Abstract
BACKGROUND: Undernutrition is the most dominant form of malnutrition among children in developing countries. Studies conducted in Tanzania have reported high levels of undernutrition among children below five years of age. However, there is limited information on differences in stunting prevalence across agroecological zones. This study aimed to determine the prevalence of undernutrition and its determinants in the lowland and highland areas in Kilosa District, Tanzania. METHODS: A cross-sectional study was conducted in a sample of 200 randomly selected households from the lowland and 141 from the highland areas of Kilosa District in Morogoro Region, Tanzania. Sociodemographic, feeding practices, hygiene, and sanitation data were collected using a structured questionnaire. Weight and height of children were measured using a standard procedure, and age was calculated from the birth date obtained from the child growth card. Anthropometric data were analyzed by using Emergency Nutrition Assessment (ENA) software. The logistic regression model was used to explore the determinants of undernutrition. RESULTS: Prevalence of stunting, underweight, and wasting was 41.0%, 11.5%, and 2.5% in lowland and 64.5%, 22.0%, and 1.4% in highland areas, respectively. The prevalence of stunting and underweight was higher in the highland compared to the lowland areas (p < 0.001). Significant determinants of underweight were areas of residence (AOR 4.21, 95% CI: 1.62-10.9), age of the children (AOR 5.85, 95% CI: 1.81-18.97), and child birth weight (AOR, 4.98 95% CI: 1.65-15.05), while determinants of stunting were the area of residence (AOR, 2.77 95% CI: 1.43-5.36), maternal age (AOR, 0.33 95% CI: 0.14-0.79), sex of a child (AOR, 1.89 95% CI: 1.03-3.50), and child birth weight (AOR, 3.29 95% CI: 1.21-8.97). CONCLUSION: The prevalence of undernutrition, especially stunting and underweight, was high in the study areas. Determinants of stunting differed between highlands and lowland areas, highlighting the needs of having properly integrated interventions based on the geographical location.