Dula Etana (PhD)Negesse Gebissa2024-06-052024-06-052022-06https://etd.aau.edu.et/handle/123456789/3119Under-five malnutrition is a major public health issue contributing to mortality and morbidity, especially in developing countries like Ethiopia where the rates remain unacceptably high. Identification of critical risk factors of malnutrition Among Children Aged 6-59Months using appropriate and advanced statistical methods can help formulate appropriate health programmes and policies aimed at achieving the United Nations SDG Goal. This study attempts to develop a quantile regression, an in-depth statistical model to identify critical risk factors of Children Aged 6-59Months chronic malnutrition (stunting). Based on the quantitative cross sectional study design was conducted from march-1/2022 to March-30/2022, height-for-age z-scores (HAZ) was estimated. Multivariable quantile regression model was employed to identify critical risk factors for with the lower cut off height-for-Age-Zscores (a measure of chronic malnutrition in populations). Quantiles of HAZ with focus on the lower cut off HAZ were modeled and the impact of the risk factors determined. Significant test of the difference between slopes at different selected quantiles of HAZ and other quantiles were performed. Quantile regression plots of slopes were developed to visually examine the impact of the risk factors across these quantiles. Data on a total of 489 children were analyzed out of which 15 (3.1 %) were stunted and 25% of the children were height-for-age Z-score value <-1. The models identified child level factors such as Child’s age, Educational level of mother/caregiver, marital status, household wealth quantile, Latrine/Sanitation status, Disposal of solid wastes, Availability water &soap at hand washing facility, Hygienic practice, Complementary Feeding and Colostrums milk at birth. Highly significant differences exist in the slopes between the lower cut 0.25 and the higher cut 0.97 quantiles. The quantile regression plots for the selected quantiles for the lower cut 0.25 and the higher cut 0.97 showed substantial differences in the impact of the covariates across the quantiles of HAZ considered. Critical risk factors that can aid formulation of child nutrition and health policies and interventions that will improve child nutritional outcomes and survival were identified. Modelling under-five chronic malnutrition using multivariable quantile regression models could be beneficial to addressing the under-five chronic malnutrition.enQuantile Regression ModelChild MalnutritionBurayu TownChronic Malnutrition.Thesis