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


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

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


by Olivia Maynard

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 Prolific

Aged 18-24

Bristol, ALSPAC

 

Aged 23-25

CSEW

 

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).
b 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)
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 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.

Leaving the Lab: Rising to the Challenge of Remote Research

Written by Angela Attwood and Maddy Dyer

COVID-19 impact on research

The coronavirus (COVID-19) pandemic has forced millions of us to embrace remote working, and researchers are no exception. Universities are closed and face-to-face research with human participants has been temporarily halted. This has created challenges for our research, and laboratory and field studies are particularly affected.

As part of a large research group at the University of Bristol, we had to respond to this new situation and develop contingency plans for our research. Our first step was to review ongoing research and identify which studies could be suspended. We were fortunate on two counts: 1) we were able to put data collection on hold for many of our studies because there was enough flexibility in our planned completion times, and 2) we were able to stay busy with other work (such as analysing or writing up data from completed studies or developing new grant applications).

In all honesty, we would probably have stopped there with our contingency plans if it hadn’t been for one study that did not have this flexibility. This study investigates smokers’ experiences of switching to e-cigarettes, and requires participants to vape and complete various tasks across a two-week period. If we weren’t able to deliver on this study by a fixed deadline of the end of 2020, the funding would be withdrawn and one of our research staff would have been out of work. This forced us to think again…

Challenges and opportunities

There have been more challenges than opportunities in the context of the COVID-19 outbreak. However, one thing this global crisis has encouraged is innovation and creativity. We responded to the pressing need to complete this project by adapting our protocol so that the study could be run remotely (with no face-to-face communication). Some studies can move onto online platforms (and some of our changes include the use of online surveys), but our study involves participation over a two-week period with multiple “visits” and the use of electronic cigarettes. This required substantial adaption of the study methods (see below for some examples), but we were able to produce a comprehensive revision that retained the necessary components to ensure valid testing of our original research question.

We had to overcome several practical challenges, such as how to screen participants for smoking status and pregnancy. In the laboratory, we typically verify smoking status using a carbon monoxide (CO) breath monitor – equipment that cannot be used during lockdown. To overcome this, we replaced the CO monitor with a cotinine urine test, which verifies smoking status by detecting a metabolite of nicotine in urine. These are dipstick tests that participants can take themselves and we verify the outcome via a video call by asking them to show us the used dipstick.

Another challenge was how to safely deliver the e-cigarettes, e-liquids, and screening tests to participants. We are doing this via post (using pre-paid postage), with carefully constructed information packs and cleaning instructions. All test sessions that collect primary outcome data are now taking place online. This includes a cue reactivity procedure that participants are led through via pre-recorded instructions that link to our online study materials. We are also exploiting ecological momentary assessment methods (daily messaging via mobile phones) to collect real-time data across the test weeks, and all face-to-face communication has been replaced with phone and video calls. We worked closely with our faculty research ethics committee and university IT services as we developed this protocol to ensure any new ideas were feasible and ethically sound (or to identify problems early and seek alternative solutions).

Our aim was to complete a project that otherwise would not have been possible. However, the important learning point was that in developing essentially what was an “emergency response” protocol, we have unlocked other important benefits. Before the COVID-19 outbreak, our biggest challenge was recruiting participants (we require smokers who are willing to abstain from smoking for one week!). As with all university-based research, we often rely on opportunistic recruitment that means recruiting from the local area (i.e., people who can easily attend the laboratory sessions during university opening hours), and our samples often comprise a relatively high number of students. This not only means we have difficulty recruiting, but that our samples are not always representative, and our results may not generalise to the wider population. This new model of working means we have no geographical restriction (as long as the post delivers and there is Internet provision – we can collect data!), hugely improving our reach and the diversity in our participant samples.

Another benefit is that data are collected in more naturalistic settings (although this comes with a loss of control that needs to be considered or may not be appropriate for some studies). For studies that require participants to attend multiple sessions, it is also likely there will be lower attrition (i.e., fewer drop-outs) as there is less burden on participants to travel to a testing laboratory.

