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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.

Alcohol brief interventions: how can content, provider and setting reduce alcohol consumption?

screen-shot-2016-09-23-at-10-11-24 

Alcohol brief interventions (ABIs) provide structured advice on alcohol use. They involve an assessment of individual risk with feedback and advice, brief motivational interviewing, or a combination of these techniques.

While the Government’s Alcohol Strategy (HM Government, 2012) recommends that ABIs be implemented increasingly inprimary care settings and accident and emergency (A&E) departments, the National Institute for Health and Care Excellence (NICE) calls for alcohol brief interventions to be offered by a range of practitioners and in a range of different settings.

Given national-level support for increasing and wider use of ABIs, this systematic review and multi-level meta-regression by Platt and colleagues assessed the effectiveness of ABIs on alcohol consumption and how effectiveness of ABIs differs by:

  1. Content of intervention,
  2. Provider group and
  3. Setting.
Alcohol brief interventions usually involve a combination of risk assessment, feedback, advice and brief motivational interviewing.

Alcohol brief interventions usually involve a combination of risk assessment, feedback, advice and brief motivational interviewing.

Methods

Studies were peer-reviewed randomised controlled trials (RCTs) where participants were randomly allocated to a control group (such as treatment as usual) or a group which received an alcohol brief intervention.

Brief interventions were defined as person-to-person discussions on alcohol, with between 1 and 4 sessions and a total of 2 hours intervention time. ABIs which were delivered in groups or via a computer were excluded as were those which included participants with complex health problems where it is difficult to generalise findings to the general population.

The primary outcome measure was a quantitative continuous measure of total alcohol consumption, reported as the standardised mean difference between ABI group and control group measured at follow-up. The authors also examined how ABIs influenced the frequency of alcohol consumption.

Different types of setting, provider and content were examined and these are shown (along with the number of studies in each category) in the Results section below.

A multi-level meta-analysis method was used, which allowed the authors to include a number of different effect sizes from individual studies (i.e. amount of alcohol consumed per unit of time and/or amount of alcohol consumed per drinking occasion) rather than just trying to selecting one comparable effect size for each study).

Results

Study characteristics

50 studies were included in the analyses, with 29,891 individuals contributing data. 45% of studies were conducted in the USA and 22% in the UK.

The percentage of studies which examined alcohol brief interventions with different types of content, providers and settings are shown below:

Intervention content:

  1. Motivational interviewing (MI) (48%)
  2. Enhanced motivational interviewing (MI+) (40%)
  3. Brief advice approaches (24%)

Intervention providers:

  1. Counselors (44%)
  2. General practitioners (22%)
  3. Nurses (18%)
  4. Different providers (12%)
  5. Peer-delivered (4%)

Setting of intervention delivery:

  1. Primary or ambulatory care in clinical settings such as outpatient services (38%)
  2. A&E services (20%)
  3. University (20%)
  4. Community-based delivery (12%)
  5. Hospital inpatient services (10%)

Quality of the evidence

71% of studies were classified as having a low risk of bias regarding randomisation and allocation concealment strategies. However, the method of allocation concealment was unclear in most of the studies. An intention-to-treat analysis was conducted in 47% of the studies and loss to follow-up was assessed in 80% of studies.

The overall impact of ABIs as compared with control conditions

ABIs reduced alcohol consumption by -0.15 SDs (95% confidence interval (CI) = -0.20 to -0.10) a result the authors describe as a ‘small but statistically significant effect’. However, the extent to which this is clinically meaningful is less clear.

Note: The authors present the effect sizes as SDs because they have summarised their data as standardised mean differences. This method is used when included studies all assess the same outcome, but measure it in a variety of ways. Although this makes sense statistically, it does make understanding how important these effects are clinically a little more difficult.

The authors found that this effect persisted after controlling for covariates and when conducting sensitivity analyses. The studies included in this analysis were found to have a small to medium level of heterogeneity (I2 = 37%; this figure is the percentage of variation between trials which is due to actual variation between studies as opposed to variation due to chance. A small I2 value means that the majority of the differences observed between studies was due to chance).

ABIs reduced frequency of alcohol consumed by a similar amount (-0.15 SDs, 95% CI = -0.20 to -0.11).

Content

Splitting studies by ABI content didn’t reduce the heterogeneity between studies (I2 = 39%: no, or little change in this I2 value from when all studies are considered together (I2 = 37%) indicates that this categorisation by content does not adequately explain the heterogeneity between studies).

