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.

Smoking and chronic mental illness: what’s the best way to quit or cut down?

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 11th December 2015.

Smoking rates in the US and UK are 2-4 times higher in people with mental illnesses compared to those without (Lasser at al., 2000; Lawerence et al., 2009).

What’s more, smokers suffering from mental illness have higher nicotine dependence and lower quit rates (Smith et al.,2014; Weinberger et al., 2012; Cook et al 2014).

About half of deaths in people with chronic mental illness are due to tobacco related conditions (Callaghan et al., 2014; Kelly et al 2011).

A new ‘state of the art’ review in the BMJ by Tidey and Miller (2015) is therefore much needed, focusing as it does on the treatments currently available for smoking and chronic mental illness, such as schizophrenia, unipolar depression, bipolar depression, anxiety disorders and post-traumatic stress disorder (PTSD).

42% of all cigarettes smoked in England are consumed by people with mental health problems.

42% of all cigarettes smoked in England are consumed by people with mental health problems.

Methods

Tidey and Miller (2015) identified studies by searching keywords in PubMed and Science Direct, using relevant guidelines, reviews and meta-analyses, and data from the authors’ own files. Two authors reviewed the references and relevant studies were chosen and summarised. Only peer-reviewed articles published in English were reviewed.

It’s important to stress that this was not a systematic review, so the included studies were not graded, but simply summarised with a particular focus on outcomes.

BMJ State of the Art reviews are not systematic reviews, so are susceptible to the same biases as other literature reviews or expert opinion pieces.  

BMJ State of the Art reviews are not systematic reviews, so are susceptible to the same biases as other literature reviews or expert opinion pieces.

Results

Schizophrenia

Nicotine replacement therapy (NRT) plus psychosocial

Overall, in studies of NRT with psychosocial treatment (such as CBT) 13% of smokers with schizophrenia averaged 6 to 12 month quit rates. Additionally, those continuing to receive NRT had reduced relapse rates.

Bupropion

Studies investigating bupropion in smokers with schizophrenia found initial abstinence, but were followed by high relapse rates with treatment discontinuation, suggesting the need for longer treatment duration. One study found bupropion coupled with NRT and CBT reduced relapse rates. 

Varenicline

Studies investigating varenicline in smokers with schizophrenia achieved abstinence at the end of the trial (compared to placebo), but not at 12-month follow up. One study found varenicline and CBT had higher abstinence rates at 52 weeks (compared to controls). Psychiatric side effects reported did not differ between groups, suggesting varenicline is well tolerated in schizophrenia.

Psychosocial

Studies investigating psychosocial treatments in smokers with schizophrenia were varied. Studies implementing CBT displayed high continuous abstinence, and those receiving motivational interviewing were more likely to seek treatment. However, in contingency management trials (receiving monetary reward for abstinence) it appeared individuals might only be staying abstinent long enough for their reward, therefore longer trials are needed.

E-cigarettes

One (uncontrolled) study provided e-cigarettes for 52 weeks to smokers with schizophrenia, finding half reduced their smoking by 50% and 14% quit. None of the participants were seeking treatment for cessation at the start of the trial, suggesting a need for further RCTs of e-cigarettes in smokers with schizophrenia.

The Mental Elf looks forward to reporting on RCTs of e-cigarettes in smokers with schizophrenia.

The Mental Elf looks forward to reporting on RCTs of e-cigarettes in smokers with schizophrenia.

Unipolar depression

A review of the cessation treatments available to smokers with unipolar depression found little differences in outcomes between individuals with and without depression. However, women with depression were associated with poorer outcomes. Previous studies indicate bupropion, nortriptyline, and NTR with mood management all effective in smokers with depression. Additionally, a long-term study of varenicline displayed continuous abstinence up to 52 weeks without any additional psychiatric side effects.

Bipolar depression

Few studies investigated cessation treatments in smokers with bipolar depression; two small-scale studies of bupropion and varenicline indicated positive results. However a long-term varenicline study found increased abstinence rates at the end of the trial, but not at 6 month follow-up. Some individuals taking varenicline reported suicidal ideation, but this did not differ from the control group.

Anxiety disorders

An analysis investigating both monotherapy and combination psychotherapies found anxiety disorders to predict poor outcomes at follow-up. Despite combination psychotherapy doubling the likelihood of abstinence in non-anxious smokers, neither monotherapy or combination therapy were more effective than placebo in smokers with a lifetime anxiety disorder. However, unipolar and bipolar only touched on pharmaceutical treatments.

