A systematic methodology to capture the global pattern of rheumatic heart disease: the Rheumatic Heart Disease Endemicity Index (RHDEI).

backgroundRheumatic heart disease (RHD) disproportionally affects young populations in socio-economically disadvantaged settings, resulting in a skewed distribution towards low- and middle-income countries. There is currently no consistent global surveillance system to identify countries with a high risk of RHD, which is a major barrier to addressing this public health threat. This paper describes a new methodology for conceptualizing locations at risk for high RHD morbidity and mortality, or burden, globally.

methodsWe utilized a set of covariates produced by the Global Burden of Disease Study from 1990 to 2021 via principal component analysis to create the rheumatic heart disease endemicity index (RHDEI). We then demonstrate how the RHDEI could be used in forecasting for targeted policy change with the use of an ensemble time-series forecasting model, creating 20 years of estimates through 2041. The results were evaluated via out-of-sample forecasting to estimate model performance and compared to a naive model to assess goodness of fit.

resultsWe produced 203 country-level yearly estimates from 1990 to 2021 for the RHDEI. We found that countries in sub-Saharan Africa and South-East Asia had the highest RHDEI results, reflecting the burden in those regions. The largest decrease in RHDEI was estimated for South Sudan, and the largest increase was estimated for Angola. Our forecast through 2041 further highlighted the heterogeneity of RHD burden, demonstrating how without intervention some regions will likely see worse outcomes in relation to RHD.

conclusionThe RHDEI provides a much-needed method for capturing global RHD distributions that can improve our understanding of the changing patterns in a data scarce landscape. The evidence the index provides can help researchers, policy makers, and clinicians better understand RHD burden and act to reduce it.