Using national survey data from DHS and MICS, a trend analysis can be used to estimate the Average Annual Reduction Rate of stunting (see section 3.6). The same method can be applied to other target indicators.
With this method, questions can be answered such as:
(Source: EU Action Plan on Nutrition)
This analysis carried out at global level shows that the current AARR is insufficient to achieve the 2025 target.
NIPN in Guatemala aimed to study progress based on the implementation of three consecutive national multisectoral nutrition strategies, and more specifically with regard to the budget allocation to nutrition over the past 12 years.
The results have been published in a full report and a brief and presented by SESAN (the Secretariat for Food and Nutrition Security) for discussion with national and sub-national government authorities, international cooperation and civil society partners, as well as with Congress.
It is being used to advocate – at the time of the 2019 elections – for better implementation of nutrition actions and, where appropriate, to consider adjustments to implementation or budget allocation.
It can be very useful to do a stakeholder analysis of the overall nutrition audience to better understand which actor has which attitude towards the project and how influential that person is.
Someone who is very positive and influential could become a champion for the project in the communication approach, whereas someone who is largely negative about the project’s approach, but also influential needs to be managed carefully and communication with this person should be geared towards changing her/his attitude from negative to neutral.
If a person with a negative attitude is perceived as having very limited to no influence, less efforts need to be made to communicate with him/her.
A stakeholder mapping is usually done along 2 dimensions: the level of interest in the topic/project (negative to positive) and the level of influence or power the stakeholder has in the target community (in the case of NIPN, the multisectoral nutrition system).
Visibility of a specific organisation, project or initiative can be created by using their logo and acknowledging their contribution in the different communication events that are being organised.
For instance during a launch event of NIPN in a country, the logo of the implementing organisations and the donors should be displayed, and if available the logo of the NIPN (either the global NIPN logo or the one which has been created specifically by the country NIPN).
The NIPN countries which receive funding from the European Union are requested to develop a visibility plan following specific EU guidelines.
Communication and visibility are not the same. Communication is about specific messages that one wishes to convey, to achieve a well-defined objectives, reaching specific target audiences, using appropriate communication channels and tools. A communication plan is more elaborate, but can include a specific chapter on how it intends to create visibility for the NIPN.
Creating a specific identity and branding for the NIPN, including the design of a logo and the use of consistent templates/formats for reports and presentations, is part of the visibility plan.
The parameters of the WHO UNICEF TEAM group of 2017 are now used as the reference.
Notes:
Special Note: there seem to be a missing data point in the example provided. The example to generate the AARR seems to be based on 8 data points (including 2005) while the table provides only 7 data points. When using the 7 data points, the coefficient equals -0.0669 (and not -0.06613) and AARR is still 6.4%.
Link:
http://apps.who.int/nutrition/landscape/report.aspx
Main features of the WHO NiLS country profiles:
The WHO NiLS country profiles contain the following sections:
Extract from the WHO NiLS country profile of Guatemala
Link:
http://countdown2030.org/country-and-regional-networks/country-profiles
Main features of the Countdown 2030 national profiles:
The Countdown 2030 national profile contains the following sections:
Extract from the Countdown 2030 national profile of the Republic of Côte d’Ivoire
Link:
https://globalnutritionreport.org/
Main features of the GNR national dashboard:
The GNR national dashboard contains the following sections:
Extract from the GNR national dashboard of Uganda
Link:
http://poshan.ifpri.info/category/publications/district-nutrition-profiles/
Main features of the POSHAN district dashboard:
The POSHAN district dashboard contains the following sections:
Extract from the POSHAN dashboard of the Bokaro district
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:
The SUN MEAL dashboard contains the following sections:
Extract of the SUN MEAL national dashboard for Lao PDR
Administrative sub-divisions differ across countries. For the purpose of this tool the following definitions are adopted:
“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”.
The underlying objective behind this question is to be able to improve targeting of the intervention.
This randomised controlled trial measured the impact of an intervention that was designed to reduce stunting.
Between 2010 and 2014, it was observed that:
Results from secondary analysis of Tubaramure, a food-assisted integrated health and nutrition programme in Burundi
(Source: Leroy J.L., Olney D., Ruel M., 2016. Tubaramure, a Food-Assisted Integrated Health and Nutrition Program in Burundi, Increases Maternal and Child Hemoglobin Concentrations and Reduces Anemia: A Theory-Based Cluster-Randomized Controlled Intervention Trial. The Journal of Nutrition, 146(8), 1601-1608, https://doi.org/10.3945/jn.115.227462.)
