Simulated 2017 nationwide sampling at 13,940 major U.S. sewage treatment plants to assess seasonal population bias in wastewater-based epidemiology

Abstract

Wastewater-based epidemiology (WBE) is an economical technique for monitoring and managing the health and behavior of human populations. Using 2017 nationwide data on geospatial population demographics as a test case, we simulated repeated sampling at all major U.S. wastewater treatment plants (WWTPs; n = 13,940) under constant biomarker loading conditions, to explore the potential sensitivity of WBE for generating skewed data. Simulation of repeated sewage sampling over all four seasons of 2017 yielded a number of expected, inter-dependent phenomena triggered by cooler wintertime temperatures compared to summertime results, including relatively (i) slower in-sewer biomarker decay, (ii) longer distal reach of WBE, (iii) larger effective sewershed monitoring areas, and (iv) an increase in the population represented. Additional important but not necessarily anticipated simulation outcomes included (v) distinct, non-random changes in demographic parameters of monitored subpopulations (e.g., by household income, educational attainment, military service, unemployment, and lack of health insurance), (vi) recurring observation of the latter demographic patterns across various geospatial scales and regions, and (vii) more evenly distributed results in the winter. In contrast, data obtainable by WBE in the summertime were dominated by households residing closest to the WWTP and subpopulations of relatively lesser wealth, educational achievement, healthcare access and employability. The analytical approach presented here should be readily applicable to other regions worldwide and may help to improve the design, robustness and interpretation of future WBE studies.

Keywords: Demographics; Modeling; Spatial analysis; Wastewater epidemiology.