Cost-Effectiveness Diagnostics for Kenyan Water Treatment Systems: A Multilevel Regression Analysis, 2000–2026

{ "background": "Persistent inefficiencies in water treatment infrastructure investment and operation constrain universal access to clean water in many African nations. Existing cost-effectiveness analyses often fail to account for the hierarchical structure of facility data and regional policy heterogeneity, leading to suboptimal resource allocation.", "purpose and objectives": "This policy analysis develops and applies a novel diagnostic framework to evaluate the cost-effectiveness of water treatment systems. It aims to identify key technical and managerial determinants of efficiency and to provide a robust model for forecasting future performance under different policy scenarios.", "methodology": "A multilevel regression model is employed, nesting individual treatment facilities within counties and hydrological regions. The core statistical model is specified as ij = \β0j + \β1ij + \β2ij + uj + eij, where uⱼ represents random intercepts for region j. Analysis uses a longitudinal national dataset, with inference based on robust standard errors clustered at the county level.", "findings": "The analysis reveals that economies of scale are significant only for facilities with a design capacity exceeding 10,000 m³/day. A one-year increase in infrastructure age is associated with a 2.3% rise in operational costs (95% CI: 1.7% to 2.9%), highlighting a pronounced depreciation effect. Regional policy variables account for approximately 30% of the observed variance in cost-effectiveness.", "conclusion": "Cost-effectiveness in Kenyan water treatment is predominantly influenced by regional governance and asset management practices, rather than solely by facility-level engineering parameters. The multilevel approach provides a more accurate diagnostic tool than conventional single-level analyses.", "recommendations": "Policy should prioritise targeted rehabilitation programmes for ageing mid-capacity facilities and strengthen county-level regulatory and technical capacity. Future infrastructure planning must integrate the developed forecasting model to optimise capital and operational expenditure across different regional contexts.", "key words":