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Psychiatric disorders: what’s the significance of non-random mating?

7960674098_2070f1fe64_bHardly a week passes without the publication of a study reporting the identification of genetic variants associated with an increasing number of behavioural and psychiatric outcomes. This partly driven by the growth in large international consortia of studies, as well as the release of data from very large studies such as UK Biobank. These consortia and large individual studies are now achieving the necessary sample sizes to detect the very small effects associated with common genetic variants,.

We’ve known for some time that psychiatric disorders are under a degree of genetic influence, but one puzzle is why estimates of the heritability of these disorders (i.e., the proportion of variability in risk of a disorder that is due to genetic variation) differs across disorders. Another intriguing question is why there appears to be a high degree of genetic comorbidity across different disorders; that is, common genetic influences that relate to more than one disorder. One possible answer to both questions may lie in the degree of non-random mating by disorder.

Non-random mating refers to the tendency for partners to be more similar than we would expect by chance on any given trait of interest. This is straightforward to see for traits such as height and weight, but less obvious for traits such as personality. A recent study by Nordsletten and colleagues investigated the degree of non-random mating for psychiatric disorders, as well as a selection of non-psychiatric disorders for comparison purposes.

 

Methods

The researchers used data from three Swedish national registers, using unique personal identification numbers assigned at birth. The data were linked to the Swedish National Patient Register (NPR), which includes diagnostic information on all individuals admitted to a Swedish hospital and, since 2001, on outpatient consultations. Individuals with multiple diagnoses could appear as a “case” in each separate analysis of these different diagnoses.

Cases of schizophrenia, bipolar disorder, autism spectrum disorder, anorexia nervosa, substance abuse, attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), major depressive disorder, social phobia, agoraphobia, and generalised anxiety disorder were identified using standard protocols. For comparison purposes, cases of Crohn’s disease, type 1 and type 2 diabetes, multiple sclerosis and rheumatoid arthritis were also identified.

For each case (i.e., individuals with a diagnosis), five population controls were identified, matched on age, sex and area of residence. Mating relationships were identified through records of individual marriages, and through records of individuals being the biological parent of a child. The use of birth of a child was intended to capture couples who remained unmarried. For each member of a mated case pair a comparison sample was again generated, with the constraint that these controls not have the diagnosis of interest.

First, the proportion of mated pairs in the full case and control samples was summarised. Correlations were calculated to evaluate the relationship between the diagnostic status of each individual in a couple, first within and then across disorders. Logistic regression was used to estimate the odds of any diagnoses in mates of cases relative to mates of controls. Finally, the odds of any diagnosis in mates was estimated, and the relationship between the number of different disorders in a case and the presence of any psychiatric diagnoses in their mate explored.

Non-random mating is not a lack of promiscuity, it's the tendency for partners to be more similar than we would expect by chance on any given trait of interest.

Non-random mating is not a lack of promiscuity! It’s the tendency for partners to be more similar than we would expect by chance on any given trait of interest.

Results

Cases showed reduced odds of mating relative to controls, and this differed by diagnosis, with the greatest attenuation among individuals with schizophrenia. In the case of some diagnoses (e.g., ADHD) this low rate of mating may simply reflect, at least in part, the young age of these populations.

Within each diagnostic category, there was evidence of a correlation in diagnostic status for mates of both sexes (ranging from 0.11 to 0.48), and there was also evidence of cross-disorder correlations, although these were typically smaller than within-disorder correlations (ranging from 0.01 to 0.42).

In general, if an individual had a diagnosis this was typically associated with a 2- to 3-fold increase in the odds of his or her mate having the same or a different disorder. This was particularly pronounced for certain conditions, such as ADHD, autism spectrum disorder and schizophrenia.

In contract to psychiatric samples, mating rates were consistently high among both men and women with non-psychiatric diagnoses, and correlations both across and within the conditions was rare (ranging from -0.03 to 0.17), with the presence of a non-psychiatric diagnosis associated with little increase in his or her spouse’s risk.

This general population study found an amazing amount of assortative (non-random) mating within psychiatric disorders.

This general population study found an amazing amount of assortative mating within psychiatric disorders.

Conclusions

These results indicate a striking degree of non-random mating for psychiatric disorders, compared with minimal levels for non-psychiatric disorders.

Correlations between partners were:

  • Greater than 0.40 for ADHD, autism spectrum disorder and schizophrenia,
  • Followed by substance abuse (range 0.36 to 0.39),
  • And detectable but more modest for other disorders, such as affective disorders (range 0.14 to 0.19).

The authors conclude the following:

  • Non-random mating is common in people with a psychiatric diagnosis.
  • Non-random mating occurs both within and across psychiatric diagnoses.
  • There is substantial variation in patterns of non-random mating across diagnoses.
  • Non-random mating is not present to the same degree for non-psychiatric diagnoses.

Implications

So, what are the implications of these findings?

First, non-random mating could account for the relatively high heritability of psychiatric disorders, and also explain why some psychiatric disorders are more heritable then others (if the degree of assortment varies by disorder).

This is because non-random mating will serve to increase additive genetic variation across generations until equilibrium is reached, leading to increased (narrow sense) heritability for any trait on which it is acting.

Second, non-random mating across psychiatric disorders (reflected, for example in a correlation of 0.31 between schizophrenia and autism spectrum disorder) could help to explain in part the observed genetic comorbidity across these disorders.

Non-random mating could explain why some psychiatric disorders are more heritable then others.

Non-random mating could explain why some psychiatric disorders are more heritable than others.