PTSD (Post Traumatic Stress Disorder)

Studies investigating cessation in PTSD sufferers found higher abstinence rates in integrative care treatment, in which cessation treatment is integrated into pre-existing mental healthcare where therapeutic relationships and a set schedule already exist. A pilot study investigating integrative care with bupropion found increased abstinence at 6 months. However, a contingency management trial found no differences between controls, although it’s possible this was due to small numbers.

Standard treatments to help people quit smoking are safe and effective for those of us with mental illness.

Standard treatments to help people quit smoking are safe and effective for those of us with mental illness.

Discussion

Clinical practice should prioritise cessation treatments for individuals suffering mental illnesses, in order to protect against the high rates of tobacco related death and disease in this population.

This review shows that smokers with mental illness are able to make successful quit attempts using standard cessation approaches, with little adverse effects.

Several studies suggested bupropion and varenicline effective in schizophrenia, and varenicline in unipolar and bipolar depression. However, it should be noted, these studies only investigated long-term depression, not situational depression.

Furthermore, all the participants in the studies reviewed were in stable condition, therefore it’s possible outcomes may be different when patients are not as stable. Individuals whom are not stable will have additional psychiatric challenges, may less likely to stick with their treatment regime, and may be more sensitive to relapse.

It should be noted that this was a ‘state of the art’ review, rather than a systematic review or meta-analysis. Therefore- as all literary reviews-it’s subject to bias and limitations, with possible exclusion of evidence, inclusion of unreliable evidence, or not being as comprehensive as if this were a meta analysed. For example, some of the author’s own files are used along side the literary search, but (presumably unpublished) data from other researchers are not sought out or included. Many of the studies included differed in design (some placebo controlled, some compared against a different active treatment ect.) therefore caution should be taken when drawing comparisons across studies.

Additionally, some sections appeared to be much more thorough than others. For example, schizophrenia is covered extensively, including NTR, psychosocial, and pharmaceutical approaches. While all anxiety disorders appeared to be gaped together as one (as opposed to looking at social anxiety, GAD, or panic disorder) and were not explored in detail, drawing little possible treatment conclusions. Finally, this was great literary review, which provided much information, but at times it did feel a bit overwhelming to read and difficult to identify the key information from each sections.

Service users who smoke are being increasingly marginalised, so practical evidence-based information to support quit attempts at the right time is urgently needed.

Service users who smoke are being increasingly marginalised, so practical evidence-based information to support quit attempts at the right time is urgently needed.

Links

Primary paper

Tidey JW and Miller ME. Smoking cessation and reduction in people with chronic mental illness. BMJ 2015;351:h4065

Other references

Lasser K, Boyd JW, Woolhandler S, et al. Smoking and mental illness: a population-based prevalence study.JAMA 2000;284:2606-10 [PubMed abstract]

Lawrence D, Mitrou F, Zubrick SR. Smoking and mental illness: results from population surveys in Australia and the United States. BMC Public Health 2009;9:285

Smith PH, Mazure CM, McKee SA. Smoking and mental illness in the US population. Tob Control 2014;23:e147-53.[Abstract]

Weinberger AH, Pilver CE, Desai RA, et al. The relationship of major depressive disorder and gender to changes in smoking for current and former smokers: longitudinal evaluation in the US population. Addiction 2012;107:1847-56. [PubMed abstract]

Cook BL, Wayne GF, Kafali EN, et al. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. JAMA 2014;311:172-82 [PubMed abstract]

Callaghan RC, Veldhuizen S, Jeysingh T, et al. Patterns of tobacco-related mortality among individuals diagnosed with schizophrenia, bipolar disorder, or depression. J Psychiatr Res 2014;48:102-10 [PubMed abstract]

Kelly DL, McMahon RP, Wehring HJ, et al. Cigarette smoking and mortality risk in people with schizophrenia. Schizophr Bull 2011;37:832-8 [Abstract]

Photo credits

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/smoking-and-chronic-mental-illness-whats-the-best-way-to-quit-or-cut-down/#sthash.NvTaK7E6.dpuf

A behavioural insights bar: How wine glass size may influence wine consumption

by Olivia Maynard @OliviaMaynard17

Now that the festive season is almost upon us, I’ve been wading through the list of jobs I’ve been putting off for longer than I can remember, with the hope of starting afresh in 2016.