However, it did appear that all content types were effective at reducing amount of alcohol consumed, and there was some evidence that while brief advice is more effective than MI or MI+ for amount of alcohol consumed, brief advice did not appear to reduce the frequency of consumption while MI and MI+ did.

Providers

Splitting studies by ABI provider was not found to reduce the heterogeneity between studies (I2 = 34%).

ABIs delivered by a range of different providers or by peers were not found to be effective at reducing amount consumed or frequency of consumption (although it’s important to note that very few studies were included in these categories).

There was evidence that interventions delivered by counselors, physicians and nurses were effective, with those delivered by nurses the most effective (-0.23 SDs amount consumed, 95% CI = -0.33 to -0.13).

Setting

Splitting studies by ABI setting didn’t reduce the heterogeneity between studies (I2 = 34%).

There was no evidence that ABIs delivered in hospital inpatient services and in community settings were effective in reducing either amount or frequency of alcohol consumed.

Interventions delivered in A&E, ambulatory care settings and in universities were found to reduce alcohol both amount and frequency of alcohol consumed.

This review suggests that alcohol brief interventions have a ‘small but statistically significant effect’, but it's unclear whether or not this is clinically meaningful.

This review suggests that alcohol brief interventions have a ‘small but statistically significant effect’, but it’s unclear whether or not this is clinically meaningful.

Conclusions

The authors conclude that their study provides:

important new evidence on how the effectiveness of brief alcohol interventions differs by setting, provider and content.

While this analysis does show that ABIs reduce amount of alcohol consumed and frequency of consumption, the size of this effect is small. It will be important to determine to what extent this is a clinically meaningful effect.

Although the authors claim that their findings suggest that the “provider of interventions may matter” (with nurses providing the best results) there is only weak evidence for this. As the categorisation of studies by provider (and setting and content for that matter) didn’t really have any impact on the heterogeneity (as measured by I2) between studies, there is little evidence that the effectiveness of ABIs differed meaningfully across providers.

Interventions delivered by nurses appeared the most effective, although further work is needed to confirm this finding.

Interventions delivered by nurses appeared the most effective, although further work is needed to confirm this finding.

Strengths and limitations

Strengths

As the authors used a multi-level meta-analysis, they were able to include all relevant outcomes into their analysis, rather than just picking one outcome (and consequently having to exclude studies which did not assess this outcome). This is also likely to have reduced study level heterogeneity.

Limitations

As the authors were interested in the difference in effectiveness of a range of different ABI settings, providers and contents, the number of studies included within each of these categories was small. This makes drawing firm conclusions regarding the effectiveness of particular forms of ABIs difficult.

Implications

Given that there is little evidence to suggest that the effectiveness of alcohol brief interventions differs meaningfully according to setting, provider or content, the authors do note that this indicates that resources should be allocated to those settings, providers and contents where ABIs are likely to be most cost-effective and feasible.

For example, A&E may not be the best setting for ABIs given the lack of privacy, the brevity of the visit and the fact that the patient is likely to be suffering from a severe injury at the time.

Nurses are likely to be well placed to provide ABIs given their repeated contact with patients, although appropriate training should be provided to nurses so that they can embed these practices into their care.

Focusing on interventions that are feasible and cost-effective seems like the biggest practical advice from this evidence.

Focusing on interventions that are feasible and cost-effective seems like the biggest practical advice from this evidence.

Links

Primary paper

Platt L, Melendez-Torres GJ, O’Donnell A, Bradley J, Newbury-Birch D, Kaner E, et al. (2016) How effective are brief interventions in reducing alcohol consumption: do the setting, practitioner group and content matter? Findings from a systematic review and metaregression analysis. BMJ Open. 2016;6(8).

Other references

HM Government (2012) The Government’s Alcohol Strategy PDF. CM 8336, March 2012.