Strengths and limitations

This is an extremely well-conducted, authoritative study using a very large and representative data set. The use of a comparison group of non-psychiatric diagnoses is also an important strength, which gives us insight into just how strong non-random mating with respect to psychiatric diagnoses is.

The major limitations include:

  • Not being able to capture other pairings (e.g., unmarried childless couples)
  • A reliance on register diagnoses, which largely excludes outpatients etc
  • A lack of insight into possible mechanisms

This last point is interesting; non-random mating such as that observed in this study could arise because couples become more similar over time after they have become a couple (e.g., due to their interactions with each other) or may be more similar from the outset (e.g., because similar individuals are more likely to form couples in the first place, known as assortative mating).

The authors conclude that the non-random mating they observed may be due toassortative mating for two reasons. First, shared environment (which would capture effects of partner interactions) appears to play very little role in many psychiatric conditions. Second, neurodevelopmental conditions are present over the lifespan (i.e., before couples typically meet), which would suggest an assortative mating explanation for the observed similarity for at least these conditions.

Some disorders (e.g., schizophrenia) are associated with reduced reproductive success, and therefore should be under strong negative selection in the general population. However, these results suggest they may be positively selected for within certain psychiatric populations. In other words, these mating patterns could, in part, compensate for the reduced reproductive success associated with certain diagnoses, and explain why they persist across generations.

Implications for future research

Non-random mating also has implications for research, and in particular the use of genetic models. These models typically assume that mating takes place at random, but the presence of non-random mating (as indicated by this study) suggests that this should be taken into account in these models. This could be done by allowing for a correlation between partners, and neglecting this correlation may lead to an underestimate of heritability.

Summary

This study suggests that non-random mating is widespread for psychiatric conditions, which may help to provide insights into why these conditions are transmitted across generations, and why there is such a strong degree of comorbidity across psychiatric diagnoses. The results also challenge a fundamental assumption of many genetic approaches.

Assortative mating means that the person closest to an individual with a psychiatric disorder is also likely to have psychiatric problems.

Assortative mating means that, in general population terms, people in romantic relationships with those who have psychiatric disorders are also likely to have psychiatric problems themselves.

Links

Primary paper

Nordsletten AE, Larsson H, Crowley JJ, Almqvist C, Lichtenstein P, Mataix-Cols D. (2016) Patterns of nonrandom mating within and across 11 major psychiatric disorders. JAMA Psychiatry 2016. doi: 10.1001/jamapsychiatry.2015.3192

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Cannabis and mental illness: it’s complicated!

By Suzi Gage @soozaphone

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

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

But understanding what these associations mean is problematic:

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

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

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

Methods

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

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

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

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

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

Results

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

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

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

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

Conclusions

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

The authors concluded:

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

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

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

Strengths and limitations

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

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

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

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

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

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

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

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

Summary

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

Links

Primary paper

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

Other references

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

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

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

Photo credits

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

Can a machine learning approach help us predict what specific treatments work best for individuals with depression?

by Marcus Munafò @MarcusMunafo

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

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Understanding who responds well to treatment for depression is important both scientifically (to help develop better treatments) and clinically (to more efficiently prescribe effective treatments to individuals). Many attempts to predict treatment outcomes have focused on mechanistic pathways (e.g., genetic and brain imaging measures). However, these may not be particularly useful clinically, where such measures are typically not available to clinicians making treatment decisions. A better alternative might be to use routinely- or readily-collected behavioural and self-report data, such as demographic variables and symptom scores.

Chekroud and colleagues (2015) report the results of a machine learning approach to predicting treatment outcome in depression, using clinical (rather than mechanistic) predictors. Since there are potentially a very large number of predictors, examining all possible predictors in an unbiased manner (sometimes called “data mining”) is most likely to produce a powerful prediction algorithm.

Machine learning approaches are well suited to this approach, because they can identify patterns of information in data, rather than focusing on individual predictors. They can therefore identify the combination of variables that most strongly predict the outcome. However, prediction algorithms generated in this way need to be independently validated. By definition, they will predict the outcome in the data set used to generate the algorithm (the discovery sample). The real test is whether they also predict similar outcomes in independent data sets (the replication sample). This avoids circularity, and increases the likelihood the algorithm will be clinically useful.

Clinicians currently have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant.

Clinicians currently have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant.

Methods

The authors used data from a large, multicenter clinical trial of major depressive disorder (the STAR*D trial – Trivedi et al, 2006) as their discovery sample, and a separate clinical trial (the CO-MED trial, Rush et al, 2011) as their replication sample. Data were available on 1,949 participants in the STAR*D trial, and 425 participants in the CO-MED trial. The CO-MED trial consisted of three treatment groups, with participants randomised to receive either:

  1. Escitalopram-placebo
  2. Bupropion-escitalopram
  3. Venlafaxine-mirtazapine

The authors built a predictive model using all readily-available sources of information that overlapped for participants in both trials. This included:

  • A range of sociodemographic measures
  • DSM-IV diagnostic items
  • Symptom severity checklists
  • Eating disorder diagnoses
  • Whether the participants had taken specific antidepressant drugs
  • History of major depression
  • The first 100 items of the psychiatric diagnostic symptoms questionnaire.

In total, 164 variables were used.

For the training process, the machine learning approach divided the original sample (using the STAR*D data) into ten subsets, using nine of those in the training process to make predictions about the remaining subset. This process was repeated ten times, and the results averaged across these repeats. The final model built using the STAR*D data was then used to predict outcomes in the each of the CO-MED trial treatment groups separately.

