Urban primary care networks in South Africa have been established to improve access to healthcare services for underserved populations. However, their effectiveness remains uncertain due to varying implementation and data quality. A multilevel regression model was employed to analyse clinical outcome measures across different levels (individual, network) within the urban primary care systems. The model is represented as y₈₉ = eta₀ + eta₁ X₈₉ + uᵢ + vⱼ + e₈₉, where y₈₉ are the clinical outcomes for individuals in a given network (i), X₈₉ represent individual-level covariates, uᵢ captures network-level effects, and vⱼ represents the effect of the urban primary care network. Robust standard errors were used to account for potential heteroscedasticity. The multilevel regression analysis revealed a significant positive coefficient for the number of healthcare providers in each network (eta₁ = 0. 25, p < 0. 01), indicating that an increase in provider numbers correlates with improved clinical outcomes. This study contributes to the methodological understanding of urban primary care networks by providing a robust framework for evaluating their impact on clinical outcomes using multilevel regression analysis. Further research should explore the scalability and sustainability of these findings across different geographical regions in South Africa. Urban Primary Care, Multilevel Regression, Clinical Outcomes, Methodological Assessment
Mankwane et al. (Thu,) studied this question.