{ "background": "The adoption of community health centre (CHC) systems is a critical component of public health infrastructure development in sub-Saharan Africa. However, longitudinal assessments of adoption rates, which are essential for strategic planning and resource allocation, have been hampered by methodological limitations in existing forecasting approaches.", "purpose and objectives": "This study aimed to methodologically evaluate longitudinal data on CHC systems and to develop a robust time-series forecasting model for predicting future adoption rates. The objective was to generate reliable, long-term projections to inform health policy and infrastructure investment.", "methodology": "A longitudinal study design was employed, analysing national-level panel data on operational CHCs. The core forecasting model was an autoregressive integrated moving average (ARIMA) model with exogenous variables (ARIMAX), specified as $\\Delta^d yt = c + \\sum{i=1}^{p}\\phii \\Delta^d y{t-i} + \\sum{i=1}^{q}\\thetai \\epsilon{t-i} + \\sum{j=1}^{m}\\betaj X{j,t} + \\epsilont$, where $Xj$ represents socioeconomic covariates. Model parameters were estimated using maximum likelihood, with robust standard errors to account for heteroskedasticity.", "findings": "The methodological evaluation revealed significant autocorrelation in annual adoption rates, which the ARIMAX(1,1,1) model effectively captured. The forecast indicates a continued positive trajectory in system adoption, with a projected increase of approximately 22% (95% prediction interval: 18% to 26%) over the next forecast horizon. The inclusion of health workforce density as an exogenous variable significantly improved model fit.", "conclusion": "The developed ARIMAX model provides a methodologically sound framework for forecasting CHC adoption, demonstrating the utility of time-series analysis in health systems research. The projections offer a credible evidence base for anticipating future infrastructure needs.", "recommendations": "Health policymakers should integrate formal forecasting models into strategic planning