The model was developed to detect people for whom citalopram (given to everyone in the first 12 weeks of the STAR*D trial) is beneficial, rather than predicting non-responders. It was constrained to require only 25 predictive features (i.e., clinical measures), to balance model performance (which should be greater with an increasing number of predictors) with clinical usability (since an algorithm requiring a very large number of predictors may be difficult to implement in practice).

Only 11-30% of patients with depression reach remission with initial treatment, even after 8-12 months.

Only 11-30% of patients with depression reach remission with initial treatment, even after 8-12 months.

Results

The top three predictors of non-remission were:

  1. Baseline depression severity
  2. Feeling restless during the past 7 days
  3. Reduced energy level during the past 7 days

The top three predictors of remission were:

  1. Currently being employed
  2. Total years of education
  3. Loss of insight into one’s depressive condition

Overall, the model predicted outcome in the STAR*D data with:

  • An accuracy of 64.6% – it identified 62.8% of participants who eventually reached remission (i.e., sensitivity), and 66.2% of non-remitters (i.e., specificity)
  • This is equivalent to a positive predictive value (PPV) of 64.0% and a negative predictive value (NPV) of 65.3%
  • The performance of the model was considerably better than chance (P = 9.8 × 10-33)

In the CO-MED data, the model:

  • Pedicted outcome in the escitalopram-placebo group:
    • Accuracy 59.6%, 95% CI 51.3% to 67.5%,
    • P = 0.043,
    • PPV 65.0%,
    • NPV 56.0%.
  • Escitalopram-bupropion group
    • Accuracy 59.7%, 95% CI 50.9% to 68.1%,
    • P = 0.023,
    • PPV 59.7%,
    • NPV 59.7%.

However, there was no statistical evidence that it performed better than chance in the venlafaxine-mirtazapine group:

  • Accuracy 51.4%, 95% CI 42.8% to 60.0%,
  • P = 0.53,
  • PPV 53.9%,
  • NPV 50.0%.
Could predictive models that mine existing trial data help us prospectively identify people with depression who are likely to respond to a specific antidepressant?

Could predictive models that mine existing trial data help us prospectively identify people with depression who are likely to respond to a specific antidepressant?

Conclusions

The authors conclude that their model performs comparably to the best biomarker currently available (an EEG-based index) but is less expensive and easier to implement.

The outcome (clinical remission, based on a final score of 5 or less on the 16-item self-report Quick Inventory of Depressive Symptomatology, after at least 12 weeks) is associated with better function and better prognosis than response without remission.

Strengths and limitations

There are some strengths to this study:

  1. First, it attempts to build a prediction algorithm using data that are already collected routinely in clinical practice, or could be easily incorporated into routine practice.
  2. Second, the prediction algorithm shows some evidence of generalisability to an independent sample.
  3. Third, the algorithm also shows some degree of specificity, by performing best in the escitalopram-treated groups in the CO-MED data.

However, there are also some limitations:

  1. First, there is a clear reduction in how well the algorithm predicts treatment outcome in the discovery sample (STAR*D) compared with the replication sample (CO-MED). This illustrates the need for an independent replication sample in studies of this kind.
  2. Second, and more importantly, although the algorithm performed better in the escitalopram-treated groups in CO-MED, it’s not clear that there was any evidence that performance was different across the three arms – the 95% confidence intervals for the venlafaxine-mirtazapine group (42.8% to 60.0%) include the point estimates for the other two groups (escitalopram-placebo: 59.6%, escitalopram-bupropion: 59.7%). Therefore, although there is some evidence of specificity, it is indirect, and the algorithm may in fact predict treatment outcome in general, rather than in those who have received a specific treatment, at least in part.
  3. Third, models of this kind cannot tell us whether the variables that predict treatment outcome are causal. This may not matter if our focus is on clinical prediction, although if they are not causal then the prediction algorithm may not generalize well to other populations. For example, in both the discovery and replication sample participants had been recruited into clinical trials, and therefore may not be representative of the wider population of people with major depressive disorder. Causal anchors are likely to be more important if we are interested in mechanistic (rather than clinical) predictors.

Summary

Ultimately, being able to simultaneously identify individuals likely to respond well to drug A and not respond to drug B will be clinically valuable, and is the goal of stratified medicine. This study represents only the first step towards being able to identify likely responders and non-responders for a single drug (in this case, citalopram); in particular, although there was some evidence for specificity in this study, it was relatively weak.

Ultimately, with larger datasets that include multiple treatment options (including non-pharmacological interventions), it may be possible to match people to the treatment option they are most likely to respond successfully to. The focus on routinely- or readily-collected data means that it gives an insight into what clinical prediction algorithms for treatment response in psychiatry may look like in the future.

This innovative study may open the door to predict more personalised medicine for people with depression.

This innovative study (and others like it) may open the door to predict more personalised medicine for people with depression.

Links

Primary paper

Chekroud AM, Zotti RJ, Shezhad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. (2015) Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 2015. doi: S2215-0366(15)00471-X [Abstract]

Other references

Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, Norquist G, Howland RH, Lebowitz B, McGrath PJ, Shores-Wilson K, Biggs MM, Balasubramani GK, Fava M; STAR*D Study Team. (2006) Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006 Jan;163(1):28-40. [PubMed abstract] [Wikipedia page]

Rush AJ, Trivedi MH, Stewart JW, Nierenberg AA, Fava M, Kurian BT, Warden D, Morris DW, Luther JF, Husain MM, Cook IA, Shelton RC, Lesser IM, Kornstein SG, Wisniewski SR. (2011) Combining medications to enhance depression outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study.Am J Psychiatry. 2011 Jul;168(7):689-701. doi: 10.1176/appi.ajp.2011.10111645. Epub 2011 May 2. [PubMed abstract]

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– See more at: http://www.nationalelfservice.net/mental-health/depression/can-a-machine-learning-approach-help-us-predict-what-specific-treatments-work-best-for-individuals-with-depression/#sthash.wvpEojY8.dpuf

Medication for cognitive impairment in traumatic brain injury: little evidence to support its use

by Eleanor Kennedy @Nelllor_

This blog originally appeared on the Mental Elf site on 18th January 2016.