This has not been an easy transition (although we will certainly be well equipped to do it again if we need to). It has been time consuming, and some aspects of the study were simply not possible in the context of the fixed time constraints and funding in place, and without the laboratory facilities. The utility of this approach needs to be considered on a case by case basis. But, for our study, it was doable. We are only at the start of this process – the study will be running throughout 2020 and we look forward to the ongoing challenge and reflecting on how we can optimise this process in future.

The important take-home message is that remote research is not something we will discard after the COVID-19 restrictions are lifted. We will refine these methods and embrace the benefits they offer. Remote research will not be an emergency response option only, but instead it will be an integral part of our research toolkit.

If you are interested in finding out more, please visit our website:

http://www.bristol.ac.uk/psychology/research/brain/targ/participants/smoker-experience-ecigs/

You can also follow us on Twitter: @BristolTARG @AngelaAttwood @MaddyLDyer

 

Understanding Anorexia – Promoting Life through Prevention

An essay by Caitlin Lloyd.

Emma was an anxious child, always worrying. At thirteen, her anxiety became centered on interactions at school – she was terrified of being judged negatively by classmates. Around this time Emma began dieting, intending to lose just a small amount of weight. It turned out she could do so relatively easily, and enjoyed the sense of achievement resulting from the numbers on the scale going down. Her diet continued, becoming more and more extreme. Emma’s weight plummeted.

Eight years later, having had two inpatient hospital admissions, Emma maintains a dangerously low body weight, achieved by setting strict rules around eating. A daily calorie limit is followed, and foods containing fat and sugar avoided. Eating takes place only at certain times, and each mouthful must be chewed ten times before swallowing. Any deviation from these rules, and the day is ruined.

Emma retook two years at school, falling behind her peers, but secured a place at Durham University to study mathematics. It is difficult to concentrate on her work though, because all Emma can think about is food: what she has eaten; and what she will eat. Her focus on food makes it hard to maintain friendships, and Emma has few. Emma spends university holidays with her family, the time dominated by arguments over food.

Sometimes Emma wishes things were different. But that means eating more, which feels impossible. Deviating from the rules makes Emma unbearably anxious. No amount of support can dispel the intense fear of becoming fat, or feelings of self-disgust that accompany weight-gain.

Emma is fictional but typical of someone with anorexia nervosa, an eating disorder characterised by persistent starvation in the context of a low weight and fear of weight-gain. In the UK it is estimated that as many as one in 25 women will experience anorexia in the course of their lifetime. Men develop anorexia too; roughly one in ten people with anorexia is male.

Anorexia usually develops during adolescence, and has many adverse yet long-lasting physical and mental health consequences. Starvation compromises the function of almost all major organ systems, and feelings of despair increase the risk of suicide; anorexia has the highest death rate of any mental health disorder.

Full recovery from anorexia is a lengthy process, and unfortunately not common. Treatments exist but not one is consistently effective. Fewer than half of those diagnosed with anorexia make a full recovery, and relapse rates are high – around 30-40% of people fall back into the disorder’s grip following initial recovery. For some, weight-gain is sustained, but a strict diet and overconcern with eating and weight remains, severely impacting quality of life.

The difficulty treating anorexia makes effective prevention vital. For this we need to target the factors that cause anorexia, requiring knowledge of what those factors are. My research investigates whether anxiety disorders play a causal role in anorexia development, to help us understand whether it would be beneficial to address anxiety in young people to prevent eating disorders.

It has long been suggested that the starvation of anorexia reduces anxiety. This would make dieting helpful (in this narrow sense) to those experiencing anxiety symptoms, encouraging the dieting to continue. Anxiety disorders and anorexia often co-occur. But correlation is not causation, and determining cause-and-effect is notoriously challenging.

As an example, for anxiety to cause anorexia development, anxiety must precede anorexia. Existing findings support this, however studies have tended to ask people with anorexia to recall the time before their illness developed. Experiencing anorexia may affect memory recall; to try and explain how their anorexia developed, someone with anorexia might believe themselves to have been more anxious in childhood than they actually were. In this case the conclusion that anxiety causes anorexia may be invalid. Many sources of potential error exist in research, meaning that many findings could be inaccurate, at least to some degree.