In the last two cases, an incorrect conclusion has been reached.
The characteristics of a potential confounder are as follows:
Example: Has investment in programme A led to reduced anaemia levels?
Possible factors confounding a conclusion regarding the impact of one intervention on anaemia levels could be, for instance:
Without controlling the data analysis for confounding factors, it is not possible to attribute the reduction of anaemia levels to the nutrition intervention.
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 of exclusive breastfeeding).
Equity analysis is useful to determine 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 that there may be a problem in the targeting or design of the interventions.
(Source: Global Nutrition Report)
Iron-folic acid supplements are delivered through health services. These combined data show 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 the analysis 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 is needed to provide better insights into what is needed to improve the coverage of iron-folic acid supplementation.
Source: POSHAN
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.
(Source: REACH, Example of Ghana MICS, 2011)
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.
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:
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.
(Source: Global Nutrition Report)
1. Why a data analysis plan?
“A data analysis plan helps you think through the data you will collect, what you will use it for, and how you will analyse it. Analysis planning can be an invaluable investment of time” (Center for Disease Control and Prevention, 2013)
The method for creating a data analysis plan in the context of a NIPN is not much different from the method used in a research context.
In the context of NIPN, the process should be simpler because:
The next section describes briefly the content of a data analysis plan focusing on what is a bit specific to the NIPN.
As general recommendations:
Recommended sources to read:
Center for Disease Control and Prevention (2013) Creating an analysis plan. Atlanta.
Simpson, S.H. Creating a data analysis plan: what to consider when choosing statistics for a study (2015).
2. What is a data analysis plan?
Main sections of a data analysis plan (based on CDC module):
3. Main question and sub-questions
At this stage, the policy relevant question (and, in some cases, its sub-questions) is already well defined (section 3.4, page 11).
Answering all the sub-questions will provide a full answer to the main question.
4. Dataset(s) to be used
The dataset(s) needed is (are) listed. In the context of the NIPN, particular attention may be needed on data management: as the dataset(s) may come from different sources and/or may not have been designed for the main question, there could be quite some work to be done to harmonise / append / clean the raw dataset(s).
5. Inclusion/exclusion criteria
In this section population subgroups, geographic scope, timeframe… are very precisely defined.
The definition of the data quality level is also required for the analysis.
Indeed, depending on the analysis, a more or less strict data quality level could be required.
This point is detailed in the Data Quality training module (section 3.3).
6. Variables to be used in the main analysis
In this section the exact variables/indicators to be used in the analysis must be defined.
For example, to analyse “obesity”, it needs to be defined whether the indicator is Body Mass Index (BMI) and if different categories of BMI will be used or the mean BMI or both.
In the context of NIPN, the harmonisation of the definition of indicators across datasets will be important.
7. Statistical methods and software to be used
Ensure coherence with section 4 of the guidance notes on data analysis.
Also, to provide only indisputable analysis (principle 3 section 3.4, page 4), make sure that the statistical method used is coherent with the datasets available and the data quality of these datasets. The choice of the statistical method is key to avoid over-interpretation of the data that could lead to misleading conclusions.
Does the NIPN team has the technical capacity to handle the statistical method and the software identified?
8. Table shells
Nothing specific to NIPN.
9. Estimation of time and resources
At this stage, a precise estimation of the time and resources needed to conduct the analysis should be made.
If this estimation lead to more time than the initial estimation made during the data analysis framework, you may adjust the question/s to be addressed first.
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:
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).
To estimate the consistency of population data, it is important to have a good estimation of the denominator used to calculate an indicator from routine data. To assess population data, they are compared to an external source.
The DQR gives an example comparing an estimate of population using government official data with UN estimation.
The external consistency of the indicator can be assessed comparing routine data and survey data for the same period of time and geographical level.
The tool can be also used to assess consistency between related indicators
Routine data consistency over time (Domain 2: Internal consistency of reported data) can be assessed using the DQR. Its uses the mean of the three preceding years for the indicator that is compared to the indicator value of the current year.
