Case study

  • What do we mean by “Sub-National” divisions in NIPN countries?

    Administrative sub-divisions differ across countries. For the purpose of this tool the following definitions are adopted:

    • The generic term “Region” is equivalent to the “Sub-National Administrative Level N-1”, which typically splits the country into 10-20 sub-divisions.
    • The generic term “District” is equivalent to the “Sub-National Level N-2”, which typically splits the country into 30-100 sub-divisions.

    “Region” is used as a generic term in the tool for “Sub-National Level 1”. The notable exception is Côte d’Ivoire where “Region” actually refers to “District”.

    Sub-national divisions in NIPN countries
    Generic term used in the guidance notesRegionDistrict
    CountriesLevel N-1Level N-2
    Bangladesh 8 divisions 64 districts
    Burkina Faso 13 regions 45 provinces
    Côte d’Ivoire 12+2 districts 31 regions
    Ethiopia 11 regional states 68 zones
    Guatemala 22 departments 335 municipalities
    Kenya 47 counties
    Lao PDR 17 provinces + Vientiane districts
    Niger 7 regions + Niamey 63 departments
    Uganda 15 sub-regions districts
    Zambia 10 provinces 103 districts
  • The SUN MEAL national and sub-national dashboards


    National dashboards have been produced for the SUN countries. Sub-national dashboards are currently being produced, which are slightly different and designed to compare data across districts.

    Main features of the SUN MEAL dashboard:

    • Clear sub-sections that correspond directly to the SUN Theory of Change and also align in many respects with the UNICEF and Lancet nutrition frameworks.
    • Indicators with “no data” appear clearly (although not shown in this example).
    • Most recent year of when the indicator was collected is included.
    • Data sources are included.
    • Colour coding clearly shows performance compared with median performance of other countries (or national median for sub-national dashboard).
    • Sub-national dashboards that are not yet fully finalised are colour coded to compare one region with another.
    • A summary narrative is included.
    • A page to explain the colour classification is included.
    • 6 pages in total for national dashboards; 8 pages for initial draft sub-national dashboards (not yet published).

    The SUN MEAL dashboard contains the following sections:

    • Enabling environment
    • Finance for nutrition
    • Intervention and food supply
    • Enacted legislations
    • SDGs drivers of nutrition
    • IYCF and dietary Intake
    • Nutrition status
    • SDGs linked to nutrition

    Extract of the SUN MEAL national dashboard for Lao PDR

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  • The POSHAN District dashboards (India)


    Main features of the POSHAN district dashboard:

    • District level dashboard
    • Clear sub-sections
    • Very clear and simple bar chart with average of the district and average of the state. One issue with this sort of bar chart is the scale as it is difficult to compare indicators ranging from 10 to 20% with those ranging from 40 to 50%.
    • Indicators with “no data” appear clearly.
    • Data sources are included.
    • A section with “Possible points of discussion” is included.
    • A page describing the determinants of malnutrition and sources of information
    • 4 pages in total for sub-national dashboards

    The POSHAN district dashboard contains the following sections:

    • Demographic profile
    • The state of nutrition
    • Immediate determinants of nutrition
    • Coverage of nutrition-specific interventions
    • Underlying and basic determinants of nutrition
    • Interventions that affect basic and underlying determinants

    Extract from the POSHAN dashboard of the Bokaro district

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  • The Global Nutrition Report national dashboards


    Main features of the GNR natonal dashboard:

    • Clear sub-sections
    • Helpful visual lay out with variety of colours but quite dense
    • Some graphs difficult to interpret right away
    • Data sources are included
    • Analysis of inequalities as defined by wealth quintile included
    • Section on progress against Global Nutrition Targets included
    • 2 pages in total for national dashboards

    The GNR national dashboard contains the following sections:

    • Economics and demography
    • Child anthropometry
    • Adolescent and adult nutrition status
    • Progress against Global Nutrition Targets
    • Intervention coverage and child feeding practices
    • Underlying determinants
    • Financial resources and policy, legislation and institutional arrangements

    Extract from the GNR national dashboard of Uganda

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  • Countdown 2030 maternal, new-born and child survival national profiles