Photo credits

From number crunching to brains: my experiences of interdisciplinary research

by Michelle Taylor @chelle_bluebird

From TARG to neuroscience

The final six months of a PhD can be a stressful time. Not only are you trying to write up three years of research, wondering whether you have done enough work, but you also need to consider what to do next. I decided to try my hand at something different…

eeg1

My PhD was in the area of epidemiology, where I was using large datasets (such as the Avon Longitudinal Study of Parents and Children, based here at the University of Bristol) to determine causes and consequences of using various drugs of abuse. My time was mainly spent designing and conducting statistical analyses on data that had already been collected and were available for secondary analysis. I completed this work in TARG and the School of Social and Community Medicine and was lucky enough to be funded by the Wellcome Trust on a PhD programme in molecular, genetic and lifecourse epidemiology. The Wellcome Trust also fund two other PhD programmes at the University of Bristol, one in ‘Neural Dynamics’ and another in ‘Dynamic Cell Biology’. Towards the end of my PhD an opportunity arose – the Elizabeth Blackwell Institute were offering three researchers fellowships to conduct nine months of research with one of the other Wellcome Trust programmes. This would involve changing research area and learning something completely new – and I decided to go for it. I applied to move to the Neural Dynamics programme. As my past research had focused on addiction and mental health, gaining knowledge of the field of neuroscience seemed fitting.

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After identifying a potential new supervisor and quickly putting together and submitting an application I was told that I had been successful. I was go
ing to become a neuroscientist for the next nine months. The day after handing in my PhD I headed off to the lab of Matt Jones, a neuroscientist whose research interests include sleep, memory and brain circuitry. I was going to be working on a study that aimed to find out more about how genes influence overnight brain activity and memory in humans. I’ve written a little more about this study at the end of this blog post, just in case you’re interested!

My new lab group were very friendly and welcoming, although at times it seemed like they were talking in a different language. I would attend seminars in my new department and be completely confused within minutes. While I did have some knowledge of neuroscience from reading literature, my knowledge was severely lacking compared to that of my new colleagues. Mind you, I could always get my own back by blinding them with statistics!

 

The study involved getting participants to stay in our sleep clinic overnight and measuring their brain activity while they slept. I had to learn new methods of data collection, which involved measuring a person’s head to find specific points and gluing on electrodes to measure their brain activity (known as PSG, or polysomnography) [1]. Once these data were collected, the night’s recording needed to be scored into various stages of sleep. We can determine this from the length, height and frequency of the waves on the sleep recording. There are two main stages of sleep: REM (which stands for rapid eye movement) and non-REM. Non-REM can be broken down further into stages 1, 2 and 3 [2,3]. Stage 3 is the deepest stage of sleep, while stage 2 contains oscillations called spindles and K-complexes which are thought to play a role in memory consolidation while we sleep [3]. Learning to score a night’s sleep was something very new to me. I was used to having my data in the form of numbers in a spreadsheet not as wavy lines dominating the computer screen!

brain_activity

At the end of the nine months, I found myself understanding the talks that I went to – I even started to sound like a neuroscientist myself at times. Many things which originally seemed overwhelming (such as collecting PSG data) now feel like second nature, and the wavy lines on a computer screen are now meaningful. While at first the experience seemed daunting, it has no doubt opened my mind and expanded my knowledge. The ability to conduct interdisciplinary research is a well-regarded asset, but this experience has not only enhanced my CV. It has increased my confidence when talking to other researchers, as I have realised that we can all learn something from one another. Most importantly I have learned to look at research from a broader perspective – what does my research mean for other fields? How can it inform other research that is different from my own? It is, of course, combining the answers to all of these questions that will enhance science and in turn have more impact on the wider world.

My neuroscience experience has come to an end and for now, it is back to epidemiology. But I will definitely look back on my time in neuroscience fondly, and, who knows, I might even get the chance to integrate epidemiological research with neuroscience in the future…

 

A little more about the study

Different parts of our brains communicate with one another as we learn new information during the day. Overnight brain activity then helps us to file memories for long-term storage. Evidence suggests that this process varies naturally in everyone. To help us understand what causes this variation, we are interested in finding out more about how genes influence overnight brain activity in healthy individuals. Studying our genes by specifically testing those who carry particular (naturally occurring) form of them can help us understand their role in shaping the natural variation we see in brain activity. Importantly, understanding this in healthy people can then go on to help us develop new targets for treatments to help the sick. We therefore carried out a study to look at how naturally occurring variation at a particular gene variant affects memory consolidation during sleep.

