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:

• What is the current trend of stunting reduction?
• Has the reduction of stunting accelerated over the period of implementation of the last Multisectoral 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 insufficient to achieve the 2025 target.

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).

MAP STAKEHOLDERS ACCORDING TO LEVEL OF INTEREST AND INFLUENCE

TV Show

Radio show

Social media campaign

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.

• 2007: UNICEF produced the technical note n°185 to generate AARR of underweight prevalence .
• 2012: Endorsement of WHO nutrition targets for 2025.
• 2013: The European Commission developed the EU 2014-2020 Nutrition Action Plan. In collaboration with WHO, the European Commission developed an excel tool & a method for generating AARR.
• 2013: WHO modified the Excel tool using the methodology described by De Onis et al. to develop the Global Nutrition Targets Tracking Tool (de Onis, G. Dewey, Borghi, W. Onyango, Blössner, Daelmans, Piwoz and Branca. 2013. The World Health Organization’s global target for reducing childhood stunting by 2025: rationale and proposed actions).
• 2014: The Global Nutrition Report (GNR) is released including estimation of AARR using methodology described by De Onis et al. in 2013.
• 2015-2016: The GNR of 2015 and 2016 are released using different monitoring progress indicators than GNR 2014.
• 2017: The WHO and UNICEF Technical Expert Advisory Group on Nutrition (TEAM) published a key technical report recommending an updated methodology for monitoring AARR.
• 2017: The European Commission published a methodological note explaining why they maintain their initial methodology.
• 2017: The GNR 2017 is released endorsing the June 2017 TEAM recommendations.
• 2017: The WHO tracking tool v2.3 produces a technical note ensuring coherence with TEAM recommendations.

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:

• Clear sub-sections
• Year of when the indicator was collected is included
• Direct link to source of information (good for web readers, less good 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 profiles contain 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)

EXAMPLE

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:

• 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
• Child health
• Policies, system, financing
• Environmental

EXAMPLE

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:

• 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

EXAMPLE

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:

• 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

EXAMPLE

Extract from the POSHAN dashboard of the Bokaro district

Link:
https://scalingupnutrition.org/progress-impact/monitoring-evaluation-accountability-and-learning-meal/

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

EXAMPLE

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:

• 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

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:

• the control group (without the intervention) saw their stunting levels increase from 68.2% to 74.8%;
• the stunting levels of the intervention group remained almost stable.

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.)

• It is only by comparing the delta of pre- and post interventions for the control group and the treatment group that a robust conclusion can be reached: the intervention reduced stunting levels by 6.4 percentage points (74.8-68.2) – (64.3-64.1).
• Without a control group, the measurements “before and after intervention” for the treatment group could be compared, leading to the conclusion that the intervention had no impact (increase of 0.2 pp).
• Without baseline measurements, the populations “with and without” interventions could be compared, leading to the conclusion that the intervention had a very high impact (reduction of 10.5 pp).

In the last two cases, an incorrect conclusion has been reached.

The characteristics of a potential confounder are as follows:

• It must be associated with the outcome;
• It must be associated with the exposure of interest;
• It must not be an intermediate step in the causal pathway between the study exposure of interest and the outcome.

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:

• the coexistence of another programme with a potential impact on anaemia reduction, targeting the same population groups;
• lower malaria incidence in the intervention area due to reduced rainfall during the baseline measurement.

Without controlling the data analysis for confounding factors, it is not possible to attribute the reduction of anaemia levels to the nutrition intervention.

• While measuring an association and checking the temporality is relatively easy, controlling for all (known and unknown) confounding factors is very challenging, as data on all confounding factors is often not available in population-based surveys. This typically requires a randomised controlled trial in a research setting to measure and compare rates of anaemia and numerous potential confounding factors between the intervention group and a control group (not exposed to the intervention), before and after the intervention.
• National population-based surveys and routine monitoring data typically do not have a control group.
• Without a control group, trying to interpret an association or a causal relationship can be very misleading. There is a high risk of drawing the incorrect conclusion as in the above-mentioned example, where the conclusion was that the nutrition intervention has an impact on anaemia while in reality the change in anaemia rates were caused by a confounding factor, and not by the nutrition intervention.
  Data analysis of this type of survey data is not suited for the analysis of a causal relationship and could lead to incorrect and misleading policy decisions.

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.

CHILD FEEDING PRACTICES BY WEALTH QUINTILES IN ONE AFRICAN COUNTRY

(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.

COVERAGE OF INTERVENTIONS

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.

OPEN DEFECATION AND STUNTING RATES BY REGION

(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.

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.

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)

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:

• A data analysis framework is already produced (step 3 of question formulation process) and forms the basis for the more detailed data analysis plan (after step 4 of question formulation process).
• Section 3.4, pages 7 to 9 describes data analysis methodologies.
• NIPN is about the use of existing data, it is not about designing a protocol for new data to be collected.

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:

• Don’t panic!
• Use the advice and experiences from colleagues and experts
• Quickly contact an expert when necessary

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):

• Main question and sub-questions
• Dataset(s) to be used
• Inclusion/exclusion criteria
• Variables to be used in the main analysis
• Statistical methods and software to be used
• Table shells
  => Estimation of time and resources needed

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).