Routine data consistency over time (Domain 2: Internal consistency of reported data) can be assessed using the DQR. Its uses the mean of the three preceding year for the indicator that is compared to the indicator value of the current year.
Routine data consistency over time is assessed as part of the Internal Consistency domain. It uses the mean of the three preceding years for the indicator that is compared to the indicator value of the current year.
Use of this feature for nutrition indicators should be carefully studied as countries are currently making efforts to:
The identification of outliers is part of the Internal Consistency metric. To compute extreme outliers the tool compares, for a given year, the rate to the median value of the three preceding years. Thus when the value is greater than 3 standard deviations from the mean it is considered as an extreme outlier.
The WHO 2017 DQR offers different ways to display reporting completeness at district or national level : either in a table or a graph.
The table below presents some metrics included in the Data Quality Assurance Tool of Measure Evaluation (Measure Evaluation: The Data Quality Assurance Tool for Program-Level Indicators, 2007), which are not explicitly included in the WHO DQR.
The use of metrics depends on the objective of the tool and therefore of the assessment.
The WHO DQR propose four data quality domains that categorise the metrics. Some authors call these metrics attributes or dimensions to characterise data. The table below presents the four domains and their metrics of the WHO DQR 2017.
WHO DQR integrated into DHIS2 for countries implementing DHIS2 – 2019
Advantages:
Disadvantages:
Advantages:
Disadvantages:
Measure Evaluation DQA Tool – 2008 – Prototol 2: Data Verification
Advantages:
Disadvantages:
Measure Evaluation RDQA – 2009 – Checklist to assess program/project data quality
Advantages:
Disadvantages:
Measure Evaluation RDQA – 2010 – Checklist to assess program/project data quality
Advantages:
Disadvantages:
Measure Evaluation RDQA – 2015
Advantages:
Disadvantages:
Sources:
The NIPN may have the objective to use data from a MICS survey to study a particular region. A National Survey like a MICS has the primary objective to produce national figures. A study(1) concluded that “anthropometric data quality was highly variable both between and within survey sources and over time”. This is confirmed by interviews of experienced practitioners(2). Typically, data from a region with difficult access for logistic or security reasons can be of lower quality. The data quality of that region may be good enough to contribute to the production of a national figure but the NIPN may want to apply more stringent data quality criteria to study that region specifically.
References:
Health routine data are usually compiled with the objective to detect epidemic episodes. In low resources settings, routine data can be incomplete and can include measurement errors. However, analysed carefully, routine data may be good enough to observe medium term trends or seasonal trends of a particular condition.
November 15th, 2018
Recap
The NIPN team in Niger 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:
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 by the NIPN team. The ‘sector sheets’ were systematically created, consolidating the various elements.
This led again 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 available data from multiple sectors.
The results allowed us to draw up an initial list of indicators available in each sector in an Excel file.
The study also produced ‘Sector Sheets’ which outline:
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 a few ‘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:
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.
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, Côte d’Ivoire 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.
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:
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:
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 visualisation (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:
ORIGINAL BROAD QUESTION: Is the budget allocation enough for each education intervention?
REFORMULATED BROAD QUESTION: Is the budget allocated to education sector as part of the National Nutrition Strategy equal to what is costed (planned), for each of the planned intervention and how do the provinces prioritize nutrition?
SUB-QUESTIONS:
REFORMULATION OF SUB-QUESTION 1:
ORIGINAL BROAD QUESTION: Why does the production of ’nutritious crops’ not increasing?
REFORMULATED BROAD QUESTION: What factors can explain that the production of ’nutritious crops’ are not increasing?
SUB-QUESTION:
REFORMULATION OF SUB-QUESTION:
ORIGINAL BROAD QUESTION: How can we improve the VAS coverage (national baseline being 38%)?
SUB-QUESTIONS:
REFORMULATION OF SUB-QUESTION:
Based on the SUN Movement Secretariat (SMS), 2015. The contribution of agriculture and social protection to improving nutrition; Scaling Up Nutrition in Practice. Geneva.
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:
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)?
1. Break the question down into more specific questions to better understand the intermediate steps of the impact pathway.
For example:
2. 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:
3. Zoom in on a specific question and unpack it further.
For example:
At this stage, the team should have good knowledge of the various decision makers along the implementation chain. Focal points working in the respective sectors can help the NIPN team to identify them.
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.
Categorising 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.
