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

Area of Science:

• Environmental Science
• Public Health
• Epidemiology

Background:

• Wastewater-based epidemiology (WBE) is crucial for public health and environmental monitoring.
• Accurate population estimation and demographic profiling in wastewater treatment plant (WWTP) catchments are significant challenges.

Purpose of the Study:

• To refine population estimates in WWTP catchments by integrating UK Census and NHS data.
• To assess demographic influences on wastewater composition.
• To compare the performance of four statistical models for population estimation.

Main Methods:

• Integration of high-resolution 2021 UK Census data, NHS patient registration records, and WWTP chemical parameters.
• Comparison of four statistical models: Simple Approach (SA), Density-Adjusted Bootstrapping (BtD), Density-Adjusted Bootstrapping with Overlap (BtDO), and Bayesian Hierarchical Modeling (ByD).
• Analysis of demographic factors (age, deprivation) and their correlation with pharmaceutical loads.

Main Results:

• The Density-Adjusted Bootstrapping with Overlap (BtDO) method yielded the most consistent population estimates with low relative uncertainties (as low as 1.5% in large catchments).
• Older populations and higher deprivation levels correlated with increased pharmaceutical loads (e.g., analgesics, r = 0.92, p < 0.01).
• Traditional indicators like Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) overestimated population, especially in agricultural areas, due to nonhuman sources.

Conclusions:

• The developed framework enhances the precision of WBE for public health surveillance and environmental risk assessment.
• The BtDO model provides a reliable method for population estimation in WBE.
• Understanding demographic influences is key to interpreting wastewater data accurately.