Different research methods have different strengths and limitations, and are thus prone to different biases. This can be used to our advantage: if findings across studies of different research methods point to the same conclusion, we can be more confident the conclusion is correct. I am using a variety of research methods, each designed to minimise the potential for erroneous conclusions, to determine the role of anxiety in anorexia. If a causal role is supported across the different studies, trialing interventions designed to reduce anxiety for eating disorder prevention is encouraged. If not, the search for other factors to target for improved eating disorder prevention continues.

We are at an early stage in understanding anorexia, but we do know that many people with the illness become ill at a young age, with their whole lives ahead – like Emma. My research matters because it aims to stop people losing their lives, and quality of life, to anorexia.

 

Does schizophrenia influence cannabis use? How to report the influence of disease liability on outcomes in Mendelian randomization studies

The recent Nature Neuroscience paper by Pasman et al entitled “GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia” (see below) provides important and novel insights into the aetiology of cannabis use and its relationship with mental health. However – in its title and elsewhere – it subtly misrepresents what the Mendelian randomization (MR) [1] analyses it presents actually show. MR analyses are increasingly being reported as demonstrating the effect of a disease (in this case schizophrenia) on the outcome, through using genome wide significant variants associated with risk of the disease on the outcome (which can be a behavior, such as cannabis smoking in the present paper, a measured trait or a second disease).

MR analyses are often carried out using summary data, where the exposure and outcome GWAS come from separate samples. In such analyses interpretation is not to apparent effects of the disease itself, but to the phenotypic effects of genetic liability to that disease. Typically, only a tiny proportion of participants in the outcome GWAS datasets will actually have experienced the disease – in this case particularly so given the low participation rate of people with schizophrenia in most studies in the general population. Indeed, MR studies can be carried out in datasets where there are no individuals with the outcome (e.g. datasets collected amongst an age group in whom the outcome will have occurred very rarely, if ever). Such analysis may reveal apparent, but impossible, effects of the disease on outcome phenotypes. To use MR analyses to investigate the causal effect of a disease on outcomes would require individual-level data with recorded disease events and subsequent follow-up. Analytical approaches to such data have, as yet, not been published.

The widespread misrepresentations of such MR studies have important implications, not just in terms of how the results are interpreted, but also how they are applied. One valuable contribution of MR studies is that they can identify modifiable exposures that can be the target of interventions. If it is recognized that what is being shown is an effect of liability to disease on an outcome, then interventions targeting the mechanisms of this liability would have benefits even in individuals who are unlikely to go on to develop the disease, including those at low risk of the disease for other reasons. For example, targeting breast cancer liability may have benefits in men if this liability influenced diseases that are common in men. If, however, it is the disease itself which has the effect, then the interventions would be targeted at those likely to develop disease: only women, in the case of breast cancer liability. It may be that schizophrenia does indeed lead to cannabis use, but the analyses reported by Pasman et al show only that liability to schizophrenia leads to cannabis use.

The point is a subtle one – we have both used similar language in the past in articles reporting MR analyses on which we are authors. Indeed, one of us (MM) was an author on the Pasman et al paper (and contributed principally to the MR analyses and their interpretation) but failed to suggest the correct phrasing.  Fortunately, the title and discussion will be changed to address this problem so that the enduring version of the paper captures this importance nuance (unfortunately, the original headline has already been repeated elsewhere [2]). However, it is a widespread and underappreciated point of interpretation in MR studies, and we feel that this presents a useful opportunity to highlight it. It also illustrates that methodologies, and the interpretation of the results they generate, continue to evolve, illustrating the need to interpret past work (including our own!) through the lens of current approaches.

[1] Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?  Int J Epidemiology 2003;32:1-22.

[2] Andrae LC. Cannabis use and schizophrenia: Chicken or egg?  Sci Transl Med 2018;10:eaav0342.

The House of Commons Science and Technology Committee reports on e-cigarettes

Written by Jasmine Khouja, PhD Student, Tobacco and Alcohol Research Group

Today sees the publication of a report on electronic cigarettes (e-cigarettes) by the House of Commons Science and Technology Committee. This compiles evidence from over 100 pieces of written submissions and five oral sessions, and highlights key issues around reducing harm, promoting smoking cessation and effectively regulating e-cigarettes. Since the report is quite long, we’ve tried to extract the main messages.

