Category: Mental elf

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

Posted on by meryemgrabski

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

Drug-using offenders with co-occuring mental illness

Posted on by meryemgrabski

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

Posted on by meryemgrabski

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 trials. Health 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.

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

Posted on by meryemgrabski

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 (QB = 0.56, df = 1, p = 0.46) in intervention efficacy between those which provided text-only support (k = 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 (QB = 0.89, df = 1, p = 0.35) in intervention efficacy between those which promoted the use of nicotine replacement therapy (NRT) (k = 7) as compared with those which did not (k = 6).
  • Message frequency:
    • There were no differences (QB = 0.96, df = 2, p = 0.62) in intervention efficacy between those which provided decreasing schedule (k = 8) as compared with fixed schedule (k = 3) or variable schedule (k = 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 (QB = 0.38, df = 1, p = 0.54) in intervention efficacy between those which used a fixed message track (k = 5) as compared with a dynamic message track (k = 8).
  • Message tailoring:
    • There were no differences (QB = 1.54, df = 2 p = 0.46) in intervention efficacy between those which used message tailoring (k = 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 (QB = 0.15, df = 1, p = 0.70) in intervention efficacy between those which used on-demand messaging (k = 11) as compared with those which did not (k = 2).
    • There were no differences (QB = 0.02, df = 1, p = 0.88) in intervention efficacy between those which provided peer-to-peer support (k = 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 (QB = 0.17, df = 1, p = 0.68) in intervention efficacy between those which included assessment messages (k = 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.

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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?

Posted on by meryemgrabski

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

Antidepressants during pregnancy and risk of persistent pulmonary hypertension of the newborn

Posted on by meryemgrabski

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 2nd July 2015.

Persistent pulmonary hypertension of the newborn (PPHN) is associated with increased morbidity and mortality of infants and occurs in 10-20 per 10,000 births.

Those who survive face chronic lung disease, seizures, and neurodevelopmental problems as a result of hypoxemia and aggressive treatment (Walsh-Sukys et al 2000; Farrow et al 2005; Clark et al 2003; Glass et al 1995).

Based on a single study in 2006, the FDA issued a public health advisory that late pregnancy exposure to SSRIs may be associated with an increased risk of PPHN (FDA 2015; Chambers 2006). However, a review yielding conflicting findings led the FDA to conclude that they were premature in their conclusion.

This is the background to a new study by Huybrechts et al (2015), which sets out to investigate SRRI and non-SSRI antidepressants and the associated risk of PPHN in late stage pregnancy.

PPHN is a potentially fatal condition affecting mainly full-term babies, in which the blood flow to the lungs shuts down because the main arteries to the lungs constrict.

Methods

Cohort and data

Participants were drawn from the Medicaid Analytic eXtract (MAX) cohort, which holds the health records of medicate beneficiaries in the United States.

Antidepressants

If women filled 1 antidepressant prescription 90 days before delivery, they were considered ‘exposed.’ Antidepressant medications were classified as either SSRIs (Selective Serotonin Re-uptake Inhibitors) or non-SSRIs. Women exposed to both types of antidepressant were excluded from the analysis. A reference group of women was created, whom had not been exposed to either SSRI or non-SSRIs at any time during pregnancy.

Persistent Pulmonary Hypertension of the Newborn (PPHN)

PPHN was defined by the ICD-9 diagnostic criteria for persistent foetal circulation or primary pulmonary hypertension in the first 30 days following delivery.

Analysis

A sensitivity analysis was conducted to control for possible misclassification, with exposure status defined as filling 2 prescriptions during 90 days before delivery, and outcome redefined as only severe cases of PPHN (respiratory assistance, extracorporeal membrane oxygenation, or inhaled nitric oxide therapy).

This very large (3.8 million pregnant women) population-based study included mothers in the US on low income and with limited resources.

Results

Within 3,789,330 pregnancies, 3.4% of women used antidepressants in the 90 days before delivery, of which 2.7% were SSRIs and 0.7% were non-SSRI antidepressants.

