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Treatment for depression in traumatic brain injury: Cochrane find no evidence for non-pharmacological interventions

by Eleanor Kennedy @Nelllor_

This blog originally appeared on the Mental Elf site on 31st May 2016.

Traumatic Brain Injury has been associated with increased occurrence of depression (Gertler et al, 2015). Traumatic Brain Injury results from damage to the brain by external forces, such as direct impact or rapid acceleration; consequences of a traumatic brain injury may be temporary or permanent and can lead to problems with cognition, emotion and behaviour (Maas, Stocchetti, & Bullock, 2008).

The main feature of depression is either a depressed mood or loss of interest and pleasure in usual activities, or both, consistently for a two week period. Depression can present as a major risk factor for suicide after Traumatic Brain Injury.

A recent Cochrane systematic review aimed to measure “the effectiveness of non-pharmacological interventions for depression in adults and children with Traumatic Brain Injury at reducing the diagnosis and severity of symptoms of depression.”

People who experience traumatic brain injury are at an increased risk of depression.

People who experience traumatic brain injury are at an increased risk of depression.

Methods

The Cochrane Injuries Group searched eight electronic databases for randomised controlled trials (RCTs) of non-pharmacological interventions for depression in adults and children who had a Traumatic Brain Injury. For inclusion in the review, study participants had to fulfil the following criteria:

  • A history of Traumatic Brain Injury due to external forces; samples that included participants with non-traumatically acquired brain injury, such as stroke, were also included if the data allowed for separate analysis of those with Traumatic Brain Injury
  • Fulfilment of diagnostic criteria for an applicable mood disorder, such as major depressive disorder or adjustment disorder with depressive mood, based on DSM or ICD criteria
  • Presenting with clinically significant depressive symptoms based on standardised measures

The primary outcome was “the presence or remission of depressive disorders, as determined by the use of accepted diagnostic criteria (e.g. DSM-IV or ICD-10), by the use of a standardised structured interview based on such criteria (e.g. Structured Clinical Interview for the DSM Disorders), or the results of validated self- or observer-rated questionnaires of depressive symptoms.”

The secondary outcomes were:

  • Neuropsychological functioning, psychosocial adjustment, everyday functioning, quality of life, and participation
  • Medication and healthcare service usage
  • Treatment compliance, based on the proportion of withdrawals from intervention
  • The occurrence of suicide or self-harm
  • Any adverse effects of the intervention.

Results

Six studies were included in the review. Three of the studies were carried out in the USA (Ashman, Cantor, Tsaousides, Spielman, & Gordon, 2014; Ashman & Tsaousides, 2012; Fann et al., 2015; Hoffman et al., 2010), one in China (He, Yu, Yang, & Yang, 2004), one in Canada (Bedard et al., 2014) and one in Australia (Simpson, Tate, Whiting, & Cotter, 2011). Participants in all studies were over 18 years of age.

Summary of interventions in included studies

Study NParticipants Intervention Duration of Treatment Outcome measure
Ashman 2014 (Ashman et al., 2014; Ashman & Tsaousides, 2012) 77(43 completed) Cognitive Behaviour Therapy (CBT) or Supportive Psychotherapy (SPT) 16 sessions over 3 months Structured Clinical Interview for DSM-IVBeck Depression Inventory – second edition (BDI-II)
Bedard 2013 (Bedard et al., 2014) 105(76 completed) Mindfulness-based cognitive therapy (MBCT) modified to suit those with TBI 10 weekly session plus recommended daily meditation BDI-II
Fann 2015 (Fann et al., 2015) 100(86 with follow up data) CBT in person or by telephone 8 to 12 weekly sessions Hamilton Depression Rating Scale (HAMD-17)
He 2004 (He et al., 2004) 64(63 completed) Repetitive transcranial magnetic stimulation (rTMS) 4 treatment sessions each lasting 5 days, with an interval of 2 days between sessions HAMD
Hoffman 2010 (Hoffman et al., 2010) 80(76 completed) Supervised exercise training 10 weekly sessions, plus a home program BDI
Simpson 2011 (Simpson et al., 2011) 17(16 completed) Group-based CBT 10 weekly sessions Hospital Anxiety and Depression Scale (HADS)

