Diarrhea is a leading cause of illness and mortality among children under five, posing a significant public health challenge in Malawi. The current study assesses the prevalence and risk factors linked to diarrhea among under-five children in Malawi. The researcher used the Malawi Multiple Indicator Cluster Survey (MICS) 2019-20 as the dataset for this study. Due to the complex sampling design, survey logistic regression was used to accomplish the study objectives. The sample size was 15569 children who were aged under five. The study found that the prevalence of diarrhea was 24.9%, with a higher percentage observed among children aged 12–23 months (38.5%) compared to other age groups. Additionally, children from the southern region had a higher prevalence of diarrhea at 27% compared to those from the northern region at 19.3%. Children from the poorest households also had a higher prevalence of diarrhea at 28.9% compared to those from the richest households at 22.6%. Furthermore, children with fever had a higher prevalence of diarrhea at 33.3% compared to those who did not have fever at 19.4%. The current study concluded that the prevalence of diarrhea was higher among children aged 12–23 months. Subsequently, policymakers should apply policies to reduce this high prevalence among this age group of children. In addition, the government needs special consideration in diarrhea control for children from the southern region because of the high prevalence of the disease compared to the other regions in Malawi. My study can help policymakers understand the scope and nature of the problem, which can notify the development of policies and programs intended to decrease the prevalence of risk factors and enhance child health outcomes.

Background: One of the health challenges in Sub-Saharan countries is child malnutrition. Body Mass Index (BMI) can be defined as a measure of nutritional status. Examining the determinants of under-five children’s BMI is a significant subject that needs to be studied. For this study, quantile regression was used to identify the determinants of under-five children's BMI in Sudan. Methods: We used the 2014 Sudan Multiple Indicator Cluster Survey (MICS) conducted by the Central Bureau of Statistics. Quantile regression was used. Results: Place of residence, state, mother’s educational level, gender, age of the child, and wealth index were an important effect significantly affecting under-five children’s BMI at different quantile levels. Conclusion: Taking measures on the nutritional status of mothers will accordingly resolve the nutritional status of their children. Therefore, the focus of policymakers should be on the influential significant factors which were found across all quantile levels to plan and develop strategies to enhance the normal or healthy weight status of under-five children in Sudan.

Kenya has a lower malaria incidence in comparison to other African malaria-endemic nations. Malaria is a significant public health concern in the country. The malaria indicator survey (MIS) data were analyzed using the logistic regression model. Nonetheless, independent data may be the cause of most MIS's hierarchical structure. This approach does not consider any association between data points within a cluster, as it assumes that the individual malaria statuses are independent of their causes. The approach may lead to biased analysis conclusions. The primary goal of this research is to determine the impact of sample enumeration areas (SEAs) and SEA features on individual malaria rapid diagnostic test (RDT) results. We are interested in identifying key factors influencing household members' malaria RDT findings or Kenya's malaria prevalence and assessing variation. Our study utilized the robust 2020 Kenya National Malaria Indicator Surveys (KMIS) dataset, which is representative of the entire nation. This dataset, comprising 301 clusters (134 urban and 167 rural areas), was instrumental in applying several multilevel models, including random sample and sample Enumeration Area (SEA) effects. We also considered the weights used in the s survey design, which is used to adjust uneven probabilities of choice within clusters, further enhancing the reliability and relevance of our findings. The methods used in this study involved a rigorous analysis of the KMIS dataset, including applying multilevel models and considering survey design weights to ensure the robustness and strength of our results. This study's findings are significant and crucial in understanding the prevalence of malaria in Kenya. The findings reveal that factors such as region, place of residence, mosquito bed net use, water source location, wealth index, age, household size, and altitude are significantly associated with malaria's prevalence. After accounting for these variables, systematic changes across SEAs accounted for approximately 47.1 % of the remaining variability in malaria occurrence in the study locations. In contrast, the remaining 52.9 % was projected to be unmeasured differences between individuals or family units. These findings provide a detailed explanation of the various processes that influence malaria prevalence in Kenya. The study's multilevel logistic regression model, which includes random effects, identified two SEA-level and eight individual/household risk factors for malaria infection. Thus, increasing the availability of insecticide-treated bed nets is one crucial element that public health policymakers should consider. Furthermore, health planners can organize spatially targeted initiatives to prevent malaria transmission with the help of spatial clustering data.

Green financing represents a great untapped opportunity, especially in Saudi Arabia and the other Gulf Cooperation Council countries, which are characterized by their developed capital markets, attracting investors from all over the world to pump their capital into projects that have an environmental and social impact, in addition to activating strong governance processes.The study aimed to examine the impact of the dimensions of sustainable development: environmental, social, and economic on green finance in Saudi national banks and fill the gap in sustainability and green finance research in Arabian countries, specifically in the Kingdom of Saudi Arabia. The study population covered the national banks in Riyadh city. The data was collected through a questionnaire and the sample size was 125 participants. The study applied linear regression models and the result revealed that the environmental dimension of sustainable development, the social dimension of sustainable development, and the economic dimension have a positive impact on green finance in Saudi banks.