CURRENT ISSUE

ABSTRACT: Despite a significant rise of reported eating disorders all over the world, data from Kazakhstan is obscured by lack of recognition of these conditions as serious health issue that have severe lifelong consequences. The attention to these conditions is long overdue and requires concentrated efforts from public, academia, and public health.

ABSTRACT: Background: Gender-based violence (GBV) and Human Immunodeficiency Virus (HIV) are critical public health issues in Sub-Saharan Africa, disproportionately affecting women. Gender-based violence (GBV)—including intimate partner violence and sexual assault—fuels HIV transmission via trauma, coercion, and healthcare barriers. This syndemic demands urgent, evidence-based solutions to break the cycle of risk.
Objectives: This study aims to explore the prevalence, types, and socio-cultural, economic, and power-related factors linking GBV to HIV risk among women in Sub-Saharan Africa, and to assess the effectiveness of integrated interventions addressing both issues.
Methodology: In carrying out this review, a scoping review design was employed. PubMed, African Journals Online (AJOL), and Google Scholar were searched for peer-reviewed studies conducted across Sub-Saharan Africa focusing on association of GBV and HIV risks. Data was extracted on the prevalence of HIV cases linked to GBV, and interventions addressing such prevalence. A thematic synthesis was used to identify common trends and gaps in the literature.
Findings: This study found GBV prevalence rates ranging from 3.4–89.3% across included studies, with significant geographic and population-based variations. GBV was identified as a major risk factor for HIV, particularly in settings with high economic dependence and gender inequality. Integrated interventions combining HIV care and GBV services were found to improve health outcomes, though access remains limited in rural areas.
Conclusion: This study underscores the urgent need for integrated, multi-sectoral approaches to address both GBV and HIV. Future research should focus on longitudinal studies and the scalability of successful interventions in diverse settings. Policymakers must prioritize these intersections to reduce the burden on women’s health in Sub-Saharan Africa.

ABSTRACT: Autoimmune diseases, including Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis (RA), and Multiple Sclerosis (MS), represent a growing global health burden. These diseases disproportionately affect women and the young, and their complex aetiology involves an interplay between genetic susceptibility and environmental triggers. In light of climate change’s increasing influence on health outcomes, this study explores the potential of machine learning (ML) models to predict climate-sensitive autoimmune diseases. We examine the integration of diverse data sources, such as electronic health records (EHRs), genomic data, and climate exposures, to enhance predictive accuracy. Current ML models in autoimmune disease prediction primarily rely on clinical and omics data, with limited consideration for environmental factors. We identify significant gaps, particularly in incorporating climate data such as particulate matter, UV radiation, and temperature variability. The study also highlights the challenges of data fusion, feature engineering, and causal inference in these models. Ethical concerns, including data privacy, model explainability, and equity, are also addressed. The research underscores the need for large-scale, prospective studies to validate climate-informed models and calls for policy-driven approaches to ensure equitable access and deployment. By bridging these gaps, climate-informed ML models hold promise for personalized, proactive disease prevention and public health planning.