Login / Signup

Micronutrient gaps during the complementary feeding period in seven countries in Southeast Asia: A Comprehensive Nutrient Gap Assessment.

Jessica M WhiteElizabeth DrummondVasundhara BijalwanAnusara SinghkumarwongArvind BetigeriJessica Blankenship
Published in: Maternal & child nutrition (2023)
The complementary feeding period is a critical stage of child development when micronutrient needs are high and challenging to meet. Understanding if specific micronutrient gaps exist during this period is critical for effective programming. A Comprehensive Nutrient Gap Assessment (CONGA) was conducted in seven countries in Southeast Asia to estimate gaps in micronutrients commonly lacking in the diets of children aged 6-23 months and to establish the certainty of available evidence for each identified gap. Sixty-eight evidence sources were identified during this analysis, and 310 micronutrient-specific data points were identified across all seven countries. Data points varied in recency, representativeness and evidence type. The CONGA methodology enabled the estimation of a gap burden rating for each micronutrient in each country, as well as a rating of their evidence certainty. Micronutrient gaps were identified in vitamin D, zinc and iron and a potential gap was identified in calcium during the complementary feeding period in the region. Evidence relevant to intake and deficiency of folate, vitamin B 12 , thiamine, niacin, vitamin C and vitamin B 6 was limited across the region. Proven strategies to address these gaps include increasing the availability and consumption of nutrient-dense foods, micronutrient supplementation, large-scale fortification of staple foods and condiments and point-of-use fortification through multiple micronutrient powders and fortified speciality foods. More recent data on micronutrient availability, intake and deficiency is urgently needed in Southeast Asia.
Keyphrases
  • electronic health record
  • mental health
  • big data
  • physical activity
  • artificial intelligence
  • climate change
  • weight gain
  • deep learning
  • oxide nanoparticles