Background: Epilepsy is the most common chronic neurological condition in children, with a prevalence of 0.3% in Jordan. It significantly impacts patients’ and their families’ lives, shaped by cultural and socioeconomic factors. This study assessed the perceived impact of epilepsy on children and their families in Jordan. Method: This was a hospital-based, cross-sectional study recruiting 184 children under 18 years with epilepsy using a custom-designed questionnaire. Results: Nearly half of the patients experienced epilepsy onset before age three, and seizures were controlled in 73%. Around 60% of parents were satisfied with their understanding of the disease. Male gender and older age at diagnosis were associated with greater perceived severity, while older age at diagnosis correlated with a negative impact on caregivers’ earning potential. Patients with more anti-epileptic drugs reported more social constraints and older children expressed concerns about medication and seizure-related injuries. Cultural factors, such as family size and history, were associated with higher caregiver burden, emphasizing the importance of culturally sensitive assessment tools. Conclusions: Effective seizure control and quality of life improvements should be priorities in managing epilepsy in children. Cultural factors are strongly linked to caregiver burden, emphasizing the need for culturally sensitive assessment tools for enhancing support and care outcomes across diverse populations.

In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy.

More than three in 10 people living in Jordan are immigrants, with the majority being Palestinian and Syrian refugees, who have a very similar non-communicable diseases (NCDs) profile to the hosting Jordanian community. We conducted a rapid review of the literature of studies, reports, and documents on the evidence of the impact of COVID-19 on vulnerable populations in Jordan with regard to NCD during the first year of the pandemic. COVID-19-related mobility constraints and often lack of awareness of NCDs put additional burden on vulnerable populations like refugees and migrants, in particular on non-registered migrants. COVID-19 pandemic and associated mitigation measures led to disruption in routine health services, significantly impacting people living with NCDs. Ensuring to deliver a people-centered and inclusive approach that works well during COVID-19 is of paramount importance toward Universal Health Coverage (all people have access to the health services they need, when and where they need them, without financial hardship).