Can interventions reduce stigma? World Psy. 2014.


Stigma towards mental disorder is recognised as a public health problem. Various programmes have been rolled out to combat stigma. Corrigan et al in their  2012 meta analysis reported that contact and educational interventions are effective in reducing personal stigma. i.e. an individual’s own attitude towards people with a mental illness. This analysis did not cover areas like self stigma and perceived stigma (individual’s beliefs about the attitudes of others to mental illness) . Clement et al‘s meta analysis (2013) looked at mass media interventions  and concluded that such interventions have a small to moderate effect.

Griffiths et al’s present meta analysis looked at various forms of stigma for various disorders. 34 controlled  studies were included in their final analysis.


Most common intervention was education. Consumer contact was the next common one. Various modes of delivery ( online, group, leaflet, video etc) were tested.

Personal stigma:  The pooled mean effect size across all conditions and interventions was small .There was also evidence that interventions incorporating a consumer contact component were effective.  Internet and non computer models had similar effects. This effect was regardless of the particular disorder studied. CBT did not show benefits.

Perceived stigma:  The interventions did not significantly reduce perceived stigma.

Internalised stigma:  The pooled mean effect size across the three studies included was not statistically significant

Conclusions: Current stigma interventions are effective in reducing personal stigma. educational and contact interventions are effective. Contact alone may not ne effective. Effects sizes are small. There are no effective interventions ( published) for perceived stigma. It is possible that public may be overestimating the stigma in others i.e. in community. Perceived stigma is a barrier to access service and hence is important to have effective interventions to reduce that. Internalised stigma is not much studied. Internet is an effective medium and this opens up many possibilities.

Summary of the article: 

Effectiveness of programs for reducing the stigma associated with mental disorders. A meta-analysis of randomizedcontrolled trials.

Griffiths KM, Carron-Arthur B, Parsons A, Reid R. World Psychiatry. 2014 Jun;13(2):161-75.

How common is self injury in non clinical populations? Suicide and life threatening behr.2014


Non suicidal self injury (NSSI= deliberate, self-inflicted destruction of body tissue resulting in immediate damage, with- out suicidal intent, not culturally sanctioned) is a risk factor for future suicide though there is no intent to end life in NSSI.

Life time prevalence in reported studies vary widely ( 50% to 10%) among secondary school students. This meta analysis and meta regression by Swannell  et al from Australia looked at various methodological factors contributing to such variations and provide a better understanding of the prevalence.

They included 128 prevalence assessments. Life time prevalence  varied  from 1.5% to 54.8%.NSSI was measured with 76 different tools, most common being Deliberate Self- Harm Inventory (DSHI; Gratz, 2001). Many methodological factors were identified as explaining the variance.( e.g. incentive given to participate, features of measurement tool like number of questions specifying methods & self administered questionnaire, anonymity etc). When adjusted for these factors, there was no increase in NSSI over time.( without adjustment, there was a trend of increasing prevalence over the years).


The overall pooled NSSI prevalence ( non clinical samples)  was 17.2% among adolescents, 13.4% among young adults, and 5.5% among adults.

Authors provide some directions to future research. Participants need to understand the concept of NSSI. They should be provided with a check list covering all methods.Frequency and intensity data need to be captured. Most studies were conducted among university students and other samples would be of interest.

Comment: Self injury disorder (DSM5)  is diagnosable after 5 NSSI incidents in one year. The fact that nearly one in five of the adolescents have resorted to inflict self injury is pointing towards the need for wider population based mental health interventions.

Summary of the article:

Prevalence of nonsuicidal self-injury in nonclinical samplessystematic reviewmeta-analysis and meta-regression. Swannell SV, Martin GE, Page A, Hasking P, St John NJ.

Suicide Life Threat Behav. 2014 Jun;44(3):273-303

How do stress cause psychosis? Epidemiology and Psych Sciences. 2014.Sept


It is generally recognised that psychosocial stress  play a role in causing mental disorders.  Brown & Burley conducted the first major work looking at stress and schizophrenia in 1960s. Nearly half of those with relapse of schizophrenia had a stressful event in the preceding 3 months. These were later replicated in 1990s. Stress was seen as working with the biological vulnerability in precipitating psychosis.

What are the biological systems that are involved in such stress response?

Mondelli reviews the major findings in this area so far  in this editorial.

1. HPA axis: First episode psychosis patients have increased HPA activation . ( cortisol increased, ACTH high, enlarged pitutary volume, blunted cortisol awaking response ). i.e. axis remain in highly activated state along with reduced reactivity to stress.  These are found to be apparent before the start of psychosis. Increased glucocorticoids can increase mesolimbic dopamine ( in animals). It can also impair neuronal plasticity. Higher cortisol levels lead to hippocampal volume loss. Antipsychotics can reduce the cortisol levels in the day time, but do not change the abnormalities in awakening response.

