Demographic-Driven Wastewater-based Epidemiology: Refining Population Estimates for Enhanced Chemical Monitoring

Wastewater-based epidemiology (WBE) is a vital tool for public health and environmental monitoring, yet accurate population estimation and demographic profiles within wastewater treatment plant (WWTP) catchments remain a challenge. This study integrates high-resolution 2021 UK Census data, NHS patient registration records, and WWTP chemical parameters to refine population estimates and assess demographic influences on wastewater composition. We compared four statistical models: Simple Approach (SA), Density-Adjusted Bootstrapping (BtD), Density-Adjusted Bootstrapping with Overlap (BtDO), and Bayesian Hierarchical Modeling (ByD), across four diverse UK catchments (12,000–960,000). The BtDO method provided the most consistent estimates, with relative uncertainties as low as 1.5% in larger catchments. Demographic analysis revealed that older populations and higher deprivations correlated with elevated pharmaceutical load (e.g., analgesics, r = 0.92, p < 0.01). Traditional load-based indicators (Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD)) significantly overestimated population due to nonhuman contributions, particularly in agricultural areas. This framework, transferable to regions with spatially referenced data, enhances WBE precision for public health surveillance and environmental risk assessment.