• Are the datasets comparable?
• Are the indicators harmonised?
• Is there a need to transform the data for the analysis?
  To answer these question, access to the datasets in question is required.

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:

• 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
  • Global initiatives using modelling techniques provide data at national and sub-national levels: NCD Risc (http://www.ncdrisc.org/index.html) or IHME for Africa (https://vizhub.healthdata.org/lbd/cgf#).

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:

• improve coverage of nutrition indicators and this can increase the mean of cases over years;
• invest to prevent and manage malnutrition events which can decrease the number of cases over years and reduce the mean;
• indicators such as wasting can quickly change over time due to shocks or improvement of the situation.
  Interpretation of this feature for nutrition indicators (e.g. stunting, wasting or overweight) should be carefully assessed.

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:

• Provides access to facility data recorded in DHIS2 Software
• Can be used to assess quality of indicator of interest available in DHIS2

Disadvantages:

• Does not include examples of nutrition data indicators
• Requires skills to use DHIS2

WHO DQR 2017

Advantages:

• Needs to be fed with data to be assessed

Disadvantages:

• Does not include examples of nutrition data indicators

Measure Evaluation DQA Tool – 2008 – Prototol 2: Data Verification

Advantages:

• More suited to evaluation of field supervision data
• Provides recommendations and actions to improve data quality

Disadvantages:

• Does not include examples of nutrition data indicators
• Requires skills to use DHIS2

Measure Evaluation RDQA – 2009 – Checklist to assess program/project data quality

Advantages:

• More suited to evaluation of field supervision data
• Provides recommendations and actions to improve data quality

Disadvantages:

• Does not include examples of nutrition data indicators
• Requires skills to use DHIS2

Measure Evaluation RDQA – 2010 – Checklist to assess program/project data quality

Advantages:

• More suited to evaluation of field supervision data
• Provides recommendations and actions to improve data quality

Disadvantages:

• Does not include examples of nutrition data indicators
• Requires skills to use DHIS2

Measure Evaluation RDQA – 2015

Advantages:

• More suited to evaluation of field supervision data
• Provides recommendations and actions to improve data quality

Disadvantages:

• Does not include examples of nutrition data indicators
• Requires skills to use DHIS2

HMIS

• Definition: An information system specially designed to assist in the management and planning of health programmes, as opposed to delivery of care (WHO 1993)
• Advantages: Once it has been set up, it is costs less and allows monitoring of health outcomes and indicators
• Disadvantages: Only captures information for users of the health system and not representative for entire population, denominator need to be estimated for indicator calculation

IDSR

• Definition: Integrated Disease Surveillance programme ensures the availability of guidelines for integrated disease surveillance and response. It provides necessary technical support for strengthening national communicable disease surveillance systems through the implementation of the Regional Strategy for Integrated Disease Surveillance and the International Health Regulations.
• Advantages: useful and relevant in monitoring communicable diseases, it has the advantage to alert about sudden increases of MAM/SAM cases and deaths thus providing alerts in case of context deterioration
• Disadvantages: Not nutrition relevant

Sentinel Surveillance

• Definition: Limited case ascertainment area, traditionally includes largest hospitals in geographic area; Should do pre-evaluation to select appropriate sentinel sites
• Advantages: Can easily collect individual patient-related data; Less costly and burdensome on resources; Flexible system design; Useful for documenting trends; Allows for routine monitoring of antibiotic non-susceptibility
• Disadvantages: Although less costly than population-based surveillance, sentinel system may still require significant financial investments in personnel and resources; Data may provide biased or skewed findings; Data are not generalisable to geographic population; This method does not collect incidence data

Sectors data systems

• Definition: Data routinely collected by sectors based on a set indicators defined to track activities
• Advantages: Source of variety of information (Weather, Climate change, Pollution, Water, sanitation, Education, Food chain, Agriculture data, Budget/finance data, etc.)
• Disadvantages: Aren’t usually well organised and harmonized ones with others in terms of representativeness

Sources:

• WHO, Developing Health Management Information System
• Measure Evaluation – Health Information System Facilitator Guide of training of trainers
• CDC, Sentinel Surveillance method

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:

1. Corsi, D. J., J. M. Perkins and S. V. Subramanian (2017). Child anthropometry data quality from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and National Nutrition Surveys in the West Central Africa region: are we comparing apples and oranges? Global Health Action 10(1): 1328185.
2. Interviews of 5 senior nutrition data analysts conducted by the GSF early 2019.

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

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

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:

1. How the Niger team has used the results to develop a capacity building plan for sectors
2. How the Niger 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 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:

1. The institutional framework;
2. The organisational framework;
3. The collection mechanism;
4. The mechanism for the validation and quality assurance of data;
5. The data management mechanism;
6. The output;
7. The dissemination and exploitation of information;
8. The indicators and data available;
9. The nutrition-sensitive indicators;
10. 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 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:

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.

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, 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.

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 Côte d’Ivoire 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 multisectoral 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, etc.) 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.

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:

• 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.