AN INVESTMENT FRAMEWORK FOR NUTRITION IN ZAMBIA: REDUCING STUNTING AND OTHER FORMS OF CHILD MALNUTRITION DISCUSSION PAPER NOVEMBER 2016 Julia Dayton Eberwein Jakub Kakietek Meera Shekar Ali Subandoro Audrey Pereira Zia Hyder Rosemary Sunkutu AN INVESTMENT FRAMEWORK FOR NUTRITION IN ZAMBIA: Reducing Stunting and Other Forms of Child Malnutrition Julia Dayton Eberwein, Jakub Kakietek, Meera Shekar, Ali Subandoro, Audrey Pereira, Zia Hyder, Rosemary Sunkutu and Jonathan Kweku Akuoku November 2016 i 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. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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. RIGHTS AND PERMISSIONS The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. © 2016 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 An Investment Framework for Nutrition in Zambia: Reducing Stunting and Other Forms of Malnutrition Julia Dayton Eberwein, Jakub Kakietek, Meera Shekar, Ali Subandoro, Audrey Pereira, Zia Hyder, Rosemary Sunkutu, and Jonathan Kweku Akuoku Health, Nutrition and Population Global Practice, World Bank, Washington, DC, USA The authors are grateful for the support from the Bill & Melinda Gates Foundation Abstract: This paper builds on global experience and Zambia’s specific context to identify an effective nutrition approach along with costs and benefits of key nutrition interventions. It is intended to help guide the selection of the most cost-effective interventions as well as strategies for scaling these up. The paper considers both relevant “nutrition-specific” interventions, largely delivered through the health sector, and multisectoral “nutrition-sensitive” interventions, delivered through other sectors such as agriculture, education, and water and sanitation. We estimate that the costs and benefits of implementing 10 nutrition-specific interventions would require an annual public investment of $40.5 million and would avert over 112,000 DALYs, save over 2,800 lives, and prevent 62,000 cases of stunting. Economic productivity could potentially increase by $915 million annually over the productive lives of the beneficiaries, with an impressive internal rate of return of 32 percent. However, because it is unlikely that the Government of the Zambia or its partners will find the $40.5 million necessary each year to reach full coverage, we also consider scale-up scenarios based on considerations of their potential for impact, burden of stunting, resource requirements, and implementation capacity. The two scenarios that scale up the nine most cost-effective nutrition-specific interventions (excluding the public provision of complementary foods) are the most advantageous in terms of cost-effectiveness and resource requirements and would require $11 million to scale up to partial levels and $23 to scale up to full- coverage levels. Among the 8 nutrition-specific interventions we consider, school-based deworming is low cost and effective. The interventions we reviewed in the agriculture sector are expensive when compared to nutrition-specific interventions, although very little cost- effectiveness data are available for the nutrition-sensitive interventions to make careful comparisons. 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 Zambia. Keywords: nutrition-specific interventions, nutrition-sensitive interventions, cost-effectiveness of nutrition interventions, Zambia, 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 TABLE OF CONTENTS TABLE OF CONTENTS................................................................................................. IV ACKNOWLEDGMENTS ................................................................................................ VI ABBREVIATIONS AND ACRONYMS .......................................................................... VII GLOSSARY OF TECHNICAL TERMS .......................................................................... IX EXECUTIVE SUMMARY .............................................................................................. XII PART I – BACKGROUND ............................................................................................ 15 COUNTRY CONTEXT ..................................................................................................... 15 NUTRITIONAL STATUS IN ZAMBIA .................................................................................... 16 THE IMPORTANCE OF INVESTING IN NUTRITION................................................................ 21 A MULTISECTORAL APPROACH FOR IMPROVING NUTRITION ............................................. 23 NATIONAL AND PARTNER EFFORTS TO ADDRESS MALNUTRITION IN ZAMBIA ...................... 26 PART II – COSTED SCALE-UP SCENARIOS: RATIONALE, OBJECTIVES, METHODOLOGY, AND RESULTS .............................................................................. 27 RATIONALE AND OBJECTIVES OF THE ANALYSIS .............................................................. 27 SCOPE OF THE ANALYSIS AND DESCRIPTION OF THE INTERVENTIONS ................................ 28 ESTIMATION OF TARGET POPULATION SIZES, CURRENT COVERAGE LEVELS AND UNIT COSTS ................................................................................................................................... 30 ESTIMATION OF COSTS AND BENEFITS ........................................................................... 33 SCENARIOS FOR SCALING UP NUTRITION INTERVENTIONS ............................................... 35 PART III – RESULTS FOR NUTRITION-SPECIFIC INTERVENTIONS ....................... 37 TOTAL COST, EXPECTED BENEFITS, AND COST EFFECTIVENESS ...................................... 37 POTENTIAL SCALE-UP SCENARIOS ................................................................................ 40 Scenario 1: Scaling Up by Province........................................................................ 40 Scenario 2: Scaling Up by Intervention ................................................................... 42 Scenario 3: Scaling Up by Province and by Intervention ........................................ 44 Scenario 4: Scaling up by varying program coverage............................................. 46 COST-BENEFIT ANALYSIS OF ALL SCENARIOS ................................................................. 47 ESTIMATED COSTS OVER A FIVE-YEAR SCALE-UP PERIOD .............................................. 48 ESTIMATED ECONOMIC BENEFITS AND ECONOMIC ANALYSIS............................................ 49 FINANCING NUTRITION IN ZAMBIA ................................................................................... 50 UNCERTAINTIES AND SENSITIVITY ANALYSES .................................................................. 51 PART IV – RESULTS FOR NUTRITION-SENSITIVE INTERVENTIONS..................... 52 NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE AGRICULTURE SECTOR . 52 NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE EDUCATION SECTOR..... 53 COMPARING NUTRITION-SPECIFIC AND NUTRITION-SENSITIVE INTERVENTIONS.................. 53 Part V: Limitations................................................................................................... 55 PART VI – CONCLUSIONS AND POLICY IMPLICATIONS ........................................ 57 APPENDIXES ............................................................................................................... 59 iv APPENDIX 1: PARTNERS COLLABORATING ON NUTRITION IN ZAMBIA ................................. 59 APPENDIX 2: TARGET POPULATION BY PROVINCE ........................................................... 61 APPENDIX 3: DATA SOURCES AND RELEVANT ASSUMPTIONS FOR UNIT COSTS IN ZAMBIA .. 62 APPENDIX 4: METHODOLOGY FOR ESTIMATING COSTS FOR ZAMBIA .................................. 64 APPENDIX 5: METHODOLOGY FOR ESTIMATING DALYS FOR ZAMBIA ................................. 66 1. Estimate the effectiveness of each intervention on mortality and morbidity for each targeted cause ............................................................................................... 66 2. Calculate the rate of YLL and YLD ..................................................................... 66 3. Calculate counterfactual DALYs averted ............................................................ 67 4. Calculate total DALYs averted under intervention coverage ............................... 67 5. Calculate net DALYs averted .............................................................................. 67 APPENDIX 6: METHODOLOGY FOR ZAMBIA LIST ESTIMATES............................................. 68 Note on Estimates of Cases of Stunting Averted .................................................... 69 APPENDIX 7: METHODOLOGY FOR ESTIMATING ECONOMIC BENEFITS ............................... 70 APPENDIX 8: SENSITIVITY ANALYSIS............................................................................... 73 Full Coverage ......................................................................................................... 73 Partial Coverage ..................................................................................................... 74 REFERENCES .............................................................................................................. 75 v ACKNOWLEDGMENTS This analytical work was carried out at the request of the Zambia National Food and Nutrition Commission (NFNC) and is the result of collaboration with many partners. We are grateful to Secretary to the Cabinet, Dr. Rowland Msiska and his team and to Dr. Peter Mwaba, Permanent Secretary MOH and his team for support and valued guidance. The Executive Director and other officials of the NFNC provided overall coordination support, communicating with various sectors including MOH, MOA, MOE, for accessing necessary information. Sincere thanks are extended to Nutrition Cooperating Partners including DfID, WHO, UNICEF, USAID, Sida, EU, CDC. CHAI, Concern Worldwide for providing technical and other inputs. The Bill & Melinda Gates Foundation (BMGF) was also a strong partner with the World Bank in advancing this work, and provided financial support. Ellen Piwoz from the BMGF provided valuable technical inputs. Sophie Naudeau and Jamele Rigolini gave useful comments during the peer review process. Finally, the team is grateful to Musonda Rosemary Sunkutu, Sr HNP Specialist, World Bank, for continued guidance and coordination support, and Charity Inonge Mbangweta, Project Assistant for providing logistical support. Trina Haque, HNP Practice Manager, Health, Nutrition and Population Global Practice, World Bank, also provided guidance and support. The authors are grateful for the skilled editing provided by Hope Steele. The authors are grateful to the World Bank for publishing this report as an HNP Discussion Paper. vi ABBREVIATIONS AND ACRONYMS BMGF Bill & Melinda Gates Foundation BMI body mass index CCT Conditional cash transfers CHW community health worker CIDA Canadian International Development Agency DALYs disability-adjusted life years DFID Department for International Development DHS Demographic and Health Survey EDF European Development Fund EU European Union FAO United Nations Food and Agriculture Organization GAFSP Global Agriculture and Food Security Programme GBD global burden of disease GDP gross domestic product GHE Global Health Estimates HIV human immunodeficiency virus IHME Institute for Health Metrics and Evaluation IITA International Institute of Tropical Agriculture LiST Lives Saved Tool LBW low body weight M&E monitoring and evaluation MCDMCH Ministry of Community Development, Mother and Child Health MCDP First 1000 Most Critical Days Programme MOH Ministry of Health NFNC National Food and Nutrition Commission NFNSP National Food and Nutrition Strategic Plan NIH National Institutes of Health NPV net present value OECD Organisation for Economic Co-operation and Development ORS oral rehydration solution PAF population attributable fractions REACH Renewed Efforts Against Child Hunger and Malnutrition STH soil-transmitted helminth SUN Scaling Up Nutrition UNDP United Nations Development Programme UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund USAID United States Agency for International Development WASH Water, Sanitation and Hygiene WAZ weight-for-age Z-score WDI World Development Indicators WFP World Food Programme WHZ weight-for-height Z-score 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) vii All dollar amounts are U.S. dollars. viii 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: = � − 0 (1 + ) =1 where is net cash inflows, 0 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 ix 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: ℎ - = ℎ 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. 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 x 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. 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: ( ) − ( ) - = xi EXECUTIVE SUMMARY The overall objective of this paper is to provide technical assistance to the Government of Zambia in the implementation of its nutrition policy and programs. It provides the Government of Zambia with the tools needed to leverage adequate resources from domestic budgets, as well as from development partners in support of implementing the nutrition scale-up plan. The executive summary highlights the main findings and discusses the implications for nutrition policy in Zambia. The remainder of the report is more technical in nature and is written for a broader audience, including planners and programmers. The analysis is expected to bring to bear evidence of potential for impact and allocative efficiency into Zambia’s nutrition programming. The prevalence of chronic undernutrition, as measured by stunting in children under five, was 40 percent in 2013–14 and, although this represents a decline from 53 percent in 2001–02, it nevertheless represents a heavy burden of undernutrition in Zambia. Micronutrient deficiencies (hidden hunger) are also prevalent, with vitamin A deficiency and anemia rates particularly high. There is geographical disparity in stunting rates in the country; the highest rates are in the Northern region (almost 50 percent), followed by Muchinga, Eastern, Luapula, and Central provinces, which all had stunting rates above 40 percent in 2013–14. Malnutrition, particularly in very young children, leads to increased mortality, increased illness, and longer-term adverse effects on cognitive abilities and schooling outcomes, 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 child mortality and one-fifth of maternal mortality in developing countries. In the longer term, stunting results in a 10 to 17 percent in loss in wages. Furthermore, Zambia loses over $186 million in GDP annually to vitamin and mineral deficiencies alone (World Bank 2013). At the same time, nutrition interventions are consistently identified as among the most cost- effective development actions and the costs of scaling up nutrition interventions are modest. Cost- benefit analysis shows that nutrition interventions are highly effective (World Bank 2010, 2012; Hoddinott et al. 2013). It is estimated that investing in nutrition can increase a country’s gross domestic product (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, avert 30 million disability-adjusted life years (DALYs), and reduce the number of stunted children by about 30 million worldwide. The paper identifies costs and benefits of key nutrition programs in Zambia and is intended to help guide the prioritization of the most cost-effective interventions. The report uses the costing framework established by Scaling Up Nutrition: What Will It Cost? (World Bank 2010) and applies it to the specific context of Zambia. Combining costing with estimates of impact (in terms of lives saved, DALYs averted, and cases of stunting averted) and cost-effectiveness analysis, the results will aid in priority setting by identifying the most cost-effective packages of interventions in situations where financial and human resources are constrained. xii We first estimate costs and benefits of Box 1: Key Findings implementing 10 high-impact nutrition-specific interventions in Zambia. We refer to this as the The full scale-up of 10 interventions nationwide “full coverage” scenario and estimate that it would require $40.5 million per year in public would require an annual public investment of investment and generate these benefits $40.5 million. The expected benefits are huge: annually: over 112,000 DALYs and 2,800 lives would be  Over 112,000 DALYs averted averted and saved respectively annually, while  Over 2,800 lives saved nearly 62,000 cases of stunting among children  Over 62,000 cases stunting averted under five would be averted annually (see Box 1  $915 million added to the economy for a summary of key findings).  