"background": "Maternal healthcare systems in Nigeria face significant challenges, yet robust methodological frameworks for evaluating facility systems and forecasting clinical outcomes are lacking. Existing assessments often rely on cross-sectional data, failing to capture temporal dynamics and system-level interactions that influence maternal health indicators. ", "purpose and objectives": "This review critically evaluates methodological approaches for assessing maternal care facility systems and develops a novel time-series forecasting model to project key clinical outcomes. The objective is to provide a validated analytical tool for health systems planning and intervention targeting. ", "methodology": "We conducted a systematic methodological review of studies evaluating maternal health facilities. The proposed forecasting model integrates autoregressive integrated moving average (ARIMA) components with exogenous health system variables. The core model is specified as Yt = \ + =1^{p\ Yt-i + \ + =1^q\ -j + =1^m\ Xk, t, where Yt is the clinical outcome, X₊, ₓ are system covariates, and parameters are estimated via maximum likelihood with robust standard errors. ", "findings": "The methodological evaluation revealed a predominant reliance on facility checklists, with less than 20% of reviewed studies employing longitudinal designs suitable for causal inference. Application of the forecasting model indicates a likely stagnation in the facility-based delivery rate, with projections showing a marginal increase of only 2–4 percentage points over the forecast horizon (95% prediction interval: 0. 5 to 5. 1). System readiness indices for emergency obstetric care were the most significant exogenous predictors. ", "conclusion": "Current methodologies for evaluating maternal care facilities are insufficient for dynamic systems analysis. The integrated ARIMA model provides a superior framework for understanding trends and generating evidence-based forecasts, highlighting an urgent need for investment in health system inputs beyond current levels to alter trajectory. ", "recommendations": "Health policymakers should adopt longitudinal, model-based forecasting for
Okonkwo et al. (Thu,) studied this question.