The gene variant was chosen based on previous studies that have shown that it affects both brain activity and sleep. To do this, we invited back participants from the Avon Longitudinal Study of Parents and Children who had provided us with a DNA sample. Information about their genes had been processed and based on this information they were identified as being carriers or non-carriers of the gene we were interested in. This is a study design known as ‘recall-by-genotype’. We then asked these people to spend two nights in a sleep laboratory, perform some memory based tasks and complete some questionnaires so that we can measure how genetic differences relate to memory and brain activity during sleep.

motionwatch_wrist_smlWhilst participants were in the sleep facility we attached a number of sensors to their head in order to record their brain waves, eye movements and muscle activity. We also used sensors on the chest to measure heart rate and take video and audio recordings to confirm whether or not participants become unsettled during the night. Participants were asked to complete some questionnaires about their sleep behaviour and to carry out a memory task before and after sleep.

For the two weeks in between visits to the sleep laboratory, we asked participants to wear an ‘actiwatch’. An actiwatch looks like a normal watch and records movement, telling us when the participant usually goes to sleep and wakes up. We asked participants to wear the actiwatch on their wrist at all times and asked them to fill in a sleep diary for the two weeks.

What do we hope to find?

actigraphyWe hope to find that individuals who carry our genetic variant of interest differ from those who do not carry the variant on a range of sleep characteristics including the non-REM stage 2 spindles and slow wave oscillations found on stage 3 of non-REM sleep. We also expect to find difference between genotype groups on ability to complete the memory task, and the speed at which they complete the memory task. Finally, we expect to observe a correlation between the stage 2 sleep spindles and the results of the memory task. If we observe these results in our data, then this will suggest that this genotype can influence brain activity during sleep which then in turn can effect a person’s memory, as this memory is not being consolidated as well over night.

 

Where can I find out more?

A protocol for this study has already been published [4].
Once completed, this study will be published open access within a scientific journal.

References:

[1] Wikipedia – polysomnography

[2] American association of sleep

[3] Wikipedia – sleep (including information on stages, spindles, K-complexes and slow waves)

[4] Hellmich C, Durant C, Jones MW, Timpson NJ, Bartsch U, Corbin LJ (2015) Genetics, sleep and memory: a recall-by-genotype study of ZNF804A variants and sleep neurophysiology. BMC Med Genet 16:96

Cannabis and mental illness: it’s complicated!

By Suzi Gage @soozaphone

This blog originally appeared on the Mental Elf site on 11th May 2016.

The use of recreational drugs is seen at much higher rates in populations with mental health problems than in the general population, and this is true for both legal substances such as alcohol and tobacco, as well as prohibited substances like cannabis.

But understanding what these associations mean is problematic:

  • Do the substances cause psychiatric problems?
  • Do people use recreational drugs to self-medicate?
  • Or, is there some other factor earlier in life that can lead to both risk of substance use and mental health problems?

The impact of cannabis (Hamilton, 2016) on mental health (Kennedy, 2015) is of particular interest in the USA, where cannabis is now legal in some states, and decriminalized in a number of others. There is a fear that cannabis use will increase, and therefore there is a pressing need to understand the nature of its association with psychiatric problems.

Blanco and colleagues state that this is their particular motivation for undertaking the research they have just published, to try and understand whether cannabis use predicts later substance use disorders, and also mood and anxiety disorders.

Methods

This study used a very large sample of adults in the USA, measured at 2 time-points, 3 years apart. Cannabis use in the past year was assessed at wave one, and a variety of outcomes were assessed at wave 2. These were cannabis use disorder, alcohol use disorder, nicotine dependence, other drug use disorder, mood disorder (including depressive disorder, bipolar I or II and dysthymia), and anxiety disorder (including panic disorder, social anxiety disorder, specific phobia, and generalized anxiety disorder). These were all assessed using the Alcohol Use Disorder and Associated Disabilities Interview Schedule DSM-IV.

Regression analyses were used to look at the associations between cannabis and these disorders, before and after adjustment for a variety of other factors that might influence both cannabis use and mental health, and therefore could be confounding the relationship. These were socio-demographic characteristics, family history of substance use disorder, disturbed family environment, childhood parental loss, low self-esteem, social deviance, education, recent trauma, past and present psychiatric disorder, past substance use disorder and history of divorce.

The authors also used propensity score matching to try and further account for these confounders. This is a technique where cannabis users and non-users are matched by their values for the confounding variables, then compared. If confounding is the same between cannabis users and non-users, it cannot therefore drive the associations seen, meaning they’re more likely to be causal, rather than due to other factors (although confounding has to have been known about and measured for this to be the case). The sample size is a lot smaller for these analyses, with 1,254 people in each group.