One of these jobs is wrapping up some of the studies I’ve been running this year, tidying up the data files and deciding what to do with the results. Obviously it’s best practice to write up all studies for publication in peer-reviewed journals, but sometimes this isn’t possible straight away (for example, when we’ve collected pilot data which will inform larger studies or research grants), although journals specifically for pilot and feasibility work do exist. However, it’s still important to share the findings, at the very least to prevent other research groups from running exactly the same pilot study (avoiding the file drawer effect).

The pilot study I’m trying to wrap up was conducted in September this year and is worth reporting, not only because the research is interesting, but also because the method of data collection was novel.

In December 2014 we were approached by the Behavioural Insights Team (BIT), who asked whether we’d be interested in running an experiment at their annual conference. Alongside a star-studded list of speakers, the BIT had planned to demonstrate to conference delegates the power of behavioural insights, by running a series of mini-experiments throughout the conference. We were asked to contribute, not only because I had previously worked in the BIT as part of a placement during my PhD, but also because of TARG’s track record in running behavioural experiments to influence alcohol consumption, both in the lab and in the ‘real-world’.

glassThe team asked us to run an experiment in the Skylon bar in the Royal Festival Hall – the venue of the conference drinks reception. After an initial assessment of the bar (yes, this is a tough job!) and discussing various possible experiments we could conduct, we finally decided to examine the impact of glass size on alcohol consumption. While considerable previous research has shown that plate size is an important driver in food consumption, and we have shown that glass shape (i.e., curved versus straight) influences alcohol consumption, there is very little research on the impact of glass size on alcohol consumption. Larger wine glasses are increasingly common and these may increase wine consumption and drinking speed by suggesting larger consumption norms to consumers, or by tricking consumers into thinking there’s a smaller amount in the glass than in a smaller glass which is equally full.

The primary aim of this pilot study was to determine the feasibility of implementing a glass size intervention study in a real-world drinking environment in order to inform future studies in this area.

Method

Prior to starting the study, as with every TARG study, we published the protocol online on the Open Science Framework. Depending on the side of the bar they were stood in, delegates attending the drinks reception were provided with either a small or a large wine glass, each of which was filled to the same volume. Every 15 minutes we counted the number of delegates on the two sides of the bar and every hour (for three hours) we counted the number of empty wine bottles on each side of the bar. We calculated the average volume of wine consumed per delegate each hour and then compared these between the two groups.

Results

From a feasibility point of view, the study worked as well as expected. Follow-up interviews with the manager of the bar indicated that bar staff enjoyed the process of participating in a study and were happy to participate again in future studies.

However, because we were conducting this in the real-world, rather than in our carefully controlled laboratory environment, we encountered a few logistical challenges. Here are the key points we learned from running this pilot study:

  1. In the real-world, there’s a necessary trade-off between collecting the data and not disrupting normal behaviour

bottles

Ideally we would have counted the number of empty bottles more frequently than every hour in order to get a more accurate measure of how much was consumed by the delegates. However prior to the start of the study, the bar manager suggested that this would interfere with their service and the bar staff reiterated this after the study had finished. As the bar staff were vital to the success of this pilot study, we didn’t think it was appropriate to push for more data collection than they felt comfortable with.

  1. Complete control of the environment isn’t possible in the real-world

controlkey

To prevent delegates from moving between the two sides of the bar we placed physical barriers between them, such as sofas, plants and lamps. However, inevitably, some delegates who wanted to ‘work the room’ at what was essentially a networking event did make their way past the barriers we set up. Other than instructing the waiters to replace the glass of those who had moved sides with the glass size appropriate for the side of the bar they were now in, there was very little we could do about this, short of frog-marching delegates back to their original side (which we thought wouldn’t go down very well on this occasion!)

  1. Accurate enforcement of study conditions is more difficult in the real-world

pouring

If we had conducted this study in the laboratory, we would have randomised participants to receive one of two glass sizes and carefully poured the exact volume of wine into their glass. In this real-world study, however, we had to rely on the waiters to accurately pour the wine into the glasses. Although highly trained, the waiters may also have fallen foul of the visual illusion the different glasses present (an effect which has been shown in previous real-world experiments). Future studies could monitor waiter pouring behaviour before and during the study.

  1. Studies in real bars have some other unexpected challenges…

full glassess

The BIT had asked that we present the results at 9am the following morning, allowing a nine hour turnaround from the end of the study to data presentation. This time pressure was not helped by the large quantities of complimentary champagne being served at the event, which considerably slowed down data entry and analysis at midnight!

Despite this substantial challenge, the results of the study were presented the following morning.