cost per DALY averted = $410 Given resource constraints, few countries are Most of the 10 interventions are very cost- able to effectively scale-up all ten nutrition- effective, although the public provision of specific interventions to full national coverage complementary food for the prevention of immediately. We therefore consider four moderate acute malnutrition is much less cost- potential scale-up scenarios for Zambia, based effective. on considerations of burden of stunting, potential for impact, costs, and capacity for In the event that scale-up to full coverage is not implementation. immediately feasible, the two most cost-effective gradual scale-up scenarios are: • Scenario 1: Scale up by region • Scenario 2: Scale up by intervention 1) Lowest cost & most cost-effective. • Scenario 3: Scale up by region and Implementing all interventions except the intervention public provision of complementary foods at • Scenario 4: Scale up by varying partial coverage levels nationwide (Scenario program coverage 4b) would require $11.2 million and save over 69,000 DALYs and 1,600 lives: cost per The two scenarios that scale up the nine most DALY averted = $166. cost-effective interventions (excluded is the 2) Second most cost-effective with greater public provision of complementary foods) are impact. Implementing all interventions the most advantageous when considered in except the public provision of complementary terms of cost-effectiveness and resource foods nationwide (Scenario 2) would require requirements (see Box 1). One scenario $23.7 million and save over 99,000 DALYs (Scenario 4b) would scale up all nine and over 2,300 lives: cost per DALY averted interventions nationwide to partial coverage = $232. levels and would require $11.2 million in public resources annually. This scenario is the most Preliminary evidence suggests that at least one cost-effective, with a cost per DALY averted of intervention outside the health sector (nutrition- $166. The second most cost-effective scenario sensitive interventions) would be cost-effective (Scenario 2) would scale up the same in improving nutritional outcomes. For Zambia, interventions to full coverage levels and would school-based deworming is low cost and require an annual investment of $23.7 in public effective, whereas our review of some other resources. Although not quite as cost-effective nutrition-sensitive interventions suggests that as Scenario 4b, Scenario 2 would reach more these are more expensive when compared to beneficiaries and therefore reduce more nutrition-specific interventions, although very malnutrition. The choice between the two little cost-effectiveness data are available to scenarios will likely depend on the level of make careful comparisons. More robust data are resources that can be leveraged for nutrition. needed to build on these findings and to identify other effective nutrition-sensitive interventions. xiii The costs presented in the preceding analysis assume scale-up to full coverage in one year. However, recognizing that a slower, incremental scale-up may be more feasible, we also estimate the cost for scaling up selected scenarios over a five-year scale-up time frame: to achieve the “full coverage” scenario would require an estimated $134.5 million, Scenario 2 would require $70.8 million, and Scenario 4b $34.7 million over five years. These investments in nutrition could also yield tremendous economic benefits for Zambia. The full scale-up investment of $40.5 million also has the potential to increase economic productivity over the productive lives of the beneficiaries by $915 million annually and yield an impressive rate of return on the investment of 32 percent. The investment would also yield large net present values of between $4.4 and $13.8 billion depending on the discount rate assumed. We have identified an annual financing gap for nutrition-specific interventions of at least $11.2 million on top of the current expenditures on nutrition in Zambia. Only $1.5 million of the $15.6 million current annual expenditures on nutrition came from the domestic government budget and the rest from international donors (see Table 18 in the main report). Donor pledges for 2014–16 are estimated at more than $36 million for the three-year period. This represents a dramatic increase and will go a long way toward financing the needed interventions. Nevertheless, it will not cover both the current expenditures (about $16 million per year) and the additional resources needed for the most modest scenario (4b) of $11.2 million per year. It will therefore be essential to leverage additional government resources for nutrition interventions, rather than to continue to rely exclusively on donors. The analysis presented here takes an innovative approach by estimating the costs and benefits not only of nutrition-specific interventions but also of selected nutrition-sensitive interventions implemented outside of the health sector. While recognizing that the evidence base for the impact of nutrition-sensitive interventions is less conclusive, we considered seven nutrition-sensitive interventions in the agriculture and education sectors that have shown some potential for improving nutrition outcomes. These include four interventions in the agriculture sector: biofortification of vitamin A-rich maize and sweet potato, aflatoxin control in maize, and the promotion of diet diversity. Four interventions in the education sector are also considered: school- based based deworming, school-based promotion of good hygiene, school-based treatment of bilharzias, and school-based feeding. The estimated annual costs are $58 million for scaling up all four of the agricultural interventions and $22 million for the four in the education sector. However, these must be considered rough approximations, because there are significant limitations in both the available data and in the methodological approaches. In addition, we were not able to estimate the benefits of these interventions because of data and methodological shortcomings, although we do report benefits calculated by researchers for other countries. Nevertheless, this preliminary costing analysis suggests that the nutrition-sensitive interventions considered here—with the exception of school-based deworming—are significantly more costly than the nutrition-specific interventions. In summary, 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 Zambia. xiv PART I – BACKGROUND COUNTRY CONTEXT Zambia is a landlocked southern African country with a vibrant economy primarily dependent on mining and agriculture. 1 The county’s estimated 14.1 million inhabitants live in an area of 752,618 square kilometers, which is subdivided into 10 provinces and 89 districts (Figure 1). The climate is generally tropical with some modifications by elevation. The Zambian economy has been historically dependent on the copper industry, although agriculture now provides more jobs. Since 2003 the economy has been growing at over 5 percent per year; its growth rate was over 7 percent in 2012. Despite having one of the higher per capita GNI in the region—of $1,540 in 2013 (World Bank 2014)—Zambia has a poverty rate of over 60 percent (World Bank 2012b). Further, Zambia ranks low at 164 out of 187 countries on the Human Development Index (UNDP 2012). Figure 1. Map of the Republic of Zambia Source: World Bank Group, internal map, 2009. 1 Zambia is bordered by the Democratic Republic of Congo and Tanzania to the north, Malawi and Mozambique to the east, Zimbabwe, Botswana, and Namibia to the south, and Angola to the west. 15 NUTRITIONAL STATUS IN ZAMBIA Overall health status in Zambia has been steadily improving in recent years. Life expectancy rose dramatically, from 42 years in 2000 to 57 years in 2012 (World Bank 2014). Under-five child mortality declined from 143 per 1,000 live births in 2000 to 89 in 2012. Infant mortality rates also declined from 86 per 1,000 live births in 2000 to 56 in 2012 (World Bank 2014). Zambia’s rate of improvement compares favorably with that of other Africa countries, as shown in Figure 2. Between 2005 and 2010, under-five mortality rates in Zambia declined by more than 5 percent. Figure 2. Changes in Child Mortality in Various Countries in Sub-Saharan Africa, 2005–2010 Source: World Bank analysis, based on DHS datasets. The prevalence of chronic malnutrition, as measured by stunting in children under five years of age, remains very high: this was 40 percent in 2013–14. This represents a decline since the recent high of 53 percent of children under five stunted in 2001–02 (Figure 3). Stunting is a measure of chronic undernutrition and reflects long-term poor caloric intake and poor quality of nutrition, including micronutrient deficiencies. Rates of wasting (acute undernutrition) are much lower, at 5 to 6 percent, and have remained unchanged since the early 1990s. Taken together, these findings suggest that children are not suffering from short-term or seasonal food shortages as much as they are from relying on a poor diet that lacks sufficient nutrients for healthy growth over the long run. On a positive note, the share of children who are underweight has declined from 23 percent in 2001–02 to 15 percent in 2013–14. 16 Figure 3. Trends in Nutritional Status among Children Under Five Years in Zambia, 1992–2014 60 50 Percent children under 5 40 30 20 10 0 Stunting Wasting Underweight 1992 1996 2001-02 2007 2013-14 Source: DHS 2013–14. There is regional variation in malnutrition as well an association between malnutrition, as measured by stunting, and poverty rates (Figure 4). In addition, Figure 4 shows the change in the geography of both stunting and poverty between 2007 and 2013–14. Although provinces in the northeast and central area (Luapula, Northern, Eastern, and Central provinces) all had stunting rates at or above 50 percent in 2007, these rates have all come down to the mid and low 40s. This compares with lower rates in Lusaka, Southern, and Western provinces. Poverty rates were highest in the Northern province in 2007 but declined there in 2010, while increasing in the south and western provinces. Overall there was a slight increase in the poverty rate nationally, with 60 percent of Zambians living in poverty in 2010 as compared to 57 percent in 2007. 17 Figure 4. Poverty Rates for 2007 and 2010 and Stunting Rates for 2007 and 2013–14 2007 2007 Poverty Rates by Province Stunting Rates by Province 2010 2013-14 (79,83] (43,49] (72,79] (40,43] (64,72] (36,40] [24,64] [36,36] Data Sources: Poverty rates from World Bank 2012b; stunting rates from DHS. Note: The data shown in the keys for the panels of Figure 4 are as follows: Poverty rates for 2007: dark blue = (70,80]; medium blue = (60,70]; pale blue = (50,60]; white = (40,50]. Poverty rates for 2010: dark blue = (79,83]; medium blue = (72,79]; pale blue = (64,72]; white = (24,64]. Stunting rates for 2007: very dark blue = (55,60]; dark blue = (50,55]; medium blue = (45,50]; pale blue = (40,45]; white = (35,40]. Stunting rates for 2013–14: dark blue = (34,49]; medium blue = (40,43]; pale blue = (36,40]; white = (36,36]. Rates of childhood stunting also vary with income, although poverty is not the only explanation for undernutrition in Zambia. Stunting rates in the bottom three quartiles are higher than 40 percent, while these rates are 28 percent in the richest quartile (Figure 5). Nevertheless, it is noteworthy that over one-quarter of children in the richest are stunted. This shows that while poverty is associated with stunting, other factors are also at play. Non-food factors, such as 18 disease, optimal feeding and caregiving practices have a major role to play in causing malnutrition. Figure 5. Prevalence of Stunting Among Children under Five by Wealth Quintiles in Zambia, 2013–14 Highest 28% Fourth 38% Wealth Quintile Middle 40% Second 42% Lowest 47% 0% 10% 20% 30% 40% 50% Prevalence of stunting in children under five Source: DHS 2013–14. Vitamin and mineral deficiencies (hidden hunger) are also pervasive in Zambia. As shown in Figure 6, over 54 percent of children under five and 13 percent of pregnant women in Zambia were deficient in vitamin A during 1993–2003, the most recent time period for which data are available (World Bank 2011). Vitamin A deficiency increases child mortality, increases vulnerability to infectious diseases such as measles, and can lead to blindness among children under five years old (Mayo-Wilson et al. 2011). Based on the 2013–14 Demographic and Health Survey (DHS), 77 percent of children aged 6–59 months received vitamin A supplementation in the previous 6 months, up from 60 percent in 2007. In addition, the data from 1993–2003 show that about half of young children and pregnant women in Zambia were anemic. Although data on current levels of anemia are not available, the 2103–14 DHS reported that about half of children aged 6–23 months consumed iron-rich foods in the previous 24-hour period. The share of households that consumed iodized salt in 2013–14 was 96 percent (DHS 2013–14). Overall, it is estimated that Zambia loses $186 million annually to vitamin and mineral deficiencies (World Bank 2011). 19 Figure 6. Vitamin A Deficiency and Anemia in Zambia, 1993–2003 Source: WHO Global Database on Child Growth and Malnutrition. Another health burden in Zambia is the high levels of aflatoxins, which are naturally occurring carcinogenic byproducts of common fungi on crops such as maize and groundnuts. Although there are no prevalence data for Zambia, experts consider aflatoxin contamination to be extensive. Evidence from other countries shows that high aflatoxin consumption can lead to liver cirrhosis (Kuniholm et al. 2008) and liver cancer in adults (Abt Associates 2014). Further, it is widely understood that there is a relationship between aflatoxin exposure and child stunting, although the evidence base for this relationship is still tentative and it has not yet been adequately quantified in the published literature (Unnevehr and Grace 2013; Abt Associates 2014). Approximately 12,700 children under five die each year in Zambia from diarrhea. Over half of these deaths are directly attributed to poor water, sanitation, and hygiene (WHO 2009). Diarrheal episodes exacerbate the relationship between malnutrition and infection because children 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). Poor sanitation is also a contributing factor—through its impact on malnutrition rates—to other leading causes of child mortality including malaria, acute respiratory infections, and measles. Parasitic intestinal worms are considered to be widely prevalent in Zambia, although no national- level prevalence data are available. A summary of all survey data of helminth infections in Zambia from 1983 through 2007 shows a wide range of infection rates, with the majority having a prevalence of over 20 percent, as depicted in the red-colored areas in Figure 7 (Global Atlas of Helminth Infections 2014). One recent study of the Kafue District in the Lusaka Province found a soil-transmitted helminth (STH) infection rate of 18 percent among preschool children (Siwila et al. 2010). The high rates of anemia among children in Zambia also point to a significant problem with STH. In the short term, helminthic infections potentially cause anemia and increase morbidity, undernutrition, and impairment of 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 less as adults (43 percent) than those who grow up free of worms (Bleakley 2007). 