Author Archives: meryemgrabski

New alcohol guidelines: what you need to know

by Olivia Maynard @OliviaMaynard17

This blog originally appeared on the Mental Elf site on 9th Febraury 2016.

Last month the UK Chief Medical Officers (CMOs) published new guidelines for alcohol consumption. These are the first new guidelines since 1995 and are based on the latest evidence on the effects of alcohol consumption on health.

The guidelines provide recommendations for weekly drinking limits, single drinking episodes and recommendations for pregnant women, drawing heavily on the Sheffield Alcohol Policy Model, which uses the most up to date evidence on both the short- and long-term risks of alcohol.

What are the new guidelines?

Guidelines for weekly drinking

For the new weekly drinking guidelines, the CMOs recommend that:

  • It’s safest for both men and women to not regularly drink more than 14 units of alcohol per week;
  • It’s best to spread these units over 3 days or more;
  • Having several drink-free days each week is a good way of cutting down the amount you drink;
  • The risk of developing a range of illnesses increases with any amount you drink on a regular basis.

There are two key changes here from the guidelines we’ve been used to:

First, there’s no difference in recommendations for men and women. This is because there is increasing evidence that although women are more at risk from the long-term health effects of alcohol, men are more at risk from the short-term effects of drinking (they’re more likely to expose themselves to risky situations while drinking).

Second, there is an explicit statement that there is no ‘safe’ level of alcohol consumption. Over the past 21 years, the link between alcohol and cancer has become much clearer. For example, we now know that while the lifetime risk ofbreast cancer is 11% among female non-drinkers, the lifetime risk for a woman drinking within the new guidelines is 13%. A woman drinking over 35 units a week increases her risk of breast cancer to 21%.

In their report, the CMOs are also at pains to point out that the evidence supporting alcohol’s protective effects on ischaemic heart disease is now weaker than in 1995. Furthermore, any potential protective effect of alcohol is mainly observed among older women at very low levels of consumption. Previously some have used this to claim that drinking is better than abstinence – the new guidelines refute this.

The new guidance says it's safest for men and women to drink no more than 14 units each week.

The new guidance says it’s safest for men and women to drink no more than 14 units each week.

Guidelines for single drinking episodes

The new guidelines are the first to provide guidance on drinking on single occasions, recommending drinkers:

  • Limit the total amount consumed on any occasion;
  • Drink slowly, with food and alternating with water;
  • Avoid risky places and activities and ensure they have a safe method of getting home.

These new recommendations reflect the fact that many alcohol consumers may drink heavily on occasion and provide guidance to avoid the risk of injury and ischaemic heart disease which increase with heavy drinking.

Heavy drinking episodes are linked with a higher risk of injury.

Heavy drinking episodes are linked with a higher risk of injury.

Guidelines for drinking during pregnancy

The new guidelines suggest that:

  • The safest approach for pregnant women is not to drink alcohol at all, to keep risks to the baby to a minimum.
  • Drinking during pregnancy can lead to long-term harm to the baby, with the risk increasing with the more alcohol consumed;
  • The risk of harm to the baby is likely to be low if a woman has drunk only small amounts of alcohol before she knew she was pregnant or during pregnancy.

The CMOs report that while the evidence on the effects of low alcohol consumption during pregnancy remains ‘elusive’, taking a precautionary approach is most prudent when it comes to a baby’s long term health. However, given the elusive evidence, the guidance is also careful to note that mothers should not be too concerned if they have drunk early in their pregnancy, as this kind of stress may be even more harmful to the developing baby.

Pregnant women are advised not to drink alcohol at all.

Pregnant women are advised not to drink alcohol at all.

A note on risk

These recommendations are based on a level of alcohol consumption which confers a 1% lifetime risk of death from alcohol. Their purpose is therefore tominimise risk from alcohol, rather than eliminate it. Indeed, the guidelines explicitly state that there is no safe level of alcohol consumption. So what does a 1% lifetime risk mean and how does this compare to other health behaviours?

Lifetime mean risks

  • Being killed through BASE jumping (0.3%);
  • Being killed in a car accident (0.4%);
  • Being diagnosed with bowel cancer from eating three rashers of bacon every day (1%);
  • Dying from an alcohol related disease, if drinking within the new guidelines (1%);
  • Smokers dying from a smoking related disease (50%, although new estimates suggest that this may be as high as 67%).

