Background The COVID-19 pandemic has impacted the nutrition and health of individuals, households, and populations globally. Through exposing fragilities in food, health, and social welfare systems, the negative influence of COVID-19 continues to affect the global burden of malnutrition. The nature and scale of these impacts are not yet well understood thus the body of evidence for informing policy is limited. Collating and monitoring relevant data in real-time from multiple levels, sectors and sources is essential in preparing and responding to the ongoing COVID-19 pandemic.
Objectives To identify key data sources related to food, nutrition, and health indicators in the context of the COVID-19 pandemic.
Methods A COVID-19, food, nutrition and health framework was developed through multiple iterative rounds of online multidisciplinary discussions including the NNEdPro COVID-19 taskforce and the Swiss Re Institute’s Republic of Science, which comprised researchers and clinicians with expertise in data science, food, nutrition, and health.
Results The proposed framework encompasses five socio-ecological levels which were further sub-divided by six categories of the food and nutrition ecosystem, including food production & supply, food environment & access, food choices & dietary patterns, nutritional status & comorbidities, health & disease outcomes, health & nutrition services. A limited number of exemplar variables for the assessment of global status of food, nutrition and health are identified under each category.
Discussion/Conclusion This collaborative framework is the first step towards the development of a better understanding of the impact of COVID-19 on food, nutrition, and health systems. Limited data availability and disruption in routine data collection as well as other nutrition assessments during the pandemic are challenges that might limit the potential of the proposed framework. Next steps will include formal research and data gap analysis and the identification, as well as utilisation, of other indicators that could be used as proxies of the variables identified.
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