Child stunting prevalence determination at sector level in Rwanda using small area estimation.

Background: Stunting among children under 5 years of age remains a worldwide concern, with 148.1 million (22.3%) stunted in 2022.

Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors.

Universal Health Coverage (UHC) is a global objective aimed at providing equitable access to essential and cost-effective healthcare services, irrespective of individuals' financial circumstances. Despite efforts to promote UHC through health insurance programs, the uptake in Kenya remains low. This study aimed to explore the factors influencing health insurance uptake and offer insights for effective policy development and outreach programs.

Application of random forest model to predict the demand of essential medicines for non-communicable diseases management in public health facilities.

Introduction: recent initiatives in healthcare reform have pushed for a better understanding of data complexity and revolution. Given the global prevalence of Non-Communicable Diseases (NCD) and the economic and clinical burden they impose, it is recommended that the management of essential medicines used to treat them be renovated and optimized through the application of predictive modeling such a RF model.

Methods: in this study, a series of data pre-processing activities were used to select the top seventeen (17) NCD essential medicines most commonly used for treating common and frequent NCD.

Adoption of RTS, S malaria vaccine for children younger than 5 years in Rwanda: A budget impact analysis.

Background: In Rwanda, malaria affects one in six children under five years old. Despite being preventable and treatable, malaria causes substantial morbidity, mortality, and economic burden on the Rwandan government and healthcare donors. Recently, the World Health Organization (WHO) agreed to consider the new malaria vaccine (RTS, S) as an additional prevention strategy.