Using wastewater-based epidemiology to estimate drug consumption—Statistical analyses and data presentation

Aim Analysis of wastewater samples can be used to assess population drug use, but reporting and statistical issues have limited the utility of the approach for epidemiology due to analytical results that are below the limit of quantification or detection. Unobserved or non-quantifiable—censored—data are common and likely to persist as the methodology is applied to more municipalities and a broader array of substances. We demonstrate the use of censored data techniques and account for measurement errors to explore distributions and annual estimates of the daily mean level of drugs excreted per capita. Measurements Daily 24-hour composite wastewater samples for 56 days in 2009 were obtained using a random sample stratified by day of week and season for 19 municipalities in the Northwest region of the U.S. Methods Methamphetamine, benzoylecgonine (cocaine metabolite), 3,4-ethylenedioxymethamphetamine (MDMA), methadone, oxycodone and hydrocodone were identified and quantified in wastewater samples. Four statistical approaches (reporting censoring, maximum likelihood estimation, Kaplan-Meier estimates, or complete data calculations) were used to estimate an annual average, including confidence bounds where appropriate, dependent upon the amount of censoring in the data. Findings The proportion of days within a year with censored data varied greatly by drug across the 19 municipalities, with MDMA varying the most (4% to 94% of observations censored). The different statistical approaches each needed to be used given the levels of censoring of measured drug concentrations. Figures incorporating confidence bounds allow visualization of the data that facilitates appropriate comparisons across municipalities. Conclusions Results from wastewater sampling that are below detection or quantification limits contain important information and can be incorporated to create a more complete and valid estimate of drug excretion

Analysis of Androgenic Steroids in Environmental Waters by Large-Volume Injection Liquid Chromatography Tandem Mass Spectrometry

A new method was developed for the analysis of natural and synthetic androgenic steroids and their selected metabolites in aquatic environmental matrices using direct large-volume injection (LVI) high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). Method accuracy ranged from 88 to 108% for analytes with well-matched internal standards. Precision, quantified by relative standard deviation (RSD), was less than 12%. Detection limits for the method ranged from 1.2 to 360 ng/L. The method was demonstrated on a series of 1-hr composite wastewater influent samples collected over a day with the purpose of assessing temporal profiles of androgen loads in wastewater. Testosterone, androstenedione, boldenone, and nandrolone were detected in the sample series at concentrations up to 290 ng/L and loads up to 535 mg. Boldenone, a synthetic androgen, had a temporal profile that was strongly correlated to testosterone, a natural human androgen, suggesting its source may be endogenous. An analysis of the sample particulate fraction revealed detectable amounts of sorbed testosterone and androstenedione. Androstenedione sorbed to the particulate fraction accounted for an estimated five to seven percent of the total androstenedione mass

Estimating daily and diurnal variations of illicit drug use in Hong Kong: A pilot study of using wastewater analysis in an Asian metropolitan city

The measurement of illicit drug metabolites in raw wastewater is increasingly being adopted as an approach to objectively monitor population-level drug use, and is an effective complement to traditional epidemiological methods. As such, it has been widely applied in western countries. In this study, we utilised this approach to assess drug use patterns over nine days during April 2011 in Hong Kong. Raw wastewater samples were collected from the largest wastewater treatment plant serving a community of approximately 3.5 million people and analysed for excreted drug residues including cocaine, ketamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA) and key metabolites using liquid chromatography coupled with tandem mass spectrometry. The overall drug use pattern determined by wastewater analysis was consistent with that have seen amongst people coming into contact with services in relation to substance use; among our target drugs, ketamine (estimated consumption: 1400-1600. mg/day/1000 people) was the predominant drug followed by methamphetamine (180-200. mg/day/1000 people), cocaine (160-180. mg/day/1000 people) and MDMA (not detected). The levels of these drugs were relatively steady throughout the monitoring period. Analysing samples at higher temporal resolution provided data on diurnal variations of drug residue loads. Elevated ratios of cocaine to benzoylecgonine were identified unexpectedly in three samples during the evening and night, providing evidence for potential dumping events of cocaine. This study provides the first application of wastewater analysis to quantitatively evaluate daily drug use in an Asian metropolitan community. Our data reinforces the benefit of wastewater monitoring to health and law enforcement authorities for strategic planning and evaluation of drug intervention strategies

