Vol. 1 No. 1 (2025)

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Methodological Evaluation and Time-Series Forecasting of Clinical Outcomes in South African Emergency Care Units: A Meta-Analysis (2000–2026)

Pieter van der Merwe, Department of Internal Medicine, Tshwane University of Technology (TUT) Kagiso Mokoena, Council for Scientific and Industrial Research (CSIR) Anika Pretorius, Department of Surgery, Tshwane University of Technology (TUT) Thandiwe Nkosi, SA Medical Research Council (SAMRC)
DOI: 10.5281/zenodo.18947459
Published: December 15, 2025

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

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How to Cite

Pieter van der Merwe, Kagiso Mokoena, Anika Pretorius, Thandiwe Nkosi (2025). Methodological Evaluation and Time-Series Forecasting of Clinical Outcomes in South African Emergency Care Units: A Meta-Analysis (2000–2026). African Food Systems Research (Interdisciplinary - incl Agri/Env), Vol. 1 No. 1 (2025). https://doi.org/10.5281/zenodo.18947459

Keywords

Emergency medicineSouth AfricaMeta-analysisTime-series forecastingClinical outcomesHealth systems evaluationLow-resource settings

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Vol. 1 No. 1 (2025)
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African Food Systems Research (Interdisciplinary - incl Agri/Env)

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