Put in the context of smoking, the risk posed by drinking within the new guidelines seems tiny (although it’s still more risky than BASE jumping!) However, it’s important to note that alcohol consumption and smoking are quite different. Alcohol consumption is perceived as normal in our society and is much more prevalent than cigarette smoking. By contrast, the acceptability of smoking is reducing and unlike social alcohol consumers, smokers are constantly being told to quit smoking.

This 1% risk level is that which is deemed ‘acceptable’ by the CMO. However, everyone will have a different ‘acceptable’ level of risk, which depends in part on how much pleasure is obtained from drinking. While some will think that increasing their risk of death from alcohol to 5% is acceptable, others will not accept any risk and will use these guidelines to cut out alcohol completely.

Criticisms of the new guidelines

As expected, the ‘nanny state’ criticism has been bandied around in pubs, on message boards and on social media since the publication of these guidelines. Others claim that these new guidelines are simply scaremongering. However, it’s important to remember that these are recommendations, not rules.

The last word must go to CMO Professor Dame Sally Davies, who addressed this criticism by saying that:

What we are aiming to do with these guidelines is give the public the latest and most up- to-date scientific information so that they can make informed decisions about their own drinking and the level of risk they are prepared to take.

What do you think? Are these new guidelines useful? Will they help reduce alcohol related harm?

What do you think? Are these new guidelines useful? Will they help reduce alcohol related harm?

Links

Primary paper

Department of Health (2016) Health risks from alcohol: new guidelines. Open Consultation, 8 Jan 2016 (Consultation closes on 1 April 2016)

Department of Health (2016). Alcohol Guidelines Review – Report from the Guidelines development group to the UK Chief Medical Officers.

Other references

Centre for Public Health (2016). CMO Alcohol Guidelines Review – A summary of the evidence of the health and social impacts of alcohol consumption. Liverpool John Moores University.

Centre for Public Health (2016). CMO Alcohol Guidelines Review – Mapping systematic review level evidence. Liverpool John Moores University.

Department of Health (1995). Sensible drinking: Report of an inter-departmental working group.

Photo credits

 

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

SMS text messaging interventions for healthy behaviour change

by Olivia Maynard @OliviaMaynard17

This blog originally appeared on the Mental Elf site on 28th September 2015.

shutterstock_216388930

There’s a lot to like about text messaging (Short Message Service: SMS) interventions for behaviour change: they can deliver cost-effective, brief, real-time and tailored messages at moments when individuals need them most. They reduce time demands on both the individual and health care practitioners, maintain the privacy of the individual and what’s more, given that the majority of the world’s population own a mobile phone, text messaging interventions can be delivered at a global scale.

Given these advantages, there’s been a great deal of research on the effectiveness of SMS interventions for health behaviours, finding mixed results. Last month, I blogged about a meta-analysis which found weak evidence for the efficacy of SMS interventions for smoking cessation. However, smoking cessation is a complex health behaviour and recent reviews have found that SMS interventions are more effective for more simple health behaviours such as medicine adherence and attending medical appointments.

Another recent meta-analysis by Orr and King (2015) published in Health Psychology Review was the first to examine the overall effectiveness of SMS interventions to enhance healthy behaviour, rather than focus on any one health behaviour (i.e. smoking cessation). Similar to the meta-analysis I blogged about last month, the authors also aimed to identify which SMS features have the biggest impact on intervention effectiveness.

Mobile phones are now so ubiquitous that they are a great tool to deliver interventions globally.

Methods

The authors searched for randomised controlled trials (RCTs) which compared SMS interventions targeting health behaviour change to a non-SMS control, which did not attempt to change behaviour (i.e. RCTs which compared two active treatments were not included).

The authors examined the influence of six moderators on the effectiveness of SMS interventions:

  1. SMS dose (i.e. the frequency of the messages: multiple per day, weekly, once only etc);
  2. SMS message tailoring (i.e. standardised, tailored, personalised);
  3. SMS directionality between researcher and participant (i.e. one-way, two-way);
  4. Category of health behaviour targeted (i.e. unhealthy behaviour modification, chronic disease management, medication adherence, appointment attendance, disease or pregnancy preventive behaviours);
  5. Complexity of these behaviours (i.e. complex: chronic disease management, disease-related medication adherence, unhealthy behaviour modification; simple: appointment attendance, non-disease-related medication adherence, preventive behaviour);
  6. Participants’ mean age.