    Main features of the Countdown 2030 national profiles:

    • Clear sub-sections
    • Helpful visual lay out with variety of colours but quite dense
    • Interactive web-based profile with a lot of potential
    • Equity plots that are quite easy to interpret are included
    • Data sources are included through a link

    The Countdown 2030 national profile contains the following sections:

    • Demographics
    • Continuum of care coverage
    • Equity
    • Maternal & newborn health
    • Women’s and children’s nutrition
    • Demographics
    • Child health
    • Policies, system, financing
    • Environmental

    Extract from the Countdown 2030 national profile of the Republic of Côte d’Ivoire

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  • Nutrition Landscape Information System (WHO NLiS) country profiles


    Main features of the WHO NiLS country profile:

    • Clear sub-sections
    • Year of when the indicator was collected is included
    • Direct link to source of information (good for web readers, less for readers of printed dashboards)
    • Very clear information on the meaning and interpretation of each indicator (click window)
    • Section on policies and programmes listed in the GINA database included

    The WHO NiLS country profile contains the following sections:

    • Child malnutrition
    • Malnutrition in women
    • Vitamin and mineral deficiencies
    • Health services
    • Food security
    • Caring practices
    • Commitment
    • Capacity
    • Meta indicators
    • Policies and actions in the Global Database on the Implementation of Nutrition Action (GINA)

    Extract from the WHO NiLS country profile of Guatemala

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  • Setting up the Multisectoral Advisory Committee: The experience of Guatemala

    Key messages

    • The NIPN in Guatemala has been fully integrated into the national coordination structures for food security and nutrition.
    • A working committee of the existing Inter-sectoral Technical Committee (CTI), which was created by law, will perform the function of Multi-sectoral Advisory Committee.
    • This set-up not only ensures institutionalization of the MAC, but also its legitimacy and authority to create a data-driven policy dialogue and influence policy decisions.
    Download the MAC Guatemala case study in pdf format:
    PDF - 829.2 kb
  • Setting up the Multisectoral Advisory Committee: The experience of Ethiopia

    Key messages

    • Ethiopia has taken a dual approach to ensuring high-level multisectoral advise: it makes use of an existing committee and in addition will establish a dedicated NIPN Advisory Committee
    • Integrating the functions of a MAC into an existing coordination structure ensures its authority, strategic influence and sustainability.
    • At the same time, it allows the awareness and capacity building across all sectors with regard to the NIPN operational cycle
    Download the MAC Ethiopia case study in pdf format:
    PDF - 479.9 kb
  • Definitions: information system, dataset, indicator

    An information system is a system of interrelated components that work together for collecting, processing, storing and disseminating information amongst stakeholders/beneficiaries to support decision-making, coordination, control, analysis and visualization (e.g. DHIS-2; National Statistics Office repository; DHS platform).

    A dataset is a file that contains all the individual records of one specific survey (e.g. DHS survey in country “X” of 2005). A dataset can be available in one or more information systems. For example, the Niger DHS survey of 2012 can be found in the STATcompiler information system (DHS official platform) and in the information system of the National Statistical Office of Niger.

    An indicator is calculated based on one or more variables from a dataset.
    The same indicator can be found in different datasets but may or may not be comparable because:

    • The definition of the indicator is not exactly the same. For example, the definition of some indicators of DHS surveys have evolved over time.
    • The survey sampling methodology is different.
  • Example of data provider mapping

    StakeholderInformation system managedCapacityContact
    Ministry of Health HMIS (DHIS-2) Lack of capacity for nutrition-related data analysis
    IT specific staff available
    Fully equipped
    Name, position and contact
    National Statistical Office NADA repository; DEVINFO socio-economic indicators platform; nutrition surveys Lack of capacity for nutrition-related data analysis, particularly for food consumption analysis
    IT specific staff available
    Fully equipped
    Name, position and contact
  • Two options to overcome challenge n°1

    Option 1: Limit the exercise to the level of the datasets (exclude the indicator matrix)

