It’s been almost 9 years since the introduction of SmokeFree legislation in the UK (although we elves still return from a night out smelling of campfire smoke). However, secondhand smoke is still accountable for 600,000 deaths annually.
Smoke free policies can be implemented at the micro-level (i.e. the individual level or in homes), the meso-level (i.e. in organisations, such as public healthcare facilities, higher education centres and prisons) or the macro-level (i.e. in an entire country). In many countries, smokefree legislation is at the macro-level, although exemptions exist at the meso-level. For example, in the UK, specific rooms in prisons and care homes are exempt from this legislation.
In their Cochrane review, Frazer and colleagues review the evidence for meso-level smoking bans (in venues not typically included in smokefree legislation) on 1) passive smoke exposure, 2) other health-related outcomes and 3) active smoking, including tobacco consumption and smoking prevalence.
Identification of included studies
The authors searched online databases of clinical trials, reference lists of identified studies and contacted authors to identify ongoing studies. Studies were included if they:
- Were conducted from 2005 onwards;
- Were conducted in public healthcare, higher educational or correctional facilities (i.e. prisons or military institutions);
- Assessed the impact of either partial or complete bans;
- Had a minimum of 6 months follow-up for measures of smoking behaviour;
- Were quasi-experimental (i.e. controlled before-and-after studies), interrupted time series or uncontrolled before-and-after studies);
- Assessed the impact of tobacco bans or policies on either the:
- Primary outcomes: reducing secondhand smoke exposure or otherhealth outcomes; or
- Secondary outcomes: tobacco consumption and smoking prevalence.
Characteristics of included studies
No randomised controlled trials (RCTs) were found. 17 observational studies were identified (three using a controlled before-and-after design with another site for comparison and 14 using an uncontrolled before-and-after design). Of these 17 studies:
- 12 were based in hospitals;
- 3 in prisons;
- 2 in universities.
Five studies investigated the impact of smoking bans on two participant groups (i.e. staff and either patients or prisoners).
The 17 studies were conducted in 8 countries: the USA (6 studies), Spain (3 studies), Switzerland (3 studies), Australia, Canada, Croatia, Ireland and Japan (all 1 study). Eight of these were conducted in US states or countries with macro-level (i.e. national) smoke-free legislation, eight with no legislative bans and one which compared all 50 US states (some with national bans and others without).
There was considerable heterogeneity between the 17 studies and so a meta-analysis of all studies was not conducted. Instead studies were analysed using aqualitative narrative synthesis according to each of the outcome measures:
Reducing secondhand smoke exposure
Four studies assessed secondhand smoke exposure, finding that a reduction in exposure was observed in all three settings after smoking bans. However, none of the studies in the review used a biochemically validated measure of smoke exposure such as cotinine or carbon monoxide levels.
Other health outcomes
Four studies examined the impact of partial or complete smoking bans on health outcomes including smoking-related mortality. Two were conducted in prisons, one in a hospital and one in a secure mental hospital (Etter et al, 2007). All of these studies observed improvements in smoking-related morbidity and mortality after smoking bans. One of these assessed the impact of smoking bans in prisons in all 50 US states and found that smoking-related mortality was reduced in those prisons that had a smoking ban for more than 9 years.
Tobacco consumption and smoking prevalence
Thirteen studies reported data on the effect of smoking bans on smoking prevalence and five of these reported data on two populations within settings (i.e. prisoners and prison staff).
Eleven of these studies were included in a meta-analysis (using the Mantel-Haenszel fixed-effect method) and the data from the 12,485 participants in these studies was pooled. Although there was considerable heterogeneity between these studies (I2 = 72%; where a higher I2 value is evidence of higher levels of heterogeneity), this heterogeneity was lower within subgroups (e.g. in prisoners or hospital staff).
