Underestimation of Drug Use: A Perennial Problem with Implications for Policy

by Olivia Maynard

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Photo by Louie Castro-Garcia on Unsplash

In a paper recently published in the journal Addiction, Hannah Charles and colleagues suggest that the prevalence of illicit drug use among 23-25 year olds in a Bristol-based birth cohort (ALSPAC) is over twice that reported in the Crime Survey for England and Wales (CSEW). The team propose that these figures reflect under-reporting in the CSEW, although they note that they may reflect higher levels of illicit drug use in Bristol. Here I present some preliminary data supporting their view that the CSEW underestimates illicit drug use.

In March 2020, I recruited 683 UK university students to participate in a short survey on drug use via the online survey platform Prolific which has been shown to produce reliable data. I recruited only students aged 18 to 24 years who reported using alcohol in the past 30 days, and participants reported whether they had used any of MDMA/ecstasy, cocaine or cannabis in the past two years.

Table 1. Prevalence of self-reported illicit drug use across three surveys of young people in the UK

via ProlificAged 18-24
Bristol, ALSPAC

Aged 23-25


Aged 23-25

2 years 1 year Lifetime 1 year Lifetime
Any illicit drug usea 52.7 (360) 36.7 62.8 16.4 40.6
Cannabis 50.2 (343) 29.2 60.5 13.8 37.3
MDMA/ecstasy/amphetaminesb 23.3 (159) 17.0 32.9 3.6 11.1
Cocaine 21.1 (144) 19.6 30.8 4.8 13.9

Notes: Values represent percentage of participants (number of participants). Percentages for CSEW and ALSPAC are taken from Charles et al (1) and are weighted percentages.
a ‘Any illicit drug use’ refers only to the illicit drugs assessed in the respective surveys (only cannabis, MDMA and cocaine in our survey), more drugs in ALSPAC and CSEW – see Charles et al (1).
Our Prolific survey asked about ‘MDMA / ecstasy’ use, ALSPAC categorised ecstasy/MDMA use along with other ‘amphetamine’ use and CSEW asked about ‘ecstasy’ use.

Over half of my sample reported using at least one of cannabis, cocaine or MDMA in the past two years (Table 1). This is markedly higher than the CSEW’s estimates of either past year or lifetime use, and more in line with those reported in ALSPAC. Comparing across drugs, past two-year use of the three drugs is higher in my survey than either past year or lifetime use in the CSEW, and higher than past year, but lower than lifetime use in ALSPAC. Perhaps of more interest than ever use of the drugs over the past two years, I also examined the combinations of drugs students in my survey were using (Table 2). I find that the majority of students who report using illicit drugs have only used cannabis in the past two years (25% of all students), although the second largest group (15%) have used all three of cannabis, MDMA and cocaine.

Table 2. Prevalence of self-reported illicit drug among UK university students

Qualtrics survey of university students (past two years)
Illicit drug use 
Cannabis 50.2 (343) 48.5 (163) 53.5 (167) 37.1 (13)
MDMA / ecstasy 23.3 (159) 19.3 (65) 29.2 (91) 8.6 (3)
Cocaine 21.1 (144) 17.6 (59) 26 (81) 11.4 (4)
Illicit drug use profiles
Alcohol only (no illicit drug use) 47.3 (323) 48.2 (162) 44.6 (139) 62.9 (22)
Any illicit drug usea 52.7 (360) 51.8 (174) 55.4 (173) 37.1 (13)
Cannabis only 24.5 (167) 27.4 (92) 21.5 (67) 22.9 (8)
Cannabis + Cocaine + MDMA 15.4 (105) 11.3 (38) 20.8 (65) 5.7 (2)
Cannabis + MDMA 6.3 (43) 6 (20) 7.1 (22) 2.9 (1)
Cannabis + Cocaine 4.1 (28) 3.9 (13) 4.2 (13) 5.7 (2)
Cocaine only 0.9 (6) 1.2 (4) 0.6 (2) 0 (0)
MDMA only 0.9 (6) 0.9 (3) 1 (3) 0 (0)
Cocaine + MDMA 0.7 (5) 1.2 (4) 0.3 (1) 0 (0)

Notes: Values represent percentage of participants (number of participants).
‘Illicit drug use’ refers to participants reporting any use of the three drugs in the past two years.
‘Illicit drug use profiles’ refers to the combinations of drugs participants report using in the past two years.
a ‘Any illicit drug use’ refers only to use of cannabis, MDMA and cocaine.

There are some important differences between my sample and both the CSEW and ALSPAC samples. Some differences may mean that my figures are overestimates, including sampling university students who are more affluent than the general population (although drug use is not necessarily higher among students than non-students) and only including those who reported drinking alcohol (although according to the study authors, over 95% of the ALSPAC participants report past year drinking). Other differences may mean my figures are underestimates, including only asking about use of three drugs (thereby underestimating ‘any illicit drug use’), and the younger average age of my sample. I also report on past two-year use, rather than either lifetime or past year use as per CSEW and ALSPAC. Given these differences, I would like to run a larger, more representative sample on the Prolific platform (Prolific allows researchers to recruit a sample which is representative of the general population), to get an estimate of illicit drug use which is more comparable to ALSPAC and CSEW.

Despite these differences, my data support those reported by Charles and colleagues. Indeed, I find it unsurprising that illicit drug use is under-reported in the Home Office’s CSEW. The validity of self-reports for sensitive issues has long been a concern. Over-reporting of illicit drug use is not considered to be a concern and numerous methods have been developed for preventing under-reporting (see a 1997 NIDA report on this issue, as well as more recent techniques for estimating prevalence of use such as the crosswise method). It is important to consider the context in which surveys are administered, including participants’ perception of who is asking the questions and for what reason. It seems that if drug use is asked about in a research context, (e.g., with a clear research objective, informed consent and no possibility of repercussions), the validity of responses may be higher than when questions are asked by organisations that are perceived to be involved in the punishment of people who use drugs (e.g., governments, universities).

