protection, water and and treating disease. Examples include: sanitation, and poverty reduction programs  Community nutrition programs have the potential to strengthen nutritional  Micronutrient (e.g., vitamin A) supplementation outcomes in several ways (Box 2). Examples  Deworming of these include (1) improving the context in which the nutrition-specific interventions are Nutrition-sensitive interventions are delivered through delivered—for example, through investment the agriculture; education; and water, sanitation, and hygiene sectors and have the potential to have an impact in food systems, empowerment of women, on nutrition outcomes more indirectly than nutrition- and equitable education; (2) integrating specific interventions. Examples include: nutrition considerations into programs in other sectors as delivery platforms (such as  Biofortification (e.g., vitamin-A rich sweet potato or conditional cash transfer programs) that will cassava) potentially increase the scale and coverage of  Conditional cash transfers nutrition-specific interventions; and (3) by  Water and sanitation sector infrastructure improvements increasing policy coherence through government-wide attention to policies or strategies and trade-offs, which may have positive or unintended negative consequences for nutrition. The synergy with other sectors is critical to breaking the cycle of malnutrition and sustaining the gains from direct nutrition-specific interventions (World Bank 2013a). Guidance on costing for nutrition-sensitive interventions is currently very limited for at least two reasons. First, evidence of the effectiveness of nutrition-sensitive interventions with respect to nutritional outcomes is limited. Second, compared with nutrition-specific interventions, estimating and attributing the costs of nutrition-sensitive interventions is quite complex since these interventions have multiple objectives and improved nutrition outcomes is only one of them. Notwithstanding these limitations, the availability of costing information is crucial to assess the cost-effectiveness of these interventions. This series of papers on nutrition interventions makes a first-ever attempt to address these issues. For Mali, beyond 10 key nutrition-specific interventions, we identify and cost six nutrition- sensitive interventions that are well aligned with the current challenges, for which there is some evidence of positive impact on nutrition outcomes, and for which there is some cost information. First, we consider the incremental costs of adding a nutrition component to cash transfer programs for poor households in Mali. We also consider two potential interventions delivered through the agriculture sector—aflatoxin control in groundnuts and nutrition education through agricultural extension—as well as two delivered through the education sector—school-based deworming and school-based promotion of good hygiene. Additionally, we include cost and benefit estimates for achieving the MDG targets for access to improved water and sanitation infrastructure. Costs and benefit estimates (where possible) for the six nutrition-sensitive interventions described here are presented in Part IV. 19 The World Bank Group is currently supporting a cash transfer program entitled Emergency Safety Nets Project (Jigiséméjiri), which will be complemented by a package of preventive nutrition interventions provided to project beneficiaries. In certain villages, all households with children 0–59 months of age and/or with pregnant women are selected to receive the preventive package while also participating in behavior communication sessions. The package will deliver key vitamins and minerals (micronutrient powders, vitamin A supplementation, oral rehydration solution with zinc, iron-folic acid supplementation, and deworming tablets) for young children and pregnant women. While the nutrition package will be piloted only in select villages during the first several years, this study uses the project budget as the basis for estimating the costs of reaching full national coverage (World Bank 2013c). Control of aflatoxins has the potential to reduce aflatoxins in groundnuts by at least 50 percent (IFPRI 2012). Several promising pre- and post-harvest interventions have been analyzed in the Malian context, including the use of improved seed varieties, better granaries, and practices such as hand sorting and drying on wooden mats. Evidence shows that consuming high levels of aflatoxins can lead to liver cirrhosis and liver cancer in adults (Kuniholm et al. 2008; Abt Associates 2014). Furthermore, it is widely understood that there is a relationship between aflatoxin exposure and child stunting, albeit the evidence base for this relationship is more tentative and it has not yet been adequately quantified in the published literature (Unnevehr and Grace 2013; Abt Associates 2014). However, although the evidence of the links between aflatoxins and child stunting is still tentative, its links with liver cancer are well established: aflatoxin-induced liver cancer in Mali could lead to nearly 10,000 DALYs each year.9 This provides sufficient impetus for actions to control aflatoxin exposure in Mali. Providing farmers with information directed at behavior change can increase the likelihood of positive nutritional outcomes (World Bank 2007). When producers understand the nutritional significance of the foods that they produce and consume, it allows them to make better consumption choices for themselves and their families. Using agricultural extension agents to provide nutrition education on food safety and preparation, child feeding practices, and growth monitoring and promotion allows behavior change communication strategies to achieve greater coverage by building on existing program capacity. School-based deworming has been proven to be an efficient and cost-effective intervention to address health and nutrition outcomes in other settings, with cost per DALY saved estimated at $4.55 (J-PAL 2012). Delivering deworming tablets through schools is inexpensive because it uses existing infrastructure and delivery platforms in schools and community links with teachers. Teachers need only minimal training to safely administer the tablets, so their workloads are not significantly increased. On the other hand, the benefits of school-based deworming are enormous. Bi-annual deworming significantly boosted school attendance and reduced self- reported illness and anemia, while providing modest gains in height-for-age Z-scores (J-PAL 2012). In the long term, deworming improved self-reported health, increased total schooling years, and raised earnings by 20 percent (Baird et al. 2011). 9 Authors’ calculations, based on Liu and Wu (2010). 20 Improved hygiene behaviors through school-based promotion of handwashing and other good hygiene behavior could decrease the risk of stunting in one in three children. Diarrheal episodes exacerbate the relationship between malnutrition and infection, as children experiencing these episodes tend to eat less, absorb fewer nutrients, and exhibit reduced resistance to infections. Prolonged diarrheal episodes lead to impaired growth and development (Ejemot et al. 2008). Correct handwashing at critical times can reduce the severity of diarrhea by 42 to 47 percent, lower the incidence of diarrhea for children by 53 percent, and reduce the incidence of acute respiratory infections by 44 percent in Nigeria (where the World Bank conducted a study on the impact of poor sanitation; World Bank 2012a)—thereby reducing child stunting. A recent campaign (WASH) to promote handwashing with soap in primary schools in China, Colombia, and Egypt demonstrated significant reduction in absenteeism related to diarrhea and respiratory illness (UNICEF 2012). A study in Brazil showed a relationship between the effects of early childhood diarrhea on later school readiness and school performance, revealing the potential long-term human and economic costs of early childhood diarrhea (Lorntz et al. 2006). The effectiveness of promoting good hygiene behavior in schools is demonstrated by the long- term impact and broad effect of good hygiene on communities. Schools are ideal settings for hygiene education: children can learn and sustain lifelong proper hygiene practices through peer- to-peer teaching, classroom sessions with focused training materials, and role playing or interactive songs. A study on the long-term effects of hygiene education programs for both adults and children found that hygiene behaviors are sustained beyond the end of an intervention. The study also found that educated students can also influence family members by sharing this information, which may in turn affect behavior change at the community level (Bolt and Cairncross 2004). At the same time, water, sanitation, and hygiene (WASH) interventions—which provide improved water sources, hygienic latrines, and behavior change communication programs—can help to reduce the incidence of diarrhea and child mortality. Gunther and Fink (2011) argue that the reduction in diarrhea from improved WASH ultimately depends on both the quality of existing WASH infrastructure and child mortality levels in the country. Given the high levels of child mortality in Mali and the poor quality of its current infrastructure, it follows that WASH interventions could have a significant impact. In studying the potential benefits of scaling up WASH interventions globally, the WHO cites estimates that the benefits of reaching MDG targets for water and sanitation in Mali would outweigh the costs 2 to 1 (Hutton 2012). NATIONAL AND PARTNER EFFORTS TO ADDRESS MALNUTRITION IN MALI Given the country’s ongoing political and food crises, efforts to improve nutrition in Mali are integrated into both the humanitarian response and long-term development programming. These nutrition interventions are coordinated by the Nutrition Division (Division Nutrition; DN) of Mali’s Ministry of Health’s National Health Directorate (Direction Nationale de la Santé; DNS). While the humanitarian response has prioritized the prevention and treatment of acute 21 malnutrition, development efforts continue to focus on preventing chronic malnutrition and micronutrient deficiencies. National efforts to address widespread malnutrition are guided by the government’s 2013 National Nutrition Policy. In line with this policy, the government is currently in the process of elaborating a costed Multisectoral Strategic Action Plan for Nutrition, which will cover the period from 2013 to 2017 and aim to coordinate interministerial efforts to combat malnutrition by scaling up both nutrition-specific and nutrition-sensitive interventions. REACH has supported the elaboration of the government’s Strategic Plan, including the costing of its activities conducted in collaboration with Inner City Fund (ICF) International. Although consolidated information on the partner landscape for nutrition is lacking, this analysis attempts to provide a broad overview of support for nutrition in Mali. Despite efforts to provide a holistic review of nutrition activities in Mali, significant gaps likely remain. A more detailed mapping exercise is currently being undertaken by REACH and ICF as part of the collaborative Scaling Up Nutrition (SUN) process. The Ministry of Health is in charge of six nutrition-specific programs, including the Acute Malnutrition Management Program, the Food Standards and Procedures Policy, the People Living with HIV/AIDS Nutrition Management Program, the national campaigns for the intensification of nutrition activities (SIAN), the Infant and Young Child Feeding Program, and the Essential Nutrition Actions Program (SUN 2012). Because national nutrition resources and capacities are limited, international and local partners provide funding, technical support, and implementation assistance for many of these interventions. Several other government ministries and agencies also contribute to multisectoral efforts to address malnutrition (SUN 2012). These stakeholders are regularly convened by the Comité Technique Intersectoriel de Nutrition (Multisectoral Technical Committee on Nutrition), which is chaired by the Ministry of Health and includes representatives from the Ministries of Agriculture, Education, Social Development, and Humanitarian Action, as well as the Food Security Commission. The National Nutrition Policy also oversees the creation of a National Nutrition Development Council under the direction of the Prime Minister; this council is responsible for coordinating national multisectoral nutrition activities (SUN 2012). REACH, which is funded by Canada and consists of representatives from the United Nations Children’s Fund (UNICEF), the WHO, the World Food Program (WFP), and the United Nations Food and Agriculture Organization (FAO), has been involved in developing the government’s multisectoral approach. Direct aid from the international community to the Malian government was suspended following the 2012 coup that overthrew the country’s elected leadership. The suspension of bilateral official development assistance (ODA), combined with a food crisis in the South and an ongoing insurgency in the North, led donors and implementing partners to focus their efforts on delivering an effective emergency response. However, following the government’s adoption of the Road Map for the Transition in early 2013, donors have begun to resume bilateral development assistance. 