Ten studies conducted in hospital settings found mixed evidence for the impact of smoking bans on smoking prevalence. Eight of these studies were included in the meta-analysis and there was evidence that smoking bans reduced active smoking rates among hospital staff (risk ratio (RR) 0.71, 95% confidence interval (CI) 0.64 to 0.78, n = 4,544, I2 = 76%) and patients (RR 0.84, CI 0.76 to 0.98, n = 1442, I2 = 20%).
The one study in a prison setting found no evidence of a change in smoking prevalence among staff or prisoners after a smoking ban (RR 0.99, CI 0.84 to 1.16, n = 130).
Two studies in university settings observed reductions in smoking prevalence after smoking bans (RR 0.72, CI 0.64 to 0.80, n = 6,369, I2 = 59%), although one study only observed this among male ‘frequent’ smokers.
Quality of the evidence
The evidence was judged to be of low quality as all of the studies wereobservational (none used a RCT design) and the risk of bias was rated as high.
Overall, this review finds evidence of smoking bans on:
- reducing smoking prevalence in hospitals and universities, with the greatest reductions among hospital staff;
- reduced mortality and exposure to secondhand smoke in hospitals, universities and prisons.
The quality of the evidence was low and the authors conclude that ‘we therefore need more robust studies assessing evidence for smoking bans and policies in these important specialist settings’. Limitations with the studies included in the review include: small sample sizes in some studies, a lack of a control location for comparison in all but three studies and a high level of heterogeneity between and within the different settings (e.g. the hospital settings included a cancer hospital, psychiatric hospitals and general hospitals).
The authors report that given this evidence, smoking bans at the meso-level should be considered as part of multifactorial tobacco control activities to reduce secondhand smoke exposure and smoking prevalence.
Given that the introduction of these bans particularly in psychiatric hospitals and prisons is controversial, the introduction of these bans should be sensitive to the needs of populations. For example, bans in psychiatric hospitals should be implemented in consultation with psychiatrists to ensure that the improved health outcomes of patients is considered first and foremost. As the evidence is currently weak, with a high risk of bias, any interventions should be closely monitored.
More robust studies are needed, using a control group for comparison, assessing smoke exposure using biochemically validated measures, using long-term follow-ups of at least 6 months and reporting smoking prevalence both before and after the introduction of the ban.
Frazer K, McHugh J, Callinan JE, Kelleher C. (2016) Impact of institutional smoking bans on reducing harms and secondhand smoke exposure. Cochrane Database of Systematic Reviews 2016, Issue 5. Art. No.: CD011856. DOI: 10.1002/14651858.CD011856.pub2.
Acceptability and impact of a partial smoking ban in a psychiatric hospital.. Preventive Medicine 2007;44(1):64–9. [PubMed abstract], . (2007)
Hardly 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.
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.
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.
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.
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.
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.
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.
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
by Eleanor Kennedy @
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.”
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.
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)|
The review reported on four comparative analyses:
- 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).
- 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).
- 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.
- 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 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.
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.
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.
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.
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]
– See more at: http://www.nationalelfservice.net/mental-health/depression/treatment-for-depression-in-traumatic-brain-injury-cochrane-find-no-evidence-for-non-pharmacological-interventions/#sthash.oqwXaf7W.dpuf
By Suzi Gage @
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.
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.
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.
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.
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.
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.
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]
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.
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– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/cannabis-and-mental-illness-its-complicated/#sthash.U9604663.dpuf
by Olivia Maynard @
This blog originally appeared on the Mental Elf site on 25th April 2016.
Although many women spontaneously quit smoking when they find out they’re pregnant, approximately 11% of women in the UK continue to smoke during their pregnancy. The health implications of this are estimated to amount to an annual economic burden of approximately £23.5 million.
The NHS Stop Smoking Service provides support for pregnant women to quit smoking during their pregnancy at an annual cost of over £5 million (or £235 per successful quitter). However, despite successful smoking abstinence during pregnancy using this service, many women restart smoking after giving birth (i.e. postpartum), increasing their risk of smoking related diseases and their offspring’s risk of passive smoking and becoming smokers themselves.