These data suggested that there was no difference in volume of wine consumed between the groups drinking from larger glasses and those drinking from tablesmaller glasses. As this study wasn’t powered to detect a meaningful difference between the two groups, we weren’t really surprised by this finding. However, these pilot data, along with the lessons learned from conducting the study will be used to inform our future research studies and grant applications.

And there we have it – another pilot study out of the file drawer and another item crossed off my ‘to-do’ list.

I’d like to thank the entire Behavioural Insights Team, in particular Ariella Kristal and Gabrielle Stubbs, for making this study happen, Carlotta Albanese from the Skylon bar and David Troy and Jim Lumsden from TARG for helping with all the data collection (and data entry at midnight).

Drug-using offenders with co-occuring mental illness

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 15th October 2015.

shutterstock_314454056

Many individuals in the criminal justice system have both mental health and substance use problems. There is little evidence targeting the treatment programmes for offenders, alongside the additional challenges faced by those with co-occurring mental illnesses.

The Cochrane Drugs and Alcohol Group have published a set of four reviews centred on interventions for drug-using offenders. This is an updated review, targeting offenders with co-occurring mental illnesses, which was originally published in 2006. We blogged about the review when it was last updated in March 2014, but this new version has more evidence (3 new RCTs) included.

About 30% of acquisitive crime (burglaries, theft and robberies) are committed by individuals supporting drug use.

Methods

The review authors searched the usual comprehensive list of databases to identify randomised controlled trials (RCTs) to identify whether treatments for drug using offenders with co-occurring mental illnesses:

  • Reduced drug use
  • Reduced criminal activity
  • Whether the treatment setting affected the intervention
  • Whether the type of treatment affected the outcome

All participants, regardless of gender, age or ethnicity, were included in this analysis.

The updated search (from March 2013 – April 2014) added 3 new trials to the review, totalling 14 publications representing 8 trials published between 1999 and 2014.

Study characteristics

  • 6 studies were conducted in secure settings and 2 studies were conducted in a court setting
  • No studies assessed pharmacological treatments or were conducted in the community
  • All studies were conducted in the United States
  • Study duration varied from 3 months to 5 year follow-up
  • 7 studies investigated adult offenders, while one study investigated adolescent offenders (aged 14 -19)
  • 3 studies included female offenders, while adult male offenders filled the majority of the population in the remaining studies.

Results

Therapeutic community and aftercare versus treatment as usual

Impact on drug use (self-report)

  • Two studies reported a reduction in drug use:
    • (Sacks, 2004) (RR 0.58 95% CI 0.36 to 0.93, 139 participants)
    • (Sacks, 2008) (RR 0.73, 95% CI 0.53 to 1.01, 370 participants)
  • One study reported no reduction:
    • (Wexler, 1999) (RR 1.11 95% CI 0.82 to 1.49, 576 participants)

Impact on criminal activity

  • Two studies reported no reduction in re-arrests following treatment:
    • (Sacks, 2008) (RR 1.65, 95% CI 0.83 to 3.28, 370 participants)
    • (Wexler, 1999) (RR 0.96, 95% CI 0.82 to 1.13, 428 participants)
  • Three studies evaluated the impact of therapeutic community treatment using re-incarceration measures
    • Two studies reported reductions:
      • (Sacks, 2004) (RR 0.28, 95% CI 0.13 to 0.63, 193 participants)
      • (Sacks 2011) (RR 0.49, 95% CI 0.27 to 0.89, 127 participants)
    • One study found no effects:
      • (Sacks, 2008) (RR 0.73, 95% CI 0.45 to 1.19, 370 participants)

Mental health court and case management versus treatment as usual (standard court proceedings)

Impact on drug use (self-report)

  • No data available

Impact on criminal activity

  • One study reported no reduction in criminal activity:
    • (Cosden, 2003) (RR 1.05, 95% CI 0.90 to 1.22, 235 participants)

Motivational interviewing and cognitive skills versus relaxation therapy

Impact on drug use (self-report)

  • Two studies reported no reduction in drug use:
    • (Stein 2011) (MD -7.42, 95% CI -20.12 to 5.28, 162 participants)
    • (Lanza 2013) (RR 0.92, 95% CI 0.36 to 2.33, 41 participants)

Impact on criminal activity

  • No data available

Interpersonal psychotherapy versus a psychotherapy versus a psycho-educational intervention

Impact on drug use (self-report)

  • One study reported no reduction in drug use:
    • (Johnson 2012) (RR 0.67, 95% CI 0.30 to 1.50, 38 participants)

Impact on criminal activity

  • No data available

This review suggests that mental health programmes and drug interventions can help reduce criminal activity and re-incarceration rates, but are less effective at reducing drug use.

