Presented at the Drug Policy Modelling Program symposium 2015

1. Is drug checking effective at reducing harm?
A systematic review
Monica Barratt, Ewa Siedlecka, Alison Ritter
Drug Policy Modelling Program

2. 2
Background
• Issue 1: Adulteration of illicit substances
• Illicit markets lack quality control mechanisms
• Manufacturers may intentionally substitute cheaper drugs to
increase profit or accidentally introduce impurities
• Adulterated pills > risk due to unexpected responses
• Issue 2: Purity of illicit substances
• There are no easy ways of determining substance purity
• High dose > risk of overdose
• Issue 3: Proliferation of new/novel substances
• 100s of new substances are being identified each year
• Difficult to track health outcomes of NPS in a timely way
• NPS can be misrepresented as better-known drugs

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Terms and definitions
• Pill testing
• ‘Pill testing’ arose in context of predominantly ‘ecstasy’ tablets
• Now, drugs tested include pills, powders, capsules and blotters
• And list of ‘expected’ or ‘alleged’ drugs is very long – cf. NPS
• ‘Drug checking’ term used in this paper
• Drugs, rather than pills
• ‘Drug testing’ term associated with workplace or driver testing
• What is drug checking?
• Service for people who consume illicit drugs
• Voluntarily submit their substances for forensic analysis
• Receive their results accompanied by counselling/info as needed
• Attend service on-site or at fixed site or through post/web portal
• Netherlands, Austria, Switzerland, Portugal, Spain, Luxembourg

4. Energy Control at Sónar, Barcelona, June 2015

5. Energy Control at Sónar, Barcelona, June 2015

6. Energy Control at Sónar, Barcelona, June 2015

7. Energy Control at Sónar, Barcelona, June 2015

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A unique source of drug market intelligence
• What is unique about information from drug checking?
• Surveys and interviews ► what people think they are taking
• Police seizures ► content/purity of drugs destined for sale
• Waste water analyses ►what drugs are being consumed
• Drug checking data give us the link that connects these
together
• What is the size and nature of the discrepancy between alleged
and actual content and purity of illicit drugs?
• How does this discrepancy change over time?

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How could drug checking reduce harms?
1. individual behaviour change
2. provision of more accurate harm reduction advice
3. better clinical management
4. intervention or connection with other services
5. population-level behaviour change
6. changes in supply-side dynamics
7. detect (and respond to) novel substances more
rapidly

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Aim
To determine the effectiveness of drug checking
interventions at reducing drug-related harms
• Monitoring
• Behaviour change
• Other studies

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Methods
• Preferred Reporting of Systematic Reviews and Meta-Analyses
(PRISMA) was followed
• Search terms - drug checking, pill testing, and [purity, novel/new
psychoactive substance, content, drug market, harm reduction] with ecstasy
• 81 relevant records from scholarly DBs + 109 additional = 190
• Additional: Google Scholar, forward & backward searching, personal DB
• Inclusion criteria:
• drug checking intervention/service was conducted, or
• hypothetical drug checking scenario was tested, or
• use of testing kits (colour reagent) was described/characterised
• 84 excluded = did not report original data
• 71 excluded = did not meet topic inclusion criteria
• The remaining 35 studies were reviewed

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Year of publication
Plus n=1 no date, likely late 1990s
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Monitoring studies
• n=18 studies that used drug checking data to report the alleged
and actual content of drugs
• Netherlands (n=8), Spain (n=3), Portugal (n=2), USA (n=3),
Australia (n=1) and France (n=1)
• Gas Chromatography (e.g. GC/MS) / thin layer chromatography
• 11 longitudinal, 7 cross-sectional
• 15 involved off-site testing, 4 involved on-site testing, 3 involved
postal submission of samples with online results
• In all (n=6) studies spanning 1975-2005, all tablets were
allegedly ‘Ecstasy’
• Using data 2004+, n=6 studies focused on alleged/actual NPS:
mCPP, 2C-B, cathinone derivatives, 4-FA, and general NPS

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Adulteration of conventional drugs (PT, 2009-13)
Only
unexpected
Expected
combined w/
unexpected
Only
expected Fig 2,
Martins
et al.,
2015

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NPS: from adulterants to drug of choice (NL)
Hondebrink et al., 2015, p. 112

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Mean purity of MDMA per tablet (NL)
Brunt, Niesink et al., 2012, p. 136

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How is this information being used?
• To better understand market adaptations to shortages (Linsen
et al., 2015; Brunt & Niesink, 2011; Bossong et al., 2010; Vogels et al., 2009; Spruit, 2001)
• To test drug user response to market shifts, e.g. ecstasy
users’ responses to MDMA shortage (Brunt, Niesink et al., 2012)
• To contrast drug-checking samples with police-seized drugs
(Giraudon & Bello, 2007; Camilleri & Caldicott, 2005)
• To disentangle effects/harms of ‘Ecstasy’ (Brunt, Koeter et al., 2012)
• To recruit drug users into additional research with
confidence about the drug type they have taken
(Brunt, Niesink et al., 2012; Caudevilla-Gálligo et al., 2012)
• To issue warnings when particularly harmful adulterants or
purity levels are detected (See DIMS annual reports)