Jones and colleagues conducted a systematic review and meta-analysis to investigate just how high the rates of restarting smoking postpartum are among those women who have received support to quit smoking during their pregnancy.
Selection of included studies
Studies were included if:
- Participants were pregnant smokers who were motivated to quit smoking (to ensure that participants were similar to those women who actively seek out Stop Smoking Services during their pregnancy)
- Interventions aimed to encourage smoking cessation during pregnancy, with control group participants receiving placebo, another cessation intervention or no intervention
- Outcome measures were continuous abstinence from the end of pregnancy to at least one postpartum follow-up, or 7-day point prevalence abstinence (i.e. not smoking for the past 7 days) at both the end of pregnancy and at least one postpartum follow-up. Where biochemically validated abstinence was not available, self-reported abstinence was accepted.
Primary outcome measure
- Longitudinally collected continuous abstinence data, among those women who reported abstinence at the end of their pregnancy and were in the intervention condition (i.e. had received Stop Smoking Service support).
Secondary outcome measure
- The overall rates of smoking prevalence (using point-prevalence data) following childbirth across all women.
27 studies were included in the review. Of these:
- 4 reported continuous abstinence only (i.e. can be used for the primary outcome measure only);
- 7 reported both continuous abstinence and point-prevalence (i.e. can be used for both the primary and secondary outcome measures);
- 16 reported point-prevalence only (i.e. can be used for the secondary outcome measure only);
20 studies were randomised controlled trials (RCTs) with individual randomisation, 5 were cluster randomised and 2 were quasi-randomised.
To minimise differences between the included studies, only data from similar time-points were synthesised. Postpartum follow-up time-points were as follows:
- 6 weeks (including data from 10 days and 4, 6 and 8 weeks postpartum);
- 3 months (data from 3 and 4 months);
- 6 months (data from 6 and 8 months);
- 12 months;
- 18 months;
- 24 months.
Risk of bias assessment
- Studies were screened and data extracted by two reviewers;
- The quality of included studies was generally judged to be poor;
- Only 8 (of 27) studies included an intention to treat analysis;
- Only 18 studies used biochemically validated abstinence;
- There was evidence of publication bias.
Primary analysis: proportion re-starting smoking
The primary analysis only included those 11 studies reporting continuous abstinence, including a total of 571 women who reported being abstinent at the end of their pregnancy.
By 6 months postpartum, 43% (95% CI = 16 to 72%, I2 = 96.7%) of these women had restarted smoking.
The subgroup analysis of those studies using biochemically validated abstinence measures included only 6 studies and found that by 6 months 74% of women (95% CI = 64 to 82%) had restarted smoking.
Secondary analysis: proportion smoking
The secondary analysis only included those 23 studies reporting point-prevalence abstinence, including a total of 9,262 women.
At the end of pregnancy, 87% (95% CI = 84 to 90%, I2 = 93.2%) of women were smoking and at 6 months this was 94% (95% CI = 92 to 96%, I2 = 88.0%).
The 17 studies using biochemically validated abstinence observed rates of smoking at the end of pregnancy of 89% (95% CI = 86 to 91%, I2 = 91.2%) and 96% at 6 months postpartum (95% CI = 92 to 99%, I2 = 70.7%).
Using these cross-sectional point-prevalence data, it is also possible to estimate the proportion of women restarting smoking postpartum. These data suggest that 13% of women were abstinent at the end of their pregnancy, but only 6% were abstinent at 6 months, which is equivalent to 54% restarting smoking postpartum.
The authors conclude that:
Most pregnant smokers do not achieve abstinence from smoking while they are pregnant, and among those that do, most will re-start smoking within 6 months of childbirth.
They also note that this means that the considerable expenditure by NHS Stop Smoking Services to help pregnant women quit smoking is not having as big an impact on improving the health of women and their offspring as it might.
