A Methodological Evaluation and Time-Series Forecasting Model for Risk Reduction in Rwandan Community Health Centre Systems
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| Format: | Recurso digital |
| Langue: | anglais |
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2000
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| _version_ | 1866901890140209152 |
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| author | Mukamana, Marie Aimee Niyonzima, Jean Paul Uwimana, Jean de Dieu |
| author_facet | Mukamana, Marie Aimee Niyonzima, Jean Paul Uwimana, Jean de Dieu |
| contents | <p>{ "background": "Community health centres are critical nodes in Rwanda's healthcare system, yet their operational resilience is challenged by fluctuating demand and supply chain vulnerabilities. A robust, predictive methodology for quantifying systemic risk is lacking.", "purpose and objectives": "This study aimed to develop and methodologically evaluate a novel time-series forecasting model to measure and predict risk reduction in the operational continuity of community health centres.", "methodology": "We conducted an intervention study using longitudinal, facility-level data on stock-outs, patient attendance, and referral rates. The core analytical framework was an autoregressive integrated moving average with exogenous variables (ARIMAX) model, specified as $yt = \\mu + \\sum{i=1}^{p}\\phii y{t-i} + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\sum{k=1}^{r}\\betak X{t,k} + \\epsilont$, where $Xt$ represents intervention covariates. Model performance was assessed via rolling-origin forecast evaluation and robust standard errors.", "findings": "The ARIMAX(2,1,1) model demonstrated superior forecasting accuracy against benchmarks, reducing one-step-ahead forecast error for essential medicine stock-out risk by 34% (95% CI: 28 to 40). The inclusion of community health worker deployment density as an exogenous variable was a significant predictor of reduced operational risk.", "conclusion": "The proposed forecasting model provides a validated methodological tool for proactively quantifying risk in decentralised health systems, demonstrating significant predictive utility.", "recommendations": "Health system planners should integrate predictive, model-based risk assessments into routine supply chain management and resource allocation decisions for community health centres.", "key words": "health systems resilience, predictive modelling, supply chain management, ARIMAX, operational research, public health", "contribution statement": "This paper provides the first application of a tailored ARIMAX forecasting framework to quantify dynamic risk in a community-based health system, offering a</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18956399 |
| institution | Zenodo |
| language | eng |
| publishDate | 2000 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Methodological Evaluation and Time-Series Forecasting Model for Risk Reduction in Rwandan Community Health Centre Systems Mukamana, Marie Aimee Niyonzima, Jean Paul Uwimana, Jean de Dieu Community health centres Rwanda Time-series analysis Risk reduction Operational resilience Sub-Saharan Africa Health systems evaluation <p>{ "background": "Community health centres are critical nodes in Rwanda's healthcare system, yet their operational resilience is challenged by fluctuating demand and supply chain vulnerabilities. A robust, predictive methodology for quantifying systemic risk is lacking.", "purpose and objectives": "This study aimed to develop and methodologically evaluate a novel time-series forecasting model to measure and predict risk reduction in the operational continuity of community health centres.", "methodology": "We conducted an intervention study using longitudinal, facility-level data on stock-outs, patient attendance, and referral rates. The core analytical framework was an autoregressive integrated moving average with exogenous variables (ARIMAX) model, specified as $yt = \\mu + \\sum{i=1}^{p}\\phii y{t-i} + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\sum{k=1}^{r}\\betak X{t,k} + \\epsilont$, where $Xt$ represents intervention covariates. Model performance was assessed via rolling-origin forecast evaluation and robust standard errors.", "findings": "The ARIMAX(2,1,1) model demonstrated superior forecasting accuracy against benchmarks, reducing one-step-ahead forecast error for essential medicine stock-out risk by 34% (95% CI: 28 to 40). The inclusion of community health worker deployment density as an exogenous variable was a significant predictor of reduced operational risk.", "conclusion": "The proposed forecasting model provides a validated methodological tool for proactively quantifying risk in decentralised health systems, demonstrating significant predictive utility.", "recommendations": "Health system planners should integrate predictive, model-based risk assessments into routine supply chain management and resource allocation decisions for community health centres.", "key words": "health systems resilience, predictive modelling, supply chain management, ARIMAX, operational research, public health", "contribution statement": "This paper provides the first application of a tailored ARIMAX forecasting framework to quantify dynamic risk in a community-based health system, offering a</p> |
| title | A Methodological Evaluation and Time-Series Forecasting Model for Risk Reduction in Rwandan Community Health Centre Systems |
| topic | Community health centres Rwanda Time-series analysis Risk reduction Operational resilience Sub-Saharan Africa Health systems evaluation |
| url | https://doi.org/10.5281/zenodo.18956399 |