The Brief Addiction Science Information Source (BASIS)

The WAGER, Vol. 13(2) - Chickens, Eggs, and Psychiatric Comorbidity Among PGs

People with gambling-related problems also are likely to qualify for other psychiatric disorders  (McIntyre et al., 2007; Shaffer et al., 2007). Understanding the temporal sequence of PG and comorbid disorders can provide information about how PG relates to other disorders and suggests causal links. Unfortunately, there is little research that clarifies how the onset of PG relates temporally to the onset of other disorders. This week’s WAGER reviews a large epidemiological study by Kessler, Hwang, LaBrie, Petukhova, Sampson, Winters & Shaffer (2008), which investigated comorbid disorders among people with PG. In addition, this study examined the temporal onset of PG and the onset of other disorders.

The National Comorbidity Survey Replication (NSC-R), a nationally representative sample of 9,282 English speaking adults (Kessler & Merikangas, 2004), used the Composite International Diagnostic Interview (CIDI; Kessler & Ustun, 2004) to assess DSM-IV criteria (American Psychiatric Association, 1994) for Axis I disorders, and age of onset (AOO; i.e., the age at which people first reported the first symptom of a given disorder) for those with disorders. 

Table 1.  Lifetime psychiatric comorbidity among participants with lifetime PG (adapted from Kessler et al., 2008)

* Prevalence significantly greater among PGs compared to the rest of the sample (p < .05).

Note: Any mood disorder = major depressive disorder or dysthymia and bipolar disorder.  Any anxiety disorder = phobias, generalized anxiety, panic, and post-traumatic stress disorder.  Any impulse control disorder = oppositional-defiant, conduct, attention deficit hyper activity, and intermittent explosive disorders.   Any substance use disorder = alcohol or drug abuse, dependence, and nicotine dependence.

The lifetime prevalence of pathological gambling (PG) within the sample was 0.6%.  Almost all participants who had lifetime PG also had another lifetime disorder (96.3%) and 64.3% suffered from three or more disorders.  Table 1 shows that mood disorders, anxiety disorders, and substance use disorders were significantly elevated among participants with PG, and that other disorders were more likely to precede PG than to occur afterward or begin at the same time.  In fact, 74.3% of participants with PG and another disorder experienced the other disorder before PG.

These results provide important information about the temporal relationships among disorders. However, this study cannot conclusively determine whether any disorder caused PG, or PG caused another disorder. Furthermore, the data in this study derived from retrospective self-report; consequently, AOO might not be accurate because of recall errors.  However, the results suggest that PG rarely exists alone. This study adds important new data about typical pattern of onset.  The study also illuminates the need for mental health and medical professionals to assess PG along with the variety of other disorders with which it is often comorbid.

What do you think?  Comments can be addressed to Leslie Bosworth.

References

American Psychiatric Association. (1994). DSM-IV: Diagnostic and statistical manual of mental disorders (Fourth ed.). Washington, D.C.: American Psychiatric Association.

Kessler, R. C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (in press). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine [preprint available]

Kessler, R. C., & Merikangas, K. R. (2004). The National Comorbidity Survey Replication (NCS-R): Background and aims. International Journal of Methods in Psychiatric Research, 13(2), 60-68.

Kessler, R. C., & Ustun, T. B. (2004). The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13(2), 93-121.

McIntyre, R. S., McElroy, S. L., Konarski, J. Z., Soczynska, J. K., Wilkins, K., & Kennedy, S. H. (2007). Problem gambling in bipolar disorder: Results from the Canadian Community Health Survey. Journal of Affective Disorders, 102(1), 27-34.

Shaffer, H. J., Nelson, S. E., LaPlante, D. A., LaBrie, R. A., Albanese, M., & Caro, G. (2007). The epidemiology of psychiatric disorders among repeat DUI offenders accepting a treatment sentencing option Journal of Consulting and Clinical Psychology, 75(5), 795-804.

STASH Vol. 4(2) - College students going green: Can college students use cannabis responsibly?

Despite the illegality of cannabis in the United States, between 30 and 40 percent of college students report having used cannabis during the past year (Eaton et al., 2006; Johnston, O'Malley, Bachman, & Schulenberg, 2007; Mohler-Kuo, Lee, & Wechsler, 2003). There is little evidence about the prevalence of Cannabis Use Disorder (American Psychiatric Association, 1994) among college students. This week’s STASH examines research conducted by Caldeira, Arria, O'Grady, Vincent, & Wish  (2008) examining rates of Cannabis Use Disorder and cannabis-related problems among first-year college students.

Researchers administered a brief survey to 3,401 incoming first-year students (89% of the freshman class) at a large public university in the U.S. mid-Atlantic region. Using a purposive sampling strategy ¹, researchers over-sampled experienced drug users and selected 1,457 students to participate in a longitudinal study beginning with a two-hour face-to-face interview ². Researchers collected responses for 1,253 students in the longitudinal cohort (86%). Students answered questions about their illicit drug use (e.g. lifetime, past-year, and past month), problems related to drug use, and past-month use of alcohol and tobacco. 

Table 1: Cannabis Use Disorder and other problems among first year college students and past year cannabis users (adapted from Caldeira et al. 08)


Table 1 shows the results for three groups: weighted prevalence estimates for the entire first-year class, past-year cannabis users, and “at-risk” cannabis users (i.e., students who used cannabis at least five times in the last calendar year). Among all past-year cannabis users (N = 739), approximately 25% of students met the criteria for Cannabis Use Disorder. Ten percent (10.1%) met the criteria for dependence by endorsing three or more of the six DSM-IV dependence criteria, and 14.5% met the criteria for abuse by not qualifying for a dependence diagnosis and endorsing one or more of the abuse criteria. Sixty four percent (64.1%) of cannabis users consumed cannabis five or more times in the past year. Among students who used cannabis five or more times in the past-year the most common problems associated with past 12-month cannabis use were: concentration problems 40.1%, driving a car while high 18.6%, and oversleeping and missing class 13.9%.

Several factors limit the study findings. The research data is based on self-report and subject to the inaccuracies associated with that data source. The researchers did not measure Cannabis Use Disorder among students who used less than 5 times in the past 12-months. Instead, researchers automatically coded them for the absence of the DSM-IV criteria for Cannabis Use Disorder. In addition, researchers only asked “at-risk” students about cannabis-related problems. Students who used cannabis 4 or fewer times might have experienced some cannabis related problems. The survey instrument only had six questions about Cannabis-related problems. It is possible that individuals experienced problems not addressed in the survey. Leading to an under reporting of the number of students that experienced cannabis-related problems.

The study’s findings, weighted to represent the entire first-year class, indicate that 17% of the class used cannabis in the past year without problems, and 9% satisfied the DSM-IV criteria for abuse or dependence. Among past-year cannabis users, 75%, of the students did so without experiencing the DSM-IV criteria for abuse. This might be an over estimation because students who used cannabis four or fewer times, representing 36% of cannabis users, in the past year were not asked the DSM-IV Cannabis Use Disorder criteria. Although the majority of students report controlled cannabis use, some of these students did incur problems related to their use. The question the researchers did not ask was: why were 75% of students able to use cannabis in a controlled manner, while 25% were not? A long-term follow-up is needed in the area of Cannabis Use Disorder to determine if the results presented here are typical of college students. Substance use researchers also might consider examining the potential factors that allow most cannabis using students to do so in a controlled fashion, while others are not able to.

What do you think? Comments can be addressed to John H. Kleschinsky.

Note

1. A procedure in which researchers over sample strata of interest.

2. The research presented in this review is part of the College Life Study, a five-year prospective study under the supervision of Amelia Arria at the Center for Substance Abuse Research (CESAR), the University of Maryland. The BASIS will continue to follow and report study findings as they become available through peer-reviewed journals.

References

American Psychiatric Association. (1994). DSM-IV: Diagnostic and statistical manual of mental disorders (Fourth ed.). Washington, D.C.: American Psychiatric Association.

Caldeira, K. M., Arria, A. M., O'Grady, K. E., Vincent, K. B., & Wish, E. D. (2008). The occurrence of cannabis use disorders and other cannabis-related problems among first-year college students. Addictive Behaviors, 33, 397 - 411.

Eaton, D. K., Kann, L., Kinchen, S., Ross, J., Hawkins, J., Harris, W. A., et al. (2006). Youth risk behavior surveillance--United States, 2005. MMWR Surveill Summ, 55(5), 1-108.

Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2007). Monitoring the Future national survey results on drug use: Volume II, college students and adults ages 19 - 45 (NIH Publication No. 07-6206). Retrieved 2/11/08. from http://www.eric.ed.gov/ERICWebPortal/custom/portlets/recordDetails/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED498426&ERICExtSearch_SearchType_0=no&accno=ED498426.

Mohler-Kuo, M., Lee, J. E., & Wechsler, H. (2003). Trends in marijuana and other illicit drug use among college students: results from 4 Harvard School of Public Health College Alcohol Study surveys: 1993-2001. Journal of American College Health, 52(1), 17-24.

Addiction & The Humanities Vol. 4(2) - Live Fast, Die Young? Maybe Not.

Media reports and speculation associate celebrity status with early death. Supporting this relationship, qualitative research has concluded that celebrities are more likely to suffer from stress, substance use, and depression, which are risk factors for early death (Patalano, 2000; Raeburn, 1999). This week’s BASIS explores quantitative research about this topic. In “Elvis to Eminem,” Mark Bellis and his colleagues hypothesized that “popstars” would have a higher mortality rate than individuals from matching demographics (e.g., age, sex, and race) in the general population (Bellis et al., 2007).

Bellis et al. focused on European and North American music stars within six genres: rock, punk, rap, R&B (rhythm and blues), electronica, and new age. The researchers calculated celebrities’ total years of life since becoming famous and compared the result to the UK and US general populations’ expected rates of survival. They standardized the general population survival rates for age, sex, and for North American analyses, ethnicity. Bellis et al. found a significant difference between US and UK music stars’ survival rate after 25 years of fame. For European music stars, the relative survival probability increases until the difference between it and the general population’s survival rate is not significant. As shown in Figure 1, for North American “popstars,” the relative survival rate continues to deviate from that of their European counterparts and from that of their matched populations.

Figure 1: Comparative survival curves for North American and European pop stars and demographically matched general populations (Bellis et al., 2007).

The study of the 1,064 music stars also found that an overdose or a chronic drug or alcohol problem caused more than a quarter of music star deaths; however, the crude mortality (i.e., death rate for the entire population including all sexes, ages, and causes) of European music stars was only half of that for North American stars.

The findings of Bellis et al were limited by their operational definition of stardom (i.e., choice of an international poll from the year 2000 that determined the All-Time 1000 Albums). Another limitation was their definition of date of fame, which was not based on any epidemiological standards, but on the artists’ earliest date of chart success, which resulted in slightly more conservative results. A third limitation was their failure to use official cause of death records in their research, rather than a range of non-traditional sources (i.e., cross- referenced over 430 websites, books, etc.). Lastly, the use of US and UK life tables as substitutes for North American and European measures also limited the study.

Bellis et al propose greater collaboration between the health and music industries to help assure that pop star influence leads to better health among fans and rising stars that look to them as leaders. However, it may be more interesting to propose that the music industry provide newest music stars with preventative tools, treatment, and resources to prevent unhealthy lifestyles. Moreover, the same research conclusions and suggestion could be applied to other non-music celebrities. The nature of stress that celebrities face in combination with the easily accessible substances suggest that a certain amount of preparation for this change in lifestyle is necessary to successfully navigate the celebrity world and avoid widely publicized addiction or premature death.

What do you think? Comments can be addressed to Ingrid Maurice.

References

Bellis, M. A., Hennell, T., Lushey, C., Hughes, K., Tocque, K., & Ashtan, J. R. (2007). Elvis to Eminem: quantifying the price of fame through early mortality of European and North American rock and pop stars. Journal of Epidemiology & Community Health, 61(10), 896-901.

Patalano, F. (2000). Psychological stressors and the short life spans of legendary jazz musicians. Perceptual & Motor Skills, 90(2), 435-436.

Raeburn, S. (1999). Psychological issues and treatment strategies in popular musicians. A review, part 1. Medical Problems of Performing Artists, 4(1), 171-179.

ASHES Vol. 4(2) - Can You Teach an Old Dog New Tricks? Smoking Cessation Interventions among Older Adults

Smoking cessation interventions vary in effectiveness (West, McNeill, & Raw, 2000) and scant research has examined how age impacts effectiveness.  Most participants in smoking cessation studies are relatively young (Connolly, 2000).  Perhaps investigators assume that older adults would not be interested in, or benefit from, smoking treatment, or that it would be easier to enroll younger adults in research.  However, there is evidence that older adults are as responsive to cessation programs as younger adults (Glynn, 1988; Morgan et al., 1996).  In this week’s ASHES, we examine recent research on a smoking cessation intervention among older adults.

Tait, Hulse, Waterreus, Flicker, Lautenshlager, Jamrozik, and Almeida (2007) recruited 215 current smokers, aged 68 years or older, from a community study of older men (Norman et al., 2004).  This sample hailed from Perth, Western Australia and surrounding communities. The volunteers responded to media advertisements soliciting participants.  Fifty participants (23%) reported that they did not want to quit smoking (i.e., continuing smokers) and 165 participants (77%) reported that they were interested in quitting (i.e., non-randomized intervention group).  All participants provided demographic information, detailed smoking histories, and responded to the Fagerström Test of Nicotine Dependence (FTND).  Investigators provided participants in the intervention group counseling, personalized educational materials, information about local services that help people to quit, and if requested, nicotine replacement therapy (NRT) patches.  Trained staff members also called participants in the intervention group six different times during the study to offer support and guidance with weight management and coping strategies.  Nearly all participants returned six months later (88% follow-up rate) for interviews; during this interview, they exhaled carbon monoxide (ECO) for a confirmation assessment.

Follow-up interviews indicated that 88.5% of the intervention group had attempted to quit smoking during the previous six months; at follow-up, 29% of the intervention group had remained abstinent for the 30 days prior to the follow-up, and an additional 20% of the group reported abstinence during the entire six-month period.  None of the continuing smokers attempted to quit.  In fact, Figure 1 shows, participants in the intervention group significantly decreased the median number of cigarettes smoked per day (Z=6.30, p<0.001), whereas the median number of daily cigarettes among continuing smokers remained the same. 

Figure 1:  Median daily cigarettes among continuing smokers and the intervention group   

This study has limitations. These results might not generalize to other older adults.  Participants self-selected either the control group or intervention group.  Therefore, the cessation rates among the intervention group, a group of adults electing to quit smoking, might be higher than that of older adults in general.  Moreover, the effectiveness of the specific intervention used in this study cannot be assessed; the success of the intervention group cannot be compared to that of the control group since the control participants did not share a fundamental characteristic with the intervention participants (i.e., expressing interest in quitting smoking).

This study’s findings indicate that some older adults are both interested in and capable of quitting smoking.  Although this conclusion is simple, its implications are substantial.  Firstly, the success of these older adults should influence future examinations of smoking cessation interventions (e.g., the populations targeted).  Developing a greater understanding of older adults’ motivations to quit smoking could help engage a greater percentage of older adults in smoking cessation interventions.  Secondly, the significant responses to this intervention should encourage physicians to provide advice about smoking cessation to all patients, no matter how old. 

What do you think?  Comments can be addressed to Sara Kaplan.

References

Connolly, M. J. (2000). Smoking cessation in old age:  Closing the stable door? Thorax, 29, 193-195.
Glynn, T. J. (1988). Relative effectiveness of physician-initiated smoking cessation programs. Cancer Bulletin 40, 359-364.

Morgan, G. D., Noll, E. L., Orleans, C. T., Rimer, B. K., Amfoh, K., & Bonney, G. (1996). Reaching midlife and older smokers:  Tailored interventions for routine medical care. Preventative Medicine 25, 346-354.

Norman, P. E., Jamrozik, K., Lawrence-Brown, M. M., Le, M. T., Spencer, C. A., Tuohy, R. J., et al. (2004). Population based randomized controlled trial on impact of screening on mortality from abdominal aortic aneurysm. British Medical Journal 329, 1259-1262

Tait, R. J., Hulse, G. K., Waterreus, A., Flicker, L., Lautenshlager, N. T., Jamrozik, K., et al. (2007). Effectiveness of a smoking cessation intervention in older adults. Addiction 102, 148-155.

West, R., McNeill, A., & Raw, M. (2000). Smoking cessation guidelines for health professionals:  An update. Thorax, 55, 987-999.