Community health centres in Tanzania have been established to improve access to healthcare services, yet their effectiveness varies widely across different locations and populations. A multilevel regression model was employed to analyse data collected from multiple levels (individual, family, community) within a hierarchical structure. Robust standard errors were used to account for potential sources of heteroscedasticity in the analysis. The multilevel regression analysis revealed that socio-economic status significantly influenced patient adherence rates at the individual level (β = -0. 75, p < 0. 05). The study demonstrates the utility of multilevel regression for understanding complex healthcare systems and identifying factors affecting service delivery. Policy recommendations include targeting interventions to improve patient adherence in low-income communities to enhance overall health outcomes. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kamasi Mvila (Sat,) studied this question.
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