The report takes a relatively positive stance on e-cigarettes, encouraging use for smoking cessation and suggesting a more accepting approach to e-cigarettes in public spaces. This is in contrast to other countries, such as Australia, where a ban is in place due to the lack of long-term research on the health impact of using e-cigarettes.

Reducing harm

The general consensus from a variety of sources is that e-cigarettes are less harmful than combustible cigarettes. However, a frequent theme is that this does not mean that e-cigarettes are ‘safe’, and the report is careful to emphasise that e-cigarettes are not completely harmless. The relative harm of heat-not-burn tobacco products compared to combustible cigarettes is less clear. There is a lack of independent evidence as the majority of data on the safety and emissions of these product has come from Philip Morris, a major tobacco company.

The long-term effects of using e-cigarettes are currently unknown. It is difficult to assess the comparative harm of e-cigarettes without also measuring the effects of prior smoking, since there are very few long-term e-cigarette users who have never smoked. Exposure to second-hand e-cigarette vapour has been similarly difficult to assess, but since potentially harmful compounds emitted are present only at very low levels second-hand vapour is unlikely to be harmful.

E-cigarettes have become a popular tool for quitting smoking and an estimated 16,000 to 22,000 people who would not have quit using alternative products or willpower alone have successfully quit each year by using e-cigarettes. Although these figures are promising, there is a lack of high-quality evidence from randomised control trials showing how effective e-cigarettes are when quitting smoking. Other evidence has been inconclusive due to the low quality of some studies.

Despite fears that e-cigarettes may act as a ‘gateway’ to smoking, current evidence does not show that using e-cigarettes causes people to start smoking. Although there is a link between e-cigarette use and subsequent smoking initiation, very few never smokers regularly use e-cigarettes, so any causal link would have a limited impact on smoking rates.

Smoking cessation

Providing e-cigarettes on prescription could encourage smokers to try e-cigarettes without barriers such as money as well as give them more confidence in the product being less harmful than cigarettes. The report concludes that e-cigarettes should be available to those in NHS mental health services given high rates of smoking in this group.

NHS England were unable to provide evidence for how they were addressing this issue. They were unable to provide a representative because there is no one individual responsible centrally with ‘oversight’ of e-cigarette policies across NHS mental health trusts. The report criticises this, stating it was concerning and that a position should be created as a matter of urgency.

E-cigarettes are generally prohibited in closed spaces such as workplaces, public transport and restaurants and vapers are usually encouraged to vape outside within designated ‘smoking’ areas. Since second-hand vapour is unlikely to be harmful, these policies may be more harmful than beneficial; frequently exposing vapers to cigarettes and cigarette smoke may increase the likelihood that they will relapse to smoking.

Regulation

E-cigarettes are currently regulated under the Tobacco Products Directive (TPD; see our previous blog) if you want to learn more about these regulations). As part of this directive, the Medicines and Healthcare products Regulatory Agency must be notified before any e-cigarette or e-liquid can be sold in the UK.

Four key criticisms of the TPD were identified in the report: i) unnecessary limits on nicotine strength of refill liquids which may lead to failed quit attempts, ii) unnecessary tank size restrictions which may lead to failed quit attempts, iii) blocking advertising the relative harm-reduction of e-cigarettes which may discourage quit attempts, and iv) the ineffective notification scheme for e-cigarette ingredients which slows innovation.

Some TPD regulations are optional and give freedom to governments to be as restrictive as they feel necessary. Scotland has been more restrictive than England in their regulations by banning certain advertising of vapour products. Currently, health claims are banned from all media advertising of e-cigarettes without a medical license (of which none is currently available). The Advertising Standards Authority is currently reviewing the legislation on e-cigarette advertising and health claims and are considering allowing this in the future.

Unsurprisingly, there is uncertainty about the future regulation of e-cigarettes due to Brexit. Regulation of e-cigarettes may change after leaving the European Union and it is unclear what these changes may be or what potential impact increased flexibility in regulating e-cigarettes could have.

Conclusions

The report is comprehensive and raises some interesting questions particularly about the lack of NHS involvement in developing strategies for smoking cessation that utilise e-cigarettes. It will be interesting to see if the NHS responds to these criticisms by taking action. I am also interested to see what Brexit will mean for the regulation of e-cigarettes in the UK, given the criticisms of TPD regulations.