20 Figure 7. Prevalence of Parasitic Intestinal Worms in Zambia between 1981 and 2012 Source: Global Atlas of Helminth Infections 2014. Note: STH = soil-transmitted helminths. 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). Behrman 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 21 result in a 5 to 17 percent increase in adult productivity. Annually Zambia loses over $186 million in GDP to vitamin and mineral deficiencies alone (World Bank 2013). 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 occur in the first 1,000 days of a child’s life, , the window of opportunity for preventing these effects is the before 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 8 shows that the rates of return from human capital (including 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 8. 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 22 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. A MULTISECTORAL APPROACH FOR IMPROVING Box 2: Nutrition-Specific and Nutrition- Sensitive Interventions Distinguished NUTRITION Nutrition-specific interventions address The determinants of malnutrition are the immediate determinants of child nutrition, multisectoral. Therefore, to successfully and such as adequate food and nutrition intake, sustainably improve nutrition outcomes, a feeding and caregiving practices, and treating multisectoral approach is needed. At a proximate disease. Examples include: level, access to food, health, hygiene, and adequate child care practices is key to reducing • Community nutrition programs malnutrition. At a more distal level, poverty, • Micronutrient (e.g., vitamin A) women’s status, and other social factors play an supplementation important role. It has been demonstrated that • Deworming direct actions taken to address the proximate determinants of malnutrition can be further Nutrition-sensitive interventions are enhanced by action on some of the more distal delivered through the agriculture; education; levels. For example, programs supporting and water, sanitation, and hygiene sectors improved infant and young child feeding and have the potential to have an impact on practices will be more effective if they are nutrition outcomes more indirectly than complemented with programs to address gender nutrition-specific interventions. Examples issues by reducing women’s workloads, thus include: allowing women more time for child care. Similarly, conditional cash transfer programs that • Biofortification (e.g., vitamin-A rich sweet target the poor, if designed appropriately, have potato or cassava) the potential not just to address poverty but also • Conditional cash transfers to increase demand for nutrition services and • Water and sanitation sector infrastructure good nutrition behaviors. improvements 23 Although the health care sector is key in delivering nutrition-specific interventions to the poor (such as vitamin A supplementation and deworming), multisectoral nutrition-sensitive actions through the agriculture sector and social protection, water and sanitation, and poverty reduction programs have the potential to strengthen nutritional outcomes in several ways (Box 2). Examples of these include (1) improving the context in which the nutrition-specific interventions are delivered—for example, through investment in food systems, empowerment of women, and equitable education; (2) integrating nutrition considerations into programs in other sectors as delivery platforms (such as conditional cash transfer programs) that will potentially increase the scale and coverage of nutrition-specific interventions; and (3) by 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 2013). 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. We identify and cost seven selected nutrition-sensitive interventions that are relevant for scale- up in the Zambian context, for which there is evidence of the positive impact on nutrition outcomes, and for which there is some cost information. These include three interventions delivered through the agriculture sector—vitamin A biofortification of orange maize and sweet potato, aflatoxin reduction through biocontrol intervention, and promotion of diet diversity through agriculture extension workers—and four delivered through the education sector—school-based deworming, school-based treatment of bilharzia, school-based promotion of good hygiene, and school feeding. Other potential nutrition-sensitive interventions include the reduction of women’s workloads through appropriate technologies in agriculture, social safety nets and conditional cash transfers targeted to the poor and designed to have an impact on nutrition outcomes, and water and sanitation programs that reduce the exposure to infections and childhood diseases. However, because of data unavailability or the absence of delivery platforms, these interventions were not included. Cost and benefit estimates (where possible) for the seven nutrition-sensitive interventions described here are presented in Part II. Biofortification has the potential to reduce micronutrient deficiencies in a highly cost-effective manner. Biofortification uses plant breeding techniques to enhance the micronutrient content of staple foods; in Zambia the focus is on fortifying maize with beta-carotene, which the body converts to vitamin A. The crossbreeding process causes the maize to take on an orange color. Over 60 percent of the total area planted in major crops in Zambia has been planted in maize and maize dominates the Zambian diet, accounting for more than half of available energy. A recent study by HarvestPlus that ranked countries according to their suitability for investment in biofortification interventions identified Zambia as a “top priority” country in its ability to benefit from the biofortification of maize (Asare-Marfo et al. 2013). A more recent ex-ante cost study of high vitamin A maize in Zambia suggests that the cost per DALY averted is considered cost-effective, but requires a long-term commitment for implementation (Fiedler and Lividini forthcoming). In another analysis, Fiedler and Lividini show how high vitamin A maize biofortication is a key 24 component in addressing vitamin A deficiency in Zambia in a cost-effective way (Fiedler and Lividini 2014). Based on analysis from other countries, biocontrol of aflatoxins has the potential to reduce aflatoxins in maize and groundnuts by at least 80 to 90 percent (Bandyopadhyay and Cotty 2013). Field testing of biocontrol products in Burkina Faso, Kenya, Nigeria, and Senegal, although not formally published, is producing extremely positive results. The method involves a single application of a product (aflasafeTM) containing strains unique to that country. The U.S. Agency for International Development (USAID) is currently supporting a project on the mitigation of aflatoxin in maize and groundnuts in Zambia (2012–15) (USAID Zambia 2011). During 2012, researchers from the International Institute of Tropical Agriculture (IITA), the Zambia Agriculture Research Institute (ZARI), and the National Institute of Scientific and Industrial Research (NISIR) developed nonpoison forming strains of fungi to test in the field. In January 2013, the Zambian aflasafeTM began being applied to fields in the Eastern Province of Zambia (IITA 2013). IITA recommends that farmers apply aflasafeTM -Zambia 30 to 40 days after planting and at a rate of 10 kg/ha for optimum efficacy (IITA 2014). Research from other countries has shown the success of agricultural programs to promote diet diversity in improving nutrition and feeding practices of young children. The promotion of diet diversity includes the delivery of basic food security and nutrition messages around specific crops via agricultural extension agents. The approach is most effective if messages are targeted toward the farmers growing the crop, and packaged together with delivery of seeds or other pertinent information about the crop, including storage and food preparation methods. In many contexts, it is usual for such a worker to talk to farm households about both food production and consumption decisions. Given the reality that food security and nutrition messages are most likely to be delivered effectively by and to women, it is important to employ female extension workers in contexts where the social norm prevents women from interacting with non-family males. 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 averted 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). Evidence from India also suggests that deworming has the potential to reduce cases of childhood stunting and underweight (Awasthi et al. 2013). In the long term, deworming improved self-reported health, increased total schooling years, and raised earnings by 20 percent (Baird et al. 2011). In Zambia, a USAID-supported program called Changes2 was implemented in 2005–09; this program trained teachers in school-based deworming in four southern provinces. Zambia hopes to expand this program to provide semi-annual deworming to all primary and secondary students. 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. 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 25 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). NATIONAL AND PARTNER EFFORTS TO ADDRESS MALNUTRITION IN ZAMBIA Nutrition interventions in Zambia are coordinated by the National Food and Nutrition Commission (NFNC), which has set out an initiative to expand nutrition interventions during 2011–15 (NFNC of Zambia 2012). This Five-Year National Food and Nutrition Strategic Plan (NFNSP) 2011–15 is Zambia's first multisectoral response to combat malnutrition. It focuses on 10 key strategic directions related to improving food and nutrition, with a major focus on strengthening and expanding interventions to promote the First 1000 Most Critical Days Programme that prevents stunting in children less than two years of age. Major external support for nutrition comes from the U.K. Department for International Development (DFID), the European Union (EU), Irish Aid, teh Swedish International Development Cooperation Authority (Sida), the World Bank, and the Global Agriculture and Food Security Programme (GAFSP) (REACH 2014). DFID, Irish Aid, and Sida support a program managed by CARE to support the government’s First 1000 Most Critical Days Programme. The World Bank will soon support the Health Services Improvement project, which has a component to improve primary health care, including a plan to scale up high-impact nutrition interventions (World Bank 2013). The EU supports a focus on maternal and child care at the district level. (Appendix 1 provides a detailed list of donor activities.) Several partners are working to improve nutrition by investing in the agricultural sector. The EU is supporting conservation agriculture with a nutrition component (REACH 2014). The GAFSP supports a food security and nutrition program. HarvestPlus is also active in Zambia, developing and disseminating a variety of vitamin A–rich maize that is intended to provide 50 percent of mean requirements for vitamin A requirements (Fiedler and Lividini 2014, forthcoming). USAID is supporting a program to reduce aflatoxins in maize and groundnuts and thereby improve nutritional status (USAID 2011; IITA 2013, 2014). 26 PART II – COSTED SCALE-UP SCENARIOS: RATIONALE, OBJECTIVES, METHODOLOGY, AND RESULTS RATIONALE AND OBJECTIVES OF THE ANALYSIS The overall objective of this costing analysis is to support the Government of Zambia in developing a costed scale-up plan for nutrition. It will provide the government with the tools it needs to leverage adequate resources from domestic budgets as well as from development partners in support of the costed scale-up plan. Within this context, the objectives of this analysis are as follows: • To estimate scale-up costs in Zambia 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 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 of nutrition-specific interventions. However, these global estimates did not capture the nuances and context in each country, nor were these estimates 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 contextualize the cost estimates for Zambia. 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 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. 27 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 have strong evidence of impact and 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 1. 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 Zambian context. These 10 interventions are based on current scientific evidence and there is general consensus from the global community about their impact. 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 Zambia. Others—such as calcium supplementation for women and prophylactic zinc supplementation— are excluded because delivery mechanisms are not available in client countries, including Zambia, 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 Zambian 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 1. Nutrition-Specific Interventions Delivered Primarily Through the Health Sector Current Intervention Description Target population coverage Breastfeeding and complementary Behavior change communication feeding focusing on optimal breastfeeding promotion and complementary feeding Children 0–23 practices months of age Negligible Vitamin A supplementatio Children 6–59 n (children Semi-annual doses months of age 76.50% Therapeutic zinc supplementatio n with ORS As part of diarrhea management Children 6–59 (children) with ORS months of age Negligible For in-home fortification of complementary food (60 sachets between 6 and 11 months of age, Children 6–23 Multiple 60 sachets between 12 and17 months of age who micronutrient months of age, and 60 sachets are not receiving powders between 18 and 23 months of complementary (children) age). foods 0.00% 28 Deworming Children 12–59 (children) Two rounds of treatment per year months of age 59.80% Iron-folic acid supplementatio n (pregnant Iron-folic acid supplementation women) during pregnancy Pregnant women 95.40% Iron fortification of staple foods Fortification of wheat flour with (general public) iron General population 0.00% Salt iodization Iodization of centrally-processed (general public) salt General population 95.60% Complementary Provision of a small amount (~250 Twice the food for the kilocalories per day) of nutrient- prevalence of treatment of dense complementary food for the underweight (WAZ moderate acute treatment of moderate malnutrition < −2) among malnutrition (moderate acute malnutrition children 6–23 (children) and/or moderate stunting) months of age* Negligible Includes the identification of Incidence severe acute malnutrition, (estimated as twice Community- community or clinic-based the prevalence) of based treatment treatment (depending on the severe wasting of severe acute presence of complications), and (WHZ <−3) among malnutrition therapeutic feeding using ready- children 6–59 (children) to-use therapeutic food months of age 15.64% Note: ORS = oral rehydration salts; WHZ = weight-for-height Z-score. The analysis in the following section focuses on nutrition-sensitive interventions that are relevant to the Zambian 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 2. As discussed above, the evidence base for nutrition-sensitive interventions is not as strong as it is for nutrition-specific interventions. Therefore, these estimates are exploratory and are limited to six potential interventions relevant to the Zambian context that can be scaled up and have 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 2013), because this is an exploratory instead of an exhaustive effort, or because they were not considered relevant to the needs of Zambia. Furthermore, cost attribution is complex because these nutrition-sensitive interventions are designed for multiple purposes. 