Use of Mobile Device Data To Better Estimate Dynamic Population Size for Wastewater-Based Epidemiology

Current state and future perspectives on de facto population markers for normalization in wastewater-based epidemiology: A systematic literature review

Wastewater-based epidemiology (WBE) and wastewater surveillance have become a valuable complementary data source to collect information on community-wide exposure through the measurement of human biomarkers in influent wastewater (IWW). In WBE, normalization of data with the de facto population that corresponds to a wastewater sample is crucial for a correct interpretation of spatio-temporal trends in exposure and consumption patterns. However, knowledge gaps remain in identifying and validating suitable de facto population biomarkers (PBs) for refinement of WBE back-estimations. WBE studies that apply de facto PBs (including hydrochemical parameters, utility consumption data sources, endo- and exogenous chemicals, biological biomarkers and signalling records) for relative trend analysis and absolute population size estimation were systematically reviewed from three databases (PubMed, Web of Science, SCOPUS) according to the PRISMA guidelines. We included in this review 81 publications that accounted for daily variations in population sizes by applying de facto population normalization. To date, a wide range of PBs have been proposed for de facto population normalization, complicating the comparability of normalized measurements across WBE studies. Additionally, the validation of potential PBs is complicated by the absence of an ideal external validator, magnifying the overall uncertainty for population normalization in WBE. Therefore, this review proposes a conceptual tier-based cross-validation approach for identifying and validating de facto PBs to guide their integration for i) relative trend analysis, and ii) absolute population size estimation. Furthermore, this review also provides a detailed evaluation of the uncertainty observed when comparing different de jure and de facto population estimation approaches. This study shows that their percentual differences can range up to ±200 %, with some exceptions showing even larger variations. This review underscores the need for collaboration among WBE researchers to further streamline the application of de facto population normalization and to evaluate the robustness of different PBs in different socio-demographic communities.

Combining wastewater surveillance and case data in estimating the time-varying effective reproduction number

Wastewater surveillance has been increasingly acknowledged as a useful tool for monitoring transmission dynamics of infections of public health concern, including the coronavirus disease (COVID-19). While a range of models have been proposed to estimate the time-varying effective reproduction number (Rt) utilizing clinical data, few have harnessed the viral concentration in wastewater samples to do so, leaving uncertainties about the potential precision gains with its use. In this study, we developed a Bayesian hierarchical model which simultaneously reconstructed the latent infection trajectory and estimated Rt. Focusing on the 2022 and early 2023 COVID-19 transmission trends in Singapore, where mass community wastewater surveillance has become routine, we performed estimations using a spectrum of data sources, including reported case counts, hospital admissions, deaths, and wastewater viral loads. We further explored the performance of our wastewater model across various scenarios with different sampling strategies. The results showed consistent estimates derived from models employing diverse data streams, while models incorporating more wastewater samples exhibited greater uncertainty and variation in the inferred Rts. Additionally, our analysis revealed prominent day-of-the-week effect in reported case counts and substantial temporal variations in ascertainment rates. In response to these findings, we advocate for a hybrid approach leveraging both clinical and wastewater surveillance data to account for changes in case-ascertainment rates. Furthermore, our study demonstrates the possibility of reducing sampling frequency or sample size without compromising estimation accuracy for Rt, highlighting the potential for optimizing resource allocation in surveillance efforts while maintaining robust insights into the transmission dynamics of infectious diseases.

Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Surveillance of COVID-19 and influenza A(H1N1) prevalence in China via medicine-based wastewater biomarkers

The simultaneous monitoring of individual or multiple diseases can be achieved by selecting therapeutic medicines used to treat the primary symptoms of the condition as biomarkers in wastewater. This study proposes a novel approach to monitor the prevalence of COVID-19 and influenza A (H1N1) by selecting nine medicines to serve as biomarkers, including three antipyretics, three antivirals, and three cough suppressants. To verify our approach, wastewater samples were collected from seventeen urban and five rural wastewater treatment plants (WWTPs) in a Chinese city over a period of one year. The use of antipyretics increased notably during the COVID-19 pandemic, while the consumption of antivirals for influenza A (H1N1) rose in the post-COVID-19 pandemic period, indicating a minor spike in the occurrence of influenza A (H1N1) after the COVID-19 pandemic. Fever is a significant symptom of COVID-19 and can serve as a reliable indicator of disease prevalence. Our research found that the prevalence of COVID-19 in urban areas was significantly higher (at 78.5 %, 95 % CI: 73.4 % - 83.9 %) than in rural areas (with a prevalence of 48.1 %, 95 % CI: 42.4 % - 53.8 %). The prevalence of COVID-19 in urban areas in this study was consistent with the data reported by the Chinese center for Disease Control and Prevention (82.4 %). Continuous monitoring of WWTPs in urban areas with fluctuating populations and complex demographics can provide early disease warning. Our results demonstrate the feasibility of evaluating community disease prevalence by selecting major therapeutic medicines as biomarkers in wastewater.

Analysis of SARS-CoV-2 in wastewater for prevalence estimation and investigating clinical diagnostic test biases

Here we analyze SARS-CoV-2 genome copies in Catalonia's wastewater during the Omicron peak and develop a mathematical model to estimate the number of infections and the temporal relationship between reported and unreported cases. 1-liter samples from 16 wastewater treatment plants were collected and used in a compartmental epidemiological model. The average correlation between genome copies and reported cases was 0.85, with an average delay of 8.8 days. The model estimated that 53% of the population was infected, compared to the 19% reported cases. The under-reporting was highest in November and December 2021. The maximum genome copies shed in feces by an infected individual was estimated to range from 1.4×108 gc/g to 4.4×108 gc/g. Our framework demonstrates the potential of wastewater data as a leading indicator for daily new infections, particularly in contexts with low detection rates. It also serves as a complementary tool for prevalence estimation and offers a general approach for integrating wastewater data into compartmental models.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

Do the lockdown-imposed changes in a wastewater treatment plant catchment's socio-demographics impact longitudinal temporal trends in psychoactive pharmaceutical use?

