The Prevalence of Malnutrition in Under-five Years Old Children by Socioeconomic Status in Turkana, Bungoma and Kakamega Counties, 2013 - 2014.

Abstract

Background. Insufficient protein or energy intake can cause malnutrition, affecting children's growth and well-being. This may lead to stunted physical and mental development, with under-five years old children facing heightened risks of illness and death. Additionally, malnutrition in childhood can impact socioeconomic status in adulthood. Broad Objective. To examine the occurrence of malnutrition among children under the age of five in Turkana, Bungoma, and Kakamega counties. Additionally, to explore the connection between malnutrition and socioeconomic status. Study design. This study utilized data from a cross-sectional household survey conducted during the fifth round of the MICS5 in Turkana, Kakamega, and Bungoma counties in Kenya in the period of 2013-2014. Methodology. This study utilized data from a secondary household survey conducted in Kenya's Turkana, Kakamega, and Bungoma counties during 2013-14 by UNICEF's Multiple Indicator Cluster Survey (MICS) program. Statistical analysis involved SAS and R programming to examine the relationship between childhood malnutrition and demographic variables. Malnutrition indicators included underweight, stunting, and wasting. Logistic regression models used the Composite Index of Anthropometric Failure (CIAF) as the dependent variable, categorizing children into seven groups. Prevalence was calculated considering various factors. Malnutrition prevalence differences between counties were analysed. The study ensured the validity of logistic regression assumptions, incorporating exploratory analysis and comparison with WHO distributions. Results. The study enrolled 2,594 under-five-year-olds in Turkana, Bungoma, and Kakamega counties, revealing a malnutrition prevalence of 48.0% (95% CI: 46.1 - 50) using the Composite Index of Anthropometric Failure (CIAF), surpassing traditional anthropometric indicators. Stunting, wasting, and underweight rates were 25.4% (95% CI: 23.8 - 27), 11.7% (95% CI: 10.6 - 13), and 18.1% (95% CI: 16.6 – 19.7), respectively. Turkana exhibited significantly higher undernutrition rates than Bungoma and Kakamega, with differences of 17.2 (95% CI: 12.67-21.74) and 12.5 (95% CI: 7.87-17.18) percentage points, respectively. Bungoma had a lower undernutrition rate than Kakamega, but this wasn't statistically significant, showing a difference of -4.7 (95% CI: -9.57-0.21) percentage points. Children in the richest households in comparison to those in poorest, had lower malnutrition risk (AOR = 0.37, 95% CI: 0.27-0.51, p: <0.001), while females had significantly lower odds than males (AOR = 0.77, 95% CI: 0.66-0.91, p = 0.002). Malnutrition odds increased with age, particularly for children aged 24-35 months (AOR = 2.22, 95% CI: 1.61-3.08, p < 0.001) and 36-47 months (AOR = 1.94, 95% CI: 1.41-2.67, p < 0.001) in compassion to aged 0-5 months. Coughing elevated malnutrition odds (AOR = 1.21, 95% CI: 1.01-1.44, p = 0.038). Urban/rural residence and recent diarrhoea or fever showed no significant differences. Turkana children had higher malnutrition odds than Luhya (AOR = 2.28, 95% CI: 1.35-3.86, p = 0.002). Maternal education level of secondary or higher, was associated with lower malnutrition odds (AOR = 0.64, 95% CI: 0.47-0.89, p = 0.008). Socioeconomic status strongly correlated with malnutrition, indicating significantly lower odds in richer and richest households compared to poorer households. Conclusion: This study sheds light on undernutrition in under-five-year-olds in Turkana, Bungoma, and Kakamega counties, emphasizing the need for tailored interventions. Turkana's significantly higher prevalence requires focused efforts, while Bungoma and Kakamega could benefit from similar strategies. The strong link between CIAF and socioeconomic status underscores poverty reduction's critical role in addressing child malnutrition. Targeted measures should address specific county needs while promoting equitable economic conditions for effective intervention.