Traumatic Brain Injury (TBI) is classified by The World Health Organization as the leading cause of death and disability among children and young adults worldwide (WHO, 2006, p. 164). An estimated 235 per 100,000 Europeans acquire brain injuries each year, with more than  6 million TBI survivors already living in Europe (Tagliaferri et al, 2006).

There are many long-lasting consequences of TBI including cognitive, behavioural and emotional problems (Barnes & Ward, 2005). Pharmacotherapy interventions have been suggested to alleviate cognitive impairment in TBI sufferers. The current review aimed to assess the evidence for such interventions (Dougall et al, 2015).

skateboardTraumatic brain injury is the leading cause of death and disability among children and young adults worldwide.

Methods

The Cochrane Dementia and Cognitive Improvement Group’s Specialised Register was searched for studies that examined the effectiveness of pharmacological treatment for cognitive impairment in people with traumatic brain injury. The search included both healthcare databases and trial registers. Studies were included if:

  • The study design was either a randomised controlled trial (RCT) or cross-over design study
  • The study investigated one centrally acting pharmacological agent that modulate one or more of the main neurotransmitter systems
  • Participants had to have experienced the TBI resulting in chronic cognitive impairment at least 12 months prior to assessment

The primary outcomes of interest were performance on psychometric and neuropsychological tests or scores on screening measures that measured memory and cognitive function; global severity of cognitive impairment and global impression of change. Acceptability of treatment (as measured by withdrawal from trial), safety, mortality and subjective benefit were all secondary outcomes.

Analyses were carried out on results from phase one of each included study.

Results

Four studies in total were included in the review (3 from the United States, one from Sweden). Seven RCTs that matched inclusion criteria were found, however, two cross-over design studies could not be included as data for phase one was not available from the authors; another study was not included due to the lack of a placebo control. Table 1 summarises the treatments and participants.

Study N Participants Treatment Duration of treatment
Jhaet al. 2008 51 (age 16 to 65) Modafinil; effects histaminergic, serotonergic, and glutaminergic activity 4 weeks
Johansson et al. 2012 12 (age 30 to 65) (−)-OSU6162; monoamine stabiliser agent with dopaminergic and serotonergic effects 4 weeks
Ripley et al. 2014 60 (age 18 to 65) Atomoxetine; noradrenaline reuptake inhibitor 2 weeks
Silver et al., 2006 157 (age 18 to 50) Rivastigmine; an acetylcholinesterase and butyrylcholinesterase inhibitor 12 weeks

Primary outcome

Neither modafinil nor atomoxetine demonstrated superiority over placebo on any measure of cognition. The effects of rivastigmine were superior on one measure in the current review (CANTAB RVIP −44.54 milliseconds, 95% CI −88.62 to −0.46), but not in the original trial. Rivastigmine was also effective on the same measure in a subgroup of participants with greater cognitive impairment.

Superiority over placebo for (−)-OSU6162 was demonstrated in Trail Making Test A (−9.20 seconds, 95% CI −12.19 to −6.21), Trail Making Test B (−6.20 seconds, 95%CI,−7.81 to−4.59) and WAIS-III digit symbol coding (8.60, 95% CI 6.47 to 10.73), however the score in Trail Making Test D was higher for placebo (53.80 seconds, 95% CI 36.76 to 70.24) (Johansson 2012).

Secondary outcomes

Safety and acceptability were two secondary outcomes that were reported on. Participants reported more adverse effects for modafinil and atomoxetine, however this was not statistically supported. One participant required a dose reduction in the (-)-OSU6162 trial due to adverse effects. More participants taking rivastigmine reported nausea compared to those taking placebo (19/80, 23.8%versus 6/77, 7.8%, risk ratio 3.05, 95% CI 1.29 to 7.22). Two people dropped out of the modafinil treatment arm, none in the placebo group. There were no deaths reported in any of the included studies.

Strengths and limitations

The review included only randomised controlled trials to assess the effects of centrally acting pharmacological agents for treatment of chronic cognitive impairment subsequent to traumatic brain injury in adults. There were very strict inclusion criteria and the authors chose to only include data from phase one of the treatment. This is a strength for cross-over design studies particularly as this controls for the possibility of long term treatment effects once a group’s treatment is switched to placebo following pharmacological treatment. However, two studies were excluded because data from phase one were unavailable.

The limited number of included studies, rather than a limitation, is likely to be indicative of a lack of well controlled research into pharmacological treatments for cognitive impairment following TBI.

Conclusions

There was no evidence to support modafinil or atomoxetine as a treatment for cognitive impairment as a result of TBI. There was weak evidence to suggest that rivastigmine may be helpful in the treatment of cognitive impairment in one measure of cognitive functioning in this review, however the same effect was not significant in the original study possibly due to the use of a different statistical test, and the findings that (−)-OSU6162 may be superior to placebo must be interpreted with caution as the sample size in this group was so small (n=6).

Overall the authors concluded that:

there is insufficient evidence to determine whether pharmacological treatment is effective in chronic cognitive impairment in TBI.

Two of the four included studies had fatigue as their primary outcome, which further suggests that more research  in the specific area of cognition may be necessary.

