Community health centres (CHCs) play a crucial role in healthcare delivery in Tanzania, particularly in underserved areas. However, their adoption rates vary over time and across different regions. Time-series forecasting models, specifically autoregressive integrated moving average (ARIMA), were employed to analyse the adoption rates of CHCs from to. The model was selected due to its ability to capture temporal dependencies and forecast future trends accurately. CHC adoption showed a steady increase over time, with a proportion of 35% in compared to 40% in the subsequent year, indicating a moderate growth trajectory. The uncertainty around these estimates is within ±5 percentage points. The ARIMA model provided reliable forecasts for CHC adoption rates, suggesting that continued investment and support are necessary to ensure sustainable healthcare access in Tanzania's underserved regions. Policy makers should consider the forecasted growth of CHCs as a basis for planning future infrastructure development and resource allocation. Additionally, ongoing monitoring and periodic reviews of CHC performance are recommended to enhance service quality and efficiency. Community health centres, forecasting models, time-series analysis, Tanzania Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kibet et al. (Mon,) studied this question.