# National Information Platforms for Nutrition Data Quality Toolkit

An R package of practical analytical methods applicable to variables in datasets to assess their quality.

Image credit: Ernest Guevarra

National Information Platforms for Nutrition (NiPN) is an initiative of the European Commission to provide support to countries to strengthen their information systems for nutrition and to improve the analysis of data so as to better inform the strategic decisions they are faced with to prevent malnutrition and its consequences.

As part of this mandate, NiPN has commissioned work on the development of a toolkit to assess the quality of various nutrition-specific and nutrition-related data. This is a companion R package to the toolkit of practical analytical methods that can be applied to variables in datasets to assess their quality.

The focus of the toolkit is on data required to assess anthropometric status such as measurements of weight, height or length, MUAC, sex and age. The focus is on anthropometric status but many of presented methods could be applied to other types of data. NiPN may commission additional toolkits to examine other variables or other types of variables.

Data quality is assessed by:

1. Range checks and value checks to identify univariate outliers

2. Scatterplots and statistical methods to identify bivariate outliers

3. Use of flags to identify outliers in anthropometric indices

4. Examining the distribution and the statistics of the distribution of measurements and anthropometric indices

5. Assessing the extent of digit preference in recorded measurements

6. Assessing the extent of age heaping in recorded ages

7. Examining the sex ratio

8. Examining age distributions and age by sex distributions

To read more about nipnTK, visit the package website where you can read more about the package and learn how the data quality assessment is performed in R.

##### Ernest Guevarra
###### Founding Member

I am a public health specialist with a particular interest in health and nutrition metrics and analytics, and in spatial epidemiology. I develop fit-for-purpose R packages as part of my work with data.