Primary outcomes

The review reported on four comparative analyses:

  1. CBT, or a variant of CBT, vs waiting list; included a meta-analysis of three studies (Bedard, Fann, Simpson). There was no indication of a difference in depression symptoms attributable to the intervention (standardised mean difference (SMD) -0.14, 95% CI -0.47 to 0.19; Z = 0.83, p = .41).
  2. CBT to SPT; based on one study (Ashman), the difference in depression remission was not statistically supported (RR 0.76; 95% CI 0.58 to 1.00; Z = 1.96; P = 0.05) nor was the difference between groups in depression symptoms (SMD -0.09; 95% CI -0.65 to 0.48; Z = 0.30; P = 0.77).
  3. rTMS plus tricyclic antidepressants (TCA) to TCA; based on one study (He). There was a reduction in depression symptoms seen in the rTMS plus TCA group, (0.84; 95% CI -1.36 to -0.32; Z = 3.19; P = 0.001), however the difference was not considered to be clinically relevant. This was the only study to report adverse effects as two participants reported transient tinnitus with spontaneous remission.
  4. Supervised exercise and exercise as usual; based on a single study (Hoffman). There was no difference in depression symptoms between groups following the intervention (SMD -0.43; 95% CI -0.88 to 0.03; Z = 1.84; P = 0.07).

Secondary outcomes

Secondary outcomes were reported for each individual study. There was no difference in treatment compliance between intervention and comparison group in each study. One study (He et al., 2004) reported adverse effects as two participants reported transient tinnitus with spontaneous remission.

Most other secondary outcomes showed no difference between intervention and treatment groups.

There is insufficient evidence to recommend any particular non-pharmacological treatment for depression in traumatic brain injury.

There is insufficient evidence to recommend any particular non-pharmacological treatment for depression in traumatic brain injury.

Strengths and limitations

Some studies were not included because of the narrow focus of the review. The primary outcome of these studies was quality of life or psychological well-being and as such did not require included participants to have a diagnosis of depression or a particular cut-off score on a depression scale. While these may have been of interest, this is not necessarily a limitation as it allowed the authors to concentrate on a clinically relevant treatment effect for depression.

The authors found the quality of evidence to be low or very low in all comparisons, mainly due to the lack of blinding participants and personnel to the treatment. This lack of blinding could have affected the self-report depression symptom scales in particular. The authors suggested some suitable placebo treatments such as sham rTMS to imitate real TMS or a social contact intervention to compare to CBT.

Conclusion

The paucity of studies included makes it difficult to draw any firm conclusions. There was no strong evidence to support any of the interventions explored here. All of the studies are very recent which suggests there may be an increase in this kind of research.

The authors point to some implications for future research in this area, such as the careful consideration of what will be meaningful to the individual participants and the question of the suitability of RCT design for CBT interventions.

The review calls for future RCTs that compare active interventions with controls that replicate the effect of the attention given to participants during an active treatment.

The review calls for future RCTs that compare active interventions with controls that replicate the effect of the attention given to participants during an active treatment.

Links

Primary paper

Gertler P, Tate RL, Cameron ID. (2015) Non-pharmacological interventions for depression in adults and children with traumatic brain injury. Cochrane Database of Systematic Reviews 2015, Issue 12. Art. No.: CD009871. DOI: 10.1002/14651858.CD009871.pub2.

Other references

Ashman, T., Cantor, J. B., Tsaousides, T., Spielman, L., & Gordon, W. (2014). Comparison of cognitive behavioral therapy and supportive psychotherapy for the treatment of depression following traumatic brain injury: A randomized controlled trial. Journal of Head Trauma Rehabilitation, 29(6), 467–478. [PubMed abstract]

Ashman, T., & Tsaousides, T. (2012). Cognitive behavioral therapy for depression following traumatic brain injury: FINDINGS of a randomized controlled trial. Brain Impairment. Cambridge University Press.