22 Since 2013 a number of nutrition-related programs have been implemented in Mali as emergency assistance. The European Commission for Humanitarian Aid and Civil Protection (ECHO) has been providing funding for the treatment of severe acute malnutrition and food distribution, as well as growth monitoring in community settings and nutrition-related training. Those activities have been implemented by UNICEF, the WFP, and a number of nongovernmental organizations. The WFP is also implementing blanket feeding programs as well as targeted supplementary feeding and treatment for moderate acute malnutrition. As part of the Food for Peace program, the US Department of Agriculture and the Office of Disaster Assistance, in collaboration with nongovernmental organizations including ACTED, Mercy Corps, Near East Foundation, Save the Children, and Catholic Relief Services are providing emergency assistance including food distribution. There are also ongoing programs to strengthen technical capacity in the nutrition sector. USAID, in collaboration with the University Research Center and as part of the Assist project, is providing technical assistance at primary health care facility level to strengthen the health infrastructure, including technical assistance related to deworming, management of acute malnutrition, and the SIAN (the intensification of nutrition activities week) initiative. A project to establish a systeme d’information sanitaire (sanitation information system) is in the preparatory stage. Similarly, a project aimed at developing local capacity and expertise in nutrition by establishing a Master’s program in nutrition at the University of Bamako, funded by Canada and spearheaded by UNICEF, is underway. In addition to nutrition-specific interventions and programs described above, a number of activities aimed at strengthening resilience and decreasing vulnerability and food insecurity are also being implemented in Mali. These include activities implemented as part of the US government–funded Feeding the Future program, which includes several projects focusing on horticulture, agricultural innovation, the provision of cattle and the commercial integration of animal husbandry, enhancement of the cereal value chain and improvement in the production of rice, millet, and sorghum. The European Union (EU) has funded several iterations of the Programmes d’appui à la sécurité alimentaire (Program to Support Food Security; PASA) project (PASA 5 is currently ongoing), which aims to improve food security among the most vulnerable communities in Mali. The EU is also providing funding for Africa’s Nutrition Security Partnership, which is implemented in partnership with UNICEF in Mali, Burkina Faso, Ethiopia, and Uganda. In Mali, this partnership includes infant and young child feeding interventions, as well as multisectoral interventions to improve nutrition at the community level. The EU is also funding nutrition-sensitive interventions aimed at improving resilience and decreasing food insecurity under two other programs: AGIR (Global Alliance for Resilience Initiative) and the Initiative pour le renforcement de la Résilience par l’Irrigation et la Gestion appropriée des Ressources (Initiative for Strengthening the Resilience of Irrigation and proper management of resources; IRRIGAR). The WFP has also engaged in activities related to improving resilience and decreasing vulnerability and has implemented activities concerned with the distribution of food and nutritional inputs, cash transfer programs, and capacity strengthening at the community level through sensitization, training, institutional development, and support for income- generating activities. The US government is currently funding the Annual Program Statement project whose goal is to improve the nutritional status of women and children through behavior 23 change communication and nutrition-sensitive interventions such as support for small-scale agriculture and the promotion of community and household vegetable gardens to encourage the consumption of foods rich in micronutrients, as well as interventions related to water, sanitation, and hygiene. Appendix 2 provides an overview of implementing partners involved in nutrition activities. 24 PART II – COSTED SCALE-UP SCENARIOS: RATIONALE, OBJECTIVES, AND METHODOLOGY RATIONALE AND OBJECTIVES OF THE ANALYSIS The overall objective of this report is to support the Government of Mali in developing a strategic approach to its malnutrition challenge and in providing a costed scale-up plan for nutrition. It is designed to provide the Government of Mali with the rationale needed to design an effective nutrition strategy and the tools required to leverage adequate resources from domestic budgets as well as from development partners. Within this context, the objectives of the analysis that follows are:  To estimate scale-up costs in Mali for a set of well-proven nutrition-specific interventions that have the potential to be scaled up through tested delivery mechanisms  To conduct a basic economic analysis to calculate the potential benefits and cost- effectiveness associated with the proposed scale-up  To propose a series of scenarios for a costed scale-up plan that rolls out this package of nutrition-specific interventions in phases, based on considerations of impact, geography, implementation capacity, and cost  To explore initial costs for a limited number of nutrition-sensitive interventions through social protection programs as well as through the agriculture, education, and water and sanitation sectors Although the economic arguments for increasing investments in nutrition are sound, one of the first questions raised by key decision makers in any country is “How much will it cost?” In 2010, the World Bank spearheaded a study called Scaling Up Nutrition: What Will It Cost? to answer that question at the global level. The analysis estimated the level of global financing required to scale up 10 evidence-based nutrition-specific interventions in 36 countries that account for 90 percent of the world’s stunting burden and 32 smaller countries that also have a high prevalence of undernutrition. The results of the study highlighted the global financing gap, underscored the importance of investing in nutrition at the global level, and laid out a methodology for estimating the costs for nutrition-specific interventions. However, these global estimates did not capture the nuances and context in each country, nor were they contextualized to every individual country’s policy and capacity setting or its fiscal constraints. This report builds on the early work to address this gap and to contextualize the cost estimates for Mali. The multisectoral approach requires nutrition-sensitive approaches or interventions that can be delivered through other sectors. As discussed above, globally there is currently very limited guidance on costing for nutrition-sensitive interventions. Therefore this present report provides an exploratory analysis to be used primarily to engage other sectors in planning for improved nutritional outcomes. This initial exercise will contribute to a broader discussion about methodological and other issues for costing nutrition-sensitive interventions, and will thereby encourage the formulation of standard definitions, methodologies, and guidance for costing these interventions in the future. 25 SCOPE OF THE ANALYSIS AND DESCRIPTION OF THE INTERVENTIONS The costed scale-up plan is presented in two sections. The first section presents estimated costs and benefits for the set of 10 nutrition-specific interventions that were included in the World Bank’s Scaling Up Nutrition report (2010) and are delivered primarily through the health sector. These interventions and the associated target population and current coverage for each intervention are specified in Table 2. The nutrition-specific interventions considered are a modified package of the interventions included in the 2008 and 2013 Lancet series on Maternal and Child Undernutrition, tailored to the Mali context. These 10 interventions are based on current scientific evidence and there is general consensus from the global community about the impact of these interventions. Some interventions—such as deworming and iron-fortification of staple foods—that were included in the 2008 Lancet series but not listed in the 2013 Lancet series are included here because they remain relevant to Mali. Others—such as calcium supplementation for women and prophylactic zinc supplementation—are excluded because delivery mechanisms are not available in client countries, including Mali, and/or there are no clear WHO protocols or guidelines for large-scale programming. In other cases, there are limited capacities for scaling up the interventions. Only those nutrition-specific interventions that are relevant to the Mali context and that have strong evidence of effectiveness, a WHO protocol, and a feasible delivery mechanism for scale-up are included in the proposed scale-up package below. As this evidence base grows, other interventions can be added over time. Table 2. Nutrition-Specific Interventions Delivered Primarily Through the Health Sector Target Intervention Description population Current coverage Behavior change communication focusing on Community optimal breastfeeding and nutrition programs complementary feeding for growth practices, proper handwashing, Families of 48% (based on coverage promotion of sanitation and good nutrition children 0–59 of community health children practices months of age centers, MOH 2013) Vitamin A supplementation Children 6–59 85.03% (SMART 2012; (children) Semi-annual doses months of age DHS 2006) Therapeutic zinc supplementation As part of diarrhea Children 6–59 with ORS (children) management with ORS months of age 0% (MICS/ELIM 2010) 26 For in-home fortification of complementary food (60 Children 6–23 sachets between 6 and 11 months of age not months of age, 60 sachets receiving Multiple between 12 and 17 months of fortified 6% (UNICEF micronutrient age, and 60 sachets between 18 complementary Programmatic Coverage powders (children) and 23 months of age). food Data) Deworming Two rounds of treatment per Children 12–59 63.8% (DHS 2006; (children) year months of age SMART 2012) Iron-folic acid supplementation for Iron-folic acid supplementation pregnant women during pregnancy Pregnant women 60.8% (DHS 2006) Iron fortification of staple foods (general Fortification of wheat flour General public) with iron population Negligible Salt iodization Iodization of centrally General 64% (MICS/ELIM (general public) processed salt population 2010) Provision of a small amount Twice the Public provision of (~250 kilocalories per day) of prevalence of complementary food nutrient-dense complementary underweight for prevention of food for the prevention of (WAZ < −2) moderate acute moderate malnutrition among children malnutrition (moderate acute malnutrition 6–23 months of (children) and/or moderate stunting) age 6.43% (WFP 2014) Includes the identification of severe acute malnutrition, community or clinic-based Community-based treatment (depending on the treatment of severe presence of complications), and Burden of severe acute malnutrition therapeutic feeding using acute (children) ready-to-use therapeutic food malnutrition 63.9% (MOH 2013) Note: ORS = oral rehydration salts; WAZ = weight-for-age Z-score. The analysis in the following section focuses on nutrition-sensitive interventions that are relevant to the Mali context and that have the potential to have an impact on nutrition outcomes. A description of these interventions, associated target populations, and responsible sectors are listed in Table 3. As discussed above, the evidence base for nutrition-sensitive interventions is not as strong as that for nutrition-specific interventions. Therefore these estimates are exploratory and are limited to six potential interventions, relevant to the Mali context, that can be scaled up and that have some potential for impact on nutrition outcomes. Additional interventions were not included in these initial estimates because their impact on nutrition is yet to be clearly documented (Masset et al. 2011; Ruel et al. 2013; World Bank 2013a), because this is an exploratory instead of an exhaustive effort, or because they were not considered relevant to Mali’s needs. Furthermore, cost attribution is complex because nutrition-sensitive interventions are designed for multiple purposes. 27 Table 3. Multisectoral, Nutrition-Sensitive Interventions: An Exploratory Process Target Intervention Description population Potential for Impact Interventions delivered through social protection Low-income* Preventive nutrition Preventive nutrition package children aged Improve intake of package delivered provided to pregnant mothers and 0–59 months, micronutrients and child as part of children under five targeted by pregnant nutrition status (Leroy, conditional cash national Emergency Safety Nets women, and Ruel, and Verhofstadt transfer program Projects mothers 2009; Ruel et al. 2013) Interventions delivered through the agricultural sector Improved child feeding practices lead to Nutrition education Use agricultural extension improvements in child via agricultural workers to educate producers on Producers and nutrition outcomes extension workers good nutrition practices their families (World Bank 2007) Some evidence for improved child Aflatoxin control nutritional status with improved Invest in improved granaries in (stunting) and reduced granaries for order to reduce aflatoxin Groundnut morbidity (Khlangwiset groundnuts contamination in groundnuts producers and Wu 2011) Interventions delivered through the education sector Distribution of praziquantel and Reduce anemia and albendazole to school-aged morbidity, improve children and training to school cognitive outcomes School-based teachers, community workers and School-aged (Miguel and Kremer deworming health workers children 2004) Reduce morbidity (diarrhea and respiratory School-based infection) and thereby promotion of good Handwashing campaign focusing School-aged improve growth hygiene on school-aged children children (APHCR 2010) Interventions delivered through the water and sanitation sector Reach MDG targets Public investment in increasing for improved access access to improved water and to water and sanitation infrastructure so as to Reduction in diarrhea sanitation reach the MDG targets for both General related illnesses infrastructure urban and rural areas population (Gunther and Fink 2011) *Low income is defined as those living under the poverty line. 28 ESTIMATION OF TARGET POPULATION SIZES, CURRENT COVERAGE LEVELS, AND UNIT COSTS Target population estimates are presented in Appendix 3. These estimates are based on projections from the 2009 Census that used 2.84 percent as the annual population growth rate. Data on the prevalence of child stunting (height-for-age Z-score <−2), underweight (weight-for- age Z-score <−2), and severe wasting (weight-for-height Z-score <−3) among children under five years of age in each region were obtained from the 2010 Mali MICS/ELIM, complemented as needed with recent Demographic and Health Survey (DHS) and Standard Monitoring and Assessment of Relief and Transitions (SMART) data. Data on current coverage levels were obtained from various sources. To avoid underestimating costs, the current coverage levels for iron fortification of staple foods and therapeutic zinc with oral rehydration salts (ORS) were set at 0 because either program coverage of these treatments is currently minimal or current reliable coverage data were not available. Programmatic data on the coverage of the treatment of severe acute malnutrition were provided by UNICEF. Coverage data on salt iodization were obtained from the 2010 Mali MICS/ELIM. Because of a lack of more recent data, coverage of iron-folic acid supplementation for pregnant women comes from DHS 2006. For coverage of vitamin A supplementation and deworming campaigns, survey data from SMART 2013 were used for the South; data from DHS 2006 were used for the North because of a lack of more recent data. Finally, because there are no consolidated national data on the coverage of behavior change interventions delivered through community nutrition programs, the percent of households living within 5 kilometers of a community health center was used as a proxy; these data are from the Ministry of Health (2013). The unit costs and delivery platforms for the nutrition-specific interventions are listed in Table 4. Whenever possible, the unit costs of the nutrition-specific interventions were estimated using programmatic data based on the preliminary results of a nutrition costing exercise conducted by the ICF and REACH, Ministry of Health sources, or implementing partners such as UNICEF and the WFP. In cases where the intervention was not being implemented or local data were not available, unit cost estimates from Nigeria or from the global costing study World Bank (2010) were used. A complete index of data sources and relevant assumptions for each intervention can be found in Appendix 4. The unit costs and the delivery platforms for selected nutrition-sensitive interventions are listed in Table 5. These interventions are delivered through a range of sectors and are based on programmatic data and ex-ante estimates from both Mali and neighboring countries. 