29 Table 2. Multisectoral, Nutrition-Sensitive Interventions: An Exploratory Process Target Intervention Description Population Potential for impact Interventions to be delivered through the agricultural sector Biofortification of Increase in vitamin A orange maize Promote use of vitamin A-rich intakes and improve and orange maize and vitamin A-rich sweet General vitamin A status (Hotz sweet potato potato population 2012a, 2012b) Improve child nutrition status (stunting) and Promote use of biocontrols such reduce morbidity Aflatoxin control as aflasafeTM for maize and General (Khlangwiset and Wu for maize groundnuts population 2011) Promote diet diversification and Improve consumption nutrition messages to farmers and nutrition impact in Promotion of diet growing crops through General home gardening diversity agricultural agents population (Chakravarty 2000) Interventions to be delivered through the education sector Distribution of mebendazole/ Reduce anemia and albendazole to school-aged morbidity, improve Deworming for children and training to school cognitive outcomes school-aged teachers, community workers, School-aged (Miguel and Kremer children and health workers children 2004) Promotion of Hygiene education program to Improve child nutrition good hygiene teach healthy practices in School-aged outcomes (stunting) behaviors schools children (Spears 2013) Reduce anemia and morbidity, improve cognitive outcomes Treatment for School-aged (Miguel and Kremer bilharzias School-based treatment children 2004) Some evidence of improved nutritional School-aged status (World Bank School feeding School-based meal provision children 2012) *Low income is defined as those living under the poverty line. ESTIMATION OF TARGET POPULATION SIZES, CURRENT COVERAGE LEVELS AND UNIT COSTS Target population estimates are based primarily on demographic data obtained from the population projection derived from the Population Census 2010 (see Appendix 2). 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 province were obtained from the Zambia 2013–14 DHS. 30 Data on current coverage levels for interventions were obtained from various sources. Current coverage levels for community nutrition programs for behavior change communication, zinc supplementation, multiple micronutrient powders for children to fortify homemade complementary food, maternal multiple micronutrients supplementation, iron fortification of staple foods, and public provision of complementary food for the prevention of moderate acute malnutrition were set to 0 percent either because interventions were not being implemented or because coverage was very minimal or reliable data were not available. Coverage data for vitamin A supplementation, deworming, iron-folic acid supplements for pregnant women, and households consuming adequately iodized salt were obtained from the Zambia 2013–14 DHS. 2 Finally, the Ministry of Health provided data on the number of children treated for severe acute malnutrition where programs for integrated management of acute malnutrition are in operation. The estimates of the current incidence of severe acute malnutrition and moderately acute malnutrition, underweight, and stunting (which were used to classify the provinces according to levels of stunting) were all obtained from the Zambia 2013–14 DHS. Whenever possible, the unit costs of the nutrition-specific interventions were estimated using programmatic data provided by the Ministry of Health and local implementing partners based on program experience. For some of the interventions, the delivery cost from the district plan (Kaputa and Mumbwa) was used as estimates for the unit cost at district level, which then combined with national-level data to obtain full estimates of intervention costs. The estimated unit costs and the delivery platforms are listed in Table 3. In cases where the intervention was not yet implemented or local data were not available, the global unit cost estimate from the World Bank (2010) was used. A complete index of data sources and relevant assumptions for these interventions is provided in Appendix 3. Table 3. Unit Costs and Delivery Platforms Used to Estimate Nutrition-Specific Intervention Costs Unit cost (US$) per Intervention beneficiary per year Delivery platform Breastfeeding and Community and facility nutrition complementary feeding 4.99 programs promotion Biannual campaign with deworming Vitamin A supplementation 0.94 and prophylactic zinc supplementation Therapeutic zinc 0.90 Health system delivery supplementation with ORS Multiple micronutrient 3.60 Community nutrition programs powders Biannual campaign with vitamin A Deworming 1.10 and prophylactic zinc supplementation Iron-folic acid Health system delivery during first 2.11 supplementation antenatal care visit 2 After discussion with the NFNC, it was decided to use the DHS data because they represent uptake of micronutrient supplements, which may not necessarily be captured in the programmatic data. 31 Iron fortification of staple 0.16 Market-based delivery foods Salt iodization 0.05 Market-based delivery Complementary food for the treatment of moderate 87.50 Community nutrition programs acute malnutrition (children) Community-based Primary health care and community treatment of severe acute 120.00 nutrition programs malnutrition Note: ORS = oral rehydration salts. For nutrition-sensitive interventions in the agriculture sector, the unit costs were estimated using programmatic cost data from Zambia with the exception of aflatoxin control, which comes from Nigeria. The unit costs and the delivery platforms are listed in Table 4. The unit costs for biofortification of orange maize and orange sweet potato are composed of two main elements: the cost of the seeds and the cost of the promotion. Estimates of seed costs come from HarvestPlus and estimates of promotion activity costs come from the Ministry of Health. The unit cost does not include research and development or adaptive breeding activities since these phases were already completed. The unit cost for aflasafeTM per hectare comes from the International Institute for Tropical Agriculture (IITA) in Nigeria (Bandyopadhyay 2013) and is considered a best approximation of costs for Zambia. This cost estimate includes material and distribution costs. The unit cost for the promotion of diet diversification comes from the Ministry of Agriculture, which estimates the total cost for promotional activities through the agriculture sector for each district: assuming 20,000 farmers per district, that yields a unit cost of $2.10 per farmer. Unit costs for interventions in the education sector come mostly from the Ministry of Education. The unit costs for school-based deworming, school-based treatment of bilharzias, and school feeding intervention used in the calculation are obtained from the Ministry of Education. 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 estimates are available for school-based promotion of good hygiene for Zambia, so the unit cost of $2 per student, obtained from the UNICEF report (2012) on WASH in schools, is used as a proxy. This includes the cost of capacity building, monitoring, advocacy, and social mobilization. 32 Table 4. Unit Costs and Delivery Platforms Used to Estimate Selected Nutrition-Sensitive Intervention Costs Unit cost (US$) per Intervention benefit unit per year Delivery platform Interventions delivered through the agriculture sector Biofortification of vitamin A- rich (orange) maize 426.52 per hectare Agriculture production Biofortification of vitamin A- rich (orange) sweet potato 229.52 per hectare Agriculture production Aflatoxin control through biocontrol application (aflasafeTM) 15.60 per hectare Agriculture production Agriculture extension Promotion of diet diversity 2.10 per farmer services Interventions delivered through the education sector School-based deworming School-based deworming 0.09 per student distribution School-based promotion of School-based hygiene good hygiene 2.00 per student education campaign School-based treatment of School-based deworming bilharzias 0.27 per student distribution School-based feeding 17.00 per student School-based meal provision ESTIMATION OF COSTS AND BENEFITS The program experience methodology employed in Scaling Up Nutrition. What Will It Cost? (World Bank 2010) is used for calculating the cost of scaling up in Zambia. This approach generates unit cost data that capture all aspects of service delivery (e.g., costs of commodities, transportation and storage, personnel, training, supervision, monitoring and evaluation, relevant overhead, wastage, etc.) for each intervention from actual programs that are already in operation in Zambia and considers the context in which they are delivered. 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 that are higher than 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: = (1 + 2 ) − 3 where: 33 Y = annual public investment required to scale up to full coverage 3 1 = additional total cost to scale up to full coverage 2 = additional cost for capacity development, M&E, and technical assistance 3 = cost covered by households living above poverty line for selected interventions Appendix 4 describes the methodology in detail. The expected benefits from scaling up nutrition interventions are calculated in terms of (1) DALYs averted, (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. 2004; Ezzati et al. 2002) to estimate the burden of infectious diseases attributable to different forms of undernutrition using most recent Global Burden of Disease Study (IHME 2010). The estimation of DALYs averted is limited to 8 of the 10 interventions in the package. 4 Appendix 5 describes the detailed methodology for estimating DALYs. The projected number of lives saved and cases of childhood stunting averted is calculated using the Lives Saved Tool (LiST), which translates measured coverage changes into estimates of mortality reduction and cases of childhood stunting averted. Because of the limitations of LiST, it was possible to estimate the lives saved for only seven interventions, 5 and the cases of child stunting averted for five interventions. 6 Appendix 6 describes the methodology for the LiST estimates. The increased program coverage is calculated by subtracting the current coverage rate from full coverage and multiplying this difference by the size of the target population for each intervention. The measures for cost-effectiveness of nutrition-specific interventions are calculated in terms of cost per DALY averted, 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 the cost- effectiveness ratio in terms of DALYs averted is based on the categorization used by WHO- CHOICE (Choosing Interventions that are Cost-Effective): 7 an intervention is considered to be “very cost-effective” if the range for the cost per DALY averted is less than GDP per capita; 8 “cost- 3 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. 4 Because of limitations in the estimation tool, it was not possible to calculate estimates for two interventions: iron fortification of staples and salt iodization. 5 The seven interventions are breastfeeding and complementary feeding promotion, vitamin A supplementation, therapeutic zinc supplementation with oral rehydration solution, iron-folic acid supplementation, maternal multiple micronutrient supplementation, the public provision of complementary food for the prevention of moderate acute malnutrition, and community-based management of severe acute malnutrition. 6 The five interventions are breastfeeding and complementary feeding promotion, vitamin A supplementation, iron-folic acid supplementation, maternal multiple micronutrient supplementation, and the public provision of complementary food for the prevention of moderate acute malnutrition. 7 Information on the cost-effectiveness thresholds used by WHO-CHOICE can be found at http://www.who.int/choice/costs/CER_levels/en/ 8 Zambia GDP per capita in current U.S. dollars was $1,540 in 2013 (World Bank 2014). 34 effective” if it is between one and three times GDP per capita; and “not cost-effective” if it exceeds three times GDP per capita (WHO 2014). 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 for the impact on mortality and morbidity of a one-year investment in reaching full national coverage, we use estimates of the number of lives saved and the reduction in stunting prevalence produced by the LiST tool. Consistent with the methodology used to quantify the benefits of health and nutrition interventions in Stenberg et al. (2014), a statistical 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. 9 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 present value (NPV) and internal rate of return of the investment. A detailed explanation of the benefit estimation methodology can be found in Appendix 7. The annual increase in economic productivity over the productive lives of beneficiaries attributable to each package of interventions is calculated based on the same estimates of future benefits. Although these benefits occur several years after the investment, we assume that they serve as an approximation of 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 (either local Zambian unit costs or regional unit costs are used, depending on 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 averted. 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, followed by estimates for various scale-up scenarios. The full-coverage estimates can be considered the medium-term policy goal for the Government of Zambia, but resource constraints will likely limit the government’s ability to achieve full national coverage in the short term. Therefore we also propose four scenarios for prioritizing the scale-up of nutrition interventions over a short-term time frame of five years: 9 Hoddinott et al. (2013) assume that stunted individuals lose an average of 66 percent of potential lifetime earnings. 35 • Scenario 1: Scale up by province • Scenario 2: Scale up by intervention • Scenario 3: Scale up by province and intervention • Scenario 4: Scale up by varying program coverage Within each scenario we consider variations and analyze their cost-effectiveness in terms of cost per DALY averted, cost per life saved, and cost per case of childhood stunting averted. After our initial analysis, we present the most attractive scale-up scenarios and discuss them in more detail. Full coverage is defined as 100 percent of the target population for all interventions except the 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. Under Scenario 4 we also propose several “partial coverage” scenarios that reduce coverage targets. 