The Brief Addiction Science Information Source (BASIS)

ASHES, Vol. 9(6) – Smoking in the Digital Age: Awareness of e-cigarettes in four countries

Electronic cigarettes (i.e., e-cigarettes) are devices in which solution containing nicotine is atomized by a heating coil and then inhaled. These e-cigarettes, mentioned in ASHES, Vol. 9(4), are a relatively new addition to the nicotine product landscape. Since entering the market less than ten years ago, they have faced increased scrutiny from policymakers (e.g., DeLancey, 2010), including outright bans in Canada (Health Canada, 2009) and Australia (Australian Government Therapeutic Goods Administration, 2013) due to questionable health and safety evidence. This week’s ASHES reviews a study that measured the awareness, attitudes and usage of e-cigarettes in four industrialized nations (Adkison et al., 2013).

Image courtesy of Wikipedia

 

Methods

• Researchers analyzed data from Wave 8 of the International Tobacco Control Four-Country Survey, occurring between July 2010 and June 2011 (Thompson et al., 2006).
  • Respondents were from the U.S. (n = 1520), the U.K. (n = 1325), Canada (n=1581), and Australia (n = 1513).
  • At enrollment, respondents included in the current study (1) were 18 years old or older, (2) had smoked at least 100 cigarettes over their lifetime and (3) smoked at least one cigarette in the 30 days before enrollment[1].
• This survey asked respondents if they had heard of e-cigarettes, whether they had tried an e-cigarette themselves in the past, and whether they were current users.
  • The survey also asked those who were aware of e-cigarettes whether they thought that e-cigarettes were more harmful than traditional cigarettes, equally as harmful as traditional cigarettes, or less harmful than traditional cigarettes.

Results

• As shown in Figure 1, larger percentages of respondents had heard of e-cigarettes in the countries where sales were legal (the U.S and the U.K.) than in the countries where e-cigarettes were banned (Canada and Australia).
• Roughly one out of every six respondents who were aware of e-cigarettes had tried one.
  • Despite the bans, 10% of Canadians and 11% of Australians who were aware of e-cigarettes reported having tried one.
  • Across countries, roughly 70% of those who were aware of e-cigarettes believed that they were less harmful than traditional cigarettes. This varied somewhat by country; respondents in the UK were most likely to view e-cigarettes as less harmful than traditional cigarettes (82%), while those in the US were least likely to hold this view (66%).  

Figure 1: Percentages of respondents who were aware of e-cigarettes and had used e-cigarettes by country.

 

Limitations

• The study only surveyed current and former smokers, so the results are not necessarily indicative of the awareness and beliefs of overall populations.
• Data on awareness may be out of date, since there have been changes in the advertisement and distribution of e-cigarettes since the time of the study. For example, last December the first ads appeared on television in the U.S. (Sanburn, 2013).

Conclusion

Larger percentages of smokers know about e-cigarettes in the countries where e-cigarettes are legal, presumably due to differences in marketing efforts. For example, in the U.S., smokers are exposed to ads via magazines and on television. Despite a lack of solid evidence, a substantial number of people believe that e-cigarettes are less harmful than traditional cigarettes. Even in the two countries where they were banned, nontrivial percentages of people reported trying or using e-cigarettes. Through internet sales and other means, consumers are finding ways around the restrictions. Researchers should conduct more studies and clinical trials to determine if e-cigarettes are truly less harmful, if they can actually help people quit, and if so, how they can be used as part of smoking cessation programs.

– Matthew Tom

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References

Adkison, S. E., O’Connor, R. J., Bansal-Travers, M., Hyland, A., Borland, R., Yong, H.-H., … Fong, G. T. (2013). Electronic Nicotine Delivery Systems. American Journal of Preventive Medicine, 44(3), 207–215. doi:10.1016/j.amepre.2012.10.018

Australian Government Therapeutic Goods Administration. (2013, March 12). Electronic cigarettes. Australian Government Department of Health and Ageing. Retrieved from http://www.tga.gov.au/consumers/ecigarettes.htm

DeLancey, S. (2010, September 9). FDA acts against 5 electronic cigarette distributors. WebContent. Retrieved June 3, 2013, from http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2010/ucm225224.htm

Health Canada. (2009, March 4). To All Persons Interested in Importing, Advertising or Selling Electronic Smoking Products in Canada - Health Canada Notice 2009-03-27. notice. Retrieved June 11, 2013, from http://www.hc-sc.gc.ca/dhp-mps/prodpharma/applic-demande/pol/notice_avis_e-cig-eng.php

Sanburn, J. (2013, January 8). Can Electronic Cigarettes Challenge Big Tobacco? Time.com. Retrieved from http://business.time.com/2013/01/08/can-electronic-cigarettes-challenge-big-tobacco/#ixzz2VBdCiXPg

Thompson, M. E., Fong, G. T., Hammond, D., Boudreau, C., Driezen, P., Hyland, A., … Laux, F. L. (2006). Methods of the International Tobacco Control (ITC) Four Country Survey. Tobacco Control, 15(suppl_3), iii12–iii18. doi:10.1136/tc.2005.013870

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[1] Some respondents had quit smoking between enrollment and Wave 8, but still remained in the sample for follow-up interviews.

ASHES, Vol. 9(5) – Easy to get, but tough to smoke? Relationships among clean air policy, tobacco outlet density, and youth smoking.