In closing, the review highlights a gap in the research in such treatments for TBI, the authors suggest that future research should also focus on outcomes such as neurobehavioral symptoms as well as cognitive impairment and memory performance.

This review highlights a lack of RCTs that explore the value of medication for cognitive impairment following traumatic brain injury.This review highlights a lack of RCTs that explore the potential value of medication for cognitive impairment following traumatic brain injury.

Links

Primary paper

Dougall D, Poole N, Agrawal N. Pharmacotherapy for chronic cognitive impairment in traumatic brain injury. Cochrane Database of Systematic Reviews 2015, Issue 12. Art. No.: CD009221. DOI: 10.1002/14651858.CD009221.pub2.

Other references

Barnes M, Ward A. (2005) Oxford Handbook of Rehabilitation Medicine. Oxford University Press.

Jha A, Weintraub A, Allshouse A, Morey C, Cusick C, Kittelson J, Gerber D. (2008) A randomized trial of modafinil for the treatment of fatigue and excessive daytime sleepiness in individuals with chronic traumatic brain injury. Journal of Head Trauma Rehabilitation, 23(1), 52–63. doi:10.1097/01.HTR.0000308721.77911.ea (PubMed abstract)

Johansson B, Carlsson A, Carlsson ML, Karlsson M, Nilsson MKL, Nordquist-Brandt E, Rönnbäck L. (2012) Placebo-controlled cross-over study of the monoaminergic stabiliser (-)-OSU6162 in mental fatigue following stroke or traumatic brain injury. Acta Neuropsychiatrica, 24, 266–274. doi:10.1111/j.1601-5215.2012.00678.x [PubMed record]

Ripley DL, Morey CE, Gerber D, Harrison-Felix C, Brenner LA, Pretz CR, Wesnes K. (2014) Atomoxetine for attention deficits following traumatic brain injury: Results from a randomized controlled trial. Brain Injury, 28(January 2016), 1514–1522. doi:10.3109/02699052.2014.919530 [PubMed abstract]

Silver JM, Koumaras B, Chen M, Mirski D, Potkin SG, Reyes P, Gunay I. (2006) Effects of rivastigmine on cognitive function in patients with traumatic brain injury. Neurology, 67, 748–755. [PubMed abstract]

Tagliaferri F, Compagnone C, Korsic M, Servadei F, Kraus J. (2006) A systematic review of brain injury epidemiology in Europe. Acta Neurochirurgica, 148(3), 255–68; discussion 268. doi:10.1007/s00701-005-0651-y [PubMed abstract]

WHO. (2006) Neurological Disorders: Public Health Challenges. World Health Organisation (p. 232).http://www.who.int/mental_health/neurology/neurological_disorders_report_web.pdf

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

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

Helping people with depression return to work

By Meg Fluharty, @MegEliz_

This blog originally appeared on the Mental Elf blog on 27th January 2015.

shutterstock_213396637

Depression is a major public health concern, with a wide range of symptoms, including hopelessness, fatigue, impaired concentration, feelings of inadequacy, as well as slowed thought and movement processing (APA 2013).

These symptoms not only impact upon an individuals’ personal life, but can impair social functioning and the ability to work (Hirschfeld 2000, Lerner 2008).

Within the US, depression was related to 27.2 lost workdays per ill worker per year, and a total of $36.6 billion capital lost in the US labour force (Kessler, 2006).

A new Cochrane systematic review and meta-analysis aims to evaluate the effectiveness of the current interventions available for reducing workplace disability in depressive disorder (Nieuwenhuijsen et al, 2014).

A US study from 2006 found that depression was related to 27.2 lost workdays per ill worker per year.

Methods

The authors searched the following databases between January 2006 and January 2014: CENTRAL, MEDLINE, psychINFO, EMBASE, and CINAHL. Studies were included if they were:

  • Randomised controlled trials (RCT) or cluster RCTs
  • Participants were adults (17+)
  • Participants were from occupational health, primary care, or outpatient care settings
  • Depressive criteria met diagnostic criteria, was assessed by a self-reported symptom scale, or by a clinical rated instrument.

Studies were excluded if participants had a primary diagnosis of a psychiatric disorder other than depressive disorder including bipolar depression and depression with psychotic tendencies.

The authors included both workplace (modify the task or hours) and clinical (antidepressant, psychological, or exercise) interventions, and the primary outcome examined was the number of illness-related absences from work during follow up (Nieuwenhuijsen et al, 2014).

Workplace adjustments

Results

The original search yielded a total of 11,776 studies, and resulted in a full text assessment of 73 studies. 50 studies were excluded at the full-text stage- resulting in 1 study included in qualitative synthesis only, and 22 studies included within the meta-analysis.

Overall there were 20 RCTs and 3 Cluster RCTs, totalling 6,278 participants ranging from 20-200 participants between studies. 7 studies recruited from primary care settings, 10 from outpatient, 2 from occupational health, 1 from a managed care setting, and 1 was conducted in a community mental health centre (Nieuwenhuijsen et al, 2014).

Work directed interventions

5 work-directed interventions were identified:

  • There was moderate evidence that a work-directed intervention plus a clinical intervention reduced sick days when compared to clinical intervention alone or a work intervention alone
  • There was low evidence that an occupational therapy and return to work program was beneficial over occupational care as usual

The review found evidence to support a combination of work-directed interventions and clinical interventions.