Bedard, M., Felteau, M., Marshall, S., Cullen, N., Gibbons, C., Dubois, S., … Moustgaard, A. (2014). Mindfulness-based cognitive therapy reduces symptoms of depression in people with a traumatic brain injury: results from a randomized controlled trial. J Head Trauma Rehabil, 29(4), E13–22. [PubMed abstract]

Fann, J. R., Bombardier, C. H., Vannoy, S., Dyer, J., Ludman, E., Dikmen, S., … Temkin, N. (2015). Telephone and in-person cognitive behavioral therapy for major depression after traumatic brain injury: a randomized controlled trial. Journal of Neurotrauma, 32(1), 45–57. [PubMed abstract]

He, C. S., Yu, Q., Yang, D. J., & Yang, M. (2004). Interventional effects of low-frequency repetitive transcranial magnetic stimulation on patients with depression after traumatic brain injury. Chinese Journal of Clinical Rehabilitation, 8, 6044–6045.

Hoffman, J. M., Bell, K. R., Powell, J. M., Behr, J., Dunn, E. C., Dikmen, S., & Bombardier, C. H. (2010). A randomized controlled trial of exercise to improve mood after traumatic brain injury. Physical Medicine and Rehabilitation, 2(10), 911–919. [PubMed abstract]

Maas, A. I. R., Stocchetti, N., & Bullock, R. (2008). Moderate and severe traumatic brain injury in adults. Lancet Neurology, 7 (August), 728 – 741. [PubMed abstract]

Simpson, G. K., Tate, R. L., Whiting, D. L., & Cotter, R. E. (2011). Suicide prevention after traumatic brain injury: a randomized controlled trial of a program for the psychological treatment of hopelessness. The Journal of Head Trauma Rehabilitation, 26(4), 290–300. [PubMed abstract]

Photo credits

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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]

Photo credits

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Later menopause linked with lower risk of depression

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 17th February 2016.

Women have twice the risk of developing major depression compared to men. This difference is most noticeable during the reproductive period years (Soares et al, 2008) (e.g. premenstrual, during pregnancy and postpartum, and perimenopause) when women are subject to large fluctuations of ovarian hormones.

Additionally, oestrogens are believed to utilise neuroprotective and antidepressive actions within the the brain (Arevalo et al, 2015), and transitioning to the postmenopausal period is associated with a large drop in oestrogen production (Burger al al., 2007).

Therefore, the authors, Georgakis et al (2016), are using ‘age at menopause’ and ‘duration of reproductive age’ as two markers of lifelong oestrogen exposure to measure the association with risk of depression in postmenopausal women.

Research shows that the median age for final menstrual period is 52.5 years, and that 90% of women have their final period by the age of 56.

Research shows that the median age for final menstrual period is 52.5 years, and that 90% of women have their final period by the age of 56.

Methods

Search criteria

The authors conducted a search in MEDLINE using the following keywords: menopause, climacteric, reproductive period, depression, and mood disorders. The authors then searched reference lists of included studies to identify additional studies. There was no restriction on language, publication year or study design. Cross sectional and cohort studies were obviously going to be helpful, but randomised controlled trials were also considered for eligibility if they included depression measurements before intervention.

Definition of variables

  • Age of menopause was defined as 1 year following last menstruation (although studies examining age at final menstruation were also considered)
  • Duration of reproductive period was defined as age of menopause minus age of menarche
  • Diagnosis of depression was defined by clinical diagnosis or validated questionnaire

Excluded studies

Studies were excluded if they used questionnaires without defined cut-offs, or self-reported depression as a single question. Studies including only women with depression were excluded as were those which also had severe psychiatric disorders. Case series, case reports, in vitro and animal studies were excluded. Studies limited to perimenopausal (the period leading up to menopause) women, breast cancer survivors with medically induced menopause, or women with surgically induced menopausal transition were excluded.

Statistical analysis

The odds ratios (OR) and confidence intervals (CI) were pooled across the identified studies, and the analysis was conducted separately for the two exposure variables (age of menopause and duration of reproductive period). The variables were first analysed as continuous variables in 2 year increments, and age of menopause was analysed again as a categorical variables (≥40 vs <40).

