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Predicators for weight gain in children treated for severe acute malnutrition: a prospective study at nutritional rehabilitation center.

Jyoti SanghviSudhir MehtaRavindra Kumar
Published in: ISRN pediatrics (2014)
Introduction. Despite being an important health problem in developing countries, there is little information available on factors affecting the severe acute malnutrition, especially nondietary factors. Objective. To study the impact of various factors, especially nondietary ones affecting directly or indirectly the weight gain in children with severe acute malnutrition. Method. A total of 300 children in the age group of 6 to 60 months meeting the WHO criteria for severe acute malnutrition were enrolled in the study. These children were provided special therapeutic diet as recommended by WHO/UNICEF protocol. Children were called for followup every 15 days up to 2 months after discharge to evaluate whether these children have achieved a final target weight gain of 15% of their admission weight. The impact of nondietary factors related to child, mother, and socioeconomic status was evaluated. Data collected through structured questionnaire were analyzed. Result. 172 (57.4%) of the total 300 children did not gain final target weight despite giving adequate diet. We observed that impact of various nondietary factors like mother's educational status and her knowledge about feeding practices, socioeconomic status, previous history, and present evidence of infection in child was important in determining the weight of child. No association was found with gender of child, BMI of mother, and father's educational status on the weight gain of child. Conclusion. The findings of this study confirm the association of many nondietary factors with weight gain in children treated for severe acute malnutrition. To reduce malnutrition emphasis should be given on these factors.
Keyphrases
  • weight gain
  • body mass index
  • birth weight
  • young adults
  • weight loss
  • mental health
  • healthcare
  • physical activity
  • emergency department
  • machine learning
  • big data
  • cross sectional
  • climate change