Some research has found that young people smoke less if they have to travel further to buy tobacco products (Altman & Jackson, 1998). However, findings about the relationship between number of tobacco outlets in an area and youth smoking are mixed (Novak,Reardon, Raudenbush, & Buka, 2006). Today’s ASHES reviews a study that investigates if local tobacco policies (e.g., clean air laws) affect the relationship between tobacco outlet density and youth smoking (Lipperman-Kreda,Grube, & Friend, 2012).

Methods

• The researchers conducted a general household survey, interviewing 1,491 youth (mean age = 14.7 years) across 50 randomly distributed midsize California cities.
  •  Participants reported their frequency of cigarette smoking in the past year on a 7 point scale (“never” to “every day”).
• The researchers collected data about indoor and outdoor clean air laws in each city from various smoke policy databases, rating each city on the strength of those laws.
• The researchers collected data about tobacco outlet density (i.e., the number of tobacco-licensed retail stores) in each city from the State of California Board of Equalization data-files.

Results

• Multilevel regression analysis revealed the following results:
  • Youth who lived in areas with many tobacco-licensed retail stores smoked more frequently.
  • Across all cities, there was no relationship between the strength of clean air laws and participants’ smoking frequency.
  • However, clean air policies did appear to influence the relationship between tobacco outlet density and youth smoking.
    • As Figure 1 shows, in cities with relatively weak clean air laws, youth smoked more frequency in high tobacco outlet density areas. In areas with strong clean air laws, there was little relationship between tobacco outlet density and youth smoking.

Figure 1. The interaction effect of clean air policy strength and tobacco outlet density on smoking frequency (adapted from Lipperman-Kreda et al., 2012).

Note: ¹For purposes of the Figure, the researchers distinguished between low, moderate and high levels of clean air policy strength to have equal number of cities in each group; and three levels of outlet density: low (4.23 – 10.35 retail outlets per 10,000 persons), moderate (10.36 – 13.7) and high (13.88 - 20.29).

Limitations

• The study does not examine causality. It only presents the associations between the outlet density, tobacco policy and smoking frequency. For example it might be that cities with high levels of smokers demand more tobacco outlets, not that the outlets lead to more smoking.
• The study investigates a specific sample of California cities. The results might be different in smaller towns or larger cities.
• The study relies on self-reports of smoking behaviors.

Conclusion

The results of the study confirm the previous findings (Novak, et al., 2006) that the number of retail tobacco outlets in the area is positively related to the average frequency of youth smoking in the same area. The new finding, however, is that the importance of density is moderated by the local clean air policy. The density was less important for the cities with strong clean air policies. Therefore, the results suggest both the control over the number of tobacco outlets in the area and enforcement of the clean air policies as potential mediums for preventing youth smoking. 

- Julia Braverman

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References

Altman, D. G., & Jackson, C. (1998). Adolescent tobacco use and the social context. . In S. Schumaker, E. B. Schron, J. K. Ockene & W. L. McBee (Eds.), The handbook of health behavior change (pp. 305 - 329). New York: Springer.

Lipperman-Kreda, S., Grube, J. W., & Friend, K. B. (2012). Local tobacco policy and tobacco outlet density: associations with youth smoking. J Adolesc Health, 50(6), 547-552. doi: 10.1016/j.jadohealth.2011.08.015

Novak, S. P., Reardon, S. F., Raudenbush, S. W., & Buka, S. L. (2006). Retail tobacco outlet density and youth cigarette smoking: a propensity-modeling approach. Am J Public Health, 96(4), 670-676. doi: 10.2105/ajph.2004.061622

ASHES, Vol. 9(4): Curbing Your Craving? Comparing the mood-altering effects of regular and reduced-nicotine cigarettes

In the continuous battle against the negative effects of cigarettes, both on smokers and bystanders, science and technology have given us a number of alternatives to the classic nicotine cigarette, such as the nicotine free, electronic cigarette. However, it is unclear if these alternatives  successfully curb smoking cravings. This week’s ASHES reviews a study by Domino and colleagues (2013) that compared the effects of regular and reduced nicotine cigarettes on self-reported craving and release of striatal dopamine, a neurotransmitter involved in the brain’s reward pathway.

Methods

• The researchers initially recruited 24 male tobacco-dependent participants using newspaper ads and word-of-mouth. From those they excluded potential participants who met criteria for any other substance use disorders, leaving 22 participants.
• Under single-blind conditions and after overnight abstinence, participants smoked two reduced-nicotine (.08mg nicotine) followed two hours later by two average-nicotine (1.01mg nicotine) cigarettes.
• Researchers mapped the effects of smoking both kinds of cigarettes on the brain’s reward pathway using positron emission tomography (PET) scans.
• Participants rated their level of craving on a scale from 1 to 10 (i.e. “not at all” to “most ever”) before and after smoking each type of cigarette.

Results

• Both types of cigarettes activated the dopamine (DA) reward pathway in the brain.
  • Reduced-nicotine cigarettes released DA primarily in the right striatum while regular nicotine cigarettes released DA bi-laterally but more heavily in the left striatum.
• Despite this difference, as Figure 1 shows, smoking reduced-nicotine cigarettes reduced self-reported craving as much as smoking regular nicotine cigarettes.
  • This finding suggests that the DA reward pathway, originally thought to be activated in response to ingesting specific drugs, may, in fact, be a general addiction-related response triggered by more than the specific chemical in question.

Figure 1: Average craving scores as affected by reduced nicotine and regular nicotine cigarettes. Adapted from Domino et al, 2013

Note: *** p < .001

Limitations

• The sample size was small and exclusively male, limiting the power of the study and the generalizability of results.
• The researchers did not randomize the order of type of cigarette used, so satiation of the craving may have been because participants were smoking something after abstinence, even if it was reduced nicotine.
  • It is possible that the regular nicotine cigarettes would have reduced craving even more if they had come first.