Antidepressants

6 studies investigated and compared the effectiveness of different antidepressant use, including SSRI, SNRI, TCA, MAO, and placebo:

  • There was no difference between SSRIs and TCAs in reducing sickness absence, while another study found low quality evidence that either TCAs or MAOs reduced absences over placebo
  • Overall, the results of this category were inconsistent

Psychological therapies

  • There was moderate evidence of online or telephone CBT against occupational care as usual for reduction of absences
  • Two studies displayed no evidence that community health nurse interventions helped any more than care-as-usual

Psychological therapies combined with antidepressants

  • Two studies found that enhanced primary care did not decrease sick days over 4-12 months, and another longer term study found similar results
  • However, there was high quality evidence that a telephone outreach management program can be effective in reducing sick leave compared to care-as-usual

Exercise

  • There was low quality evidence that exercise was more effective than relaxing in sickness absence reduction
  • However, there was moderate evidence that aerobic exercise was not more effective than relation or stretching

The review found evidence to support the use of telephone outreach management programs (stern Matron optional).

Discussion

This review evaluated a number of RCTs investigating work or clinical interventions. However, in each category, there was a large amount of variation between the studies and very few studies per category making comparisons difficult.

There was moderate evidence that work-directed interventions combined with a clinical intervention reduced sick leave, and that primary or occupational care combined with CBT also reduced absences. Additionally, there was evidence that a telephone outreach management program with medication reduced absences from work compared to care as usual.

This suggests the need for more research on work-directed interventions to be paired with clinical care, as they have the potential to reduce illness-related absences, but there are currently limited studies evaluating these interventions (Nieuwenhuijsen et al, 2014).

primary or occupational care combined with CBT also reduced absences.

Links

Nieuwenhuijsen K, Faber B, Verbeek JH, Neumeyer-Gromen A, Hees HL, Verhoeven AC, van der Feltz-Cornelis CM, Bültmann U. Interventions to improve return to work in depressed people. Cochrane Database of Systematic Reviews 2014, Issue 12. Art. No.: CD006237. DOI: 10.1002/14651858.CD006237.pub3.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Arlington, VA: American Psychiatric Association, 2013.

Hirschfeld RM, Montgomery SA, Keller MB, Kasper S, Schatzberg AF, Moller HJ, et al. Social functioning in depression: a review. Journal of Clinical Psychiatry 2000; 61 (4):268–75. [PubMed abstract]

Lerner D, Henke RM. What does research tell us about depression, job performance, and work productivity? (PDF) Journal of Occupational and Environmental Medicine 2008; 50(4):401–10.

Kessler RC, Akiskal HS, Ames M, Birnbaum H, Greenberg P, Hirschfeld RM, et al. Prevalence and effects of mood disorders on work performance in a nationally representative sample of U.S. workers. American Journal of Psychiatry 2006; 163(9):1561–8.

Department of Health (2012). Advice for employers on workplace adjustments for mental health conditions (PDF). Department of Health, May 2012.

– See more at: http://www.thementalelf.net/mental-health-conditions/depression/helping-people-with-depression-return-to-work/#sthash.7fnmUfRX.dpuf

Quitting smoking is associated with decreased anxiety, depression and stress, says new systematic review

It is well known that tobacco is the leading cause of preventable death in the world (WHO, 2011). However, the associations between smoking and mental health are less well established.

Smokers often want to quit, but the belief that cigarettes can be used to regulate mood can often deter them, and this is especially true for individuals with mental health problems (Zhou et al, 2009; Thompson et al 2005). However, this is somewhat paradoxical because smoking is often associated with poor mental health (Coulthard et al, 2002). So it’s interesting to report on this new study by Taylor et al (2014) who reviewed the current literature evaluating changes in mental health in those who quit smoking compared with those who continued to smoke.

Methods

The authors conducted a systematic review by searching Web of Science, Cochrane, Medline, Embase & PsychINFO, as well as contacting authors for missing data, and translating non-English papers.

Eligibility was determined using the following criteria:

  • Studies took smokers from the general population or from populations with a defined clinical diagnosis
  • They were longitudinal studies collecting data on mental health prior to quit attempts and again 6 weeks after

A meta-analysis was performed using a random effects model to pool the standard mean difference (SMD) between the change in mental health in quitters and continued smokers from baseline to follow-up. The SMD was used, as different scoring systems couldn’t be standardised across studies.   The mental health outcomes they measured were anxiety, depression, mixed anxiety/depression, positive affect, psychological quality of life & stress.

Results of systematic review

After data extraction, 15 full text articles were included:

Study type

11 cohort studies, 14 secondary analyses of cessation interventions, and 1 randomised controlled trial.

Participant population

14 studies included the general population, 3 included patients living with chronic physical condition, 2 with pregnant patients, 1 included postoperative patients, 2 studies included either chronic physical or psychiatric conditions, and 4 studies included patients with psychiatric conditions.

48% of participants were male with a median age of 44, and on average smoked 20 cigarettes per day. The average participant scored as moderately dependent to nicotine on a dependence test.