Antidepressant versus non-use

  • 31.0 (95% CI, 28.1 to 34.2) per 10,000 infants exposed to antidepressant use had PPHN
  • 20.8 (95% CI, 20.4 to 21.3) per 10,000 infants not exposed to antidepressant use had PPHN

SSRI versus non-SSRI antidepressant use

  • 31.5 (95% CI 28.3 to 35.2) per 10,000 infants exposed to SSRIs had PPHN
  • 29.1 (95% CI 23.3 to 36.4) per 10,000 infants exposed to non-SSRIs had PPHN

Depression diagnosis

After restricting to a diagnosis of depression:

  • 33.8 (95% CI, 29.7 to 38.6) per 10,000 infants exposed to SSRIs had PPHN
  • 34.4 (95% CI, 26.5 to 44.7) per 10,000 infants exposed to non-SSRIs had PPHN
  • 14.9 (95% CI 23.7 to 26.1) per 10,000 infants not exposed to antidepressant use had PPHN

Sensitivity analysis

  • Women who filled 2 prescriptions in the 90 days before delivery did not have stronger associations
  • Changing the definition for PPHN did not alter associations in either SSRIs or non-SSRIs

The chances of a baby getting PPHN when its mother was not taking an SSRI are around 2 in 1,000, compared to around 3 in 1,000 when the mother had taken an SSRI in the last 90 days of pregnancy.

Discussion

Overall, the authors found evidence that SSRI exposure in the last 90 days of pregnancy may be associated with an increased risk of PPHN. However, the magnitude of risk observed is less than has previously been reported. Furthermore, sensitivity analyses did not amplify these risks.

The authors conclude by suggesting clinicians should take the increase of risk of PPHN into consideration when prescribing these drugs during pregnancy.

Limitations

There are a few limitations in this study to be noted:

  • Possible misclassification of the exposure or outcome, (e.g. filling a prescription does not guarantee it was taken as prescribed) which may bias the results. However, the authors did conduct a sensitivity analysis in order to control for this.
  • The baseline characteristics varied between women taking antidepressants and those who did not, with women prescribed antidepressants more likely to be older, white, taking other psychotropic medicines, be chronically ill, be obese, smoke, and have health care issues. While the SSRI and non-SSRI groups were more comparable, non-SSRI women had higher overall illness, more comorbidities, and co-medication use. Additionally, the participant population was drawn from a relatively low-income group, in which comorbid illness is likely to be higher than general populations, which may account for the difference in risk of previous studies.

This evidence would suggest that the benefits of antidepressants taken during pregnancy outweigh the risks of rare events such as PPHN.

Professor Andrew Whitelaw, Professor of Neonatal Medicine at the University of Bristol, said of the study:

Taking this study with the previous evidence, I conclude that there is a slightly increased risk of PPHN if a pregnant woman takes an SSRI but this only brings the risk up to 3 per 1000 births. I do not suggest that seriously depressed pregnant women should be denied SSRI treatment, but it would be wise for them to deliver in a hospital with a neonatal intensive care unit in case PPHN does occur.

Links

Primary paper

Huybrechts K, Bateman B, Palmsten K, Desai R, Patorno E, Gopalakrishnan C, Levin R, Mogun H, Hernandez-Diaz S. (2015) Antidepressant Use Late in Pregnancy and Risk of Persistent Pulmonary Hypertension of the Newborn. 2015: 313(21). [Abstract]

Other references

Walsh-Sukys MC, Tyson JE, Wright LL et al. (2000) Persistent pulmonary hypertension of the newborn in the era before nitric oxide: practice variation and outcomes. Pediatrics. 2000;105(1 pt 1):14-20. [PubMed abstract]

Farrow KN, Fliman P, Steinhorn RH. (2005) The diseases treated with ECMO: focus on PPHN. Semin Perinatol. 2005;29(1):8-14. [PubMed abstract]

Clark RH, Huckaby JL, Kueser TJ et al. (2003) Clinical Inhaled Nitric Oxide Research Group.  Low-dose nitric oxide therapy for persistent pulmonary hypertension: 1-year follow-up. J Perinatol. 2003;23(4):300-303. [PubMed abstract]

Glass P, Wagner AE, Papero PH et al. (1995) Neurodevelopmental status at age five years of neonates treated with extracorporeal membrane oxygenation. J Pediatr. 1995;127(3):447-457. [PubMed abstract]

US Food and Drug Administration. (2006) Public health advisory: treatment challenges of depression in pregnancy and the possibility of persistent pulmonary hypertension in newborns.

Chambers  CD, Hernández-Diaz  S, Van Marter  LJ,  et al.  Selective serotonin-reuptake inhibitors and risk of persistent pulmonary hypertension of the newborn. N Engl J Med. 2006;354(6):579-587. [PubMed abstract]

– See more at: http://www.nationalelfservice.net/treatment/antidepressants/antidepressants-during-pregnancy-and-risk-of-persistent-pulmonary-hypertension-of-the-newborn/#sthash.kEFM7Ik8.dpuf

Smoking and risk of schizophrenia: new study finds a dose-response relationship

Posted on by meryemgrabski

by Marcus Munafo @MarcusMunafo

This blog originally appeared on the Mental Elf site on 1st July 2015.