36 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 at the national level in Zambia is estimated to be $40.5 million annually (Table 5). This cost includes the additional cost of scaling up all 10 interventions across the entire country ($46.1 million per year) plus additional resources for monitoring and evaluation (M&E), operations research and technical support, and capacity development for program delivery (estimated at $5.0 million). Of this total amount of $51.2 million, part of the costs for iron fortification, multiple micronutrient powders for children, salt iodization, and the public provision of complementary foods for moderate acute malnutrition could be covered from private resources from households above the poverty line (estimated at $10.7 million), which leaves a financing gap of $40.5 million for a full scale-up nationwide. Table 5. Estimated Annual Cost of Scaling Up Nutrition-Specific Interventions to Full Coverage Annual Cost (US$ Intervention millions) Breastfeeding and complementary feeding promotion (children) 5.7 Vitamin A supplementation (children) 0.6 Therapeutic zinc supplementation with ORS (children) 2.3 Multiple micronutrient powders (children) 2.3 Deworming (children) 1.0 Iron-folic acid supplementation (pregnant women) 0.05 Iron fortification of staple foods (general public) 2.2 Salt Iodization (general public) 0.03 Public provision of complementary food for prevention of moderate acute malnutrition (children) 23.0 Community-based treatment of severe acute malnutrition (children) 8.9 Total cost for scaling up all 10 interventions 46.1 Capacity development for program delivery 4.1 M&E, operations research, and technical support 0.9 Household contributions from private resources ,<10.7> ANNUAL PUBLIC INVESTMENT REQUIRED 40.5 37 The expected benefits from scaling up these 10 nutrition-specific interventions across the entire country are enormous (Table 6). Once the interventions are scaled up to 100 percent, 112,545 DALYs and 2,859 lives would be saved annually, while 62,195 cases of stunting among children under five would be averted annually. Program coverage is assumed to expand as follows: • 1.1 million families with children 0–23 months of age would be reached by community programs for breastfeeding and complementary feeding promotion • 601,000 children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation • 2.6 million children 6–59 months of age would receive zinc supplementation as part of diarrhea management • 640,000 children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders • 909,000 children 12–59 months of age would receive deworming medication • 24,000 additional pregnant women would receive iron-folic acid tablets as part of their antenatal care • 14.0 million people would be able to consume staple foods fortified with iron • 617,000 people who do not currently use iodized salt would be able to obtain it • 263,000 children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices • 95,000 children aged 6–23 months of age would receive a small amount of nutrient-dense complementary food for the prevention or treatment of moderate malnutrition Table 6. Estimated Annual Benefits for Scaling Up 10 Nutrition-Specific Interventions to Full Coverage Number of Number of stunting beneficiaries DALYs Lives cases Intervention covered averteda saved averted Breastfeeding and complementary feeding promotion (children) 1,147,221 16,019 334 21,292 Vitamin A supplementation (children) 601,814 6,321 155 5,043 Therapeutic zinc supplementation with ORS (children) 2,560,912 31,968 960 — Micronutrient powders (children) 640,203 — — — Deworming (children) 908,835 3,035 — — Iron-folic acid supplementation (pregnant women) 23,935 264b 6 280 Iron fortification of staple foods (general public) 14,024,707 — — — Salt iodization (general public) 617,087 — — — Public provision of complementary food for 262,988 28,898 475 56,872 38 prevention of moderate acute malnutrition (children) Community-based treatment of severe acute malnutrition (children) 95,062 42,992 1,293 — Total when all interventions implemented simultaneouslyc — 112,545 2,859 62,195 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 IHME 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. For the package as a whole, the total cost per DALY averted is estimated to be $410, the total cost per life saved is estimated to be $16,126, and the total cost per case of stunting averted is estimated to be $741 (Table 7). 10 Variation in cost-effectiveness among the interventions is high, with costs per DALY averted ranging from $72 for zinc supplementation to $796 for the public provision of complementary foods. Overall, these cost estimates translate into an increase in annual public resource requirements of $14.42 per child. This compares favorably to global estimates of $30 per child calculated in World Bank (2010). All 10 proposed nutrition-specific interventions are “very cost-effective” according to the WHO/CHOICE criteria (WHO 2014). As shown in Table 7, these 10 intervention are “very cost- effective” in that the cost per DALY averted is below the Zambian GDP per capita of $1,540 in 2013 (World Bank 2014). Although public provision of complementary food for the prevention of moderate acute malnutrition is cost-effective with the cost per DALY averted estimated of $796, it is the least cost-effective of the 10 interventions considered. In countries such as Zambia where fiscal and capacity constraints limit scale-up, the public provision of complementary foods may be a lower priority. Issues of governance, accountability, and supply logistics will further constrain the scale-up of the public provision of complementary foods. 10 For the total cost per benefit unit, we divide the total annual program cost for all 10 interventions (excluding M&E, and capacity development costs, but before subtracting household contributions) by the benefits estimates available. Because of limitations of the LiST tool, DALYs averted estimates are available for only 7 interventions, lives saved estimates are available for 6 interventions, and stunting reduction estimates are available for 4 interventions. 39 Table 7. Cost-Effectiveness of Scaling Up 10 Nutrition Interventions to Full Coverage, US$ Cost/DALY averted Cost/case of Cost/life stunting Intervention Zambia Global saved averted Breastfeeding and complementary 357 a 53–153 17,140 269 feeding promotion (children) Vitamin A supplementation 89 a 3–16 3,628 112 (children) Therapeutic zinc supplementation 72 a 73 2,401 — with ORS (children) Micronutrient powders (children) — 12.2 — — Deworming (children) 327 a — — — Iron-folic acid supplementation 188 a 66–115 8,290 178 (pregnant women) Iron fortification of staples — — — — (general public) Salt iodization for general — — — — population Public provision of complementary 796 a 500–1,000 48,455 405 food for prevention of moderate acute malnutrition (children) Community-based treatment of 207 a 41 6,867 — severe acute malnutrition (children) Total when all interventions 410 a — 16,126 741 implemented simultaneouslyb 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. POTENTIAL SCALE-UP SCENARIOS Scenario 1: Scaling Up by Province Table 8 shows the estimated costs and benefits of scaling up the 10 nutrition-specific interventions by province. The provinces are grouped according to the rate of stunting prevalence in 2013–14: those with higher prevalence (stunting over 40 percent) and those with lower prevalence (stunting between 35 and 39 percent). The lack of wide variation in 40 stunting rates across provinces, combined with the fact that provinces in the lower prevalence category have (collectively) a greater population and therefore a higher total number of stunted children, means that it is hard to prioritize some provinces over others, as demonstrated in Table 8. Concentrating on the five highest prevalence provinces would require $20.7 million and avert over 58,000 DALYs and save over 1,200 lives, whereas addressing stunting in the lower prevalence provinces would cost $19.8 million and avert almost 70,000 DALYs and save over 1,500 lives. Table 8. Scenario 1: Cost and Benefits of Scaling Up 10 Nutrition-Specific Interventions by Province Annual public Annual benefits investment Province (US$, DALYs Lives millions) averted saved Higher prevalence (over 40% stunting) a $20.7 58,405 1,274 Rural lower prevalence (35–39% stunting) b $9.2 26,743 581 Urban lower prevalence (35–39% stunting) c $10.5 43,128 970 Total when all interventions implemented $40.5 112,545* 2,859* simultaneously Note: Cells in red indicate recommended interventions under this scenario. a. Central, Eastern, Luapula, Muchinga, and Northern provinces b. Northwestern, Southern, and Western provinces c. Copperbelt, Lusaka, * 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 differ from the individual sums. Concentrating only on the higher stunting prevalence provinces would increase program coverage as follows: • 0.5 million families with children 0–23 months of age would be reached by community programs for breastfeeding and complementary feeding promotion • 0.3 million children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation • 1.1 million children 6–59 months of age would receive zinc supplementation as part of diarrhea management • 0.3 million children 6–23 months would receive vitamins and minerals through multiple micronutrient powders • 0.4 million children 12–59 months would receive deworming medication; • 0.01 million pregnant women would receive iron-folic acid tablets as part of their antenatal care • 6.2 million people would be able to consume staple foods fortified with iron • 0.3 million people who do not currently use iodized salt would be able to obtain it • 0.1 million children aged 6–23 months of age would receive the public provision of a small amount of nutrient-dense complementary food for the prevention of moderate malnutrition 41 • 0.04 million children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices Scenario 2: Scaling Up by Intervention Scenario 2 is based on a stepwise scale-up by intervention. The primary considerations for choosing the interventions in each step are cost effectiveness, recommended phasing of interventions, implementation capacity, and delivery mechanisms. The proposed plan for a stepwise scale-up by intervention is summarized below and illustrated in Figure 9. • Step 1 focuses on a package of primarily preventive interventions that can be scaled up quickly, either with existing capacities or with modest investment in capacity-building for community nutrition programs for breastfeeding and complementary feeding promotion and child health days. It also includes community-based treatment of severe acute malnutrition at 30 percent coverage. The cost of Step 1 is $17.6 million. An additional $1.6 million for capacity development and $0.4 million for monitoring and evaluation, operations research, and technical support is budgeted, which brings the total cost of Step 1 to $19.5 million. The portion of scale-up costs that could be covered by households above the poverty line is estimated at $1.9 million. Therefore the total public investment required for Step 1 is estimated at $17.6 million. • Step 2 includes the costs of a full scale-up of treatment for severe acute malnutrition from a base of 30 percent in Step 1 to 50 percent coverage, with the assumption that additional implementation capacities are built in Step 1. The estimated cost of this scale-up is $5.5 million. We also include an additional $0.5 million for capacity development and $0.1 million for monitoring and evaluation, operations research, and technical support in Step 2, which brings the total public investment required for Step 2 to $6.2 million. (There is no household contribution for this intervention.) • Step 3 adds scaling up of the public provision of complementary foods to prevent and treat moderate malnutrition among children under two years of age. The total cost of this intervention is $23.0 million. We also include an additional $2.1 million for capacity development and $0.5 million for monitoring and evaluation, operations research, and technical support. Of the total cost of $25.5 million, an estimated $8.8 million could be resourced from private resources in households above the poverty line. This brings the estimated public investment required for Step 3 to $16.7 million. Step 3 interventions are assigned the lowest priority for the following reasons: (1) the 2013 Lancet nutrition series concluded that there are no additional benefits to public provision of complementary foods beyond those provided by dietary counseling and education; (2) at $796 per DALY averted, the public provision of complementary foods for the preventions of moderate acute malnutrition is not as cost-effective as other key interventions (as discussed above); (3) the total cost of complementary foods is overwhelming and accounts for more than half cost of the total cost of scaling up all interventions, while the benefits as estimated lives saved or DALYs averted are lower than other interventions; (4) governance, accountability, supply-chain, and logistics are key challenges associated with large-scale food distribution. The estimated benefits do not outweigh these risks, and the costs are high when compared with other interventions. Under these circumstances, rapid scale-up is neither feasible nor recommended. 42 Figure 9. Scenario 2: Stepwise Scale-Up by Intervention Note: There is no household contribution for Step 2 interventions. The preferred scale-up scenario would be to scale up Step 1 and Step 2 interventions, requiring an annual public investment of $23.7 million. As shown in Table 9, this would avert 99,379 DALYs and save 2,358 lives. It would also provide the following additional program coverage: • 1.1 million families with children 0–23 months of age would be reached by community programs for breastfeeding and complementary feeding promotion • 601,000 additional children 6–59 months of age would receive twice-yearly doses of life- saving vitamin A supplementation • 2.6 million children 6–59 months of age would receive zinc supplementation as part of diarrhea management • 640,000 children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders • 909,000 million children 12–59 months of age would receive deworming medication • 24,000 million pregnant women would receive iron-folic acid tablets as part of their antenatal care • 14.0 million people would be able to consume staple foods fortified with iron • 617,000 people who do not currently use iodized salt would be able to obtain it 43 • 95,000 children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices Table 9. Scenario 2: Costs and Benefits of Scaling Up Nutrition-Specific Interventions by Intervention Annual benefits Annual public Interventions investment (US$, millions) DALYs averted Lives saved Step 1: Community nutrition programs, all micronutrient and deworming interventions, 30% of $17.6 73,234 1,617 community-based treatment of severe acute malnutrition Step 2: 50% of community-based treatment of severe acute malnutrition $6.2 26,145 741 Subtotal (step 1 and 2 interventions when implemented simultaneously)a $23.7 99,379 2,358 Step 3: Public provision of complementary food for the $16.7 28,898 467 prevention of moderate malnutrition TOTAL when all interventions $40.5 112,545 2,859 implemented simultaneouslya Note: Cells in red indicate recommended interventions under this scenario. a. 