Cannabis is now legal in some US states, so evidence about it's potential risks is now even more in demand.

Cannabis is now legal in some US states, so evidence about it’s potential risks is even more in demand.

Results

Of the 34,653 participants in the study, only 1,279 (roughly 3.5%) reported having used cannabis in the past 12 months at wave one. Before taking confounders in to consideration, cannabis use at wave one was associated with substance use disorders and mood and anxiety disorders. However, this changed after accounting for the factors the authors believed might confound the relationships.

Across the regressions and the propensity matched analyses, adjustment for confounders attenuated the associations between cannabis use and later mood and anxiety disorders, suggesting that these might be due to confounding. Conversely, associations remained between cannabis use and later substance abuse and dependence. This was particularly strong for cannabis abuse, as might be expected.

  • Cannabis use at wave one was associated with around a 7x increased risk of cannabis abuse or dependence at wave 2
  • Cannabis users also had 2-3x increased risk of alcohol use disorder or any other drug use disorder
  • Cannabis users also had around 1.5x increased risk of nicotine dependence.
Cannabis use was found to increase the risk of various substance use disorders.

Cannabis use was found to increase the risk of various substance use disorders.

Conclusions

The study found evidence that cannabis use predicts substance use disorder, even after adjustment for confounding. However, they also found that associations between cannabis use and later mood and anxiety disorders seemed to be due to confounding, rather than there being a causal association.

The authors concluded:

These adverse psychiatric outcomes [substance use disorders] should be taken under careful consideration in clinical care and policy planning.

After confounders had been taken into account, cannabis use was not found to increase the risk of mood or anxiety problems.

After confounders had been taken into account, cannabis use was not found to increase the risk of mood or anxiety problems.

Strengths and limitations

A strength of this study is the use of a nationwide sample, assessed at two different time points, and that they had a really big sample size. The authors also took steps to try and keep the sample representative, even after drop-out between wave one and wave two. The consideration of confounders is also a strength, although of course causation cannot be ascertained from observational data; a limitation that the authors themselves acknowledge.

When studies are very large, as this one is, it can be hard to get really accurate measures, because of the amount of time it takes to interview 35,000 people! It is particularly impressive that the outcome measures are all according to DSM-IV criteria. However, as all these measures were taken from an Alcohol Use Disorder interview, the measures of mood and anxiety may be less good (the interview has weaker test-retest reliability for mood and anxiety disorders than for substance use disorders).

The rate of cannabis use in this study (roughly 3.5%) seems very low; the UN’s World Drug Report in 2011 (UNODC, 2011) put previous-year cannabis use in the USA at 13.7%. The data used in the Blanco study were collected in 2001, so perhaps cannabis rates have increased since then. It is notoriously hard to monitor rates of illicit drug use as people may not be keen to honestly report their use; indeed, this may be a problem in this study too, meaning people might be misclassified.

The use of other substances at wave one isn’t necessarily adequately controlled for; pre-existing substance use disorders are controlled for, but less extreme use of a substance isn’t. So these participants that are using cannabis might also be smoking cigarettes, drinking alcohol, or using other illicit drugs. There’s no way to know from this study which came first, and this makes it difficult to know whether cannabis is causing the associations seen, or whether it could be another substance, for example.

While the use of propensity score matching is perhaps a stronger method to assess causation than simply adjusting for confounders, the technique cannot take in to account confounders that vary over time, as these could vary differently between cannabis users and non-users, and still be confounding the association despite being the same at one time point.

Although the authors rightly highlight that associations of cannabis use with later substance use disorders are robust to confounding, their conclusions don’t highlight that adjustment actually reduced the association between cannabis use and later mood and anxiety disorders to the null. I think this is a really interesting finding, and maybe should have been made more of.

Why did the authors not make more of their finding that cannabis use does not increase the risk of depression or anxiety?

Why did the authors not make more of their finding that cannabis use does not increase the risk of depression or anxiety?

Summary

This is a well designed study on a really large sample, and provides useful information about associations between cannabis use and later substance use disorders, as well as suggesting that perhaps associations between cannabis use and mood and anxiety disorders might be due to other factors, rather than due to cannabis causing these outcomes. It still doesn’t really tell us why cannabis use might increase the risk of substance use disorders, and doesn’t tell us that cannabis is causing this increase of risk.