Almost exactly a year ago, a landmark study identified 108 genetic loci associated with schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). In a Mental Elf post on that study I wrote: “Genetic studies also don’t rule out an important role for the environment – [genome-wide association studies] might even help identify other causes of disease, by identifying loci associated with, for example, tobacco use.”

I mentioned this because one of the loci identified is strongly associated with heaviness of smoking. There are two possible explanations for this: either this locus influences both smoking and schizophrenia, or smoking causes schizophrenia.

Smoking and schizophrenia are highly co-morbid; the prevalence of smoking among people with a diagnosis of schizophrenia is much higher than in the general population. It is widely believed that this is because smoking helps to alleviate some of the symptoms of schizophrenia, or the side-effects of antipsychotic medication.

The possibility that smoking itself may be a risk factor for schizophrenia has generally not been widely considered. Now, however, intriguing evidence has emerged that it may be, from a large study of data from Swedish birth and conscript registries (Kendler et al, 2015).

The leading causes of premature mortality in people with schizophrenia are ischaemic heart disease and cancer, both heavily related to smoking.

Methods

The authors linked nationwide Swedish registers via the unique 10-digit identification number assigned at birth or immigration to all Swedish residents. Data on smoking habits were collected from the Swedish Birth Register (for women) and the Military Conscription Register (for men). The date of onset of illness was defined as the first hospital discharge diagnosis for schizophrenia or non-affective psychosis.

Cox proportional hazard regressions were used to investigate the associations between smoking and time to schizophrenia diagnosis. To evaluate the possibility that smoking began during a prodromal period (where symptoms of schizophrenia may emerge prior to a full diagnosis), buffer periods of 1, 3 and 5 years were included in the models. In the female sample, data from relatives (siblings and cousins) were also used to control for familial confounding (genetic and environmental).

Results

Smoking status information was available for 1,413,849 women, and 233,879 men.

There was an association between smoking at baseline and a subsequent diagnosis of schizophrenia for:

  • Women
    • Light smoking: hazard ratio 2.21, (95% CI 1.90 to 2.56)
    • Heavy smoking: hazard ratio 3.45 (95% CI 2.95 to 4.03)
  • Men
    • Light smoking: hazard ratio 2.15 (95% CI 1.25 to 3.44)
    • Heavy smoking: hazard ratio 3.80 (95% CI 1.19 to 6.60)

Adjustment for socioeconomic status and prior drug abuse (i.e., confounding) weakened these associations slightly.

Taking into account the possibility of smoking onset during a prodromal period also did not weaken these associations substantially, irrespective of whether the buffer period (from smoking assessment to the date at which a first schizophrenia diagnosis would be counted) was 1-, 3- or 5-years. Theoretically, if prodromal symptoms of schizophrenia lead to smoking onset (i.e., reverse causality) the smoking-schizophrenia association should weaken with longer buffer periods.

Finally, the co-relative analyses compared the association between smoking and schizophrenia in the female sample, within pairs of relatives of increasing genetic relatedness who had been selected on the basis of discordance for smoking (i.e., one smoked and one did not). If the smoking-schizophrenia association arises from shared familiar risk factors (genetic or environmental) the association should weaken with increasing familial relatedness. Instead, only modest decreases were observed.

As a validation check on the accuracy of their measure of smoking behaviour, the authors confirmed that heavy smoking was more strongly associated with both lung cancer and chronic obstructive pulmonary disease, two diseases known to be caused by smoking.

These results show a dose-response relationship between smoking and risk of schizophrenia, i.e. the more you smoke, the stronger the association. 

Conclusion

This study provides clear evidence of a prospective association between cigarette smoking and a subsequent diagnosis of schizophrenia. However, observational associations are notoriously problematic, because these associations may arise because of confounding (measured and unmeasured), or reverse causality. Since these analyses were conducted on observational data, these limitations should be borne in mind and we cannot say with certainty that smoking is a causal risk factor for schizophrenia.

Nevertheless, the authors conducted a number of analyses to attempt to rule out different possibilities. First, the associations were weakened only slightly when adjusted for socioeconomic status and prior drug abuse, so the impact of measured confounders appears to be modest (although other confounding could still be occurring). Second, the inclusion of a buffer period to account for smoking onset during a prodromal period also weakened the associations only slightly, which is not consistent with a reverse causality interpretation. Finally, the co-relative analysis did not indicate that the association differed strongly across levels of familial relatedness, suggesting that the impact of unmeasured familial confounders (both genetic and environmental) is relatively modest.

