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Sep 10, 2025Keywords:
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Main Article Content
Spatial distribution and risk factor profiling of heart failure across Rwanda's healthcare landscape
Abstract
Purpose: This research paper aims to investigate heart failure (HF) epidemiology in Rwanda, with special emphasis on distribution and mapping, clinical characteristics and risk factors of HF, and its New York Heart Association (NYHA) classification in Rwandan society.
Design/Methodology Approach: The study design is retrospective, and secondary data were abstracted from 4,085 available HF files of patients hospitalised in seven hospitals in Rwanda from 2008 to 2019. This study applied a combination of descriptive, spatial, and inferential statistical methods to analyse the distribution and patterns of HF across Rwanda
Research Limitation: The study was limited to existing data on the classification of heart failure cases recorded in Rwanda from 2008 to 2019, using the New York Heart Association (NYHA) classification.
Findings: The findings revealed an unequal distribution of HF patients across all 30 administrative districts of Rwanda, as 71.8 percent of HF patients resided in only 10 districts. The results further showed that the significant symptoms of HF in Rwanda were dyspnea, edema, persistent cough, and abdominal swelling. Furthermore, dilated cardiomyopathy, valvular heart disease, hypertension, and congenital heart defects were found to be the common risk factors for HF in Rwanda.
Practical Implication: This research can contribute to the development of clinical practice guidelines for heart failure (HF) in Rwanda, ensuring standardised and evidence-based care for HF patients.
Social Implication: Promoting awareness about heart failure (HF), facilitating access to strengthened health facilities in all districts of Rwanda, and diagnosing, treating, and controlling the risk factors of HF at earlier stages may significantly reduce the impact of HF on sufferers, their families, and health systems in Rwanda.
Originality and value: The geographic mapping of HF prevalence identifies hotspots, facilitating targeted interventions and resource allocation to the areas of greatest need.