Wastewater-based epidemiology (WBE) includes the analysis of human metabolic biomarkers of xenobiotics in influent wastewater. WBE complements existing drug utilization approaches and provides objective, spatio-temporal information on the consumption of pharmaceuticals in the general population. This approach was applied to 24-h composite influent wastewater samples from Leuven, Belgium. Daily samples were analysed from September 2019 to December 2019 (n = 76), and on three days of the week (Monday, Wednesday, Saturday) from January 2020 to April 2022 (n = 367). Sample analysis consisted of 96-well solid-phase extraction and liquid chromatography coupled to tandem mass spectrometry. Measured concentrations of 21 biomarkers for antidepressant and opioid use were converted to population-normalized mass loads (PNML) by considering the flow rate and catchment population. To capture population movements, mobile phone data was used. Amitriptyline, hydroxy-bupropion, norcitalopram, citalopram, normirtazapine, trazodone, O-desmethylvenlafaxine, codeine, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), methadone, morphine, O-desmethyltramadol, and tramadol were included in the temporal assessment since concentrations were above the lower limit of quantification. The PNML of most biomarkers increased (with 3-119 %) throughout the sampling period. The population disruption during the COVID-19 pandemic led to a major change in the socio-demographics of the catchment area, resulting in temporal differences in the PNML of the different biomarkers. As such, higher PNML were observed during the different lockdown phases, which were characterized by the outflow of university students and a decreasing commuting in and out the catchment area. The effects of the fluctuating socio-demographics of the catchment population were further evidenced by the different week-weekend pattern of PNMLs over the course of the sampling campaign. Mean parent/metabolite ratios (i.e., citalopram/norcitalopram, tramadol/O-desmethyltramadol, venlafaxine/O-desmethylvenlafaxine, and methadone/EDDP) remained relatively stable throughout the entire sampling campaign (RSD% below 25 % for all ratios, except for methadone/EDDP) and therefore were not affected by this population change.

Dynamic population normalisation in wastewater-based epidemiology for improved understanding of the SARS-CoV-2 prevalence: a multi-site study

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring the circulation of COVID-19. However, while variations in population size are recognised as major sources of uncertainty, wastewater SARS-CoV-2 measurements are not routinely population-normalised. This paper aims to determine whether dynamic population normalisation significantly alters SARS-CoV-2 dynamics observed through wastewater monitoring, and whether it is beneficial or necessary to provide an understanding of COVID-19 epidemiology. Data from 394 sites in England are used, and normalisation is implemented based on ammoniacal nitrogen and orthophosphate concentrations. Raw and normalised wastewater SARS-CoV-2 metrics are evaluated at the site and spatially aggregated levels are compared against indicators of prevalence based on the Coronavirus Infection Survey and Test and Trace polymerase chain reaction test results. Normalisation is shown, on average, to have a limited impact on overall temporal trends. However, significant variability in the degree to which it affects local-level trends is observed. This is not evident from previous WBE studies focused on single sites and, critically, demonstrates that while the impact of normalisation on SARS-CoV-2 trends is small on average, this may not always be the case. When averaged across many sites, normalisation strengthens the correlation between wastewater SARS-CoV-2 data and prevalence indicators; however, confidence in the improvement is low.

Long-term wastewater-based surveillance and impacts of the COVID-19 pandemic on drug use trends in a U.S. Northeast rural town

Herein we discuss the findings of a two-year wastewater-based drug use surveillance from September 2018 to August 2020 and present objective evidence on the impacts of the COVID-19 pandemic on drug use in a rural community. 24-h composite wastewater samples were collected twice each month from a university town in Northeastern United States and were analyzed for ten priority opioids and stimulants: morphine, codeine, hydrocodone, methadone, fentanyl cocaine, methamphetamine, amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), and 3,4-methylenedioxy-N-ethylamphetamine (MDEA). All target drugs were detected at 100 % frequency in wastewater samples. On a mass basis, the average estimated per capita drug consumption were highest for cocaine, morphine, and amphetamine, and lowest for MDMA, MDEA, and hydrocodone. Furthermore, the estimated per capita consumption of fentanyl was higher than previous reports from rural and university settings in the U.S. Generally, drug consumption was higher during the spring semesters, with year-on-year semester increases also noted over the 2-y study period. Except for methadone and cocaine, the estimated average per capita consumption of drugs increased over the pandemic period, with the highest increase noted for MDMA (286 % increase compared to baseline, p = 0.016). Estimated average consumption of methadone and cocaine decreased slightly by 6 % and 7 %, respectively. These results demonstrate the utility and strength of wastewater-based approaches in capturing long-term and evolving trends in drug use within communities. Our study findings reflect the regionwide problem with opioid-related overdoses and increasing stimulant prescription rates. Our findings also provide objective data and insights for health policymakers on the effects of the pandemic period on community drug use in a rural U.S. town.

Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence - Insights from long-term surveillance

Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic waves. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3-4 among sites during both waves and were on average 45 % higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag/lead time, discharge and detection probability improved model performance in all cases significantly, but the importance of these components was also site and time specific. In all cases, models with lag/lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics, but revealed pronounced differences throughout the epidemic waves and among the different sites.

Evaluation of population estimation methods for wastewater-based epidemiology in a metropolitan city

This study evaluated the effect of population estimation on the calculation of drug biomarker consumption using wastewater-based epidemiology. Population estimates using mobile phone data, census data, and wastewater quality parameters, such as biological oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP), were evaluated in six different wastewater treatment plant catchment areas of Busan Metropolitan City, South Korea. The population based on mobile phone data was affected by the patterns of non-resident population movements in each area. The population-normalized daily loads (PNDLs) of methamphetamine were compared according to the different population results. The PNDLs using the population based on mobile phone data (PNDL_Mobile) was 5.87-27.0 mg/d/1000 people. The PNDL_Mobile values were notably different from the PNDLs using wastewater quality parameters (PNDL_Wastewater) (PNDL_Wastewater/PNDL_Mobile: 51-148 %, mean 93 %, relative standard deviation (RSD) 36 %), indicating the unsuitability of population estimation using BOD, TN, and TP. In areas with a large concentration of workplaces, the PNDLs using census data (PNDL_Census) differed from the PNDL_Mobile values (PNDL_Census/PNDL_Mobile: 57-124 %, mean 94 %, RSD 27 %), whereas other areas showed similar values for PNDL_Census and PNDL_Mobile (PNDL_Census/PNDL_Mobile: 95-108 %, mean 102 %, RSD 4.2 %). In particular, the total population estimates of the six survey areas using census data were approximately the same as those based on mobile phone data (RSD: 0.8 %), indicating a decrease in the influence of the non-residential active population in the entire metropolitan city.

Evaluating the impact of COVID-19 countermeasures on alcohol consumption through wastewater-based epidemiology: A case study in Belgium

Wastewater-based epidemiology (WBE) is a complementary approach to monitor alcohol consumption in the general population. This method measures concentrations of xenobiotic biomarkers (e.g., ethyl sulphate) in influent wastewater (IWW) and converts these to population-normalized mass loads (PNML, in g/day/1000 inhabitants) by multiplying with the flow rate and dividing by the catchment population. The aims of this case study were to: (i) investigate temporal trends in alcohol use during the COVID-19 pandemic; and (ii) measure the effect of policy measures on alcohol consumption. Daily 24-h composite IWW samples (n = 735) were collected in the wastewater treatment plant of the university city of Leuven (Belgium) starting from September 2019 to September 2021. This is the first study that investigates alcohol use through WBE for a continuous period of two years on a daily basis. Mobile phone data was used to accurately capture population fluxes in the catchment area. Data was evaluated using a time series based statistical framework to graphically and quantitatively assess temporal differences in the measured PNML. Different WBE studies observed temporal changes in alcohol use during the COVID-19 pandemic. In this study, the PNML of ethyl sulphate decreased during the first lockdown phase, potentially indicating that less alcohol was consumed at the Leuven area during home confinement. Contrastingly, alcohol use increased after the re-opening of the catering industry. Additionally, a decrease in alcohol use was observed during the exam periods at the University of Leuven and an increase during the holiday periods. The present study shows the potential of WBE to rapidly assess the impact of some policy measures on alcohol consumption in Belgium. This study also indicates that WBE could be employed as a complementary data source to fill in some of the current knowledge gaps linked to lifestyle behavior.

Assessment of Commonly Measured Wastewater Parameters to Estimate Sewershed Populations for Use in Wastewater-Based Epidemiology: Insights into Population Dynamics in New York City during the COVID-19 Pandemic

Understanding per capita rates of disease incidence or prevalence from wastewater surveillance data requires an estimate of the population contributing to wastewater samples, given that populations in large urban areas are dynamic, especially if major events, such as the onset of the COVID-19 pandemic, cause large population shifts. To assess whether commonly measured wastewater parameters can be used to estimate sewershed populations, we used wastewater data collected from New York City's (NYC) 14 wastewater treatment facilities to evaluate the relationship between influent loads of four wastewater parameters-ammonia, total Kjeldahl nitrogen, total suspended solids, and five-day carbonaceous biochemical oxygen demand-and census-based population estimates of the corresponding sewersheds during 2019, when populations were assumed to be relatively stable. Ammonia mass load had the most consistent relationship with sewershed population, regardless of wet weather contributions to NYC's predominantly combined sewer system. Changes in ammonia loads due to COVID-19 restrictions enacted in March 2020 generally reflected population shifts in sewersheds serving areas of Manhattan and Brooklyn, for which previous studies report decreased commuter mobility and residential populations. Our findings highlight the utility of ammonia mass load in influent wastewater as a population indicator to normalize wastewater-based epidemiology data and track sewershed population dynamics.

Triangulating Amsterdam's illicit stimulant use trends by wastewater analysis and recreational drug use monitoring

Drug consumption estimates are of relevance because of public health effects as well as associated criminal activities. Wastewater analysis of drug residues enables the estimation of drug consumption and drug markets. Short-term and long-term trends of cocaine, MDMA (ecstasy), amphetamine (speed) and methamphetamine (crystal meth), were studied for the city of Amsterdam. MDMA (+41%) and cocaine (+26%) showed significantly higher weekend vs. week consumption, while no differences were observed for the other drugs. The consumption of MDMA, cocaine, amphetamine and methamphetamine significantly increased between 2011 and 2019. Weekly trends emerging from wastewater analyses were supported by qualitative and quantitative data from a recreational drug use monitoring scheme. However, information collected in panel interviews within nightlife networks and surveys among visitors of pubs, clubs and festivals only partially reflected the long term increase in consumption as registered from wastewater analysis. Furthermore, methamphetamine use was not well presented in survey data, panel studies and test service samples, but could be monitored trough wastewater analysis. This illustrates that wastewater analysis can function as an early warning if use and user groups are small or difficult to reach trough other forms of research. All in all, this study illustrates that wastewater-based epidemiology is complementary to research among user groups, and vice versa. These different types of information enable to connect observed trends in total drug consumption to behaviour of users and the social context in which the use takes place as well as validate qualitative signals about (increased) consumption of psychoactive substances. Such a multi angular approach to map the illicit drug situation on local or regional scale can provide valuable information for public health.

Detection of SARS-CoV-2 RNA in wastewater and importance of population size assessment in smaller cities: An exploratory case study from two municipalities in Latvia

Wastewater-based epidemiology (WBE) has regained global importance during the COVID-19 pandemic. The mobility of people and other factors, such as precipitation and irregular inflow of industrial wastewater, are complicating the estimation of the disease prevalence through WBE, which is crucial for proper crisis management. These estimations are particularly challenging in urban areas with moderate or low numbers of inhabitants in situations where movement restrictions are not adopted (as in the case of Latvia) because residents of smaller municipalities tend to be more mobile and less strict in following the rules and measures of disease containment. Thus, population movement can influence the outcome of WBE measurements significantly and may not reflect the actual epidemiological situation in the respective area. Here, we demonstrate that by combining the data of detected SARS-CoV-2 RNA copy number, 5-hydroxyindoleacetic acid (5-HIAA) analyses in wastewater and mobile call detail records it was possible to provide an accurate assessment of the COVID-19 epidemiological situation in towns that are small (COVID-19 28-day cumulative incidence r = 0.609 and 35-day cumulative incidence r = 0.89, p < 0.05) and medium-sized towns (COVID-19 21-day cumulative incidence r = 0.997, 28-day cumulative incidence r = 0.98 and 35-day cumulative incidence r = 0.997, p < 0.05). This is the first study demonstrating WBE for monitoring COVID-19 outbreaks in Latvia. We demonstrate that the application of population size estimation measurements such as total 5-HIAA and call detail record data improve the accuracy of the WBE approach.

Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

BACKGROUND

The effective reproductive number, R e , is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, R e estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.

OBJECTIVES

We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate R e in near real time, independent of clinical data and without the associated biases.

METHODS

We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based R e from this incidence.

RESULTS

The method to estimate R e from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the R e estimates from case report data as R e estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer R e .

DISCUSSION

To our knowledge, this is the first time R e has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

Interpreting per capita loads of organic matter and nutrients in municipal wastewater: A study on 168 Italian agglomerations

The size of an agglomeration is expressed in population equivalent, referring to the maximum average weekly load during the year according to the European Directive 271/91. This quantity, multiplied by the daily biochemical oxygen demand (BOD) produced by one population equivalent (which is 60 g d^-1 PE^-1, by definition), yields the design biodegradable organic load of the wastewater treatment plant. The same agglomeration size is compared against the capacity of the wastewater treatment plant (WWTP) for plant conformity verification by the European Commission. However, field observations show remarkable fluctuations of the daily mass flows entering a WWTP, often considerably below or above the expected load calculated according to the Directive prescriptions. A wrong estimation of the real influent load adversely affects the plant design and operation, and may lead to a misleading verification of its conformity to the agglomeration. In this work, a statistical data analysis on 168 agglomerations was performed, aiming at verifying the consistency between the expected loads of BOD, chemical oxygen demand (COD), nitrogen and phosphorus and the mass flows measured at the treatment plant inlet. Only 30-40% of the total analysed cases were found having an actual load compatible with the expected one. In these cases, the average per capita daily loads of BOD, COD, nitrogen and phosphorus, calculated over 2-3 years data pools, resulted: 44.6 ± 5.9, 82.4 ± 11, 9.4 ± 1.1, and 1.08 ± 0.13 g d^-1, respectively, while the daily BOD per capita production of 60 g represented a value in between the 70th and the 90th percentiles of the actual daily load distributions. For the remaining 60-70% of the total cases, variably-remarkable positive or negative discrepancies between the nominal pollutant loads generated by the agglomeration and those measured at the plant inlet could be detected and possible causes were pointed out.

Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

BACKGROUND

The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.

OBJECTIVES

We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate Re in near real time, independent of clinical data and without the associated biases.

METHODS

We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence.

RESULTS

The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re.

DISCUSSION

To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

Effects of sewer biofilm on the degradation of drugs in sewage: A microcosm study

A thorough understanding of the in-sewer stability of chemical biomarkers is critical in applying wastewater-based surveillance of community drug use. In this study, we examined the effects of sewer biofilm on the degradation of commonly abused drugs, namely, morphine, fentanyl, cocaine, and amphetamine, in wastewater using 48-h batch degradation tests. The experiments were designed to distinguish among abiotic, biochemical, and physical degradation processes, and used mature biofilm obtained from an actual sewer line. Parallel microcosm tests were conducted using wastewater with and without suspended biofilm. Results indicate that first order kinetics describe the degradation of the drugs in both wastewater and wastewater-biofilm microcosms. Amphetamine was most stable in all microcosms, with a maximum removal of only 34% after 48 h. Abiotic chemical transformation played a major role in the degradation of morphine (k_ab = 0.018 h^-1), fentanyl (k_ab = 0.022 h^-1) and cocaine (k_ab = 0.049 h^-1) in wastewater. Fentanyl removal from wastewater was also influenced by the presence of biofilm (k_f = 0.015 h^-1). This study is the first to report on the effect of sewer biofilm on fentanyl degradation, and highlights the need to account for in-sewer drug stability in wastewater-based drug use estimation, particularly for chemicals with high affinity for organics.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

Building knowledge of university campus population dynamics to enhance near-to-source sewage surveillance for SARS-CoV-2 detection

Wastewater surveillance has been widely implemented for monitoring of SARS-CoV-2 during the global COVID-19 pandemic, and near-to-source monitoring is of particular interest for outbreak management in discrete populations. However, variation in population size poses a challenge to the triggering of public health interventions using wastewater SARS-CoV-2 concentrations. This is especially important for near-to-source sites that are subject to significant daily variability in upstream populations. Focusing on a university campus in England, this study investigates methods to account for variation in upstream populations at a site with highly transient footfall and provides a better understanding of the impact of variable populations on the SARS-CoV-2 trends provided by wastewater-based epidemiology. The potential for complementary data to help direct response activities within the near-to-source population is also explored, and potential concerns arising due to the presence of heavily diluted samples during wet weather are addressed. Using wastewater biomarkers, it is demonstrated that population normalisation can reveal significant differences between days where SARS-CoV-2 concentrations are very similar. Confidence in the trends identified is strongest when samples are collected during dry weather periods; however, wet weather samples can still provide valuable information. It is also shown that building-level occupancy estimates based on complementary data aid identification of potential sources of SARS-CoV-2 and can enable targeted actions to be taken to identify and manage potential sources of pathogen transmission in localised communities.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

Estimating dynamic population served by wastewater treatment plants using location-based services data

Wastewater-based epidemiology is a useful approach to estimate population-level exposure to a wide range of substances (e.g., drugs, chemicals, biological agents) by wastewater analysis. An important uncertainty in population normalized loads generated is related to the size and variability of the actual population served by wastewater treatment plants (WWTPs). Here, we built a population model using location-based services (LBS) data to estimate dynamic consumption of illicit drugs. First, the LBS data from Tencent Location Big Data and resident population were used to train a linear population model for estimating population (r² = 0.92). Then, the spatiotemporal accuracy of the population model was validated. In terms of temporal accuracy, we compared the model-based population with the time-aligned ammonia nitrogen (NH₄-N) population within the WWTP of SEG, showing a mean squared error of < 10%. In terms of spatial accuracy, we estimated the model-based population of 42 WWTPs in Dalian and compared it with the NH₄-N and design population, indicating good consistency overall (5% less than NH₄-N and 4% less than design). Furthermore, methamphetamine consumption and prevalence based on the model were calculated with an average of 111 mg/day/1000 inhabitants and 0.24%, respectively, and dynamically displayed on a visualization system for real-time monitoring. Our study provided a dynamic and accurate population for estimating the population-level use of illicit drugs, much improving the temporal and spatial trend analysis of drug use. Furthermore, accurate information on drug use could be used to assess population health risks in a community.

Performance- and image-enhancing drug use in the community: use prevalence, user demographics and the potential role of wastewater-based epidemiology

Performance- and image-enhancing drug (PIED) misuse is a significant public health issue. Currently, seizure data, surveys, anti-doping testing, and needle service provider data are used to estimate PIED use in populations. These methods are time consuming, single point-in-time measurements that often consist of small sample sizes and do not truly capture PIED prevalence. Wastewater-based epidemiology (WBE) has been used globally to assess and monitor licit and illicit drug consumption within the general community. This method can objectively cover large populations as well as specific subpopulations (gyms, music festivals, prisons), and has potential as a complementary monitoring method for PIED use. Information obtained through WBE could be used to aid public health authorities in developing targeted prevention and education programmes. Research on PIED analysis in wastewater is limited and presents a significant gap in the literature. The focus is on anabolic steroids, and one steroid alternative currently growing in popularity; selective androgenic receptor modulators. This encompasses medical uses, addiction, prevalence, user typology, and associated public health implications. An overview of WBE is described including its benefits, limitations and potential as a monitoring method for PIED use. A summary of previous work in this field is presented. Finally, we summarise gaps in the literature, future perspectives, and recommendations for monitoring PIEDs in wastewater.

Current and future perspectives for wastewater-based epidemiology as a monitoring tool for pharmaceutical use

The medical and societal consequences of the misuse of pharmaceuticals clearly justify the need for comprehensive drug utilization research (DUR). Wastewater-based epidemiology (WBE) employs the analysis of human metabolic excretion products in wastewater to monitor consumption patterns of xenobiotics at the population level. Recently, WBE has demonstrated its potential to evaluate lifestyle factors such as illicit drug, alcohol and tobacco consumption at the population level, in near real-time and with high spatial and temporal resolution. Up until now there have been fewer WBE studies investigating health biomarkers such as pharmaceuticals. WBE publications monitoring the consumption of pharmaceuticals were systematically reviewed from three databases (PubMed, Web of Science and Google Scholar). 64 publications that reported population-normalised mass loads or defined daily doses of pharmaceuticals were selected. We document that WBE could be employed as a complementary information source for DUR. Interest in using WBE approaches for monitoring pharmaceutical use is growing but more foundation research (e.g. compound-specific uncertainties) is required to link WBE data to routine pharmacoepidemiologic information sources and workflows. WBE offers the possibility of i) estimating consumption of pharmaceuticals through the analysis of human metabolic excretion products in wastewater; ii) monitoring spatial and temporal consumption patterns of pharmaceuticals continuously and in near real-time; and iii) triangulating data with other DUR information sources to assess the impacts of strategies or interventions to reduce inappropriate use of pharmaceuticals.

Application of wastewater-based epidemiology to investigate stimulant drug, alcohol and tobacco use in Lithuanian communities

WBE was applied to evaluate illicit drug (i.e. amphetamine, cocaine, MDMA and methamphetamine), alcohol and tobacco use in three Lithuanian cities in 2018 and 2019. Considerable concentrations of methamphetamine and MDMA were found in the three locations, suggesting a specific Lithuanian consumption pattern. Yet, unexpected high concentrations of amphetamine (>4 μg/L) were detected in two samples of Kaunas in 2018. Through the use of chiral analysis and non-target and suspect drug precursor compound screening, these extreme values were confirmed to be the result of direct disposal of amphetamine in the sewers. Furthermore, substantial alcohol use was measured in the three investigated catchment populations of Lithuania with almost 4 standard drinks/day/inhabitant aged 15+ on average in 2019. For tobacco, an average of 5.6 cigarettes/day/inhabitant aged 15+ in 2019 was reported with large discrepancies between WBE figures and sales data, potentially highlighting illegal trade of tobacco products.

Mining chemical information in Swedish wastewaters for simultaneous assessment of population consumption, treatment efficiency and environmental discharge of illicit drugs

Consumption of illicit drugs poses health risks to the public and environment. Knowledge on their usage helps better implementations of intervention strategies to reduce drug-related harms in the society and also policies to limit their releases as emerging contaminants to recipient environments. This study aimed to investigate from the daily consumption to treatment efficiency and subsequent discharge of illicit drugs by the Swedish urban populations based on simultaneous collection and analysis of influent and effluent wastewater. Two different weekly monitoring campaigns showed similar drug prevalence in Stockholm and Uppsala, with amphetamine as the most popular drug. Almost all target drug residues were still measurable in effluent wastewater. High removal efficiencies (> 94%) were observed for amphetamine, cocaine and benzoylecgonine, whereas ketamine, 3,4-methylenedioxymethamphetamine (MDMA), mephedrone and methamphetamine were the least removed substances (< 64%), with the highest discharge observed for MDMA in both catchments (~ 3.0 g/day in Uppsala; ~ 18 g/day in Stockholm). Our study provides new insights into short-term changes in the use and related discharge of illicit drugs by urban populations. Such wastewater monitoring can provide useful information to public health, forensic and environmental authorities in planning future intervention and regulation policies.

Application of catecholamine metabolites as endogenous population biomarkers for wastewater-based epidemiology

Wastewater-based epidemiology studies use catchment populations to normalise chemical marker mass loads in 24-h composite wastewater samples. However, one of the biggest uncertainties within the field is the accuracy of the population used. A population marker in wastewater may significantly reduce the uncertainty. This study evaluated the catecholamine metabolites - homovanillic acid (HVA) and vanillylmandelic acid (VMA) - as potential population biomarkers. Influent wastewater 24-h composite samples were collected from 38 wastewater catchments from around Australia (representing ~33% of Australia's population), extracted and analysed by liquid chromatography tandem mass spectrometry. Measured mass loads were compared to population sizes determined by mapping catchment maps against high-resolution census data. Both biomarkers correlated with coefficient of determinations (r²) of 0.908 and 0.922 for HVA and VMA, respectively. From the regression analysis, a slope (i.e. the daily per-capita excretion) of 1.241 and 1.067 mg.day^-1.person^-1 was obtained for HVA and VMA, respectively. The mass load ratio between VMA:HVA were very similar to that reported in literature for urinary analysis among all catchments. Overall, this study provided further evidence that catecholamine metabolites are suitable candidates as population biomarkers for future studies.

Evaluation of Illicit Drug Consumption by Wastewater Analysis Using Polar Organic Chemical Integrative Sampler as a Monitoring Tool

Illicit drug abuse is a worldwide social and health problem, and monitoring illicit drug use is of paramount importance in the context of public policies. It is already known that relevant epidemiologic information can be obtained from the analysis of urban residual waters. This approach, named wastewater-based epidemiology (WBE), is based on the measurement of specific markers, resulting from human biotransformation of the target drugs, as indicators of the consumption of the compounds by the population served by the wastewater treatment installation under investigation. Drug consumption estimation based on WBE requires sewage sampling strategies that express the concentrations along the whole time period of time. To this end, the most common approach is the use of automatic composite samplers. However, this active sampling procedure is costly, especially for long-term studies and in limited-resources settings. An alternative, cost-effective, sampling strategy is the use of passive samplers, like the polar organic chemical integrative sampler (POCIS). POCIS sampling has already been applied to the estimation of exposure to pharmaceuticals, pesticides, and some drugs of abuse, and some studies evaluated the comparative performances of POCIS and automatic composite samplers. In this context, this manuscript aims to review the most important biomarkers of drugs of abuse consumption in wastewater, the fundamentals of POCIS sampling in WBE, the previous application of POCIS for WBE of drugs of abuse, and to discuss the advantages and disadvantages of POCIS sampling, in comparison with other strategies used in WBE. POCIS sampling is an effective strategy to obtain a representative overview of biomarker concentrations in sewage over time, with a small number of analyzed samples, increased detection limits, with lower costs than active sampling. Just a few studies applied POCIS sampling for WBE of drugs of abuse, but the available data support the use of POCIS as a valuable tool for the long-term monitoring of the consumption of certain drugs within a defined population, particularly in limited-resources settings.

Basketball and drugs: Wastewater-based epidemiological estimation of discharged drugs during basketball games in Kentucky

High school sports gather a significantly larger number of fans than college and professional sports in the U.S. Adolescent and adult students in high schools and colleges (aged 12-25) are among the most vulnerable population to substance use. Event planners, risk managers, and emergency medical service personnel can extrapolate the mass loads of drugs in wastewater in this study to evaluate the spectator behavior in relatively larger basketball gatherings. Thirty-three illicit and prescribed psychotic drug residues (out of target 36) and five new psychoactive substances (NPS, out of target 40) were quantified in wastewater, using ultra-performance liquid chromatography and tandem mass spectrometry, discharged during a college and a high school basketball games that were played in the same stadium in Kentucky. The wastewater concentrations of amphetamine, methylphenidate, hydromorphone were significantly higher (p ≤ 0.040) during a high school basketball game whereas cocaine, hydrocodone, and gabapentin was significantly higher (p ≤ 0.006) in a college basketball game. Higher cocaine to its metabolite ratio suggested that a significant amount of cocaine may have directly discharged down the drain during the college basketball game. Two synthetic cathinones (methcathinone and 4-methyl pentedrone) and three other NPSs (4-ANPP, mCPP, and 4-methylamphetamine) were also quantified in wastewater indicate the prevalence of NPSs in Kentucky. This is the first report of quantified substances of potential abuses at basketball games.

Can wastewater analysis be used as a tool to assess the burden of pain treatment within a population?

Pain is a global health priority that is challenging to asses. Here we propose a new approach to estimating the burden of pain treatment in a population using wastewater-based epidemiology (WBE). WBE is able to quantify multiple pharmaceutical compounds in order to estimate consumption by a population. Wastewater samples collected from areas representing whole communities can be analysed to estimate the consumption of drugs used to treat pain, such as nonsteroidal anti-inflammatory drugs (NSAIDs) and opioids. The collection and analysis of wastewater can be conducted systematically to estimate the total consumption of NSAIDs and/or opioids in the population of a catchment area and to compare changes over time within the catchment or between different catchment populations. Consumption estimates can be combined by standardising the mass consumed to Defined Daily Doses (DDD) or morphine equivalents in order to assess, the population burden of pain treatment from mild to moderate (for NSAIDs) and for strong and severe pain (for opioids). We propose this method could be used to evaluate the total pain treatment burden between locations and over time. While this concept shows promise, future studies should evaluate the applicability as a tool to measure the burden of pain receiving treatment in a community.

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

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.

A comparison of trends in wastewater-based data and traditional epidemiological indicators of stimulant consumption in three locations

AIMS

To compare long-term trends in wastewater data with other indicators of stimulant use in three locations and to test the reliability of estimates based on 1 week of sampling.

DESIGN

Comparison of trends in quantities ('loads') of stimulants or their metabolites in wastewater with trends in other indicators of stimulant use (e.g. treatment, police, population survey data).

SETTING AND PARTICIPANTS

Populations in Oslo (Norway), South-East Queensland (Australia) and Eindhoven (the Netherlands).

MEASUREMENTS

Wastewater data were modelled for MDMA (3,4-methyl​enedioxy​methamphetamine), benzoylecgonine (a metabolite of cocaine), amphetamine and methamphetamine in Oslo; benzoylecgonine in Eindhoven; and methamphetamine in South-East Queensland. Choice of stimulants modelled in each region was primarily determined by availability of useable data.

FINDINGS

In Oslo, wastewater data, driving under the influence of drugs statistics and seizure data all suggested increasing MDMA use between 2009 and 2017. In South-East Queensland, there was an estimated 31.1% [95% confidence interval (CI) = 29.4-32.9%] annual increase in daily loads of methamphetamine in wastewater between 2009 and 2016, compared with a 14.1% (95% CI = 10.9-17.3%) annual increase in seizures. Some of the increase in wastewater can be explained by increased purity. In Eindhoven, there was no evidence of a change in cocaine consumption from wastewater, but a reduction was observed in numbers in treatment for cocaine use from 2012 to 2017. In approximately half the cases examined in Oslo, credible intervals around estimates of annual average loads from a regression model versus estimates based on a single week of sampling did not overlap.

CONCLUSIONS

Long-term trends in loads of stimulants in wastewater appear to be broadly consistent with trends in other indicators of stimulant use in three locations. Wastewater data should be interpreted alongside epidemiological indicators and purity data. One week of wastewater sampling may not be sufficient for valid inference about drug consumption.

Prevalence of illicit and prescribed neuropsychiatric drugs in three communities in Kentucky using wastewater-based epidemiology and Monte Carlo simulation for the estimation of associated uncertainties

A cost-effective alternative approach capable of determining the prevalence of substance use in communities can complement the existing efforts of combating drug abuse and addiction. In this study, the prevalence of 10 illicit and 19 prescribed psychoactive drugs of potential abuse was determined utilizing wastewater-based epidemiology, and compared in two adjoined urban communities and a rural community. This is the first application of the Monte Carlo simulation method to account multiple uncertainties and propagation of errors associated with the individual parameter of wastewater based epidemiological estimations in the U.S. A significantly higher prevalence of cocaine [3830 (mean difference, MD: 2960) mg/d/1000 people] was found in the central business district while the per-capita consumption rates of amphetamine [738 (MD: 338) mg/d/1000 people] and methamphetamine [1660 (MD: 629) mg/d/1000 people] were higher in a rural community. Among narcotics, the per-capita consumption rate of fentanyl and morphine was significantly higher in urban communities while codeine, hydrocodone, hydromorphone, and buprenorphine were dominant in a rural community. The significantly higher prevalence of buprenorphine (˜20-30 folds), oxycodone (˜2-3 folds), and alprazolam (˜2-3 folds) determined in these communities compared to the conventional estimates based on the electronically reported prescriptions and drug-related inpatient hospitalizations suggest the abuse of these drugs.

Harnessing the Power of the Census: Characterizing Wastewater Treatment Plant Catchment Populations for Wastewater-Based Epidemiology

Wastewater studies that provide per capita estimates of consumption (influent) or release (effluent) via wastewater systems rely heavily on accurate population data. This study evaluated the accuracy of Wastewater Treatment Plant (WWTP) reported populations, as well as hydrochemical parameters, against accurate populations from a population census. 104 catchment maps were received from WWTPs, geolocated in geospatial software and overlaid with the smallest area unit of the Australian census, equating to 14.9 million Australians or 64% of the national population. We characterized each catchment for population counts, as well as by age profile, income profile, and education level. For a subset of sites, population estimates using hydrochemical parameters BOD, COD, and dissolved ammonia were evaluated for accuracy against census populations. Population estimates provided by WWTP personnel were on average 18% higher than census-based populations. Furthermore, hydrochemical-based population estimates had high RSD (>44%) for BOD, COD, and ammonium between sites, suggesting that their applicability for use in population estimation may not be appropriate for every WWTP. Catchment age distributions were evaluated and 46% of catchments had skewed age distributions: 6% were skewed older, and 40% were skewed younger. Through this process WWTP catchment populations can be characterized in a way that will enhance the interpretations of per capita estimates.

Can water quality indicators and biomarkers be used to estimate real-time population?

The precise population estimation, short-term or real-time, is crucial to social and civil management, such as public resource distribution, education budgets, health care, and public safety. In this paper, we reviewed the methods for estimation of real-time population. For real-time population estimation, especially for a certain wastewater treatment plant catchment, many water quality indicators and biomarkers were selected as potential markers and their stability, consumption coefficient, and uncertainty were assessed. The conventional water quality indicators, such as wastewater discharge volume, chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, and total phosphorus, were used to calculate the serving population within a WWTP catchment. These parameters are all affected by the behavior, living habits and health conditions of people in different regions. Among them, wastewater discharge volume and ammonia might be more suitable for population estimation than the other parameters which may be influenced by additional industrial discharge. Moreover, acesulfame could be used to estimate the general population, while caffeine, tobacco, and carbamazepine could be applied as biomarkers for a specific population. Furthermore, the per capita loading differ should be considered and measured independently. To reduce the uncertainty of population, comprehensive model with multi-parameters should be developed and applicability should be checked.

Wastewater-based epidemiology in Beijing, China: Prevalence of antibiotic use in flu season and association of pharmaceuticals and personal care products with socioeconomic characteristics

Wastewater-based epidemiology is an emerging field that has mostly been applied to investigate consumption of illicit drugs. In this study, the wastewater-based epidemiology approach was employed to study consumption of pharmaceuticals and personal care products (PPCPs) and measure their prevalence of use in eight densely populated, urban areas of Beijing, China. Ammonium loads were used to estimate the population equivalents of each sewershed. These estimates were applied to calculate population-normalized antibiotic consumption and prevalence of use during flu season, when antibiotics are frequently misused as a medical treatment. Results indicated that 21.9 g d^-1 (10⁴ people)^-1 of ten popular antibiotics were consumed across the eight sewersheds, indicating that 1.98‰ of the 12.5 million population equivalents used these antibiotics during the sampling period. A comparison of these results to calculations made using previously reported data from 2013 suggest that recent Chinese antibiotic control policies have been effective. Uncertainty analyses were conducted to identify the 95% confidence range for antibiotic prevalence of use as 1.44-3.61‰. Human excretion factors were identified as the most sensitive variable. The wastewater-based epidemiology methods were also applied to a wider range of PPCPs, and the results indicated positive relationships between consumption and socioeconomic factors, such as housing price and population density. Overall, this work provides important public health information on antibiotic use and elucidates relationships between PPCP consumption and socioeconomic characteristics.

Alcohol and nicotine consumption trends in three U.S. communities determined by wastewater-based epidemiology

Wastewater-based epidemiology (WBE), an emerging tool for monitoring public health in near real-time, is used extensively in Europe but applications to U.S. populations are still scarce. In this longitudinal study, raw wastewater was collected monthly from three U.S. cities as 24-h weekday composites and analyzed for evidence of alcohol and tobacco consumption. Over the 11-month sampling period, biomarkers of stimulant use were detected in wastewater by isotope dilution liquid chromatography tandem mass spectrometry in units of μg/L (ethyl sulfate, 1.6-25.1; nicotine, 0.6-26.7; cotinine, 0.2-3.8; and 3‑hydroxycotinine, 0.3-3.8). Average consumption rates in the three communities were calculated using detected biomarker levels in conjunction with wastewater flow rates, metabolic excretion factors, and population size data. Computed average per-capita consumption rates estimated for the sub-population aged 15 and above for alcohol (13.4 ± 5.6 L/y/person) and daily consumption of nicotine by smokers (14.2 ± 3.6 cigarettes/d/person) were in good agreement with U.S. survey data (9.0 L/y/person; 14.2 cigarettes/d/smoker). The WBE approach also captured impacts of temporal population influx on substance consumption patterns. This first U.S. WBE study to track recreational use of stimulants longitudinally and concurrently in multiple American cities highlights opportunities for collecting robust public health information from wastewater anonymously, economically and in near real-time.

Assessing Alternative Population Size Proxies in a Wastewater Catchment Area Using Mobile Device Data

Modeling and prediction of a city's (Oslo, Norway) daily dynamic population using mobile device-based population activity data and three low cost markers is presented for the first time. Such data is useful for wastewater-based epidemiology (WBE), which is an approach used to estimate the population level use of licit and illicit drugs, new psychoactive substances, human exposure to a wide range of pollutants, such as pesticides or phthalates, as well as the release of endogenous substances such as oxidative stress and allergen biomarkers. Comparing WBE results between cities often requires normalization to population size, and inaccuracy in the measured population can introduce high levels of uncertainty. In this study mobile phone data from 8-weeks in 2016 was used to train three linear models based on drinking water production, electricity consumption and online measurements of ammonium in wastewater. The ammonium model showed the best correlation with R² = 0.88 while drinking water production and electricity consumption showed more discrepancies. The three models were then re-evaluated against 5-week of mobile phone data from 2017 showing mean absolute errors <10%. The ammonium-based estimated mean annual population for Oslo in 2017 was 645 000 inhabitants, 4% higher than the "de jure" population reported by the wastewater treatment plant. Due to changing conditions and seasonality, drinking water production underestimated the population by 27% and electricity consumption overestimated the population by 59%. Therefore, the results of this work showed that the ammonium mass loads can be used as an anthropogenic proxy to monitor and correct the fluctuations in population for a specific catchment area. Furthermore, this approach uses a simple, yet reliable indicator for population size that can be used also in other areas of research.

A circular toxicity approach to isoprostanes: From markers of oxidative stress, to epidemiological warning systems and agents of aquatic toxicity

Isoprostanes (IsoPs) are a class of oxidation products naturally formed in vivo that are indicative of endogenous oxidative stress. In individuals with chronic and oxidative stress related diseases, IsoPs are increased to pathological levels. Since they are excreted through urine into sewage systems, IsoPs can be detected in wastewater treatment plants' (WWTPs) effluents and thus can be used to evaluate the health status of a given population. The underlying principle is that higher isoprostanes WWTPs' levels correspond to populations undergoing higher levels of oxidative stress, and thus disease. However, IsoPs are not eliminated by WWTPs and will end up being released into the aquatic environment, where they will be available for uptake by aquatic species. Being bioactive molecules, it has been suggested that IsoPs in the environment may elicit oxidative stress in aquatic organisms. In this context, we have critically reviewed the available data on IsoPs as products and effectors of toxicity, and propose the new concept of "circular toxicity". In general, IsoPs excreted by humans as a consequence of oxidative stress are released into the aquatic environment where they may interact with aquatic organisms and induce the production of more IsoPs. These stress markers, in turn, will also be excreted, increasing the already high levels of stressors in the aquatic environment and thus create an escalating cycle of oxidative stress.

Estimation of the consumption of illicit drugs during special events in two communities in Western Kentucky, USA using sewage epidemiology

Sewage epidemiology is a cost-effective, comprehensive, and non-invasive technique capable of determining semi-real-time community usage of drugs utilizing the concentration of drug residues in wastewater, wastewater inflow, and the population size served by a wastewater treatment plant. In this study, semi-real-time consumption rates of ten illicit drugs were determined using sewage epidemiology during special events including Independence Day, the 2017 solar eclipse, and the first week of an academic semester in the Midwestern United States. The average per-capita consumption rate of amphetamine, methamphetamine, cocaine, and THC were significantly different between two similar-sized communities during Independence Day observation week (p<0.046) and a typical week (p<0.001). Compared to a typical day, the consumption rate of amphetamine, methamphetamine, cocaine, morphine, and methadone was significantly higher on Independence Day (p<0.021) and during solar eclipse observation (p=0.020). The estimated percentage of the population that consumed cocaine in a community is similar to the conventionally estimated consumption of cocaine; however, the combined estimated population that consumed amphetamine and methamphetamine based on sewage epidemiology was ~2 to 4 fold higher than the conventional estimates. This study is the first to compare community use of drugs during special events in the USA using sewage epidemiology.

Analysis of stimulant drugs in the wastewater of five Nordic capitals

Wastewater-based epidemiology is an efficient way to assess illicit drug use, complementing currently used methods retrieved from different data sources. The aim of this study is to compare stimulant drug use in five Nordic capital cities that include for the first time wastewater samples from Torshavn in the Faroe Islands. Currently there are no published reports that compare stimulant drug use in these Nordic capitals. All wastewater samples were analyzed using solid phase extraction and ultra-high performance liquid chromatography coupled to tandem mass spectrometry. The results were compared with data published by the European Monitoring Centre for Drugs and Drug Addiction based on illicit drugs in wastewater from over 50 European cities. Confirming previous reports, the results showed high amphetamine loads compared with other European countries. Very little apparent abuse of stimulant drugs was detected in Torshavn. Methamphetamine loads were the highest from Helsinki of the Nordic countries, indicating substantial fluctuations in the availability of the drug compared with previous studies. Methamphetamine loads from Oslo confirmed that the use continues to be high. Estimated cocaine use was found to be in the lower range compared with other cities in the southern and western part of Europe. Ecstasy and cocaine showed clear variations between weekdays and weekends, indicating recreational use. This study further demonstrates geographical trends in the stimulant drug market in five Nordic capitals, which enables a better comparison with other areas of the continent.

Population mobility and urban wastewater dynamics

Dynamic influent models, which have been proposed to test control strategies using virtual wastewater treatment plants, should be as realistic as possible. The number of inhabitants in the catchment at any given time and their ways of life are among the parameters affecting the quality of these models. Census data related to work and school commutes were used to evaluate the number of people present in a given urban area. Based on the example of a large urban catchment (Grand Nancy, France), the results show that a population increase of 30% could occur during working hours resulting from the imbalance between workers leaving and coming into the catchment. Combined with information related to the local way of life, variation in the population helps to explain changes in wastewater flow rate and pollution (carbon, nitrogen, phosphorus and heavy metals), which present several maxima reflecting daily activities, such as bladder voiding, meals, the use of washrooms, etc. However, no well-defined variation patterns for pH and conductivity, which are linked to the concentrations of anions and cations in the wastewater, were observed. Slight reductions (up to 10% on Sundays) in the flow and pollution load were observed on weekends as the commuter flow decreased. Census data proved to be efficient in helping to understand the daily pattern of urban wastewater characteristics.

Monitoring wastewater for assessing community health: Sewage Chemical-Information Mining (SCIM)

Timely assessment of the aggregate health of small-area human populations is essential for guiding the optimal investment of resources needed for preventing, avoiding, controlling, or mitigating human exposure risks, as well as for maintaining or promoting health. Seeking those interventions yielding the greatest benefit with respect to the allocation of resources is critical for making progress toward community sustainability, reducing health disparities, promoting social justice, and maintaining or improving collective health and well-being. More informative, faster, and less-costly approaches are needed for guiding investigation of cause-effect linkages involving communities and stressors originating from both the built and natural environments. One such emerging approach involves the continuous monitoring of sewage for chemicals that serve as indicators of the collective status of human health (or stress/disease) or any other facet relevant to gauging time-trends in community-wide health. This nascent approach can be referred to as Sewage Chemical-Information Mining (SCIM) and involves the monitoring of sewage for the information that resides in the form of natural and anthropogenic chemicals that enter sewers as a result of the everyday actions, activities, and behaviors of humans. Of particular interest is a specific embodiment of SCIM that would entail the targeted monitoring of a broad suite of endogenous biomarkers of key physiologic processes (as opposed to xenobiotics or their metabolites). This application is termed BioSCIM-an approach roughly analogous to a hypothetical community-wide collective clinical urinalysis, or to a hypothetical en masse human biomonitoring program. BioSCIM would be used for gauging the status or time-trends in community-wide health on a continuous basis. This paper presents an update on the progress made with the development of the BioSCIM concept in the period of time since its original publication in 2012, as well as the next steps required for its continued development.