The full report can be accessed here: [The House of Commons Science and Technology Committee reports on e-cigarettes]

 

Why I took part in the “Preregistration Challenge”

By Sarah Peters

The preregistration of study protocols has a long history in clinical trials, but is a more recent innovation in many other areas. The hope is that it will help counter the “reproducibility crisis” in psychological science – the failure of many published findings to replicate reliably. Here I discuss my experience with the Open Science Framework “Preregistration challenge”, and argue for more widespread adoption of preregistering reports.

There is an ongoing methodological crisis in psychological science – the reproducibility crisis refers to the failure of many scientific findings to be replicated. The Reproducibility Project, a recent initiative led by Professor Brian Nosek at the University of Virginia, aimed to identify the scale of this crisis. A large collaboration between 270 project members reran 100 published psychological experiments, and found that just 36% of the initial findings were replicated. Similarly, some classic textbook experiments have proven difficult to replicate, and publication bias – whereby positive findings are more likely to be published and negative findings to be dismissed – plagues the field.

Given this, scientists are exploring how to improve the way we conduct research and thereby improve the quality of what we produce. One suggestion is to preregister our research question, methods and analysis plan in advance of data collection. It is hoped that public preregistration will limit analytical flexibility and post hoc hypothesising, thereby improving the transparency and robustness of research findings.

Curious about the benefits of preregistration, and to see how it differed from the way I’d previously conducted my research, my colleagues and I published a preregistration for a recent study on Open Science Framework (OSF). We were interested in whether Cognitive Bias Modification, a psychological intervention designed to shift the emotional interpretation of faces, would impact clinically-relevant outcomes. We also entered the study into OSF’s (ongoing!) Preregistration Challenge, which offers the chance to win a $1,000 prize to 1,000 researchers who go from preregistration to publication.

Preregistering our study did require a greater time commitment prior to running it, but thinking about our predictions, design, and analyses meant that we could spot any potential issues and improve our experimental design before we collected data (i.e., before it was too late!). As a preregistration is public and cannot be changed after it’s published, it forced us to think more carefully about our decisions. For example, thinking more carefully about whether our data would truly answer our question made us wonder whether the emotional biases we wanted to study might be more prominent when an individual is under stress, so we decided to include another task to measure this. Also, by knowing which statistical analyses we would conduct before recruiting participants we could ensure that our study was adequately powered and would meet the assumptions of the planned analyses.

Initially I was concerned that this approach could be limiting. What if we found something interesting that we hadn’t expected and wanted to run additional analyses to probe it? But a preregistered report doesn’t prevent that – it simply means that you would (honestly and transparently!) report those analyses as exploratory. This protection against HARKing (hypothesising after the results are known) is important; separating analyses as planned versus exploratory can prevent overconfidence in weaker findings and the publication of attractive, but uncertain, positive findings.

Following data collection, we went back to our preregistration. It was here that our earlier time investment paid off; once our data were cleaned we could immediately run our planned analyses, and much of the manuscript writing (introduction and methods) was already done. We also ran a number of exploratory analyses, such as whether our results were moderated by participants’ anxiety scores. We subsequently published our findings in the academic journal Royal Society Open Science, and were thrilled to receive one of the latest $1,000 Preregistration Challenge prizes for bringing our study from preregistration to publication!

While interpreting findings and making discoveries is an important aim of scientific research, it is just as important to continuously scrutinise the scientific method. As a scientist, there is no question that seeing data can influence my decisions and interpretations. However, the adoption of preregistration can eliminate this, make the process easier in the long term, and improve research quality overall.

Professor Nosek and other members of the Reproducibility Project argue that, “Progress in science is marked by reducing uncertainty about nature”. But, if scientific findings have not or cannot be replicated, we can’t be certain that they exist. Preregistration is a simple change to the way we do research that can help to halt the reproducibility crisis and produce effective and credible science.

Read more about how to take part in the Preregistration Challenge here.

See Peters et al.’s preregistration here, and the published study here.

Sarah Peters can be contacted via email at: s.peters@bristol.ac.uk.