This study provides clear evidence of a prospective association between cigarette smoking and a subsequent diagnosis of schizophrenia.

Limitations

There are some limitations to the study that are worth bearing in mind:

  1. First, there were no data on lifetime smoking, although the authors validated their measure of smoking against outcomes known to be caused by smoking.
  2. Second, the authors used clinical diagnoses, and included both schizophrenia and non-affective psychosis, so the specificity of the findings to these outcomes is uncertain.
  3. Third, because of the small number of schizophrenia diagnoses the co-relative analyses used non-affective psychosis only.

This study is not enough to say with certainty that smoking is a causal risk factor for schizophrenia.

Summary

There are three main ways in which the association between smoking and schizophrenia might arise:

  1. Schizophrenia causes smoking,
  2. Smoking causes schizophrenia, and
  3. The association arises from risk factors common to both.

This study suggests that the first mechanism cannot fully account for the association; if anything there was more support for the third mechanism, including stronger evidence for a role for familial factors than for socioeconomic status and drug abuse. However, critically, this study also finds support for the second mechanism, including a dose-response relationship between smoking and risk of schizophrenia.

Despite this study’s strengths, and the care taken by the authors to explore the three possible mechanisms that could account for the association between smoking and schizophrenia, no single study is definitive. However, evidence is emerging from other studies that support the possibility that smoking may be a causal risk factor for schizophrenia.

Recently, McGrath and colleagues have reported that earlier age of onset of smoking is prospectively associated with increased risk of non-affective psychosis (McGrath et al, 2015).

In addition, Wium-Andersen and colleagues report that tobacco smoking is causally associated with antipsychotic medication use (but not antidepressant use), in a Mendelian randomisation analysis that uses genetic variants as unconfounded proxies for heaviness of smoking (Wium-Andersen et al, 2015).

Identifying potentially modifiable causes of diseases such as schizophrenia is a crucial part of public health efforts. There is also often reluctance among health care professionals to encourage patients with mental health problems (including schizophrenia) to attempt to stop smoking. If smoking is shown to play a causal role in the development of schizophrenia, there may be more willingness to encourage cessation. Since the majority of the mortality associated with schizophrenia is due to tobacco use (Brown et al, 2000), helping people with schizophrenia to stop is vital to their long-term health.

There is now mounting evidence that supports the possibility that smoking may be a causal risk factor for schizophrenia.

Links

Primary paper

Kendler, K.S., Lonn, S.L., Sundquist, J & Sundquist, K. (2015). Smoking and schizophrenia in population cohorts of Swedish women and men: a prospective co-relative control study. American Journal of Psychiatry. doi: 10.1176/appi.ajp.2015.15010126 [Abstract]

Other references

Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421-427. doi: 10.1038/nature13595

McGrath, J.J., Alati, R., Clavarino, A., Williams, G.M., Bor, W., Najman, J.M., Connell, M. & Scott, J.G. (2015). Age at first tobacco use and risk of subsequent psychosis-related outcomes: a birth cohort study. Australian and New Zealand Journal of Psychiatry. [PubMed abstract]

Wium-Andersen, M.K., Orsted, D.D. & Nordestgaard, B.G. (2015). Tobacco smoking is causally associated with antipsychotic medication use and schizophrenia, but not with antidepressant medication use or depression. International Journal of Epidemiology, 44, 566-577. [Abstract]

Brown S, Inskip H, Barraclough B. (2000) Causes of the excess mortality of schizophrenia. Br J Psychiatry. 2000 Sep;177:212-7.

– See more at: http://www.nationalelfservice.net/mental-health/schizophrenia/smoking-and-risk-of-schizophrenia-new-study-finds-a-dose-response-relationship/#sthash.u3UiDOlG.dpuf

CBT for substance misuse in young people

Posted on by meryemgrabski

by Eleanor Kennedy @Nelllor_

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

According to 2011 figures for the UK, over 11% of people seeking treatment for substance use were aged between 15-19 years old (Emcdda.europa.eu, 2015).

Cognitive-Behavioural Therapy (CBT) is a treatment that uses cognitive and behavioural techniques to target drug-related beliefs and to alter how these beliefs impact on actions. The individualised nature of CBT may especially be beneficial for young people whose needs differ from those of an adult due to the developmental stage of adolescence.

The factors that moderate the success of CBT treatment among young people are not well-defined. The authors of the current review aimed “to assess the effectiveness of CBT for young people in outpatient non-opioid drug use and to explore any factors that may moderate outcomes” (Filges et al 2015). Non-opioid drugs refers to cannabis, cocaine, ecstasy and amphetamines.