29 Table 4. Unit Costs and Delivery Platforms Used in the Calculations for Nutrition-Specific Interventions Unit cost (US$ per beneficiary Intervention per year) Costed delivery platform Community nutrition programs for Community nutrition growth promotion (children)a 5.00 programs Vitamin A supplementation (children)a 0.37 Campaigns Therapeutic zinc supplementation with Community nutrition ORS (children)c 0.86 programs Multiple micronutrient powders Community nutrition (children)a 0.98 programs Deworming (children)a 0.04 Campaigns Iron-folic acid supplementation for Provided at the primary health pregnant womena 1.27 care center Iron fortification of staple foods (general public)b 0.20 Market-based delivery system Salt iodization (general public)b 0.05 Market-based delivery system Public provision of complementary food for prevention of moderate acute Community nutrition malnutrition (children)a 158.97 programs Primary health care centers Community-based treatment of severe and community nutrition acute malnutrition (children)a 135.33 programs a. denotes unit cost based on cost data from Mali. b. denotes unit cost based on global estimates. c. denotes unit cost from Nigeria. The first nutrition-sensitive intervention provides a package of preventive nutrition interventions to pregnant mothers and children in poor households that are receiving cash transfers as part of the Emergency Safety Nets Project..10 The unit cost is derived from the World Bank Group– financed Emergency Safety Nets Project in Mali, which includes a nutrition pilot to be implemented in 10 percent of target villages over five years (World Bank 2013c). Because this is based on the planned budget for an upcoming pilot project, actual costs may be higher or lower, especially when the nutrition package is scaled to full national coverage as envisioned in this study. For the nutrition-sensitive interventions in the agriculture sector, unit costs were estimated using cost data from both Mali and Niger. The unit cost (per producer per year) for nutrition education delivered through agricultural extension workers comes from program budgets from FAO Niger 10 A preventive nutrition package would include behavior change communication, vitamin A supplementation, therapeutic zinc supplementation as part of diarrhea treatment with ORS, micronutrient powders, deworming treatment, iron-folic acid supplementation, and iodized oil capsules. 30 (FAO 2014). Unit costs for aflatoxin control in groundnuts through the use of improved granaries (per 40 kilograms of groundnuts per year) come from an IFPRI (2012) working paper that reviewed the cost-effectiveness of various aflatoxin control methods in Mali. Table 5. Unit Costs and Delivery Platforms Used in the Estimations for Selected Nutrition-Sensitive Interventions Unit cost (US$) per benefit Intervention unit per year Delivery platform Interventions delivered through the social protection sector Preventive nutrition package delivered as part of conditional cash transfers program 36.27 per child Social safety nets Interventions delivered through the agriculture sector Nutrition education via agricultural Agricultural extension extension workers 4.17 per producer workers Aflatoxin control with improved 0.15 per 40 kg of granaries for groundnuts groundnuts Agriculture sector Interventions delivered through the education sector School-based deworming School-based deworming 0.08 per student distribution School-based promotion of good School-based hygiene hygiene 2.00 per student education campaign Interventions delivered through the water and sanitation sector Reach MDG targets for improved access to water and sanitation Public investment in infrastructure n.a. improved infrastructure Note: n.a. = not applicable. Unit costs for interventions in the education sector are based on the cost of similar programs delivered in other African countries. For school-based deworming, the unit cost ($0.08) used in the calculation is obtained from regional estimates of delivery cost in schools for Ghana (Guyatt 2003), assuming twice-a-year treatment. These estimates compare well with recent regional bottom-up cost analysis, based on the neglected tropical disease national plans from 36 Sub- Saharan countries (Seddoh et al. 2013),11 which estimates the unit cost of preventive chemotherapy of five neglected tropical diseases in the Africa region at $0.26. The major cost components for deworming are human resources, surveillance and mapping, non-donated drugs, advocacy, infrastructure and logistics, and implementation and management. No unit cost 11 The five NTDs are lymphatic filariasis, onchocerciasis, schistosomiasis, trachoma, and soil-transmitted helminthiasis. 31 estimate is available for school-based promotion of good hygiene for Mali, so the unit cost of $2 per student obtained from UNICEF (2012) on WASH in schools is used as the unit cost. This includes the cost of capacity building, monitoring, advocacy, and social mobilization. ESTIMATION OF COSTS AND BENEFITS The program experience methodology employed in World Bank (2010) is used for calculating the cost of scaling up in Mali. This approach generates unit cost data that capture all aspects of service delivery, including the costs of commodities, transportation and storage, personnel, training, supervision, monitoring and evaluation (M&E), relevant overhead, wastage, and so on for each intervention from actual programs that are already in operation in Mali. Another commonly used method is the ingredients approach, in which selected activities are bundled into appropriate delivery packages (for example, number of visits to a health center) (see, e.g., Bhutta et al. 2013). Although the program experience approach tends to yield cost estimates higher than those of the ingredients approach, these estimates more accurately reflect real programmatic experience, including inefficiencies in service delivery. It should, however, be noted that the calculated costs are reported in financial or budgetary terms. They do not capture the full social resource requirements, which account for the opportunity costs of the time committed by beneficiaries accessing the services. We calculate the annual public investment required to scale up the interventions as follows: where: Y = annual public investment required to scale up to full coverage12 = additional total cost to scale up to full coverage = additional cost for capacity development, M&E, and technical assistance = cost covered by households living above poverty line for selected interventions Appendix 5 describes the methodology in detail. The expected benefits from scaling up nutrition interventions are calculated in terms of (1) DALYs saved, (2) number of lives saved, (3) cases of childhood stunting averted, and (4) increased program coverage. To calculate the number of DALYs, we use the method employed by Black et al. (2008) to estimate the averted morbidity and mortality from scaling up different nutrition interventions. The method uses population attributable fractions (PAF) based on the comparative risk assessment project (Ezzati et al. 2002; Ezzati et al. 2004) to estimate the burden of infectious diseases attributable to different forms of undernutrition using most recent Global Burden of Disease Study (IHME 2010). DALY estimates in this study are neither discounted nor 12 Full coverage is defined as 100 percent of the target population for all interventions except for community-based treatment of severe acute malnutrition, for which full coverage is assumed to be 80 percent. 32 age-weighted, in line with the methodology used in the Global Burden of Disease Study and the WHO Global Health Estimates (2012). Appendix 6 describes the methodology for estimating DALYs. The projected number of lives saved and the cases of childhood stunting averted are calculated using the Lives Saved Tool (LiST), which translates measured coverage changes into estimates of mortality reduction and changes in the prevalence of under-five stunting. This analysis included all ten interventions to calculate the number DALYs saved. However, because of methodological limitations of the LiST tool, the calculation for number of lives saved is based on only six of the ten interventions,13 and cases of childhood stunting averted is based on only four of the ten.14 As such, the estimates presented here are likely to underestimate the number of lives saved and cases of childhood stunting averted. Appendix 7 describes the methodology for the LiST estimates. The measures for cost-effectiveness of nutrition-specific interventions are calculated in terms of cost per DALY saved, cost per life saved, and cost per case of stunting averted. Estimates of benefits were combined with information on costs to produce the cost-effectiveness measures for each intervention as well as for the overall package of intervention. The evaluation of cost- effectiveness ratio in terms of DALYs saved is based on the categorization used by WHO- CHOICE (Choosing Interventions that are Cost-Effective):15 an intervention is considered to be “very cost-effective” if the range for the cost per DALY averted is less than GDP per capita;16 it is considered to be “cost-effective” if it is between one and three times GDP per capita; and it is considered “not cost-effective” if it exceeds three times GDP per capita (WHO 2014). The cost-benefit analysis is based on the estimated economic value of the benefits attributable to nutrition-specific interventions. In order to arrive at a dollar value of the impact on mortality and morbidity of a five-year scale-up plan, we use estimates of the number of lives saved and the reduction in stunting prevalence produced by the LiST tool. Following established practice, a life year saved is valued as equivalent to gross national income (GNI) per capita; this is considered to be a conservative measure because it accounts for only the economic, not the social, value of a year of life. In order to estimate the value of the reduction in stunting, we follow the methodology used in Hoddinott et al. (2013), which values a year of life lived without stunting based on the assumption that stunted individuals lose an average of 66 percent of lifetime earnings. Future benefits are then age-adjusted and discounted at three potential discount rates (3, 5, and 7 percent) in order to arrive at their present value. The present value of future benefits is then compared with the annual public investment required, which allows us to estimate the net 13 The six interventions are community nutrition programs for growth promotion, vitamin A supplementation, therapeutic zinc supplementation with ORS, iron-folic acid supplementation, the public provision of complementary food for the prevention of moderate acute malnutrition, and community-based management of severe acute malnutrition. 14 The four interventions are community nutrition programs for growth promotion, vitamin A supplementation, iron-folic acid supplementation, and the public provision of complementary food for the prevention of moderate acute malnutrition. 15 Information on the cost-effectiveness thresholds used by WHO-CHOICE can be found at http://www.who.int/choice/costs/CER_levels/en/ 16 Mali’s GDP per capita in current U.S. dollars was $715 in 2013 (World Bank 2014a). 33 present value (NPV) and internal rate of return of the investment. A detailed explanation of the benefit estimation methodology can be found in Appendix 8. The annual increase in economic productivity attributable to each package of interventions is calculated based on the same estimates of future benefits. Although these benefits occur only once beneficiaries have reached productive age, we assume that these benefits approximate the present value of economic productivity lost each year as a result of mortality and morbidity that would otherwise be prevented by scaling up nutrition interventions. Values presented are taken from a year in which all beneficiaries have reached productive age. The approach for estimating the potential costs and benefits of nutrition-sensitive interventions differs from the methodology used for nutrition-specific interventions. Similar to nutrition- specific interventions, the total cost for scaling up the interventions is calculated by multiplying the unit cost by the target population (whether local unit costs or regional unit costs are used depends on their availability). However, since most nutrition-sensitive interventions have multiple objectives, it is not always feasible to attribute the nutrition-related benefits to the overall costs of the interventions. Because these constraints limit the accuracy of cost- effectiveness estimates, we instead rely on secondary sources and published literature when available, with cost-effectiveness presented in terms of cost per DALY saved. SCENARIOS FOR SCALING UP NUTRITION INTERVENTIONS When estimating the costs and benefits of scaling up nutrition interventions, we begin with estimates for scaling up all 10 interventions to full national coverage and follow this with estimates for various scale-up scenarios. The full-coverage estimates can be considered the medium-term policy goal for the Government of Mali, but resource constraints will probably limit the government’s ability to achieve full national coverage in the short term. Therefore we also propose three scenarios for prioritizing the scale-up of nutrition interventions, which also allow for some alignment with Mali’s most pressing structural and crisis-induced needs:  Scenario 1: Scale up by region  Scenario 2: Scale up by intervention  Scenario 3: Scale up by intervention and region Full coverage is defined as 100 percent of the target population for all interventions except for community-based treatment of severe acute malnutrition, for which full coverage is assumed to be 80 percent. This definition is consistent with the methodology used in World Bank (2010), and is based on the reality that few community-based treatment programs have successfully achieved more than 80 percent coverage at scale. 