    The exercise is still vast and the scope of the exercise can be further reduced by considering the following:
    1) The range of datasets to investigate
    For example, Ivory Coast decided to investigate the datasets managed by key sectors involved in nutrition (health, education, gender, social affairs, agriculture, animal resources, water, economy, finance & planning) and other agencies known for managing databases (National Agency for Rural Development, National Water Offices, etc.)
    “Key sectors” are those involved in the Multi-sectoral Plan of Action for Nutrition. In some countries this list can be much more vast, including more than ten ministries. For the sake of the data landscape exercise, it is recommended to reduce the list of data providers to investigate to keep the exercise feasible. Other sectors could be investigated in a second stage using the experience of the first exercise.

    2) The level of detail of information to collect for each dataset
    NIPN teams can decide the level of detail necessary to collect for each dataset. One option is to request a detailed description of datasets only for some prioritised sectors.

    Option 2: Include datasets and indicator matrix in the data landscape exercise

    The scope of the data landscape exercise can be reduced by looking at:
    1) The number and range of indicators to investigate
    There are several ways to decide the number and range of indicators to include in the matrix:

    • Select key data providers instead of selecting indicators. For example, the Ivory Coast NIPN country team decided to investigate all the data available in key ministries (health, education, gender, social affairs, agriculture, animal resources, water, economy, finance & planning) and other agencies known for managing databases (National Agency for Rural Development, National Water Offices, etc.)
    • Refer to the M&E plan and list of multi-sectoral indicators that are attached to a national multi-sectoral plan of action for nutrition. If this list is still too long, key indicators can be identified and selected. For example, the Guatemala NIPN country team selected 70 key indicators from the M&E plan. This would mean that the indicators matrix is limited to those 70 indicators but the description of the datasets would need to have a wider scope.
    • Adapt the SUN MEAL system which includes a list of multi-sectoral indicators (about 300) and a sub-selection of key indicators (about 70) for nutrition.

    2) The level of detail of information to collect for each indicator
    There are several options to consider when deciding the level of detail for each indicator, which include:

    • Complete a full indicator matrix. The indicator matrix available here is an example of an in-depth assessment for each selected indicator.
    • Compile only basic information for each indicator. For example, the NIPN in Burkina Faso listed all the indicators available in the datasets and their definition only.
    • Complete the indicator matrix in full for key sectors only (Health, Agriculture, Education, …) and collate less detailed information for the sectors that are of lower priority. For example, the NIPN in Ethiopia decided to focus on nutrition & WASH in the first phase. This means that a detailed indicator matrix can be completed for these two domains with less detailed information for the other domains.
  • Interview with the NIPN Niger team about the data mapping study, 15/11/2018

    • Issiak Balarabé Mahamane, Assistant to the General Secretary, INS
    • Guillaume Poirel, Head of Mission for NIPN Technical Assistance, SOFRECO

    The NIPN Niger team conducted a data mapping study between November 2017 and May 2018, during the start-up phase of the project, using an external consultant. The aim of the study was to carry out “an inventory and analysis of the information and data systems for nutrition in Niger”. The study was carried out by the INS (National Statistical Office), under the strategic direction of the High Commissioner to the 3N “Nigeriens Nourish Nigeriens” initiative. This interview highlights:

    1. How the Niger NIPN team has used the results to develop a capacity building plan for sectors
    2. How the Niger NIPN team dealt with the lack of official definition of key indicators for nutrition
    3. Methodological recommendations for collecting the information

    How did the study go? What were the main challenges encountered?
    The structures to investigate were identified based on the institutions named as “responsible” or “collaborative” in the eight commitments of the National Policy for Nutrition Security (PNSN).
    Questionnaires were given to the sectors, resulting in a significant loss of time. The best method is to meet with the institutions directly and carry out this mapping work with them. The necessary information was obtained after just two or three visits to each institution and only the institutions at the central level were investigated. This was a limiting factor because certain information, notably on the data quality monitoring mechanism, is available at sub-national level.
    Due to the sheer volume of information collected, there was an enormous amount of simplifying and restructuring work to be done which mobilised the NIPN teams. The ‘sector sheets’ were systematically created, consolidating the various elements.
    This led to considerable delays: instead of being finalised by the end of December 2017, the study was finalised in May 2018 and pushed forward until February 2019.

