TARG – IEUREKA!

Erasing the stain: Challenging the stigma of opioid substitution treatment. Findings from a stakeholder workshop

Posted on October 4, 2021October 4, 2021 by lindsey.pike

Author: Vicky Carlisle. Twitter: @Vic_Carlisle, Email: vicky.carlisle@bristol.ac.uk

On Wednesday 7th July 2021, I brought together key stakeholders with an interest in improving opioid substitution treatment (OST) from across the UK. This included people with lived experience, Public Health England staff, local authority public health practitioners, treatment service leads, pharmacists and academics. We discussed the findings of my recently completed PhD, and together we considered the next stages of developing an intervention to improve OST.

A summary of my research

For those not familiar with the topic, OST refers to the treatment of opioid dependency with either methadone or buprenorphine (alongside psychosocial support). Through my research, I wanted to understand what the key facilitators and barriers are to people ‘recovering’ in OST. To do this, I drew on both quantitative and qualitative methodologies. I found that loneliness, isolation and experiences of trauma and stigma were key barriers to recovery; whereas positive social support, discovering a sense of purpose and continuity of care were valuable facilitators.

Importantly, some factors appear to act as both facilitators and barriers to recovery in OST. For instance, I found that some service users used isolation as a form of self-protection (to shield themselves from negative influences), however this often left them feeling lonely and disconnected from the potential benefits offered by developing more positive social support networks.

Undoubtedly, the strongest barrier to recovery was stigma. Service users told me that they experience stigma from a range of sources, including from family and friends, healthcare professionals and members of the wider community. I found similar patterns in the literature review that I carried out (Carlisle et al, 2020). Stigma is like a stain where an individuals’ entire identity is defined by a single, negative attribute. In the case of OST, individuals may possess overlapping stigmatised identities of ‘OST service user’, ‘drug user’ and ‘injecting drug user’. Some will be further stigmatised due to experiencing homelessness, being HIV or Hepatitis C positive or through involvement in sex-work.

“I found that loneliness, isolation and experiences of trauma and stigma were key barriers to recovery”

Community pharmacies are one environment where service users report experiencing a great deal of stigma. Unlike customers collecting other prescriptions, many OST service users receive their medications (methadone/buprenorphine) through an arrangement known as ‘supervised consumption’. This means they must be observed taking their medication by a pharmacist to ensure that it is not diverted to others. This is often conducted in full view of other customers, despite guidelines which recommend that this takes place in a private room or screened area. This leaves OST service users open to the scrutiny of the ‘public gaze’.

My findings have several implications in relation to stigma. Firstly, OST service users receive poorer care than other members of society in healthcare settings, which may result in them avoiding seeking help from drug treatment and for other health conditions. Secondly, stigmatising OST service users makes community re-integration extremely challenging and this has been directly linked to individuals returning to drug using networks as it is somewhere they feel a sense of belonging. The ultimate impact of being repeatedly exposed to stigma is an internalisation of these judgements, resulting in feelings of shame and worthlessness – again impacting on individuals’ ability to seek help and develop supportive new relationships with others.

Figure 1: Key facilitators and barriers to recovery, retention and completion in OST at each level of the socioecological model. Stigma is present at every level of the system.

What we discussed during the workshop

Being able to present these findings to key stakeholders was a real highlight of my PhD work; it’s not often that you have the ear of so many invested and engaged individuals in one ‘room’ (albeit a Zoom room!). The findings of my PhD chimed closely with the experiences of those in the room and would be further reflected the next day when Dame Carol Black’s Review of Drugs Part 2 was published, which made specific reference to stigma.

After I presented a short overview of my PhD findings, attendees spent time in small groups discussing how we might address OST stigma at each level of the socioecological system (see figure 1, above). A common thread that ran through each of the groups’ discussions was the importance of embedding interventions within trauma-informed frameworks. Attendees felt that increasing others’ understanding of the impact of trauma and ‘adverse childhood experiences’ (ACEs) may be a key mechanism by which to reduce stigma towards OST service users.

Indeed, a recent study found promising results in relation to this – that increasing the public’s awareness of the role of ACEs in substance use reduced stigmatising attitudes towards people who use drugs (Sumnall et al, 2021). Workshop attendees suggested that this outcome could be achieved through trauma-informed training of all individuals who might work with OST service users, such as pharmacists, the police and medical professionals, as well as those who work in healthcare settings, such as receptionists.

At the individual level there was a discussion about the way that stigma trickles down the socioecological system, resulting in self-stigma or internalised stigma. People felt that the best way to reduce this was to tackle stigma higher upstream first.

When thinking about reducing stigma in everyday inter-personal interactions, people highlighted the importance of using non-stigmatising language. For those who are interested (and I think we all should be!) the Scottish Drug Forum has published an excellent guide here.