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 Province and by Intervention Scenario 3 is a hybrid of Scenarios 1 and 2 and proposes the scaling up by province and by selected interventions as listed in Table 10 below. The preferred scenario is to scale up Step 1 and Step 2 interventions in provinces where stunting rates exceed 40 percent (the higher prevalence provinces). This requires an annual public investment of $10.8 million and averts 45,056 DALYs and saves 1,042 lives (Table 11). Scenario 3 would provide the following program benefits: • 0.5 million families with children 0–23 months of age would be reached by community programs for breastfeeding and complementary feeding promotion • 0.2 million children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation • 1.1 million children 6–59 months of age would receive zinc supplementation as part of diarrhea management 44 • 0.2 million children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders • 0.4 million children 12–59 months of age would receive deworming medication • 0.01 million pregnant women would receive iron-folic acid tablets as part of their antenatal care • 6.2 million people would be able to consume staple foods fortified with iron • 0.3 million people who do not currently use iodized salt would be able to obtain it • 0.04 million children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices Table 10. Scenario 3: Costs of Scaling Up Nutrition-Specific Interventions by Province and by Intervention, US$ millions Higher Rural and prevalence Urban lower Intervention/Province provincesa prevalence provincesb Step 1 interventions: Community nutrition programs, all micronutrient $8.1 $9.5 and deworming interventions, 30% of community- based treatment of severe acute malnutrition Step 2 interventions: 50% of community-based $2.7 $3.5 treatment of severe acute malnutrition Step 3 interventions: Public provision of complementary food for prevention of moderate $9.9 $6.9 acute malnutrition Note: Cells in red indicate recommended interventions under this scenario. a. Central, Eastern, Luapula, Muchinga, and Northern provinces. b. Copperbelt, Lusaka, Northwestern, Southern, and Western provinces. Table 11. Scenario 3: Costs and Benefits of Scaling Up Nutrition-Specific Interventions by Intervention and Province Annual public Annual benefits Interventions and provinces investment DALYs Lives (US$, millions) averted saved Step 1 and 2 interventions in higher $10.8 44,056 1,042 prevalence provincesa Step 1 and 2 interventions in rural and urban $12.9 55,323 1,316 lower prevalence provincesb Step 3 interventions in all provinces $16.7 28,898 467 TOTAL when all interventions are $40.5 112,545 2,859 implemented simultaneouslyc Note: Cells in red indicate recommended interventions under this scenario. a. Central, Eastern, Luapula, Muchinga, Northern provinces. b. Copperbelt, Lusaka, Northwestern, Southern, Western provinces. 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. 45 Scenario 4: Scaling up by varying program coverage As discussed above, the previous scenarios assume full program coverage. Scenario 4 allows for partial coverage rates as a way to scale-up to the eventual full coverage scenario (Table 12). Table 12. Full and Partial Program Coverage Targets Percent of target population covered by Year 5 Intervention Full Partial coverage coverage (Scenarios 1, (Scenario 4) 2, and 3) Community programs for growth promotion 100 80 Vitamin A Supplementation 100 100 Therapeutic zinc Supplementation with ORS 100 80 Micronutrient powders for children 100 80 Deworming 100 100 Iron-folic acid supplementation for pregnant women 100 80 Maternal multiple micronutrient supplementation 100 80 Iron fortification of staple foods 100 80 Salt iodization 100 80 Public provision of complementary food for 100 50 prevention of moderate acute malnutrition Community-based treatment of severe acute 80 50 malnutrition The first possibility (4a) under Scenario 4 would scale up all interventions to the partial coverage levels and would require total annual public investment of $24.7 million (Table 13). This scenario would avert 73,805 DALYs, save 1,893 lives and avert 33,619 cases of stunting among children under five. Table 13. Scale up Possibilities Considered Under Scenario 4 Annual benefits Annual public Scale up option investment (US$, millions) DALYs Lives averted saved Scenario 4a: All interventions in all $24.7 73,805 1,893 provinces at partial coverage rates Scenario 4b: Step 1 and Step 2 interventions in all provinces at partial $11.2 69,184 1,659 coverage rates Note: Cells in red indicate recommended interventions under this scenario. 46 Scenario 4b would scale up all interventions except the public provision of complementary foods in all provinces. This would require an annual public investment of $11.2 million, and would also avert 69,184 DALYs, save 1,659 lives and avert 22,076 cases of stunting among children under five. This option would provide the following program benefits: • 1.1 million children 6–59 months of age would receive twice-yearly doses of life-saving vitamin A supplementation • 0.6 million families with children 0–23 months of age would be reached by community programs for breastfeeding and complementary feeding promotion • 2.6 million children 6–59 months of age would receive zinc supplementation as part of diarrhea management • 0.6 million children 6–23 months of age would receive vitamins and minerals through multiple micronutrient powders • 0.9 million children 12–59 months of age would receive deworming medication • 14.0 million people would be able to consume staple foods fortified with iron • 0.6 million people who do not currently use iodized salt would be able to obtain it • 0.1 million children 6–59 months of age would be treated for severe acute malnutrition using community-based management practices COST-BENEFIT ANALYSIS OF ALL SCENARIOS When considered in terms of cost-effectiveness (cost per benefit unit) and total resources required, Scenarios 2 and 4b stand out as the most attractive. Table 14 presents a comparison of the costs and benefits of the scenarios considered in this analysis. Based on cost per DALY averted, the two most cost-effective scenarios (2 and 4b) both involve scaling up nine of the 10 interventions. The excluded intervention is the public provision of complementary foods for the prevention of moderate acute malnutrition, which is not nearly as cost-effective as the other nine interventions. Scenario 4b is the most cost-effective, with a cost per DALY averted of $166, and would scale up the nine interventions to partial coverage levels nationwide. This scenario would require $11.2 million annually and would avert over 69,000 DALYs, save almost 1,700 lives, and prevent over 22,000 cases of stunting. Although Scenarios 2 and 3 are equally cost-effective, at a cost of about $230 per DALY averted and $9,750 per life saved, Scenario 2 would provide benefits nationwide, making it more politically feasible. This option would require a larger annual public investment of $23.7 million, but would also avert over 99,000 DALYs and save over 2,300 lives. The choice between these two scenarios will mainly depend on the level of resources Zambia is able to leverage for nutrition interventions. 47 Table 14. Costs and Benefits of All Scenarios Annual Benefits Cost per Benefit Unit Annual public Cost/ investment Cost/ Cases of Cost/ case of (US$, DALYs Lives DALY stunting life stuntin millions) averted saved averte averted saved g d Scenarios averted Full coverage $40.5 112,545 2,859 62,195 $410 $16,126 $741 Scenario 1 $20.7 58,405 1,274 — $370 $16,947 — $23.7 99,379 2,358 — $232 $9,739 — Scenario 2 Scenario 3 $10.8 44,056 1,042 — $231 $9,756 — Scenario 4a $24.7 73,805 1,893 33,619 $374 $14,574 — $11.2 69,184 1,659 22,076 $166 $6,933 — Scenario 4b Note: Cells in red indicate recommended interventions under this scenario. — = not available. ESTIMATED COSTS OVER A FIVE-YEAR SCALE-UP PERIOD Until this point in the analysis, the costs are presented assuming scale-up over a one year. However, recognizing that a slower, incremental scale up may be more feasible, we also estimate the cost for scaling up selected scenarios over a five-year time frame (Table 15). A five-year scale up of all 10 interventions nationwide is estimated to cost $134.5 million, whereas Scenario 2 would require an investment of $70.8 million and Scenario 4b would require $34.7 million. Interventions are assumed to scale from current coverage as follows: 20 percent of scale up in Year 1, 40 percent in Year 2, 60 percent in Year 3, 80 percent in Year 4, and 100 percent in Year 5. For these calculations, we consider the expenditures on capacity development and system strengthening required to scale to full coverage to be a fixed cost, with some additional funds allocated to refresher training and rehiring in the years after scale has been reached. Thus, the average annual amount spent on capacity development is allocated across the five years, rather than increasing in proportion to coverage as is the case with the other costs. 48 Table 15. Scale Up over Five Years for Selected Scenarios (US$, millions) Year 2 Total Year 1 Year 3 Year 4 Year 5 (40% Scale Up Scenario (20% (60% (80% (100% Coverage Costs Over coverage) coverage) coverage) coverage) ) 5 Years Scale up of all 6.8 16.8 27.6 37.0 46.4 $134.5 10 interventions nationwide Scenario 2 $4.8 $9.5 $14.2 $18.9 $23.6 $70.8 Scenario 4b $2.2 $4.5 $7.0 $9.3 $11.7 $34.7 Note: Expenditures on capacity development and system strengthening required to scale to full coverage treated as a fixed cost, with some additional funds allocated to refresher training and rehiring in the years after scale has been reached. ESTIMATED ECONOMIC BENEFITS AND ECONOMIC ANALYSIS A high burden of malnutrition negatively impacts a nation’s human capital. An investment in improving nutrition outcomes among Zambian children is therefore also an investment in the country’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 of a particular package of interventions. The economic benefits of investing in these effective nutrition interventions are tremendous (Table 16). Scaling up all 10 interventions nationwide could produce an annual increase of $915 million in national economic productivity over the productive lives of the children affected. Investing in the lower-cost Scenario 4a would also produce an annual increase, of $496 million, in national economic productivity over the lives of the children affected. (Because of methodological limitations, we were not able to calculate the increases in economic productivity for the other scenarios.) These estimates of economic benefits are based on a conservative methodology that does not necessarily account for all of the potential benefits associated with improving nutrition outcomes among Zambian children. For example, these figures do not account for future growth in GDP per capita, which would also be expected to increase with improved nutritional outcomes. Furthermore, it is likely that these estimated increases in GDP would also improve equity in Zambia because productivity among the poor would benefit the most from improved nutritional outcomes. Our analyses also show that these nutrition interventions are excellent economic investments (Table 16). Because an increase in the assumed discount rate reduces the present value of future 49 benefits, we present the results using three possible discount rates: 3, 5, and 7 percent. Both scenarios show highly positive net present values across this range of discount rates, indicating that they are an excellent economic investment. In addition, each of the scenarios would yield a highly positive internal rate of return, another indicator that it is an excellent economic investment. Table 16. Estimated Economic Benefits and Economic Analysis of Selected Scenarios Economic measure Full scenario Scenario 4a Annual increase in economic $915 million $496 million productivitya Internal rate of return 31.5% 30.6% Net present value (US$, billions) at 3% $13.8 $7.5 discount rate Net present value (US$, billions) at 5% $7.6 $4.1 discount rate Net present value (US$, billions) at 7% $4.4 $2.4 discount rate Note: Because of methodological limitations of the LiST tool, this analysis is limited to scenarios that include all 10 interventions nationwide. a. Annual increase in productivity over the productive lives of the beneficiaries. FINANCING NUTRITION IN ZAMBIA The cost-effectiveness analysis presented here identifies an annual financing gap of at least $11.2 million (Scenario 4b) to $23.7 million (Scenario 2). This is above and beyond the $15.6 million of government and donor funds currently financing nutrition interventions in Zambia (Table 17). Some of the donor contributions, especially the $7.5 million from the United States, may be overestimated. The government budget for 2013 appropriated $1.5 to nutrition interventions. This is an increase of only $300,000 over the previous year and represents only 2 percent of the additional resources needed for even the most modest scale-up scenario. Some preliminary information is available regarding donor pledges for 2014 to 2016; this information suggests that more than $37 million is pledged for this three-year period (Table 18). 11 This represents a dramatic increase and will go a long way toward financing the needed interventions. Nevertheless, it will not cover both the current expenditures (about $16 million per year) and the additional resources needed for either of the most cost-effective scenarios identified in this analysis: Scenario 2 ($23.7million per year) or Scenario 4b ($11.2 million per year). It will therefore be essential to leverage additional government resources for nutrition interventions, rather than continued exclusive reliance on donor aid. 11 The nutrition portion of the European Union budget has not yet been finalized and therefore is not included in this figure. 50 Table 17. Estimated Annual Allocations for Nutrition, 2012 Estimated allocations for Source of financing 2012 (US$, millions) United States 7.5 DFID 5.2 Government of Zambia 1.5* UNICEF 1.0 Irish Aid 0.7 World Food Programme 0.04 Total available financing 15.6 Sources: Scaling Up Nutrition 2014 for all figures except Government of Zambia, which is from Zambia Civil Society Scaling Up Nutrition Alliance 2014. *Denotes budget for 2013. Table 18. Estimated Donor Funding for 2014–16 Estimated allocations for 2014–2016 (€, Donor millions) European Union €50 for health and nutrition (≈US$62.5 million) SUN Funding (DFID, Irish Aid, and US$29 million Sida) USAID (for both FY 2014 and 2015) US$7.6 million UNCERTAINTIES AND SENSITIVITY ANALYSES Because actual unit costs may differ from our estimates, it is important to consider the effects of both an increase and a 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 higher degree of certainty around their estimated costs and financing needs. 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 very little information concerning the costs of public provision of complementary foods for prevention of moderate acute malnutrition. In Zambia, there is no delivery mechanism that can be used as a reference for the public provision of complementary foods, while unit costs depend on the type of food provided as well as the choice of targeting method. Other factors, such as widespread governance concerns 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 8. 