© Juozas Cernius/Save the Children
To turn a commitment to scale up nutrition into reality, we need high-quality actionable data – in the right places at the right times. Without this, we are at best myopic, and at worst, flying blind. Data are needed to characterize different types of nutrition problems, to highlight magnitude, distribution, and variability, over time and space; to understand what’s driving the problem; to design, deliver and monitor appropriately targeted interventions and determine their effectiveness; to track national and global levels and trends, and to hold responsible actors accountable for progress (or lack thereof) toward goals they have signed up to.
In this paper, we argue that a value chain approach is key for progress. We start by reviewing recommendations on a package of high-impact nutrition-specific interventions. The “best bet” recommendations from the 2013 Lancet Maternal and Child Nutrition series were combined with current WHO global guidance to generate a list of 24 high-impact interventions. We then proposed a list of indicators to capture their coverage, and assess the extent to which this is done or feasible using available data sets. Three case studies are presented to highlight the kind of innovations that are feasible, using published literature and empirical data from large-scale initiatives.
This paper is part of a series from the Countdown to 2030 initiative that aims to address the challenges in measurement and monitoring women’s, children’s and adolescents’ health in the context of the sustainable development goals. The series includes improved ways to measure and monitor inequalities, drivers of women’s, children’s and adolescents’ health especially governance, early childhood development, reproductive maternal and child health in conflict settings, nutrition intervention coverage and effective coverage of interventions.
What’s needed to advance the nutrition intervention coverage measurement agenda?
- A process for generating global consensus on a minimum core set of validated coverage indicators on proven, high-impact nutrition-specific interventions;
- The inclusion of coverage measurement and indicator guidance in WHO intervention recommendation and guidance documents;
- The incorporation of these indicators into data collection mechanisms (via revisions of survey questionnaires and routine nutrition and health information systems) as well as relevant intervention delivery platforms.
- An agenda for continuous measurement improvement, including indicator validation and means for adjusting coverage estimates to take into account delivery quality and effectiveness (effective coverage);
More broadly, we argue that to accelerate progress toward the Sustainable Development Goals we need the following:
- The adoption of a value chain approach to data, which views the entire ecosystem, encompassing an integrated and interoperable set of hardware, software, data, people and procedures that produces relevant data, that translates data into useful information, and (via communication) into improved knowledge for action.
- In-country mechanisms for priority-setting and coordination of the collection and use of quality and appropriately timed data.
- Operational guidance for data prioritization, harmonization of indicators and consistent incorporation of nutrition into routine management information systems.
- Development of national data plans that are well costed, resourced and implemented over the long term (including financing for updating and maintaining national and global databases).
- Implementation research, innovation and learning across the data value chain.
- A strengthened enabling environment to support a culture for data and evidence use for planning and action. Data need to be persuasively communicated to policymakers and program managers, in a way that facilitates action. Enabling policy and institutional environments also need to be built on strengthened commitment, governance, capacity and leadership at all levels.
- The capture and dissemination of tacit and experiential knowledge (e.g. “stories of the data revolution”) to both inform and inspire change.