34 PART III – RESULTS FOR NUTRITION-SPECIFIC INTERVENTIONS TOTAL COST, EXPECTED BENEFITS, AND COST-EFFECTIVENESS The total additional public investment required to scale up 10 nutrition-specific interventions from current coverage levels to full coverage (so that 100 percent of the target population is covered) at the national level in Mali is estimated at nearly $64 million annually (Table 6). This cost includes the additional cost of scaling up all 10 interventions ($91 million per year) from current coverage levels, plus additional resources for M&E, operations research, technical support, and capacity development for program delivery (about $10 million). We anticipate that, of this total amount, part of the costs of iron fortification, multiple micronutrient powders, salt iodization, and public provision of complementary food could be covered by private resources from households above the poverty line, accounting for an estimated $37 million. Table 6. Estimated Cost of Scaling Up 10 Nutrition-Specific Interventions to Full Coverage Annual cost Intervention (US$, millions) Community nutrition programs for growth promotion (children) 8.1 Vitamin A supplementation (children) 0.2 Therapeutic zinc supplementation with ORS (children) 2.5 Micronutrient powders (children) 0.4 Deworming (children) 0.03 Iron-folic acid supplementation for pregnant women 0.4 Iron fortification of staple foods (general public) 3.5 Salt iodization (general public) 0.3 Public provision of complementary food for prevention of moderate acute 67.2 malnutrition (children) Community-based treatment of severe acute malnutrition (children) 8.2 Total cost for scaling up all 10 interventions 90.6 Capacity development for program delivery 8.2 M&E, operations research, and technical support 1.8 Household contributions from private resources <37.0> ANNUAL PUBLIC INVESTMENT REQUIRED 63.6 35 The expected benefits from scaling up these 10 nutrition-specific interventions are large (Table 7). About 482,000 DALYs and more than 14,000 lives would be saved annually,17 while over 260,000 cases of stunting among children under five would be averted.18 Programs are estimated to expand as follows:  The families of 1.6 million children 0–59 months of age would be reached by community programs for behavior change communication and growth promotion  429,000 children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation  377,000 children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders  2.9 million children 6–23 months of age would be treated for diarrhea with zinc and oral rehydration solution  820,000 children 12–59 months of age would receive deworming medication  311,000 pregnant women would receive iron-folic acid tablets as part of their antenatal care  17.3 million people would be able to consume staple foods fortified with iron  6.2 million people who do not currently use iodized salt would be able to obtain it  71,000 children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices  431,000 children 6–23 months of age would receive a small amount of nutrient-dense complementary food for the prevention or treatment of moderate acute malnutrition 17 From six of the ten interventions. 18 From four of the ten interventions. 36 Table 7. Estimated Annual Benefits for Scaling Up 10 Nutrition Interventions to Full Coverage Number of Cases of beneficiaries DALYs stunting Intervention covered saveda Lives saved averted 1,620,822 Community nutrition programs 293,726 6,582 126,428 for growth promotion (children) Vitamin A supplementation 428,485 7,898 713 16,423 (children) Therapeutic zinc supplementation — 2,861,447 11,874 3,361 with ORS (children) 405,296 53,271 — — Micronutrient powders (children) — — Deworming (children) 819,722 3,905 Iron-folic acid supplementation 311,128 17,658b 128 620 for pregnant women Iron fortification of staple foods — — — 17,309,000 (general public) 6,162,004 — — — Salt iodization (general public) Complementary food for prevention of moderate acute 430,935 13,316 1,093 145,640 malnutrition (children) Community-based treatment of 71,003 severe acute malnutrition 80,376 5,149 — (children) TOTALc — 482,025 14,439 263,392 Note: ORS = oral rehydration salts; — = not available. a. DALY estimates in this study are neither discounted nor age-weighted, in line with the methodology used in the IMHE Global Burden of Disease 2010 and the WHO Global Health Estimates 2012. For more information on the methodology used to calculate DALYs averted, see Appendix 5. b. DALY estimates for iron-folic acid supplementation are calculated for DALYs averted among pregnant women. They do not include the DALYs averted among children born to mothers who received these supplements. c. The total of the interventions implemented simultaneously does not equal to the sum of the individual interventions. This is because some interventions affect nutrition outcomes via similar pathways causing their combined impact to be different than the individual sums. Most of the proposed nutrition-specific interventions are highly cost-effective, but there are two exceptions (Table 8). Seven of the ten interventions are considered “very cost-effective” according to the criteria set forth by WHO-CHOICE (WHO 2014), which considers an intervention to be “very cost-effective” if the cost per DALY saved is less than GDP per capita ($715 for Mali). These include community nutrition programs for growth promotion, vitamin A supplementation, zinc supplementation, micronutrient powders, deworming, iron-folic acid supplementation for pregnant women, and the community-based management of severe acute 37 malnutrition. The public provision of complementary food for the prevention of moderate acute malnutrition is $5,034—a substantially higher cost per DALY saved. This is more than three times the Mali GDP per capita and is therefore not considered to be cost-effective. Given the low benefit-cost ratio and fiscal constraints in Mali, the public provision of complementary food for the prevention of moderate acute malnutrition should be assigned the lowest priority in terms of scale up. Logistical constraints and potential issues of governance and accountability can further hinder a successful scale-up of this intervention. Table 8. Cost-Effectiveness of Scaling Up 10 Nutrition Interventions to Full Coverage (US$) Cost/case of Cost/DALY saved Cost/life stunting (US$) Intervention saved averted Mali Global Community nutrition programs for growth promotion (children) 1,231 64 28a 53–153 Vitamin A supplementation (children) 222 10 20a 3–16 Therapeutic zinc supplementation with ORS (children) 732 — 207a 73 Micronutrient powders (children) — — 7a 12 Deworming (children) — — 8a — Iron-folic acid supplementation for pregnant women 3,051 630 22a 66–115 Iron fortification of staple foods (general public) — — — — Salt iodization (general public) — — — — Public provision of complementary food for prevention of moderate acute malnutrition (children) 61,452 461 5,034 500–1,000 Community-based treatment of severe acute malnutrition (children) 1,584 — 101a 41 Total when all interventions implemented simultaneouslyb 6,276 344 188 n/a Note: ORS = oral rehydration salts; — = not available. a.Very cost-effective according to WHO-CHOICE criteria (WHO 2014). b. The total of the interventions implemented simultaneously does not equal to the sum of the individual interventions. This is because some interventions affect nutrition outcomes via similar pathways causing their combined impact to be different than the individual sums. THREE POTENTIAL SCALE-UP SCENARIOS Scenario 1: Scale Up by Region Table 9 shows the estimated costs and benefits of scaling up the 10 nutrition-specific interventions according to the stunting burden found in each region. As discussed above, the 38 regions display significant disparities in malnutrition rates, with Sikasso, Ségou, Mopti, and Tombouctou (highest burden regions) all suffering from under-five stunting prevalence of 40 percent or above, while stunting rates in the other provinces fall between 30 and 40 percent (middle burden regions), with the exception of Bamako (the lowest burden region), where stunting prevalence is 21 percent (DHS Program 2012).19 The highest burden regions account for an overwhelming portion (over two-thirds) of the total cost of scaling up nutrition interventions. These regions will disproportionately benefit from such interventions, as demonstrated by their high expected benefits in terms of DALYs saved, lives saved, and cases of stunting averted (Table 9).20 Table 9. Scenario 1: Costs and Benefits of Scaling Up 10 Nutrition Interventions by Region Annual public Annual benefits investment Region by stunting rate (US$, millions) DALYs saved Lives saved Highest burden regions:a stunting 44 280,075 9,203 rates greater than 40 percent Middle burden regions:b stunting 16 141,116 3,710 rates between 30 and 40 percent Lowest burden region:c stunting 4 60,834 1,526 rates less than 30 percent TOTAL 64 482,025 14,439 a. Regions with the highest burden of stunting (40 percent or more) are Sikasso, Ségou, Mopti, and Tombouctou. b. Regions exhibiting the middle burden of stunting (30 to 40 percent) are Kayes, Koulikoro, Gao, and Kidal. c. The region with the lowest burden of stunting (21 percent) is Bamako. Given the high burden of stunting as well as the population’s vulnerability to malnutrition in Sikasso, Ségou, Mopti, and Tombouctou, these regions should be prioritized for scale-up. This scenario would require an investment of $44 million annually, and would save over 280,000 DALYs and 9,000 lives. It would also increase program reach as follows:  The families of 259,000 children aged 0–59 months would be reached by community programs for behavior change  147,000 children aged 6–59 months would receive twice-yearly doses of life-saving vitamin A supplementation 19 Stunting rates for Gao, Tombouctou, and Kidal are from DHS 2006. The DHS 2006 data are an overestimate of the 2010 MICS/ELIM data but may be more closely aligned with the latest 2013 numbers when taking into account the strong effects of the crisis on the North. 20 These calculations take into account population variation across the regions and, accordingly, the numbers of children not currently served who can benefit from each of the interventions. The later are reported in Appendix 3. 39  1.6 million children aged 6–59 months would receive zinc supplementation as part of diarrhea management  148,000 children aged 6–23 months would receive vitamins and minerals through multiple micronutrient powders  363,000 children aged 12–59 months would receive deworming medication  161,000 pregnant women would receive iron-folic acid tablets as part of their antenatal care  9.2 million people would be able to consume staple foods fortified with iron  2.5 million people would gain access to iodized salt  59,000 children aged 6–59 months would be treated for severe acute malnutrition using community-based management practices  136,000 children aged 6–23 months would receive a small amount of nutrient-dense complementary food (~250 kilocalories/day) for the prevention or treatment of moderate acute malnutrition Scenario 2: Scale Up by Intervention The primary considerations for choosing the interventions in each step are cost-effectiveness, recommended phasing of interventions, implementation capacity, and the presence of already- existing delivery mechanisms. The proposed plan for a step-wise scale-up by intervention is summarized below and illustrated in Figure 14.  Step 1 includes community nutrition programs for behavior change and growth promotion, in addition to all micronutrient and deworming interventions. The cost of scaling up Step 1 interventions to reach full coverage (100 percent of the target population) is $15.7 million per year. In this step, an additional $1.4 million is allocated for capacity development and approximately $300,000 is allocated to M&E and operations research. Households above the poverty line are expected to cover $2.6 million toward the costs of micronutrient powders, salt iodization, and iron fortification of flour, bringing the total public resources required to $14.9 million.  Step 2 includes the scale up of community-based treatment of severe acute malnutrition (CMAM) from existing coverage levels to 80 percent. We estimate that the cost of increased CMAM coverage is $8.2 million per year. We also allocate approximately $734,000 for capacity development and $163,000 for M&E and operations research, for a total of $9.1 million.  Step 3 includes only the public provision of complementary food for the prevention of moderate acute malnutrition. The estimated cost of this intervention is $67.2 million. However, we anticipate that households above the poverty line would be able to contribute $34.7 million. After allocating approximately $6 million to capacity development and $1.3 million for M&E, the total public investment required for Step 3 is $39.9 million. It should be noted that when the public provision of complementary food for moderate acute malnutrition is included in the package of interventions, the cost of providing multiple micronutrient powders will decline. This is because of an overlap between the populations targeted by micronutrient powders and the public provision of complementary food: when 40 children aged 6–23 months who suffer from moderate malnutrition are provided with complementary food, they do not receive micronutrient powders. Those children 6–23 months old who are not at risk of developing moderate malnutrition will continue to receive micronutrient powders even when the provision of complementary food is included. In this case, the total public resources required for micronutrient and deworming interventions declines from $14.9 million to $14.7 million. Figure 14. Proposed Scenario 2: Stepwise Scale-Up by Intervention Note: When the public provision of complementary food for moderate acute malnutrition is included in the package of interventions, the cost of providing multiple micronutrient powders will decline because of an overlap between the populations targeted by micronutrient powders and the public provision of complementary food. See text for details. Given cost-effectiveness and implementation capacity considerations, we propose that Mali scale up only Step 1 and Step 2 interventions, which would require an annual public investment of $24 million (Table 10), and save at least 456,000 DALYs and 14,000 lives. Step 3 is given the lowest priority for three main reasons: (1) recent findings from the 2013 Lancet series suggest that, in most situations, the prevention of moderate malnutrition with a strong focus on counseling and education achieves similar results with or without public provision of complementary foods; (2) 41 at $39.9 million, the cost of providing complementary food accounts for nearly half of the overall cost. In contrast, the cost of all micronutrient and deworming interventions (in the absence of the provision of complementary food) is only $5.9 million, and these have much more attractive benefit-cost ratios; and (3) governance and accountability challenges for providing complementary food, as well as logistics and supply chain constraints, are considerable. The scale-up of Step 1 and Step 2 interventions