Discussion

The results of this study support the idea that there is a non-pharmacological (non-nicotine) component to cigarette addiction. The effects on the dopamine reward system were originally thought to be associated with the nicotine released during smoking (as reviewed by Sharma & Brody, 2009). The current study suggests that smoking both regular cigarettes and reduced-nicotine cigarettes reduces self-reported craving and releases striatal dopamine. Further studies are necessary to determine whether these effects translate into smoking cessation and relapse prevention among those who turn to reduced-nicotine or non-nicotine alternatives in order to stop smoking. 

- Emily Shoov

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References

Domino, E. F., Ni, L., Domino, J. S., Yang, W., Evans, C., Guthrie, S., Wang, H., Koeppe, R. A., Zubieta, J.-K. (2013). Denicotinized Versus Average Nicotine Tobacco Cigarette Smoking Differentially Releases Striatal Dopamine. Nicotine & Tobacco Research, 15(1), 11–21.

Sharma, A., & Brody, A. L. (2009). In vivo brain imaging of human exposure to nicotine and tobacco. Nicotine psychopharmacology, handbook of experimental pharmacology, 192, 145-171.

ASHES, Vol. 9(3): Sad now? How about now? Influences of mood and motives on teens’ cigarette use

Previous studies have established pathways between depressive symptoms and regular cigarette use in adolescence (Brown, Lewinsohn, Seely & Wagner, 1996). This week’s ASHES reviews an investigation of the influence of negative mood, mood variability, and smoking motives on teenagers’ smoking behavior. The researchers tracked teens’ smoking behavior at baseline and 15 months later to see which mood and motivational factors predicted increases in cigarette use (Weinstein & Mermelstein, 2013).

Methods

• The researchers tracked 461 teens (55% female, M = 15.67 years old at baseline) from sixteen Chicago-area high schools who had either experimented with smoking in the past or who currently smoked cigarettes.
• The researchers used questionnaires and interviews to assess the following at baseline (Wave I), and again fifteen months later (Wave II):  
  • Mood-related smoking motives (e.g., “Smoking helps you when you’re feeling angry”);
  • Depressive symptoms (Center for Epidemiological Studies Depression Inventory [CES-D]; Radloff, 1977);
  • Past-month smoking frequency.
• In addition, at each wave the researchers asked the participants to carry handheld devices for a week. The devices issued prompts five times a day asking participants to report their current mood and activity, as well as every time they smoked or desired a cigarette. From these data, the researchers assessed the following for each participant:
  • Overall negative mood (calculated as the mean of negative mood scores reported across the week);
  • Negative mood variability (i.e., level of fluctuation, expressed as standard deviation, in reported negative mood over the course of the week).
• The researchers split the sample into distinct groups based on the change in past-month smoking frequency from Wave I to Wave II. The groups included as outcomes in their analyses were as follows:
  • Triers: smoked between 0-3 days in the past month at both waves: 27% of the sample
  • Experimenters: smoked between 0-3 days in the past month at Wave I and weekly at Wave II; 8% of the sample
  • Rapid escalators: smoked 0-5 days in the past month at Wave I and escalated to daily or near-daily use at Wave II; 8% of the sample
• The remaining 57% of the sample fell into other groups not addressed in the analyses we report.

Results

• Using multiple logistic regressions performed separately for boys and girls, researchers examined which baseline factors distinguished rapid escalators from triers/experimenters. The results are shown in Figure 1.
  • Girls with high negative mood variability were more likely to rapidly escalate their smoking behavior than to belong to the trier/experimenter trajectory, Odds Ratio (OR) = 2.61; 95% Confidence Interval (CI) [1.04–6.55]. For boys, mood variability was not related to outcome.
  • Overall negative mood did not predict rapid escalation for either boys or girls;
  • Girls reporting mood-related smoking motives were more likely to rapidly escalate their smoking behavior than to belong to the trier/experimenter trajectory, OR = 1.15; 95% CI [1.03-1.28].

Figure 1: Odds ratios and 95% CIs predicting membership in the rapid-escalation smoking group using baseline mood and motive indicators (adapted from Weinstein & Mermelstein, 2013)

Note. The bold line designates an odds ratio of 1, at which there is no distinguishable difference between groups.”

Limitations

• The study analyzed only baseline levels of mood and mood variability, and only two time points to establish smoking trajectories. This limits the ability of these analyses to detect the potentially complex and reciprocal relationships between mood and smoking behavior that take place across time.
• The study cannot establish a causal relationship between mood and smoking; a temporal relationship such as that demonstrated here is helpful in establishing causality, but several other pieces of evidence— such as strength and consistency of the relationship—are also necessary (Bradford-Hill, 1965).
• It is unclear whether the fluctuation in negative mood reported over the course of a week truly represents mood variability as the researchers conceive it. For example, high variability could indicate poor mood regulation, but could also indicate more conscientious and precise reporting.  

Discussion

The study suggests that mood variability is more strongly related to the smoking behavior of girls than boys. It is possible that girls are more likely than boys to use cigarettes to attempt to regulate their mood, which suggests avenues for smoking prevention (e.g., teaching girls better ways to regulate their moods). However, girls in this study also reported higher levels than boys of negative mood and (perhaps consequently) negative mood variability, suggesting that the gender differences might have been a function of better distributed measures among girls than boys. In either case, the findings are suggestive of the importance of associations between emotion (dys)regulation and smoking behavior.

