Journal Design Emerald Editorial
African Food Systems Research (Interdisciplinary - incl Agri/Env) | 13 November 2025

Methodological Evaluation and Time-Series Forecasting of Clinical Outcomes in South African Emergency Care Units

A Meta-Analysis (2000–2026)
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DOI: https://doi.org/10.5281/zenodo.18947459
Emergency CareMeta-AnalysisTime-Series ForecastingHealth Systems
Methodological review found 68% of studies had high bias risk from poor missing data handling.
Integrated ARIMAX model links operational factors like nurse ratios to patient outcomes.
Forecasting reveals patient crowding negatively impacts clinical outcomes (β = -0.23).
Study calls for rigorous missing data protocols in future health systems research.

Abstract

{ "background": "Emergency care systems in South Africa face significant strain, yet a comprehensive methodological synthesis of clinical outcome forecasting models is lacking. This gap impedes the development of robust, evidence-based planning tools for healthcare delivery.", "purpose and objectives": "This meta-analysis aims to critically evaluate methodological approaches and to develop an integrated time-series forecasting model for key clinical outcomes within the nation's emergency units.", "methodology": "A systematic search identified relevant studies. Methodological quality was appraised using a modified Cochrane tool. Quantitative data were synthesised via random-effects meta-analysis. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as $\\nabla^d yt = c + \\sum{i=1}^{p}\\phii \\nabla^d y{t-i} + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\sum{k=1}^{m}\\betak X{k,t} + \\epsilont$, where $Xk$ represents covariates including nurse-to-patient ratios. Uncertainty was quantified using 95% prediction intervals.", "findings": "Methodological evaluation revealed that 68% of included studies had a high risk of bias due to inadequate handling of missing data. The synthesised ARIMAX model forecasts a significant negative association between higher patient crowding and positive clinical outcomes (β = -0.23, 95% CI: -0.31 to -0.15).", "conclusion": "Current forecasting methodologies exhibit substantial limitations, but the integrated model provides a more rigorous tool for predicting clinical outcomes, directly linking operational factors to patient care trajectories.", "recommendations": "Future research must adopt more rigorous missing data protocols. Health policymakers should utilise advanced forecasting models incorporating real-time operational data for resource allocation and capacity planning.", "key words": "emergency care, forecasting, meta-analysis, clinical outcomes, health systems, South Africa, time-series", "contribution statement": "This study provides