{ "background": "Emergency care systems in sub-Saharan Africa face significant challenges in resource allocation and outcome measurement. Current evaluations often rely on cross-sectional data, which fail to capture temporal dynamics and unobserved heterogeneity, limiting the robustness of policy-relevant evidence.", "purpose and objectives": "This protocol outlines a methodological framework for applying panel-data econometrics to estimate the effect of systemic factors on clinical outcomes in emergency units. The primary objective is to establish a replicable model for quantifying the impact of variables such as staffing ratios, drug availability, and diagnostic capacity on patient mortality and morbidity.", "methodology": "We propose a longitudinal observational study using routinely collected administrative data from a nationally representative sample of units. The core analytical model is a two-way fixed effects regression: $Y{it} = \\alpha + \\beta X{it} + \\mui + \\lambdat + \\epsilon{it}$, where $Y{it}$ is the clinical outcome for unit $i$ in period $t$, $X{it}$ is a vector of time-varying covariates, $\\mui$ and $\\lambdat$ are unit and time fixed effects, and $\\epsilon{it}$ is the error term. Inference will be based on cluster-robust standard errors to account for intra-unit correlation.", "findings": "As a research protocol, this paper does not present empirical results. The anticipated findings will include estimated coefficients and their confidence intervals for key systemic determinants. For example, a preliminary power analysis suggests the model is powered to detect a minimum 5-percentage-point reduction in adverse outcomes associated with a one-standard-deviation increase in nurse-to-patient ratios.", "conclusion": "This protocol provides a rigorous methodological foundation for analysing complex, time-varying determinants of emergency care performance. The panel-data approach directly addresses limitations of prior static analyses.", "recommendations": "We recommend that health systems researchers and policymakers adopt panel-data methods for evaluating service delivery interventions. National health management information systems should be strengthened to support the longitudinal data architecture required