A visual analytics approach to characterising disease progression among adults with chronic diseases in rural Agincourt northeast South Africa

Abstract

Chronic diseases pose a significant challenge to the healthcare systems in South Africa, calling for innovative approaches for comprehensive understanding and management. This research study utilizes the Agincourt HDSS-Clinic dataset to design and implement a visual analytics system using the R Shiny web application framework. Focused on adults with chronic diseases, the tool employs dynamic visualizations to show patterns of healthcare utilization and disease progression. Through the R Shiny platform, the system provides a user-friendly interface for exploring and interpreting complex data, offering valuable insights into patient healthcare behaviours and the dynamics of chronic illnesses. The study used data from a total of 26 426 patients consisting of 19 265 (73%) females and 7 161 (27%) males. The study revealed previously unrecognized associations between specific chronic conditions including the existence of a substantial intersection between HIV, Hypertension, and Diabetes with 101 patients experiencing the coexistence of all the three conditions. Notably, the visual analytics system facilitated the identification of distinct healthcare utilization patterns across different demographic groups highlighting the most frequently visited health facility accounted for 5 912 patient visits overall while the least visited health facility accounted for 1 447 patient visits. The findings underscore the effectiveness of visual analytics in uncovering trends within complex datasets. The implications of these findings extend beyond the immediate research scope, influencing healthcare strategies and contributing to the ongoing discussions on innovative solutions for chronic disease management. This study contributes to the evolving field of visual analytics in healthcare, demonstrating the potential for such tools to inform decision-making and enhance patient outcomes