51 PART IV – RESULTS FOR NUTRITION-SENSITIVE INTERVENTIONS We present cost-benefit estimates for four nutrition-sensitive interventions delivered through the agriculture sector and four delivered through the education sectors. Table 19 summarizes the cost of scaling up these interventions and, when available, DALYs averted and cost per DALY averted. Table 19. Preliminary Results for Costing Nutrition-Sensitive Interventions Annual cost Cost/DALY DALYs Intervention (US$, averted averted millions) (US$) Delivered through the agriculture sector Biofortification of vitamin A-rich 20.8 1,208,012 a $24a (orange) maize Biofortification of vitamin A-rich 11.2 343,750 b $32b (orange) sweet potato Aflatoxin control through biocontrol 22.7 511,628c $43c application (aflasafeTM) Promotion of diet diversity 3.4 — — TOTAL for agriculture 58.1 — — Delivered through the education sector School-based deworming 0.3 — $4.6d School-based promotion of good 7.0 — — hygiene School-based treatment of bilharzias 0.3 — — School-based feeding 14.6 — — TOTAL for education 22.2 — — Note: — = not available. a. Based on estimates for Zambia (Fieldler and Lividini 2014). b. Based on ex-ante global estimates (Meenakshi et al. 2010). c. Based on estimates from Nigeria (Khlangwiset 2011). d. Cost per DALY averted based on a similar program with similar costs in Kenya (J-PAL 2012). NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE AGRICULTURE SECTOR The estimated total cost of scaling up biofortification of vitamin A-rich (orange) maize is $20.8 million and of vitamin A-rich (orange) sweet potato is $11.2 million. This is based on the assumption that 50,000 hectares of each breed would be planted in Zambia. Cost per DALY averted for vitamin A-rich maize is estimated at $24 for Zambia, resulted in an estimated 1,208,012 DALYs averted (Fiedler and Lividini 2014). Ex-ante global estimates for vitamin A-rich sweet potato are estimated at $32 (Meenakshi et al. 2010). Meenakshi and colleagues also 52 estimated benefit-cost ratios for maize in Ethiopia and Kenya of between 2 (a pessimistic estimate) and 47 (an optimistic one), and benefit-cost ratios for sweet potato in Uganda of between 17 and 58 (Meenakshi et al. 2010). Taken together, these benefit estimates suggest large potential benefits from biofortication in Zambia. Nevertheless, these interventions are much more costly than the nutrition-specific interventions discussed in the previous section. The total cost of scaling up aflatoxin reduction through biocontrol of maize is estimated to be $22.7 million. The cost calculation uses the unit cost of aflasafeTM biocontrol developed by IITA and tested in Nigeria, with a cost per hectare of approximately $15.6, including material and distribution costs (Bandyopadhyay 2013). Crop area is based on the FAO’s 2010 projections of Zambia maize planting area, which is estimated at a little over a million hectares. It is assumed that aflasafeTM will be applied to all maize fields. 12 The estimated DALYs averted for biocontrol is estimated at 511,628 and assumes a cost per DALY averted of $43 (Khlangwiset 2011). No estimations are currently available for lives saved through aflatoxin control. NUTRITION-SENSITIVE INTERVENTIONS DELIVERED THROUGH THE EDUCATION SECTOR The cost of scaling up both school-based deworming and school-based treatment for bilharzia is estimated to be $570,000 annually. The unit cost for deworming ($0.09) and treatment for bilharzia ($0.27) used in the calculation is obtained from Ministry of Education. For deworming, the unit cost estimate compares well with regional estimates of delivery cost in schools for Ghana (Guyatt et al. 2003). The major cost components are human resources, surveillance and mapping, non-donated drugs, advocacy, logistics and implementation, and management. The estimated target population for school-based deworming is 3.5 million school-aged children (6–15 years old) enrolled in primary and secondary schools; current coverage is assumed to be negligible. The cost of scaling up school-based promotion of handwashing and good hygiene behavior is estimated to be $7 million. Although the promotion of water, sanitation, and hygiene (WASH) in schools normally includes sustainable, safe water supply points, handwashing stands, and sanitation facilities, the costing includes only the component for hygiene education. The target population is 3.5 million school-aged children (6–15 years old) enrolled in primary and secondary schools; current coverage is assumed to be negligible. The cost of scaling up school-based feeding program is estimated to be us $14.6 million. The unit cost per child is estimated at $17 and is obtained from Ministry of Education. The school-based feeding program is part of School Health and Nutrition program and is expected to reach 860,000 students. COMPARING NUTRITION-SPECIFIC AND NUTRITION-SENSITIVE INTERVENTIONS Overall, the nutrition-sensitive interventions considered here are much more costly than the nutrition-specific interventions discussed in the previous section, with the exception of school- based deworming. For example, the biofortification and aflatoxin reduction costed for the agriculture sector would cost more than all of the nutrition-specific interventions proposed under 12 Most maize in Zambia is consumed by humans, but some is also consumed by livestock. We were not able to estimate how many hectares produce maize for human consumption versus other purposes, so we estimate the cost for all hectares of maize production. 53 Scenarios 2 and 4b. In addition, the evidence base demonstrating effectiveness in improving nutrition through nutrition-sensitive interventions is significantly weaker than that for nutrition- specific interventions. Taking these two factors together—the costly nature of the interventions and the weaker evidence on effectiveness—we conclude that more pilot projects are needed before prioritizing scarce resources for the nutrition-sensitive interventions considered here. The exception is school-based deworming interventions, which are highly cost-effective and relatively low cost. 54 PART V: LIMITATIONS Service delivery assumptions. These analyses focus on the costs of delivering direct nutrition services using services modalities currently in place or that have the potential to be delivered at scale. Where programs did not exist at scale, service delivery assumption were made for the purpose of modeling scale-up costs. However, such decisions need to be reviewed and modified based upon the discretion of implementing agencies. Efficiency gains could potentially be made using alternative service delivery channels. Our cost estimates are based on a number of assumptions regarding the supply of nutrition services and interventions. First, we assume supply is the limiting factor and that additional demand-generation activities are not necessary to stimulate the consumption of direct nutrition interventions; improving the quality and availability of supply-side will be sufficient to increase coverage to desired levels. This may not be true, particularly for interventions such as rice fortification and the promotion of optimum infant and young child feeding, where non-costed promotion efforts may be necessary to stimulate consumption. Second, we assume that the capacity to absorb the additional financing and scale up the intervention either exists currently or will be developed in the future. Further assessments of health system capacity—in terms of infrastructure, supply chain effectiveness, and human resources for health—are needed to determine whether this is indeed the case. Specifically, as a result of the absence of a human resource time use study, we are limited in our assumption of the incremental nature of the human resource costs included in our estimates. Assumptions about unit costs. We have assumed that the average unit cost will not change over the next 10 years. It is possible that the delivery of certain services will improve over time thanks to economies of scale, increased efficiency in service delivery, cheaper technologies and products, or other factors. It is also possible that, as the coverage increases, reaching the last 20 percent or 10 percent of beneficiaries will be more expensive and have higher marginal cost. Empirical literature on the over-time changes in the service delivery cost of nutrition interventions is very limited and it is impossible to determine which of the potential cost escalation or de- escalation scenarios are more realistic. Given this level of uncertainty, we selected to assume no changes in service deliver cost. Cost-benefit and cost-effectiveness calculations. Although this analysis compares effectiveness and cost-effectiveness of different interventions, it does not address various different service modalities options for delivering a specific intervention. Such analysis is beyond the scope of this report. However, it would be very important for planning the expansion of nutrition interventions in Zambia. For example, a study by Puett et al. (2013) showed that outpatient treatment of severe acute malnutrition using ready-to-use therapeutic foods is much more cost-effective than the traditional inpatient treatment. Further analyses comparing cost-effectiveness of different service delivery platforms should be conducted. Different data sources are used to generate costs versus benefits, which may result in an overstatement of the benefits per dollar spent. Impact analyses using the LiST model use effect estimates from controlled clinical or community trials. During such trials, the delivery of the interventions is closely monitored and supervised and, consequently, services delivered are of very high quality (for example, they exhibit high fidelity with guidelines and protocols, are delivered by highly trained staff, and so forth). The real-life, large-scale programs are likely to be less effective than that of controlled trials. Therefore, the results presented above are likely to be 55 overestimating the cost-effectiveness and economic benefits of the interventions. Nevertheless, given the absence of more accurate data from large-scale national programs, our estimates can be interpreted as reasonable upper-bound estimates. On the other hand, the economic benefits presented, based largely on mortality, morbidity, and cognitive deficits avoided in children under age five, do not include benefits from reductions in anemia and micronutrient deficiency resulting from the fortification of staples in the general population. Therefore, it is likely that the benefits are, in fact, underestimated. Scope of the analysis. Finally, even though this report focuses almost exclusively on direct nutrition interventions, the causes of malnutrition are multisectoral, so any longer-term approach to improving nutrition outcomes must also include nutrition-sensitive interventions. An important next step will be to extend the cost-effectiveness analysis to nutrition-sensitive interventions implemented outside of the health sector. More robust data on nutrition-sensitive interventions are needed to do this. 56 PART VI – CONCLUSIONS AND POLICY IMPLICATIONS Systematic costing of highly effective nutrition interventions is important for priority-setting, resource mobilization, and advocacy. Combining costing with estimates of impact (in terms of lives saved, DALYs averted, and cases of stunting averted) and a cost-effectiveness analysis will make the case for 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 (i.e., the Ministry of Finance) when making budget allocations because it provides useful evidence on what the government can “buy” (in terms of lives saved, DALYs averted, or cases of stunting averted) given available resources. Reaching full national coverage would be expensive and would require a significant increase in the amount of public resources devoted to nutrition in Zambia. Because it is unlikely that the government or its partners will find the $40.5 million annually necessary to reach full national coverage for all nutrition-sensitive interventions, it is important to consider scenarios that make the most of the resources available. Therefore, the findings and recommendations presented here are based on cost-benefit analyses that can help policy makers to prioritize the allocation of resources more effectively so as to achieve maximum impact. The scenario recommended in this report represents a compromise between the need to increase coverage and the constraints imposed by limited resources and capacities. Scaling up the nine most cost-effective interventions (excluding the public provision of complementary foods) nationwide at either full or partial coverage levels (Scenarios 2 and 4b, respectively) is the most advantageous way to scale up when considered in terms of resource requirements and cost-effectiveness (cost per benefit unit). Scenario 4b is the most cost-effective in terms of cost per DALY averted of $166, and it is also the least costly overall ($11.9 million). It would avert over 69,000 DALYs and save over 1,600 lives. However, Scenario 4b would only scale up interventions to partial coverage levels. Scenario 2 requires more resources ($23.7 million) but is also cost-effective ($232 cost per DALY averted) and scales up all nine interventions to full coverage levels nationwide. Scenario 2 also provides more benefits: over 99,000 DALYs averted and 2,300 lives saved. Although this report focuses extensively on nutrition-specific interventions, the causes of malnutrition are multisectoral and therefore any longer-term approach to improving nutrition outcomes must also include nutrition-sensitive interventions. This analysis takes an innovative approach to nutrition costing by not only estimating the costs and benefits of nutrition-specific interventions but also exploring costs for a selected number of nutrition-sensitive interventions implemented outside of the health sector. We have identified seven candidate nutrition-sensitive interventions with high impact potential for Zambia: the biofortication of vitamin A-rich maize and sweet potato, aflatoxin control in maize, promotion of diet diversity, school-based deworming, school-based promotion of good hygiene, school-based treatment of bilharzias, and school-based feeding. The most promising of these is school-based deworming, which is very cost-effective and requires fewer resources than the other nutrition-sensitive interventions considered here. However, these interventions are just a starting point, and as the government begins to develop a multisectoral nutrition policy, it would be useful consult across sectors and ministries in order to identify other possible nutrition-sensitive interventions that are cost-effective. Overall, these findings point to a powerful set of nutrition-specific interventions and a candidate list of nutrition-sensitive approaches that represent a cost-effective approach to reducing the high 57 levels of child malnutrition in Zambia. 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 therefore offers a window of opportunity to permanently lock in human capital. 