A Multilevel Regression Analysis of Water Treatment System Performance and Risk Reduction in Tanzania

{ "background": "Inadequate access to safe drinking water remains a critical public health challenge in many regions. The performance of centralised water treatment systems is a key determinant of health risk reduction, yet comprehensive, system-level evaluations linking technical performance to quantified risk are limited.", "purpose and objectives": "This study aimed to develop and apply a multilevel regression framework to evaluate the technical performance of urban water treatment systems and quantify its association with microbial risk reduction.", "methodology": "We conducted a cross-sectional field study of centralised treatment facilities. Performance data (turbidity, chlorine residual) and microbial water quality indicators (E. coli, total coliforms) were collected at multiple points within each system. A two-level hierarchical model was fitted, with sampling points nested within facilities. The core model was: log() = \β0 + \β1X + u + e, where u \~ N(0, \σ²u). Inference was based on robust standard errors.", "findings": "A one-unit increase in final treated water turbidity (NTU) was associated with a 17.3% increase in estimated microbial risk (95% CI: 9.8% to 25.4%). Substantial variation in performance was attributable to facility-level management factors, accounting for approximately 35% of the total variance in risk outcomes.", "conclusion": "The technical performance of treatment, particularly final turbidity control, is a statistically significant and modifiable predictor of microbial risk. Facility-level management practices are a critical source of performance variation.", "recommendations": "Routine monitoring should prioritise final turbidity as a key performance indicator. Regulatory frameworks should mandate and audit facility-specific performance management plans to reduce inter-facility variability.", "key words": "water safety, hierarchical model, turbidity,