Introduction Stroke is a leading cause of mortality and disability globally, with a disproportionate and growing burden in low- and middle-income Countries. However, evidence on national temporal trends and potential impacts in Uganda remains limited. This study examined trends in stroke cases and deaths in Uganda and forecast of future stroke burden through December 2028. Methods We conducted a retrospective time-series analysis of District Health Information System (DHIS2) from 2016 to 2025. Monthly stroke cases, and deaths were analyzed. Monthly populations of Uganda were estimated using exponential growth interpolation and incorporated as an offset term in Bayesian negative binomial regression models to estimate incidence rate ratios (IRR) at 95% confidence interval (CI). Decomposition analysis using the Kitagawa method was applied to derive contributions from population growth and epidemiological change, based on model estimates. Stationarity and autocorrelation were assessed using the unit root and Ljung–Box tests. Forecasting was performed using the exponential smoothing state-space (ETS) model at 80% prediction intervals (PI). Results Trend analysis showed sustained monthly increases in stroke incidence and mortality with IRR; 1.005 (95% CI: 1.004–1.006), and 1.006 (95% CI: 1.005–1.007), respectively (all p < 0.001). The epidemiological effect was approximately twice as large as the population effect, indicating that the majority of the increase in stroke cases and deaths is attributable to rising incidence rates rather than population growth alone. ETS model demonstrated that by December 2028, monthly cases and deaths are projected to reach approximately 927 (80% PI: 709–1,156) and 126 (80% PI: 94–160), respectively. Conclusion Stroke burden in Uganda is projected to continue increase further over the next years in the absence of effective prevention measures. Strengthened primary prevention, expanded acute stroke care capacity, and improved surveillance systems are urgently needed to mitigate the growing health system impact.
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Nakasendwa et al. (2026) studied this question.
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