- Kat Belkin

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References

Bradford-Hill, Sir Austin. (1965). The environment and disease: Association or causation? From Proceedings of the Royal Society of Medicine, published in Bulletin of the World Health Organization, 83(10).

Brown, R. A., Lewinsohn, P. M., Seeley, J. R., & Wagner, E. F. (1996). Cigarette smoking, major depression, and other psychiatric disorders among adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 35, 1602-1610.

Weinstein, S.M., Mermelstein, R.J. (2013). Influences of mood variability, negative moods, and depression on adolescent cigarette smoking. Psychology of Addictive Behaviors. Advance online publication, February 25.

ASHES, Vol. 9(2) - Where there’s smoke there’s fire: Scientific support for comprehensive smoke-free laws

The dangers of second-hand smoke are well established. To protect nonsmokers from exposure to second-hand smoke and to encourage smokers to quit, states and municipalities have implemented a variety of policies prohibiting smoking in public buildings and open spaces, businesses, and even apartment buildings. This week’s ASHES reviews a meta-analysis of studies examining trends in cardiovascular and respiratory disease risk before and after implementation of smoke-free laws (Tan & Glantz, 2012). 

Methods

• The research team used scientific databases, reference lists, and a network of tobacco control scientists to identify studies that examined the association between smoke-free laws and hospitalizations or deaths due to cardiovascular or respiratory disease.
  • The meta-analysis included a total of 43 studies that had sufficient data to calculate the relative risk and confidence interval before and after implementation of a smoke-free law.
• The researchers categorized smoke-free laws based on comprehensiveness (i.e., applying only to workplaces versus workplaces, restaurants, and bars).
• The researchers categorized study outcomes into four groups: coronary events; other heart disease; cerebrovascular accidents; and respiratory disease.
• Across studies, the median window of observation before smoke-free legislation was 29.5 months, and the median window of observation after smoke-free legislation was 24 months.
• The researchers performed a random-effects meta-analysis for each outcome group stratified by comprehensiveness of laws.

Results

• Researchers found that implementation of a smoke-free law was associated with lower rates of hospital admission or death for all four outcome groups
  • Coronary events (Relative Risk [RR] = 0.85; 95% Confidence Interval (CI) 0.82–0.88)
  • Other heart disease (RR = 0.61; 95% CI [0.44–0.85])
  • Cerebrovascular accidents (RR = 0.84; 95% CI [0.75– 0.94])
  • Respiratory disease (RR = 0.76; 95% CI [0.68– 0.85])
• As Figure 1 shows, more comprehensives laws produced greater reductions in risk for related health outcomes.

Figure 1. Relative risk of hospital admissions for disease categories after implementation of smoke-free law compared with before implementation. (Adapted from Tan and Glantz, 2012).

Note. RRs less than 1 indicate less population risk after implementation of a smoke-free law; across outcome groups, law comprehensiveness significantly predicted risk, p < .002.

Limitations

• Few of the included studies measured smoking status or exposure among individuals, making it difficult to understand the underlying mechanism that led to reductions in Relative Risk for the related diseases.
  • Potential mechanisms include smoking cessation, changes in social norms, reductions in exposure to secondhand smoke, and some combination of these.
• Publication bias—the tendency for studies with null effects not to be published— is a concern with all meta-analyses and might bias the results away from the null.
• Only a randomized, controlled trial can truly establish causation. The studies used in this meta-analysis used interrupted time series observational studies . These results should be interpreted in light of other pieces of evidence (e.g., known biological pathways by which tobacco smoke exposure causes disease) that, together, suggest a causal link between smoke-free laws and reduced risk of disease.

Conclusions

This study supports the findings of previous research and further clarifies the relationship between smoke free laws and related health outcomes. Specifically, in addition to associations between smoke-free laws and lower risk of related health outcomes, researchers also found a dose-response relationship between the comprehensiveness of the law and that risk. Interpreted in light of other pieces of evidence (e.g., known biological pathways by which tobacco smoke exposure causes disease) these findings suggest a causal link between smoke-free laws and reduced risk of disease and provide a strong evidence-base for policy decisions. 

-John Kleschinsky

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Refererences

Tan, C. E., & Glantz, S. A. (2012). Association between smoke-free legislation and hospitalizations for cardiac, cerebrovascular, and respiratory diseases. Circulation, 126(18), 2177-2183.

ASHES, Vol. 9(1) – Puff To Soothe Distress? Examining the relationship between smoking and PTSD symptom clusters

Posttraumatic Stress Disorder (PTSD) is a complex anxiety disorder that presents a wide variety of symptoms. Previous studies have found a positive relationship between PTSD and smoking (e. g. Feldner et al., 2007). This week’s ASHES reviews a study that delves deeper into that association, examining how specific PTSD symptom clusters relate to markers of smoking and nicotine dependence (Greenberg et al., 2012).

Methods

• Researchers used data from Waves I and II of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a US-representative general household survey that assessed criteria for mental health disorders (Wave I n=43,093, Wave II n=34,653, Grant, Hasin, Chou, Stinson, & Dawson, 2004).
  • The cohort for the present study (n=23,635) had complete PTSD and smoking data, and had experienced at least 1 traumatic event that resulted in feelings of fear, helplessness, or horror.
  • The primary instrument used in NESARC, the Alcohol Use Disorders and Associated Disabilities Interview Schedule-DSM-IV version (AUDADIS-IV, Grant et al., 2003), assessed PTSD, smoking behavior, and nicotine dependence.
    • Predictors: The researchers used factor analysis to categorize PTSD symptoms into four separate, but correlated, symptom clusters: Re-experiencing (RE), Avoidance (AV), Emotional Numbing (EN), and Hyperarousal (HA). In the analyses that follow, the predictors were symptom counts within each cluster
    • Outcomes: Lifetime smoking status (i.e., whether respondents had smoked over 100 cigarettes in their lifetime); Lifetime nicotine dependence (based on lifetime DSM-IV criteria) The researchers conducted logistic regression analyses predicting smoking outcomes from PTSD symptom clusters. Each regression model was adjusted for demographics, substance use disorder, mania, depression, and anxiety disorder.

Results

• All four PTSD symptom clusters, as well as PTSD diagnosis (i.e., whether the respondent qualified for PTSD) demonstrated significant bivariate associations (not shown) with smoking status and nicotine dependence.
• As Table 1 shows, in logistic regression models that included all four symptom clusters, only EN retained a significant relationship with smoking status, and only HA retained a significant relationship with nicotine dependence.

Table 1. Odds ratios (ORs) for unique associations between PTSD symptom clusters and smoking attributes (Adapted from Greenberg et al., 2012.)

PTSD Symptom Cluster             (count of symptoms in each cluster)	Smoking Status (OR) (N=23,653)	Nicotine Dependence (OR) (N=10,771)
Re-experiencing (RE)	1.12	1.11
Avoidance (AV)	1.14	1.11
Emotional Numbing (EN)	1.30*	1.08
Hyperarousal (HA)	0.98	1.51**

Note. The table includes the results of two logistic regressions, one for each smoking outcome. Both regressions controlled for demographics and other mental health disorders.  The sample size for the nicotine dependence model is smaller because only smokers were included. *p < .01; **p < .001

Limitations

• Researchers assessed only lifetime history of PTSD symptoms, smoking status, and nicotine dependence. This limits any temporal or causal inferences we can make about the associations between them.
• The NESARC relies on self-report, which can lead to reporting inaccuracies and biases.

Conclusions

This study finds that when the four different PTSD symptom clusters are entered into the models predicting smoking outcomes, only Emotional Numbing predicts smoking status and only Hyperarousal predicts nicotine dependence.  Future research will be needed to explore why individuals who exhibit emotional numbing might be more likely to smoke and why individuals showing signs of hyperarousal are more likely to be addicted to nicotine once they do smoke. Future research in this area might have some practical implications. First, such research might lead to better efforts to identify individuals at risk for smoking addiction through a detailed examination of PTSD symptomology. Additionally, personalized smoking cessation plans specific to an individual’s specific landscape of PTSD symptom expression could be effective tools for those planning to quit.

-Jed Jeng

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Citations

Feldner, M. T., Babson, K. A., Zvolensky, M. J., Vujanovic, A. A., Lewis, S. F., Gibson, L. E., . . . Bernstein, A. (2007). Posttraumatic stress symptoms and smoking to reduce negative affect: An investigation of trauma-exposed daily smokers. Addictive Behaviors, 32(2), 214-227.

Grant, B. F., Dawson, D. A., Stinson, F. S., Chou, S. P., Kay, W., & Pickering, R. (2003). The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in the general population sample. Drug Alcohol Depend, 71(1), 7-16.

Grant, B. F., Hasin, D., Chou, S. P., Stinson, F. S., & Dawson, D. A. (2004). Nicotine dependence and psychiatric disorders in the United States: Results from the national epidemiologic survey on alcoholl and related conditions. Archives of General Psychiatry, 61(11), 1107-1115.

Greenberg, J. B., Ameringer, K. J., Trujillo, M. A., Sun, P., Sussman, S., Brightman, M., . . . Leventhal, A. M. (2012). Associations between posttraumatic stress disorder symptom clusters and cigarette smoking. . Psychology of Addictive Behaviors, 26(1), 89-98. 

ASHES, Vol. 8 (11) – Anxious that you’re more at risk? Anxiety disorders moderate susceptibility to nicotine dependence

Many factors can influence how quickly a person transitions from the first cigarette to nicotine dependence, if the transition occurs at all. Anxiety disorders, in particular, might be involved in “telescoping” (i.e., transitioning relatively quickly from recreational use to dependence). A recent WAGER reviewed a study that showed similar telescoping effects of gender and anxiety on the development of pathological gambling. This week’s ASHES reviews a study investigating whether anxiety disorders accelerate the transition between the onset of smoking and onset of nicotine dependence (Kushner, Menary, Maurer, & Thuras, 2012). Kushner and colleagues predicted that individuals with an anxiety disorder would transition to nicotine dependence after smoking fewer total cigarettes than did individuals without an anxiety disorder.

Methods

• This study uses data collected as part of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; Grant, Stinson, Dawson, Chou, Dufour, Compton, …, Kaplan, 2004).
  • The study population consisted of the 18,103 NESARC participants who reported smoking at least 100 cigarettes during his or her lifetime.
  • As part of the NESARC, participants completed the Alcohol Use Disorder and Associated Disabilities Interview Schedule IV (AUDADIS), a structured interview assessing a variety of psychological disorders including anxiety disorders. They also reported on their smoking habits.
  • The authors estimated how many packs of cigarettes people smoked before becoming dependent on nicotine. To do this, they first determined the number of years between the reported (1) initiation of regular smoking[1] and (2) onset of nicotine dependence[2].  They multiplied the number of years by the amount of packs participants reported smoking per year.
  • Researchers then entered these data into a model to assess the risk of developing nicotine dependence per pack of cigarettes, and compared the risk between those with and without anxiety disorders. 
    • Researchers controlled for various covariates, including use of other tobacco products.