Generation of (synthetic) influent data for performing wastewater treatment modelling studies

The success of many modelling studies strongly depends on the availability of sufficiently long influent time series - the main disturbance of a typical wastewater treatment plant (WWTP) - representing the inherent natural variability at the plant inlet as accurately as possible. This is an important point since most modelling projects suffer from a lack of realistic data representing the influent wastewater dynamics. The objective of this paper is to show the advantages of creating synthetic data when performing modelling studies for WWTPs. This study reviews the different principles that influent generators can be based on, in order to create realistic influent time series. In addition, the paper summarizes the variables that those models can describe: influent flow rate, temperature and traditional/emerging pollution compounds, weather conditions (dry/wet) as well as their temporal resolution (from minutes to years). The importance of calibration/validation is addressed and the authors critically analyse the pros and cons of manual versus automatic and frequentistic vs Bayesian methods. The presentation will focus on potential engineering applications of influent generators, illustrating the different model concepts with case studies. The authors have significant experience using these types of tools and have worked on interesting case studies that they will share with the audience. Discussion with experts at the WWTmod seminar shall facilitate identifying critical knowledge gaps in current WWTP influent disturbance models. Finally, the outcome of these discussions will be used to define specific tasks that should be tackled in the near future to achieve more general acceptance and use of WWTP influent generators

Inferring transmission fitness advantage of SARS-CoV-2 variants of concern from wastewater samples using digital PCR, Switzerland, December 2020 through March 2021

BackgroundThroughout the COVID-19 pandemic, SARS-CoV-2 genetic variants of concern (VOCs) have repeatedly and independently arisen. VOCs are characterised by increased transmissibility, increased virulence or reduced neutralisation by antibodies obtained from prior infection or vaccination. Tracking the introduction and transmission of VOCs relies on sequencing, typically whole genome sequencing of clinical samples. Wastewater surveillance is increasingly used to track the introduction and spread of SARS-CoV-2 variants through sequencing approaches.AimHere, we adapt and apply a rapid, high-throughput method for detection and quantification of the relative frequency of two deletions characteristic of the Alpha, Beta, and Gamma VOCs in wastewater.MethodsWe developed drop-off RT-dPCR assays and an associated statistical approach implemented in the R package WWdPCR to analyse temporal dynamics of SARS-CoV-2 signature mutations (spike Δ69-70 and ORF1a Δ3675-3677) in wastewater and quantify transmission fitness advantage of the Alpha VOC.ResultsBased on analysis of Zurich wastewater samples, the estimated transmission fitness advantage of SARS-CoV-2 Alpha based on the spike Δ69-70 was 0.34 (95% confidence interval (CI): 0.30-0.39) and based on ORF1a Δ3675-3677 was 0.53 (95% CI: 0.49-0.57), aligning with the transmission fitness advantage of Alpha estimated by clinical sample sequencing in the surrounding canton of 0.49 (95% CI: 0.38-0.61).ConclusionDigital PCR assays targeting signature mutations in wastewater offer near real-time monitoring of SARS-CoV-2 VOCs and potentially earlier detection and inference on transmission fitness advantage than clinical sequencing. Keywords: B.1.1.7; SARS-CoV-2; digital PCR; drop-off assays; transmission fitness

Understanding the uncertainty of estimating herbicide and nutrient mass loads in a flood event with guidance on estimator selection

The aim of this study was to understand the uncertainty of estimating loads for observed herbicides and nutrients during a flood event and provide guidance on estimator selection. A high-resolution grab sampling campaign (258 samples over 100 h) was conducted during a flood event in a tropical waterway in Queensland, Australia. Ten herbicides and three nutrient compounds were detected at elevated concentrations. Each had a unique chemograph with differences in transport processes (e.g. dependence on flow, dilution processes and timing of concentration pulses). Resampling from the data set was used to assess uncertainty. Bias existed at lower sampling efforts but depended on estimator properties as sampling effort increased: the interpolation, ratio and regression estimators became unbiased. Large differences were observed in precision and the importance of sampling effort and estimator selection depended on the relationship between the chemograph and hydrograph. The variety of transport processes observed and the resultant variability in uncertainty suggest that useful load estimates can only be obtained with sufficient samples and appropriate estimator selection. We provide a rationale to show the latter can be guided across sampling periods by selecting an estimator where the sampling regime or the relationship between the chemograph and hydrograph meet its assumptions: interpolation becomes more correct as sampling effort increases and the ratio becomes more correct as the r2 correlation between flux and flow increases (e.g. > 0.9); a stratified composite sampling approach, even with random samples, is a promising alternative