Results

Thirty eight studies met the criteria for inclusion in this meta-analysis.

The meta-analysis found that there was an overall (pooled) positive effect of SMS interventions on healthy behaviour (g = 0.291, 95% CI = 0.219 to 0.363, p < 0.001). The heterogeneity between studies was low (I2 = 38.619, p = 0.009­).

Planned sub-group analyses explored the impact of the six moderators on health behaviour change. There was little evidence that any of the six moderators impacted SMS intervention efficacy. However, when the authors regrouped the studies (i.e. unplanned analyses) for the tailoring, dose and complexity moderators, only SMS dose was found to impact SMS efficacy, with studies using multiple messages per day being more effective than those with reduced frequency.

The effect size for SMS interventions was small, but given that this is a cheap and simple intervention to deliver, it may be worthwhile.

Strengths

The quality of the evidence in the 38 studies was judged to be relatively high, with all but three of the studies assessed as having high to moderate methodological quality. However, the authors judged that the risk of incomplete data was high in 40% of studies, despite efforts to contact the authors of the original studies for further information.

Further analyses of the data found that publication bias was not a threat to the validity of the estimated effect of SMS interventions for healthy behaviour.

Weaknesses

The majority of included studies relied on self-report outcome measures, rather than actual behaviour, which is likely to have increased the observed effects of the studies. Future studies should use objective outcome measures.

The observation that more frequent text messages are more effective than less frequent messages was only found after regrouping the studies and running multiple unplanned comparisons between groups. This finding should therefore be treated with caution.

The authors only included studies which compared SMS intervention to no intervention at all. These findings therefore tell us nothing about how SMS interventions compare to other established interventions, such as verbal or other written reminders and messages.

Very few of the studies included in this meta-analysis were grounded in any health behaviour theory. The authors suggest that future research should examine the impact of established theoretical components on health behaviour change outcomes.

This study does not provide reliable evidence that more frequent text messages are more effective than less frequent messages.

Discussion

Using strict inclusion criteria for studies, this meta-analysis found that SMS interventions have a positive, albeit small, effect on healthy behaviour change. There was little evidence that moderators such as tailoring, directionality, health behaviour category or complexity or participant age influence efficacy. There was some evidence that higher SMS dose might increase efficacy.

These findings echo those in a recent meta-analysis of studies exploring the effectiveness of SMS interventions for smoking cessation where no moderator was found to be more effective than any other at increasing quit success and only a small positive effect of SMS intervention was observed.

Although this and previous meta-analyses have found only modest benefits of SMS interventions over control, given the low cost of delivery of SMS interventions and the potential to target large numbers of individuals, the public health benefits are still considerable and future research should continue to examine the efficacy of these interventions.

Despite the small effect sizes, SMS text messages remain a potentially effective intervention that can work on a truly global scale.

Links

Primary paper

Orr JA, King RJ. (2015) Mobile phone SMS messages can enhance healthy behaviour: a meta-analysis of randomised controlled trialsHealth psychology review (just-accepted), 1-36.

Other references

Maynard O. (2015) SMS texting to quit smoking: a meta-analysis of text messaging interventions for smoking cessation. The Mental Elf, 26 Aug 2015.

Supervised injectable heroin for refractory heroin addiction

by Eleanor Kennedy @Nelllor_

This blog originally appeared on the Mental Elf site on 28th August 2015.

Opioid use is the number one reason for seeking substance misuse treatment across 30 European countries. Opioids are drugs derived from the opium poppy and these include the drug heroin (EMCDDA, 2015).

Heroin dependence has negative consequences for both the individual and society as persistent use of the drug is associated with poor health, criminal offences and damaged personal relationships (Ferri et al, 2011). Drug-free treatments and substitution treatments are the two interventions used to overcome heroin dependence.

Methadone is the most common substitution treatment in the EU, however, heroin prescribing is well established in Denmark, Germany and The Netherlands, an option in the UK and Spain, and currently under investigation in Belgium and Luxembourg (EMCDDA, 2015).

