Modeling the Risk Factors of Undernutrition among Children below Five Years of Age in Uganda Using Generalized Structural Equation Models.

Introduction: The prevalence of undernutrition among children below five years of age, in Uganda and the world over, remains very high. About 45% of all global deaths among children below five years of age are attributed to undernutrition. A number of studies using different statistical approaches affirm this effect, yet some factors indicate the influence of other factors within the system.

Undernutrition among the children below five years of age in Uganda: a spatial analysis approach.

Background: Undernutrition is a health condition caused by a lack of enough food intake, not having enough of the right combination of food nutrients, or the body's failure to utilize the food eaten resulting in either, stunting, being underweight, or wasting. Globally, undernutrition affects more than 149 million under-five children, while in Uganda about 3 in every 10 children suffer from undernutrition. Undernutrition and its risk factors among under-five children in Uganda were unevenly distributed across the country and a study that focused on spatial distribution was prudent to examine the nature of the problem and salient factors associated with it.

Economic attributes and childhood stunting in Rwanda: case study of the City of Kigali.

Introduction: stunting in under five children is a great concern in low and middle-income countries including Rwanda. While over the past decades different developing countries have made remarkable efforts improving their economic growth, there is mixed evidence and lack of consensus on the impact of economic development on nutrition improvement. The objective of this study was to assess the relationship between economic attributes and childhood stunting in the City of Kigali.

Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset.

Background: Extensive research on infant mortality (IM) exists in developing countries; however, most of the methods applied thus far relied on conventional regression analyses with limited prediction capability. Advanced of Machine Learning (AML) methods provide accurate prediction of IM; however, there is no study conducted using ML methods in Rwanda. This study, therefore, applied Machine Learning Methods for predicting infant mortality in Rwanda.

Socio-demographic and clinical characteristics of diabetes mellitus in rural Rwanda: time to contextualize the interventions? A cross-sectional study.

Background: Existing prevention and treatment strategies target the classic types of diabetes yet this approach might not always be appropriate in some settings where atypical phenotypes exist. This study aims to assess the socio-demographic and clinical characteristics of people with diabetes in rural Rwanda compared to those of urban dwellers.

Methods: A cross-sectional, clinic-based study was conducted in which individuals with diabetes mellitus were consecutively recruited from April 2015 to April 2016.