2.Low magnitude, general and  long-term inflammation: Increased inflammatory status is seen prominently in depression. It is also seen at onset of psychosis. Increase in IL6, TNF alpha, IL-1beta are the most consistent findings. These may be interacting with mono amine synthesis pathways, neuro endocrine systems and neural plasticity. It looks like antipsychotics show an initial anti inflammatory effect but this is followed by a gradual rise in cytokines along with an increase in body weight. So may be it have a direct an indirect effect. higher inflammation is also correlated with poorer outcome in psychosis.

The next questions are 1. Can we use the above biomarkers to predict psychosis among those who face stress? 2. Can it predict who is going to be resistant to antipsychotics? 3. Can we develop new therapeutic options along these pathways?

Comments: Reducing stress and providing support is key in maintaining individuals with psychosis well. At least this is what we can practically offer now. At population level, more awareness & options of support  for all who face stress is crucial. Low key interventions might be useful tools in that.

Summary of the article: 

From stress to psychosiswhom, how, when and why?

Mondelli V. Epidemiol Psychiatr Sci. 2014 Sep;23(3):215-8.

Can we predict bipolar disorder in patients presenting with depression? Jl Aff dis. Oct.2014


In half of patients who are diagnosed with bipolar disorder, onset episode is one of depression. The proportion is 2/3 in bipolar 2 disorder.Many may have multiple depressive episodes before manic or mixed episode appears facilitating a diagnosis of bipolar disorder.  Predicting who will go on to develop bipolar , if possible, would be huge benefit.

Several factors have been identified previously as associated with a later diagnosis of bipolar disorder in some one  presenting with depression. This include history of bipolar disorder, (2) cyclothymic or hyperthymic temperament, (3) illness-onset before age 25 years, (4) multiple shorter depressive episodes, (5) stressful precipitants at onset,( 6) presence of ADHD and (7) pathological mood-elevation during treatment with an antidepressant or other mood-elevating agent (stimulant, corticosteroid). Symptoms like prominent agitation, anxiety, psychotic features and atypical symptoms (hypersomnia, hyperphagia, psychomotor retardation) are also suggested as predictors in some studies.

Leonardo Todo  and team from International Consortium for Bipolar Disorder Research ( USA & Italy) report the clinical characteristics that predict later bipolar diagnosis. Of the 2146 patients in an episode of  depression , 30% met criteria for bipolar disorder later. Average time of observation was  around 13 years.


Seven factors were found to be significantly predictive of bipolar disorder.

1. 4 or more depressive episodes before intake 2. suicidal acts (gestures or acts) 3. cyclothymic  temperament 4. First degree family history of bipolar disorder 5. substance abuse 6. younger age of onset 7. male sex. First 4 factors were more strongly associated with later bipolar diagnosis.

if your patient has two factors,  prediction has a sensitivity (71 %) and specificity (62%). i.e.  69% of diagnostic assumption  of bipolar will be correct.

Limitations: all patients did not provide all information about predictive factors, recall of past events may be biased.

Questions like “are unipolar and bipolar two different disorders or two expressions of  one disorder?”  are still hotly debated. Is it that the potential for bipolarity is there at the start of expression of illness or is it a spontaneous change that occurs in the course of illness ( ? due to environmental factors or treatment factors)? The present study does not address these issues, but are relevant for future discussion.

Conclusions: Certain clinical factors can predict the possibility of a patient presenting with depression as eventually diagnosed as bipolar disorder. It is important that clinicians make such assessments to ensure optimum treatments.

Summary of the article:

Bipolar disorders following initial depressionmodeling predictive clinical factors.

Tondo L, Visioli C, Preti A, Baldessarini RJ.

J Affect Disord. 2014 Oct;167:44-9

Would blue lights reduce suicide? Jl Affective disorders. Dec. 2014


Restricting access to suicide hotspots  is an effective public health intervention. Suicide barrier that is going to be erected at Golden Gate Bridge, San francisco is going to be the costliest suicide barrier in the world. The board has set aside 76 million dollars for this. There are concerns from previous research that substitution ( i.e. increased suicide rates in nearby areas) phenomenon might reduce the effect. AN interesting study from Japan adds to this discussion.

Installation of platform screen doors have shown to reduce metro suicides. In japan, blue lights were installed in some railway stations  and this was found to reduce suicides there. Matsubayashi et al. (2013) showed that blue light installation reduced suicide rate by 83% in the 10 year period. The same group then examined whether substitution phenomenon occurred with this reduction, the results are reported in this article.

They studies the number of suicides at blue light stations and compared this with neighbouring stations ( to see whether there is a shift),They used the number of suicides in all other stations without blue light as the control group.


The updated data still strongly suggest that blue light reduce suicides. Almost no suicides occurred in stations where blue light was turned on in the evening hours. There was no systematic substitution in neighbouring stations. It also shows that blue light has not become less effective over time.