Discussion

This updated review included eight studies conducted within secure settings and in the judicial system. There were no studies for drug abusing offenders with mental illnesses under parole identified for inclusion within this review. Therefore, it’s difficult to compare if interventions are more beneficial within the community or under probation services.

Additionally, as all studies were conducted in the United States, it’s possible the treatments may not be generalisable outside the American judicial system, and as drug-use was self-report rather than biological measures, some caution needs to be taken when interpreting the results.

Generally, there was large variation across the studies, making comparisons difficult. However, two of the five trials displayed some evidence for therapeutic aftercare in relation to reducing subsequent re-incarceration.

All of the studies in this review were conducted in the US, so there may be issues of generalisability to other countries and judicial/health systems.

Links

Primary paper

Perry AE, Neilson M, Martyn-St James M, Glanville JM, Woodhouse R, Godfrey C, Hewitt C. Interventions for drug-using offenders with co-occurring mental illness. Cochrane Database of Systematic Reviews 2015, Issue 6. Art. No.: CD010901. DOI: 10.1002/14651858.CD010901.pub2.

Other references

Sacks S, Sacks JY, McKendrick K, Banks S, Stommel J. Modified TC for MICA inmates in correctional settings: crime outcomes. Behavioural Sciences and the Law 2004;22(4):477-501. [PubMed abstract]

Sullivan CJ, McKendrick K, Sacks S, Banks S. Modified therapeutic community treatment for offenders with MICA disorders: substance use outcomes. American Journal of Drug and Alcohol Abuse 2007; Vol. 33, issue 6:823-32. [0095-2990: (Print)] [PubMed abstract]

Sacks JY, McKendrick K, & Hamilton ZK. A randomized clinical trial of a therapeutic community treatment for female inmates: outcomes at 6 and 12 months after prison release. Journal of Addictive Diseases 2012;31(3):258-69. [PubMed abstract]

Sacks JY, Sacks S, McKendrick K, Banks S, Schoeneberger M, Hamilton Z, et al. Prison therapeutic community treatment for female offenders: Profiles and preliminary findings for mental health and other variables (crime, substance use and HIV risk). Journal of Offender Rehabilitation 2008;46(3-4):233-61. [: 1050-9674] [Abstract]

Prendergast ML, Hall EA, Wexler HK. Multiple measures of outcome in assessing a prison-based drug treatment program. Journal of Offender Rehabilitation 2003;37:65-94. [Abstract]

Prendergast ML, Hall EA, Wexler HK, Melnick G, Cao Y. Amity prison-based therapeutic community: 5-year outcomes. Prison Journal 2004;84(1):36-50. [Abstract]

Wexler HK, DeLeon G, Thomas G, Kressel D, Peters J. The Amity prison TC evaluation – re incarceration outcomes. Criminal Justice and Behavior 1999a;26(2):147-67. [Abstract]

Wexler HK, Melnick G, Lowe L, Peters J. Three-year re incarceration outcomes for Amity in-prison therapeutic community and aftercare in California. The Prison Journal1999b;79(3):321-36. [Abstract]

Cosden M, Ellens JK, Schnell JL, Yamini-Diouf Y, Wolfe MM. Evaluation of a mental health treatment court with assertive community treatment. Behavioral Sciences and the Law2003;21(4):415-27. [Abstract]

Stein LA, Lebeau R, Colby SM, Barnett NP, Golembeske C, Monti PM. Motivational interviewing for incarcerated adolescents: effects of depressive symptoms on reducing alcohol and marijuana use after release. Journal of Studies on Alcohol and Drugs2011;72(3):497-506. [PubMed abstract]

Lanza PV, Garcia PF, Lamelas FR, Gonzalez-Menendez A. Acceptance and commitment therapy versus cognitive behavioral therapy in the treatment of substance use disorder with incarcerated women. Journal of Clinical Psychology 2014;70(7):644-57. [DOI:10.1002/jcip.22060]

Johnson JE, Zlotnick C. Pilot study of treatment for major depression among women prisoners with substance use disorder. Journal of Psychiatric Research 2012;46(9):1174-83. [DOI: 10.1016/j.jpsychires.2012.05.007]

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/drug-using-offenders-with-co-occurring-mental-illness/#sthash.CnpCuCWr.dpuf