Can cognitive interventions change our perception from negative to positive, and might that be useful in treating depression?

By Sarah Peters

Have you ever walked away from a social interaction feeling uncomfortable or anxious? Maybe you felt the person you were talking to disliked you, or perhaps they said something negative and it was all you could remember about the interaction. We all occasionally focus on the negative rather than the positive, and sometimes ruminate over a negative event, but a consistent tendency to perceive even ambiguous or neutral words, faces, and interactions as negative (a negative bias), may play a causal role in the onset and rate of relapse in depression.

A growing field of psychological interventions known as cognitive bias modification (CBM) propose that by modifying these negative biases it may be possible to intervene prior to the onset of depression, or prevent the risk of subsequent depressive episodes for individuals in remission. Given that worldwide access to proven psychological and pharmacological treatments for mood disorders is limited, and that in countries like the UK public treatment for depression is plagued by long waiting lists, high costs, side effects, and low overall response rates, there is a need for effective treatments which are inexpensive, and both quick and easy to deliver. We thought that CBM might hold promise here, so we ran a proof of principle trial for a newly developed CBM intervention that shifts the interpretation of faces from negative to positive (a demonstration version of the training procedure can be seen here). Proof of principle trials test an intervention in a non-patient sample, which is important to help us understand a technique’s potential prior to testing it in a clinical population – we need to have a good idea that an intervention is going to work before we give it to people seeking treatment!

In this study, we had two specific aims. Firstly, we aimed to replicate previous findings to confirm that this task could indeed shift the emotional interpretation of faces. Secondly, we were interested in whether this shift in interpretation would impact on clinically-relevant outcomes: a) self-reported mood symptoms, and b) a battery of mood-relevant cognitive tasks. Among these were self-report questionnaires of depressive and anxious symptoms, the interpretation of ambiguous scenarios, and an inventory of daily stressful events (e.g., did you “wait too long in a queue,” and “how much stress did this cause you on a scale of 0 to 7”). The cognitive tasks included a dot probe task to measure selective attention towards negative (versus neutral) emotional words, a motivation for rewards task which has been shown to measure anhedonia (the loss of pleasure in previously enjoyed activities), and a measure of stress-reactivity (whereby individuals complete a simple task under two conditions: safe and under stress). This final task was included because it is thought that the negative biases we were interested in modifying are more pronounced when an individual is under stress.

We collected all of our self-report and cognitive measures at baseline (prior to CBM), after which participants underwent eight sessions (in one week) of either CBM or a control version of the task (which does not shift emotional interpretation). We then collected all of our measures again (after CBM). In order to be as sure of our results as possible, there were a number of critical study design features we used. Our design, hypotheses, and statistical analyses were pre-registered online prior to collecting data (this meant that we couldn’t fish around in our data until we found something promising, then re-write our hypotheses to make that result seem stronger). We also powered our study to be able to detect an effect of our CBM procedure. This meant running a statistical calculation to ensure we had enough participants to be convinced by any significant findings, and their potential to be clinically useful. This told us we needed 104 individuals split evenly between groups. Finally, our study was randomised (participants were randomly allocated to the intervention group or the control group), controlled (one group underwent an identical “placebo” procedure), and double-blind (only an individual who played no role in recruitment or participant contact knew which group any one participant was in).

So, what did we actually find? While the intervention successfully shifted the interpretation of facial expressions (from negative to positive), there was only inconclusive evidence of improved mood and the CBM procedure failed to impact most measures. There was some evidence in our predicted direction that daily stressful events were perceived as less stressful by those in the intervention group post-CBM, and weaker evidence for decreased anhedonia in the intervention group. In an exploratory analysis, we also found some evidence that results in the stress-reactivity task were moderated by baseline anxiety scores – for this task, the effects of CBM were only seen in individuals who had higher baseline anxiety scores. However, exploratory findings like this need to be treated with caution.

Therefore, as is often the case in scientific research, our results were not entirely clear. However, there are a few limitations and directions for future research that might explain and help us to interpret our findings. Our proof of principle study only considered effects in healthy individuals. Although these individuals were clearly amenable to training, and may indeed have symptoms of depression or anxiety without a clinical diagnosis, our observation that more anxious individuals appeared to be more affected by the intervention warrants research in clinical populations. In fact, a reasonable parallel to the effects observed in this study may be working memory training, which does not transfer well to other cognitive operations in healthy samples, but shows promise as a tool for general cognitive improvement in impaired populations.