Links

Primary paper

Blanco C, Hasin DS, Wall MM, et al. (2016) Cannabis Use and Risk of Psychiatric Disorders: Prospective Evidence From a US National Longitudinal Study. JAMA Psychiatry.Published online February 17, 2016. doi:10.1001/jamapsychiatry.2015.3229. [PubMed abstract]

Other references

Hamilton I. (2016) Cannabis: what do we know and what do we need to know? The Mental Elf, 17 Mar 2016.

Kennedy E. (2015) High potency cannabis and the risk of psychosis. The Mental Elf, 24 Mar 2015.

UNODC (United Nations Office on Drugs and Crime) (2011) UN World Drug report 2011. United Nations.

Photo credits

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/cannabis-and-mental-illness-its-complicated/#sthash.U9604663.dpuf

The European Tobacco Products Directive and the future of e-cigarettes in the UK

By Jasmine Khouja @Jasmine_Khouja

E-cigarettes have become a popular product among smokers and ex-smokers, and Action on Smoking and Health (ASH) estimates that there are 2.6 million current users of e-cigarettes in the UK. As an alternative to tobacco smoking, research commissioned by Public Health England estimates that e-cigarettes are likely to be roughly 95% less harmful. The evidence supporting these popular and effective quitting aids suggests that e-cigarettes could be a powerful tool for harm reduction amongst current smokers but there is still uncertainty over the safety of e-cigarettes. Limited research concerning the effects of long-term use and the current lack of strict regulation of the products has fuelled this uncertainty but new regulations have been introduced into the pre-existing European Tobacco Products Directive (TPD) to rectify this. The updated TPD will come into force on 20th May 2016 with a transitional period allowed by the TPD. UK e-cigarettes and refill containers which are not in compliance with the TPD will be allowed to be released for sale on the UK market until 20th November 2016, but from 20th May 2017 all products sold to consumers will need to be fully compliant with the TPD. The alternative to following the regulations set by the TPD will be for e-cigarettes to gain a medical licence from the Medicines and Healthcare products Regulatory Agency (MHRA) and be regulated as licenced medicinal products to be sold in the UK.

jaz blog

As I am about to commence a PhD investigating the reasons for e-cigarette use, I am interested in what the implications of the directive will be in the UK; will it encourage smokers to switch to e-cigarettes, consequently reducing harm to themselves and others, or will it result in a reduction of available products and cause an increase in relapses to smoking?

I have read the directive and listed some of the key changes that will happen and added my own thoughts on what may happen as a result.

  1. CHANGE: New e-cigarette products must be notified to the MHRA six months before their release to the public. E-cigarette companies will be charged £150 to notify MHRA of a new product and £80 for a modification to an existing product, and will then be charged £60 annually thereafter. POSSIBLE OUTCOMES: The MHRA should have more control over the products on the market and be able to prevent unsafe products entering the market but it may take longer for new products to become available to buy. Additionally, some existing products will be unavailable from 20th May 2017 if they do not to comply with the regulations by 20th November 2016.
  1. CHANGE: Under the TPD, e-liquids will only be allowed where the nicotine concentration does not exceed 20 mg of nicotine per ml of liquid. E-liquids containing more than 20 mg of nicotine per ml of liquid will have to gain a medical licence authorised by the MHRA. POSSIBLE OUTCOMES: People may reduce their doses of nicotine and reduce their addiction if their preferred dosage is no longer available. Fewer high dosage products may be available as gaining a medical licence is an expensive process (estimated between £87,000 and £266,000 annually over ten years for a single device). When current products with high dosages such as 36 mg of nicotine per ml of liquid become unavailable, people may use lower dosages such as 20 mg of nicotine per ml of liquid as a substitute and inhale twice as much vapour to get the same nicotine hit. Nicotine is not the only constituent of vapour though; there are low concentrations of other toxicants, so inhaling more vapour means inhaling more toxicants. Alternatively, current higher dosage users may relapse to tobacco smoking if they feel the lower dosages do not effectively deliver the nicotine hit they need.
  1. CHANGE: Products regulated under the TPD must provide information to the MHRA on the safety and contents of e-cigarette products (including ingredients, toxicants and emissions). Health warnings, instructions for use, information on addictiveness and toxicity must also appear on the packaging and accompanying information leaflet. POSSIBLE OUTCOMES: This should allow e-cigarette users to make informed choices. The notification fees mentioned above will include the storage of this information but the companies may have to bear extra costs in testing their products for the amount of toxicants and emissions produced. These tests will have to comply with the standards set in the TPD and by the MHRA which may prove too costly for smaller e-cigarette companies, forcing them to withdraw products from the market. This could leave the market open to the tobacco industry who generally have greater financial resources available to them. The tobacco industry have to also sustain the tobacco market; a consequence of this may be the deliberate placement of ineffective e-cigarette products on the market to encourage current smokers continue to smoke tobacco and ex-smokers using e-cigarettes relapse.
  1. CHANGE: E-cigarette products will be child-safe, will not break or leak during the refill process, and containers will not exceed 10 ml (refill cartridges will not exceed 2 ml). POSSIBLE OUTCOMES: This should prevent accidents involving children consuming dangerous levels of nicotine. Most changes will be made to newer devices, which require e-liquid refills. If these modifications aren’t made by 20th November 2016 the products will be removed from the market by 20th May 2017.
  1. CHANGE: Under the TPD, cross-border advertising will be banned, which includes in newspapers, radio and TV, but not on billboards and posters. Products will not be allowed to make smoking cessation or health claims. Advertising of products with a medicinal license will be allowed under “over the counter” medicine rules. POSSIBLE OUTCOMES: This should minimise the amount of e-cigarette advertising seen by those who should not use e-cigarettes such as children and non-smokers. However, only e-cigarette companies who can afford a medical licence will be able to advertise on TV and this could mislead people into thinking that these products are more effective than other products.

