Bayesian Hierarchical Model for Measuring Cost-Effectiveness of Water Treatment Facilities in Tanzanian Settings

Water treatment facilities are essential for improving water quality in Tanzania's rural areas, where access to clean water is limited. A Bayesian hierarchical regression model was employed to account for heterogeneity among facilities, incorporating fixed effects for geographic regions and random effects for individual facilities, thereby providing robust estimates of cost-effectiveness. Uncertainty in parameter estimation was quantified through credible intervals. The analysis revealed that the marginal benefit-cost ratio varied significantly across different water treatment systems and geographical locations, with some systems showing a positive return on investment (ROI) while others had negative ROIs. This study provides a nuanced understanding of cost-effectiveness for water treatment facilities in Tanzania, offering insights into system selection based on economic performance. Based on the findings, policymakers should prioritise investments in systems with positive ROIs to maximise overall benefits and minimise costs in Tanzanian settings. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.