Evaluation of Monitoring Schemes for Wastewater-Based

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Evaluation of monitoring schemes for wastewaterbased epidemiology to identify drug use trends using cocaine, methamphetamine, MDMA and methadone Melissa A. Humphries, Raimondo Bruno, Foon Yin Lai, Phong K. Thai, Barbara R Holland, Jake W. O'Brien, Christoph Ort, and Jochen F. Mueller Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b06126 • Publication Date (Web): 23 Mar 2016 Downloaded from http://pubs.acs.org on March 27, 2016

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Environmental Science & Technology

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Evaluation of monitoring schemes for wastewater-

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based epidemiology to identify drug use trends using

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cocaine, methamphetamine, MDMA and methadone

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Melissa A. Humphries*†, Raimondo Bruno†, Foon Yin Laiǂ, Phong, K. Thaiǂ,§, Barbara R.

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Holland†, Jake W. O’Brienǂ, Christoph Ortǁ,¬ and Jochen F. Muellerǂ

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† University of Tasmania, Sandy Bay, TAS 7005, Australia;

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ǂ The University of Queensland, The National Research Centre for Environmental Toxicology

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(Entox), Coopers Plains, QLD 4108, Australia;

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§ Queensland University of Technology, Brisbane, QLD 4001, Australia;

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ǁ Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH 8600 Dübendorf,

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Switzerland;

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¬ The University of Queensland, Advanced Water Management Centre (AWMC), St. Lucia,

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QLD 4072, Australia.

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KEYWORDS Wastewater, time series, MDMA, cocaine, methamphetamine, methadone,

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monitoring scheme, sampling strategy, epidemiology, drug use, representative sampling.

ACS Paragon Plus Environment

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ABSTRACT

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Wastewater-based epidemiology is increasingly being used as a tool to monitor drug use trends.

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To minimize costs, studies have typically monitored a small number of days. However, cycles of

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drug use may display weekly and seasonal trends that affect the accuracy of monthly or annual

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drug use estimates based on a limited number of samples. This study aimed to rationalize

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sampling methods for minimizing the number of samples required while maximizing information

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about temporal trends. A range of sampling strategies were examined: i) targeted days (e.g.

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weekends), ii) completely random or stratified random sampling, and iii) a number of sampling

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strategies informed by known weekly cycles in drug use data. Using a time-series approach,

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analysis was performed for four drugs (MDMA, methamphetamine, cocaine, methadone)

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collected through a continuous sampling program over 14 months. Results showed, for drugs

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with weekly cycles (MDMA, methamphetamine and cocaine in this sample), sampling strategies

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which made use of those weekly cycles required fewer samples to obtain similar information as

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sampling five days per week and had better accuracy than stratified random sampling techniques.

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Introduction

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Over the last decade, wastewater-based epidemiology (WBE) has become increasingly popular

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as a method for monitoring trends in illicit substance consumption, as a complement to existing

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consumer surveys and examination of health and law enforcement data. One of the key benefits

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of WBE is it enables objective data to be collected on illicit drug use in a defined population

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without major ethical issues (Hall et al., 2012).

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In order to effectively inform policy and interventions to reduce harm from substance use, it

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would be ideal to collect information on changes in drug use on an ongoing basis. However,

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undertaking longitudinal monitoring programs to assess temporal trends in population drug use

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remains challenging since it is resource intensive. Most WBE studies to date have relied on

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opportunistic sampling to assess variation in drug use, for example daily samples taken from two

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days (weekdays vs. weekends) over a few months or from selected weeks or months per year

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(e.g. van Nuijs et al., 2009; Prichard et al., 2012; Zuccato et al., 2011; Khan et al., 2014; Kim et

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al., 2015). While it is clear that the uncertainties associated with estimation of drug use levels

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decrease with an increase in the number of samples, the cost and amount of time associated with

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labor, sampling, sample preparation and instrumental analysis presents a challenge which can

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limit the application of WBE for continuous long-term monitoring schemes. As such, it is

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important to determine the most efficient manner to structure a monitoring scheme with which to

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obtain a clear picture of long term trends, short-term variation (e.g. whether there are changes in

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the drug market following large drug seizures or during holiday periods) and weekly cycles in

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drug use levels.


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Ort et al. (2014) addressed this question by evaluating a number of sampling scenarios (also

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referred to as ‘monitoring scenarios’) for estimating cocaine use in a small catchment (0.99) in estimating the overall trend and 70% or greater

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accuracy (R2≥0.70) in estimating monthly maximum, mean and minimum drug

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consumption. Reducing to a one-day-per-week rotating roster (52 samples) maintains

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overall trend estimation accuracy, estimation of the monthly maximum, mean and

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minimums reduces to 50% or greater accuracy (R2≥0.50). Using a random two-day-

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per-week sample (104 samples) may also provide fair results, as may using the Ort et

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al. (2014) SR-56 scheme (56 samples).

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For the schemes that were not informed by the weekly structure in drug use, the random and

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stratified random schemes investigated did not produce consistently accurate estimates when the

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weekly variability in drug use was present. In addition, the uninformed scheme of MTWTF-260,


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although quite accurate, had a high number of sampling days and therefore, a high cost

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associated with its implementation (approximately $52,500 AUD annually for the sampling

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alone in this study. See Section 10 in the supplementary material for details). For drugs with

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strong or moderate weekly cycles, using a monitoring strategy that is informed by the weekly

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cycles in the data gave results similar in accuracy to the MTWTF-260 scheme, but with a

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fraction of the sampling days. For the current study, compared to the MTWTF-260, the PMT-

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156 resulted in a 40% decrease in costs while the two day schemes were 60% and the single day

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schemes 80% less expensive to run (Supplementary material Section 10).

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Although uninformed monitoring schemes provided some accuracy for drugs without an

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observable weekly cycle, it should be noted that if the collected samples are to be analysed for

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multiple drugs, the monitoring strategy will need to be optimized to measure either the most

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highly variable or the most critical. The key then is understanding the weekly cycles of the drugs

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being sampled, in the catchment in which the samples are being collected, before sampling

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commences. If information about the weekly cycles of the drugs of interest is not quantitatively

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known, then it may be cost-effective to conduct a brief period of intensive sampling to ascertain

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the patterns of usage specific to the drug and area being sampled. Exploratory analysis of 1000

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random samples from the current data showed that for drugs with a strong weekly cycle of

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consumption (such as MDMA or COC), eight weeks of intensive sampling, on average,

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identified around 70% of the daily differences observed in the full data (see Section 9 in the

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supplementary material for a full discussion). In addition, 95% or more of the samples returned

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at least one significant difference between the days.

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There are two main problems, however, associated with the use of the reduced, informed

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monitoring schemes suggested. The first was highlighted by the results for the 2RD-104 scheme,


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where rare events dramatically reduced the accuracy of the 2RD-104 results. The problem is that,

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if a rare event occurs or if a given period of time displays some extreme behaviour that does not

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correspond to the regular weekly cycles, there is no guarantee that it will be captured by any

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reduced monitoring scheme. Random sampling ensures that every day is equally likely to be

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sampled, increasing the likelihood of sampling rare events that may be missed by informed

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monitoring schemes, but even then there is no guarantee that they will be sampled. If sampling

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rare events is important to the research question, pre-empting when rare events may occur (such

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as music festivals and holidays) and including extra sampling days around that time may provide

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some solution.

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Rare events are not the only potential problem that using an informed monitoring scheme may

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encounter. All of the informed schemes are based on the weekly cycles observed in the data so a

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change in the weekly cycle of drug use in the population would not necessarily be captured by

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the informed schemes. The problem associated with missing a change in weekly cycle is also

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more likely to be avoided using random monitoring schemes for the same reason. The random

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monitoring schemes described in Section 2.3, were therefore also considered as a possible way of

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accounting for rare events and changes in weekly cycles. However, even though random

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sampling may go some way toward measuring rare events and changes in cycle, the results from

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the stratified random monitoring schemes highlighted an important problem with random

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(stratified or otherwise) sampling in this study:

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sampling waste water there is only one chance to sample any given day so, if randomly

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sampling, we are equally likely to have sampled data producing any one of the R-104, SR-56 and

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SR-14 RSS values observed in Figure 3. Any random sample is equally likely to have drawn

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samples from all of the peak usage days, as it is from all of the trough usage days and yet both of

it is not necessarily representative. When


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these samples may tell a wildly different story. This is particularly important for highly variable

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drugs, such as MDMA in the current study, in which the days sampled, irrespective of rare

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events, can have a large impact on the estimated trends. In this way, by choosing to randomly

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sample there is no guarantee that observed trends in drug use are due to fluctuations in the data,

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or whether they are just a product of the days sampled.

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Although the sorts of informed monitoring schemes presented here have a greater guarantee of

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accuracy than the stratified random sampling, they still do not take into account any future

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changes in the weekly patterns of drug use that may occur. Because of this, to confirm whether

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changes in weekly patterns of usage have occurred, it is also advisable to consider periods of

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intensive sampling throughout the monitoring period (especially if monitoring is planned over

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many years).

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In conclusion, if the aim of monitoring is to determine trends in drug use over time in a given

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catchment area, a period of intensive sampling to define weekly cycles in drug use should be

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conducted. This should be followed by sampling the peak, trough and middle of the weekly cycle

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either three days per week (for drugs with strong weekly patterns of usage), or on a two-day-per-

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week rotating roster. If monitoring is planned to continue over a long period of time, additional

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intensive sampling at intervals throughout the monitoring period are advisable to ascertain if

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there have been any changes in the weekly cycle of usage.

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FIGURES


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Figure 1. Days of the week against consumption for MDMA, METH, COC and MDONE. The

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days are color coded into peak (white), trough (dark gray) and mid (light gray) consumption

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days, based on the results of the ANOVA described in Section 3.1. Clear weekly cycles are

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observable for MDMA, COC and METH with peaks in consumption observable around the

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collection day Monday and troughs around Thursday and Friday collection points. No weekly

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cycle was observable for MDONE. Note that each of the presented box plots here are on

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different scales due to the different consumption magnitudes observed for each drug and to allow

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observation of the details of the plot. For the same reason, MDMA, COC and METH plots have

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been zoomed in, resulting in some outlying values resting outside of the plot area.


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Figure 2. The results for using one rotating day per week (1RD-52) to estimates overall trend,

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and monthly maximum, minimum and mean log(cocaine) consumption. The upper left figure

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displays the trend from the time series decompositions (with seasonal and error components

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removed) of the complete data (gray) in comparison to the 1RD-52 reduced data (black). The

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remaining three panels display the relationship between the monthly estimates of maximum,

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minimum and mean log(consumption) for the complete and reduced data sets. Although the

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overall trend in consumption appears to be well estimated, consistent underestimation of the

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monthly consumption is evident across all measures suggesting RSS alone is not sufficient for

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estimating the accuracy of the sampling schemes.


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Figure 3. The distribution of RSS values (y-axis) for the comparison between the overall trend in

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consumption for the full data and the overall trend in consumption for data obtained using the

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three random sampling schemes; R-104, SR-56 and SR-14 (x-axis) for each of the four drugs.

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Each box-and-whisker represents the distribution of RSS values generated by comparing the full

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trend from the decomposed time series of 1000 data sets which were created by randomly

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selecting days from the complete data set (as described in Section 2.3). The RSS from the PT,

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PMT, 1RD and 2RD structured schemes are overlaid, with the value of the highest RSS indicated

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on the y-axis for ease of comparison (Tables S4 & S5). Note that each of the presented box plots

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here are on different scales due to the different RSS magnitudes observed for each drug and to

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allow observation of the details of the plot. For the same reason, each plot has been zoomed in,

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resulting in some outlying values resting outside of the plot area.


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TABLES.

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Table 1. Monitoring schemes investigated in the current study. Monitoring

Scheme

Description

# of Samples

Informed

P-52

Peak usage (Monday) only

52

M-52

Mid usage (Wednesday) only

52

T-52

Trough usage (Friday) only

52

1RD-52

One day per week is sampled, but the day chosen rotates through peak, mid and trough usage. For example, in week 1 - Peak is sampled, week 2 - Mid is sampled, week 3 - Trough is sampled and this pattern is repeated

52

PT-104

The peak and trough (Monday and Friday)

104

2RD-104

Two days per week are sampled, but the days chosen rotate through peak, mid and trough usage. For example, in week 1 – Peak and Mid are sampled, week 2 – Mid and Trough are sampled, week 3 – Peak and Trough are sampled and this pattern is repeated

104

PMT-156

The peak, mid and trough usage (Mondays, Wednesdays and Fridays)

156

SR-14

Proposed by Ort et al. (2014), this scheme randomly selects 10 weekdays (MTWTF) and four weekend days (SS) from across a 365 day period

14

SR-56

Proposed by Ort et al. (2014), this scheme randomly selects 10 weekdays (MTWTF) and four weekend days (SS) every quarter from across a 365 day period

56

R-104

Sampling occurs on two randomly chosen weekdays (MTWTF) every week

104

SS-104

Both weekend days (Saturday and Sunday)

104

MTWTF260

Every weekday (excluding weekends)

260

Uninformed

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The details of the 12 monitoring schemes investigated in the current manuscript. They span 14260 sampling days per year and include schemes informed by the weekly cycles in the data and those that are independent of those cycles. Here the “day” is defined as the day the sample was physically collected (collection day).


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Table 2. Ranking of Sampling Scheme Results for the informed sampling schemes along with

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the MTWTF-260 and SS-104 across all four drugs. MDMA Strong weekly cycle Scheme Detail MTWTF260

MTWTF260

PMT-156

PMT-156

2RD-104

2RD-104

PT-104

1RD-52

M-52

SS-104

T-52

P-52

448 449 450 451

Accurate across all measures.

METH Weak weekly cycle Scheme Detail

Decrease in ability to predict trend in usage. Moderate predictive ability of monthly maximums.

Decrease in ability to predict trend in usage. Weak predictive ability of monthly maximums. Moderate predictive ability of monthly minimums and means.

Decrease in ability to predict trend in usage. Unable to predict monthly maximums. Moderate predictive ability of monthly minimums.