    How did you use the results of the study?
    Firstly, the study provided objective information about the multi-sectorial data available.
    The results allowed us to draw up an initial list of indicators available in each sector in an Excel file.

    1. The study also produced ‘Sector Sheets’ which outline:
    2. The institutional framework;
    3. The organisational framework;
    4. The collection mechanism;
    5. The mechanism for the validation and quality assurance of data;
    6. The data management mechanism;
    7. The output;
    8. The dissemination and exploitation of information;
    9. The indicators and data available;
    10. The nutrition-sensitive indicators;
    11. The indicators selected for the NIPN.

    Following the data mapping, it became apparent that there was an urgent need to put together a referential database of NIPN indicators. In fact, in Niger there is no existing framework providing an official list of the multi-sectoral indicators for nutrition. We are working to adopt nutrition-sensitive indicators in each sector. These indicators will serve as a basis for the NIPN platform.

    Taking into account the institutions’ capacities, we decided to recruit ‘Sectoral Study Officers’ for four months.

    The Sectoral Support Officers have access to clearly identified indicators and will have to ensure that the values for each indicator are collected (15 October 2018 – 15 February 2019), which is a necessary prerequisite for organising the data and building the ‘Nutrition Info’ module. The work of the Sectoral Support Officers will enable us to:

    1. Consolidate the database of nutrition indicators. As a result, for example, we have determined that in the health sector 27 nutrition indicators were missing in the initial mapping. To date, there are several stages to the consolidation: 1. Verifying the comprehensiveness of the indicators, 2) Sorting the indicators according to their link with or ‘sensitivity’ to nutrition, 3) Completing the 23 fields for each indicator (definition, frequency, method of calculation, etc.) and 4. Validating the set of fields for the indicators selected for the NIPN. Also, 239 indicators have been selected for the 3 sectors currently covered (123 indicators for health, 23 indicators for education, 93 indicators for farming and agriculture);
    2. Analyse the nutritional statistics situation in the beneficiary sectors;
    3. Create and validate data sheets for each indicator;
    4. Collect information and documents;
    5. Update the contact list.

    The ‘Sectoral Study Officers’ also make it possible to strengthen capacities in each sector and build relationships that will facilitate access to multi-sectoral data at a later stage.

  • Example of indicator matrix

    Download an example of indicator matrix in Excel format (based on the experience of Niger):

    Excel - 24.2 kb
  • The National Evaluation Platform (NEP) project

    The NEP is an initiative of Johns Hopkins University in four African countries (Mali, Malawi, Mozambique and Tanzania) that was supported by the Government of Canada between 2014 and 2018.
    Its objective is to equip government decision makers with the tools and skills needed to critically evaluate the state of maternal, newborn, and child health and nutrition in their countries, and support sound decision making. It is built through a cycle-based approach that progressively adds new types of data, analytical tools and communications skills, and disseminates findings to policy makers concerned with maternal and child health and nutrition.

    NEP has been implemented through a country-owned and government-led approach. It works with multiple national stakeholders concerned with maternal and child health, nutrition data and decision making, who all have an interest in improving health and nutrition and decreasing mortality outcomes.
    To consult the NEP country experience, or to review the outputs generated and lessons learnt, you can consult the NEP website.

  • How the lack of coherence between sectoral plans contributed to policy question formulation in Mali

    In 2014, the Mali Government was in the process of drafting a ten-year Plan for Sanitary and Social Development (PDDSS) 2014-2023. At that time, a review by the National Evaluation Platform (NEP) team of the draft PDDSS and of the Programme of Sanitary and Social Development III (PRODESS III), the five-year PDDSS programme revealed the following:

