Childhood adversity linked to psychotropic drug use in later life

This post was originally written by Andrew Jones for the Mental Elf. You can see the original here.

 

Mental health problems such as depression and anxiety are significant contributors to burden of disease, the loss of quality of life and major societal and economic costs. The prevalence of mental health disorders in the European Union is 27% of the adult population, which is estimated at 83 million people (WHO, 2015).

 

Psychotropic drugs (i.e. medicines that alter chemical levels in the brain which impact mood and behavior) are often the first line of defence for mental health problems in developed countries. In Finland, psychotropic drugs are the largest drug group in terms of sales, with most recent figures demonstrating 8% of the total population receiving antidepressants at some point.

 

Relationships between childhood adversities and adult mental disorders are widely reported throughout the literature (see Chapman et al, 2004; Chen et al 2010; and also our own elf blogs Shepherd, 2014). However, these studies tend to only focus on single circumstances of adversity. Furthermore, population based research examining the use of psychotropic drugs as an indicator of poor mental health in adulthood is potentially important, but this research is lacking.

 

 

Therefore, a recent study published in the Journal of Epidemiology and Community Health (Koskenvuo et al, 2014) set out to examine whether multiple adverse experiences in childhood, such as familial conflicts or poor parent-child relationships, predict the use of psychotropic drugs in adulthood.

 

Methods

The data was derived from the Health and Social Support follow up study on a randomly selected sample of the Finish population. The initial survey was carried out via a postal questionnaire in 1992, and a follow up in 2003/4 to all those who responded to the initial questionnaire. Individual data was linked to national registers (N = 24,284).

 

Childhood adversities were coded by asking whether the respondents had experienced any of the following during childhood;




 

The cohort study used a randomly selected sample (24,284) of the Finnish population between the ages of 20 and 54.

• Divorce or separation of parents
• Long-term financial difficulties
• Serious familial conflicts
• Fear of a family member
• Severe illness of a family member or an alcohol problem
• Parent-child relationships were also assessed

The use of psychotropic drugs was taken from Finland’s National Drug Prescription Register, which holds data on outpatient purchases of all psychotropic drugs prescribed by healthcare professionals.

Results

• Use of any psychotropic drugs was found for 24.3% (N = 5,896) of the participants

• The most commonly prescribed drugs were:
  • Antidepressants (17.6%)

• The least commonly prescribed drugs were:
  • Drugs for bipolar disorder (2.4%)
  • Antipsychotics (3.6 %)

• Frequent fear of a family member demonstrated the strongest association with psychotropic drug use:
• A 3-fold increase was found for multiple antidepressant exposure (OR = 3.08, 95% CIs 2.72 to 3.94)

• There was a 2-fold increase in multiple exposure to:
  • Anxiolytics (OR = 2.69, 95% CIs 2.27 to 3.20)
  • Drugs for bipolar disorder (OR = 2.09, 95% CIs 1.49 to 2.95)
  • Antipsychotics (OR = 2.28, 95% CIs 1.72 to 3.02)
  • Hypnotics/sedatives (OR = 2.45, 95% CIs 2.06 to 2.93)

• Serious familial conflicts were also associated with 2-fold increases in:
  • Drugs for bipolar disorder (OR = 2.07, 95% CIs 1.54 to 2.77)
  
  
  • Antidepressants (OR = 2.10, 95% CIs 1.87 to 2.35)

• Divorce of parents demonstrated the weakest relationship with psychotropic drugs

• A graded association was found between childhood adversities and psychotropic drug use. As number of childhood adversities increased, number of purchases of each drug also increased:
• For example, 1-2 childhood adversities was associated with an increase in 1-2 purchases of antipsychotics (OR = 1.97, 95% CIs 1.46 to 2.67)

• Whereas 5-6 childhood adversities was associated with a larger increase in >16 purchases of antipsychotics (OR = 5.63, 95% CIs 3.52 to 9.00)

• The authors also examined the effects of multiple childhood adversities and found a significant effect on any psychiatric drug use, with the association remaining after adjustments for alcohol and smoking use, BMI work status and recent life events (OR = 2.82 95% CIs 2.42 to 3.28)

The number of psychotropic drugs used as an adult corresponded
 to the number of adversities experienced as a child.