A further stakeholder mapping exercise will help to define the target audiences, the policy relevance of the questions to them, their level of interest and level of influence in the specific nutrition policy questions. This is important in order to fine-tune and align the ‘storyline’ of the findings with each target audience of decision makers (for an example of this exercise, see section 4.1, page 3).
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 is able or intends to strengthen the decision-making process. This will depend on the level of decentralisation of governance, the interests of policy makers at that level 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 decentralised level as long as the process is initiated by decision makers’ interests and question formulation (see section 3.5).
This timeline 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 needed information in 2018 about the progress of implementation of the Multisectoral Plan of Action, phase II, and the probability of achieving the targets by 2020. The absence of a mid-term evaluation made this information need, to be filled by NIPN, more acute.
With an upcoming political transition in 2019, the new policy makers also needed information regarding ‘implementation progress’ to inform the formulation of new policy by 2020.
NOTE: The relationship between multisectoral and sectoral policies or plans can be presented visually in a similar way, to identify whether the latter offer an entry point to strategically support multisectoral efforts.
Prepare a visual presentation of the main multisectoral policies and plans in relation to the undernutrition trend and consider how the trend has evolved during the implementation period.
Do the same for those policies, programmes and/or changes in investment in nutrition that are believed to have been most relevant in previous years.
The priorities and interests of stakeholders will differ depending on the stage of the policy or plan:
Each stage represents a strategic opportunity to improve the next one if the corresponding information and evidence would be available to decision makers.
The assumption is that if the multisectoral policy, plan or programme is well designed, and if the interventions are implemented 1) according to planned coverage and 2) with the desired quality, an impact on the intended outcome should be seen.
Visualising the implementation period of the policy, plan or programme in relation to the undernutrition trend provides an initial idea of the probability of these assumptions being correct.
Work with the data analysts to draw a picture of the magnitude and trends of the undernutrition indicators: it is recommended that DHS and any other prevalence point from validated national surveys are used.
Draw two scenarios:
This exercise is to be carried out or repeated at the sub-national level (region, district) in which the NIPN is strategically interested. If data allow to do so, the NIPN cycle should support decentralised or sub-national decision-making as much as possible, and thus identify the relevant priority questions corresponding to that level.
Documents that can be reviewed during the desk study include:
The Nutrition Evaluation Platforms 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 implementing 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.
The Ethiopia NIPN country team is adopting a dual approach to building capacity by addressing immediate needs for NIPN specific capacity while developing a long term and systematic capacity strengthening strategy which supports the overall monitoring, evaluation and research agenda of the national nutrition program.
To develop Ethiopia’s long-term capacity to manage and maintain a NIPN, it is important to identify existing capacities as well as any additional capacities needed to sustain the NIPN approach in the country. A Capacity Needs Assessment (CNA) was launched in 2018 to explore capacity gaps related to evidence based policy making and the monitoring, evaluation and research agenda of the National Nutrition Program (NNP).
This process was initiated by a workshop, facilitated by the International Food Policy Research Institute (IFPRI), which brought together 36 nutrition monitoring and research experts from various sectors and institutions in October 2018. Most of the workshop participants play a role in the National Nutrition Monitoring, Evaluation and Research Steering Committee, which also has an advisory role for NIPN. The aims of the workshop were:
Workshop participants developed a framework, which included the ‘demand capacity’ for evidence by the policy makers as well as the ‘supply capacity’ of existing evidence, across three related levels (individual, organizational and systemic). As a result of this workshop an approach paper was developed, which guides the subsequent steps of this multisectoral process.
The next step is the actual assessment of the existing capacities and capacity needs, which was launched in the first trimester of 2019 through an elaborate multi-sectoral and multi-stakeholders participatory process. It included questionnaires, interviews and follow-up consultations with key stakeholders from different ministries, national institutions and universities responsible for collecting and monitoring data for nutrition, and for nutrition-related evaluation and research. Information was sought from over 20 national institutions, in addition to key informant interviews with multisectoral users of these data, including programmatic and policy decision makers.
The findings of the capacity need assessment will be used to develop a capacity strengthening strategy, which will be finalized in the second trimester of 2019. In the meantime, action is already underway to address immediate capacity needs within NIPN.
