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A Data Science Approach to Develop Growth Cutoffs for Graded Care of Malnutrition

The study aims to calculate cut-offs using data provided by HBGDki and datasets with SAS, SJRI where weight, height, and age are available for children below five years in combination with other outcomes such as death, morbidity or hospitalization. Using WHO standards, weight for height, height for age and weight for age will be calculated, and these metrics will be used as determinants for risk of death, morbidity/hospitalization. A finer categorization of malnutrition based on the risk of mortality or significant morbidity can be used to develop and then deliver tailored optimized therapeutic options for what is essentially a far more eclectic group than what is captured by a three-category classification MAM, SAM and others.

More information about Data Science Approaches to Improve Maternal and Child Health in India

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The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.