{ "background": "Water treatment systems in many developing nations face persistent operational and water quality challenges. There is a recognised need for robust methodological frameworks to evaluate these systems and quantify the efficacy of interventions aimed at mitigating public health risks.", "purpose and objectives": "This case study aims to develop and apply a methodological framework for evaluating water treatment facilities. Its objectives are to assess systemic performance, identify key risk factors, and measure the potential for risk reduction through targeted improvements.", "methodology": "A multilevel regression modelling approach was employed, analysing operational and water quality data from a stratified sample of treatment facilities. The core statistical model was a two-level hierarchical linear model: $y{ij} = \\beta{0j} + \\beta{1}x{1ij} + ... + \\epsilon{ij}$, where $\\beta{0j} = \\gamma{00} + \\gamma{01}z{1j} + u{0j}$. Robust standard errors were used for inference to account for heteroskedasticity.", "findings": "The analysis identified coagulation-flocculation process efficiency as the most significant facility-level predictor of final water quality (p < 0.01). A one standard deviation improvement in this parameter was associated with a 22% reduction in modelled residual contamination risk (95% CI: 18% to 26%). Significant variation in performance was attributable to differences in maintenance protocols.", "conclusion": "The methodological framework successfully quantified the relationship between specific operational parameters and overall system risk. It demonstrates that targeted engineering interventions at the process unit level can yield substantial and measurable improvements in water safety.", "recommendations": "Implement routine performance assessment using multilevel modelling to prioritise maintenance. Focus engineering resources on optimising coagulation-flocculation units. Develop standardised protocols for data collection to support ongoing, data-driven facility management.", "key words": "water treatment; risk assessment; multilevel modelling; regression analysis; infrastructure management", "contribution statement": "This study provides a novel