Systematic evaluation of biomarker stability in pilot scale sewer pipes

Transformation of biomarkers (or their stability) during sewer transport is an important issue for wastewater-based epidemiology (WBE). Most studies so far have been conducted in the laboratory, which usually employed unrealistic conditions. In the present study, we utilized a pilot sewer system including a gravity pipe and a rising main pipe to investigate the fate of 24 pharmaceutical biomarkers. A programmable logic controller was used to control and monitor the system including sewer operational conditions and wastewater properties. Sequential samples were collected that can represent hydraulic retention time (HRT) of up to 8 h in a rising main and 4 h in a gravity sewer. Wastewater parameters and biomarker concentrations were analysed to evaluate the stability and transformation kinetics. The wastewater parameters of the pilot system were close to the conditions of real sewers. The findings of biomarker transformation were also close to real sewer data with seventeen biomarkers reported as stable while buprenorphine, caffeine, ethyl-sulfate, methadone, paracetamol, paraxanthine and salicylic acid degraded to variable extents. Both zero-order and first-order kinetics were used to model the degradation of unstable biomarkers and interestingly the goodness of fit R for the zero-order model was higher than the first-order model for all unstable biomarkers in the rising main. The pilot sewer system simulates more realistic conditions than benchtop laboratory setups and may provide a more accurate approach for assessing the in-sewer transformation kinetics and stability of biomarkers

Sewage-based epidemiology requires a truly transdisciplinary approach

If asked whether you had consumed illicit drugs recently, would you admit it? If yes, could you precisely recall types of drug, times and amounts used? If you were the person commissioned with the task of quantifying drug use, what approach would you use given the social stigma attached with such behavior? We measure drug residues in sewage, which represents urine of entire populations, to provide an objective estimate of total drug use in a region. In transdisciplinary projects, sewage-based results provide valuable information at unrivaled spatiotemporal resolution complementing traditional data

Comparison of pharmaceutical, illicit drug, alcohol, nicotine and caffeine levels in wastewater with sale, seizure and consumption data for 8 European cities

Background: Monitoring the scale of pharmaceuticals, illicit and licit drugs consumption is important to assess the needs of law enforcement and public health, and provides more information about the different trends within different countries. Community drug use patterns are usually described by national surveys, sales and seizure data. Wastewater-based epidemiology (WBE) has been shown to be a reliable approach complementing such surveys. Method: This study aims to compare and correlate the consumption estimates of pharmaceuticals, illicit drugs, alcohol, nicotine and caffeine from wastewater analysis and other sources of information. Wastewater samples were collected in 2015 from 8 different European cities over a one week period, representing a population of approximately 5 million people. Published pharmaceutical sale, illicit drug seizure and alcohol, tobacco and caffeine use data were used for the comparison. Results: High agreement was found between wastewater and other data sources for pharmaceuticals and cocaine, whereas amphetamines, alcohol and caffeine showed a moderate correlation. methamphetamine and 3,4- methylenedioxymethamphetamine (MDMA) and nicotine did not correlate with other sources of data. Most of the poor correlations were explained as part of the uncertainties related with the use estimates and were improved with other complementary sources of data. Conclusions: This work confirms the promising future of WBE as a complementary approach to obtain a more accurate picture of substance use situation within different communities. Our findings suggest further improvements to reduce the uncertainties associated with both sources of information in order to make the data more comparable.Jose Antonio Baz Lomba, Stefania Salvatore, Richard Bade, Erika Castrignanò, Ana Causanilles, Juliet Kinyua, Ann-Kathrin McCall, Pedram Ramin, Nikolaos I. Rousis, and Yeonsuk Ryu acknowledge the EU Marie-Skłodowska Curie Initial Training Network SEWPROF (Marie Curie-FP7-PEOPLE, grant number 317205) for their Early Stage Researcher grant and Emma Gracia-Lor for her Experienced Researcher grant. We thank the people and agencies who assisted in the collection of the wastewater samples, in particular Pia Ryrfors and colleagues at Vestfjorden Avløpselskap (VEAS, Oslo, Norway)

Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK