May 2016 – TARG Blog

Rates of restarting smoking after giving birth

Posted on May 25, 2016May 13, 2016 by rebeccalawn

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.

The NHS Stop Smoking Service costs over £5 million every year, but 11% of women in the UK continue to smoke during their pregnancy.

Methods

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.

Results

Study characteristics

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.

In clinical trials of smoking cessation interventions during pregnancy, only 13% of female smokers are abstinent at term.

Conclusion

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.

Limitations

There was considerable variability between the included studies (i.e. the I²statistic 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.

Discussion

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%.

Nearly half (43%) of the women who do stop smoking during their pregnancy, re-start smoking within 6 months of childbirth.

Links

Primary paper

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.

Photo credits

– See more at: http://www.nationalelfservice.net/populations-and-settings/pregnancy/rates-of-restarting-smoking-after-giving-birth/#sthash.iSRFc5w5.dpuf

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

Posted on May 13, 2016 by rebeccalawn

by Marcus Munafò @MarcusMunafo

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.

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

Methods

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

Escitalopram-placebo
Bupropion-escitalopram
Venlafaxine-mirtazapine

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

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

In total, 164 variables were used.

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

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

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

Results

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%.

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

Conclusions

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

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

Strengths and limitations

There are some strengths to this study:

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.

Summary

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

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

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

Links

Primary paper

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

Other references

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

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

Photo credits

– 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

Later menopause linked with lower risk of depression

Posted on May 13, 2016 by rebeccalawn

by Meg Fluharty @MegEliz_

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

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

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

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

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

Methods

Search criteria

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

Definition of variables

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

Excluded studies

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

Statistical analysis

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

Results

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

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

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

Age at menopause

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

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

Reproductive period

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

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

Discussion

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

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

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

Strengths and limitations

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

However, there were some limitations to consider:

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

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

Do you talk to your female friends about the menopause?

Links

Primary paper

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

Other references

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

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

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

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

Photo credits

– See more at: http://www.nationalelfservice.net/mental-health/depression/later-menopause-linked-with-lower-risk-of-depression/#sthash.v4Zbt9Fx.dpuf

The European Tobacco Products Directive and the future of e-cigarettes in the UK

Posted on May 6, 2016June 16, 2016 by rebeccalawn

By Jasmine Khouja @Jasmine_Khouja

E-cigarettes have become a popular product among smokers and ex-smokers, and Action on Smoking and Health (ASH) estimates that there are 2.6 million current users of e-cigarettes in the UK. As an alternative to tobacco smoking, research commissioned by Public Health England estimates that e-cigarettes are likely to be roughly 95% less harmful. The evidence supporting these popular and effective quitting aids suggests that e-cigarettes could be a powerful tool for harm reduction amongst current smokers but there is still uncertainty over the safety of e-cigarettes. Limited research concerning the effects of long-term use and the current lack of strict regulation of the products has fuelled this uncertainty but new regulations have been introduced into the pre-existing European Tobacco Products Directive (TPD) to rectify this. The updated TPD will come into force on 20^th May 2016 with a transitional period allowed by the TPD. UK e-cigarettes and refill containers which are not in compliance with the TPD will be allowed to be released for sale on the UK market until 20^th November 2016, but from 20^th May 2017 all products sold to consumers will need to be fully compliant with the TPD. The alternative to following the regulations set by the TPD will be for e-cigarettes to gain a medical licence from the Medicines and Healthcare products Regulatory Agency (MHRA) and be regulated as licenced medicinal products to be sold in the UK.

As I am about to commence a PhD investigating the reasons for e-cigarette use, I am interested in what the implications of the directive will be in the UK; will it encourage smokers to switch to e-cigarettes, consequently reducing harm to themselves and others, or will it result in a reduction of available products and cause an increase in relapses to smoking?

I have read the directive and listed some of the key changes that will happen and added my own thoughts on what may happen as a result.

CHANGE: New e-cigarette products must be notified to the MHRA six months before their release to the public. E-cigarette companies will be charged £150 to notify MHRA of a new product and £80 for a modification to an existing product, and will then be charged £60 annually thereafter. POSSIBLE OUTCOMES: The MHRA should have more control over the products on the market and be able to prevent unsafe products entering the market but it may take longer for new products to become available to buy. Additionally, some existing products will be unavailable from 20^th May 2017 if they do not to comply with the regulations by 20^th November 2016.