- There was considerable variability between the included studies (i.e. the I2 statistic was high). The authors attempted to minimise this variability by aggregating data at similar time-points and only including those studies where women consented to join (i.e. were motivated to quit smoking)
- Only a few studies reported longitudinal continuous abstinence data, restricting the amount of data which could be included in the primary analysis.
This is the first study to systematically investigate the rate of restarting smoking postpartum and provide data on the effectiveness of the Stop Smoking Services provided to pregnant women.
Using continuous postpartum abstinence rates, 43% of women who had received a smoking cessation intervention and were abstinent at the end of their pregnancy had restarted smoking after 6 months. Using data from the cross-sectional point-prevalence data, a similar rate of restarting was observed.
These results are generalisable to those pregnant women who seek support from Stop Smoking Services. Although no reviews have investigated the rates of restarting smoking among those women who spontaneously quit smoking during their pregnancy, individual studies suggest that the rates are broadly similar at between 46 and 76%.
Jones M, Lewis S, Parrott S, Wormall S, Coleman T. (2016) Re-starting smoking in the postpartum period after receiving a smoking cessation intervention: a systematic review. Addiction, doi: 10.1111/add.13309.
– See more at: http://www.nationalelfservice.net/populations-and-settings/pregnancy/rates-of-restarting-smoking-after-giving-birth/#sthash.iSRFc5w5.dpuf
by Marcus Munafò @
This blog originally appeared on the Mental Elf site on 11th Febraury 2016.
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.
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:
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).
The top three predictors of non-remission were:
- Baseline depression severity
- Feeling restless during the past 7 days
- Reduced energy level during the past 7 days
The top three predictors of remission were:
- Currently being employed
- Total years of education
- 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%.
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:
- 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.
- Second, the prediction algorithm shows some evidence of generalisability to an independent sample.
- 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:
- 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.
- 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.
- 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.
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.
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]
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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- Dean Hochman CC BY 2.0
– 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
by Meg Fluharty @
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.
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
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.
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).
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).
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 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.
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]
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.
– See more at: http://www.nationalelfservice.net/mental-health/depression/later-menopause-linked-with-lower-risk-of-depression/#sthash.v4Zbt9Fx.dpuf
by Olivia Maynard @
This blog originally appeared on the Mental Elf site on 9th Febraury 2016.
Last month the UK Chief Medical Officers (CMOs) published new guidelines for alcohol consumption. These are the first new guidelines since 1995 and are based on the latest evidence on the effects of alcohol consumption on health.
The guidelines provide recommendations for weekly drinking limits, single drinking episodes and recommendations for pregnant women, drawing heavily on the Sheffield Alcohol Policy Model, which uses the most up to date evidence on both the short- and long-term risks of alcohol.
What are the new guidelines?
Guidelines for weekly drinking
For the new weekly drinking guidelines, the CMOs recommend that:
- It’s safest for both men and women to not regularly drink more than 14 units of alcohol per week;
- It’s best to spread these units over 3 days or more;
- Having several drink-free days each week is a good way of cutting down the amount you drink;
- The risk of developing a range of illnesses increases with any amount you drink on a regular basis.
There are two key changes here from the guidelines we’ve been used to:
First, there’s no difference in recommendations for men and women. This is because there is increasing evidence that although women are more at risk from the long-term health effects of alcohol, men are more at risk from the short-term effects of drinking (they’re more likely to expose themselves to risky situations while drinking).
Second, there is an explicit statement that there is no ‘safe’ level of alcohol consumption. Over the past 21 years, the link between alcohol and cancer has become much clearer. For example, we now know that while the lifetime risk ofbreast cancer is 11% among female non-drinkers, the lifetime risk for a woman drinking within the new guidelines is 13%. A woman drinking over 35 units a week increases her risk of breast cancer to 21%.
In their report, the CMOs are also at pains to point out that the evidence supporting alcohol’s protective effects on ischaemic heart disease is now weaker than in 1995. Furthermore, any potential protective effect of alcohol is mainly observed among older women at very low levels of consumption. Previously some have used this to claim that drinking is better than abstinence – the new guidelines refute this.
