Title : Rising stroke tide in Uganda: A decade of trends and forecasts from national DHIS2 data, 2016–2028

Background: 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 short-term forecasts in Uganda remains limited. This study aimed to examine trends in stroke admissions, new cases, and stroke-related deaths in Uganda and to forecast future stroke burden through December 2027.

Methods: We conducted a retrospective time-series analysis using routinely collected national health facility data in the District Health Information System (DHIS2) from 2020 to 2025. Monthly stroke cases, and deaths were analyzed. Monthly populations of Uganda were estimated using exponential growth interpolation and incorporated as offset terms in negative binomial regression models to estimate incidence rate ratios (IRR) at 95% confidence interval (CI). Stationarity and autocorrelation were assessed visually and confirmed using unit root and Ljung–Box tests. Forecasting was performed using the naive, exponential smoothing state-space (ETS) and autoregressive integrated moving average models, with model selection based on minimization of Mean Root Square Error (MRSE) and Mean Average Percentage Error (MAPE). Forecasts were generated with 80% and 95% prediction intervals (PI).

Results: Stroke cases, and deaths increased significantly over time, with monthly IRR of 1.006 (95% CI: 1.005–1.008) and 1.005 (95% CI: 1.003–1.007), respectively (all p < 0.001). These findings indicate rising stroke rates beyond population growth alone. ETS models demonstrated the best predictive performance. By December 2027, monthly stroke cases are projected to reach approximately 1,007 per month (80% PI: 764–1,262), and stroke-related deaths approximately 111 per month (80% PI: 86–138).

Conclusion: Stroke burden in Uganda is increasing and is projected to continue rising over the next three 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.

Suzan Nakasendwa is a biostatistician and data enthusiastic with expertise in clinical trials, implementation research, and global health. She has extensive experience analyzing complex health data across infectious diseases, including tuberculosis, COVID-19, and antimicrobial resistance, to generate evidence that informs public health practice. She is a PhD candidate at the School of Public Health, Makerere University, where her research focuses on transforming stroke care through artificial intelligence by leveraging routine health data systems for improved classification and prediction of survival outcomes.