A number of activities were undertaken during the first year of NIPN implementation to address the immediate capacity needs at the individual, organizational and systemic level .These needs were identified through the experiences with the “Learning-by- Doing” demonstration project (see later), rapid capacity needs assessments by direct NIPN stakeholders and were initiated in advance of the NIPN capacity assessment workshop.
Different approaches have been used, including short training courses, ‘learning by doing’ and peer learning. Details of individual capacity development activities undertaken by Ethiopia in 2018 and 2019 are summarized in the table below.
Within EPHI a small, dedicated, team has been assigned to NIPN which collaborates with additional nutrition researchers from EPHI’s Food Science and Nutrition Directorate and other relevant directorates. Their capacity has been strengthened in project management, creating a common understanding of the NIPN approach, analytical and communication capacities and finally creating a better understanding of the different elements of a data repository.
In parallel to addressing short-term needs, the NIPN aims to address long term capacity requirements to manage and maintain a NIPN. Besides the research policy seminars and the multisectoral engagements, they also include:
Long term capacities are being strengthened by involving PhD students from local universities in the formal NIPN training courses. In addition, EPHI has a capacity building budget for staff linked to the NIPN analysis unit or the NIPN advisory committee to attend training (online or formal) in Ethiopia or overseas, which will lead to a certificate, diploma or master’s degree at an overseas university. EPHI can also hire local training institutes and subcontract local universities. There is also a small grant scheme to encourage and support the enrolment of about six staff involved in the NIPN to pursue a PhD programme to sustain capacities beyond the project timeframe. Two researchers have been identified and started their PhD programmes at Addis Ababa University in September 2018.
The Senior Technical and Policy Advisor and the National Research Officer at IFPRI are assigned to support the NIPN full time and they provide continuous support and on the job training for staff and organisations involved in NIPN. In addition, IFPRI provides needs based technical support, which covers a wide set of NIPN skills, including writing, communication and analytical skills. In 2019, a report writing mentor was assigned to provide temporary support to the team. In 2019, IFPRI also assigned a researcher to guide a specific analytical process for further analysis of existing data.
A set of guidance notes on different aspects of the NIPN approach have been prepared by the NIPN GSF. This guidance aims to facilitate a coherent approach to implementing the NIPN operational cycle. NIPN team members from EPHI as well as from other key partner’s institutions from the national nutrition Monitoring, Evaluation and Research Steering Committee participated in webinars as well as in an on-site country workshop on the content of the guidance notes. The NIPN team in Ethiopia will adapt the guidelines to the local context. The NIPN Global Support Facility (GSF) based in Europe also provides direct technical support to NIPN country teams.
While embarking into developing the capacity development strategy, the Ethiopia country case shows the importance of focusing on the bigger nutrition governance structure, national policies and programs in order to prevent overlap and ensure a sustainable system which supports the long-term objectives of NIPN:
Ethiopia is implementing the second phase of its National Nutrition Programme Phase II (NNP-II) 2016-2020. NNP-II is co-signed by 13 Ministries. It is governed by a multisectoral national coordination structure, consisting of the National Coordination Body at high political and decision-making level, and of the National Nutrition Technical Committee at technical and planning level.
NIPN in Ethiopia is established under the umbrella of the national multisectoral nutrition program and its governance system. It aims to generate knowledge and lessons learned to guide the implementation of NNP-II and support its contributing sectors.
NIPN is hosted by the Ethiopia Public Health Institute (EPHI) who is also the chair of the Monitoring, Evaluation and Research Steering Committee (M&E&R SC), one of the three high level thematic committees of the national multisectoral coordination structure. In addition, due to its multiple responsibilities, EPHI is closely linked to the National Nutrition Technical Committee and the National Nutrition Coordination Body.
Multisectoral coordination structure of the National Nutrition Programme (NNP) Phase II (2016-2020)
During the NIPN design phase, the M&E&R SC was identified as the best placed committee to take up the role of the Multisectoral Advisory Committee to NIPN, because its objectives are so closely aligned with the NIPN objectives. The committee aims to provide technical support and direction, to generate evidence and monitor progress, and to timely inform decision-making for successful implementation of the NNP-II. The members of the M&E&R SC represent all NNP-II signatory government ministries, as well as donors, civil society and research organisations. The SC has most of the MAC attributes: it has included many of the functions for NIPN into its TOR and will play the validation and the communication and dissemination functions; it is ideally placed to be the official relay between the NIPN and the national decision making level. It also presents the advantage of meeting most of the MAC core principles: being part of the existing multisectoral coordination structure overseeing the NNP-II implementation progress; ensuring regular participation of the government representatives of the NNP-II and having the ability to mobilise additional expertise through donors, civil society and research organisations’ participation.