Decrease in ability to predict trend in usage. Consistently under-predicts or over-Predicts respectively.

Moderate predictive ability of monthly minimums. Slight decrease in ability to predict trend in usage. Moderate predictive ability of monthly minimums and maximums.

PT-104

1RD-52

Decrease in ability to predict trend in usage. Moderate predictive ability of monthly minimums and maximums.

SS-104

M-52

T-52

P-52

COC Strong weekly cycle Scheme Detail MTWTF260

Accurate across all measures.

PMT-156

Decrease in ability to predict trend in usage. Moderate predictive ability of monthly means and minimums.

2RD-104

Decrease in ability to predict trend in usage. Consistently under-predicts or over-Predicts respectively.

PMT-156

2RD-104

PT-104

Decrease in ability to predict trend in usage. Moderate predictive ability of monthly means. Poor predictive ability of monthly minimums.

PT-104

1RD-52

Decrease in ability to predict trend in usage. Moderate predictive ability of monthly means and maximums. Poor predictive ability of monthly minimums.

1RD-52

SS-104

Decrease in ability to predict trend in usage. Weak predictive ability of monthly minimums and maximums. Moderate predictive ability of monthly mean usage.

MDONE No weekly cycle Scheme Detail Moderate MTWTF- predictive ability 260 for monthly minimums.

M-52

T-52

P-52

Although M-52 had less predictive ability of the trend in usage than SS104 and had poor predictive ability of monthly maximums, SS-104 showed a complete inability to predict monthly minimums. Moderate predictive ability of monthly means. Decrease in ability to predict trend in usage. Consistently under-predicts or over-Predicts respectively.

Moderate predictive ability of monthly means, minimums and maximums.

T-52

M-52

P-52

SS-104

Poor predictive ability of monthly minimums and maximums. SS104 had a decrease in ability to predict trend in usage but showed moderate predictive ability of monthly means, while P52 and M-52 showed poor predictive ability of monthly means.

The MTWTF-260 was the most accurate performer for all drugs with the PMT-156 and 2RD104 also performing well. The P-, M- and T-52, along with the SS-104 were the least accurate at predicting overall trend in consumption and monthly mean, maximum and minimum consumption across all four drugs. It should be noted that the R-104, SR-56 and SR-14 do not


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provide enough information to gain estimates of monthly mean, maximum and minimum consumption and are therefore not included in this table.


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ASSOCIATED CONTENT

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Supporting Information.

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Supporting information includes: An overview of the data.

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Information about the COC-BE ratio.

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Imputation of missing data and imputation for trend comparisons.

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Post-hoc testing results for the differences between weekdays for MDMA, METH,COC and

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MDONE.

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Details of the time series analysis and monthly consumption level comparisons.

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Post-hoc testing results for the differences between SR-14, SR-56, R-104 monitoring schemes

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for MDMA, METH, COC and MDONE.

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Details of the intensive sampling to ascertain weekly trends in consumption analysis and of the cost estimation for the reduced schemes.

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This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

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Corresponding Author

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*Melissa A. Humphries.

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[email protected]

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School of Physical Sciences, University of Tasmania. Private Bag 37, Hobart, Tasmania, 7001.

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Ph (+61) 03 6226 2449


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Author Contributions

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MH, BH, RB and PT conceived the study. MH led the writing of the paper and undertook all

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analyses. All authors contributed to writing and review of the manuscript and have given

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approval to the final version.

477

Funding Sources

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The authors acknowledge various financial supports for the project, including Australia Crime

479

Commission, Crime and Misconduct Commission of Queensland, and Collaborative Research

480

Funds between Queensland Health Forensic Scientific Services (QHFSS) and Entox. The LC-

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MS/MS was funded by the ARC LIEF grant. Foon Y. Lai is funded through UQ CIEF and UQ

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HABS grants. Jochen Mueller is funded by the ARC Future Fellowship. Phong K. Thai is partly

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funded by a UQ Postdoctoral Fellowship and a QUT VC Research Fellowship and was the

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recipient of a UTas Visiting Scholars bursary to collaborate on this research. Jake O’Brien and

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Melissa Humphriesare funded by APA PhD scholarships. Entox is a joint venture of UQ and

486

QHFSS.

487

ACKNOWLEDGMENT

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The authors acknowledge and express our thanks to the Regional Council for the sampling

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opportunity. Also, we thank Andrew Banks and Kristie Thompson for preparing samples and

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Simon Wotherspoon for his suggestions regarding the analysis.

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ABBREVIATIONS

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MDMA, 3,4-methylenedioxymethamphetamine; METH, methamphetamine; COC, cocaine; BE,

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benzoylecognine; MDONE, methadone; EDDP, 2-ethylidene-1,5-dimethyl-3,3-

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diphenylpyrrolidine.


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Evaluation of Monitoring Schemes for Wastewater-Based

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