would benefit the following target groups:  The families of 1.6 million children 0–59 months of age would be reached by community programs for behavior change communication and growth promotion  429,000 children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation  775,000 children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders  2.9 million children 6–23 months of age would be treated for diarrhea with zinc and oral rehydration solution  820,000 children 12–59 months of age would receive deworming medication  311,000 pregnant women would receive iron-folic acid tablets as part of their antenatal care  17.3 million people would be able to consume staple foods fortified with iron  6.2 million people who do not currently use iodized salt would be able to obtain it  71,000 children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices 42 Table 10. Scenario 2: Costs and Benefits for Scaling Up Nutrition-Specific Interventions, by Intervention Annual public Annual benefits investment Intervention (US$, millions)a DALYs saved Lives saved Step 1: Community nutrition programs for growth promotion, vitamin A, deworming, $15 388,333 9,243 zinc, iron-folic acid supplementation, iron fortification of staple foods, salt iodization, multiple micronutrient powders Step 2: Community-based management of $9 67,594 2,320 severe acute malnutrition Subtotal when Step 1 and Step 2 interventions are implemented $24 468,709 11,243 simultaneouslyb Step 3: Public provision of complementary food for the prevention of moderate $40 13,316 1,093 malnutrition $64 482,025 14,439 TOTALb Note: Cells in red indicate recommended interventions under this scenario. a. Micronutrient powder costs are reduced by $0.2 million when the public provision of complementary food is included. b. The total of the interventions implemented simultaneously does not equal to the sum of the individual interventions. This is because some interventions affect nutrition outcomes via similar pathways, causing their combined impact to be different than the individual sums. Scenario 3: Scaling Up by Intervention and by Region Scenario 3 seeks to balance political and local context considerations with the most effective allocation of resources. Because allocating resources based purely on technical considerations can alienate excluded groups and not be fully aligned with local needs, this scenario seeks to prioritize the scale-up of higher-cost interventions in regions with higher burdens of malnutrition; such interventions are in addition to less costly interventions implemented by the government in all regions of the country. We therefore propose that the government implement all micronutrient and deworming interventions in each of the nine regions, while prioritizing the scale-up of community-based management of severe acute malnutrition and community nutrition programs in regions with under-five stunting rates above 40 percent. This scenario would help address some of the structural chronic and acute malnutrition issues that require targeted interventions (pregnant women and children in the four regions with the highest levels of chronic malnutrition and vulnerability to malnutrition, in Tombouctou and Ségou with very high levels of acute malnutrition, and so on). At the same time, this approach would help tackle some of the effects of 43 the recent crisis, seen by the worsening nutrition outcomes of very young children and in a region such as Ségou. Scaling up interventions under Scenario 3 would require $18 million in public resources (red cells in Table 11). Scenario 3 would save approximately 319,500 DALYs and 11,000 lives. Program coverage would increase as follows:  The families of 259,000 children 0–59 months of age would be reached by community programs for behavior change communication and growth promotion  429,000 children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation  2.9 million children 6–23 months of age would be treated for diarrhea with zinc and oral rehydration solution  775,000 children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders  820,000 children 12–59 months of age would receive deworming medication  311,000 pregnant women would receive iron-folic acid tablets as part of their antenatal care  17.3 million people would be able to consume flour fortified with iron  6.2 million people who do not currently use iodized salt would be able to obtain it  59,000 children 6–59 months would be treated for severe acute malnutrition using community-based management practices Table 11. Scenario 3: Cost of Scaling Up Selected Nutrition Interventions, by Intervention and Region (US$, millions) Highest Middle Lowest burden burden burden Intervention/Region regionsa regionsb regionc Total Micronutrient and deworming 3.4 2.0 0.5 5.9 interventions Community-based management of severe acute malnutrition and 11.9 4.2 2.0 18.0 community nutrition programs Step 3: Public provision of complementary food for the prevention 29.1 9.7 1.2 39.9 of moderate acute malnutrition TOTAL 44.4 15.6 3.6 63.7 Note: Cells in red show indicate recommended interventions under this scenario. a. Sikasso, Segou, Mopti, Tombouctou. b. Kayes, Koulikoro, Gao, Kidal. c. Bamako. 44 COST-BENEFIT ANALYSIS OF THE SCALE-UP SCENARIOS Our analysis reveals significant differences in the cost-effectiveness of the various scenarios, with Scenarios 2 and 3 being the most attractive. Table 12 summarizes the costs, DALYs saved, projected number of lives saved, and projected cases of child stunting averted (where possible) for each scale-up scenario. Scenario 2 offers the most cost-effective solution, with a cost per DALY saved of just $51. However, Scenario 3 offers the lowest annual cost—$18 million— while also achieving a relatively high degree of cost-effectiveness with a cost per DALY saved of $57. Although all scenarios offer significant benefits, a combination of cost-effectiveness considerations and resource limitations make Scenarios 2 and 3 the most attractive options. Table 12. Summary of Costs and Benefits by Scenario Annual public Annual benefits Cost per benefit unit investment Cases of Case of (US$, DALYs Lives stunting DALY Life stunting Scenario millions) saved saved averted saved saved averted Full national $64 482,025 14,439 263,392 $188 $6,276 $344 coverage Scenario 1 by $44 280,075 9,203 — $197 $5,992 — region Scenario 2 by $24 468,709 11,243 — $51 $2,119 — intervention Scenario 3 by region and by $18 319,460 10,730 — $57 $1,708 — intervention Note: — = not available. Recognizing the difficulty of scaling to full coverage in one year, we also consider the costs of scaling up gradually over five years rather than in one year for all scenarios (Table 13). Scenarios 2 and 3 would require $67 and $50 million, respectively, as compared with $174 million for the full coverage scenario. Interventions are assumed to scale from current coverage as follows: 20 percent of coverage in year 1, 40 percent in year 2, 60 percent in year 3, 80 percent in year 4, and 100 percent in year 5. For these calculations, we consider the expenditures on capacity development and system strengthening required to scale to full coverage to be a fixed cost, with some additional funds allocated to refresher training and rehiring in the years after the intervention has scaled to reach 100 percent. Thus, the average annual amount spent on capacity development is allocated across the five years rather than increasing in proportion to coverage as is the case with the other costs. 45 Table 13. Cost for Scale-Up of All Scenarios (US$, millions) Total Year 1 Year 2 Year 3 Year 4 Year 5 scale-up (20% (40% (60% (80% (100% costs Scenario coverage) Coverage) coverage) coverage) coverage) over 5 years Full national coverage (all 10 14 25 34 45 56 174 interventions) Scenario 1 10 17 24 32 40 123 Scenario 2 5 9 13 18 22 67 Scenario 3 4 7 10 13 16 50 A high burden of malnutrition negatively impacts a nation’s human capital. Therefore, an investment in improving nutrition outcomes among children is also an investment in Mali’s economic future. The two main ways in which malnutrition affects economic productivity are increased mortality and morbidity—in other words, lives lost and years lived with a disease or disability. For the purposes of this analysis, we estimate the potential economic benefits of scaling up nutrition interventions in terms of lives saved (reduction in mortality) and cases of stunting averted (reduction in morbidity). Because each life lost results in one less citizen contributing to the nation’s economy, and because stunted children tend to earn and consume less, these impact estimates help us to arrive at approximations of the return on investment attributable to the scale up over five years of a particular package of interventions. Our analysis of the economic productivity of the investment in nutrition is limited to the full coverage scenario because we were only able to estimate cases of stunting averted for this one scenario. It should be noted that these estimates of economic benefits are based on a highly conservative methodology that does not necessarily account for all of the potential benefits associated with improving nutrition outcomes among Malian children. However, even such conservative estimates clearly demonstrate that the opportunity costs of not scaling up nutrition interventions are substantial: we estimate that an investment in bringing all 10 interventions to full national coverage over five years (the full coverage scenario) could produce $194 million in annual returns over the productive lives of the children affected (Table 14). We also find that the investment yields positive net present values, large benefit-cost rations and a high internal rate of return (Table 14). Because any increase in the assumed discount rate reduces the present value of future benefits, we perform a sensitivity analysis using 3 potential discount rates: 3, 5, and 7 percent. We identify net present values ranging between $0.81 and $2.7 billion depending of the discount rate (7 and 3 percent, respectively). The investment yields an internal rate of return of almost 18 percent. 46 Table 14. Economic Analysis of the Full Coverage Investment in Nutrition, Five- Year Scale-Up Economic measure Impact of full scale-up of all 10 interventions Annual increase in economic productivity $194 million Internal rate of return 17.7 % Discount rate 3% 5% 7% Net present value $2.7 billion $1.5 billion $0.81 billion Benefit-cost ratio 18.5 11.0 6.9 FINANCING NUTRITION IN MALI Despite the resumption of bilateral development assistance and the mobilization of international resources for the emergency response, there is broad agreement among stakeholders that current financing for nutrition in Mali is inadequate (SUN 2012). At the central government level, no budget line item is currently dedicated to nutrition. Instead, government allocations to nutrition share space in the budget with other health activities, including malaria and hygiene (MOH 2011). As shown in Figure 15, the budget allocation to the Nutrition Division is relatively small, amounting to CFAF 564 million in 2010 (approximately $1.2 million) and just CFAF 356 million in 2011 (approximately $750,000). In 2010, nutrition funding represented only 1.9 percent of the DNS budget and a mere 1 percent in 2011 (MOH 2011). 47 Figure 15. National Budget Allocation to Nutrition Source: MOH 2011. Over the last decade, nutrition ODA to Mali has increased significantly in both value and as a share of overall ODA (Figure 16). In fact, even as overall ODA declined sharply following the 2012 coup, international financing for nutrition grew from $23.5 million in 2011 to over $27 million in 2012 (OECD 2014). Although ODA figures have yet to be released for subsequent years, nutrition funding for the humanitarian response alone reached a total of $27.97 million in 2013, indicating sustained growth in donor funding for nutrition in the midst of an ongoing nutrition crisis (UN OCHA 2014). However, given the large proportion of donor nutrition funding currently allocated to the humanitarian response, it remains unclear whether this level of funding can be sustained over the long term. 48 Figure 16. Aid Flows to Nutrition and Health, 2004–2012 Source: OECD 2014. UNCERTAINTIES AND SENSITIVITY ANALYSES Because actual unit costs may differ from our estimates, it is important to consider the effects of either an increase or decrease in these costs on the overall price of the interventions. This uncertainty is greatest for higher-cost interventions and less significant for those with lower costs. For example, given the prevalence of information on and experience with less expensive micronutrient and deworming interventions, there is a high degree of certainty about their estimated costs. On the other hand, the costs of community nutrition programs can vary greatly depending on their context, the intensity of behavior change campaigns, the number of community health workers employed, and the amount of incentives provided to them—all of these affect unit costs. Finally, there is limited experience in the public provision of complementary food for the prevention of moderate acute malnutrition, while unit costs depend on the type of food provided as well as the choice of targeting method. Other factors, such as widespread concern about governance and diversion of the food, also need to be considered. In order to account for these uncertainties, we perform a partial sensitivity analysis that describes the impact of variation in unit costs while holding other variables constant. These results are presented in Appendix 9. 49 PART IV – RESULTS FOR NUTRITION-SENSITIVE INTERVENTIONS This section presents cost-effectiveness estimates for six nutrition-sensitive interventions that are highly relevant for Mali: a preventive nutrition package delivered as part of a targeted cash transfer program; aflatoxin reduction in groundnuts with improved granaries; nutrition education via agricultural extension workers; school-based deworming; and school-based promotion of good hygiene. The sixth intervention is the effort to reach MDG targets for improved access to water and sanitation. Table 15 summarizes the cost of scaling up these interventions and, when available, the projected DALYs saved and cost per DALY saved. Table 15. Preliminary Results for Costing Nutrition-Sensitive Interventions Annual cost Cost- Intervention (US$, millions) Benefits effectiveness Delivered through social protection Preventive nutrition package delivered as part of conditional cash 49.4 — — transfer programa Delivered through the agriculture sector Aflatoxin control with improved $272 per DALY 1.3 4,582 DALYs saved granaries for groundnutsb saved Nutrition education via agricultural 19.0 — — extension workersc Delivered through the education sector d School-based deworming $4.55 per 0.3 DALY saved School-based promotion of good 6.2 — — hygienee Delivered through the water and sanitation sector Reach MDG targets for improved 331.0 2 to 1 benefit- access to water and sanitation — infrastructure f (total cost) cost ratio Note: — = not available. a. Calculations based on the nutrition component included in the World Bank Social Safety Nets Project. b. Based on IFPRI (2012), Liu and Wu (2010), and author calculations. c. Based on financial data from FAO Niger Nutrition Education Program (FAO 2014) and author’s calculations. d. Based on unit cost estimates from Ghana school-based deworming program presented in Guyatt (2003); cost per DALY saved based on a similar program with similar costs in Kenya (J-PAL 2012). e. Based on global unit costs from UNICEF (2012) and school enrollment from World Bank (2014a). f. From Hutton (2012). 