Wasting and Underweight in Northern African Children: Findings from Multiple-Indicator Cluster Surveys, 2014-2018.
Nagwa Farag ElmighrabiCatharine A K FlemingKingsley Emwinyore AghoPublished in: Nutrients (2023)
Northern Africa faces multiple severe detrimental factors that impact child nutrition. This study aimed to identify the predictors for wasting and underweight in children aged 0-59 months in Northern Africa. We analysed pooled cross-sectional data from multiple-indicator cluster surveys conducted in four countries (Algeria, Egypt, Sudan, and Tunisia) involving 37,816 children aged 0-59 months. A logistic regression analysis was used, considering clustering and sampling weights, to identify factors associated with wasting and underweight among children aged 0-23, 24-59, and 0-59 months. Among children aged 0-59 months, the overall prevalence was 7.2% (95% CI: 6.8-7.5) for wasting and 12.1% (95% CI:11.7-12.5) for underweight. Sudan and Algeria had the highest rates of wasting, while Sudan and Egypt had the highest rates of underweight. Multiple regression analyses indicate that factors associated with wasting and being underweight include child age, country, rural residency, poor wealth index, being male, birth order, maternal education, body mass index, media use, lack of diverse foods, longer duration of breastfeeding, perceived small baby size, and diarrhoea. These findings highlight the importance of implementing targeted health and nutrition initiatives, such as maternal education, family planning, and community engagement. Priority should be given to children from underprivileged areas who lack proper dietary variety.
Keyphrases
- mental health
- cross sectional
- healthcare
- body mass index
- physical activity
- quality improvement
- young adults
- public health
- birth weight
- pregnancy outcomes
- social support
- depressive symptoms
- south africa
- preterm infants
- social media
- weight gain
- clinical trial
- drug delivery
- machine learning
- single cell
- open label
- cancer therapy
- rna seq
- weight loss
- big data
- medical students