Future research is also needed to disambiguate the tentative self-report stress and cognitive anhedonia effects observed here. One possibility, for example, is that the 104 participants we recruited were not enough to detect an effect of transference from CBM training to other measures (the size of which is unknown). Given the complexity of any mechanism through which a computerised task could shift the perception of faces and then influence behaviour, it is likely that a larger sample is necessary. While it could be argued that if such a large group of individuals is warranted to detect an effect, that effect is likely too small to be clinically useful, we would argue that even tiny effects can indeed be meaningful (e.g., cancer intervention studies often identify very small effects which can have a meaningful impact at a population level).

Another explanation for our small effects is that while one week was long enough to induce a change in bias, it may not have been long enough to observe corresponding changes in mood. For instance, positive interpretation alone may not be enough – it may be that individuals need to go out into the world and use this new framework to have personal, positive experiences that gradually improve mood, and this process may take longer than one week.

Overall, this CBM procedure may have limited impact on clinically-relevant symptoms. However, the small effects observed still warrant future study in larger and clinical samples. Given the large impact and cost of mood disorders on the one hand, and the relatively low cost of providing CBM training on the other, clarifying whether even small effects exist is likely worthwhile. Even if this procedure fails to result in clinical improvement, documenting and understanding the different steps in going from basic scientific experimentation to intervening in clinical samples is crucial for both the scientific field and the general public to know. The current study is part of a body of research which should encourage all individuals who are directly or indirectly impacted by depression or other mood disorders. Novel approaches towards understanding, preventing, and treating these disorders are constantly being investigated, meaning that we can be hopeful for a reduction in the devastating impact they currently have in the not so distant future.

Read the published study here

Sarah Peters can be contacted via email at: s.peters@bristol.ac.uk 

Does calorie and unit information influence our drinking behaviour?

By Olivia Maynard

Over the past two years we’ve invited hundreds of people into the lab to drink beer. Unfortunately, we weren’t there to socialise; this was in the name of science. We wanted to know whether giving people information about the number of units and or calories in their beer influenced how much they drank and their perceptions of drinking.

There are strong arguments for including this information: providing unit information may increase knowledge about alcohol consumption and calorie information may help drinkers choose lower calorie (and as a result lower unit) beverages. However, we also wondered whether there might be some unintended consequences of providing this information, particularly for those who are highly motivated to drink. What if unit information simply allows these drinkers to choose higher strength drinks and calorie information only discourages them from eating more, not drinking less? What if discussion around mandatory unit and calorie labelling is distracting us from the bigger issues: health warnings, minimum unit pricing, improving treatment for alcohol dependence and stopping alcohol advertising to young people, to name a few?

So, with this healthy level of scepticism, we set about inviting 264 regular alcohol consumers (mostly undergraduate students) to attend a lab session where they were given some beer and completed some taste ratings. What participants didn’t know was that they had been randomly assigned to one of four conditions. One group had information about the calorie and unit content of the beers, one group just got calorie information, another had just unit information, and the final group got no information at all. As well as measuring how much beer they drank, we also asked participants to reflect on the likely impact of unit and calorie information on their drinking behaviour.

You can read all the results in our (open access) paper that was published this week in the journal Alcohol and Alcoholism. If you want the concise version: we found no evidence that either unit or calorie information influenced how much beer people consumed and we found a lot of variation in the amount people drank.

However, it was our analysis of participants’ thoughts on unit and calorie information that proved vital to understanding what was going on here. Our participants told us that their main motivation for drinking alcohol was usually to get drunk; where unit information was perceived as being helpful, this was to help them choose the highest strength drink. Unit and calorie information was seen as distracting from the social aspect of drinking, and although some participants felt that calorie information might reduce consumption, most thought it would affect others, not themselves. Some people thought that calorie information could be misused by encouraging people to eat less (to compensate), rather than drink less.

