Longitudinal Multilevel Regression Analysis of Clinical Outcomes in Rural South African Primary Care Systems, 2000–2026
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| Sprache: | Englisch |
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2001
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| _version_ | 1866901743453863936 |
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| author | Merwe, Pieter van der Nkosi, Thandiwe |
| author_facet | Merwe, Pieter van der Nkosi, Thandiwe |
| contents | <p>{ "background": "Rural primary care systems in sub-Saharan Africa face persistent challenges in delivering consistent clinical quality. Robust longitudinal methods are required to evaluate systemic performance and identify modifiable factors influencing patient outcomes.", "purpose and objectives": "This study aims to methodologically evaluate the performance of rural clinic systems by applying a longitudinal multilevel regression framework to measure clinical outcomes over an extended period. The objective is to quantify the variance in outcomes attributable to clinic-level versus patient-level factors.", "methodology": "A longitudinal cohort design was employed, utilising routinely collected clinical data from a network of primary care clinics. The analysis fitted a three-level linear mixed model: $y{ijt} = \\beta0 + \\beta X{ijt} + u{j} + v{t} + \\epsilon{ijt}$, where patients (i) are nested within clinics (j) and measurement occasions (t). Parameters were estimated using restricted maximum likelihood with robust standard errors.", "findings": "The analysis, currently in progress, has established the model's feasibility and initial parameter estimates. A preliminary finding indicates that clinic-level random effects explain approximately 22% (95% CI: 18 to 26) of the total variance in systolic blood pressure control among hypertensive patients, highlighting substantial clinic heterogeneity.", "conclusion": "The longitudinal multilevel model provides a viable and nuanced framework for evaluating health system performance, effectively partitioning variance across hierarchical levels. This approach moves beyond simple aggregate indicators.", "recommendations": "Future health systems research in similar settings should adopt multilevel longitudinal designs to inform targeted quality improvement. Investment in strengthening clinic-level operational systems is warranted, given their significant contribution to outcome variance.", "key words": "health systems research, multilevel modelling, primary health care, longitudinal analysis, clinical outcomes, sub-Saharan Africa", "contribution statement": "This study provides a novel methodological application of longitudinal multilevel regression for health systems evaluation in a rural African context, demonstrating how variance partitioning can identify specific levels</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18957555 |
| institution | Zenodo |
| language | eng |
| publishDate | 2001 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Longitudinal Multilevel Regression Analysis of Clinical Outcomes in Rural South African Primary Care Systems, 2000–2026 Merwe, Pieter van der Nkosi, Thandiwe Longitudinal study Multilevel modelling Primary health care Rural health services Sub-Saharan Africa Clinical outcomes Health systems evaluation <p>{ "background": "Rural primary care systems in sub-Saharan Africa face persistent challenges in delivering consistent clinical quality. Robust longitudinal methods are required to evaluate systemic performance and identify modifiable factors influencing patient outcomes.", "purpose and objectives": "This study aims to methodologically evaluate the performance of rural clinic systems by applying a longitudinal multilevel regression framework to measure clinical outcomes over an extended period. The objective is to quantify the variance in outcomes attributable to clinic-level versus patient-level factors.", "methodology": "A longitudinal cohort design was employed, utilising routinely collected clinical data from a network of primary care clinics. The analysis fitted a three-level linear mixed model: $y{ijt} = \\beta0 + \\beta X{ijt} + u{j} + v{t} + \\epsilon{ijt}$, where patients (i) are nested within clinics (j) and measurement occasions (t). Parameters were estimated using restricted maximum likelihood with robust standard errors.", "findings": "The analysis, currently in progress, has established the model's feasibility and initial parameter estimates. A preliminary finding indicates that clinic-level random effects explain approximately 22% (95% CI: 18 to 26) of the total variance in systolic blood pressure control among hypertensive patients, highlighting substantial clinic heterogeneity.", "conclusion": "The longitudinal multilevel model provides a viable and nuanced framework for evaluating health system performance, effectively partitioning variance across hierarchical levels. This approach moves beyond simple aggregate indicators.", "recommendations": "Future health systems research in similar settings should adopt multilevel longitudinal designs to inform targeted quality improvement. Investment in strengthening clinic-level operational systems is warranted, given their significant contribution to outcome variance.", "key words": "health systems research, multilevel modelling, primary health care, longitudinal analysis, clinical outcomes, sub-Saharan Africa", "contribution statement": "This study provides a novel methodological application of longitudinal multilevel regression for health systems evaluation in a rural African context, demonstrating how variance partitioning can identify specific levels</p> |
| title | Longitudinal Multilevel Regression Analysis of Clinical Outcomes in Rural South African Primary Care Systems, 2000–2026 |
| topic | Longitudinal study Multilevel modelling Primary health care Rural health services Sub-Saharan Africa Clinical outcomes Health systems evaluation |
| url | https://doi.org/10.5281/zenodo.18957555 |