A recent systematic review and meta-analysis aims to compare supervised injectable heroin (SIH) as a treatment for heroin users who have not responded to more standard treatments such as methadone maintenance treatment (MMT) or residential rehabilitation (Strang et al, 2015).

NICE guidance recommends the use of methadone or buprenorphine as the first-line treatment in opioid detoxification.

Methods

Electronic databases (PubMed, Web of Science and Scopus) were searched for studies that reported on the effects of SIH treatment in participants with heroin-dependence unresponsive to standard treatments.

The studies had to have opiate use, retention in treatment, mortality and side-effects as outcome variables.

Studies were excluded if they were methodological papers, assessed unsupervised heroin treatment provision, focused on policy aspects, cost effectiveness, community perspectives or patient satisfaction.

The meta-analysis focussed on Mantel-Haenszel random effects pooled risk ratios for SIH treatment compared to the comparison groups.

Results

There were a total of six papers included in the main review and meta-analysis. These studies were based in Switzerland, The Netherlands, Spain, Germany, Canada and England.

All studies explored SIH compared to MMT (oral methadone) in chronic heroin-dependent individuals who have repeatedly failed in orthodox treatment.

The results of rate of retention and the use of illicit heroin following treatment are shown in Table 1. The rates of retention varied across studies, with only one study reporting a lower rate of retention for the SIH group (Van den Brink et al, 2003). The statistical evidence indicated a lower rate of illicit heroin use in individuals receiving SIH treatment in all six studies.

Table 1: Retention in treatment and use of illicit heroin results

Study Retention in treatment Use of illicit heroin
Perneger et al, 1998 SIH 93% vs MMT 92% p = 0.002
Van den Brink et al, 2003 SIH 72% vs MMT 85% P = 0.002
March et al, 2006 SIH 74% vs MMT 68% P = 0.02
Haasen et al, 2007 SIH 67% vs MMT 40% P < 0.001
Oviedo-Joekes et al, 2009 SIH 88% vs MMT 54% P = 0.004
Strang et al, 2010 SIH 88% vs MMT 69% P < 0.0001

Meta-analysis

A meta-analysis was conducted to explore retention in treatment, mortality outcome and side-effects.

  • Retention in treatment was significantly better for the SIH than for the MMT treatment groups as demonstrated by four studies; RR = 1.37, 95% CI = 1.03 to 1.83
  • Mortality was lower in the SIH than in the MMT treatment groups but this was not significant; RR=0.65, 95% CI = 0.25 to 1.69
  • There was a higher risk of side effects in the SIH compared to the MMT treatment groups based on analysis of five studies; RR = 4.99, 95% CI = 1.66 to 14.99

This review provides good evidence that heroin-assisted treatment works for a small group of patients with refractory heroin dependence.

Strengths and limitations

All of the included studies were randomised controlled trials comparing traditional oral MMT to SIH in participants with chronic heroin-dependence who have not been successfully treated. The review followed PRISMA guidelines and was inclusive of all languages and publication dates, so the likelihood of important papers being excluded is minimal.

In this review the authors focussed on supervised administration of heroin only, which contrasts with a 2011 Cochrane Review that also included studies where heroin was prescribed for take-home administration (Ferri et al, 2011). By restricting the inclusion criteria, stronger conclusions can be made about the efficacy of this type of treatment which may guide the introduction of new interventions. Additionally the authors’ address several key misgivings about SIH, which further supports the argument that SIH is an effective treatment for treatment-resistant heroin dependence. For example, the concern that SIH may undermine other existing treatments is countered by the difficulty in recruitment experienced by many of the six trials under review.

There are some limitations, e.g. the safety of injectable diamorphine requires further research as the instances of sudden-onset respiratory depression is at a rate of about 1 in 6,000 injections.

The supervision and administration of SIH makes it more expensive than oral forms of opioid maintenance treatment.

Conclusions

The authors concluded that:

Based on the evidence that has been accumulated through these clinical trials, heroin-prescribing, as a part of highly regulated regimen, is a feasible and effective treatment for a particularly difficult-to-treat group of heroin dependent patients.

The importance of supervision during administration is emphasised throughout the review. As mentioned above, all of the participants engaged in SIH had previously repeatedly failed in orthodox treatment, however, the evidence supports SIH as a treatment option for these individuals.