It’s interesting that even though the unit and calorie information was very visible in our study (on a piece of paper, presented for 10 minutes), those who had received this information were still very inaccurate when it came to reporting how many units and calories were in their drinks. They basically didn’t seem to have read or engaged with it. If they’re not reading it in this context, is it likely that drinkers will read this information when it’s printed in tiny font on the back of the bottle?

So, what does this all mean for any plans to introduce unit and calorie information? Our study only really tells us about the potential impact of unit and calorie information among young adults (many of whom were students) who tend to drink to get drunk. However, our findings do call into question whether mandatory unit and calorie labelling on its own would reduce how much people drink, and also highlights potential negative unintended consequences of providing this information.

Despite some of these potential unintended consequences, there are still reasons to include unit and calorie information, if only because it’s a consumer right (you know how many calories are in just about everything else you consume). However, perhaps more effort needs to be placed on making this information more engaging and embedding it into public understanding of recommended drinking levels. Coincidentally, an analysis of the public’s awareness of new national alcohol guidelines was also published yesterday. This report argues that although the public have a relatively high awareness of what the guidelines are, they should be put into context by increasing the public’s awareness of the links between alcohol and cancer. Perhaps using health messages such as ‘Drinking alcohol regularly is linked to long-term risks such as cancer’, alongside unit and calorie information, might result in more meaningful changes in attitudes and behaviours around drinking. I feel another study coming on….

Olivia Maynard can be found on Twitter at @OliviaMaynard17

Action for Brain Injury Week

By Eleanor Kennedy

It’s Action for Brain Injury Week this week (8th – 14th May), a campaign run by the non-profit brain injury association Headway.  This year the campaign is all about “A New Me”, giving a platform to survivors and their families to discuss how life-changing a brain injury can be. In honour of the campaign, I’m writing a summary about my PhD research on mild traumatic brain injury.

Traumatic brain injury (TBI) is an injury to the head that results in an alteration in consciousness. My work focuses on mild TBI, which injury involves symptoms such as confusion/disorientation, loss of consciousness of less than 30 minutes and/or memory loss around the event that led to the injury.

I’m interested in how mild TBI in youth may be associated with later behaviour. Initially I conducted a systematic review of the literature and found that there was evidence for an association between childhood mild TBI and behaviours such as substance use, committing crimes and behavioural issues. However, this was based on a small number of studies and there were some limitations to be addressed.

A key issue was the use of appropriate control participants. In this kind of research, the behaviour of participants with mild TBI has been compared to that of participants with no injuries. These control participants are usually similar to the mild TBI group in terms of demographic factors such as age, gender and socioeconomic background. However, these similarities do not consider injury factors that could also have an impact on behaviour, for example pain, absence from school, and the trauma of having an injury. A second control group that includes participants with a non-head-related injury addresses this issue.

In my own research, I use data from the Avon Longitudinal Study of Children and Adolescents (ALSPAC). This is a birth cohort that began in the early nineties when over 14, 000 pregnant women were recruited; biological, genetic, environmental and psychological information has been gathered on participating families ever since. Participants and their parents have answered questions relating to head injury and fractures at many time points across the children’s life time. It is possible to have a group with mild TBI, a group with broken bone history and a group with neither injury.

So far, we have explored the association between mild TBI from birth to age 16 years and risk behaviour at age 17 years. We found that participants with a mild TBI were more likely to use alcohol to a hazardous level than participants with a broken bone and participants with no injury. This is in line with previous research, and has important implications for recurrent TBI and recovery from TBI. Another finding was that participants with either a mild TBI or a broken bone were more likely to commit offences – suggesting that there may be common risk factors for acquiring an injury and criminal behaviour. For example, an individual who has the personality trait of sensation seeking could potentially be more likely to get into risky situations leading to injuries and to commit offences.

I recently presented these findings at the International Brain Injury Association’s 12th World Congress in New Orleans. At the conference, there was an exhibition of masks created as part of a project called ‘Unmasking Brain Injury’. Each mask was designed and decorated by a survivor of brain injury to share their experience; each mask was as unique as the individuals’ story. Projects that give a voice to people living with a brain injury, such as ‘A New Me’ campaign, are a reminder of the challenges that are faced when dealing with a brain injury. It’s a privilege to contribute research to this field and to listen to the voices of those experiencing it to promote awareness and compassion.