Conclusions

The results of this population-based cohort study demonstrate a significant effect of multiple childhood adversity measures on future mental health problems, as measured by psychotropic drug use. The strongest association was found between frequent fear of a family member and antidepressant use.



This research underlines the importance of identifying
and supporting families at risk of adversity.

Limitations of the study include no information about medications taken during periods of inpatient hospital care. Furthermore, there was significant overlap between diagnostic drugs, suggesting drugs may not be diagnostic specific.

In conclusion, many symptoms of mental health problems which appear in early adulthood or in later life are traceable to early circumstances. The results here emphasise the importance of early recognition of families at risk.

Links

Koskenvuo, K., Koskenvuo, M., (2014). Childhood adversities predict strongly the use of psychotropic drugs in adulthood: a population-based cohort study of 24 284 Finns. Journal of Epidemiology and Community Health doi:10.1136/jech-2014-204732

WHO (2015). Prevalence of mood disorders – data and statistics. WHO website, last accessed 30 Jan 2015.

Chapman, DP., Whitfield, CL. Felitti, VJ., Dube, SR., Edwards, VJ., Anda, RF. (2004). Adverse childhood experiences and the risk of depressive disorders in adulthood. Journal of Affective Disorders, 82, 217-25.

Chen, LP., Murad, MH., (…) Zirakzadeh, A. (2010). Sexual abuse and lifetime diagnosis of psychiatric disorders: systematic review and meta-analysis. Mayo Clinical Proceedings, 85, 618-29.

Shepherd, A. (2014). Childhood abuse and adverse life events interact synergistically to produce a high risk for psychotic experiences. The Mental Elf, 15 Jul 2014.

Alcohol use disorders and mortality in Nordic countries

This post was originally written by Andrew Jones for the Mental Elf. You can see the original here.

Alcohol Use Disorder (AUD) is one of the most prevalent mental disorders, affecting an estimated 3.6 % of the world population. AUDs are a major contributor of morbidity and mortality, with excessive alcohol consumption associated with increased burden of disease, accidents and social problems (Samohkvalov et al, 2010).

A recent meta-analysis including studies across many countries demonstrated that men with AUD have three-fold higher mortality, whereas women have four-fold mortality, than the general population (Roerecke et al, 2013). Increased mortality is also seen in younger people and those in TREATMENT FOR addiction. However, little is known about the mortality data of patients with AUD in Nordic countries.

Because of alcohol-related problems, Sweden, Denmark and Finland created alcohol policies to restrict availability and reduce population consumption of alcohol. However in 1995, after joining the European Union, both Sweden and Finland shifted to more liberal polices such as tax reductions and lengthened opening hours.

In a recent population based REGISTER study published in Acta Pyschiatrica Scandinavica, the authors evaluated the mortality and life expectancy rates in people diagnosed with AUD in Denmark, Finland and Sweden over a twenty year period (Westman et al, 2014). Within these countries, Sweden has the most restrictive alcohol policies, whilst Denmark has the least restrictive policies.

The study used National HEALTH Registers to follow the entire adult population of Denmark, Finland and Sweden.

Methods

The authors used National HEALTH Registers to follow the entire population of the three countries aged over 15 (approximately 20 million people in total). They identified all people who had been admitted to HOSPITAL through AUD over a twenty-year observation period, between 1987 and 2006. Data about alcohol consumption per capita was collected from an international database. Each person was followed from the date of their hospital administration until death or the end of a 5 year follow up period.

The study population was stratified into 5 age groups (15-29, 30-44, 45-59, 60-74, >75 years) and frequency of mortality was calculated for each group. For analysis of time trends the 20 year observation period was split into four periods (1987-1991, 1992-1996, 1997-2001 and 2002-2006).

The main outcome measures were the standardised mortality rate per 100,000 person-years and life-expectancy.

The authors were primarily interested in links between alcohol use disorder, mortality and life expectancy.

Results

During the entire study alcohol consumption per capita was lowest in Sweden and highest in Denmark. Small fluctuations in consumption were evident across the time periods. For example, in Finland alcohol consumption peaked during 2002-2006, coinciding with alcohol tax reductions.

