国际公共卫生与安全杂志

Determinants of Malnutrition among Children Aged 6-59 Months in Trans-Mara East Sub-County, Narok County, Kenya

Abstract

Edward olodaru Ole Tankoi, Stephen Amolo Asito and Samson Adoka

Malnutrition is associated with a lot of morbidity and more than one-third deaths in children under 5 years globally. A majority of those who suffer from the brunt of malnutrition are in developing countries. Of note Kenya is one of the countries with the greatest burden of malnutrition associated with rapid nutrition, economic and social transitions. However, there is a paucity of data on malnutrition and the factors related to it in children in rural settings. This study therefore examined the prevalence and predictors of malnutrition among children aged 6-59 months in Trans-Mara East sub-county in Narok county. The study employed a descriptive cross-sectional design and data was collected using a semi-structured questionnaire. Analysis was done using multivariate logistic regression. Of the 350 children enrolled in this study, 31%, 22% and 8% of the children were stunted, underweight and wasted, respectively. Besides, 9% and 4% of the children suffered from overweight and obesity respectively. The key determinants for stunting were number of children in the household (adjusted Odds Ratio (aOR): 1.86; 95%CI: 1.01-3.43), mother being a house wife (aOR: 3.63; 95%CI: 1.08-12.24), and being poor (aOR: 3.33; 95%CI: 1.44-7.68). For obesity, the predictors were child age with 12-23 months (Crude Odds Ratio: 2; 95%CI: 0.175-22.8); 24-35 months (odds ratio of 2.22; 95%CI: 0.22-22.3), child gender with males more likely to be obese relative to females (OR: 3.27; 95%CI: 0.856-12.5). This study indicates that there is double burden of malnutrition in rural settings characterized by high prevalence of under nutrition and low prevalence of over nutrition. The results of this study will be useful for the Ministry of Health and other developmental partners targeting child nutrition in formulating context-specific interventions that are optimized according to the level of food insecurity within different settings.