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. The recent 2019/2020 Rwanda Demographic Health Survey (RDHS) revealed that the prevalence of stunting in Rwanda among under five children was 33.5%. In Rwanda, there is no sufficient evidence on stunting status to guide prioritized interventions at the sector level, the lowest administrative unit for implementing development initiatives. This study aimed to provide reliable estimates of stunting prevalence in Rwanda at the sector level. METHODS: In this article, Small Area Estimation (SAE) techniques were used to provide sector level estimates of stunting prevalence in children under five in Rwanda. By plugging in relevant significant covariates in the generalized linear mixed model, model-based estimates are produced for all sectors with their corresponding Mean Square Error (MSE). RESULTS: The findings showed that, overall, 40 out of 416 sectors had met the national target of having a stunting rate less than or equal to 19%, while 194 sectors were far from meeting this target, having a stunting rate higher than the national prevalence of 33.5% in the year 2020. The majority of the sectors with stunting prevalence that were higher than the national average of 33.5% were found in the Northern Province with 68 sectors out of 89 and in Western Province with 64 sectors out of 96. In contrast, the prevalence of stunting was lower in the City of Kigali where 14 out of 35 sectors had a stunting rate between 0 and 19%, and all sectors were below the national average. This study showed a substantial connection between stunting and factors such as household size, place of residence, the gender of the household head, and access to improved toilet facilities and clean water. CONCLUSION: The results of this study may guide and support informed policy decisions and promote localised and targeted interventions in Rwanda's most severely affected sectors with a high stunting prevalence in Rwanda.

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.

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. The study focused on machine learning (ML) applications, whereby a random forest (RF) model was applied to predict the demand using essential medicines consumption data from 2015 to 2019 for approximately 500 medical products. Results: with a seventy-eight (78) percent accuracy rate for the training set and a 71 percent accuracy rate for the testing set, the RF model predicted the trend in demand for 17 NCD essential medicines. This was achieved by entering the month, year, district, and name of the NCD essential medicine. Based on historical consumption data, the RF model can thus be used to predict demand trends. Our findings showed that the RF model is talented to commendably perform as a predicting model. Conclusion: the study concluded that RF has the ability to optimize health supply chain planning and operational management by boosting the accuracy in predicting the demand trend for NCD essential medicines.

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. The Global Fund, a healthcare donor, is committed to donating more than fifty million US dollars over four years (2018-2021) to fight malaria in Rwanda. We estimated the potential budget impact of the adoption of RTS, S, into the Global Fund budget (as a case study) for malaria prevention in Rwanda. Methods: We developed a static budget impact model based on clinical, epidemiological, and cost (in US dollars) data from the literature, to assess the financial consequences of adding RTS, S to existing prevention strategies. Cost of treatment and prevention for the first year (without vaccine) was estimated and compared to the total cost after the fifth year (with vaccine). A one-way sensitivity analysis evaluated the robustness of the model. Results: For the 283,931children under 5 years at risk of malaria in Rwanda every year, the expected budget for first year (without vaccine) was $1,328,377.71 and for the fifth year (with vaccine) was $3,837,804, yielding a potential budget impact of $2,509,427. The cost of treating un-prevented malaria for the first year was $736,959 and for the fifth year was $61,413. The annual number of malaria treatments avoided increased from 10,095 children in the first year after introduction of vaccine to 36,701 children at the fifth year. Conclusion: With a potential budget impact of $2,509,427, the introduction of malaria vaccine for children under 5 years by Global Fund in Rwanda may be affordable when compared to the amount spent on treating children with malaria. Given that Malaria causes more harm than most parasitic diseases and disproportionally affects low-income populations, it is ethical to deploy all measures to control or eliminate Malaria, including vaccination.