While the CSEW recognises that it does not reliably measure problematic drug use, my data and that of Charles and colleagues provide evidence that CSEW’s claim that it is a ‘good measure of recreational drug use’ may be wrong. Although it may be convenient to believe that only a small subset of the population uses illicit drugs, accurate information may galvanise policy makers (both nationally and locally, including at universities) into developing drugs policies that reflect reality and which support, rather than criminalise, the large proportion of the population who choose to use drugs. Indeed, this is what we’re doing at the University of Bristol, where it has been accepted that drug use is relatively common among our students and we’re providing support and education to those students who need it.


This blog posted was originally posted on the Tobacco and Alcohol Research Group blog

stopWatch – a smartwatch system that could help people quit smoking

Dr Andy Skinner and Chris Stone

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October sees the return of Stoptober, a Public Health England initiative to encourage smokers to quit. Campaigns like this and many others have been effective in reducing smoking in the UK over a number of decades. However, on average, about 15% of the UK’s population still smoke, and this costs the NHS more than £2.5bn each year.

To help address this, the NHS Long Term Plan has identified a range of measures to encourage healthier behaviours, including the need to speed up the introduction of innovative new health interventions based on digital technologies.

Here in the MRC IEU we’ve been working on a new wearable system that could help people stop smoking; stopWatch is a smartwatch-based system that automatically detects cigarette smoking. Because the system can detect when someone is smoking a cigarette, it can trigger the delivery of interventions to help that person quit smoking at precisely the time the interventions will be most effective.

Hand and wrist wearing stopWatch and holding a cigarette
The stopWatch could help people to stop smoking

What is stopWatch, and how does it work?

stopWatch is an application that runs on a commercially available Android smartwatch. Smartwatches now come equipped with motion sensors, just like the ones in smartphones that measure step counts and activity levels. As smartwatches are attached to the wrist, the motion sensors in a smartwatch can tell us how a person’s hand is moving. stopWatch takes data from the smartwatch’s motion sensors and applies machine learning methods to look for the particular pattern of hand movements that are unique to smoking a cigarette.

How can we use stopWatch to help people quit smoking?

It’s estimated about a third of UK smokers try to stop each year, but only a fifth of those that try manage to succeed. For most smokers an attempt to stop smoking ends with a lapse (having just one cigarette), that can quickly lead to a full relapse to smoking. As stopWatch can detect the exact moment a smoker lapses and has a cigarette, it can trigger the precise delivery of an intervention aimed specifically at helping prevent the lapse turning into a full relapse back to smoking.

Will the intervention work?

A recent article highlighted the potential for using mobile and wearable technologies, like stopWatch, to deliver these kinds of ‘just-in-time’ interventions for smoking. To develop our smoking relapse intervention we will be using the person-based approach, which has an excellent track record of delivering effective health behaviour change interventions. We will also be engaging the highly interdisciplinary cohort of PhD students in the new EPSRC Center for Doctoral Training in Digital Health and care, which brings together students with backgrounds in health, computer science, design and engineering.

However, that same article also pointed out that these types of intervention are still new, and that there has been little formal evaluation of them so far. So we don’t yet know how effective these will be, and it’s important interventions of this kind are subject to a thorough evaluation.

We will be working closely with colleagues in NIHR’s Applied Research Collaboration (ARC) West and Bristol Biomedical Research Centre who have expertise in developing, and importantly, evaluating interventions. We will also be working with the CRUK-funded Integrative Cancer Epidemiology Unit at the University of Bristol, collaborating with researchers who have detailed knowledge of developing interventions for specific patient groups.

The StopWatch display
On average, stopWatch detected 71% of cigarettes smoked and of the events stopWatch thought were cigarette smoking, 86% were actually cigarette smoking.

How good is stopWatch at detecting cigarette smoking?

In any system designed to recognise behaviours there is a trade-off between performance and cost/complexity. Other systems that use wearables to detect smoking are available, but these require the wearable be paired with a smartphone and need a data connection to a cloud-based platform in order to work properly. stopWatch is different in that it runs entirely on a smartwatch. It doesn’t need to be paired with a smartphone, and doesn’t need a data connection. This makes it cheaper and simpler than the other systems, but this also means its performance isn’t quite as good.

We recently validated the performance of stopWatch by asking thirteen participants to use stopWatch for a day as they went about their normal lives. On average, stopWatch detected 71% of cigarettes smoked (the system’s sensitivity), and of the events stopWatch thought were cigarette smoking, 86% were actually cigarette smoking (its specificity). This compares with a sensitivity of 82% and specificity of 97% for the systems that require smartphones and data networks.

When will stopWatch and the smoking relapse intervention be available and what will they cost?

The stopWatch system itself is available for research purposes to academic partners now, free of charge. We’re open to discussions with potential commercial partners – please get in touch if you’d like to discuss this (contact details below).

We aim to begin work on the smoking relapse intervention based on stopWatch next year, and we expect development and evaluation to take between 18 and 24 months. The cost of the intervention has yet to be determined. That will depend on many factors, including the partnerships we form to take the intervention forward.

What’s next?

We’re currently putting stopWatch through its paces in some tough testing in occupational settings. This will stress the system so that we can identify any weaknesses, find out to how to improve the system, and develop recommendations for optimising the use of stopWatch in future studies and interventions.

We’re also developing a new smartwatch-based system for the low burden collection of self-report data called ‘dataWatch’. This is currently undergoing feasibility testing in the Children of the 90s study.

Contact the researchers

Dr Andy Skinner Andy.Skinner@bristol.ac.uk