The non-opioid drugs covered by this review were cannabis, cocaine, ecstasy and amphetamines.

Methods

Numerous online databases were searched and studies were included if:

  • The study design was either a randomised, quasi-randomised or non-randomised controlled trial (RCT, QRCT or NRCT)
  • Participants were aged 13-20 years old
  • Participants were enrolled in outpatient treatment for non-opioid drug treatment
  • CBT was the primary intervention, although CBT interventions with an add-on component, such as motivational interviewing, were included

The primary outcome measure was abstinence or reduction of drug use as measured by biochemical test, self-report estimates or psychometric scales. Secondary outcomes of interest were social and family functioning; education or vocational involvement; retention; risk behaviour such as crime rates.

Two separate meta-analyses were conducted.

Seven

Results

Study characteristics

Seven studies, reported in seventeen papers, were included in the review. All seven studies were RCTs; six were conducted in the US and one was carried out in The Netherlands. The seven studies were quite different; sample sizes ranged from 43 to 320 participants and the gender of participants enrolled ranged from 54% to 81% male.

CBT was compared to a range of interventions, namely adolescent community reinforcement approach; multidimensional family therapy; chestnut’s Bloomington outpatient program; interactional treatment; psychoeducational substance abuse treatment and functional family therapy. Three evaluated CBT only, while four studies looked at CBT with an add-on component including Assertive Continuing Care, Motivational Enhancement Intervention or Integrated Family therapy.

The studies also differed in terms of CBT delivery; one study provided individual CBT, two had group CBT session, one study included family sessions alongside peer-group therapy, another study had family sessions at the beginning and end of the treatment period, while another study provided a home-based continuing care approach.

Main findings

Separate meta-analyses were conducted on the four studies that looked at CBT with an add-on component and on the three studies that evaluated CBT without an add-on component. Analyses had differing numbers of included studies depending on the variable in question.

Outcome measures were evaluated in three different intervals: short term (beginning of treatment to < 6 months later); medium term (6 months to < 12 months after beginning treatment) and long term (12 months + after the beginning of treatment).

Drug use

  • Overall, studies that reported on the effects of CBT with an add-on component did not show a reduction of drug use relative to the comparison treatment in the:
    • Short term (SMD 0.14 95% CI -0.64 to 0.36);
    • Medium term (SMD -0.06 95% CI -0.44 to 0.32) or
    • Long term (SMD -0.15 95% CI -0.36 to 0.06)
  • The studies that evaluated CBT without an add-on component were not found to be significantly more effective than their respective comparison treatment in the
    • Short term (SMD -0.13 95% CI -0.68 to 0.42);
    • Medium term (SMD 0.08 95% CI -0.48 to 0.31) or
    • Long term (SMD 0.02 95% CI -0.48 to 0.52)

Recovery

  • Studies that reported on CBT with an add-on component showed a statistically significant relative effect on recovery status in the long term (OR = 0.63 (95% CI 0.39 to 1.00)
  • Only one study with CBT without an add-on component reported recovery status, this was not statistically significant (OR = 2.89 (95% CI 0.72 to 11.56)

Secondary outcomes

  • CBT with an add-on component was not found to have a significant relative effect on retention or risk behaviour
  • CBT without an add-on component also did not have a significant relative effect on psychological problems, family problems, school problems, retention or risk behaviour

Unfortunately, this review does not tell us whether CBT is more or less effective than other treatments for substance misuse in young people.

Strengths and limitations

The review had some strengths. A large number of databases were searched and there were no language restrictions on the literature included. Additionally, all included studies were RCTs with none of the studies classified as having a very high risk of bias.

The small number of studies included in this review is not problematic by itself, however, the choice to carry out separate meta-analyses based on the inclusion of an add-on component to the CBT, reduced the power of the analyses even further.

Additionally, caution must be taken when interpreting the findings of the meta-analyses as the studies were all very different. There was significant heterogeneity between the studies in all but one analysis and also many of the analyses were conducted on only two studies.

The qualitative review of the paper was weak, it was merely a description of the included studies without an evaluation of the differences between them.

Conclusions

The review is inconclusive in terms of CBT being more or less effective than other therapies, as the authors themselves note. No qualitative comparisons were drawn between the studies, this may have been more beneficial given the array of differences between all seven studies.

The review did not consider any factors that may moderate the efficacy of CBT as a treatment for non-opioid drug use and the authors suggest that future studies should include more information about the heterogeneity of treatment effects so that this can be explored.

Given the differences between the included studies, a meta-analysis was probably not appropriate and a good quality systematic review may have been more useful.

More qualitative analysis of the included studies may have shed more light on this discussion.

Links

Primary paper

Filges T, Knudsen ASD, Svendsen MM, Kowalski K, Benjaminsen L, Jørgensen AMK. Cognitive-Behavioural Therapies for Young People in Outpatient Treatment for Non-Opioid Drug Use: A Systematic Review. Campbell Systematic Reviews 2015:3 10.4073/csr.2015.3

Other references

Emcdda.europa.eu, (2015). EMCDDA | European Monitoring Centre for Drugs and Drug Addiction — information on drugs and drug addiction in Europe. [online] Available at: http://www.emcdda.europa.eu/ [Accessed 15 May 2015].

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/cbt-for-substance-misuse-in-young-people/#sthash.xWsGpoWk.dpuf

The effect of smoking-free psychiatric hospitals on smoking behaviour: more evidence needed

Posted on by meryemgrabski

By Olivia Maynard @OliviaMaynard17 

This blog originally appeared on the Mental Elf site on 18th May 2015.

One in three people with mental health illnesses in the UK smoke, as compared with one in five of the general population. In addition, smokers with mental illnesses smoke more heavily, are more dependent on nicotine and are less likely to be given help to quit smoking. As a result, they are more likely to suffer from smoking-related diseases, and on average die 12-15 years earlier than the general population.

Since July 2008, mental health facilities in England have had indoor smoking bans. However, NICE guidelines recommend that all NHS sites, including psychiatric hospitals become completely smoke-free, a recommendation previously examined by the Mental Elf.

This NICE recommendation has been criticised by those who argue that:

  1. Tobacco provides necessary self-medication for the mentally ill;
  2. Smoking cessation interferes with recovery from mental illness;
  3. Smoking cessation is the lowest priority for those with mental illnesses;
  4. People with mental illnesses are not interested in quitting;
  5. People with mental illness cannot quit smoking.

Many people argue that forcing people to quit smoking when they are having an acute mental health episode is tantamount to abuse.

Judith Prochaska, a researcher at Stanford University, has previously addressed each of these arguments (she calls them ‘myths’) (Prochaska, 2011). The abridged summary of the evidence surrounding myths 1, 2 and 3 is that:

  1. Smoking actually worsens mental health outcomes; in fact, the argument that nicotine provides self-medication is one which has been promoted by the tobacco industry itself;
  2. Smoking cessation does not exacerbate mental health outcomes;
  3. Smoking cessation should be a high priority, given that mental health patients are much more likely to die from tobacco-related disease than mental illness.

These are interesting and important arguments and more evidence surrounding them is also available here (Prochaska, 2010).

However, in this blog post I focus on ‘myths’ 4 and 5, drawing on a recent systematic review investigating the impact of a smoke-free psychiatric hospitalisation on patients’ motivations to quit (myth 4) and smoking behavior (myth 5) (Stockings et al., 2014).

This systematic review brings together mostly cross-sectional studies that look at the impact that smoke-free hospitals have on psychiatric inpatients who smoke.

Methods and results

Stockings and colleagues searched for studies examining changes in patients’ smoking-related behaviours, motivation and beliefs either during or following an admission to an adult inpatient psychiatric facility.

Study characteristics

Fourteen studies matched these inclusion criteria, two of which were conducted in the UK. The majority of the studies used a cross-sectional design and none were randomised controlled trials. The studies were all quite different, with the number of participants ranging from 15-467 and the length of admission ranging from 1-990 days. Crucially, the type of smoking ban varied considerably between the studies, so I’ll consider these separately.

Facilities with complete smoking bans

Six studies were conducted in facilities with complete bans. All of these offered nicotine dependence treatment, including nicotine replacement therapy (NRT) or brief advice.

  • Only one of these statistically assessed smoking behaviour, finding that cigarette consumption was lower during admission compared with prior to admission.
  • Three studies assessed smoking behaviour after discharge, finding that the majority of patients resumed smoking within five days. However, there was some evidence from the two larger studies that smoking prevalence was still lower at two weeks and three months post-discharge compared with prior to admission.
  • The one study to statistically assess smoking-related beliefs and motivations found that patients expected to be more successful at quitting following discharge compared with at admission. Higher doses of NRT were related to higher expectations of success.

Facilities with incomplete bans

Eight studies were conducted in facilities with incomplete bans. 