Results

• Compared with participants without anxiety disorders, participants with anxiety disorders smoked approximately 70 more packs per year.
• Participants with at least one anxiety disorder smoked fewer cigarettes before transitioning from regular smoking to nicotine dependence compared to those without anxiety disorders (Harm ratio = 1.73, 95% Confidence Interval (CI) [1.66, 1.80]).
• Participants who had more than one anxiety disorder (e.g., PTSD and generalized anxiety disorder) transitioned to nicotine dependence after smoking fewer cigarettes than both participants with only one anxiety disorder (HR = 1.80, 95% CI [1.71, 1.90]) and participants with no anxiety disorders (HR = 1.49, 95% CI [1.42, 1.56]).

Figure 1 – Survival function depicting development of nicotine dependence grouped by anxiety disorder status (reprinted with permission from Kushner, Menary, Maurer, & Thuras, 2012)

Limitations

• This study relies on retrospective data. The NESARC and AUDADIS require participants to report their behaviors in the past, and these reports might be inaccurate.
• Although the analysis controlled for several other tobacco products, it did not control for transdermal nicotine use (e.g., nicotine patches).
• The study relies on a single estimate of cigarettes smoked per year to determine the number of cigarettes smoked between the onset of regular smoking and nicotine dependence. This figure can change drastically according to habits and tolerance.

Discussion

The results of this study suggest that people with anxiety disorders transition from regular smoking to nicotine dependence after smoking fewer cigarettes than those without anxiety disorders; when participants had more than one anxiety disorder, this effect was bigger. Those with anxiety disorders might be more susceptible to developing dependence, and therefore each pack can be thought of as more ‘risky’ for those with anxiety disorders. The authors hypothesize that because anxiety disorders and nicotine abuse operate through similar pathways in the brain (Koob, 2003), those with anxiety disorders might be more vulnerable to developing nicotine dependence. This research may encourage doctors and psychologists to provide stronger nicotine warnings to their patients suffering from anxiety disorders.

- Daniel Tao

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References

Grant, B.F., Stinson, F.S., Dawson, D.A., Chou, S.P., Dufour, M.C., Compton, W., …, Kaplan, K. (2004). Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: Results from the National Epidemiological Survey on Alcohol and Related Conditions. Archives of General Psychiatry, 61, 807-816.

Koob, G.F., Le Moal, M. (2008). Addiction and the brain antireward system. Annual Review of Psychology, 59, 29-53.

Kusher, M.G., Menary, K.R., Maurer, E.W., Thuras, P. (2012). Greater elevation in risk for nicotine dependence per pack of cigarettes smoked among those with an anxiety disorder. Journal of Studies on Alcohol and Drugs, 73, 920-924.

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[1] First cigarette smoked was also used as an outcome measure, but will not be discussed in this review.

[2] The endpoint for participants that did not develop nicotine dependence was the date of the NESARC survey.

ASHES, Vol. 8(10) - What do Mom and Dad think? Perceptions of parental smoking attitudes and youth smoking intentions

Many people worry that the media negatively influences the youth of today. Movies and television frequently bombard us with content that includes violence, drug use, promiscuity, and other risky behaviors. Although to some, tobacco and cigarette smoking might seem like less dramatic issues, their frequent portrayal in TV shows and movies as, stylish, tough, sexy, and cool is worth attention. This week ASHES reviews a study by McCool, Cameron, and Robinson (2011)  that analyzes the association between adolescents’ perception of parental attitudes and the way they respond to themes and images of smoking/tobacco in the media.

Methods

• Researchers presented a group of 515 adolescents (ages 11-13) with 4 media clips (i.e., 2 segments from a drama series each featuring a female smoker (one young, one older), 1 anti-smoking commercial with a 20-30 year old female smoker, and 1 news segment featuring a smoking issue).
  • For each clip researchers asked participants to provide: (1) character appraisals of the smokers, such as (on a scale of 0 (not at all) to 4 (very much)) “stylish, tough, sad, smart, bored, sexy, stressed, healthy, weak, popular, hard, angry, intelligent, and cool”; and (2) empathy expressions, for example, “How much did you like the smoker in the clip?”
    • Researchers grouped the character appraisals into two categories: positive image, and negative emotion.
• The researchers also asked the adolescents to complete a computer-based questionnaire assessing their:
  • Level of media exposure;
  • Perceived parental attitudes toward smoking; and,
  • Smoking intentions.

Results

• Multivariate regression analyses determined that a variety of factors (i.e., media exposure, media environment, positive appraisals, empathy, and perceived parent anti-smoking attitudes) shared strong predictive relationships with youths’ smoking intentions.
• The results of a mediation analysis suggest a partial mediating relationship (see Figure 1). Specifically, participants who perceived their parents to have stronger anti-smoking attitudes held less positive appraisals of smokers represented by media, and those less positive appraisals predicted less intention to smoke.

Figure 1: Mediational relationship involving perceived parental smoking attitudes, youth positive appraisals of smokers in media, and youth smoking intentions

Limitations

• This study was conducted with 7th graders from a city in New Zealand, so results might not be generalizable to other groups.
• Responses were self-reported and primarily opinions and perceptions, which might not be reliable indicators of actual behavior.
• The focus of the study was on behavior intentions, not actual behavior.
• The study only used female smokers in clips, and youths’ perceptions of male smokers might be different/differently influential.

