Emergency care systems in Tanzania are critical for improving patient outcomes and reducing mortality rates. However, there is a lack of comprehensive methodological evaluations to assess their effectiveness. The study employs a multilevel regression model to analyse data from multiple ECUs across different regions. The model is specified as Y₈₉ = eta₀ + eta₁X₈₉ + uᵢ + vⱼ + e₈₉, where Y₈₉ represents clinical outcome measures for patient i in ECU j, X₈₉ are covariates such as staffing levels and equipment availability, uᵢ captures unobserved heterogeneity at the individual level, vⱼ represents systematic effects within ECUs, and e₈₉ is the error term. Uncertainty around coefficient estimates will be assessed through robust standard errors. The multilevel regression analysis revealed that effective staffing levels in ECUs significantly improved patient recovery rates (OR: 1. 25, CI: 1. 07-1. 46). This study provides valuable insights into the strengths and weaknesses of Tanzania's emergency care systems. Policy recommendations include increasing staffing levels in ECUs and investing in infrastructure to enhance clinical outcomes.
Ngowi et al. (Mon,) studied this question.