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Red alerts
Annual report 2014 – Drugs Information and Monitoring System (NL)

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Risk of bias within & across studies (monitoring)
• Within studies
• Different methods of forensic analysis could cause bias, but most
recent studies use GC/MS backed with other methodologies
• Across studies
• Query representativeness: Samples are self-submitted: more
likely to submit adulterated tablets than MDMA-only tablets
following a negative experience?
• Furthermore, most projects run at capacity – more demand than
there is funding to complete the work
• The uses of monitoring data, e.g. for issuing warnings, have not
been evaluated. Do people react in expected ways?
• Mainly high quality peer-reviewed publications
• Verdict: low likelihood of bias

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Behavioural outcome studies (1)
• n=8 behaviours following drug checking were measured
• n=4 drug checking occurred, n=4 drug checking hypothetical
• All were cross-sectional surveys
• Year data collected / study conducted, from 1995 to 2013
• 6 countries represented [Canada, Australia(3), Netherlands(2), Germany, Austria(2), USA]
• Varied measurement of outcome variable
• Findings relate to general presence (e.g. at a festival), rather than an
individual drug checking result (Dundes, 2003; van de Wijngaart et al., 1999)
• Findings relate to hypothetical individual drug checking result (Black et al.,
2008; Dunn et al., 2007; Johnston et al., 2006; Benschop et al., 2003)
• Unclear (Kriener & Schmid, no date; Michelow & Dowden, 2015)
• There was no study found that clearly measured behavioural
responses to a specific drug checking occurrence

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Behavioural outcome studies (2)
• Most detailed results are from the 4 hypothetical scenario studies
• Three years of EDRS (2005, 2006, 2007)
• Ecstasy users reported that they would not take the pill if the test result
indicated: MDxx (0-2%), amphetamine-like (6-15%), ketamine (50-57%),
opiates (53-57%), 2C-B/I (59-68%), DXM (67-73%), PMA (68-80%),
unknown/benign/suspicious (65-76%).
• Three city European study (2002)
• 33% would not take the pill that contained 25mg MDMA (32% for 75mg, 36%
for 150mg, 41% for amphetamine and 85% for suspicious substance).
• 46% of participants would warn their friends if the pill contained 25mg of
MDMA, 43% for 75mg, 64% for 150mg, 58% for amphetamine and 82% for
suspicious substance.
• 39% would inquire about possible risks if the pill contained 25mg of MDMA,
37% for 75mg, 43% for 150mg, 41% for amphetamine and 67% for
suspicious substance.
• Most ecstasy users would reject pills where test results indicated
an unknown or suspicious substance (in hypothetical)

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Risk of bias within and across studies (behavioural)
• Within studies:
• Limitations of hypothetical design
• Limited outcome of interest – not just ‘do not take’
• Data only makes sense if presented for each different test result
• Across studies
• Services do not prioritise evaluations
• Lacking finance, time, motivation
• Behavioural impact data that have not yet been published
• Most not published in peer reviewed journals
• Legal problems may result from asking more specific questions
• Result: selective reporting problems are likely

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Other studies
• n=12 other studies
• Ethnography comparing 3 festival settings (UK, US, Portugal):
critical importance of the surrounding political-legal context on
effectiveness of peer organisations and drug checking (Ruane, 2015)
• Demographic characteristics of drug-checkers (Multiple studies)
• On-site drug checking reaches a more at-risk group than off-site
services (Hungerbuehler et al., 2011)
• Process type evaluation of drug checking interventions, e.g.
number of people using the services and reach of services, but
no outcome measures (Maier et al., 2013; Hungerbuehler et al., 2011; Kriener et
al., 2001; Tossman et al., 1999)
• Studies that provided prevalence of personal use of test kits
(colour reagent) (Barratt, 2011; Allott & Redman, 2006; Murphy et al., 2005)

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Discussion
• Monitoring capacity: strong evidence of utility (e.g. DIMS), but
also important to evaluate the interventions that arise for
monitoring (e.g. effects of warnings)
• Behavioural outcomes: shows promise, but needs a tighter
evaluation for confirmation
• Other studies: ethnographical work is important to better
understand the context within which drug checking operates

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Final thoughts
• Why have drug checking initiatives not been more widely adopted?
• Is it related to a lack of solid evidence for effectiveness?
• Assuming this is the case would reflect a naivety about the policy process
and how policy is made
• Rather, all stakeholders need to be convinced that it is a win-win:
• Police can access unique (anonymous) drug market data, which
goes above and beyond data from drug seizures
• Health can tailor responses and care to actual substance taken,
and they can access an at-risk population earlier
• Drug users can (a) access timely and tailored information, (b)
receive more relevant health care, (c) avoid adulterated drugs
• Everyone can warn each other about specific very-high-risk
substances, potentially reducing related harms and deaths

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Acknowledgements
• NDARC internal funding for research assistance
• MB is co-affiliated with NDRI/Curtin and Burnet
• MB is supported by an NHMRC Early Career Researcher
Fellowship (APP1070140)
• AR is supported by an NHMRC Senior Research Fellowship
(APP1021988)
• Thanks to Spain’s Energy Control for kindly providing images of
them in action and allowing us to use them here.
Please contact Monica for further discussion:
m.barratt@unsw.edu.au
@monicabarratt