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Prevalence and Factors Associated with Iron Deficiency Anaemia among Children Aged 6-23 Months in Southwestern Uganda.

Dickson KajobaWalufu Ivan EgesaSolomon MuyombyaYamile Arias OrtizMartin NduwimanaGrace Ndeezi
Published in: International journal of pediatrics (2024)
Iron deficiency anaemia is still a global public health concern with the highest burden among children 6 to 23 months due to their rapid growth spurt exceeding breastmilk supply. Therefore, nutritional supply is a key source of iron to attain the required nutrients for better growth and development. This was a cross-sectional descriptive study done at Ishaka Adventist Hospital (IAH) and Kampala International University Teaching Hospital (KIUTH) from April to July 2022. Participants were consecutively enrolled in the study. Structured questionnaires, 24-hour dietary recall, and clinical assessment were used to obtain data. Data analysis was done using the statistical package for social scientists (SPSS) V22.0. Bivariable and multivariable analyses were done using logistic regression for associations with significance set at P value < 0.05. A total of 364 participants were enrolled, with the majority being males (198, 54.4%) and born at term (333, 91.5%). The modal age was 12-17 months [163(44.8%)] with a mean age of 14.1 months (SD 5.32). The overall prevalence of IDA was 151/364 (41.5%). The factors associated with IDA included male sex (aOR 1.61), current episode of diarrhoea (aOR 1.71), poor meal frequency (aOR 1.78), no vegetable consumption (aOR 2.47), and consuming fruits once (aOR 1.97) in 7 days preceding the study. The study finds a high prevalence of IDA among infants 6-23 months with at least four in 10 being affected. Screening for IDA should be recommended in male children with current diarrhoea, poor intake of fruits and vegetables, and poor meal frequency. The Mentzer index is an equally good alternative screening test for IDA.
Keyphrases
  • iron deficiency
  • public health
  • data analysis
  • young adults
  • risk factors
  • emergency department
  • mental health
  • machine learning
  • preterm infants
  • physical activity
  • body mass index
  • deep learning
  • big data
  • low birth weight