Mortality

Mortality was higher overall in Denmark, than Finland or Sweden. Standardized mortality increased over the twenty years in both men and woman in Denmark. In Finland and Sweden standardized mortality decreased in both men and women over time.

In all three countries, mortality rates in people with AUD were higher in younger age groups. People with AUD had higher mortality from all causes of death, including all diseases, medication conditions and suicide.

Life expectancy

Life expectancy was highest in Sweden and lowest in Denmark. In all three countries, life expectancy was longer in woman than men. Difference in life expectancy was calculated as the life expectancy in the general population minus life expectancy of people with AUD. In Denmark this was approximately 27.6 years; Finland 26.9 years; Sweden 24.9 years.

Over the 20 year period life expectancy differences increased in Men (Denmark, 1.8 years; Finland, 2.6 years; Sweden, 1.0 years). In women, life expectancies differences increased in Denmark (0.3 years), but decreased in Finland (0.8 years) and Sweden (1.8 years).

Denmark had the highest alcohol consumption and the worst outcomes in terms of mortality and life expectancy.

Discussion

Across three Nordic countries, individuals who are HOSPITALIZED with AUD have an average life expectancy of 47-53 years if male, and 50-58 years if female. The main finding of the study was the shorter life expectancy (~26.5 years) of individuals with AUD compared with the general population.

A particular strength of this study was the comparison of mortality and life expectancy across the whole population in the three Nordic countries. The authors used nationwide HEALTH registers to provide highly reliable population data.

Limitations

• The main limitations of this study include the use of only inpatient data for establishing AUD. This may have led to selection bias towards AUD patients with the most severe HEALTH problems.
• Secondly, as the study was register-based there was no clinical data about treatment or adherence. In support of these points, it is thought only one in three individuals with dependence will ever seek treatment.
• Finally, alcohol consumption per capita was determined through aggregate data rather than individual alcohol exposure.

The results of this study have clear clinical implications for policy and treatment. The authors suggest that hazardous alcohol consumers should be a specific TARGET for preventative measures, to ensure they do not develop AUDs. Furthermore, the somatic care of people with AUD should be substantially improved.

To conclude, Alcohol Use Disorder is a significant public HEALTH concern, which severely impacts mortality and life-expectancy.

The authors suggest that public HEALTHprevention programmes should focus on hazardous drinkers, to ensure that they do not develop Alcohol Use Disorders.

Links

Key paper

Westman J, Wahlbeck K, Laursen TM, et al (2014). Mortality and life expectancy of people with alcohol use disorder in Denmark, Finland and Sweden. Acta Psychiatrica Scandinavica, doi: 10.1111/acps.12330.

Further reading

Cunningham JA, Breslin FC (2004). Only one in three people with alcohol abuse or dependence ever seek treatment. Addictive Behaviours, 29: 221-3.

Roerecke M, Rehm J (2013). Alcohol use disorders and mortality: a systematic review and meta-analysis. Addiction, 9: 1562-78.

Samohkvalov AV, Popova S, Room R, Ramonas M, Rehm J (2010). Disability associated with alcohol use and dependence. Alcohol: Clinical and Experimental Research, 34: 1871-8.

E-cigarettes and youth: are e-cigs encouraging more use of conventional cigarettes?

This post was originally written by Matt Field for the Mental Elf. You can see the original here.

Arguments about the potential benefits and harms of electronic cigarettes (E-Cigs) are repeated on a weekly basis in the news, and we covered this topic in a recent Mental Elf blog.

One area of concern is the use of E-Cigs by adolescents, not least because some products seem to be marketed at young people and because the developing adolescent brain is at increased risk of developing nicotine dependence and other addictions (Field, 2013). Although some studies have shown that E-Cigs can help people to quit smoking, the evidence is rather mixed.

Dutra and Glantz (2014a) recently reported an analysis based on cross-sectional SURVEY DATA from adolescents in the USA. They used the data to investigate associations between the use of E-Cigs and regular cigarettes, and to see if E-Cigs would help or hinder attempts to quit smoking.