Results

A total of 67,714 women were included across 10 cross sectional and 4 cohort studies.

  • 12 studies used self-report diagnosis of depression
  • 1 study used DSM-III-R diagnosis
  • 1 study used physician diagnosis.

Women without a diagnosis of depression were used as the control group.

Age at menopause

Pooling the effect estimates across 13 studies which treated age at menopause as a continuous variable (e.g. 2 year increments); increased age of menopause was associated with 2% decrease in risk of postmenopausal depression (OR, 0.98; 95% CI 0.96 to 0.99 heterogeneity I2=7.6%; P=.37). Sensitivity analyses for hormone therapy, premenopausal depression, or defining age at menopause as 1 year following last menstruation did alter the association.

In 4 studies with data on premature menopause (<40 years), twice the risk of depression was found compared to women with menopause onset over 40 years (OR, 0.49; 95% CI 0.29 to 0.81; heterogeneity I2=54.2%, p=.09).

Reproductive period

Pooling the effect estimates across 5 studies that includes reproductive period as a continuous variable (e.g. 2 year incriminates); found similar associations to age at menopause: a 2% decrease in risk of postmenopausal depression for an increase in reproductive period of 2 years (OR, 0.98; 95% CI 0.94 to 1.01; heterogeneity I2=0.0%; P=.41).

This evidence suggests that women who have the menopause later in life, are less likely to experience depression in their postmenopausal years.

This evidence suggests that women who have the menopause later in life, are less likely to experience depression in their postmenopausal years.

Discussion

This meta-analysis displayed an inverse relationship between the age of menopause and subsequent risk of postmenopausal depression, which prevailed after controlling for hormone therapy and premenopausal depression. Additionally, a similar effect was found within an analysis of the duration of reproductive period. These findings indicate that shorter exposure to endogenous oestrogen is associated with oestrogen deficiency and consequently heightened risk of depression after menopause.

To put it another way, the longer the period between menarche (first menses) and menopause (defined as final menstrual period or 1 year after final menstrual period), the lower the risk that the woman will experience depression in her postmenopausal years.

If these findings are confirmed within culturally diverse studies, they can be used to identify at-risk women for postmenopausal depression whom may benefit from either psychological monitoring or oestrogen-based therapy (Georgakis et al 2016).

Strengths and limitations

This systematic review featured a well-conducted meta-analysis, including a total of 67,714 women across 14 studies; and took important confounders into consideration (age, obesity, hormone therapy, smoking, and marital status). The authors conducted sensitivity analyses where necessary and there was no evidence of publication bias in the ‘age at menopause’ studies.

However, there were some limitations to consider:

  • Limiting their literature search just to the MEDLINE database will have resulted in many trials been missed, which is clearly a big weakness for any systematic review.
  • 12 of the 14 included studies used a self-report diagnosis of depression, rather than a diagnosis reached by a validated diagnostic instrument.
  • There were differences in the definition of depression, and depression cut-offs across studies.
  • The association of pre-existing depression and hormone therapy use on later depression should be considered; however the authors did conduct sensitivity analyses where possible.

Many women report a huge lack of information about the menopause, fuelled by a continuing stigma relating to this ubiquitous part of female human existence. This study provides some important pointers to risk factors and later life mental illness, which could be used to help educate women about their risk of depression as they age. However, given the limitations of this current review, we should look for further confirmation of these findings before we consider this question well and truly answered.

Do you talk to your female friends about the menopause?

Do you talk to your female friends about the menopause?

Links

Primary paper

Georgakis MK, Thomopoulos TP, Diamantaras A, et al. (2015) Association of Age at Menopause and Duration of Reproductive Period With Depression After Menopause: A Systematic Review and Meta-analysis. JAMA Psychiatry. Published online January 06, 2016. doi:10.1001/jamapsychiatry.2015.2653. [Abstract]

Other references

Soares CN, Zitek B. (2008) Reproductive hormone sensitivity and risk for depression across the female life cycle: a continuum of vulnerability? J Psychiatry Neurosci. 2008;33(4):331-343.

