Multilevel Regression Analysis for Evaluating Cost-Effectiveness of Process-Control Systems in Ugandan Settings

The adoption of process-control systems (PCSs) in Ugandan settings has shown varying levels of effectiveness and cost-effectiveness across different sectors. A multilevel regression model was employed to analyse data from multiple Ugandan settings. The model accounts for both fixed and random effects, incorporating variables such as sector type, geographical location, and implementation context. The analysis revealed that PCSs in healthcare sectors were more cost-effective compared to other sectors, with a marginal improvement rate of 15% in resource utilization per annum. The multilevel regression model provided insights into the varying costs and benefits of PCS implementation across different Ugandan settings, highlighting areas for further research and policy development. Policy makers should prioritise sectors with higher marginal improvement rates when considering investments in process-control systems. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.