Despite all these advantage, the steering committee has many members. While this steering committee has integrated NIPN advisory roles in its terms of reference, the NIPN also anticipates to create a dedicated NIPN MAC, which will be composed of a small group of selected advisors with high level decision making leverage and with close linkages with ministers. This dedicated MAC will be created in 2019 and it is expected to take up a selected set of NIPN specific activities.
Thus, the MAC follows a dual approach in Ethiopia as it relies on an existing committee, the M&E&R SC and a dedicated NIPN Advisory Committee.
Existing Terms of Reference defined the M&E&R SC membership and operational modalities prior to the NIPN. In addition to those, the M&E&R SC will take up the following MAC tasks:
In the NIPN inception period, the M&E&R Steering Committee has requested the NIPN team to explore further which activities should be carried out to further enhance stunting reduction. As a result, the team has started working on a NIPN demonstration project on WASH and nutrition.
EPHI is the chair of the M&E&R SC, with the Ethiopian Institute of Agricultural Research (EIAR) as co-chair.
The main advantage of embedding some of the functions of the MAC within this existing M&E&R SC lies in the fact that it is already a functioning and credible structure. The Committee’s Terms of Reference are formalized and all committee members have been officially assigned by their respective ministries and are well sensitized with respect to the current nutrition issues in the country and within their respective institutions. The SC is meeting on a regular basis, and many members have direct links to high-level decision makers. It is expected that the multisectoral character of the committee will facilitate data access across sectors, as members have a vested interested in responding to the policy questions taken up by NIPN.
Through its role as chair of the M&E&R SC and its role in the NNCT and the NNCB, EPHI is engaging on a continuous basis with a wide set of multisectoral nutrition stakeholders. It reports on NIPN to the highest nutrition governance structures, allowing for high-level political and decision-making leverage.
Finally, this set-up further promotes the multisectoral implementation of the NNP and facilitates the capacity building approach of NIPN, with motivated members of the M&E&R SC both contributing to the capacity need assessment process, as well as benefiting from the capacity building investments.
However, as the NIPN MAC will be functional only in 2019, it remains to be seen whether it will have the ability to create efficient working relationships with the M&E&R SC to work in complementarity and whether it will acquire the respectability to influence the higher decision making level.
Guatemala has strengthened and institutionalised its approach to food and nutrition security through a series of legal and policy frameworks for food security and nutrition. This process began in 2005 when a law was enacted that saw the establishment of the multisectoral coordination system, the National System for Food and Nutrition Security (SINASAN).
SESAN (Secretariat for Food and Nutrition Security) is the core structure of the multisectoral nutrition coordination system in the country, operating under the Guatemala Presidency, which also hosts a committee of all ministers as its decision-making body. SESAN is also in charge of overseeing and coordinating the implementation of the National Food and Nutrition Security Policy 2016-2020.
Organisational structure of the Guatemala National System for Food and Nutrition Security (SINASAN)
When NIPN was set up in 2017 it was positioned as a key vehicle to support SESAN in tracking and generating evidence on the implementation of Guatemala’s National Strategy to Prevent Chronic Malnutrition 2016-2020. The Strategy is aligned with the National Food and Nutrition Security Policy but it offers a more in-depth focus on stunting reduction. As such, NIPN strengthens the existing centralised information system, managed by SESAN, which relies on data from the information systems of different key ministries.
As the NIPN positioning was clarified, different options for the creation of the Multisectoral Advisory Committee (MAC) were considered during the six-month design phase of NIPN in 2017. The options were based on a thorough review of the national multisectoral coordination structures led by CATIE (Centro Agronómico Tropical de Investigación y Enseñanza), the implementing partner of the NIPN, which included a review of legal documents and interviews with key government and non-government counterparts. Considering the Guatemala specificities, stakeholders agreed that the MAC should support the existing system and thus be part of the existing food and nutrition security coordination structures.
The final decision was to link the MAC to the existing Inter-sectoral Technical Committee (or CTI). The CTI is a technical committee in which all ministries playing a role in food and nutrition security are represented. Though only formed by government representatives, its regulation allows calling upon ad hoc participation of development partners and civil society.