Present data only looked at jumping from platform. It is possible that people might choose to jump at railway crossing or they might have chosen to go to a station run by a different company. Suicide rate in the area was not compared  to show whether blue light had an impact on total suicide rate. The effect shown is only for night-time suicide, which accounts for  only 14% of all such suicides.

Comments: Influence of environment in human emotional state is well recognised. Various environmental measures are useful in reducing distress and producing calming effect. It needs to be seen whether such interventions reduce overall suicide rates. It would be interesting to see whether installation of blue lights in acute psychiatric wards have an effect on reducing self harm/aggression incidents.

Summary of the article

Does the installation of blue Lights on train platforms shift suicide to another station?: Evidence from Japan.

Matsubayashi T, Sawada Y, Ueda M. J Affect Disord. 2014 Dec 1;169:57-60

Is lithium augmentation effective in depression? J Aff Dis.Oct.2014


The 2007 Crossley & Bauer meta analysis clearly showed the benefit of lithium augmentation in depression. The effect size was large as well. (odds ratio  3.11  and NNT of 5). However,  lithium prescription do not reflect this possible benefit.Is it because the original evidence was more for lithium augmentation of TCAs and that we use less of them now a days?

Nelson et al report the results of meta analysis of all studies on this question. They included all double blind RCTS where Lithium or placebo was given after treatment failure with one antidepressant for 21 days ( at least). They identified 13 controlled studies of which 9 met all inclusion criteria. There were 237 patients in total.


OR for response was 2.89. heterogeneity was very  low ( all studies show effect in same direction). Lithium was effective whether it was combined with TCAs or newer antidepressants.NNT for response was 5. The confidence intervals for the OR is wide and hence the true NNT can be between 3 and 9.

Limitations:  Individual studies had small number of patients. ( 8 of 9 trials had fewer than 30 patients). One has to remember that met analytic results of smaller studies can sometimes be misleading.

Comments: Was bipolar status a factor in those who showed response?  There was not enough data to answer this question.

Tolerability of lithium is likely to be a factor in declining use. STARD found Lithium less tolerable than T3. Trials in this meta analysis did not show poor tolerability ( these were shorter trials and hence the tolerability issues may not have appeared).

Atypical antipsychotics as augmenting agents have taken over lithium in actual practice. The database is much larger for that as well.

Conclusion: Lithium is an effective augmenting agent when added to TCA or SSRI.

Summary of the article:

systematic review and meta-analysis of lithium augmentation of tricyclic and second generationantidepressants in major depression.

Nelson JC, Baumann P, Delucchi K, Joffe R, Katona C. J Affect Disord. 2014 Oct 15;168C:269-275.

What predict non response to first line depression treatments? Biol Psy. Oct.2014


Remission of depression with the first  antidepressant occur in  40% of patients only. Second steps benefit another 15-20%. Lack of response to initial agents usually indicate  a poor outcome.There are many predictors that can help us in choosing a particular treatment. However, for these to be clinically meaningful, these predictors should tell us the possibility of response to a range of treatments that are available rather than to a single agent.

Metabolic activity in right anterior insula has been shown to predict the response to either escitalopram or CBT. The same group (  McGrath et al Emory University) is now reporting predictors of non response two either of these two treatments ( SSRI or cBT). If we can predict who will not respond to these strategies, patents could straight away try some other treatments.

Patients received CBT or Escitalopram for 3 months ( random) and then combined for the next 3 months. Non response= no response over 6 months. Hamilton score 15 or above was needed to be randomised. Those with substance use, medical conditions,psychotic features, suicidal features etch ere excluded. Escitalopram was given 10mg/day with option to increase to 20mg/day. 16 CBT sessions were given.


82 patients were randomised. Phase 1 remission rates were similar (36% for CBT and 40% for  Escitalopram). Baseline Sub Callosal Cingulate (SCC) metabolism was higher in patients who failed to respond to both treatments compared with patients who achieved remission with either treatment. Superior temporal sulcus activity was also increased in the non remitters.

Hyperactivity of SCC was previously reported in resistant depression. Increased connectivity of SCC to default mode network is also reported in recurrent depression. Deep brain stimulation for resistant depression targets this area. All these are converging evidence to suggest that SCC activity is a crucial factor in non response.

Limitations- Patients achieving response were excluded from significance tests. This was because  researchers wanted clear differences in clinical picture and link that to structural/ functional parameters. Small number of participants, need replication.

Conclusions: SCC hyperactivity could be predictor of non response to first line treatments ( SSRI or CBT). if consistently and significantly shown, such predictors may one day help clinicians to fast track patents to most appropriate treatments.

Summary of the article:

Pretreatment brain States identify likely nonresponse to standard treatments for depression.

McGrath CL, Kelley ME, Dunlop BW, Holtzheimer Iii PE, Craighead WE, Mayberg HS.

Biol Psychiatry. 2014 Oct 1;76(7):527-35.