A possible outcome for many of these changes is the loss of products from the market because of non-compliance with the regulations. Although increased reassurance that e-cigarettes on the market meet certain quality standards may encourage new users, the removal of any e-cigarette product from the market will provide an opportunity for e-cigarette users to relapse to smoking; without their favourite brand or flavour, it may be easier for them to resume smoking again than to find a replacement that suits their needs and taste. This in turn could lead to increased levels of smoking, and therefore harms to both individuals and society as a whole. Additionally, high nicotine dosage e-cigarette users may be encouraged to inhale more vapour and therefore unnecessary amounts of other constituents. However, recent preliminary research findings from ASH UK suggest there are few high dosage users meaning that this should not affect many.

The withdrawal of products is likely to be determined by the cost of making products compliant. Tobacco companies generally have greater financial resources than e-cigarette companies, with the top companies making billions in profit each year, meaning they can afford to make the necessary changes to meet the new regulations. The few e-cigarette companies that are owned by tobacco companies mainly produce ‘cigalikes’ which are the least effective design of e-cigarettes and there is a higher chance of relapsing to smoking when using them compared to later-generation devices. Given that the tobacco-owned e-cigarette companies will probably have greater resources available to them, they could end up with a monopoly on the e-cigarette industry. In fact, this may already be happening; the first medically licensed e-cigarette is a ‘cigalike’ owned by British American Tobacco. This means British American Tobacco could own the only TV-advertised e-cigarette (until another company gains a licence). Consequently, smokers looking to try e-cigarettes may choose less effective devices because they are more widely advertised.

These changes may reassure the general public that the devices will be safe but may lead to many ex-smokers relapsing because they are forced to use e-cigarettes and e-liquids that do not meet their needs, all the while lining the pockets of the tobacco industry by allowing them a monopoly on higher nicotine dosage products. Of course, the possible outcomes stated here are speculative; research will need to be undertaken to evaluate the ongoing impact of the new guidelines.

Links

  1. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/489981/TPD_Cons_Gov_Response.pdf).
  2. http://www.telegraph.co.uk/news/health/news/12079130/E-cigarettes-win-first-approval-as-a-medicine-opening-way-for-prescription-by-the-NHS.html
  3. https://www.gov.uk/government/consultations/regulatory-fees-for-e-cigarettes
  4. http://ash.org.uk/files/documents/ASH_1011.pdf
  5. https://nicotinepolicy.net/documents/reports/Impacts%20of%20medicines%20regulation%20-%2020-09-2013.pdf

Photo Credits

http://ecigarettereviewed.com/ – Lindsay Fox

Journey to the front of the lecture theatre

Sally Adams reflects on the journey from student to lecturer as she begins a lectureship in health psychology at the University of Bath and looks forward to continued collaboration with TARG.

