Share on facebook
Share on twitter
Share on linkedin
Share on email

Bridging the Micronutrient Data Gap Requires a New Era of Collaboration

The health, societal, and economic consequences of micronutrient deficiencies have been well documented. Over time, the global health community has developed a suite of effective, cost-efficient, and safe solutions to address these deficiencies, including the promotion of diet diversity, biofortification, large scale food fortification, and supplementation.   

Salt iodization, for example, is one of the most prominent success stories in global health.  Health problems, like goiters and hypothyroidism, were commonplace until the introduction of salt iodization programs beginning in the 1920s. By following the WHO’s recommended levels of iodization, countries have been able to reduce iodine deficiencies in millions of people through cost-effective programs.  

But even with effective, well-researched solutions an estimated two billion people worldwide suffer from micronutrient deficiencies, and that number is only expected to rise. Global crises including the COVID-19 pandemic weaken nutrition and food security worldwide, which results in lower quality diets 1 and a greater need for interventions.   

If micronutrient interventions are impactful and cost-effective, why do population levels of deficiencies remain so high?  

One major hurdle standing in the way of micronutrient interventions is an inadequate and underinvested micronutrient data system. Accurate data are sparse, available data must be analyzed, and existing analysis is not adequately used in national-level policy and programming. And the segmentation of micronutrient work often means data collection and analysis is segmented, which often limits and impedes data sharing. Together, these challenges mean it’s sometimes impossible for researchers to glean meaningful insights from existing data.  

But reliable and timely data on micronutrients are critical to address and reduce deficiencies. It allows national governments to create robust programs, understand nuances within populations, and make improvements to long-running interventions. Improved data on vitamin A status in Guatemala, for instance, allowed the government to revitalize its sugar fortification program and, at the same time, scale back vitamin A supplementation. This meant the government could reduce vitamin A toxicity while reducing program costs (1). And in Nepal, new findings on urinary iodine concentrations meant the government could discontinue its iodized oil capsule program (1). Without good data, these programs may have continued to run, but at sub-optimal levels and at a higher cost.  

Fortunately, we’re starting to see a shift in the nutrition field when it comes to data. It’s becoming abundantly clear that good data are a critical component to successful interventions, and the field is excited for this shift in focus. But to successfully incorporate data, the field needs programs and entities purpose-built to unify and coordinate stakeholders. 

That’s why the Micronutrient Forum is launching the Micronutrient Data Innovation Alliance (DInA) with support from the Bill and Melinda Gates Foundation. DInA is convening diverse stakeholders to improve the availability, quality, accessibility, and use of data across the micronutrient value chain. This convening will support national-level decision-makers to better design, implement, measure, and optimize programs, resulting in more efficient and effective programs. It will bring researchers, normative agencies, data systems, statisticians, national leaders, tech sectors together to tackle data challenges and to create a more holistic approach to big micronutrient challenges.  

Working in silos will only get us so far- collaboration is key when tackling the complex challenges surrounding micronutrient deficiencies and the global micronutrient data gap. By leveraging the power of innovation and new ways of working with leading cross-sectoral expertise, DInA will galvanize collective action and revolutionize our approach to data.  

Citations:

  1. https://academic.oup.com/ajcn/article/114/3/862/6283778 

Our website uses cookies to ensure you have the best experience.
Please visit our Privacy Policy page for more information.