Guidelines for single drinking episodes
The new guidelines are the first to provide guidance on drinking on single occasions, recommending drinkers:
- Limit the total amount consumed on any occasion;
- Drink slowly, with food and alternating with water;
- Avoid risky places and activities and ensure they have a safe method of getting home.
These new recommendations reflect the fact that many alcohol consumers may drink heavily on occasion and provide guidance to avoid the risk of injury and ischaemic heart disease which increase with heavy drinking.
Guidelines for drinking during pregnancy
The new guidelines suggest that:
- The safest approach for pregnant women is not to drink alcohol at all, to keep risks to the baby to a minimum.
- Drinking during pregnancy can lead to long-term harm to the baby, with the risk increasing with the more alcohol consumed;
- The risk of harm to the baby is likely to be low if a woman has drunk only small amounts of alcohol before she knew she was pregnant or during pregnancy.
The CMOs report that while the evidence on the effects of low alcohol consumption during pregnancy remains ‘elusive’, taking a precautionary approach is most prudent when it comes to a baby’s long term health. However, given the elusive evidence, the guidance is also careful to note that mothers should not be too concerned if they have drunk early in their pregnancy, as this kind of stress may be even more harmful to the developing baby.
A note on risk
These recommendations are based on a level of alcohol consumption which confers a 1% lifetime risk of death from alcohol. Their purpose is therefore tominimise risk from alcohol, rather than eliminate it. Indeed, the guidelines explicitly state that there is no safe level of alcohol consumption. So what does a 1% lifetime risk mean and how does this compare to other health behaviours?
Lifetime mean risks
- Being killed through BASE jumping (0.3%);
- Being killed in a car accident (0.4%);
- Being diagnosed with bowel cancer from eating three rashers of bacon every day (1%);
- Dying from an alcohol related disease, if drinking within the new guidelines (1%);
- Smokers dying from a smoking related disease (50%, although new estimates suggest that this may be as high as 67%).
Put in the context of smoking, the risk posed by drinking within the new guidelines seems tiny (although it’s still more risky than BASE jumping!) However, it’s important to note that alcohol consumption and smoking are quite different. Alcohol consumption is perceived as normal in our society and is much more prevalent than cigarette smoking. By contrast, the acceptability of smoking is reducing and unlike social alcohol consumers, smokers are constantly being told to quit smoking.
This 1% risk level is that which is deemed ‘acceptable’ by the CMO. However, everyone will have a different ‘acceptable’ level of risk, which depends in part on how much pleasure is obtained from drinking. While some will think that increasing their risk of death from alcohol to 5% is acceptable, others will not accept any risk and will use these guidelines to cut out alcohol completely.
Criticisms of the new guidelines
As expected, the ‘nanny state’ criticism has been bandied around in pubs, on message boards and on social media since the publication of these guidelines. Others claim that these new guidelines are simply scaremongering. However, it’s important to remember that these are recommendations, not rules.
The last word must go to CMO Professor Dame Sally Davies, who addressed this criticism by saying that:
What we are aiming to do with these guidelines is give the public the latest and most up- to-date scientific information so that they can make informed decisions about their own drinking and the level of risk they are prepared to take.
Department of Health (2016) Health risks from alcohol: new guidelines. Open Consultation, 8 Jan 2016 (Consultation closes on 1 April 2016)
Department of Health (2016). Alcohol Guidelines Review – Report from the Guidelines development group to the UK Chief Medical Officers.
Centre for Public Health (2016). CMO Alcohol Guidelines Review – A summary of the evidence of the health and social impacts of alcohol consumption. Liverpool John Moores University.
Centre for Public Health (2016). CMO Alcohol Guidelines Review – Mapping systematic review level evidence. Liverpool John Moores University.
Department of Health (1995). Sensible drinking: Report of an inter-departmental working group.
by Eleanor Kennedy @
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).
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.
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|
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).
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.
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.
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.
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)
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