    • The target mortality rates were already achieved and the baseline information of several interventions and targets were not coherent between the PDDSS and PRODESS III.
    • The target population groups of the different health and nutrition programmes of the health sector were not harmonised.
    • The proposed intervention package in PDDSS did not seem to correspond with the ambition of the mortality reduction targets of the PDDSS.
      Confronted with this lack of coherence between the MPPA and sector-specific policies and plans, the relevant stakeholders decided to work towards a common and harmonised mortality reduction target which could be realistically achieved within the PDDSS time frame. Policy questions were formulated by the analysis team focusing on the PDDSS targets and the proposed intervention packages, and they were validated by the government stakeholders.
      To allow the use of the Lives Saved Tool, a modeling software which could provide findings in a relatively short time frame (six months), the finalisation of the PDDSS was postponed until 2015.
      The analyses were carried out within the promised time frame and results were available on time to redefine the PDDSS targets and refine the intervention packages, through the mid-year review process of the PRODESS III.

    This example is based on the experience of the National Evaluation Platforms (NEP) project, by Johns Hopkins University.

  • EXERCISE 1: Start with understanding the magnitude and the trends of the undernutrition problem and the trends in relation to national or sub-national targets

    Work with the data analysts to gain a picture of the magnitude and trends of the undernutrition problem: it is recommended that DHS and any other prevalence point from validated national surveys are used.
    Draw the scenarios 1) if trends continue as usual, and 2) if national targets are to be met.
    This exercise is to be repeated at the sub-administrative level (region, district) at which the NIPN cycle ‘questions-analysis-findings’ is strategically interested in focusing on. As data allow to do so, the NIPN cycle should strengthen the decentralized decision-making level as much as possible, and thus the questions identified also respond to that level.

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  • EXERCISE 2: Map out key multi-sectoral policies and plans of action in relation to the undernutrition trend

    Present visually the main multi-sectoral policies and plans in relation to the undernutrition trend and consider how the trend evolved during the implementation period.
    Do it for the most relevant policies, programmes and/or changes in investment in nutrition that are believed to have occurred in previous years.

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    The priorities and interests of stakeholders will differ depending on the stage of the policy or plan:

    • At formulation stage, the focus will be on the definition of targets, the selection of interventions, the required coverage, etc.
    • During implementation, the priorities will be around progress of implementation and probability of reaching targets.
    • At evaluation, the focus will be on impact: what worked, what did not.

    Each stage represents a strategic opportunity to improve the next one, should the corresponding information be available to decision makers.
    The assumption is that if the multi-sectoral policy, plan or programme is well designed, and the interventions are implemented 1) according to planned coverage and 2) with the desired quality, an impact should be seen on the intended outcome.
    Visualising the implementation period of the policy, plan or programme in relation to the undernutrition trend will provide an initial idea of the probability of these assumptions being correct.

  • EXERCISE 3: Gain a picture of the implementation time frame of the multi-sectoral policies and plans

    • Display the implementation periods of the multi-sectoral policies, plans and programmes on one timeline.
    • Add complementary information on the context of implementation, as shown in the fictional country example below.

    This chronogram can help to confirm the priorities of policy makers for the next 12-24 months and will help to identify possible windows of opportunity for influencing planning, formulation or evaluation cycles.

    The fictional example below shows that policy makers in this country need information in 2018 on the progress of implementation of the Multi-sectoral Plan of Action, phase II, and the probability that the plan will achieve its targets by 2020. The absence of a mid-term evaluation makes this information need, which can be filled by NIPN, more acute.
    With an upcoming political transition in 2019, the new policy makers will also need information regarding ‘implementation progress’ to inform the formulation of new policy by 2020.

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    NOTE: The relationship between multi-sectoral and sectoral policies or plans can be presented visually in a similar way, to identify whether the latter could offer an entry point to strategically supporting multi-sectoral efforts.

  • EXERCISE 4: Identify which policies, programmes or investment decisions are more likely to be influenced and decide which administrative level to influence

    Ultimately, the review process will give the NIPN team an initial idea of the decision makers’ priorities and the corresponding time frame on which the ‘questions-analysis-findings’ cycle should focus.
    The initial selection of the priorities will be based on the information pulled together in the policy review, using the previous tips, and on the feedback from stakeholders.
    At the same time, the team will need to decide at which administrative level the NIPN cycle intends to strengthen the decision-making process. This decision will depend on how decentralized nutrition interventions are, the interests of policy makers and the availability of data at sub-national level. The demand for information is likely to be higher at sub-national level, where capacity to use collected data is often sub-optimal and a feedback loop is often lacking. The sub-national dashboard can support analysis at the decentralized level as long as the process is initiated by decision makers’ interests and question formulation.

