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March 13, 20260 citationsOpen Access

Methodological Evaluation of District Hospital Systems in South Africa: A Multilevel Regression Analysis for Risk Reduction

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LMLebo MokoenaCape Peninsula University of TechnologyAMAnika van der MerweCape Peninsula University of Technology

Key Points

  • This evaluation aims to assess a novel analytical framework for quantifying risk in district hospitals using multilevel regression modeling.
  • Conducted secondary analysis of anonymised audit data from district hospitals.
  • Utilized a two-level random intercepts model to analyze data.
  • Examined risk indices against system performance scores for infrastructure and clinical processes.
  • Significant variation in risk indices linked to hospital-level system performance (intra-class correlation coefficient = 0.31).
  • One-standard-deviation improvement in clinical process scores resulted in a 17.2% reduction in risk index.
  • Infrastructure scores had a weaker, non-significant association with risk indices.

Abstract

"background": "District hospitals are critical nodes in the South African healthcare system, yet systematic evaluations of their operational systems for patient safety and risk reduction are methodologically underdeveloped. Existing assessments often lack the statistical rigour to account for hierarchical data structures inherent in hospital networks. ", "purpose and objectives": "This short report aims to methodologically evaluate a novel analytical framework for assessing systemic risk in district hospitals. The objective is to demonstrate the application of multilevel regression modelling to quantify risk reduction potential across different hospital system domains. ", "methodology": "We conducted a secondary analysis of anonymised, cross-sectional audit data from a national sample of district hospitals. A two-level random intercepts model was specified: y{ij = \0 + \1Xij + uj + eij, where i denotes wards and j denotes hospitals. Risk indices were modelled against system performance scores for infrastructure, clinical processes, and administration. Inference was based on 95% confidence intervals derived from robust standard errors. ", "findings": "The multilevel model revealed significant variation in risk indices attributable to hospital-level system performance (intra-class correlation coefficient = 0. 31). A one-standard-deviation improvement in clinical process scores was associated with a 17. 2% reduction in the composite risk index (95% CI: 12. 5% to 21. 9%). Infrastructure scores showed a weaker, non-significant association. ", "conclusion": "The methodological approach successfully disentangles ward-level from hospital-level system effects, providing a more precise tool for targeting risk reduction interventions. Clinical process systems emerge as the most leverageable domain for systemic improvement. ", "recommendations": "Health authorities should adopt hierarchical modelling in routine hospital audits to identify priority hospitals and system domains for quality improvement investments. Future research should apply this method longitudinally to assess intervention impact. ", "key words": "health systems evaluation, multilevel modelling, patient safety, health services research, quality improvement",

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Cite This Study

Mokoena et al. (2006) studied this question.

synapsesocial.com/papers/69b3ac2b02a1e69014ccdb2fhttps://doi.org/10.5281/zenodo.18949826
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