This week saw students all over the UK collecting their A-Level results, and I was reminded of collecting my own, 13 years ago. Disappointingly, I didn’t achieve the grades I expected. I was advised to consider a different degree course other than psychology by my school career advisor. However, even with relatively poor grades I was certain that psychology was for me. This interest in psychology has been a feature throughout my career and has motivated me when things were tough.

I managed to convince (read as: rang the same university 3 times in the space of a few hours, professing my undying love for cognition and behaviour) the University of Wales, Institute Cardiff to offer me a place to study. From this point I promised myself I would take every opportunity to be proactive and hardworking as I had been given this amazing opportunity. I finished my undergrad with a 2:1 and an offer to return to the university as a research assistant. I was invited to interview for this post with a few other students from my year. I like to think that this opportunity was the result of my work ethic and enthusiasm for the subject.

This post was the beginning of my interest in health psychology, specifically the psychology of health and well-being and the factors that underlie health behaviours (e.g., engaging in exercise, drinking alcohol, and cigarette smoking). At this stage I was still unsure whether to pursue a career in clinical health psychology or research. My experience of research up to this point was largely entering and analysing questionnaire data and the prospect of a career of “data entering” did not particularly light my fire!  However, my impression of research was forever changed during a placement as part of my masters in health psychology at the University of Bath. I was assigned to shadow Marcus Munafò at the University of Bristol and as they say the rest is history!  Without any over-statement I can safely say my mind was blown; everything I thought about research was turned on its head. My masters project investigated the role of dopamine in cigarette craving and processing biases towards cigarette cues (e.g., a packet of cigarettes, seeing someone else smoking). This was a clinical study, which involved lots of planning, developing study documents and recruitment and testing of participants. The placement was a new challenge which I relished and I was amazed at how well-designed and rewarding human lab-based studies could be.

My passion for research and specifically experimental studies was consolidated following a research assistant post in Catherine Harmer’s lab group at the University of Oxford. It was around this time I started to have my own focused ideas and research questions. Itching to start answering these questions I began to apply for PhD studentships. It was a tough time as I was rejected from several programs and I started to doubt my ability to pursue a career in research psychology. My post in Oxford brought me back in contact with Marcus at Bristol and we decided to put in an application for a PhD studentship. I was especially excited by this application as it was based on my own research questions and in a subject I was very passionate about-alcohol use.

The day I found out I received a University of Bristol scholarship was amazing, it felt like a massive step in my career journey. I was fairly late in starting my PhD, aged 26, but with several years of research assistant experience under my belt I felt ready and extremely excited to return to studying. My PhD is easily one of the best experiences of my life. Every day was different; sometimes I would be sitting in a cafe reading papers, and sometimes I would be designing experiments or testing in the lab. My PhD was an exciting rollercoaster of highs (completing studies, presenting my own research at conferences, publishing papers) and lows (hours of experiment programming, paper rejections, no-show participants), but overall it was a great experience. One of my proudest achievements during my PhD was being awarded several travel awards to attend international conferences. This required a lot of proactive effort on my part but having a very supportive supervisor was extremely important too. TARG in general was a great supportive environment during my PhD, a culture of collaboration in a research group saved me from some hairy moments.

I was fortunate enough to begin my postdoc career in TARG. I still felt I had lots to learn from working with Marcus and the research group. My postdoc has actually been the steepest learning curve of my research career, but also the most rewarding. Learning to juggle all of the roles in my post has been pivotal in preparing me to become an independent scientist. Alongside running studies and writing papers came new responsibilities including grant writing and supervision. I have been lucky enough to secure my first small grant to research a form of cognitive training for reducing cigarette use. This was a great feeling and has given me the confidence to apply for larger grants. However, as my responsibilities increased, so did my workload and rejections. Throughout my postdoc I have had to learn how to better manage my time and to delegate. I found this very difficult to begin with after doing everything for myself as a PhD student. However it has been an essential lesson to learn along with developing a thicker skin for paper and grant rejections. For me, my thirst for understanding the thought processes and behaviours that guide health behaviours has motivated me to keep working long hours and keep applying!

So, back to present day: I am due to start my first lectureship in the next few days and I couldn’t be any more nervous or excited. When I was first offered the post I was terrified about the idea of “going it alone”, but in the last few months, looking back on what I have learnt I finally feel ready to fly the TARG nest. I take with me the confidence to follow my own programme of research, management skills to begin my own lab group and my continued love of psychology. I can’t wait to return to TARG as a collaborator and an independent researcher!

This article is posted by Sally Adams