Methods

Data were obtained from the 2011 and 2012 National Youth Tobacco SURVEY, a nationally representative cross-sectional sample of students from middle and high schools (grades 6-12) across the USA. There were 18, 866 respondents in the 2011 SURVEY and more (24, 658) in the 2012 SURVEY, with an equal male/female split in both years and average age of 14.7 (2011) and 14.6 (2012).

The 81 items in the SURVEY included questions about use of tobacco and other nicotine-delivery products, including conventional cigarettes and E-Cigs. There were also questions about attempts to quit smoking in the previous year and intentions to quit in the coming year, as well as questions about the duration of abstinence from conventional cigarettes, to identify former smokers who had managed to quit.

6.5% of young people in the 2012 SURVEY reported having ever used an e-cigarette, which was more than double the number in 2011.

Results

The well-documented recent trend for increasing prevalence of E-Cigarette use was confirmed here, with 3.1% of the 2011 sample reporting ‘ever use’ of E-Cigs, which more than doubled to 6.5% in 2012.

The number of current E-Cig users (use at least once per day in the previous 30 days) showed a similar trend, rising from 1.1% in 2011 to 2.0% in 2012.

In both years, about half of E-Cig users were also current cigarette smokers, and the other half were not, (i.e. they only used E-Cigs).

Beyond this prevalence data, the main thrust of the results is that E-Cig use was associated with more rather than less cigarette smoking. Specifically, pooled analyses (comprising data from 2011 and 2012) revealed that:

• E-Cig users were more likely to also smoke regular cigarettes than non-users
• 
  
  • Current E-Cig use was positively associated with ever smoking cigarettes (OR = 7.42, 95% CI = 5.63 to 9.79)
  • 
    
  • Current E-Cig use was positively associated with current cigarette smoking (OR = 7.88, 95% CI = 6.01 to 10.32)
  • 
    
• The relationship between E-Cig use and intention to quit smoking regular cigarettes was weak
• 
  
  • Among current cigarette smokers, regular E-Cig use was not associated with having attempted to quit smoking regular cigarettes in the previous 12 months (OR = 0.89, 95% CI = 0.61 to 1.30)
  • 
    
  • Regarding future intentions to quit smoking, current users of E-Cigs were not more likely to intend to quit within the next year (OR = 1.34, 95% CI = 0.62 to 2.90)
  • 
    
  • Although those who had ever used E-Cigs (but were not current users) were more likely to intend to quit within the next year (OR = 1.53, 95% CI = 1.03 to 2.28)
  • 
    
• E-Cig users were less likely to have abstained from regular cigarettes in the past year
• 
  
  • For example, current use of E-Cigs was associated with reduced abstinence from cigarettes within the past year, and this was seen both in those who had only experimented with conventional cigarettes (OR = 0.12, 95% CI = 0.07 to 0.18), as well as in current smokers (OR = 0.34, 95% CI = 0.13 to 0.87)
  • 
    

E-Cig use was associated with more rather than less cigarette smoking.

Discussion 

The authors’ interpreted their findings as follows:

"Use of E-cigarettes does not discourage, and may encourage, conventional cigarette use among US adolescents"

and

"…E-cigarette use is aggravating rather than ameliorating the tobacco epidemic among youth. These results call into question claims that e-cigarettes are effective as smoking cessation aids."

Indeed, one interpretation of the data is that smokers may be using E-Cigs as a way to obtain nicotine in situations where regular smoking is not allowed, rather than as an aid to smoking cessation. For this reason, and in addition to concerns about their toxicity, an Editorial in the journal called for tighter regulation of the E-Cig market (Chaloupka, 2014).

However, the authors’ claims about the causal influence of E-Cig use on the initiation and maintenance of cigarette smoking attracted a number of critical commentaries, and these should be read alongside the TARGET article to get a balanced view. The most important point is that this was a cross-sectional SURVEY that revealed associations between E-Cig use and cigarette smoking. It is never possible to infer causation from correlation, a point that the authors acknowledged in their conclusion. However the majority of the article strongly implies such a causal relationship.

This type of study can establish correlation, but never causation.