Will this systematic review and meta-analysis be sufficient for policy makers to start recommending supervised injectable heroin for heroin users who have not responded to other standard treatments?

Links

Primary paper

Strang J, Groshkova T, Uchtenhagen A. et al. (2015) Heroin on trial: systematic review and meta-analysis of randomised trials of diamorphine-prescribing as treatment for refractory heroin addictionBr. J. Psychiatry 2015;207:5-14. doi:10.1192/bjp.bp.114.149195.

Other references

EMCDDA (2015) European Monitoring Centre for Drugs and Drug Addiction. emcdda.europa.eu. 2015. Available at: http://www.emcdda.europa.eu

Ferri M, Davoli M, Perucci CA. Heroin Maintenance for chronic heroin-dependent Individuals. Cochrane Database of Systematic Reviews 2011, Issue 12. Art. No .: CD003410. DOI: 10.1002 / 14651858.CD003410.pub4.

Van den Brink W, Hendriks VM, Blanken P, Koeter MWJ, van Zwieten BJ, van Ree JM. (2003) Medical prescription of heroin to treatment resistant heroin addicts: two randomised controlled trialsBMJ 2003;327(August):310. doi:10.1136/bmj.327.7410.310.

Perneger T V, Giner F, del Rio M, Mino A. (1998) Randomised trial of heroin maintenance programme for addicts who fail in conventional drug treatmentsBMJ 1998;317(July):13-18. doi:10.1136/bmj.317.7150.13.

March JC, Oviedo-Joekes E, Perea-Milla E, Carrasco F. (2006) Controlled trial of prescribed heroin in the treatment of opioid addiction. J. Subst. Abuse Treat. 2006;31:203-211. doi:10.1016/j.jsat.2006.04.007. [PubMed abstract]

Haasen C, Verthein U, Degkwitz P, Berger J, Krausz M, Naber D. (2007) Heroin-assisted treatment for opioid dependence: Randomised controlled  trialBr. J. Psychiatry 2007;191:55-62. doi:10.1192/bjp.bp.106.026112.

Oviedo-Joekes E, Brissette S, Marsh DC, et al. (2009) Diacetylmorphine versus methadone for the treatment of opioid addiction. N. Engl. J. Med. 2009;361:777-786. doi:10.1056/NEJMoa0810635. [Abstract]

Strang J, Metrebian N, Lintzeris N, et al. (2010) Supervised injectable heroin or injectable methadone versus optimised oral methadone as treatment for chronic heroin addicts in England after persistent failure in orthodox treatment (RIOTT): a randomised trial. Lancet 2010;375(9729):1885-1895. doi:10.1016/S0140-6736(10)60349-2. [Abstract] [Watch Prof John Strang talk about the RIOTT trial]

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/supervised-injectable-heroin-for-refractory-heroin-addiction/#sthash.hvYELhgt.dpuf

SMS texting to quit smoking: a meta-analysis of text messaging interventions for smoking cessation

by Olivia Maynard @OliviaMaynard17

This blog originally appeared on the Mental Elf site on 26th August 2015.

The efficacy of different smoking cessation interventions is always a hot topic around our woodland campfire. We’ve blogged previously about the effectiveness of both pharmacological and psychological treatments for smoking cessation, as well as their effectiveness among different populations of smokers.

A recent systematic review and meta-analysis investigated the efficacy of SMS text message interventions for smoking cessation. Unlike the majority of other smoking cessation interventions, using mobile phones to deliver health information allows for direct interaction between clients and practitioners without face-to-face interaction and permits the collection of large amounts of data. It is therefore cost-effective and easily scalable to large populations.

Previous meta-analyses have looked at the effectiveness of text messaging interventions for smoking cessation, but the review recently published by Spohr and colleagues is the first to investigate which elements or moderators of text message interventions are the most effective in supporting smoking cessation.

This review

Methods

The authors searched for randomised controlled trials which investigated the efficacy of text messaging interventions for smoking cessation. Only studies which included a follow-up measure of smoking abstinence were included. 13 articles met all of these inclusion criteria.

