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. 2019 Oct 22;116(43):21864-21873.
doi: 10.1073/pnas.1910242116. Epub 2019 Oct 7.

Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology

Phil M Choi  1 , Benjamin Tscharke  2 , Saer Samanipour  3 , Wayne D Hall  4 , Coral E Gartner  2   5 , Jochen F Mueller  2 , Kevin V Thomas  2 , Jake W O'Brien  2
Affiliations

Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology

Phil M Choi et al. Proc Natl Acad Sci U S A. .

Abstract

Wastewater is a potential treasure trove of chemicals that reflects population behavior and health status. Wastewater-based epidemiology has been employed to determine population-scale consumption of chemicals, particularly illicit drugs, across different communities and over time. However, the sociodemographic or socioeconomic correlates of chemical consumption and exposure are unclear. This study explores the relationships between catchment specific sociodemographic parameters and biomarkers in wastewater generated by the respective catchments. Domestic wastewater influent samples taken during the 2016 Australian census week were analyzed for a range of diet, drug, pharmaceutical, and lifestyle biomarkers. We present both linear and rank-order (i.e., Pearson and Spearman) correlations between loads of 42 biomarkers and census-derived metrics, index of relative socioeconomic advantage and disadvantage (IRSAD), median age, and 40 socioeconomic index for area (SEIFA) descriptors. Biomarkers of caffeine, citrus, and dietary fiber consumption had strong positive correlations with IRSAD, while tramadol, atenolol, and pregabalin had strong negative correlation with IRSAD. As expected, atenolol and hydrochlorothiazide correlated positively with median age. We also found specific SEIFA descriptors such as occupation and educational attainment correlating with each biomarker. Our study demonstrates that wastewater-based epidemiology can be used to study sociodemographic influences and disparities in chemical consumption.

Keywords: drugs; food; public health; socioeconomics; wastewater.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
IRSAD and median age of catchments featured in this study. Each catchment is depicted by a circle whose area represents its population size.
Fig. 2.
Fig. 2.
Correlations (R, Pearson or Spearman) of biomarker with catchment median age and IRSAD. Each biomarker is plotted using the highest |R| value. Biomarkers without significant correlations are faded. Amphetamine and methamphetamine are illicit drugs. R values are provided numerically in SI Appendix, Figs. S3 and S4. 4PA, 4-pyridoxic acid.
Fig. 3.
Fig. 3.
Correlations of wastewater biomarker loads with catchment-specific SEIFA descriptors, whose definitions are provided in Table 1. For each biomarker, SEIFA descriptors with the greatest positive (blue) and negative (black) R values are plotted. Linear or first-order regressions are shown for illustration purposes. SEIFA descriptor definitions are provided in Table 1.
Fig. 4.
Fig. 4.
Linear or first-order correlation coefficients (R) of normalized biomarker loads with SEIFA descriptors, whose definitions are supplied in Table 1. Correlation type is designated P (Pearson) or S (Spearman). The significance cutoff was |R| = 0.5.
See this image and copyright information in PMC

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