One commentary (Niaura, Glynn and Abrams, 2014) identified two additional explanations for the association between use of regular cigarettes and E-Cigs, both of which are plausible. First is that people may start out with regular cigarettes and then switch to E-Cigs because they are less harmful, cheaper, and (for now at least), they can be used almost everywhere. A further possibility is that demographic or personality characteristics (e.g. sensation-seeking) may predispose people to the use of nicotine-containing products and other addictive drugs in general. Including Dutra and Glantz’s explanation (E-Cig use promotes cigarette use), all three explanations are plausible but the cross-sectional nature of these survey data makes it impossible to distinguish between them.

In my favourite commentary, Farsalinas and Polosa (2014) used the same survey data to investigate if there was an association between cigarette smoking and the use of stop-smoking interventions in the previous 12 months. They consider both pharmacological (e.g. nicotine replacement products) and psychosocial (e.g. calling a quitline) interventions. This analysis revealed that, unsurprisingly, current and former cigarette smokers were far more likely to have used these interventions than never smokers. The intuitive explanation for this is that people start smoking before they seek out an intervention to help them quit; no reasonable person would argue that this correlation suggests that nicotine gum and smoking quitlines are somehow a ‘gateway’ into cigarette smoking. Yet this is exactly the logic that Dutra and Glantz applied when interpreting their E-Cig data. In their response to the Farsalinas and Polosa commentary they stated that nicotine replacement products are not approved for use by adolescents in the USA because there is a lack of evidence for their efficacy in this population. Make of that what you will.

Conclusions

Among American youth, users of Electronic cigarettes are more likely to be current cigarette smokers and less likely to have been abstinent from tobacco in the previous year. You could interpret this as evidence that E-cigarettes encourage the use of conventional cigarettes, but alternative explanations may be more plausible.

We should be careful about interpreting these findings, given the nature of the evidence on which they are based.

Links

Chaloupka, F. J. (2014). Tobacco control policy and electronic cigarettes. JAMA Pediatrics, 168, 601-602.

Dutra, L. M., & Glantz, S. A. (2014a). Electronic cigarettes and conventional cigarette use among US Adolescents: A cross-sectional study. JAMA Pediatrics, 168, 610-617.

Dutra, L. M., & Glantz, S. A. (2014b). Youth tobacco use and electronic cigarettes – in reply. JAMA Pediatrics, 168, 776.

Farsalinos, K. E., & Polosa, R. (2014). Youth tobacco use and electronic cigarettes. JAMA Pediatrics, 168, 775.

Field, M. Hard Evidence: is the teenage brain wired for addiction? The Conversation UK, 2 Oct 2013.

Niaura, R. S., Glynn, T. J., & Abrams, D. B. (2014). Youth experimentation with e-cigarettes: Another interpretation of the data. JAMA, 312, 641-642.

Internet-based interventions for harmful drinking show small beneficial effects

This post was originally written by Andrew Jones for the Mental Elf. You can see the original here.

According to the World HEALTH Organisation (WHO), alcohol misuse ranks within the top ten HEALTH conditions with the highest burden of disease. The prevalence of hazardous and harmful drinking in the UK is approximately 7.6 million people. The health implications of such drinking patterns can be severe and there is increased risk of progression to dependence if not treated.

Research suggests that the majority of hazardous drinkers would like support to reduce their drinking, however they would prefer help outside of conventional health-care settings. Furthermore, these individuals are unlikely to engage with abstinence-based treatments and thus controlled drinking is a more realistic goal. Individuals often remain hidden to conventional treatments because of these factors. However, internet self-help interventions appear to be a promising strategy in overcoming this treatment gap.

Internet-based interventions for harmful drinking

Studies of web-based interventions suggest they reach first time help seekers, and are taken up much more readily than low-intensity face-to-face interventions. Most of these interventions are unguided and delivered as standalone procedures in the community. A previous meta-analysis focusing on these unguided interventions reported significant reductions in alcohol consumption, compared to control groups (Riper et al, 2011).

Recently there has been increased research into these unguided internet-based interventions, but also interventions that are therapist-led (guided). As a result, a meta-analysis published in PLoS ONE set out to examine the effectiveness of both guided and unguided low-intensity internet interventions for adult alcohol misuse (Riper et al, 2014).

