Time-Series Forecasting Model for Evaluating District Hospital Systems in South Africa: A Longitudinal Study
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2013
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| _version_ | 1866901853918199808 |
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| author | Motombwe, Qhunou Nxumalo, Nontaba Mkhize, Sipho |
| author_facet | Motombwe, Qhunou Nxumalo, Nontaba Mkhize, Sipho |
| contents | <p>South Africa's district hospitals play a critical role in healthcare delivery, but their performance varies over time. Understanding these variations is essential for policy-making and resource allocation. A mixed-method approach combining quantitative time-series modelling with qualitative interviews was employed. The Box-Jenkins ARIMA (AutoRegressive Integrated Moving Average) model was used for forecasting future performance based on historical data. The forecasted models showed a consistent upward trend in hospital capacity utilization, indicating improved system reliability over the study period. This study provides evidence of increasing system efficiency and highlights the need for continued monitoring and support to ensure sustainable healthcare services. District health authorities should prioritise investment in infrastructure and human resources to maintain and enhance service delivery. district hospitals, time-series forecasting, reliability measurement, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18982278 |
| institution | Zenodo |
| language | eng |
| publishDate | 2013 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Time-Series Forecasting Model for Evaluating District Hospital Systems in South Africa: A Longitudinal Study Motombwe, Qhunou Nxumalo, Nontaba Mkhize, Sipho African geography longitudinal analysis time-series analysis forecasting models system reliability data mining econometrics <p>South Africa's district hospitals play a critical role in healthcare delivery, but their performance varies over time. Understanding these variations is essential for policy-making and resource allocation. A mixed-method approach combining quantitative time-series modelling with qualitative interviews was employed. The Box-Jenkins ARIMA (AutoRegressive Integrated Moving Average) model was used for forecasting future performance based on historical data. The forecasted models showed a consistent upward trend in hospital capacity utilization, indicating improved system reliability over the study period. This study provides evidence of increasing system efficiency and highlights the need for continued monitoring and support to ensure sustainable healthcare services. District health authorities should prioritise investment in infrastructure and human resources to maintain and enhance service delivery. district hospitals, time-series forecasting, reliability measurement, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p> |
| title | Time-Series Forecasting Model for Evaluating District Hospital Systems in South Africa: A Longitudinal Study |
| topic | African geography longitudinal analysis time-series analysis forecasting models system reliability data mining econometrics |
| url | https://doi.org/10.5281/zenodo.18982278 |