Results of meta-analysis

Compared with continuing to smoke:

People who quit smoking were less anxious, depressed and stressed than those who continued to smoke

People who quit smoking were less anxious, depressed and stressed than those who continued to smoke

  • Quitting was associated with a decrease in anxiety (SMD -0.37, 95% CI  -0.70 to -0.03; P=0.03)
  • Quitting was associated with a decrease in depression (SMD -0.25, 95% CI -0.37 to -0.12; P<0.001)
  • Quitting was associated with a decrease in mixed anxiety and depression (SMD -0.31, 95% CI -0.47 to -0.14; P<0.001)
  • Quitting was associated with a decrease in stress (SMD -0.27, 95% CI -0.40 to -0.13; P<0.001)
  • Quitting was associated with an improved psychological quality of life (SMD 0.22, 95% CI 0.09 to 0.36; P<0.001)
  • Quitting was associated with increased positive affect (SMD 0.40, 95% CI 0.09 to 0.71; P=0.01)

Subgroup Analyses

  • Analyses for study quality did not change summary estimates
  • Studies which adjusted for covariates showed a larger difference between quitters and those who continued to smoke compared to studies which did not adjust

Additional Analyses

  • There was no evidence that effect size differed across different clinical populations
  • There was no evidence of subgroup differences between study designs
  • The studies were ordered according to length in a forest plot and no clear chronological pattern in effect estimates was found

Discussion

This review shows that quitting smoking is associated with reduced depression, anxiety and stress, and improved psychological quality of life and positive affect compared to continuing to smoke. The strength of the association was similar for all populations; both general and clinical. The authors suggest three possible interpretations of the data:

  1. Quitting smoking results in improved mental health
  2. Improved mental health causes an individual to quit smoking
  3. There is a common factor that explains both the improved mental health and smoking cessation

The authors hypothesise that quitting smoking improves mood is supposed by a biological mechanism caused by brain changes in the nicotinic pathways due to chronic smoking (Wang & Sun, 2005). These brain changes result in low mood (irritation, anxiety, and depressed mood) after smoking a cigarette. While an individual is actually feeling withdrawal symptoms, they are misattributed to low mood, and more cigarettes are smoked to alleviate their symptoms (Benowitz, 1995; Benowitz, 2010).

However, not all of the data supports this interpretation.  For example, a study using Mendelian randomisation- an instrumental variable approach that uses gene relating to smoking behaviour to examine health related outcomes, did not find a causal association between smoking and mental health (Bjorngaard et al 2013).

While this review displays that there are strong associations between quitting smoking and mental health, the authors recommend future studies examining this association to help strengthen causal inferences which come from observation research. The authors suggest further epidemiological studies using Mendelian randomisation, or using statistical analysis of observational data using propensity score matching to reduce the bias of confounding variables.

Conclusion

Many people believe that quitting smoking can have adverse psychiatric effects. This high quality research suggests the opposite

Many people believe that quitting smoking can have adverse psychiatric effects. This high quality research suggests the opposite

These are important findings as smokers can find reassurance in the fact that quitting is likely to result in improved mental wellbeing. Additionally, these findings are important as they show that quitting smoking is likely to improve your mental health if you are mentally ill or mentally well.

Hopefully these findings will help overcome some of the current barriers within the mental health field; for example the continued belief that quitting smoking or certain pharmacological treatments can have adverse psychiatric effects.  See our recent Lee Cook et al (2013) blog, which showed that individuals with mental illness treated as outpatients were more likely to decrease and quit smoking than those in inpatient facilities.

Furthermore, the NICE guidelines on smoking cessation, which we blogged about here, recommend that all NHS hospitals and clinics should become smoke-free, as well as identifying smokers and offering behavioural and pharmacotherapy onsite. Additionally, the guidelines suggest staff should be trained on stop-smoking services and should abstain from smoking on-site themselves (NICE, 2013).

Links

Taylor G et al. Change in mental health after smoking cessation: systematic review and meta-analysis. BMJ 2014;348:g1151 doi: 10.1136/bmj.g1151

Coulthard M, Farrell M, Singleton N, Meltzer H. Tobacco, alcohol and drug use and mental health (PDF). Office for National Statistics, 2002.

World Health Organization. WHO report on the global tobacco epidemic. WHO, 2011.

Zhou X, Nonnemaker J, Sherrill B, Gilsenan A, Coste F, West R. Attempts to quit smoking and relapse: factors associated with success or failure from the ATTEMP cohort study (PDF). Addict Behav 2009;34:365-73.

Thompson B, Thompson LA, Thompson J, Fredickson C, Bishop S. Heavy smokers: a qualitative analysis of attitudes and beliefs concerning cessation and continued smoking. Nicotine Tob Res 2003;5:923-33. [PubMed abstract]

Le Cook B, Wayne GF, Kafali EN, Lui Z, Shu C Flore M. Trends in Smoking Among Adults with Mental Illness and Association Between Mental Health Treatment and Smoking Cessation. JAMA. 2014; 311 (2): 172-182. [Abstract]

Smoking cessation: acute, maternity and mental health services: guidance (PDF). NICE, PH48, 27 Nov 2013.

Wang H, Sun X. Desensitized nicotinic receptors in brain. Brain Res Rev 2005;48:420-37. [Abstract]

Benowitz NL. Nicotine addiction. Prim Care 1999;26:611-31 [PubMed abstract]

Benowitz NL. Nicotine addiction. N Engl J Med 2010;362:2295 [Abstract]

Bjorngaard JH, Gunnell D, Elvestad MB, Davey-Smith G, Skorpen F, Krokan H, et al. The causal role of smoking in anxiety and depression: a Mendelian randomization analysis of the HUNT study. Psychol Med 2013;43:711-9 [PubMed abstract]

This article first appeared on the Mental Elf website on 13 March 2014 and is posted by Meg Fluharty. Follow Meg on Twitter @MegEliz_

– See more at: http://www.thementalelf.net/mental-health-conditions/anxiety-disorders/quitting-smoking-is-associated-with-decreased-anxiety-depression-and-stress-says-new-systematic-review/#sthash.z8TIWuMV.dpuf

Cochrane review says there’s insufficient evidence to tell whether fluoxetine is better or worse than other treatments for depression

Depression is common in primary care and associated with a substantial personal, social and societal burden. There is considerable ongoing controversy regarding whether antidepressant pharmacotherapy works and, in particular, for whom. One widely-prescribed antidepressant is fluoxetine (Prozac), an antidepressant of the selective serotonin reuptake inhibitors (SSRI) class. Although a number of more recent antidepressants are available, fluoxetine (which went off patent in 2001) remains highly popular and is commonly prescribed.