Previous studies have reported significant reductions in alcohol consumption from unguided Internet-based interventions

Methods

The authors conducted a literature search in bibliographic databases up until September 2013. In order to be included in the analysis, studies had to compare a web-based intervention with a control group (e.g. a waitlist or alcohol information brochure) and include a low-intensity self-help intervention that could be performed on a COMPUTER or mobile phone, with or without professional guidance. Studies also had to include drinkers who exceeded guidelines for low-risk drinking and include an assessment of drinking behaviour as a primary outcome measure.

The authors identified 16 studies (containing 23 comparisons) for their meta-analysis. The studies included 5,612 PARTICIPANTS (3,268 in intervention and 2,344 in control / comparison conditions). This allowed the authors to detect the small effect size which they were expecting.  All studies included were published fairly recently, between 2006 and 2013.

Results

• The effect of low-intensity internet-based alcohol interventions to reduce alcohol consumption in comparison to controls was significant at post-test (g=0.20; 95% CI: 0.13 to 0.27, p<.001, NNT=8.93).
• PARTICIPANTS in a guided or unguided intervention group drank significantly less units per week at post-treatment than controls (n =14; 2.2 units; 95% CI: 0.87 to 3.46, p = .001). On average, intervention participants were drinking 22 grams of alcohol less per week. A standard UK unit is 8 grams compared to 10 grams in the Netherlands (where the meta-analysis was conducted), so this is approximately a pint of beer or glass of wine per week (i.e. not an awful lot!).
• Compared to controls, intervention participants were more likely to have reduced their alcohol consumption to within low risk-guidelines (n = 6; RD 0.13, 95% CI: 0.09 to 0.17, p<.001).
• At 6-12 month follow-ups there were no significant differences between six unguided interventions and control groups (g = 0.06, 95% CI: -0.14 to 0.25, p = .567). There were no follow-up data available for guided interventions.
• There was no significant difference in effect sizes between guided (g = 0.23) and unguided (g = 0.20) alcohol interventions.

On average, the guided or unguided intervention group members drank about a pint of beer or glass of wine less per week

Conclusions

This meta-analysis demonstrated a small but significant effect in favour of low-intensity internet-based self-help interventions for alcohol misuse. The results of this study support the use of internet-based self-help interventions for curbing alcohol use in various settings.

Although the pooled effect of the interventions was small, the authors suggest that:

The public HEALTH impact could be substantial if large numbers of people who misuse alcohol were to take part in these interventions.

Whilst these results seem promising, it is worth noting that the reduction of 22 grams of alcohol is lower than that of face-to-face interventions in primary care, and also lower than a previous meta-analysis focusing on unguided interventions only (Kaner, et al, 2007).

Limitations

The findings of this study should be interpreted with some caution, as there are some limitations:

• The subgroup analyses comparing guided to unguided interventions suffered from a lack of power
• Also, some studies reported high dropout rates (up to 42%), however this is a common occurrence in internet-based research

Future research in this area should focus on longer follow-ups to assess the maintenance of intervention effects, and also direct comparisons between guided and unguided interventions.

To conclude, low-intensity internet-based self-help interventions may be beneficial in reducing alcohol use and these interventions may provide a cost effective way of bridging the treatment gap in hazardous drinkers.

Future studies need to follow-up people for longer and compare the differences between guided and unguided interventions.

Links

Riper, H., Blankers, M., Hadiwijaya, H., et al (2014). Effectiveness of Guided and Unguided Low-Intensity Internet Interventions for Adult Alcohol Misuse: A Meta-Analysis. PLoS ONE, 9(6): e99912.

Kaner EF, Dickinson HO, Beyer FR, Campbell F, Schlesinger C, Heather N, Saunders JB, Burnand B, Pienaar ED. Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database of Systematic Reviews 2007, Issue 2. Art. No.: CD004148. DOI: 10.1002/14651858.CD004148.pub3.

Riper, H., Spek, V., Boon, B., et al (2011). Effectiveness of E-self-help interventions for curbing adult problem drinking: a meta-analysis. J Med Internet Res, 12(2):e42.