58 APPENDIXES APPENDIX 1: PARTNERS COLLABORATING ON NUTRITION IN ZAMBIA Amount in US$ Donor Program Coverage Status millions • Social protection through cash transfer -— 50 districts Ongoing programs Government • First 1000 Most Critical Days Programme • 60% of the funds $27 managed by CARE support government- 14 districts Ongoing of which $17 is supported programs on for nutrition both national and DFID, Irish subnational levels Aid, Sida • Maternal Child and $67 of which Neonatal Health $24 is directed through the MOH and toward strengthening MCDMCH To start basic health 4 provinces in July services including nutrition World Bank interventions • Millennium Development Goal initiative, to be implemented by UNICEF, WHO, 50 million euros Inception UNFPA 11 districts (US$68) stage • Focus on maternal and child health care at district level • Support to FAO for conservation 11 million euros Inception 31 districts agriculture (US$15) stage European • EDF 11- 2014–20 110 million Design N/A Union euros (US$150) stage 59 • Conservation agriculture with a built in nutrition element Global • Agriculture investment Agriculture plan and Food 31 6 districts Security • Food security and Programme nutrition (GAFSP) Source: REACH 2014. Note: DFID = Department for International Development (U.K.) EDF = European Development Fund FAO = Food and Agriculture Organization of the United Nations MCDMCH = Ministry of Community Development, Mother and Child Health MOH = Ministry of Health UNFPA = United Nations Population Fund UNICEF = United Nations Children’s Fund WHO = World Health Organization 60 APPENDIX 2: TARGET POPULATION BY PROVINCE Childre n 0-23 Children 6– months 59 months Children Children to cover to cover 6–23 6–59 Populat under under months months ion not the therapeutic not Childre Pregnant not using commu Children 6– zinc covered n 12–59 women not Populati Children 6– treated fortified nity 59 months supplement by not receiving on not 23 months for foods nutritio not covered ation with multiple covered iron-folic consum to receive severe n by vitamin A oral micronutr by acid ing complemen acute progra supplement rehydration ient deworm supplement iodized tary malnutrit Province ms ation salts powders ing ation salt feeding ion (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 1,396,76 Central 114,255 69,628 255,049 62,426 87,136 2,073 54,474 27,525 6,856 5 Copperbel 2,228,15 182,263 66,725 406,860 103,028 97,337 3,224 89,126 40,465 17,088 t 0 1,721,99 Eastern 140,859 77,666 314,436 82,507 141,013 2,939 70,602 28,389 7,396 3 1,010,35 Luapula 82,647 36,160 184,490 37,478 55,375 2,774 69,714 27,588 18,287 2 2,512,37 Lusaka 205,512 108,726 458,759 126,201 172,122 2,423 100,495 35,595 17,726 0 Muchinga 67,643 39,562 150,999 36,639 56,653 1,902 826,936 25,635 16,615 2,537 1,244,84 Northern 101,828 61,601 227,309 49,704 84,883 3,833 94,608 30,464 5,346 5 North- 48,193 24,528 107,580 27,470 42,832 874 589,154 20,031 10,472 5,241 Western 1,544,84 Southern 126,368 67,137 282,089 73,422 98,117 1,777 83,422 26,066 4,739 4 Western 77,653 46,456 173,342 41,327 67,179 1,867 949,298 6,645 19,808 7,280 1,147,22 14,024,7 NATIONAL 601,814 2,560,912 640,203 908,835 23,935 617,087 262,988 95,062 1 07 Sources and Notes: Column 1: Population Census 2010 (Children 0–23 months); Column 2: Population Census 2010 (Children 6-–59 months), Vitamin A coverage from DHS 2013-14; Column 3: Population Census 2010 (Children 6–59 months); Column 4: Population Census 2010 (Children 6–23 months), Percent < −2 WAZ from DHS 2013-14; Column 5: Population Census 2010 (Children 12–59 months), Deworming coverage from DHS 2013-14; Column 6: Population Census 2010 and Pregnancy Rate of 3.71%, iron-folic acid supplementation coverage from DHS 2013-14; Column 7: Population Census 2010 (Total population); Column 8: Population Census 2010 (Total population), Percent of households consuming iodized salt from DHS 2013-14; Column 9: Population Census 2010 (Children 6–23 months), Percent < −2 WAZ from DHS 2013-14; Column 10: Population Census 2010 (Children 6–59 months), Percent < −3 WHZ from DHS 2013-14, SAM treatment coverage (16%) from the Zambian Ministry of Health. This represents the total population of children 6–59 months not treated for severe acute malnutrition although we only expect to reach 80 percent of this population. 61 APPENDIX 3: DATA SOURCES AND RELEVANT ASSUMPTIONS FOR UNIT COSTS IN ZAMBIA Unit cost Costed (US$ per delivery beneficiary Intervention platform per year) Source Assumptions Calculated based on district work plan and combined with central- district level budget (MOH, Mumbwa district Community budget). Focus is on nutrition MOH 2013; exclusive breastfeeding, programs NFNC 2013; infant and young child growth Community Mumbwa District complementary food, and promotion nutrition Nutrition Plan hygiene practices, (children) programs $7.25 2013 among others. Supplements are distributed through biannual maternal, newborn and child health weeks, with overhead costs (for planning, advocacy, social mobilization, health worker and volunteer Vitamin A MOH 2013; training, monitoring, supplementation Child Health Fiedler et al. supervision) shared with (children) Weeks $ 0.94 2013 other interventions Primary health Therapeutic zinc care and Assuming diarrhea three supplementation community episodes/year and each with ORS nutrition required 12 tablets for (children) program $0.90 MOH 2013 treatment Multiple micronutrient Estimates based on powders for Community micronutrient powder trial children nutrition currently underway (children) programs $3.60 UNICEF 2013 managed by UNICEF Supplements are distributed through biannual MNCH weeks, with overhead costs (for planning, advocacy, social mobilization, health worker and volunteer training, MOH 2013; monitoring, supervision) Deworming Child Health Fiedler et al. shared with other (children) Weeks $1.10 2013 interventions 62 Unit cost Costed (US$ per delivery beneficiary Intervention platform per year) Source Assumptions Assume daily IFA supplements for last two trimesters of pregnancy (about 180 tablets) Primary health delivered through primary 6. Iron-folic acid care and health care and child supplementation community health weeks; includes of pregnant nutrition MOH 2013; cost of prenatal visits and women programs $2.11 NFNC 2012 of supplement. 7. Iron Based on total annual fortification of capital and recurrent staple foods Market-based costs divided by total (general public) delivery system $0.16 Fiedler 2013 population 8. Salt Horton et al. Global estimate is used; iodization Market-based 2010; Aminu no specific information on (general public) delivery system $0.05 2013 is Zambia available 9. Public provision of complementary food for prevention of moderate acute Community malnutrition nutrition Based on a ration of 200 (children) programs $87.50 WFP 2013 grams per day 10. Community- Primary health based treatment care and of severe acute community malnutrition nutrition Calculated from MOH (children) programs $120.00 MOH 2013 budget 63 APPENDIX 4: METHODOLOGY FOR ESTIMATING COSTS FOR ZAMBIA 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 province 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 province 6. Estimate the unit cost per beneficiary for each intervention from program experience in Zambia, 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 province. The formula for calculation is: 1 = 1 (100 − 2 ) where: x1 = additional costs of scaling up to full coverage z1 = unit cost per beneficiary z2 = current coverage level (percentage) 8. Estimate additional resources for (1) capacity development for program delivery and (2) M&E, operations research, 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 food from private resources 10. Calculate the annual public investment required to scale up these interventions to full coverage using the following formula: = (1 + 2 ) − 3 where: Y = annual public investment required to scale up to full coverage 1 = additional total cost to scale up to full coverage 2 = additional cost for capacity development, M&E, and technical assistance 3 = cost covered by households living above poverty line for selected interventions 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 64 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. 65 APPENDIX 5: METHODOLOGY FOR ESTIMATING DALYS FOR ZAMBIA 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 YLL and YLD due to 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 (WHO 2012b). GHE 2012 morbidity and mortality estimates were used in combination with country-specific population attributable fractions (PAF) from the Global Burden of Disease (IHME 2010). 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: U-5_population = the population of children under five 66 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 67 APPENDIX 6: METHODOLOGY FOR ZAMBIA 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 Zambia LiST estimates. Zambia LiST estimates Data sources Source First year population UN World Population Prospects, 2012 Revision UNData; Sex ratio at birth http://data.un.org/Data.aspx?q=sex+ratio&d=PopDiv&f=variableID%3a52 Life expectancy UN World Population Prospects, 2012 Revision Family planning Unmet need Bradley et al. 2012. Total fertility rate Population Reference Bureau’s 2013 World Population Datasheet Age-specific fertility rate UN World Population Prospects, 2012 Revision 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 Stunting distribution LiST default; data have been calculated using DHS and MICS datasets Wasting distribution LiST default; data have been calculated using DHS and MICS datasets Neonatal mortality UN IGME 2013 Infant mortality Population Reference Bureau’s 2013 World Population Datasheet Child mortality UNICEF (2012 data) Distribution of causes of Liu et al. 2012. death Maternal mortality ratio WHO 2013 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. LiST either can use a sequential method to calculate the impact of individual interventions or can calculate the simultaneous impact of a set of interventions implemented at the same time. The second, simultaneous method is likely to yield slightly lower estimates because interventions may have overlapping benefits. In this analysis we present the both the individual/sequential results of the individual interventions in the full coverage 68 scenario (with totals calculated using the simultaneous method) and the simultaneous impact in the various scale-up scenarios. 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 of age. Our reason for using stunting prevalence in Year 5 relates to the assumptions built into the LiST model, which 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 scale 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 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. 69 APPENDIX 7: 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 underestimate 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 include 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. 13 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). 14 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 57, which is equivalent to life expectancy at birth in Zambia. 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 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): 13 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. 14 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. 70 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: = 1 = � (1 + ) =13 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) 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: 71 Indicator Value Source GNI per capita US$1,480 World Bank 2013 Life expectancy at birth 57 years World Bank 2012 Labor force participation rate 79% World Bank 2012 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: 5 = �( ) + ( ) =1 5 1 −� ( ) (1 + ) =1 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. 72 APPENDIX 8: SENSITIVITY ANALYSIS Full Coverage Assumption change Effect on total annual cost Iron fortification of staple foods unit cost doubles Increase from $48.7 million to $50.9 million All micronutrient and deworming unit costs double Increase from $48.7 million to $57.2 million Community nutrition program unit cost doubles Increase from $48.7 million to $57 million Complementary food unit cost doubles Increase from $48.7 million to $71.7 million Community-based management of severe acute malnutrition unit cost doubles Increase from $48.7 million to $57.6 million Iron fortification of staple foods costs reduced by 50% Decrease from $48.7 million to $47.6 million All micronutrient and deworming unit costs reduced by 50% Decrease from $48.7 million to $44.5 million Community nutrition program unit cost reduced by 50% Decrease from $48.7 million to $44.5 million Complementary food unit cost reduced by 50% Decrease from $48.7 million to $37.2 million Community-based management of severe acute malnutrition unit cost reduced by 50% Decrease from $48.7 million to $44.3 million 73 Partial Coverage Assumption change Effect on total annual cost Iron fortification of staple foods unit Increase from $29.7 million to $31.5 million cost doubles All micronutrient and deworming unit Increase from $29.7 million to $36.7 million costs double Community nutrition program unit cost Increase from $29.7 million to $36.3 million doubles Complementary food unit cost doubles Increase from $29.7 million to $41.2 million Community-based management of Increase from $29.7 million to $34.1 million severe acute malnutrition unit cost doubles Iron fortification of staple foods costs Decrease from $29.7 million to $28.8 million reduced by 50% All micronutrient and deworming unit Decrease from $29.7 million to $26.1 million cost reduced by 50% Community nutrition program unit cost Decrease from $29.7 million to $26.3 million reduced by 50% Complementary food unit cost reduced Decrease from $29.7 million to $23.9 million by 50% Community-based management of Decrease from $29.7 million to $27.4 million severe acute malnutrition unit cost reduced by 50% 74 REFERENCES Abt Associates. 2014. 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Lusaka, Zambia. 80 This paper builds on global experience and Zambia’s specific context to identify an effective nutrition approach along with costs and benefits of key nutrition interventions. It is intended to help guide the selection of the most cost-effective interventions as well as strategies for scaling these up. The paper considers both relevant “nutrition-specific” interventions, largely delivered through the health sector, and multisectoral “nutrition- sensitive” interventions, delivered through other sectors such as agriculture, education, and water and sanitation. We estimate that the costs and benefits of implementing 10 nutrition-specific interventions would require an annual public investment of $40.5 million and would avert over 112,000 DALYs, save over 2,800 lives, and prevent 62,000 cases of stunting. Economic productivity could potentially increase by $915 million annually over the productive lives of the beneficiaries, with an impressive internal rate of return of 32 percent. However, because it is unlikely that the Government of the Zambia or its partners will find the $40.5 million necessary each year to reach full coverage, we also consider scale-up scenarios based on considerations of their potential for impact, burden of stunting, resource requirements, and implementation capacity. The two scenarios that scale up the nine most cost-effective nutrition-specific interventions (excluding the public provision of complementary foods) are the most advantageous in terms of cost-effectiveness and resource requirements and would require $11 million to scale up to partial levels and $23 to scale up to full-coverage levels. Among the 8 nutrition-specific interventions we consider, school-based deworming is low cost and effective. The interventions we reviewed in the agriculture sector are expensive when compared to nutrition-specific interventions, although very little cost- effectiveness data are available for the nutrition-sensitive interventions to make careful comparisons. 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 Zambia. ABOUT THIS SERIES: This series is produced by the Health, Nutrition, and Population Global Practice of the World Bank. 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 free copies of papers in this series please contact the individual author/s whose name appears on the paper. Enquiries about the series and submissions should be made directly to the Editor Martin Lutalo (mlutalo@ worldbank.org) or HNP Advisory Service (askhnp@worldbank.org). For more information, see also www.worldbank.org/hnppublications. 1818 H Street, NW Washington, DC USA 20433 Telephone: 202 473 1000 Facsimile: 202 477 6391 Internet: www.worldbank.org E-mail: feedback@worldbank.org