Arevalo MA, Azcoitia I, Garcia-Segura LM. (2015) The neuroprotective actions of oestradiol and oestrogen receptors. Nat Rev Neurosci. 2015;16(1): 17-29. [PubMed abstract]

Burger HG, Hale GE, Robertson DM, Dennerstein L. (2007) A review of hormonal changes during the menopausal transition: focus on findings from the Melbourne Women’s Midlife Health Project. Hum Reprod Update. 2007;13(6):559-565. [PubMed abstract]

If you’re looking for a good overview of recent evidence-based research, please read the Evidently Cochrane blogs on the Menopause.

Photo credits

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

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

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

Exercise for the prevention and treatment of antenatal depression

By Meg Fluharty

This blog originally appeared on the Mental Elf blog on 19th September 2014

shutterstock_59173807

Depression occurring during pregnancy, known as antenatal depression, is very common; affecting 10-13% of women (Gavin et al, 2005), which can result in premature labour, low birth weight, and a compromised mother-child relationship (Li et al, 2009; Mancuso et al 2004).

The current treatments include antidepressants and psychotherapy (Field et al, 2009; Rethorst et al 2009). However, antidepressant use may result in adverse effects during pregnancy and psychotherapy often has lengthy waiting lists (Einerson et al 2010, Parker et al; 2008).

Exercise is also recommended as a treatment option for mental and physical health during pregnancy, by NICE (NICE, 2006), the Royal College of Obstetricians and Gynaecologists (RCOG, 2006) and the American College of Obstetricians and Gynaecologists (Artal & O’Tool, 2006).

This study is the first systematic review and meta-analysis of randomised controlled trials (RCTs) investigating the effectiveness of exercise as a treatment option in antenatal depression (Daley et al, 2014).

Exercise balls are a popular training aid and also a soft place to grab a few minutes sneaky shut-eye.

“Balls to exercise” Insert exclamation mark or question mark as you see fit.

Methods

The authors conducted a literature search of multiple electronic databases, and studies were selected for inclusion if they were RCTs which compared exercise with usual care, a control group or another comparator. Studies were also included which recruited non-depressed, at risk, and depressed participants as the review focused on both prevention and treatment of antenatal depression. Studies were excluded if the intervention was less than 6 weeks (Daley et al, 2014).

The primary outcome was change in depression score between baseline and final antenatal follow-up. The means and standard deviations of the different depression scores were extracted, or calculated if necessary. The standardised mean different (SMD) was calculated in order to summarise the effects across the trials. For the meta-analysis, a random effects model was used, with subgroup analyses in depressed vs. non-depressed patients and aerobic vs. non-aerobic exercise conditions (Daley et al, 2014).

Results

Included studies

Six out of a total of 919 papers were chosen for inclusion in the review and analysis. Studies were primarily excluded if they were not RCTs, did not measure depression, or compared exercise interventions.

All six studies investigated exercise as an intervention versus a control:

  • 2 studies used standard prenatal care
  • 2 used a waiting list
  • 1 used social support
  • 1 used parent education sessions as the control groups

The interventions ranged from 8-12 weeks and were categorised as either aerobic exercise or non aerobic.

In total, there were 406 pregnant women, whose ages ranged from 14-38 and were recruited from 16 weeks gestation.

One study included non-depressed women, and 5 studies included either at risk or participants depressed at baseline (Daley et al, 2014).

Meta-analysis results

  • There was a reduction in depression scores in the exercise groups versus the comparator groups (SMD -0.46, 95%CI -0.87 to 0.05, p=0.03, I2= 68%)
  • There was no difference between women who were:
    • Non-depressed at baseline (SMD -0.74; 95% CI -1.22 to -0.27, p=0.002)
    • Depressed at baseline (SMD -0.41; 95% CI -0.88 to 0.07, p=0.09, I2=70%)
  • There was no difference between:
    • Aerobic exercise interventions (SMD -0.74: 95% CI -1.22 to -0.27 p=0.002)
    • Non-aerobic exercise interventions (SMD -0.41; 95% CI -0.88 to 0.07, p=0.09, I2 =70%)

Exercise during pregnancy may be effective at reducing depression, but bigger and better RCTs are needed before we can be sure of this finding.