50 INCORPORATING NUTRITION INTERVENTIONS INTO SOCIAL PROTECTION PROGRAMS The costs of scaling up the provision of preventive nutrition interventions to the children of all cash transfer beneficiaries are estimated to be $49 million annually. These calculations are based on the anticipated budget for the safety net program financed by the World Bank Group, which includes a pilot for providing preventive nutrition services to beneficiaries. The cost of reaching national coverage is based on a target population equivalent to the total number of children under five years of age multiplied by the poverty headcount ratio found in the MICS/ELIM 2010 survey. However, it may be possible to reduce costs by targeting the nutrition package geographically so that it is included in cash transfers only to those households living in the country’s most vulnerable regions. One weakness in the methodology is that it was not possible to calculate the incremental (or separate) cost of delivering the nutrition interventions within the bundle of services. NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE AGRICULTURE SECTOR Several pre- and post-harvest options exist for controlling aflatoxins in groundnuts in Mali, each with varying degrees of cost-effectiveness. Based on the limited data available, the use of improved granaries offers the most cost-effective approach—this approach has an estimated annual cost of $1.3 million, the potential to reduce aflatoxin concentrations in groundnuts by an average of 55 percent, and a cost per DALY averted of $272. According to Liu and Wu (2010), aflatoxin consumption in Africa leads to an average of 3 to 56 cases of liver cancer per 100,000 people each year, which in the Malian context could equate to an annual loss of nearly 10,000 DALYs.21 Table 16 provides an overview of four approaches to control aflatoxins in groundnuts, as well as the cost-effectiveness of scaling up to national coverage. 21 Authors calculations, based on 2014 population estimates from the Malian National Institute of Statistics (2012). 51 Table 16. Estimated Cost-Effectiveness of Aflatoxin Control Methods for Groundnuts Estimated effectiveness (% reduction in Estimated Cost per Annual cost aflatoxin DALYs DALY averted Control method (US$, millions) concentrations) averted (US$) Improved groundnut seed 4.4 60 4,999 889 Canvas/tarpaulin 4.6 50 4,166 1,099 Post-harvest intervention packagea 4.0 69 5,749 693 Improved granary 1.3 55 4,582 272 Sources: Unit cost and percent reduction estimates taken from IFPRI (2012); total cost of full national coverage calculated using Malian groundnut harvest area from FAOSTAT (2012); estimated DALYs averted based on the upper bound regional (Sub-Saharan Africa) average incidence of aflatoxin-induced liver cancer found in Liu and Wu (2010), with an assumed average of 13 DALYs per case of liver cancer. Note: Benefits from aflatoxin control are assumed to have a five-year lag time, with DALYs averted discounted at a 3 percent discount rate. a. Post-harvest intervention package includes education, hand sorting, drying on natural fiber mats, storing off- ground on wooden pallets, and insecticide. Extending nutrition education nationally to all persons working in agriculture would cost an estimated $19 million annually. This is based on the unit cost of $4.17 per producer from program budgets from the FAO Niger Nutrition Education Program (FAO 2014).22 These costs would probably decrease over time because they include the cost of training extension workers to deliver nutrition messages. We can assume that the impact of this nutrition education would be similar to that of community-based behavior change communication programming, which is estimated in previous sections. NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE EDUCATION SECTOR The cost of scaling up school-based deworming is estimated to be $300,000 annually. The target population is school-aged children (6–19 years old) enrolled in primary and secondary schools; current coverage is assumed to be negligible. 22 In order to calculate the cost of scaling up nutrition education programming in the agriculture sector, we derive our target population of producers by multiplying the total population by the proportion of working age adults (age 15+) found in the World Bank’s World Development Indicators. Next we multiply this number by the labor force participation rate and by the percent of the workforce employed in agriculture for 2006 from the World Development Indicators (World Bank 2014a). 52 The cost of scaling up school-based promotion of handwashing and good hygiene behavior is estimated to be $6.2 million. Although the promotion of WASH in schools normally includes sustainable, safe water-supply points, handwashing stands, and sanitation facilities, this costing includes only the component for hygiene education. The target population is school-aged children; current coverage is assumed to be negligible. IMPROVING NUTRITION THROUGH INVESTMENTS IN THE WASH INFRASTRUCTURE An ex-ante assessment completed by the WHO found that reaching the MDG targets for access to improved water and sanitation in Mali would cost a total of $331 million (Hutton 2012). Although this cost estimate does not include the recurring costs maintaining existing infrastructure, it finds that the economic benefits of improved infrastructure could be twice as high as the costs. 53 CONCLUSIONS AND POLICY IMPLICATIONS Systematic costing of highly effective nutrition interventions is important for setting priorities, mobilizing resources, and advocating. Combining costing with estimates of impact (in terms of lives saved, DALYs saved, and cases of stunting averted) and cost-effectiveness analysis will make the case for investment in nutrition stronger and will aid in priority-setting by identifying the most cost-effective packages of interventions in situations where financing is constrained. This will potentially be a powerful evidence-based advocacy tool for policy makers—for example, to assist the Ministry of Finance to make efficient budget allocations—because it provides useful evidence of what the government can “buy” (in terms of lives saved, DALYs saved, or cases of stunting averted) given available resources. Reaching full national coverage of the 10 nutrition-specific interventions will be expensive and will require a significant increase in the amount of public resources devoted to nutrition in Mali. Because it is unlikely that the government or its partners will find the $64 million annually necessary to reach full national coverage for all nutrition-sensitive interventions, it is important to consider strategies that make the most of the resources available. Thus the findings and recommendations presented here are based on cost-benefit analyses that can help policy makers prioritize the allocation of resources more effectively so as to achieve maximum impact. The scenarios recommended in this report represent a compromise between the need to increase coverage and the constraints imposed by limited resources and capacities, while taking into account Mali’s most pressing needs. Two scenarios (Scenarios 2 and 3) stand out as especially attractive. Scenario 2 would require an annual public investment of $24 million and would save more than 468,000 DALYs and over 11,000 lives. Scenario 3 would require an annual public investment of $18 million and would save about 319,000 DALYs and over 10,000 lives. Recognizing the challenges of scaling to full coverage in one year, we estimate the investment required to scale up over five years to be $67 million for Scenario 2 and $50 million for Scenario 3. These total costs for five years are significantly less than the $174 million needed for full coverage (of 100 percent of the target population), but still represent a significant increase over current spending on nutrition in Mali. Every attempt has been made to use real programming costs for these estimates, but the costs estimated here are likely to be slight overestimates while the benefits are likely to be underestimated. In effect, not all of the expected economies of scale are accounted for. With respect to the benefits, these estimates are likely to be underestimates of the true benefits because of the limitations of the LiST tool used, which make it possible to estimate the benefits of only some of the interventions that are proposed to be scaled up. In many cases, actual program costs will be lower than estimated because they can be added to existing programs. The incremental costs of adding to an existing program are lower because existing implementation arrangements can be used, thereby minimizing costs for staffing, operations, and training. For example, 54 behavior change communication programs, vitamin A supplementation, and deworming are all delivered in Mali as part of the SIAN activities. Similarly, deworming and behavior change for improved hygiene can and often are delivered together in school settings. Our analysis partially considers this overlap: the analysis of outcomes using the LiST software allows us to estimate the effects of several interventions implemented simultaneously to take account of the synergies among the interventions. On the other hand, the scope of the analysis in this paper did not include a rigorous comparison of the cost and cost-effectiveness of different delivery platforms for specific interventions. Such a comparison would be able to determine, for example, whether behavior change communication in the community setting is more effective than it is in schools. Based on the analyses, we cannot suggest specific methods to integrate the delivery of the interventions to capitalize on and coordinate the overlap across the intervention, but it is clear that policy makers in Mali could and should consider how the interventions overlap and identify how to implement overlapping interventions in a coordinated fashion in different settings or sectors. This process of coordination is already partially underway in the Multisectoral Strategic Action Plan for Nutrition: strategic axis 13 of the Plan focuses on coordinating and integrating nutrition into existing and new policies. Some of the actions incorporated under that strategic axis call for joint advocacy across different sectors (health, education, nutrition, and food security) to emphasize the need to link and coordinate across the sectors. Those actions can result in joint policy actions (e.g., a common national behavior change communication policy for nutrition) that would help capitalize on the overlap between the interventions. Even though this report focuses extensively on nutrition-specific interventions, the causes of malnutrition are multisectoral so any longer-term approach to improving nutrition outcomes must include nutrition-sensitive interventions. This report therefore also explores costs and benefits for six highly relevant nutrition-sensitive interventions implemented outside the health sector. These include a nutritional package delivered as part of a conditional cash transfer program, aflatoxin reduction in groundnuts, nutrition education via agricultural extension workers, school-based deworming, school-based promotion of good hygiene, and the program to reach MDGs for improved access to water and sanitation. However, the analysis presented here is only a starting point meant to spur more analytical work to identify interventions that substantially improve nutritional status. As the government refines its multisectoral nutrition policy, it will be useful to consult across sectors and ministries in order to identify other possible nutrition-sensitive interventions that are cost-effective. More robust data on nutrition-sensitive interventions are needed to do this. Overall, the findings presented in this report point to a powerful set of nutrition-specific interventions and a candidate list of nutrition-sensitive interventions that represent a cost- effective approach to reducing the high levels of child malnutrition in Mali. An important next step will be to leverage the additional financing needed to implement the scale-up. Resources will need to be mobilized from national budgets and from international donors and ODA. Within 55 the national budgets, it will be important to prioritize health sector funds for the nutrition-specific interventions. It will also be important for other sectors—such as water and sanitation and social protection—to engage in the cost-effective nutrition-sensitive interventions the report has identified. 