1. Training module on anthropometric data quality

2. Who is here – whom are you going to spend the next days with?
Name
Role and organization
Favorite food

3. Goal of NIPN: Evidence-informed nutrition policies and programmes
Training module on anthropometric data quality
Goal of NIPN: Evidence-informed nutrition policies and programmes
OBJECTIVES
To create country-led and country-owned multi-sectoral National Information Platform for Nutrition
To manage and analyse information and data from all sectors with an influence on nutrition
To communicate and use information, informing strategic policy and programme decisions on nutrition
NIPN is unique in bringing together and valuing multiple data sources from the different sectors that influence nutrition: health; agriculture; water, sanitation and hygiene; social protection; education; among others.
The focus of NIPN analysis is on using national and sub-national data to track progress and identify regional disparities of nutrition outcomes and determinants; coverage of interventions and programmes; as well as nutrition-specific and nutrition-sensitive investments. Such analysis is critical for taking decisions about how to direct resources.
Though primarily driven by national demand, the NIPN will not be able to answer all policy questions. Broad questions need to be broken down into more specific questions that can be answered by the NIPN taking into account the data that are available and their quality.
For questions requiring more complex analysis or additional data collection (such as causal analyses, impact evaluation or cost-effectiveness analyses), the scientific literature can be reviewed to assess existing evidence or the question can be referred to a research body.

4. NIPN Core values NIPN is country-owned.
Training module on anthropometric data quality
NIPN Core values
NIPN is country-owned.
NIPN is country-driven, responding to country demands
NIPN analyses existing data from multi-sectoral sources for national and sub-national level analysis.
NIPN communicates clear and actionable messages in a timely manner to influence policy makers.
NIPN is country-owned. It strengthens systemic, organizational and individual capacity and is rooted within existing national institutions and the existing multi-sectoral coordination system for nutrition.
NIPN is country-driven. It is led by a country’s priority issues in nutrition- specific and nutrition-sensitive policies and programmes.
NIPN analyses existing data from multi-sectoral sources for national and sub-national level analysis. As such it improves the accessibility of nutrition-relevant data from multiple datasets and makes better use of under-utilised data. NIPN is not responsible for collecting new data.
NIPN communicates clear and actionable messages in a timely manner to influence policy makers. It tells a story that traces the impact pathway as visualised below : how inputs (investments, human resources) are leading to outputs (intervention coverage) and translate into changes in outcomes (determinants of nutrition), which ultimately have an impact on indicators of nutrition status, such as stunting.
30 Jan -1 Feb 2019

5. IThe importance of data quality in the PNIN approach

6. We can only work on the quality of the data AFTER data collection
Use of existing data
We can only work on the quality of the data AFTER data collection
Here we restrict the data quality field to what can happen after the data collection. We do not take care of the training of the investigators etc ...

7. Use of existing data The data is validated
It is therefore not a question of modifying the quality controls of a validated survey but, according to the desired analysis,to define whether one will take into account or not this or that investigation in the analysis.

8. Data Quality Routine data Survey data Other indicators Anthropometry
Surveillance and M&E
For nutrition, we can use data from very different information systems. Each one applies quality controls of their own and are adapted to their objectives.
The focus of this training is on Survey data and especially of Anthropometric indicators of survey data. Main surveys with anthropometric indicators are DHS – MICS – SMART (local or national)
This training is largely based on the TEAM’s (Technical Expert Advisory Group on Nutrition Monitoring from WHO and UNICEF + international experts)
Recommendations for improving the quality of anthropometric data collection, analysis and reporting

9. Complementary module on routine data quality
Coming soon!!
Should come before July 2019

10. OBJECTIVES of this training
Training module on anthropometric data quality
OBJECTIVES of this training
Understand the SMART – DHS – MICS survey instruments and their procedure for anthropometric data quality controls
Understand the different tests made to control for data quality post data collection
Understand a data quality report
Appreciate objectively the debates around certain tests
Be able to take a decision on which data quality controls levels are necessary to be coherent with the type of analysis planned
 this module is based on TEAM  Recommendations for improving the quality of anthropometric data collection, analysis and reporting from recent presentation made in N.Y. 6th of feb 2019.

11. Training module on anthropometric data quality
DAY 1: Survey Methods, Nut Indicators
DAY 2: Perform quality control tests
DAY 3: Decide on the level of quality needed and sufficient

12. Materials available In the training folder you can find Excercises
Training module on anthropometric data quality
Materials available
In the training folder you can find
Excercises
Power Point presentation
Additional readings
Software guidelines

13. Expectations of participants
Training module on anthropometric data quality
Expectations of participants
Please write on a post-it:
Your expectations / expected achievements of this workshop
If you have more than 1, please write each expectation on a different post-it
Give the post-its to the trainer, please.