A Methodological Evaluation and Time-Series Forecasting Model for District Hospital Clinical Outcomes in Uganda: A Systematic Review (2000–2026)
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| Format: | Recurso digital |
| Language: | English |
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2025
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| _version_ | 1866901576717697024 |
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| author | Ogwal, Julius Kato, Moses Mbabazi, Nakato |
| author_facet | Ogwal, Julius Kato, Moses Mbabazi, Nakato |
| contents | <p>{ "background": "District hospitals are critical nodes in Uganda's healthcare system, yet systematic assessments of methodologies for evaluating their clinical performance and forecasting future outcomes are lacking. This gap hinders evidence-based planning and resource allocation.", "purpose and objectives": "This systematic review aims to critically appraise methodological approaches used in the evaluation of district hospital systems and to synthesise evidence on the application of time-series forecasting models for clinical outcomes.", "methodology": "A systematic search of peer-reviewed literature and grey sources was conducted. Studies were screened and selected based on pre-defined eligibility criteria. Methodological quality was assessed using a modified checklist for observational and modelling studies. The core forecasting model synthesised is an ARIMA(p,d,q) formulation: $Xt = \\mu + \\phi1 X{t-1} + ... + \\phip X{t-p} + \\epsilont + \\theta1 \\epsilon{t-1} + ... + \\thetaq \\epsilon{t-q}$, where parameter uncertainty was quantified using 95% confidence intervals.", "findings": "The review identified a predominant reliance on retrospective, facility-level data with significant heterogeneity in outcome definitions. A key finding was that models incorporating seasonal autoregressive components improved forecast accuracy for paediatric malaria admissions by approximately 18% (95% CI: 12–24%) compared to non-seasonal benchmarks. Methodological limitations included frequent under-reporting of model validation procedures.", "conclusion": "While time-series forecasting offers a viable tool for predicting clinical burdens, current methodological rigour is inconsistent. Standardised reporting guidelines and enhanced capacity in advanced statistical modelling are required for reliable health system analytics.", "recommendations": "Future research should prioritise the development and use of standardised core outcome sets. Model development must incorporate robust external validation and explicit uncertainty analysis to inform policy effectively.", "key words": "health systems research, forecasting, clinical outcomes, ARIMA modelling, sub-Saharan Africa, health services evaluation", "contribution statement": "This review provides</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18957300 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Methodological Evaluation and Time-Series Forecasting Model for District Hospital Clinical Outcomes in Uganda: A Systematic Review (2000–2026) Ogwal, Julius Kato, Moses Mbabazi, Nakato Systematic review Time-series forecasting Clinical outcomes District hospitals Sub-Saharan Africa Healthcare evaluation Uganda <p>{ "background": "District hospitals are critical nodes in Uganda's healthcare system, yet systematic assessments of methodologies for evaluating their clinical performance and forecasting future outcomes are lacking. This gap hinders evidence-based planning and resource allocation.", "purpose and objectives": "This systematic review aims to critically appraise methodological approaches used in the evaluation of district hospital systems and to synthesise evidence on the application of time-series forecasting models for clinical outcomes.", "methodology": "A systematic search of peer-reviewed literature and grey sources was conducted. Studies were screened and selected based on pre-defined eligibility criteria. Methodological quality was assessed using a modified checklist for observational and modelling studies. The core forecasting model synthesised is an ARIMA(p,d,q) formulation: $Xt = \\mu + \\phi1 X{t-1} + ... + \\phip X{t-p} + \\epsilont + \\theta1 \\epsilon{t-1} + ... + \\thetaq \\epsilon{t-q}$, where parameter uncertainty was quantified using 95% confidence intervals.", "findings": "The review identified a predominant reliance on retrospective, facility-level data with significant heterogeneity in outcome definitions. A key finding was that models incorporating seasonal autoregressive components improved forecast accuracy for paediatric malaria admissions by approximately 18% (95% CI: 12–24%) compared to non-seasonal benchmarks. Methodological limitations included frequent under-reporting of model validation procedures.", "conclusion": "While time-series forecasting offers a viable tool for predicting clinical burdens, current methodological rigour is inconsistent. Standardised reporting guidelines and enhanced capacity in advanced statistical modelling are required for reliable health system analytics.", "recommendations": "Future research should prioritise the development and use of standardised core outcome sets. Model development must incorporate robust external validation and explicit uncertainty analysis to inform policy effectively.", "key words": "health systems research, forecasting, clinical outcomes, ARIMA modelling, sub-Saharan Africa, health services evaluation", "contribution statement": "This review provides</p> |
| title | A Methodological Evaluation and Time-Series Forecasting Model for District Hospital Clinical Outcomes in Uganda: A Systematic Review (2000–2026) |
| topic | Systematic review Time-series forecasting Clinical outcomes District hospitals Sub-Saharan Africa Healthcare evaluation Uganda |
| url | https://doi.org/10.5281/zenodo.18957300 |