56 APPENDIXES APPENDIX 1: DEFINITIONS OF ADEQUACY VARIABLES These variables are recommendations; numbers in brackets are age ranges. FOOD ADEQUACY  [0–6 months] Exclusive breastfeeding  [6–23 months]: o Breastfed children  [6–8 months] Breastfed + solid/semi-solid/porridge at least twice a day  [9–23 months] Breastfed + solid/semi-solid/porridge at least three times a day o Non-breastfed children  [6–23m] solid/semi-solid/porridge/milk at least four times a day  [23–59 months]: was breastfed and had an individual diet diversity score > = 4, including 1 animal protein + 1 vitamin-rich item of food HEALTH/CARE ADEQUACY  Good immunization schedule {All children [0–5 years]}: o [2 months] Polio0 o [3 months] Polio0 + Bacille de Calmettet et Guérin (BCG) vaccine against tuberculosis o [4 months] Polio0 + BCG + Penta1 + Polio1 o [5 months] Polio0 + BCG + Penta1&2 + Polio1&2 o [6–11 months] Polio0 + BCG + Penta1&2&3 + Polio1&2&3 o [12 m+] Polio0 + BCG + Penta1&2&3 + Polio1&2&3,Measles  Mothers have received at least 4 antenatal care visits {All mothers with birth in the past two years}  All children [0–5 years] slept under insecticide-treated nets ENVIRONMENT ADEQUACY  Improved water  Improved sanitation  Place to wash hands with soap available 57 APPENDIX 2: PARTNER LANDSCAPE IN NUTRITION INTERVENTIONS IN MALI, 2013 Activities: 1. Public provision of complementary food 2. Community outreach to identify and refer malnourished children 3. Treatment of moderate acute malnutrition 4. Treatment of severe acute malnutrition 5. Programs for infant and young State Financing Implementing partners child feeding. Bamako UNICEF CRF, ACTED, ACF-E 2,3,4,5 Gobierno de ACF-E 2,3,4,5 Navarra, Spain Gao ECHO ACF-E, AVSF, MDM- 1, 2,3,4,5 Belgique, IRC UNICEF AAG-Gao, AVSF, MDM- 2,3,4 Belgique Sida CRF, Save the Children 2,3,4 RRA CRF, AVSF 2,3,4 CIDA MDM-Belgique 2,3,4 WFP IRC 2,3,4,5 Kayes World Vision World Vision 2,3,5 UNICEF CRF, CRB 2,3,5 ECHO CRF, Medicus Mundi, 2,3,5 ACF-E, CRB ICCO OMAES 2,5 AECID ACF-E, MPDL 2,3,4,5 FBSA CRB 2,4,5 Kidal CIDAI MDM Belgique 2,3,4 UNICEF MDM Belgique 2,3,4 ECHO MDM Belgique 2,3,4 Koulikoro USAID HKI 2,5 UNICEF AMCP-ALIMA, IRC 2,4,5 58 ECHO AMCP-ALIMA, IRC 2,4,5 WFP AFASO 1,2,5 BØRNEfonden BØRNEfonden 2 World Vision World Vision 2,3,5 DFID IRC 2,3,4,5 FSBA CRB 2,5 — ACF-E 1,2,4,5 Mopti World Vision World Vision 2,3,4,5 USAID HKI, IRC 2,3,5 UNICEF ASDAP, ACTED 2,3,4,5 MdM-F MdM-France 4 MSF MSF 2,4 ECHO Save the Children, MdM-F 2,4,5 WFP CARE 2 DGD-Belgium MdM-F 2,4 — YA-G-TU 2,3,4,5 Ségou UNICEF CRB, ASDAP, TDH 2,3,4,5 Lausane World Vision World Vision 2,3,5 ECHO TDH Lausane, ECHO 2,3,4,5 USAID HKI 2,5 — Millennium Village 2,3,4,5 Sikasso USAID HKI, ASDAP 2,5 BØRNEfonden BØRNEfonden 2,5 World Vision World Vision 2,3,5 MSF MSF 2,4 UNICEF ASDAP 2,5 KOICA, Save the Save the Children 2,5 Children Korea Tombouctou UNICEF AMCP-ALIMA, Save the 2,3,4 Children, AVSF WFP WHH, Save the Children 2,3,4 ECHO AMCP-ALIMA, Save the 2,3,4 Children, IMC, AVSF MSF MSF 2,4 59 AECID, CAM CRE 1,2,5 Sida CRF 2,3,4,5 RRA AVSF 2,3,4 Save the Children Save the Children 2,4,5 Korea — AVSF 2,3,4 Source: United Nations Office for Coordination of Humanitarian Affairs 2014a. Note: AAG-Gao = Association d’Aide a Gao ACCID = Associació Catalana de Comptabilitat i Direcció (Catalan Association of Accountants) ACF-E = Action Contre la Faim – Espagne (Action Against Hunger) ACTED = l’Agence d’Aide à la Coopération Technique et au Développement AECID = Agencia Española para la Cooperación Internacional y el Desarrollo (Spanish Agency for International Cooperation and Development) AFASO = Association des Femmes Actives et Solidaires AMCP-ALIMA = L'Alliance Médicale Contre le Paludisme (the Alliance for International Medical Actions) ASDAP: L’Association pour le Soutien du Développement des Activités de Population AVSF = Agronomes et Vétérinaires sans Frontieres CARE = Cooperative for Assistance and Relief Everywhere CIDA = Canadian International Development Agency CRB = Croix Rouge de Belgique CRE = Croix Rouge Espagnole CRF = Croix Rouge Française DFID = Department for International Development (U.K.) DGD-Belgium = Direction générale Coopération au développement et Aide humanitaire (Directorate General for Development Cooperation and Humanitarian Aid) ECHO = European Commission Humanitarian Aid and Civil Protection Department FBSA = Fonds Belge de Sécurité Alimentaire HKI = Helen Keller International ICCO = Interchurch Organization for Development Cooperation IMC = International Medical Corps IRC = International Relief Commission KOICA = Korea International Cooperation Agency MDM-Belgique = Médicins du Monde, Belgique (Doctors of the World, Belgium) MDM-France = Médicins du Monde, France (Doctors of the World, France) MPDL = Movimiento por la Paz, el Desarme y la Libertad (Movement for Peace, Disarmament and Freedom) MSF = Médicins Sans Frontières (Doctors Without Borders) Sida = Swedish International Development Cooperation Agency TDH Lausanne = Terre des Hommes International Federation, Lausanne UNICEF = United Nations Children’s Fund USAID = United States Agency for International Development WHH = Welthungerhilfe (International German NGO) WFP=World Food Program. YA-G-TU = Yam Giribolo Tumo (Women’s Association for the Advancement of Women) — = not available. 60 APPENDIX 3: TARGET POPULATION SIZE Moderately malnourished Children 6–23 Children children 6–23 months not Total Children 6–23 under five Severely months not receiving MNPs population months not Children 6–59 Children not covered malnourished covered by and not targeted Total not receiving zinc months not 12–59 by children not public provision for public Pregnant women population consuming supplementation covered by months not community covered by of provision of not receiving not flour with ORS for vitamin A covered by nutrition CMAM complementary complementary iron folic acid consuming fortified treatment of Region supplementation deworming programs programming food foods supplementation iodized salt with iron diarrhea Kayes 37,103 122,824 198,047 10,542 53,771 71,862 58,298 1,273,000 2,375,000 343,000 Koulikoro 110,218 181,081 268,509 (721) 72,524 76,090 54,070 1,021,290 2,885,000 400,925 Sikasso 10,518 71,776 322,859 105 98,101 64,858 57,491 702,227 3,149,000 378,093 Ségou 68,399 125,902 231,730 33,929 77,138 69,215 39,782 685,356 2,786,000 379,259 Mopti 11,536 112,478 229,561 18,135 73,411 34,366 46,711 737,504 2,426,000 318,223 Tombouctou 56,868 52,959 83,623 7,138 6,976 12,743 16,869 411,648 804,000 117,365 Gao 52,914 48,982 52,128 (1,958) 4,889 13,884 11,300 629,204 646,000 81,515 Kidal 9,607 8,876 941 (1,631) 587 1,453 1,455 66,825 81,000 11,843 Bamako 71,322 94,831 233,411 5,463 35,113 60,824 21,489 571,605 2,157,000 195,849 National total 428,485 819,709 1,620,822 71,003 422,511 405,296 307,463 6,098,659 17,309,000 2,226,074 Sources: Column 1: INS Population Projections 2014 (children 6 –59 months), SMART 2013, DHS 2006 for northern regions; Column 2: INS Population Projections 2014 (children 12–59 months), SMART 2013, DHS 2006 Vitamin A coverage as a proxy for the North; Column 3: INS Population Projections 2014 (children 0–59 months), population more than 5 kilometers from a community health center from MOH 2013; Column 4: Burden of Severe Acute Malnutrition and Coverage of CMAM Programming from UNICEF 2013; Column 5: INS Population Projections 2014 (children 6 –23 months), % WAZ <-2 DHS 2012-13 and DHS 2006 for the North, coverage estimates from WFP 2014; Column 6: INS Population Projections 2014 (children 6 –23 months); Column 7: INS Population Projections 2014 (births in last 12 months), iron supplement c overage from DHS 2006; Column 8: INS Population Projections 2014 (total population), percent families consuming adequately iodized salt from MICS/ELIM 2010; Column 9: INS Population Projections 2014 (total population); Column 10: INS Population Projections 2014 (children 6 –23 months), MICS/ELIM 2010 (coverage of ORS). Note: CMAM = community-based treatment of severe acute malnutrition; MNP = micronutrient programs; ORS = oral rehydration salts; WAZ = weight-for-age Z-score. 61 APPENDIX 4: DATA SOURCES AND RELEVANT ASSUMPTIONS Intervention Costed delivery platform Cost estimate Source Assumptions Behavior change interventions Included in community No country-specific unit cost nutrition programs Cost estimate from information available, assume Community nutrition $5.00 per participant Nigeria (World Bank same unit cost as similar Breastfeeding promotion programs per year 2014d) programming in Nigeria. Education on appropriate complementary feeding Assume zero additional cost as it practices (excluding Community nutrition Included in community is included in community nutrition provision of food) programs nutrition programs Same as above program Assume zero additional cost as it Community nutrition Included in community is included in community nutrition Handwashing programs nutrition programs Same as above program Micronutrients and deworming interventions Supplementation is distributed through biannual SIAN weeks. Delivery costs (for planning; advocacy; social mobilization; health worker and volunteer Weeks for the training; monitoring; supervision; Intensification of and logistics support to fixed Vitamin A Nutrition Activities $0.37 per child 6–59 facilities and outreach stations) supplementation (SIAN) months UNICEF Mali 2014 Input costs are $0.08 for two bi- 62 annual rounds,). Delivery costs are $0.29 for two rounds. No program exists in Mali, therefore we use unit cost estimates from Nigeria. Each child Weeks for the is assumed to have two to three Therapeutic zinc Intensification of episodes of diarrhea per year, with supplementation with Nutrition Activities $0.86 per child per an average of 12 tablets needed to ORS (SIAN) year. World Bank 2014d treat one episode. Three doses per week for three months with a unit cost of $0.87 for MNPs and 12.5% additional added for transport and Community nutrition ICF International distribution. Shares costs with Micronutrient powders programs $0.98 per child per year 2014 community nutrition programs. The cost of albendazole tablets is Weeks for the $0.04 per child per year (two Intensification of rounds). Delivery costs are Nutrition Activities $0.04 per child 12–59 included in the cost of Vitamin A Deworming (SIAN) months of age per year UNICEF Mali 2014 supplementation. Assume daily IFA supplementation for last two trimesters of pregnancy and one month after giving birth (about 210 tablets), delivered through antenatal care services. 1,000 tablets cost $5.40, 210 tablets cost Iron-folic acid $1.13, distribution costs $0.14. supplementation for ICF International Shares programmatic costs with pregnant women Health structures $1.27 per pregnancy 2014 antenatal care services. 63 Global estimate is used. No Iron fortification of Market-based delivery $0.20 per person per specific information on Mali staple foods systems year (flour fortification) World Bank 2010 available. Global estimate is used. No Market-based delivery $0.05 per person per specific information on Mali Salt iodization systems year World Bank 2010 available. Complementary and therapeutic food interventions Ready-to-Use Therapeutic Food costs about $112.11 per child per Primary health care and year, transport costs about $3.38 Treatment of severe community nutrition $135.33 per child per per child per year, programmatic acute malnutrition programs episode UNICEF Mali 2014 costs are about $19.84 per year. Based on provision of supercereal plus (CSB++), which is currently distributed by the WFP in Mali’s northern regions. Food makes up Community nutrition approximately 60% of total Prevention/treatment of programs or primary $158.97 per child per intervention cost. All costs are moderate malnutrition health care system year WFP Mali 2014 calculated by the WFP. 64 APPENDIX 5: METHODOLOGY FOR ESTIMATING COSTS FOR MALI The following steps lay out the methodology used to estimate costs for each intervention: 1. Describe each intervention 2. Define target populations for each intervention 3. Estimate the size of the target populations for each intervention in each state/region using the most current demographic data 4. Specify the delivery platform or channel(s) for each intervention, based on the country context and the accepted delivery modes 5. Identify data on the current coverage levels for each intervention in each state/region 6. Estimate the unit cost per beneficiary for each intervention from program experience in Mali, whenever possible, and/or Africa region 7. Calculate additional costs of scaling up to full coverage by multiplying the unit cost for each intervention with the size of the “uncovered” target population for each intervention by state/region. The formula for calculation is: where: = additional costs of scaling up to full coverage = unit cost per beneficiary = current coverage level (percentage) 8. Estimate additional resources for (1) capacity development for program delivery and (2) M&E and technical support, estimated at 9 percent and 2 percent of total cost of interventions, respectively 9. Estimate a portion of the total cost that can be covered by private household resources. It is assumed that households above the poverty line could cover their own cost of iron fortification, multiple micronutrient powders, salt iodization, and complementary foods from private resources 10. Calculate the annual public investment required to scale up these interventions to full coverage using the following formula: where: Y = annual public investment required to scale up to full coverage = additional total cost to scale up to full coverage = additional cost for capacity development, M&E, and technical assistance = cost covered by households living above the poverty line for selected interventions 65 Full coverage is defined as 100 percent of the target population for all interventions except the treatment of severe acute malnutrition, which is set to 80 percent. This is consistent with World Bank (2010) methods and is based on the reality that few community-based treatment programs have successfully achieved more than 80 percent coverage at scale. 66 APPENDIX 6: METHODOLOGY FOR ESTIMATING DALYS FOR MALI The following steps were undertaken to estimate the impact in DALYs averted of implementing the various nutrition interventions: 1. Estimate the effectiveness of each intervention on mortality and morbidity for each targeted cause 2. Calculate the rate of years of life lost (YLL) and years of life spent with a disability (YLD) that result from each cause-risk factor combination for the target population 3. Calculate the DALYs averted under current or counterfactual coverage scenario 4. Calculate the DALYs averted under the proposed intervention coverage scenario 5. Calculate the net DALYs averted by the proposed intervention 1. Estimate the effectiveness of each intervention on mortality and morbidity for each targeted cause To estimate the effectiveness of the interventions, key articles by Black et al. (2013) and Bhutta et al. (2013) in the Lancet series on maternal and child undernutrition were first consulted. Additional literature searches for the latest evidence were conducted in the Pubmed online database and the Cochrane Library of systematic reviews and meta-analyses. Effectiveness figures that were reported as statistically significant were extracted and used for the calculations. 