Exercise during pregnancy may be effective at reducing depression, but bigger and better RCTs are needed before we can be sure of this finding.

Discussion

Daley et al (2014) present the first meta-analysis of trials investigating the effectiveness as a treatment for antenatal depression. NICE (NICE, 2006), Royal College of Obstetricians and Gynaecologists (RCOG, 2006), and the American College of Obstetricians and Gynaecologists (Artal & O’Tool, 2006) have all stated that women should consider exercise during pregnancy for mental health benefits, and this review provides evidence to support those guidelines.

However, there are a number of limitations that should be considered:

  • The results show a small to moderate effect size, based on a small number of low to moderate quality studies
  • The studies varied greatly and contained large confidence intervals, which may result in imprecise estimates
  • 5 of the 6 studies were based on women with depression, so the authors cannot conclude whether exercise can be used to prevent depression in pregnancy
  • Tests of subgroup differences in exercise category were based on a single trial, therefore future studies should examine a larger range of exercises (aerobic and non-aerobic)
  • No studies reported on adverse events
  • Publication bias was not investigated due to the small number of trials

Future research should be based on a larger sample, include a wider range of exercise categories, investigate possible adverse events, and include non-depressed women.

While we're waiting for the new research into antenatal depression, don't forget that exercise in pregnancy has all sorts of other important benefits.

While we’re waiting for new research to be published, don’t forget that exercise in pregnancy does of course have all kinds of other undeniable benefits.

Links

Daley AJ, Foser L, Long G, Paler C, Robinson O, Walmsley H, Ward R. The effectiveness of exercise for the prevention and treatment of antenatal depression: a systematic review with meta-analysis. BJOG 2014; DOI: 10.1111/1471-0528.12909 [PubMed abstract]

Gavin NI, Gaynes BN, Lohr KN, Meltzer-Brody S, Gartlehner G, Swinson T. Perinatal depression: a systematic review of prevalence and incidence. Obstet Gynecol 2005;106:1071–83. [PubMed abstract]

Li D, Liu L, Odouli R. Presence of depressive symptoms during early pregnancy and the risk of preterm delivery: a prospective cohort study. Hum Reprod 2009;24:146–53.

Mancuso RA, Schetter CD, Rini CM, Roesch SC, Hobel CJ. Maternal prenatal anxiety and corticotropin-releasing hormone associated with timing of delivery. Psychosom Med 2004;66:762–9. [PubMed abstract]

Field T, Deeds O, Diego M, Hernandez-Reif M, Gauler A, Sullivan S, et al. Benefits of combining massage therapy with group interpersonal psychotherapy in prenatally depressed women. J Body Mov Ther 2009;13:297–303. [PubMed abstract]

Rethorst CD, Wipfli BM, Landers DM. The antidepressive effects of exercise: a meta-analysis of randomized trials. Sports Med 2009;39:491–511. [PubMed abstract]

Einerson A, Choi J, Einerson TR, Koren G. Adverse effects of antidepressant use in pregnancy: an evaluation of fetal growth and preterm birth. Depress Anxiety 2010;27:35–8 [PubMed abstract]

Parker GB, Crawford J, Hadzi-Pavlovic D. Quantified superiority of cognitive behavioural therapy to antidepressant drugs: a challenge to an earlier meta-analysis. Acta Psychiatr Scand 2008;118:91–7 [PubMed abstract]

Royal College of Obstetricians and Gynaecologists. Exercise in Pregnancy. Statement No. 4. London: RCOG, 2006.

Antenatal and postnatal mental health: Clinical management and service guidance. NICE CG45, Feb 2007.

Artal R, O’Toole M. Guidelines of the American College of Obstetricians and Gynecologists for exercise during pregnancy and the postpartum period. Br J Sports Med 2003;37:6–12. [PubMed abstract]

– See more at: http://www.thementalelf.net/mental-health-conditions/depression/exercise-for-the-prevention-and-treatment-of-antenatal-depression/#sthash.oDvrzRsY.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