Forecasting Years Lived in Poor Health in Kenya: A Comparative Analysis of ARIMA and LSTM Models with Implications for East African Health Policy

Kenya's life expectancy increased from 55.5 to 64.7 years (1990-2024), but years lived in poor health (GAP) have not declined proportionally, creating substantial public health planning challenges. Evidence-based forecasting of GAP trends is essential for health system resource allocation, yet no systematic forecasting methodologies exist for East African health systems. This study compared ARIMA and LSTM forecasting models for predicting Kenya's GAP trends and established methodological frameworks for health system planning across East Africa. Comparative time series analysis was conducted using Global Burden of Disease Study 2021 data spanning 1990-2021 for Kenya, Uganda, and Tanzania health systems, with 32 annual observations for each country. ARIMA and LSTM models were developed and validated using identical specifications, with performance evaluated using RMSE, MAE, and Diebold-Mariano statistical tests for significance. ARIMA significantly outperformed LSTM in Kenya (RMSE: 5.67 vs 6.66, p<0.001), reflecting stable health system patterns suitable for systematic planning, while LSTM demonstrated superior performance in Uganda (RMSE: 8.47 vs 15.03) and Tanzania (RMSE: 7.30 vs 10.10), indicating more complex health dynamics requiring sophisticated modelling approaches. Kenya's predictable GAP patterns enable reliable ARIMA-based forecasting for health system planning, while regional variations necessitate context-specific methodological approaches across East African health systems. This study provides the first systematic GAP forecasting framework for East Africa, offering health policy makers evidence-based tools for resource allocation while establishing methodological foundations for public health planning that can strengthen health systems across the region