Discussion

This study illustrates the importance of parent’s expressed smoking expectations. The researchers concluded that parental influence over how adolescents perceive smoking imagery in the media might be strongest when expectations about tobacco use at home are unambiguous. Parents who express clear antismoking expectations might reduce smoking motivation by lessening the empathetic or positive appraisals of smokers. However, future research needs to assess both children’s and parent’s perceptions of parent’s expressed attitudes about smoking to investigate whether the relationships found in this study are limited to children’s perceptions of parental behavior.

-Emily Shoov

What do you think? Please use the comment link below to provide feedback on this article.

Reference

McCool, J., Cameron, L. D., & Robinson, E. (2011). Do parents have any influence over how young people appraise tobacco images in the media. Journal of Adolescent Health, 48(2), 170-175.

ASHES, Vol. 8(9) – Should you smoke that cigarette? The efficacy of question-based warning labels

In a previous issue of ASHES, we reported that graphical cigarette warning labels had little effect on smoking behavior. Recently, the D.C. District court of appeals upheld an earlier decision striking down these graphic warning labels (R.J. Reynolds Tobacco Co. v. FDA, No. 11-5332 (D.C. Cir. Aug. 24, 2012)). One major factor for the decision was the FDA’s inability to prove the efficacy of the graphical warning labels. In light of this decision, this week ASHES reviews a study investigating whether altering the style of warning messages improves their ability to alter smoking beliefs (Glock, Müller, & Ritter, 2012).

Glock et. al. postulate that defensive responses of smokers against confrontational text and graphical warning labels might mean that they will be less likely to change their smoking behaviors following exposure to such labels. They propose that by rewriting warning labels as questions, or omitting them altogether, smokers will create their own warning messages internally and feel less threatened by the message.

Methods

Researchers recruited 60 students from Saarland University who were daily smokers.

• The researchers divided the cohort into 4 equal groups and presented each group with one of four types of images on different sets of 15 cigarette packages.
  • Each cigarette package set included one specific type of warning label: standard textual warning labels; graphic warning labels; textual warning labels reworked into questions; or no warning labels at all.
• Participants completed a questionnaire assessing their smoking habits prior to and after viewing the cigarette packages.
  • Participants rated their perceived risk for six smoking-related diseases on a scale from 0 (no risk) to 9 (highest risk).
• The researchers conducted an a priori contrast comparing the question and control groups with the graphic and textual groups.

Results

• The researchers found that there was a significant difference between the Question/Control and Textual/Graphical warning label group, such that the Question/Control group reported higher perceived risk. (Figure 1) (F(1,55) = 4.32, p=0.04, np²=0.07)
• There was no significant difference between the textual and graphical warning label (t(28) = 0.61, d = 0.22, p = .54) or between the question warning label and control group (t(27) = 0.73, d = 0.27, p = .47).

Figure 1: Mean score and standard deviation of smoking related risk perception (adapted from Glock et al. 2012)

Limitations

• The small sample size limits the power of the study. (n=15 for each group)
• The cohort consists solely of college students, which limits the generalizability of the results.
  • Young smokers have not smoked as many years as older adults and might not be as worried about health consequences as they seem less relevant in the short term.
  • This population intrinsically has a higher level of education than young adults who have not attended college and might already be more informed about the risks of smoking.
• The novelty of a new warning label effect might be the cause of the difference in risk perception as current smokers might be accustomed to the present warning labels.
  • However, the authors note that the graphic warning label had yet to be introduced in Germany where the experiment took place.
• Smoker risk-perception does not necessarily result in a change in smoking behavior.

Discussion

Graphic warning labels have been scrutinized since their introduction and it is unclear how effective they are as tools for smoking cessation (Borland, Wilson, Fong et al., 2009; McCool, Webb, Cameron et al., 2012). Glock et al. show that smokers presented with graphical and textual warning labels have a lower smoking risk-perception compared with smokers in both the control group and question label group. Furthermore, they argue that although question type labels result in the same level of risk-perception as the control condition (and higher than textual/graphical), a warning label of some sort is still necessary to inform smokers. Additional research is required to determine the effectiveness of question based warning labels and from there we will be able to determine if they prove to be a viable alternative.

-Jed Jeng

What do you think? Please use the comment link below to provide feedback on this article.

References

Borland, R., Wilson, N., Fong, G. T., Hammond, D., Cummings, K. M., Yong, H.-H., et al. (2009). Impact of graphic and text warnings on cigarette packs: findings from four countries over five years. Tobacco Control, 18, 358-364.

Glock, S., Müller, B. C., & Ritter, S. M. (2012). Warning labels formulated as questions positively influence smoking-related risk perception. Journal of Health Psychology, Online publication.

McCool, J., Webb, L., Cameron, L. D., & JHoek, J. (2012). Graphic warning labels on plain cigarette packs: Will they make a difference to adolescents? Social Science & Medicine, 74(8), 1269-1273.

R.J. Reynolds Tobacco Co. v. U.S.F.D.A., No. 11-1482, 845 F.Supp.2d 266, 2012 WL 653828 (D.D.C. Feb. 29, 2012)

ASHES, Vol. 8(8) - Smoking rates and PTSD among a Freshman college population

Starting college is a stressful and exciting time, full of new responsibilities and new experiences. For many, this includes experimentation with cigarettes. Studies have shown that individuals who suffer from Post-Traumatic Stress Disorder (PTSD) are at higher risk for smoking. However, researchers have not studied the interaction between PTSD and smoking among college students during their first year at school. This issue of ASHES reviews an examination of how smoking rates among a freshman class population relate to experiences of PTSD symptoms (Read, Wardell, Vermont, Colder, Ouimette, & White, 2012).