The authors also extracted information on the use of each of the moderators described below:

  • Intervention type:
    • SMS only;
    • ‘SMS plus’ (where SMS support is combined with either face-to-face or web-based support).
  • Message frequency:
    • Fixed message schedule (a consistent number of messages throughout the intervention);
    • Decreasing schedule (most messages at quit attempts, followed by a gradual reduction);
    • Dynamic schedule (depends on the stage of cessation the client is at).
  • Message track:
    • Fixed message track (users cannot influence the course of the intervention);
    • Dynamic message track (user quit status and stage of change can influence intervention messages).
  • Message tailoring:
    • Tailored messages (customised message content to a specific individual);
    • Targeted messages (customised messages to a population subgroup).
  • On-demand messaging:
    • On-demand messaging (allow users to text a keyword in emergency situations to receive additional support. Some interventions allow users to connect with other users for support and encouragement).
  • Message direction:
    • Unidirectional messaging (by the researcher);
    • Bidirectional messaging (to obtain data from the client).
  • Message interaction:
    • Researcher-initiated (containing intervention messages and assessment questions);
    • User-initiated (containing requests for additional support).

Intervention success was assessed using seven-day point prevalence as the primary outcome measure, as 11/13 of the studies reported these results. Two other studies only reported 6 month continuous abstinence.

The researchers used an intention to treat analysis.

Perhaps surprisingly (given the ubiquitous nature of smartphones) the reviewers only found 13 trials to include in their analysis.

Results

The 13 articles resulted in a cumulative sample size of n = 13,626. Participants were primarily adult smokers (six studies), but four studies recruited participants aged 15 or over and three targeted adolescents and young adults (ages 16-25).

Smoking quit rates for the text messaging intervention groups were 35% higher as compared to control groups (OR = 1.35, 95% CI = 1.23 to 1.49).

Overall, the analysis of the intervention moderators did not find strong evidence that any particular moderator was more effective than any other:

  • Intervention type:
    • There were no differences (Q= 0.56, df = 1, p = 0.46) in intervention efficacy between those which provided text-only support (= 6) as compared with text messaging plus additional support (k = 7).
    • However, text-only interventions had a slightly larger effect size than those with text messaging plus additional support.
    • There were no differences (Q= 0.89, df = 1, p = 0.35) in intervention efficacy between those which promoted the use of nicotine replacement therapy (NRT) (= 7) as compared with those which did not (k = 6).
  • Message frequency:
    • There were no differences (Q= 0.96, df = 2, p = 0.62) in intervention efficacy between those which provided decreasing schedule (= 8) as compared with fixed schedule (k = 3) or variable schedule (= 2) support.
    • However, those which had a fixed schedule had larger effect sizes than either of the other types of schedules.
  • Message track:
    • There were no differences (Q= 0.38, df = 1, p = 0.54) in intervention efficacy between those which used a fixed message track (= 5) as compared with a dynamic message track (k = 8).
  • Message tailoring:
    • There were no differences (Q= 1.54, df = 2 p = 0.46) in intervention efficacy between those which used message tailoring (= 8) as compared message targeting (k = 1) or a combination of both (k = 4).
    • All studies included some form of message content tailoring.
  • On-demand messaging:
    • There were no differences (Q= 0.15, df = 1, p = 0.70) in intervention efficacy between those which used on-demand messaging (= 11) as compared with those which did not (k = 2).
    • There were no differences (Q= 0.02, df = 1, p = 0.88) in intervention efficacy between those which provided peer-to-peer support (= 5) as compared with those which did not (k = 8).
  • Message direction:
    • All of the studies used bidirectional messaging so the effectiveness of this moderator to unidirectional messaging could not be assessed.
  • Message interaction:
    • There were no differences (Q= 0.17, df = 1, p = 0.68) in intervention efficacy between those which included assessment messages (= 7) as compared with those which did not (k = 6).

Text messaging does not compare well to many other more effective methods of smoking cessation.

Conclusions

This meta-analysis found that smoking cessation interventions which used text-messaging increased the odds of successfully quitting smoking by 35%.

To put this in perspective, other reviews have found that telephone quit lines increase smoking cessation success by 60%, social support increases success by 30% and practical counselling by 50%. NRT and other medications have been shown to increase cessation success by between 50% and 310% (Fiore et al., 2000).