  • Four banned smoking indoors and all of these offered nicotine dependence treatment:
    • Only one of these statistically assessed smoking behaviour, finding that quit attempts increased from 2.2% when smoking was permitted in specific rooms, to 18.4% after the ban.
    • One study that assessed smoking prevalence post-discharge found that all participants (n = 15) resumed smoking.
    • One study found that participants expected to be more successful in smoking cessation post-discharge as compared with at admission.
  • Three allowed smoking in designated rooms, with no nicotine dependence treatment:
    • There were mixed results among the two studies which assessed smoking prevalence during admission.
    • Compared with at admission, there was some evidence of increased motivation to quit smoking.
  • One restricted smoking to five pre-determined intervals per day, with no nicotine dependence treatment:
    • Motivation to quit was lower at discharge compared with at admission.

This review suggests that complete bans are the most effective at encouraging smoking cessation and that NRT or brief advice are crucial.

Conclusions

The authors concluded that:

Smoke-free psychiatric hospitalisation may have the potential to impact positively on patients’ smoking behaviours and on smoking-related motivation and beliefs.

Strengths and limitations

The fourteen studies included in this review were all quite different from each other and had a number of limitations including:

  • Small sample sizes;
  • Incomplete reporting of key outcomes;
  • Failure to use controlled, experimental research designs;
  • Differences in the types of smoking bans in place;
  • Inconsistent provision of nicotine dependence treatment.

These key differences and limitations prevented statistical examination of the results as a whole. This means that making firm conclusions is difficult. There is clearly a need for more research in this area.

This area of research is far from complete, so we cannot make any firm conclusions about smoke-free psychiatric hospitals at this stage.

Summary

There is evidence that people with mental illnesses are interested in quitting smoking (myth 4) and that they are able to (myth 5). However, we still need more studies to examine these questions with well-powered (i.e. large sample sizes), high-quality (i.e., experimental) research designs.

The evidence presented in this systematic review suggests that complete bans are the most effective at encouraging smoking cessation and that the provision of nicotine dependence treatment, such as NRT or brief advice, is also crucial.

Although a handful of the studies assessed smoking behaviour after discharge, none of the facilities viewed this as an important outcome. Given the high level of smoking-related disease among those with mental health illnesses, ensuring that individuals remain abstinent from smoking after discharge is important for the continuing good health of these individuals.

Importantly, none of the studies in this review explored the impact of smoke-free legislation on mental health outcomes. Although the evidence suggests that smoking cessation actually improves mental health outcomes, future research should continue to examine this relationship.

Over to you

Do you have a mental health illness yourself, or support someone who does? Do you work with people with mental health illnesses? Should psychiatric hospitals become smoke-free?

We'd love to hear your views about this systematic review and more generally on this often emotive topic. Please use the comment box below to share your knowledge and experience.

Links

Primary paper

Stockings EA. et al (2014) The impact of a smoke-free psychiatric hospitalization on patient smoking outcomes: a systematic review. Aust NZ J Psychiatry 2014 May 12;48(7):617-633. [PubMed abstract]

Other references

Prochaska, J. J. (2010). Failure to treat tobacco use in mental health and addiction treatment settings: A form of harm reduction? Drug and Alcohol Dependence, 110(3), 177-182. doi: http://dx.doi.org/10.1016/j.drugalcdep.2010.03.002

Prochaska, J. J. (2011). Smoking and Mental Illness — Breaking the Link. New England Journal of Medicine, 365(3), 196-198. doi: doi:10.1056/NEJMp1105248

 

Promoting smoking cessation in people with schizophrenia

Posted on by meryemgrabski

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 14th May 2015.

shutterstock_276469196People with schizophrenia have a considerable reduction in life expectancy compared to the general population (Osborn et al 2007; Lawrence et al 2013). A number of factors lead to cardiovascular disease (Osborn et al 2007; Lawrence et al 2013; Nielsen et al, 2010) one of which is smoking.People with schizophrenia smoke at much higher rates and more heavily than the general population (Ruther et al 2014, Hartz et al 2014).Stubbs et al (2015) carried out a review to assess the current cessation interventions available for individuals with serious mental illnesses and establish if any disparities currently lie in the delivery of these interventions.60% of premature deaths in people with schizophrenia are due to medical conditions including heart and lung disease and infectious illness caused by modifiable risk factors such as smoking, alcohol consumption and intravenous drug use.

Methods

The authors searched several electronic databases (Embase, PubMed, and CINAHL) using the following keywords: “smoking cessation”, “smoking”, “mental illness”, “serious mental illness” and “schizophrenia.”