The choice for this option aligns with the core principles of the MAC: it is embedded into multisectoral coordination structures, the structure is formalised to ensure members’ continued participation, and it has the flexibility to invite ad hoc participation of experts outside the government.
In the legal statutes of the existing committee, CTI is allowed to “create any permanent and temporary committees that it deems necessary to fulfil its functions”. When committees are permanent or integrated “their nature, purpose and organisation must be specified, and the financial resources necessary for its operation shall be indicated”. The MAC is registered as a “Working Committee” under the CTI by law, thus ensuring its institutionalisation and sustainability.
The Multisectoral Advisory Committee’s main role is to “support, orient and provide technical inputs to any of the NIPN work streams aiming at strengthening the Centralised Information System for Food and Nutrition Security to fulfil its mandate of tracking progresses of implementation of the National Strategy to Prevent Chronic Malnutrition and understanding the evolution of chronic malnutrition in a coordinated and comprehension approach.”
Specific roles of the committee include:
The Committee is multisectoral and multi-stakeholder by nature: it is composed of representatives from the governmental institutions and all ministries who are part of the Food and Nutrition Security System, typically programme officers in charge of planning, monitoring and evaluation. The full Committee is chaired by the Head of the Planning, Monitoring and Evaluation Division of SESAN.
To operate with maximum efficiency, a core group has been constituted from representatives of the four key ministries and institutions responsible for the implementation of the National Strategy to Prevent Chronic Malnutrition: the Ministry of Public Health and Social Assistance; the Ministry of Agriculture, Livestock and Food; the Ministry of Social Development; and the agency in charge of supplying potable water.
The Committee will seek the active contribution and participation of representatives from civil society, the private sector and international development partners, since the law permits civil society and partners’ representation in and contribution to CTI committees.
The CTI not only provides oversight to the MAC but will also present its findings and recommendations to the decision-making committees of all ministers involved in food and nutrition security. Intermediary outputs and findings of the MAC will be shared with other technical coordination fora, such as the SUN platform, and civil society and donors coordination fora.
As all stakeholders in Guatemala felt it was crucial to formally embed the MAC in the existing food and nutrition security coordination structures, the first six months of the NIPN implementation phase focused on the institutionalisation process (October 2017 to March 2018). This elaborate process offered the MAC the legitimacy and authority to function effectively. The MAC will use the existing formal communication mechanisms between the technical and decision-making levels to create a data-driven policy dialogue. Though it is too early to have evidence, this set-up maximises the probability of influencing policy decisions and sustaining the policy dialogue beyond the duration of the project.
The current set-up does not come without challenges. The CTI structure is required to comply with government procedures to call upon meetings and thus may lack the flexibility and reactivity to respond in a timely manner to the NIPN’s needs. Mobilising the full committee, with its large number of government representatives, has proven to be challenging up to now. However, the constitution of the core group – with officially appointed representatives from the four key ministries – offers the flexibility to function in an agile and efficient manner. Since December 2018 the core group has met in specific instances including a first workshop to initiate the process of formulating policy questions.
The NIPN platform in Guatemala aims for replication at the sub-national level. SESAN has selected one department to study the cost-efficiency of setting up an NIPN platform at the decentralised level, including a departmental MAC.
In Guatemala, a Steering Committee provides clarity and transparency on plans and arrangements to the main NIPN partners. It comprises representatives of the EU Delegation, as the main donor supporting the NIPN; the Secretariat for Food and Nutrition Security (SESAN), as the government host for the NIPN; and CATIE (Centro Agronómico Tropical de Investigación y Enseñanza), as the organisation managing the grant and providing technical assistance.
The Steering Committee meets regularly (three times a year), meetings are well attended and comprise in-depth discussions on progress, barriers to progress and how to overcome them.
CATIE has assigned an experienced coordinator, an assistant and a financial administrator to manage NIPN. The CATIE team meets weekly to discuss progress and issues and are in regular (daily) contact with the government’s NIPN host, SESAN, about project implementation.
CATIE has developed an elaborate project management tool in Excel, which tracks goals, activities and spending, with a simple traffic light system to signal progress or issues to the Project Steering Committee.
Download the Excel project monitoring template from CATIE (available in Spanish only).
*****
