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. The study utilized machine learning techniques on data from the 2021 FinAccess Survey. Among the models examined, the Random Forest model demonstrated the highest performance with notable metrics, including a high Kappa score of 0.9273, Recall score of 0.9640, F1 score of 0.9636, and Accuracy of 0.9636. The study identified several crucial predictors of health insurance uptake, ranked in ascending order of importance by the optimal model, including poverty vulnerability, social security usage, income, education, and marital status. The results suggest that affordability is a significant barrier to health insurance uptake. The study highlights the need to address affordability challenges and implement targeted interventions to improve health insurance uptake in Kenya, thereby advancing progress towards achieving Universal Health Coverage (UHC) and ensuring universal access to quality healthcare services.

Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method.

In Rwanda, the prevalence of hypertension was 15.3% in 2015. At present, there are no accurate predictions of the prevalence of hypertension and its trend over time in Rwanda to assist decision makers in making plans for prevention and more effective interventions. This study used the Gibbs sampling method in combination with the Markov Chain Monte Carlo approach to predict the prevalence of hypertension and its associated risk factors in Rwanda over a period of ten years. The data were from World Health Organization (WHO) reports. The findings showed that the prevalence of hypertension is estimated to reach 17.82% in 2025, with tobacco use, being overweight or obese, and other risk factors having a respective prevalence of 26.26%, 17.13%, 4.80%, and 33.99%, which shows the increase and, therefore, measures for prevention to be taken. Therefore, to prevent and reduce the prevalence of this disease, the government of Rwanda should take appropriate measures to promote a balanced diet and physical exercise.

Prediction of prevalence of type 2 diabetes in Rwanda using the metropolis-hasting sampling.

In this work, we predict the prevalence of type 2 diabetes among adult Rwandan people. We used the Metropolis-Hasting method that involved calculating the metropolis ratio. The data are those reported by World Health Organiation in 2015. Considering Suffering from diabetes, Overweight, Obesity, Dead and other subject as states of mathematical model, the transition matrix whose elements are probabilities is generated using Metropolis-Hasting sampling. The numerical results show that the prevalence of type 2 diabetes increases from 2.8% in 2015 to reach 12.65% in 2020 and to 22.59% in 2025. Therefore, this indicates the urgent need of prevention by Rwandan health decision makers who have to play their crucial role in encouraging for example physical activity, regular checkups and sensitization of the masses.

Mathematical Modelling of HIV-HCV Coinfection Dynamics in Absence of Therapy.

Globally, it is estimated that of the 36.7 million people infected with human immunodeficiency virus (HIV), 6.3% are coinfected with hepatitis C virus (HCV). Coinfection with HIV reduces the chance of HCV spontaneous clearance. In this work, we formulated and analysed a deterministic model to study the HIV and HCV coinfection dynamics in absence of therapy. Due to chronic stage of HCV infection being long, asymptomatic, and infectious, our model formulation was based on the splitting of the chronic stage into the following: before onset of cirrhosis and its complications and after onset of cirrhosis. We computed the basic reproduction numbers using the next generation matrix method. We performed numerical simulations to support the analytical results. We carried out sensitivity analysis to determine the relative importance of the different parameters influencing the HIV-HCV coinfection dynamics. The findings reveal that, in the long run, there is a substantial number of individuals coinfected with HIV and latent HCV. Therefore, HIV and latently HCV-infected individuals need to seek early treatment so as to slow down the progression of HIV to AIDS and latent HCV to advanced HCV.