  • EXERCISE 5: Identify and know the key ‘decision makers’ at the various administrative levels

    At this stage, the team should have good knowledge of the various decision makers along the implementation chain. Decision makers are not necessarily limited to policy makers: programme planners and implementation officers are other actors along the implementation chain who are also making decisions to improve nutrition actions at their level.
    Categorizing the decisions makers, according to the administrative level at which they intervene and according to the type of decisions they can make, will help to assess the diversity of the NIPN stakeholders’ needs.
    It will also define the target audiences and consequently help to fine-tune the policy relevance of the questions and align the ‘storyline’ of the findings with each target audience of decision makers.

  • EXAMPLE 2: Identify the different elements of a specific policy question against the impact pathway and break them down into sub-questions

    Have investments (input) in WASH interventions (activity) led to better access to WASH facilities (output), resulted in a reduction in % of children suffering from diarrhoea (outcome) and reduced child undernutrition (impact)?

    Break the question down into more specific questions to better understand the intermediate steps of the impact pathway. For example:

    • Have investments in WASH interventions changed over the past 5 years?
    • How are investments in WASH interventions distributed at sub-national level?
    • Has coverage in WASH interventions changed over the past 5 years?
    • Are WASH interventions reaching the target populations?
    • Have WASH interventions led to improved access to latrines/access to safe drinking water?

    Unpack the question to identify the indicators, the relationship between the indicators and any assumptions, in order to generate more specific questions that can be answered by the data available. For example:

    • Indicators can be investments or programme coverage.
    • A relationship is: “WASH investment leads to increased WASH programme coverage.”
    • An assumption is: “WASH investments translate directly into increased programme coverage.”

    Zoom in on a specific question and unpack it further. For example:

    • The latest assumption can in turn become a question to be unpacked into further questions: “WASH investments translate directly into increased programme coverage.”
    • Have investments in WASH translated into equal budget expenditures at regional/department level for WASH interventions?
    • In which WASH interventions have investments been made at regional level? What was the distribution of budgets across regions?
    • Has staff been hired, trained and supervised in each region equally? Were supplies and equipment equally available in each region to achieve planned coverage?
    • etc.
  • EXAMPLE 3: Formulating questions related to a social protection pathway for impact on nutrition

    Based on the SUN Movement Secretariat (SMS), 2015. The contribution of agriculture and social protection to improving nutrition; Scaling Up Nutrition in Practice. Geneva.

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    Developing more detailed impact pathways than the ones shown in this example, which highlight specific assumptions and specific relationships between activities, outputs and inputs, will allow the formulation of more specific and detailed questions to help identify bottlenecks, such as:

    • “Has the budget allocated to the implementation of social protection interventions also been used to strengthen the human resource capacity, and has this in its turn led to a higher quality of services and better targeting?”
    • “What are the reasons for a lack of increase in coverage of social protection interventions? - Insufficient number of staff, low level of training, low quality of service delivery, insufficient number of distribution points, inadequate targeting, incomplete monitoring?”
  • The types of data that can be used by a NIPN

    NIPN is unique in that it brings together and values multiple data sources shared by the various sectors that influence nutrition: health, agriculture, water, sanitation and hygiene, social protection, and education, among others.
    Typical data that NIPN would use comes from:

    • National population-based surveys (which can be representative at sub-national level)
      • Demographic and Health Surveys (DHS); Multiple Indicators Cluster Surveys (MICS); National Nutrition Surveys (NNS); Household Incomes and Expenditures Surveys (HIES); Health and Nutrition Expenditures Surveys (HNES); Services Availability and Readiness Assessment (SARA), etc.
    • Local population-based surveys (representative of one district/region)
      • Nutrition Surveys (SMART); Household surveys; Intervention coverage surveys, etc.
    • Programme data
      • Routine data collected by each sector
      • Monitoring data collected by each sector
    • Early warning system data
    • Financial data
      • Evaluation of budget needed (Nutrition Plan of Action)
      • Official commitments for financial investments
      • Budget allocation at central and sub-national levels
    • Modelling data

    This list is not meant to be exhaustive. Each country should explore the wealth of information available. The data landscape exercise is useful for this purpose (see section 3.1).

