Quality assurance of routine data (1/10)

1. Rationale

The National Information Platform for Nutrition (NIPN) values existing data from diverse sources to address nutrition policy relevant questions and provide evidence to decision-makers.

Why use routine data in this process? Surveys are expensive while routine data are not, surveys are done every 3-5 years while routine data are collected more regularly and therefore provide recent information. Moreover, at present, many countries have well-functioning routine data collection systems and increasingly implement an electronic platform.

Furthermore, routine data permit standardised analyses across geographical levels, such as district level (1). In each NIPN country data quality of routine data is being assessed using one or more tools (DQR, RDQA, etc.).

Using data from all sectors that contribute to nutrition would be ideal. However, data from routine health information system (HIS) are among the most organised and accessible data, which is not the case for other sectors. It appears crucial to give special importance to Health Management Information System (HMIS) data and their quality in NIPN.

The main goal of this guide is to ensure a standardised quality approach is being used to check routine data quality, matching with the NIPN objectives and values. Furthermore, using a published tool to assess routine data in the NIPN operational cycle could de facto be an added value to the sustainability of the NIPN approach.

(1) Cesar G Victora, Robert Black, J Ties Boerma, Jennifer Bryce. Measuring impact in the Millennium Development Goal era and beyond: a new approach to large-scale effectiveness evaluations. Lancet, 2011;377:85-95.