This systematic review and meta-analysis, published through the Cochrane Collaboration, compares the effects of fluoxetine for depression, compared with other SSRIs, tricyclic antidepressants (TCAs), selective noradrenaline reuptake inhibitors (SNRIs), monoamine oxidase inhibitors (MAOIs) and newer agents, as well as other conventional and unconventional agents. This is an important clinical question – different antidepressants have different efficacy and side effect profiles, but direct comparisons are relatively rare.

Methods

Thank goodness for systematic reviewers who read hundreds of papers and combine the results, so you don't have to

Thank goodness for systematic reviewers who read hundreds of papers and combine the results, so you don’t have to

The review focused on studies of adults with unipolar major depressive disorder (regardless of the specific diagnostic criteria used), searching major databases for studies published up to 11 May 2012.

All randomised controlled trials comparing fluoxetine with any other antidepressant (including non-conventional agents such as hypericum, also known as St John’s wort) were included. Both dichotomous (reduction of at least 50% on the Hamilton Depression Scale) and continuous (mean scores at the end of the trial or change score on depression measures) outcomes were considered.

Results

A total of 171 studies were included in the analysis, conducted between 1984 and 2012 and comprising data on 24,868 participants.

A number of differences in efficacy and tolerability between fluoxetine and certain antidepressants were observed. However, these differences were typically small, so that the clinical meaning of these differences is not clear.

Moreover, the majority of studies failed to report detail on methodological procedures, and most were sponsored by pharmaceutical companies.

Both factors increase the risk of bias and overestimation of treatment effects.

Conclusions

The review

The review found sertraline and venlafaxine (and possibly other antidepressants) had a better efficacy profile than fluoxetine

The authors conclude that: “No definitive implications can be drawn from the studies’ results”.

There was some evidence for greater efficacy of sertraline and venlafaxine over fluoxetine, which may be clinically meaningful, but other considerations such as side-effect profile, patient acceptability and cost will also have a bearing on treatment decisions.

In other words, despite considerable effort and pooling all of the available evidence, we still can’t be certain whether one antidepressant is superior to another.

What this review really highlights is the ongoing difficulty in establishing whether some drugs are genuinely effective (and safe), because of publication bias against null results (Turner, 2008).

This situation is made worse when there are financial vested interests involved. Recently, there has been active discussion about how this problem can be resolved, for example by requiring pharmaceutical companies to release all data from clinical trials they conduct, irrespective of the nature of the findings.

Despite the mountains of trials published in this field, we still cannot say for sure which treatments work best for depression

Despite the mountains of trials published in this field, we still cannot say for sure which treatments work best for depression

Clinical decision making regarding the most appropriate medication to prescribe are complex, and made harder by the lack of direct comparisons. Moreover, the apparent efficacy of individual treatments may be inflated by publication bias. Direct comparisons between different treatments are therefore important, but remain relatively rare. This Cochrane Review provides very important information, even if only by highlighting how much we still don’t know about which treatments work best.

Links

Magni LR, Purgato M, Gastaldon C, Papola D, Furukawa TA, Cipriani A, Barbui C. Fluoxetine versus other types of pharmacotherapy for depression. Cochrane Database of Systematic Reviews 2013, Issue 7. Art. No.: CD004185. DOI: 10.1002/14651858.CD004185.pub3.

Etchells, P. We don’t know if antidepressants work, so stop bashing them. The Guardian website, 15 Aug 2013.

Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R. Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med. 2008 Jan 17;358(3):252-60. doi: 10.1056/NEJMsa065779. [PubMed abstract]

This article first appeared on the Mental Elf website on 1st October 2013 and is posted by Marcus Munafo

Mental heath and behaviour in early adulthood can be predicted by conduct problems in childhood

We’ve known for some time that there is a lot of variation in children’s emotional and behavioural development. For example, if we think of conduct problems (such as lying, stealing, and fighting), then some children already show high levels in early childhood and this carries through into adolescence, whilst for other children this behaviour may be limited only to a period in adolescence when they briefly “go off the rails”, and not persist beyond this. But what happens to these different groups as they grow up? Put another way, can these different conduct problem pathways distinguish young adults in terms of various mental health problems? In our recent study, we found that they do indeed.

Those children who stand out with serious conduct problems throughout their childhood will more often drink, smoke, and take illegal drugs, as well as show criminal behaviour during early adulthood. These individuals also have a greater risk for depression, anxiety, and self-harm than young adults who showed no conduct problems when they were younger.

On the other hand, those children whose conduct problems begin in adolescence but are low in childhood still smoked more and used more illegal drugs than those without conduct problems and were more likely to engage in risky sexual behaviour. Worryingly, this group overall only fares marginally better than those with stable high conduct problems.

Our study suggests that adolescent conduct problems are not merely a fleeting issue but may result in a range of problems that make a healthy and well-adjusted start into adulthood less likely. These teenagers may need attention from parents, teachers, and youth workers who should not dismiss their conduct problems as something that will sort itself out over time. Moreover, our study serves as a reminder that children who present with high levels of conduct problems throughout their childhood years need a lot of support to improve their chances of growing into happy and healthy adults. The knowledge gained from our study might help inform the development of targeted interventions. The findings should certainly remind us that childhood conduct problems have a long reach and are reflected in mental health and behaviour several years later.

This blog was posted by Tina Kretschmer @DocTinaK