  • Example 1: Trend analysis of stunting to track progress

    Using national survey data from DHS and MICS, a trend analysis can be used to estimate the Average Annual Reduction Rate of stunting (see page XX). The same method can be applied to other target indicators.
    With this method, NIPN can answer questions such as:

    • What is the current trend of stunting reduction?
    • Has the reduction of stunting accelerated over the period of implementation of the last Multi-sectoral Plan of Action for Nutrition?
    • Is the current trend in stunting reduction sufficient to reach the target?
    Global average annual reduction rate for stunting

    (Source: EU Action Plan on Nutrition)
    This analysis carried out at global level shows that the current AARR is not sufficient to achieve the planned target of 2025.

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  • Example 2: Venn diagrams to visualise the coexistence of multiple forms of malnutrition

    This Venn diagram shows the population affected by one or more forms of malnutrition.
    It is taken from the Global Nutrition Report, which analysed national level data.
    Similar analysis can be carried out at sub-national level to show which regions are affected by which forms of malnutrition and what actions are needed.
    It allows the issue of the double burden of malnutrition to be highlighted.
    The same figure with the actual number of children affected by region can highlight where to invest.

    Coexistence of multiple forms of malnutrition in one African country

    (Source: Global Nutrition Report)

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  • Example 3: Descriptive analysis to improve the targeting of a social assistance programme in Mexico

    A National Food Assistance programme has been implemented in Mexico for decades.
    In 1994, a descriptive analysis of the levels of incomes of the beneficiaries of the programme showed that the programme was not very effective at targeting the poorest households.
    After active measures were taken, the same analysis showed that in 2000 a much greater proportion of the programme’s beneficiaries were indeed those with the lowest incomes.

    Benefit incidence of Food Oriented Social Assistance, by income decile, 1994–2000

    Source: Levy, S. (2006). Progress against Poverty. Sustaining Mexico’s POP Programme. Washington, DC: Brookings Institution Press.
    Between 1994 and 2000, the Government of Mexico improved the targeting of the Food Oriented Social Assistance programme:

    • In 1994, less than 10% of the beneficiaries belonged to the lowest income decile.
    • In 2000, more than 30% of the beneficiaries belonged to the lowest income decile.
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  • Example 4: Descriptive analysis to improve the targeting of hygiene interventions

    A descriptive analysis looking at regional disparities shows that the three regions with high levels of stunting also have high levels of open defecation practices.
    This can be a starting point to further investigate why open defecation is high in those three regions and whether other determinants are also showing high levels.

    Open defecation and stunting rates by region

    (Source: REACH, Example of Ethiopia MICS, 2011)

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  • Exemple 5: Descriptive analysis of coverage of health interventions in India

    Iron-folic acid supplements are delivered through health services. This combined data suggests that most districts with low levels of iron-folic acid supplements also have low ANC visits coverage, suggesting an issue with access to health services.
    But it also shows that quite a few districts with good ANC visits coverage also have a low coverage of iron-folic acid supplements, suggesting issues with delivery.
    Further investigation can provide interesting insights into what is needed to improve the coverage of iron-folic acid supplementation.

    Coverage of interventions

    Source: POSHAN

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  • Example 6: Equity analysis of child feeding practices

    This equity analysis of child feeding practices shows that some practices are more sensitive to the levels of incomes (minimum diet diversity) than others (early initiation).
    Equity analysis is useful for determining whether the progress achieved is benefiting all and, in particular, the most vulnerable.
    If most of the progress is only observed in households with higher incomes, this suggests a problem with the targeting or design of the interventions.

    Child feeding practices by Wealth Quintiles in one African country

    (Source: Global Nutrition Report)

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