Some excellent suggestions were made for reducing stigma that individuals experience in organisations such as pharmacies, hospitals and other settings. This is something that Dr Jenny Scott and I discussed in a recent article for the Pharmaceutical Journal (Scott & Carlisle, 2021). One attendee suggested the introduction of positive role-models within organisations who could be an exemplar of positive behaviour for others (a ‘stigma champion’ perhaps?). Training was identified as a key mechanism through which stigma could be reduced in organisations, including through exposure to people who use drugs (PWUD) and OST service users during training programmes. It was stressed however, that this should be carefully managed to ensure that a range of voices are presented and not just ones supporting dominant discourses around abstinence-based recovery.

Suggestions for improving community integration included increasing access to volunteering opportunities – something that people felt has been impacted by reduced funding to recovery services in recent years. It was also suggested that community and faith leaders could be a potential target for education around reducing stigma and understanding the impact of trauma, as these individuals may be best placed to have conversations about stigma with members of their communities.

Finally, there were some thoughtful discussions around the best way to influence policy to reduce stigma. The importance of showing policymakers the evidence-base from previous successful strategies was highlighted. Something that resulted in a lively debate was the issue of supervised consumption with arguments both for and against (this is also relevant at the organisational level). The group summarised that whilst diversion of medications was a risk for some, a blanket approach to supervised consumption and/or daily collections exposes individuals to stigma in the pharmacy, which leaves individuals vulnerable to dropping out of treatment. It was pointed out that supervised consumption policies were quickly relaxed at the start of Covid-19 restrictions – something that appears to have been done safely and with benefits to service users. It was also highlighted that supervised consumption in OST is inherently stigmatising, as users of other addictive drugs with overdose potential, such as other prescribed opioids and benzodiazepines, are not subjected to the same regulations. This sends a clear message to OST service users that they cannot be trusted. Other key suggestions were:

Communicating with CQCs and Royal Colleges, who may be particularly interested in understanding how people are treated in their services.
Drawing on existing stigma policies from other arenas e.g. mental health.
Highlighting the fiscal benefits of reducing stigma to key decision makers.
Tapping into plans for the new Police and Crime Commissioners, who have a trauma sub-group.
Linking into work with ADDER areas, which may provide the evidence for ‘what works’.

What next?

I am now planning to apply for further funding to develop an intervention to reduce organisational stigma towards OST service users. The involvement of service users and other key stakeholders will be crucial in every step of that process, so I will be putting together a steering group as well as seeking out collaborations with academics internationally that have expertise and an interest in this area. I was really pleased to see that Dame Carol Black’s second report makes some concrete recommendations around reducing stigma towards people who use drugs. I hope therefore to be able to work with the current momentum to make OST safer and more attractive to those whose lives depend on it.

I’d like to extend my gratitude to all of the attendees at the workshop and to Bristol’s Drug and Alcohol Health Integration Team (HIT) for supporting this event. If you are an individual with lived experience of OST, an academic, or any other stakeholder working in this area and would like to be involved with future developments, please get in touch with me at vicky.carlisle@bristol.ac.uk or find me on Twitter at @Vic_Carlisle.

References

Carlisle, V., Maynard, O., Padmanathan, P., Hickman, M., Thomas, K. H., & Kesten, J. (2020, September 7). Factors influencing recovery in opioid substitution treatment: a systematic review and thematic synthesis. https://doi.org/10.31234/osf.io/f6c3p

Scott, J & Carlisle, V (2021). A pharmacy resolution for 2021: let’s improve the way patients with addiction are treated. The Pharmaceutical Journal. https://pharmaceutical-journal.com/article/opinion/a-pharmacy-resolution-for-2021-lets-improve-the-way-patients-with-addiction-are-treated

Sumnall, H. R., Hamilton, I., Atkinson, A. M., Montgomery, C., & Gage, S. H. (2021). Representation of adverse childhood experiences is associated with lower public stigma towards people who use drugs: an exploratory experimental study. Drugs: Education, Prevention and Policy, 28(3), 227-239. https://doi.org/10.1080/09687637.2020.1820450

This blog was originally posted on the TARG blog on the 1 October 2021.

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

Posted on February 22, 2021February 22, 2021 by lindsey.pike

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

	University students via ProlificAged 18-24	Bristol, ALSPAC Aged 23-25		CSEW Aged 23-25
	2 years	1 year	Lifetime	1 year	Lifetime
Any illicit drug use^a	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/amphetamines^b	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).
^bOur 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)
	All (n=683)	Female (n=336)	Male (n=312)	Other (n=35)
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 use^a	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

Posted on October 24, 2019October 22, 2019 by lindsey.pike

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