None of the moderators investigated here were found to be more effective than any other. There was some evidence that interventions which used fixed schedules were more effective than those which used either decreasing or variable schedules. Similarly, there was some evidence that text-only support programs were more effective than those which provided a ‘text-plus’ service. However, there was no robust statistical evidence for these differences.

Overall, these results provide no evidence that text-messaging interventions, which are more complex and time-demanding (i.e. text-plus, on-demand messaging, variable schedules, social support communication), are any more effective than the simplest interventions.

However, given the cost-effectiveness, relative ease of delivery and promise of efficacy of these interventions, future research should continue to determine what moderators make an effective text-based intervention.

Limitations and future directions

These results should be treated with caution however, for a number of reasons:

  • The authors relied on data obtained from the original articles to compile this meta-analysis, rather than contacting the researchers themselves. As data regarding the actual use by users of some of the moderators such as social support and on-demand messaging was not reported in articles, we cannot be certain whether the failure of these moderators to increase quit success is because they are simply not more effective, or because users didn’t actually use these services.
  • The number of studies included in this meta-analysis was small (only 13 studies). This is even more the case for the moderator analyses. Drawing firm conclusions from the statistical evidence is therefore difficult.

shutterstock_117926893

Links

Primary paper

Spohr SA, Nandy R, Gandhiraj D, Vemulapalli A, Anne S, Walters ST. (2015) Efficacy of SMS Text Message Interventions for Smoking Cessation: A Meta-Analysis. Journal of Substance Abuse Treatment, 56, 1-10. doi: http://dx.doi.org/10.1016/j.jsat.2015.01.011

Other references

Fiore MC, Bailey WC, Cohen SJ, Dorfman SF, Goldstein MG, Gritz ER, … Lando HA. (2000) Treating tobacco use and dependence: a clinical practice guideline: Publications Clearinghouse.

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/sms-texting-to-quit-smoking-a-meta-analysis-of-text-messaging-interventions-for-smoking-cessation/#sthash.hjMwMixu.dpuf

Does tobacco cause psychosis?

by Marcus Munafo @MarcusMunafo

This blog originally appeared on the Mental Elf site on 30th July 2015.

Hot on the heels of a recent study suggesting a dose-response relationship between tobacco smoking and subsequent risk of psychosis, a systematic review and meta-analysis (including the data from that prospective study) has now been published, again suggesting that we should be considering the possibility that smoking is a causal risk factor for schizophrenia.

As I outlined in my earlier post, smoking and psychotic illness (e.g., schizophrenia) are highly comorbid, and smoking accounts for much of the reduced life expectancy of people with a diagnosis of schizophrenia. For the most part, it has been assumed that smoking is a form of self-medication, to either alleviate symptoms or help with the side effects of antipsychotic medication.

It's widely thought that people with psychosis or schizophrenia use smoking as a way to self-medicate and relieve their symptoms.

Methods

This new study reports the results of a systematic review and meta-analysis of prospective, case-control and cross-sectional studies. The authors hoped to test four hypotheses:

  1. That an excess of tobacco use is already present in people presenting with their first episode of psychosis
  1. That daily tobacco use is associated with an increased risk of subsequent psychotic disorder
  1. That daily tobacco use is associated with an earlier age at onset of psychotic illness
  1. That an earlier age at initiation of smoking is associated with an increased risk of psychotic disorder

The authors followed MOOSE and PRISMA guidelines for the conduct and reporting of systematic reviews and meta-analyses, and searched Embase, Medline and PsycINFO for relevant studies. They included studies that used ICD or DSM criteria for psychotic disorders (including schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, non-affective psychotic disorder, atypical psychosis, psychotic depression, and bipolar mania with psychotic features).

To test the first hypothesis, studies with a control group were used to calculate an odds ratio. To test the second, prospective studies in which rates of smoking were reported for patients who developed psychotic disorders compared to controls were included, so risk ratios could be calculated. To test the third and fourth, prospective and case-control studies were included, and for the onset of psychosis, cross-sectional studies were also included.

Effect size estimates (weighted mean difference for continuous data, and odds ratios for cross-sectional data or relative risks for prospective data) were combined in a random-effects meta-analysis.

Results

A total of 61 studies comprising 72 independent samples were analysed. The overall sample included 14,555 tobacco users and 273,162 non-users.