Studies were eligible if they included individuals with a DSM or ICD-10 diagnosis of schizophrenia and reported a cessation intervention.

The authors included both observational and intervention studies as well as systematic-reviews and meta-analyses.

This paper is a clinical overview (not a systematic review) of a wide range of different studies relevant to smoking cessation in schizophrenia and other severe mental illnesses.

Results

Pharmacological interventions

 Non-pharmacological interventions

  • The evidence for E-cigarettes was inconsistent, with the authors concluding more evidence was needed before clinicians consider e-cigarettes within mental health settings. Additionally, e-cigarette use in people with schizophrenia should have side effects monitored closely.
  • There was little research on exercise in schizophrenia, but one study found a reduction in tobacco consumption.

Behavioural approaches

  • Behavioural approaches such as offering smoking cessation advice alongside pharmacotherapy have been found successful with no harmful side effects.

Disparities in smoking cessation interventions

  • An investigation of GP practices found individuals with schizophrenia did not receive smoking cessation interventions proportional to their needs.

Support while quitting

  • People with serious mental illnesses experience more severe withdrawal symptoms compared to the general population, and therefore should be given extra support during cessation attempts (Ruther et al 2014).
  • Psychiatrists should re-evaluate choice and the dose of antipsychotic medicine being given after abstinence from smoking is achieved. This is because of nicotine’s metabolic influence on antipsychotic medicine.
  • Alongside smoking cessation, exercise should be promoted among people with schizophrenia to combat weight gain and the increased metabolic risk.

People with serious mental illness are likely to need more support when quitting smoking, because they generally suffer more severe withdrawal symptoms.

Discussion

In light of the findings, the authors suggest several steps for clinicians to help people with schizophrenia quit smoking:

  • Patients’ current smoking status, nicotine dependency, and previous quit attempts should be assessed. Assessing nicotine dependency will help predict the level of withdrawal symptoms the patient is likely to experience upon quitting.
  • Cessation attempts are best timed when the patient is stable. Patients should be thoroughly advised on the process needed to give them the best chance of quitting smoking, Thus, allowing the patient to formulate their quit plan and take ownership of their own quit attempt.
  • Cessation counselling should be provided, particularly what to expect with withdrawal symptoms (e.g. depression and restlessness) and how to cope.
  • Pharmacological support should be provided (Bupropion recommended) when there is even mild tobacco dependence.
  • Clinicians should carefully monitor patients’ medication and fluxions in weight for a minimum of 6 months after quitting smoking, and when needed recommended exercise to combat weight gain.

The authors provide a well laid out summary of their findings, alongside some excellent suggestions for clinicians to consider on how to best promote cessation in practice.

However, it should be stressed that Stubbs et al (2015) only searched for high qualities studies and provided an overview of them –  this is not a systematic review or meta-analysis. They included several types of studies, set little inclusion criteria and listed no exclusion criteria. This is quite different from a systematic review with a meta-analysis, which would set stricter predefined search and eligibility criteria, which identify a set of studies all tackling the same question, thus allowing for the statistical pooling and comparison of these studies.

This is not a systematic review, but it does offer some very useful practical advice for clinicians who are trying to promote smoking cessation.

Links

Primary paper

Stubbs B, Vancampfort D, Bobes J, De Hert M, Mitchell AJ. How can we promote smoking cessation in people with schizophrenia in practice? A clinical overview. Acta Psychiatrica Scandinavica. 2015: 1-9. 
[PubMed abstract]

Other references

Osborn DPJ, Levy G, Nazareth I, Petersen I, Islam A, King MB. Relative risk of cardiovascular and cancer mortality in people with severe mental illness from the United Kingdom’s General Practice Research Database. Arch Gen Psychiatry 2007;64:242–249.

Lawrence D, Hancock KJ, Kisely S. The gap in life expectancy from preventable physical illness in psychi- atric patients in Western Australia: retrospective analysis of population based registers. BMJ 2013;346: f2539-f.

Nielsen RE, Uggerby AS, Jensen SOW, McGrath JJ. Increasing mortality gap for patients diagnosed with schizophrenia over the last three decades – a Danish nationwide study from 1980 to 2010. Schizophr Res 2013;146:22–27.  
[PubMed abstract]

Ruther T, Bobes J, de Hert M et al. EPA guidance on tobacco dependence and strategies for smoking cessation in people with mental illness. Eur Psychiatry 2014;29:65– 82. 
[PubMed abstract]

Hartz SM, Pato CN, Medeiros H et al. Comorbidity of severe psychotic disorders with measures of substance use. JAMA Psychiatry 2014;71:248–254.