CHANGE: Under the TPD, e-liquids will only be allowed where the nicotine concentration does not exceed 20 mg of nicotine per ml of liquid. E-liquids containing more than 20 mg of nicotine per ml of liquid will have to gain a medical licence authorised by the MHRA. POSSIBLE OUTCOMES: People may reduce their doses of nicotine and reduce their addiction if their preferred dosage is no longer available. Fewer high dosage products may be available as gaining a medical licence is an expensive process (estimated between £87,000 and £266,000 annually over ten years for a single device). When current products with high dosages such as 36 mg of nicotine per ml of liquid become unavailable, people may use lower dosages such as 20 mg of nicotine per ml of liquid as a substitute and inhale twice as much vapour to get the same nicotine hit. Nicotine is not the only constituent of vapour though; there are low concentrations of other toxicants, so inhaling more vapour means inhaling more toxicants. Alternatively, current higher dosage users may relapse to tobacco smoking if they feel the lower dosages do not effectively deliver the nicotine hit they need.

CHANGE: Products regulated under the TPD must provide information to the MHRA on the safety and contents of e-cigarette products (including ingredients, toxicants and emissions). Health warnings, instructions for use, information on addictiveness and toxicity must also appear on the packaging and accompanying information leaflet. POSSIBLE OUTCOMES: This should allow e-cigarette users to make informed choices. The notification fees mentioned above will include the storage of this information but the companies may have to bear extra costs in testing their products for the amount of toxicants and emissions produced. These tests will have to comply with the standards set in the TPD and by the MHRA which may prove too costly for smaller e-cigarette companies, forcing them to withdraw products from the market. This could leave the market open to the tobacco industry who generally have greater financial resources available to them. The tobacco industry have to also sustain the tobacco market; a consequence of this may be the deliberate placement of ineffective e-cigarette products on the market to encourage current smokers continue to smoke tobacco and ex-smokers using e-cigarettes relapse.

CHANGE: E-cigarette products will be child-safe, will not break or leak during the refill process, and containers will not exceed 10 ml (refill cartridges will not exceed 2 ml). POSSIBLE OUTCOMES: This should prevent accidents involving children consuming dangerous levels of nicotine. Most changes will be made to newer devices, which require e-liquid refills. If these modifications aren’t made by 20^th November 2016 the products will be removed from the market by 20^th May 2017.

CHANGE: Under the TPD, cross-border advertising will be banned, which includes in newspapers, radio and TV, but not on billboards and posters. Products will not be allowed to make smoking cessation or health claims. Advertising of products with a medicinal license will be allowed under “over the counter” medicine rules. POSSIBLE OUTCOMES: This should minimise the amount of e-cigarette advertising seen by those who should not use e-cigarettes such as children and non-smokers. However, only e-cigarette companies who can afford a medical licence will be able to advertise on TV and this could mislead people into thinking that these products are more effective than other products.

A possible outcome for many of these changes is the loss of products from the market because of non-compliance with the regulations. Although increased reassurance that e-cigarettes on the market meet certain quality standards may encourage new users, the removal of any e-cigarette product from the market will provide an opportunity for e-cigarette users to relapse to smoking; without their favourite brand or flavour, it may be easier for them to resume smoking again than to find a replacement that suits their needs and taste. This in turn could lead to increased levels of smoking, and therefore harms to both individuals and society as a whole. Additionally, high nicotine dosage e-cigarette users may be encouraged to inhale more vapour and therefore unnecessary amounts of other constituents. However, recent preliminary research findings from ASH UK suggest there are few high dosage users meaning that this should not affect many.

The withdrawal of products is likely to be determined by the cost of making products compliant. Tobacco companies generally have greater financial resources than e-cigarette companies, with the top companies making billions in profit each year, meaning they can afford to make the necessary changes to meet the new regulations. The few e-cigarette companies that are owned by tobacco companies mainly produce ‘cigalikes’ which are the least effective design of e-cigarettes and there is a higher chance of relapsing to smoking when using them compared to later-generation devices. Given that the tobacco-owned e-cigarette companies will probably have greater resources available to them, they could end up with a monopoly on the e-cigarette industry. In fact, this may already be happening; the first medically licensed e-cigarette is a ‘cigalike’ owned by British American Tobacco. This means British American Tobacco could own the only TV-advertised e-cigarette (until another company gains a licence). Consequently, smokers looking to try e-cigarettes may choose less effective devices because they are more widely advertised.

These changes may reassure the general public that the devices will be safe but may lead to many ex-smokers relapsing because they are forced to use e-cigarettes and e-liquids that do not meet their needs, all the while lining the pockets of the tobacco industry by allowing them a monopoly on higher nicotine dosage products. Of course, the possible outcomes stated here are speculative; research will need to be undertaken to evaluate the ongoing impact of the new guidelines.

Links

Photo Credits

http://ecigarettereviewed.com/ – Lindsay Fox