  1. The overall prevalence of smoking in people presenting with their first episode of psychosis was higher than controls (12 case-control samples, odds ratio 3.22, 95% CI 1.63 to 6.33, P = 0.001). This supports hypothesis 1.
  2. Compared with non-smokers, the incidence of new psychotic disorders was higher overall (6 longitudinal prospective samples, risk ratio 2.18, 95% CI 1.23 to 3.85, P = 0.007). This supports hypothesis 2.
  3. Daily smokers developed psychotic illness at an earlier age compared with non-smokers (26 samples, weighted mean difference -1.04 years, 95% CI -1.82 to -0.26, P = 0.009). This supports hypothesis 3.
  4. Age at initiation of smoking cigarettes did not differ between patients with psychosis and controls (15 samples, weighted mean difference -0.44 years, 95% CI 1-.21 to 0.34, P = 0.270). This does not support hypothesis 4.

Daily tobacco use is associated with an increased risk of psychosis and an earlier age at onset of psychotic illness.

Conclusion

The authors conclude that the results of their systematic review and meta-analysis show that daily tobacco use is associated with an increased risk of psychotic disorder and an earlier age at onset of psychotic illness, although the magnitude of the association is relatively small.

Interestingly, the authors interpret their results in the context of the Bradford Hill criteria for inferring causality (which consider the strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy of an association). They argue that, where these criteria can be applied (the specificity criterion cannot be applied because smoking affects so many disease processes, while the experiment criterion is not met because animal models of psychotic illness that capture important features such as delusions are impossible), they do appear to be met by the evidence available.

Limitations

There are a number of important limitations to this study, which the authors themselves acknowledge:

  • The first is that all analyses relied on observational data, which makes strong causal inference impossible. Longitudinal prospective studies help somewhat in this respect, but only a small number were available for inclusion in the analysis of risk of developing psychosis between smokers and non-smokers. Moreover, even these studies cannot exclude the possibility that symptoms present before a first full episode of psychosis may have led to smoking initiation (i.e., self medication).
  • Another important limitation is that very few studies measured or adjusted for use of other substances (most importantly, perhaps, cannabis, which has been widely discussed as a potential risk factor for schizophrenia). This is a potentially very important source of bias.

Nevertheless, this is a well-conducted systematic review and meta-analysis that brings together a reasonably large literature. The results appear robust, although given the observational nature of the data, and the fact that only data that were comparable across studies could be meta-analysed, any conclusions regarding causality need to be very tentative.

Very few studies in this review, measured or adjusted for use of other substances such as cannabis.

Summary

It seems that we should seriously consider the possibility that smoking is a causal risk factor for schizophrenia. Of course, the data available to date aren’t definitive, and we need to be very cautious about inferring causality from observational data, but this does feel like an area where there is growing, converging evidence from multiple studies using multiple methods.

It’s also worth bearing in mind that even if smoking is a causal risk factor, this does not preclude the possibility that smoking is also used as a form of self-medication. There are several thousand constituents of tobacco smoke; it is possible that some of these alleviate symptoms, while others exacerbate them. For this reason, we shouldn’t assume that nicotine is necessarily the culprit if smoking is indeed a causal risk factor; it may be (and Gurillo and colleagues discuss the biological plausibility of nicotine in this context), but that will need to be tested.

This last point is particularly important in the content of ongoing debate regarding the potential harms and benefits of electronic cigarettes. If smoking does turn out to be a causal risk factor for schizophrenia, then whether nicotine or something else in tobacco smoke is identified as the culprit will have an important bearing on this debate, and attitudes towards these products.

There are several thousands constituents of tobacco smoke; it is possible that some of these alleviate symptoms, while others exacerbate them

Links

Primary paper

Gurillo P, Jauhar S, Murray RM, MacCabe J. (2015) Does tobacco use cause psychosis? Systematic review and meta-analysis. Lancet Psychiatry 2015. doi: 10.1016/S2215-0366(15)00152-2 (Open access paper: features audio interview with authors)

Munafo M. Smoking and risk of schizophrenia: new study finds a dose-response relationship. The Mental Elf, 1 Jul 2015.

– See more at: http://www.nationalelfservice.net/mental-health/psychosis/does-tobacco-use-cause-psychosis/#sthash.sxUwJPIF.dpuf