2. Calculate the rate of YLL and YLD The WHO’s 2012 Global Health Estimates (GHE 2012) data tables provide country-specific YLL and YLD rates for each cause of death or disease. GHE 2012 morbidity and mortality estimates were used in combination with country-specific population attributable fractions (PAF) from the 2010 global burden of disease (GBD). This assumes that the risk factor impacts on morbidity and mortality did not differ significantly between the two estimates. To calculate the rate of morbidity and mortality from a cause due to a specific risk factor, the first step is to calculate the PAF for the cause–risk factor combination. The PAF was extracted from the country-specific risk factor attribution table from the 2010 GBD data. This was done separately for YLL and YLD. In the second step, the country-specific YLLs and YLDs for the target population—in most cases children under five years old—were extracted from the GHE 2012 estimates. To calculate the YLL rate, the country-specific YLL is multiplied by the YLL PAF and then by 100,000. The final figure is divided by country-specific population of interest (usually children under five) to get the rate. The same final steps are followed to calculate the YLD, although instead multiplying country-specific YLDs by the YLD PAF. The population estimate for the rate calculation was extracted from GHE 2012. YLL per 100,000 = (U-5_cause_total_YLL * YLL_PAF * 100,000)/U-5_ population YLD per 100,000 = (U-5_cause_total_YLD * YLD_PAF * 100,000)/U-5_population where: 67 U-5_population = the population of children under five 3. Calculate counterfactual DALYs averted To calculate the DALYs averted if current intervention coverage were maintained, the following formula was used: YLL = U-5_population_intervention_year * current_coverage * intervention_mortality_reduction * YLL_rate YLD = U-5_population_intervention_year * current_coverage * intervention_morbidity_reduction * YLL_rate DALY_current = YLL + YLD 4. Calculate total DALYs averted under intervention coverage To calculate the potential DALYs averted under the intervention coverage, a similar formula as above was used: YLL = U-5_population_intervention_year * intervention_coverage * intervention_mortality_reduction * YLL_rate YLD = U-5_population_intervention_year * intervention_coverage * intervention_morbidity_reduction * YLL_rate DALY_intervention = YLL + YLD 5. Calculate net DALYs averted The potential net DALYs averted by the intervention is: DALYs averted = DALY_intervention – DALY_current 68 APPENDIX 7: METHODOLOGY FOR MALI LIST ESTIMATES The Lives Saved Tool (LiST) is a part of an integrated set of tools that comprise the Spectrum policy modeling system. These tools include DemProj for creating demographic projections; AIM to model and incorporate the impact of HIV/AIDS on demographic projections and child survival interventions; and FamPlan for incorporating changing fertility into the demographic projection. LiST is used to project how increasing intervention coverage would impact child and maternal survival. The table below summarizes data sources used for the Mali LiST estimates. Table 7.1: Mali LiST Estimates: Data Sources Mali LiST estimates Data sources Demographic data United Nations, Department of Economic and Social Affairs First year population 2012 Sex ratio at birth CIA World Factbook, accessed 2014 Life expectancy WHO Global Health Observatory, accessed 2014 Family planning Unmet need Bradley et al. 2012 Total fertility rate DHS 2012 DHS 2012 for base year; data have been calculated using the final year projection from LiST and interpolated for Age-specific fertility rate intervening years Health, mortality, economic status Vitamin A deficiency Black et al. 2013 Zinc deficiency Wessels and Brown 2012 Diarrhea incidence Fischer Walker et al. 2012 Severe pneumonia incidence Fischer Walker et al. 2013 Malaria exposure (women) Guerra et al. 2008 LiST default; data have been calculated using DHS and Stunting distribution MICS datasets LiST default; data have been calculated using DHS and Wasting distribution MICS datasets Neonatal mortality UN IGME 2013 69 Mali LiST estimates Data sources GAVI Vaccine Alliance, Country Hub: Mali, accessed 2014 Infant mortality GAVI Vaccine Alliance, Country Hub: Mali, accessed 2014 Child mortality Distribution of causes of death Liu et al. 2012 Maternal mortality ratio WHO 2013 World Bank low-income country estimate: Poverty headcount ratio at $1.25 a day (PPP) (% of population). Household poverty status World Bank 2012b, accessed 2014 Household size LiST default; DHS and MICS Survey results Once the demographic and health data have been updated, the coverage and scale-up plan for each intervention is introduced into LiST. With the exception of community nutrition programs (CNPs), lives saved and reduced stunting prevalence were estimated separately for each intervention. This assumed that each intervention is implemented independently, and therefore may slightly inflate the estimates for interventions with overlapping impacts on the target population. CNPs are considered to be a package of three interventions: breastfeeding promotion, complementary feeding education, and handwashing promotion. Note on Estimates of Cases of Stunting Averted In order to estimate the number of cases of under-five stunting averted attributable to the annual investment in the scaling up of nutrition interventions, we use LiST to model changes in the prevalence of stunting over five years, during which the interventions are projected to have reached 100 percent of the target population. Next, we model changes in the prevalence of stunting over five years with no scale-up of the interventions. We then take the difference between the estimated stunting prevalence in year 5 with the scale-up and the prevalence in year 5 absent the scale-up, and multiply this percentage point difference by the total population of children under five years. Our reason for using stunting prevalence in year 5 concerns the assumptions built into the LiST model. The model assumes that stunting is itself a risk factor for becoming stunted in the next time period. As a result, stunting prevalence remains flat during the first two years of the scale- up, before dropping precipitously until year 5, after which the prevalence begins to level out. We assume that continuing investments in maintaining the level of intervention after year 5 will serve to maintain the gains in stunting prevalence reduction, and therefore we present this reduction as a benefit attributable to a one-year investment in scaling up nutrition. On the other hand, when estimating stunting reduction (and lives saved) attributable to a five- year scale-up plan, we model this scale up directly in LiST and use the annual results over five years in our cost-benefit analysis. Using annual results over five years provides a more accurate 70 portrayal of the direct benefits attributable to a five-year scale up plan, and it does not assume that the scale will necessarily be maintained following the end of the period covered in the plan. 71 APPENDIX 8: METHODOLOGY FOR ESTIMATING ECONOMIC BENEFITS There is considerable debate in the literature regarding the best methodology for monetizing the value of a life saved. In this analysis, we focus solely on the economic value of a life year, which we measure as equal to GNI per capita. Other studies attempt to estimate the social value of a life year as well as its economic value; because we do not, we acknowledge that our results are an underestimate of the true value of a life year saved. Still, valuing years of life saved alone does not account for the economic benefits of reduced morbidity, which includes the long-term, nonlethal impacts of malnutrition on individuals. Although there are a number of long-term impacts of nutritional deficiencies, we choose to focus on stunting because of the availability of country-specific impact estimates produced by the LiST tool.23 In order to estimate the economic value of a case of childhood stunting averted, we follow the methodology used in Hoddinott et al. (2013), who begin by assuming that stunted individuals lose an average of 66 percent of lifetime earnings, based on direct estimates of the impact of stunting in early life on later life outcomes found in Hoddinott et al. (2011).24 This estimate for the effects of stunting on future consumption is used as a proxy for the effect of stunting on lifetime earnings. Additionally, Hoddinott et al. (2013) account for uncertainty by assuming that only 90 percent of the total gains will be realized, which we also include in our calculations. However, unlike those authors, we adjust our calculations to reflect the country’s labor force participation rate. For both lives saved and cases of stunting averted, the benefits of a five-year scale-up plan are attributed to a group of children that is assumed to enter the labor force at age 15 and exit the labor force at age 55, which is equivalent to life expectancy at birth in Mali. Benefits from both stunting and lives saved are then multiplied by a lifetime discount factor (LDF) in order to obtain the present value of benefits incurred during the expected years of productivity (years between the age of entry into and exit from the workforce). The LDF is derived from three potential discount rates (3 percent, 5 percent, and 7 percent), an adjustment for age at the time of investment (for simplicity, we assume an average age of two years for all children), and the years of lifetime productivity expected. The LDF represents the years of productivity that are “counted” in the calculation, discounted back to their present value in the year in which the investment in nutrition is made. Because we assume an average age of two years for all beneficiaries, we use an LDF that assumes that these children will enter the labor force 13 years from the time of investment. Importantly, given the time frame considered under this analysis, we 23 It should be noted that because stunting is just one of many long-term consequences of poor nutrition, actual economic benefits of improving nutrition may be much higher than estimated here. 24 Hoddinott et al. (2011) provided direct estimates of the impact of stunting in early life on later life outcomes, which found that an individual stunted at age 36 months had, on average, 66 percent lower per capita consumption over his or her productive life. 72 do not attempt to account for projected growth in the country’s GDP and per capita incomes. This downward bias contributes to the conservative nature of our estimates. The following equations are used to estimate (1) the economic value of lives saved (reduced mortality) and (2) increased future productivity (reduced morbidity): 1. Present value of reduced mortality = (lives saved attributable to intervention scale-up) *(GNI per capita) * LDF 2. Present value of reduced morbidity = (cases of child stunting averted) * (coefficient of a deficit) * (percent of income actually realized) * (GNI per capita) * (LDF) where:  Lives saved attributable to the intervention scale-up are estimated using the LiST tool.  Cases of child stunting averted are calculated by subtracting the projected under-five stunting prevalence (%) after the interventions are scaled up calculated by LiST from the projected stunting prevalence under a scenario with no scale up and multiplying it by the total under-five population.  The coefficient of deficit is equal to the reduction in lifetime earnings attributable to stunting.  The lifetime discount factor (LDF) is used to discount future benefits to their value at the time of investment. It is derived from a discount rate, age at the time of investment and the estimated age of entry and exit into the workforce. The equation used to calculate the LDF is: where: LDF = the lifetime discount factor r = is the discount rate t = the time period since the initial investment in scaling up the interventions (we assume that children are 2 years old at the time of investment and enter the labor force at 15 years old, which is reflected in the starting value for t) 73 T = the last time period before individuals exit the labor force (we assume individuals are out of the workforce at life expectancy at birth) Note, the beginning time period t and ending period T is adjusted for each cohort based on the year of investment. For example, the first cohort is assumed to enter the labor force at time period t=13 and exit at time T, the second cohort is assumed to enter the labor force at time period t=14 and exit at time T+1, and so forth. The following values and sources are used in our calculations: Indicator Value Source GNI per capita $670 World Bank 2013 Life expectancy at birth 55 years World Bank 2011 Labor force participation rate 66% World Bank 2014a Coefficient of deficit (stunting) 0.66 Hoddinott 2011 Actual gains realized 90% Hoddinott 2013 To arrive at a net present value (NPV), we use the following equation: where c is the cohort group and t is the time period. Finally, the annual addition to economic productivity is measured by taking the total economic benefits for the year in which all beneficiaries of the initial one-year investment have reached productive age. These benefits are not discounted back to their present value, as they are considered the annual opportunity cost of not investing in scaling up nutrition interventions. It should be noted that these benefits are derived from a progressive, five-year scale-up plan, and therefore subsequent investments that maintain the target scale will increase the total annual benefits as new beneficiaries are reached. 74 APPENDIX 9: SENSITIVITY ANALYSIS Assumption change Effect on total cost Iron fortification of flour costs double Increase from $63 million to $65 million All micronutrient and deworming unit cost doubles Increase from $63 million to $68 million Behavior change communication unit cost doubles Increase from $63 million to $72 million Public provision of complementary food unit cost doubles Increase from $63 million to $103 million Community-based management of severe acute malnutrition unit cost doubles Increase from $63 million to $72 million Iron fortification of flour costs reduced by 50% Decrease from $63 million to $62 million All micronutrient and deworming unit cost reduced by 50% Decrease from $63 million to $60.5 million Community nutrition program unit cost reduced by 50% Decrease from $63 million to $58.5 million Public provision of complementary food unit cost reduced by 50% Decrease from $63 million to $43 million Community-based management of severe acute malnutrition unit cost reduced by 50% Decrease from $63 million to $58.5 million 75 REFERENCES Abt Associates. 2014. 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The paper looks at both relevant “nutrition-specific” interventions, largely delivered through the health sector, and at multisectoral “nutrition-sensitive” interventions delivered through other sectors such as agriculture, social protection, and water and sanitation that have the potential to strengthen nutritional outcomes in Mali. We first estimate that the costs and benefits of implementing 10 nutrition-specific interventions in all regions of Mali would require a yearly public investment of $64 million. The expected benefits are large: annually about 480,000 DALYs and more than 14,000 lives would be saved and over 260,000 cases of stunting among children under five would be averted. However, because it is unlikely that the Government of Mali or its partners will find the $64 million necessary to reach full national coverage, we also consider three potential scale-up scenarios based on considerations of their potential for impact, the burden of stunting, resource requirements, and implementation capacity. Using cost-benefit analyses, we propose scale-up scenarios that represent a compromise between the need to move to full coverage and the constraints imposed by limited resources. We identify and cost six nutrition-sensitive interventions that are relevant to Mali’s context and for which there are both evidence of positive impact on nutrition outcomes and some cost information. These findings point to a powerful set of nutrition-specific interventions and a candidate list of nutrition- sensitive approaches that represent a highly cost-effective approach to reducing child malnutrition in Mali. . 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