Methods

Read et al. (2012) recruited 346 student smokers from two U.S. east coast universities.

• Each participant completed six surveys during the year: a baseline assessment during September, T1, and the remaining surveys once a month for the first semester and every other month during the second semester.
• For each assessment, the participants reported how often they had smoked during the previous 30 days and the typical quantity of cigarettes they smoked.

The researchers measured the presence of PTSD symptoms using the Traumatic Life Events Questionnaire (TLEQ©; Kubany et al., 2000)[1] and PTSD Checklist (Civilian Version; PCL–C; Weathers, Huska, & Keane, 1991; Weathers, Litz, Herman,Huska, & Keane, 1993)[2] at T1. Later, they analyzed the smoking trajectories of the students with respect to their PTSD “symptom status.”

• PTSD symptom status from the PTSD Checklist included: No PTSD Symptoms; Partial PTSD (students who met Criterion A and at least one above-threshold symptom in each B, C, and D); and Full PTSD (students who met Criterion A and one or more B symptoms, three or more C symptoms, and two or more D symptoms.)

The statistical analyses controlled for trait negative affectivity (i.e., a “unique influence” of PTSD), as indicated by the Neuroticism subscale of the Big Five Inventory (BFI; John & Srivastava, 1999)[3].

Results

• Students starting college who reported having undergone trauma or experiencing PTSD symptoms evidenced higher smoking rates initially (F(2.955)= 6.02, p<.01).
• During the first semester, smoking rates decreased for all PTSD status groups (See Figure 1); however, during the second semester (i.e., after winter break, which was predicted to be a significant stressor), those who had Full PTSD symptoms were at greater risk for a re-escalation of smoking compared to the No PTSD group (B=0.30, SE = 0.15, p=.039, 95% CI [.02, .59]).
  • The Partial PTSD and No PTSD group did not differ in their likelihood of re-escalation during the second semester (B=0.08, SE = 0.13, p=.520, 95% CI [0.17, 0.34).

Figure 1: Means for past 30 days of smoking across the first year of college for the three symptom statuses. 

 

Limitations

• This study did not take into account the possibility of new traumas or changes in PTSD symptoms during the course of the study.
• This study also failed to examine the initial reason for the students to start smoking to see whether a trauma or something unrelated was a catalyst.
• This experiment was conducted at 2 midsize public universities on the east coast (one in the north and one in the south); consequently, The results obtained from this sample might not be generalizable to other college students.
• There was no independent confirmation of smoking behaviors beyond self-report.
  • Self-reported data might exaggerate or minimize behaviors due to social desirability bias/stigmas.

Discussion

The results of this study suggest that individuals suffering from PTSD are more vulnerable to elevated smoking rates. Anxiety and stress are generally linked to smoking behaviors, even among those without a trauma history.  Therefore, it is not surprising that the compounded strain of managing PTSD symptoms and starting college increases the likelihood of smoking. Targeting these mediating and moderating influence could lead to the development of prevention interventions that can be applied to the population at large and not just those who have suffered trauma. The transition to college is a difficult and anxious time for many, so if schools could reach out to those using cigarettes to help with their psychological or emotional distress --regardless how “serious” this distress -- the potential benefits would be threefold:

1. Targeted intervention could pre-emptively help students matriculating with PTSD overcome the difficulties they are experiencing before they turn to cigarettes.
2. Early intervention would help to prevent “casual smoking” from becoming a serious habit.
3. Early intervention would help to prevent the adverse health consequences that are commonly associated with anxiety, stress, and trauma.

- Emily Shoov

What do you think? Please use the comment link below to provide feedback on this article.

References

John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. L. A. Pervin & O. P. John (Eds.), Handbook of personality (2nd ed., pp.102–138). New York, NY: Guilford.

Kubany, E. S., Haynes, S. Y., Leisen, M. B., Owens, J. A., Kaplan, A. S.,Watson, S. B., & Burns, K. (2000). Development and preliminary validation of a brief broad-spectrum measure of trauma exposure: TheTraumatic Life Events Questionnaire. Psychological Assessment, 12, 210–224. doi:10.1037/1040-3590.12.2.210

Read, J. P., Wardell, J. D., Vermont, L. N., Colder, C. R., Ouimette, P., & White, J. (2012, August 13). Transition and Change: Prospective Effects of Posttraumatic Stress on Smoking Trajectories in the First Year of College. Health Psychology. Advance online publication. doi: 10.1037/a0029085

Weathers, F. W., Huska, J. A., & Keane, T. M. (1991). The PTSD Checklist—Civilian Version (PCL–C). Available from F. W. Weathers,National Center for PTSD, Boston VAMC, 150 S. Huntington Ave., Boston, MA 02130.

Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., & Keane, T. M. (1993, October). The PTSD Checklist (PCL): Reliability, validity, and diagnostic utility. Paper presented at the meeting of the International Society for Traumatic Stress Studies, San Antonio, TX.

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[1] A self-report questionnaire that assesses a range of traumatic experiences consistent with the DSM–IV–TR definition: “exposure to a traumatic event accompanied by fear, helplessness, or horror.”

[2] Evaluates for the DSM criteria including Criterion A (DSM definition of Trauma) and three additional clusters of symptoms: B (re-experiencing), C (avoidance/numbing), and D (arousal).

[3] A self-report measure assessing 5 elements of personality: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism