The Brief Addiction Science Information Source (BASIS)

STASH, Vol. 6(1) – Skunked: Can high-potency cannabis use influence psychosis onset?

Research indicates that cannabis use is associated with increased risk of psychosis and that cannabis potency and patterns of use can influence this risk (Henquet, Murray, Linszen, & van Os, 2005; Moore et al., 2007; Murray, Morrison, Henquet, & Di Forti, 2007). During recent decades, the potency of cannabis being sold illicitly in the US has increased dramatically. The average percentage of delta-9 tetrahydrocannabinol (Δ-9 THC) identified within samples seized by law enforcement increased from approximately 2% in 1980 to 8.5% in 2008 (ElSohly, 2009).  This week’s STASH reviews a study by Di Forti et al. in which they investigated the correlation between the use of cannabis with higher levels of Δ -9 THC, such as skunk cannabis, and onset of psychosis (Di Forti et al., 2009).

Methods

• In this case-control study, researchers enrolled 280 cases (i.e., patients) aged 18-65 from 3 in-patient mental health units in London, UK with first hospital admission for psychosis.
• Researchers also enrolled 174 control cases from the general population within the catchments areas of the case-related hospitals via advertisements in newspapers, on the Internet, and recruitment at businesses.
• Potential controls completed the Psychosis Screening Questionnaire and were not enrolled in the study if they screened positive for psychosis or had a history of psychosis.    
• Study participants provided information about sociodemographic variables and drug use history.
• Participants reporting cannabis use completed the Cannabis Experience Questionnaire.    
• Researchers used logistic regression analyses to examine the relationships between case-control status and cannabis use characteristics.  

Results

• 57% (n = 159) of cases and 63% (n = 109) of controls reported having used cannabis at least once (OR = 0.8, 95% CI 0.6-1.5).
• Of those study participants reporting cannabis use
• 77% of cases and 33% of controls reported using cannabis daily (OR adjusted to account for potential confounders (i.e., age, gender, ethnicity, education, employment status) = 6.4, 95% CI 3.2-28.6).  See Figure 1.  
• 78% of cases reported preferential use of high-potency (Δ-9 THC levels 12-18%) “skunk” cannabis compared to 37% of controls (Adjusted OR = 6.8, 95% CI 2.6-25.4)
• When asked about duration of use, 59% of cases and 38% of controls reported using cannabis for more than 5 years (Adjusted OR = 2.1, 95% CI 0.9-8.4).  

Figure 1: Patterns of cannabis use among cases and controls (adapted from Di Forti et al., 2009)

 
* Statistically significant difference, P<0.05  

Limitations

• Non-random sampling strategies used to recruit controls and a lack of matching between cases and controls might have biased the findings of this study.
• Study relies on self-report of drug use history, including the type of cannabis used. 
• Given the retrospective nature of the study (e.g., asking questions about past cannabis use) it is impossible to establish a temporal, causal relationship between high potency cannabis use and development of psychosis. For example, people who have experienced psychotic episodes might prefer high potency cannabis – perhaps as a self-handicapping strategy.
• The authors collected data about patients’ use of other illicit drugs; however, they do not report it in the study.  It is possible that the use of other drugs might increase psychosis risk. But, this cannot be determined from the data presented.

Conclusion
Patients with a first psychotic episode onset were significantly more likely than healthy controls to use high-potency cannabis; they also were more likely to use cannabis more frequently than the controls. This finding supports the conclusion of the authors: high concentrations of Δ9-THC is an active contributor to psychosis among cannabis users.  Additional research is needed to address the limitations of this study, particularly to establish a temporal relationship between high potency cannabis exposure and onset of psychosis. As high-potency cannabis becomes more widely available to consumers, Di Forti et al. highlight the risks associated with high-potency cannabis for the public health.

-Erica Marshall

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References
Di Forti, M., Morgan, C., Dazzan, P., Pariante, C., Mondelli, V., Marques, T. R., et al. (2009). High-potency cannabis and the risk of psychosis. The British Journal of Psychiatry, 195(6), 488-491.

ElSohly, M. (2009). Quarterly Report Potency Monitoring Project: Report 104: National Institute on Drug Abuse.

Henquet, C., Murray, R., Linszen, D., & van Os, J. (2005). The environment and schizophrenia: the role of cannabis use. Schizophrenia Bulletin, 31(3), 608-612.

Moore, T. H., Zammit, S., Lingford-Hughes, A., Barnes, T. R., Jones, P. B., Burke, M., et al. (2007). Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review. Lancet, 370(9584), 319-328.

Murray, R. M., Morrison, P. D., Henquet, C., & Di Forti, M. (2007). Cannabis, the mind and society: the hash realities. Nature Reviews Neuroscience, 8(11), 885-895.

 

STASH, Vol. 5(10) – Can family interventions in early adolescence reduce teen illicit drug contact years later?

Exposure to, and use of, substances during early adolescence is associated with increased risk of escalating use, abuse, and dependence later in life (Agrawal et al., 2006; Grant & Dawson, 1997; Van Etten, Neumark, & Anthony, 1997).  Researchers have identified factors associated with reductions in opportunities to engage in substance use, such as parenting practices that emphasize adolescent monitoring and non-coercive discipline (Chen, Storr, & Anthony, 2005).  This week’s STASH reviews the findings of a family level intervention program focusing on adolescent contact with illicit drugs (Spoth, Guyll, & Shin, 2009).

Methods

• Researchers randomly assigned 22 rural Iowa schools to receive either the Iowa Strengthening Families Program (ISFP, N=11) or no intervention, with minimal researcher contact with participants (Control, N=11).
• •  Of the families eligible for participation in the study (i.e., they had children in the 6th grade), fifty three percent completed pre-testing (ISFP N = 238, Control N = 208).
  • Of those completing pre-testing in the ISFP group, 49% of families (N = 117) attended the ISFP intervention for an overall intervention response rate of 26%.
• The ISFP program provided families of sixth graders with seven evening prevention sessions including content presented to parents alone, adolescents alone, and both groups simultaneously.
• • Parents received information about parenting practices that have been shown to reduce adolescent risk of initiating drug use.
  •  Adolescents received information about peer interactions and social skill development.
• Students who completed the baseline test provided follow-up information about illicit drug exposure and drug use experience at the end of 6th, 7th, 8th, 10th, and 12th grade. As Table 1 shows, the ISFP group (N=238) included both students who attended the ISFP sessions (N=117) and those who only completed the pre-test (N=121).

Table 1: Assessment participation (adapted from Iowa State University Partnerships in Prevention Science Institute Participation figure)

 


Results

• ISFP participating adolescents were less likely to be exposed to illicit substance use than the control group adolescents, with statistically significant differences observed at the grade 8 (p=0.014) and grade 10 (p<0.001) interviews.
•  ISFP participating adolescents were about 2 times less likely than control group adolescents to have engaged in illicit substance use by grade 12 (15.5% of ISFP participants vs. 30.1% of Controls, OR=2.34; 95% CI = 1.34, 4.12; p = 0.002).

Figure 1: Illicit Substance Use Exposure (adapted from Spoth, Guyll, & Shin, 2009)

Limitations

• Researchers included all 238 pre-tested students in the ISFP group but provided no justification for expecting an intervention effect among the 121 students who did not receive the intervention.
• The recruitment (53%) and participation rates (49%) for the study are low and rate of participation at follow-up varies across study waves (Table 1).  It is possible that factors related to non-participation also might be related to substance exposure and use, which could confound the study results.
• The study population was predominantly white and from rural areas.  Consequently, these results might not generalize to urban/suburban or ethnically diverse populations.
• This study relies on self-report of illicit drug exposure and use.
• The illicit drug use assessment did not ask specifically about misuse of prescription drugs, but included the category of “other.”  The study results might underestimate the actual rates of prescription drug misuse.

Conclusion
The authors concluded that the results of this study indicate that an intervention targeting young adolescents and their parents provided a protective shield against illicit substance use initiation via reductions in exposure to illicit drugs. They attribute the protective effect to alterations in parental behaviors that limit adolescent exposure to illicit drugs. Further research will be needed to address the limitations of this study, particularly to elucidate the effects of combining ISFP participants and non-participants in the intervention group for analysis and to explore the potential impact of low rates of participation at baseline and varying follow-up rates. Until these limitations are addressed, it is difficult to judge from this study the potential of intervention techniques in early adolescence to reduce harm by effecting behavior long after program participation.
-Erica Marshall

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References

Agrawal, A., Grant, J. D., Waldron, M., Duncan, A. E., Scherrer, J. F., Lynskey, M. T., et al. (2006). Risk for initiation of substance use as a function of age of onset of cigarette, alcohol and cannabis use: findings in a Midwestern female twin cohort. Preventive Medicine, 43(2), 125-128.

Chen, C. Y., Storr, C. L., & Anthony, J. C. (2005). Influences of parenting practices on the risk of having a chance to try cannabis. Pediatrics, 115(6), 1631-1639.

Grant, B. F., & Dawson, D. A. (1997). Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse, 9, 103-110.

Spoth, R., Guyll, M., & Shin, C. (2009). Universal intervention as a protective shield against exposure to substance use: long-term outcomes and public health significance. American Journal of Public Health, 99(11), 2026-2033.

Van Etten, M. L., Neumark, Y. D., & Anthony, J. C. (1997). Initial opportunity to use marijuana and the transition to first use: United States, 1979-1994. Drug and Alcohol Dependence, 49(1), 1-7.

 

STASH, Vol. 5(9) – Can marijuana use protect adolescents from brain damage associated with binge drinking?

Studies link abnormalities in the white matter of the brain among alcohol-dependent adults with impairment of executive functioning, working memory, and attention (Chanraud et al., 2007; Pfefferbaum et al., 2000).  However, few studies have investigated white matter abnormalities in sub-clinical alcohol users, particularly adolescents.  The effect of binge drinking, which is common among adolescents, coupled with marijuana use also has been largely ignored.  This week’s STASH reviews an imaging study by Jacobus et al., which examined white matter abnormalities in adolescents with histories of sub-clinical binge drinking and binge drinking + marijuana use (Jacobus et al., 2009).

Methods

• Forty-two youths aged 16-19 years recruited from high schools and colleges were classified as controls (n=14), binge drinkers (n=14) or binge drinkers + marijuana users (n=14) using the Customary Drinking & Drug Use Record.
• • Researchers classified participants as binge drinkers if they reported at least 1 episode of at least 4 drinks for females and at least 5 drinks for males.
  • Researchers classified participants as binge drinkers + marijuana users if they met the criteria for binge drinkers and reported 180-1800 episodes of lifetime marijuana use.
• Using demographic variables, the study groups were well-matched, although binge drinkers + marijuana users had significantly more occasions of drinking than binge drinkers alone (p<0.05).
• Researchers performed diffusion tensor imaging (DTI) scans on study participants to assess the integrity of their white matter fiber clusters as measured by fractional anisotropy (FA) and mean diffusivity (MD).
• Lower FA values and higher MD values correlate positively with the abnormality of white matter structure.

Results

• Binge drinkers and binge drinkers + marijuana users had significantly lower FAs than controls.
• • Binge drinkers had significantly lower FAs than controls in all 8 fiber clusters under investigation (ps<0.016). 
  • Binge drinkers + marijuana users had significantly lower FAs than controls in 3 of 8 clusters (ps<0.01).
• Binge drinkers had significantly lower FAs than binge drinkers + marijuana users in 4 of 8 examined clusters (ps 0.014 to 0.043).
• There were no significant differences in MD between the study groups.

Figure 1: Between-group FA differences (from Jacobus et al.)
   
  

CON = Control BG = Binge drinking BG+MJ = Binge drinking + marijuana use
a = CON>BG; b = CON>BG+MJ; c = BG<BG+MJ; d = BG>BG+MJ, all p<0.05

Limitations

• Drug use history relies on self-reported data.
• Small samples, which can lead to selection bias.
• The study was not longitudinal in nature so causal relationships between drug and alcohol use and white matter abnormalities cannot be determined.
• Researchers did not investigate effect of heavy marijuana use without binge drinking on white matter fiber bundles.  It is possible that binge drinking + marijuana use is protective while either alone is more damaging.

Conclusion
The results indicate that binge drinking and binge drinking + marijuana are associated with white matter damage in adolescents.  From this result, the study authors conclude that marijuana use might have a protective effect against the damage caused by binge drinking.  Additional research that addresses the limitations of this study is needed to fully elucidate the relationship between binge drinking, marijuana use, and white matter abnormalities and to examine the potential effects of this damage on the cognitive functioning of adolescents.  Research about the broader impacts of white matter damage on adolescent health and development, such as the potential effects of white matter damage on behavior, is needed.

-- Erica Marshall

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References
Chanraud, S., Martelli, C., Delain, F., Kostogianni, N., Douaud, G., Aubin, H. J., et al. (2007). Brain morphometry and cognitive performance in detoxified alcohol-dependents with preserved psychosocial functioning. Neuropsychopharmacology, 32(2), 429-438.

Jacobus, J., McQueeny, T., Bava, S., Schweinsburg, B. C., Frank, L. R., Yang, T. T., et al. (2009). White matter integrity in adolescents with histories of marijuana use and binge drinking. Neurotoxicology Teratology, 31(6), 349-355.

Pfefferbaum, A., Sullivan, E. V., Hedehus, M., Adalsteinsson, E., Lim, K. O., & Moseley, M. (2000). In vivo detection and functional correlates of white matter microstructural disruption in chronic alcoholism. Alcoholism, Clinical and Experimental Research, 24(8), 1214-1221.

 

STASH, Vol. 5(8) – Straight dope: A randomized controlled trial of diacetylmorphine (heroin) treatment for opioid addiction

Opioid abuse affects more than 1 million North Americans and is a serious public health concern contributing to ill health and fatal overdose (United Nations Office on Drugs and Crime, 2008).  Although maintenance treatment with oral methadone is standard for opioid dependence, some opioid-dependent individuals do not respond well, have difficulty maintaining treatment, and/or continue to use illicit opioids while receiving methadone. This week’s STASH reviews the first North American randomized controlled trial of diacetylmorphine, the active component in heroin, versus methadone treatment for opioid dependence in individuals for whom standard treatments have been unsuccessful (Oviedo-Joekes et al., 2009).

Methods

• Researchers enrolled 251 opioid users in Montreal, Quebec and Vancouver, British Columbia, into an open-label, randomized controlled trial. Participants all had injected opioids daily for the past year and had at least two previous treatment attempts including one attempt with methadone treatment.
• Participants were randomized into three* groups for the 12-month study period.
• methadone (N=111): taken orally and dispensed once daily at clinic sites.
• diacetylmorphine (N=115): self-administered by injection up to three times daily by participants under medical supervision at study clinic sites.
• The primary outcomes of interest, assessed at 3, 6, 9, and 12 months were:
• Retention in treatment or drug-free status outside of the study context.
• Reduction in illicit drug use and/or other illegal activity assessed by the European Addiction Severity Index.

Results

• Figure 1 shows that treatment retention for participants receiving injectable diacetylmorphine was 87.8% compared to 51.4% of participants receiving oral methadone at 12-month follow-up, a significant difference (p < .0001).
• Figure 1 also shows that rates of illicit drug use and/or other illegal activities were reduced by 67.0% in the diacetylmorphine treatment group compared to 47.7% in the methadone treatment group (p < .01).
• The mean number of days of illicit heroin use per month was reduced from 26.6 to 5.3 in the diacetylmorphine group compared to a reduction from 27.4 to 12.0 days per month in the methadone treatment group (p < 0.001).

Figure 1 Primary Outcomes at 12 Months (adapted from Oviedo-Joekes et al., 2009).
  

Limitations

• The study relies on self-report data of illicit opioid use.
• Given the nature of the treatments under investigation, it is not possible to perform a double-blind study.
• Authors fail to acknowledge or control for the possible confounding of treatment outcome due to route of administration (i.e., injection use versus oral ingestion), social setting within which people use these drugs, with psychoactive effects of the drug.

Conclusion
This study shows that individuals treated for opioid dependence with injectable diacetylmorphine were more likely to remain in treatment and were less likely to engage in illicit drug use or other criminal behaviors compared to those treated with oral methadone.  The study authors conclude that for the subgroup of opioid-dependent individuals for which traditional (i.e., methadone) treatment options are not successful, clinically supervised treatment with diacetylmorphine appears to be a safe and effective treatment option. This interpretation must be tempered, however, because the researchers failed to account for the potential confounding effect of route of administration (i.e., oral vs. injected) on the study outcomes. It is likely that injection heroin users would prefer injection to another treatment (i.e., oral methadone) that requires significant behavioral change and places them on a different physiological and therefore psychosocial schedule. Perhaps diacetylmorphine administration is more appropriately understood as a harm-reduction strategy similar to needle exchange programs.  Further research is needed to assess the impact of diacetylmorphine administration on the lives of opioid dependent individuals. 

-Erica Marshall

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References

Haasen, C., Verthein, U., Degkwitz, P., Berger, J., Krausz, M., & Naber, D. (2007). Heroin-assisted treatment for opioid dependence: randomised controlled trial. Br J Psychiatry, 191, 55-62.

Oviedo-Joekes, E., Brissette, S., Marsh, D. C., Lauzon, P., Guh, D., Anis, A., et al. (2009). Diacetylmorphine versus methadone for the treatment of opioid addiction. N Engl J Med, 361(8), 777-786.

United Nations Office on Drugs and Crime. (2008). World drug report. Vienna.

van den Brink, W., Hendriks, V. M., Blanken, P., Koeter, M. W., van Zwieten, B. J., & van Ree, J. M. (2003). Medical prescription of heroin to treatment resistant heroin addicts: two randomised controlled trials. Bmj, 327(7410), 310.

* Twenty-five study participants were assigned to receive a treatment option not addressed in this review.

STASH, Vol. 5(7) – Schizophrenia and substance abuse: The risk for violence

Research indicates that people diagnosed with schizophrenia are at an increased risk of committing a violent act compared to those who do not suffer from this mental disorder (Joyal, Dubreucq, Grendon, & Millaud, 2007). What remains uncertain is the degree to which schizophrenic people are at risk for violence (Mullen, Burgess, Wallace, Palmer, & Ruschena, 2000; Rasanen et al., 1998; Steadman et al., 1998). This week’s STASH reviews a longitudinal study that examined how substance abuse affects risk for violent behavior among people with schizophrenia (Fazel, Langstrom, Hjern, Grann, & Lichtenstein, 2009).  

Methods
Study Groups

• 8,003 people from the nationwide Swedish registries (1973 through 2006) diagnosed with schizophrenia on at least two separate occasions (to minimize false-positive diagnoses) and who were at least 15 years old at baseline.
• Two control groups: 1) for each schizophrenic, a matched (by year of birth and gender) random selection of 10 members from the general population (n = 80,025); 2) the unaffected siblings of the 4,674 schizophrenics who had unaffected siblings (n = 7,780).

Data

• Data obtained from nationwide Swedish registries (1973 through 2006) included sociodemographic information, hospital admissions, substance abuse (i.e., diagnosis of alcohol abuse/dependence and drug abuse/dependence at hospital admission), and violent crime (e.g., homicide, sexual offense (e.g., rape, child molestation), illegal threats, and intimidation).

Statistical Analysis

• The researchers used conditional logistic regression analyses to estimate risk for violence related to substance use among patients compared to the two control groups.

Results

• Among the schizophrenics, substance abuse significantly exacerbated the risk for violence from 8.5% to 27.6%.
• Schizophrenics with substance abuse comorbidity were at a significantly higher risk for violence than the matched general population controls: 27.6% compared to 6.1%, respectively.
• Schizophrenics with substance abuse comorbidity also were more likely to commit a violent crime compared to their unaffected siblings: 28.3% compared to 17.9%, respectively.
• Table 1 displays the adjusted odds ratios of committing at least one violent crime to show that compared to the general population, but not unaffected siblings, the risk of violence by schizophrenics with substance abuse was much more than the risk of violence by schizophrenics without substance abuse.

Table 1: Adjusted Odds Ratios of Violent Crime among Schizophrenic Patients and the Controls with and without Comorbid Substance Abuse (adapted from Fazel et al., 2009).

* The comparison between patients and matched public population control group was adjusted by income (lowest vs. middle and highest tertiles), marital status (single vs. not single) and immigration status (individual or at least 1 parent born outside of Sweden). The sibling group was not matched but the comparisons were adjusted by year of birth, sex, income, and marital status.

Limitations

• This sample came from Swedish data and therefore, the results may not generalize to all populations.
• The study relied on hospital data to determine schizophrenia diagnosis and comorbid substance abuse. Because two schizophrenic diagnoses were needed for participant inclusion, some schizophrenic patients might not have been included. The diagnostic validity of substance abuse diagnoses from the Swedish Hospital Discharge Registry has been shown to be in fair agreement with more comprehensive inpatient assessments (Bergman, Belfrage, & Grann, 1999). However, many instances of substance abuse do not result in hospitalizations.
• The researchers did not have data available regarding any type of treatment that participants received. Treatment that includes the use of antipsychotic drugs has been shown to reduce the risk of violence among people with schizophrenia (Swanson et al., 2008).

Conclusion

This study shows that substance abuse is a potential moderator for violent behavior among people with schizophrenia.  However, the data also suggests the importance of considering family-related factors because the difference between comorbid schizophrenics and their unaffected siblings is small compared to the difference for the general population control group. More research is needed to examine why schizophrenia and substance abuse comorbidity are related to a higher risk of violent behavior.

References

Bergman, B., Belfrage, H., & Grann, M. (1999). Mentally disordered offenders in Sweden: Forensic and general psychiatric diagnoses. American Journal of Forensic Psychiatry, 20, 27-37.

Fazel, S., Langstrom, N., Hjern, A., Grann, M., & Lichtenstein, P. (2009). Schizophrenia, substance abuse, and violent crime. JAMA, 301(19), 2016-2023.

Joyal, C., Dubreucq, J. L., Grendon, C., & Millaud, F. (2007). Major mental disorders and violence: A critical update. Current Psychiatry Reviews, 3, 33-50.

Mullen, P. E., Burgess, P., Wallace, C., Palmer, S., & Ruschena, D. (2000). Community care and criminal offending in schizophrenia. Lancet, 355(9204), 614-617.

Rasanen, P., Tiihonen, J., Isohanni, M., Rantakallio, P., Lehtonen, J., & Moring, J. (1998). Schizophrenia, alcohol abuse, and violent behavior: A 26-year followup study of an unselected birth cohort. Schizophrenia Bulletin, 24(3), 437-441.

Steadman, H. J., Mulvey, E. P., Monahan, J., Robbins, P. C., Appelbaum, P. S., Grisso, T., et al. (1998). Violence by people discharged from acute psychiatric inpatient facilities and by others in the same neighborhoods. Archives of General Psychiatry, 55(5), 393-401.

Swanson, J. W., Swartz, M. S., Van Dorn, R. A., Volavka, J., Monahan, J., Stroup, T. S., et al. (2008). Comparison of antipsychotic medication effects on reducing violence in people with schizophrenia. British Journal of Psychiatry, 193(1), 37-43.

STASH, Vol. 5(6) –Self-treatment versus recreation as motivations for prescription drug misuse

Rates of prescription drug misuse in the United States have risen significantly during recent years, with one study reporting an increase of 2.5 million prescription drug users between 2002 and 2007 (SAMHSA, 2007; McCabe, West, & Wechsler, 2007).  Prescription drug misuse is of particular concern among adolescents and young adults who have shown higher rates of misuse than other age groups (Kroutil et al., 2006; McCabe, Cranford, & Boyd, 2006).  For example, a study by Kroutil et al. found that 18-25 year olds were 4 times as likely to misuse prescription stimulants than those 26 years old and older (2006).  This week, STASH reviews a study by McCabe, Boyd, & Teter (2009) that attempts to identify subtypes of prescription drug misusers as a step toward selecting more appropriate and, hopefully, more successful, interventions.

Methods

• Researchers mailed an invitation to participate in the study to a randomly selected sample of 5,389 undergraduate students at a large Midwestern university. Roughly two-thirds (n = 3.639, 68%) completed the self-administered web-based survey.
• The survey instrument included questions about
  • prescription drug misuse history
    • drug classes (i.e., pain medication, stimulant medication, sleeping medication, and sedative/anxiety medication)
    • motivations (i.e., self-treatment motivations, recreational motivations, and mixed motivations (reporting both self-treatment and recreational motivations))
    • routes of administration (i.e., inhaling, injecting, snorting, smoking, or ingesting orally)
    • co-ingestion with alcohol
  • drug and alcohol use history, and
  • screens for drug and alcohol abuse (CAGE and DAST-10)

Results

• The prevalence of lifetime prescription drug misuse was 19.7% for all medication classes.
  • Researchers classified 39.1% of misusers into the self-treatment subtype, 12.6% into the recreational subtype, and 48.3% into the mixed subtype (i.e., researchers did not classify drug misuse as a single type).
  • Pain medication had the highest prevalence rates of lifetime misuse for both self-treatment (5.9%) and recreational (3.3%) subtype among the four medication classes studied.
  • Self-treatment misusers were the least prevalent subtype of sedative/anxiety medication misusers (0.6%), and recreational misusers were the least prevalent of sleeping medication misusers (0.5%).
• The results included significant differences in prescription drug misuse by gender and race.
  • Men were more likely than women (3.5% vs. 1.6%) to belong to the recreational subtype while women were more likely than men (9.1% vs. 6.0%) to belong to the self-treatment subtype (p<0.001 based on Chi-square tests). 
  • White (3.0%) and Hispanic (2.7%) participants had the highest prevalence rates of recreational misuse compared to Black (1.0%) and Asian (0.7%) participants (p<0.001 based on Chi-square tests).
• As Table 1 shows, across all four measures, recreational misusers were at significantly greater risk of recent binge drinking, past year illicit drug use, alcohol and drug abuse.

Table 1:  Substance use and substance related problems based on lifetime nonmedical   prescription drug misuse subtypes (adapted from McCabe et al., 2009).

   *Click image to enlarge, or adjust your browser's zoom setting.
Limitations

• Study participants were all students from one large university in the Midwest so results might not generalize to a young adult population. 
• The study relies on self-report data.
• It is possible that self-selection bias was a factor in this study because the response rate of those approached to participate in the study was 68%.
• Researchers classified misuse subtypes based on life-time prescription drug use while other high risk behaviors were assessed more recently (i.e., past two weeks, past year).  It is possible that this time frame discrepancy could lead to different results and conclusions if the time frame affected the way study participants reported the behaviors under investigation.
  

Conclusion
This study indicates that there is heterogeneity among prescription drug misusers and that members of the recreational subtype are more likely to engage in high risk alcohol and drug use behavior than those in the self-medication subtype.  Identifying relationships between prescription misuser subtype and other drug and/or alcohol use and abuse illuminates potential pathways for prevention and intervention.  This study also found significant racial and gender differences in motivation for drug misuse. Males, White and Hispanic participants were more likely to be in the recreational misuse subtype.  Further research is needed to understand the role of these subtypes in prescription drug misuse, and to fully understand the interplay of subtype and other risky drug and alcohol use behaviors.

-Erica Marshall

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

References

Substance Abuse & Mental Health Services Administration (SAMHSA). (2007). Results from the 2006 National Survey on Drug Use and Health. Rockville, MD: Office of Applied Studies.

Kroutil, L. A., Van Brunt, D. L., Herman-Stahl, M. A., Heller, D. C., Bray, R. M., & Penne, M. A. (2006). Nonmedical use of prescription stimulants in the United States. Drug and Alcohol Dependence, 84(2), 135-143.

McCabe, S. E., Boyd, C. J., & Teter, C. J. (2009). Subtypes of nonmedical prescription drug misuse. Drug Alcohol Depend, 102(1-3), 63-70.

McCabe, S. E., Cranford, J. A., & Boyd, C. J. (2006). The relationship between past-year drinking behaviors and nonmedical use of prescription drugs: prevalence of co-occurrence in a national sample. Drug and Alcohol Dependence, 84(3), 281-288.

McCabe, S. E., West, B. T., & Wechsler, H. (2007). Trends and college-level characteristics associated with the non-medical use of prescription drugs among US college students from 1993 to 2001. Addiction, 102(3), 455-465.

 

STASH, Vol. 5(5) – Substance use and abuse among lesbian, gay, and bisexual youths: The role of rejection to disclosure

Youths who self-identify as lesbian, gay, or bisexual (LGB) are significantly more likely than other young people to report using alcohol, tobacco, and other drugs (Marshal et al., 2008). Recent research has focused on risk or protective factors that influence substance use among this population. One proposed risk factor is rejection from one’s friends and family members following the disclosure of sexual orientation (i.e., “coming out”). Rejection following disclosure might produce feelings of shame and social isolation, which might contribute to increased substance use. This week’s STASH reviews a recent study of the relationship between reactions to disclosure of sexual orientation and substance use among LGB youths (Rosario, Schrimshaw, & Hunter, 2009)

Participants
•    Participants were an ethnically diverse convenience sample of 156 14-21 year-olds (mean age = 18.3, SD = 1.65) recruited from LGB community-based and college student organizations in New York City.

•    Roughly 66% self-identified as lesbian/gay, 31% identified as bisexual, and 3% identified as “other” (e.g., “confused”).

Procedures
•    Participants completed a 2- to 3-hour structured interview at recruitment (Time 1), with follow-up interviews occurring 6 and 12 months later (Times 2 and 3, respectively).

•    The retention rates were 92% for the 6-month assessment and 90% for the 12-month assessment.

•    Attrition analyses revealed that participants lost to one or more follow-up assessments did not differ at baseline from those interviewed at all three assessments on any demographic factors or Time 1 variables.

•    Participants were paid $30 at each interview.

Primary Measures
•    Participants listed the “important people” to whom they first disclosed their sexual orientation and then indicated whether each person reacted in an accepting, neutral, or rejecting manner. Therefore, there were four disclosure measures:  the total number of people, and the number of people who had an accepting, neutral, or rejecting response.

•    Substance use (quantity and frequency of cigarette, alcohol, and marijuana use) was assessed at Times 1-3 using the Alcohol and Drugs Schedule (see Rosario, Schrimshaw, & Hunter, 2009). The Time 1 assessment measured substance use in the past 3 months; the Times 2 and 3 assessments measured substance use in the past 6 months. Investigators assessed substance abuse using 11 items derived from the Diagnostic Interview Schedule for Children (e.g., “Felt you needed or were dependent on alcohol and/or drugs”). The index of substance abuse was the number of endorsed items. 

Results
•    On average, participants first became aware of their sexual orientation at age 10 and first disclosed their sexual orientation at age 15. The majority (57.1%) first disclosed to a friend. Approximately 30% first disclosed to a family member. The typical participant’s first disclosure happened roughly 3 years before Time 1.

•    Correlations and linear regressions controlling for gender, age, and social desirability revealed that, of the four disclosure measures, only the number of rejecting reactions was directly associated with substance use or abuse. More rejecting reactions predicted greater substance use at Times 1 and 2 (beta’s ranged from 0.13-0.28) and more substance abuse symptoms at Time 2 (beta = 0.44) and Time 3 (beta = 0.20). 

•    Researchers used linear regression to assess whether the number of accepting reactions moderated the relationship between the number of rejecting reactions and substance use. The interaction term (number of accepting reactions x number of rejecting reactions) significantly added to the prediction of four measures of alcohol use. Inspection of the regression slopes revealed that when participants had fewer accepting reactions, alcohol frequency and quantity increased as the number of rejecting reactions increased. However, when participants had a high number of accepting reactions, participants reported a relatively low/moderate level of alcohol use, regardless of the number of rejecting reactions they experienced. Figure 1, adapted from Rosario et al., 2009, shows the interaction between accepting and rejecting reactions predicting frequency of alcohol use at Time 1. The pattern was similar for other alcohol measures.

Limitations
•    The sample was composed of individuals living in an urban environment who were members of LGB organizations. Therefore, this sample might differ from the wider population of LGB youths in some important ways.

•    Participants provided descriptions of their disclosure retrospectively.

•    Participants varied in the length of time between disclosure and Time 1; it is unclear whether this variable influenced the relationship between reactions to disclosure and substance use/abuse.

•    Because these data were correlational, it is premature to conclude that reactions to disclosure causally influenced substance use and abuse.

Conclusions
•    Despite the limitations noted above, these data suggest that one risk factor for substance use and abuse among LGB youths is having more friends, family members, and other people react to disclosure in a rejecting manner.
•    Accepting reactions to disclosure are protective and blunt the impact of rejection on substance use and abuse.

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

References
Marshal, M. P., Friedman, M. S., Stall, R., King, K. M., Miles, J., Gold, M. A., et al. (2008). Sexual orientation and adolescent substance use: A meta-analysis and methodological review. Addiction, 103(4), 546-556.

Rosario, M., Schrimshaw, E. W., & Hunter, J. (2009). Disclosure of sexual orientation and subsequent substance use and abuse among lesbian, gay, and bisexual youths: Critical role of disclosure reactions. Psychology of Addictive Behaviors, 23(1), 175-184.

STASH, Vol. 5(4) – Risk factors for substance use among women who were teenage mothers

According to the recent Center for Disease Control’s National Vital Statistics Reports, after years of decline, the rates of teenage pregnancy in the U.S. are rising to 41.9 births per 1000 females (Center for Disease Control and Prevention, 2009). This week the STASH reviews research by De Genna, Cornelius, and Donovan (2009) who conducted a 10-year follow-up examining the risk factors for substance use among women who were teenage mothers.

Method

• Pregnant teenagers (n=445, mean age 16.32 ± 1.89, range 12-18 years) were recruited from a Pittsburgh, PA prenatal clinic and interviewed about past year substance use related experiences at three different time points:
  • (1) during pregnancy at recruiting;
  • (2) 6 years after recruitment;
  • (3) 10 years after recruitment.
• 292 participants provided complete data (68% follow-up rate)
• Data collection:
  • Demographic information: race, education, family income.
  • Substance use information (cigarette, alcohol, marijuana, cocaine, and other drug): age at initiation of substance use, peer substance use, frequency, and quantity of substance use.
  • Physical and mental health information: depression, aggression, and hostility.

Results

• Researchers did not observe any difference between the original recruited cohort and the follow-up cohort with respect to demographic information, substance use information, and physical and mental health information.
• As Table 1 shows, teenage mothers in this study cohort remained at higher risk for substance use compared to other young women or adults in the state of Pennsylvania (Pennsylvania Department of Health, 2004)

Table 1. Tobacco use and binge-drinking prevalence among different populations.

• Investigators used logistic regression models to identify factors that influence tobacco use, binge-drinking, and marijuana use, among teenage mothers at follow-up (all p < 0.05).

Figure 1. Factors associated with tobacco and marijuana use at both 6-year and 10-year follow-up.

• As Figure 1 shows, for tobacco use at 6-year follow-up, women who engaged in binge-drinking at 6-year follow-up were more likely to smoke.
• Tobacco use before pregnancy was a significant predictor of tobacco use at 10-year follow-up, while concurrent marijuana use and higher levels of hostility were associated with higher tobacco use at 10-year follow-up.
• For marijuana use at 6-year follow-up, tobacco use pre teen pregnancy and higher levels of adolescent anxiety/depression are significantly predictors of marijuana use, and higher levels of hostility at follow-up were associated with higher marijuana use.
• Race is a significant predictor for marijuana use at 10-year follow-up, indicating that Black mothers were more likely to use marijuana than white mothers; further, use of tobacco and hostility were associated with marijuana use during the 10-year follow-up.

Limitations

• We might not be able to generalize the results of this investigation to teenage mothers from other parts of the country, or to those from other socioeconomic or different racial/ethnic backgrounds.
• This study relied on self-report of substance use, with no biochemical verification.
• This study lacks comparison groups. For example, un-pregnant female teenagers who use substances and were matched for age, socioeconomic, and other demographic features.

Conclusions

• The investigators found that higher levels of hostility were associated with both tobacco and marijuana use at follow-up; this indicates that psychological issues might be associated with substance use.
• The investigators also identified that other substance use was associated with tobacco use, binge-drinking, and marijuana use among teenager mothers.

-Evelyn Chao

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

References
Center for Disease Control and Prevention. (2009). Births: Final Data for 2006. National Vital Statistics Reports, 57(7).

De Genna, N. M., Cornelius, M. D., & Donovan, J. E. (2009). Risk factors for young adult substance use among women who were teenage mothers. Addictive Behaviors, 34(5), 463-470.

Pennsylvania Department of Health. (2004). Behavioral Health Risks of Pennsylvania Adults. http://www.dsf.health.state.pa.us/health/cwp/view.asp?a=175&Q=243554

STASH, Vol. 5(3) - Gender Disparities: What Traumatic Events Place Young Adults at Risk for Substance Use Disorders?

Recent research indicates that a number of risk factors are linked to the development of Substance Use Disorders (SUDs) (Kilpatrick et al., 2000); these risks include traumatic events (Breslau, Davis, & Schultz, 2003; Simpson & Miller, 2002). Most of the research suggests that traumatic event(s) increase the risk for SUDs for young women, but not for young men (Becker & Grilo, 2006; Breslau et al., 2003; Lipschitz, Grilo, Fehon, McGlashan, & Southwick, 2000). However, this week’s STASH reviews a study of  young adults (Danielson et al., 2009) that reports an association between traumatic events and SUDs among both young men and women, but the events differed across gender. 

Methods:

• Participants were 43.5% (N=1753) of those who took part in the 7-8 year follow-up survey to the original National Survey of Adolescents (Kilpatrick et al., 2000).
  • Investigators administered telephone interviews to participants between 18 and 26 years old (mean age = 22.12) who were residing in the United States and selected by using a random digit dialing procedure.  
  • 881 (50.2%) of the sample were male and 872 (49.8%) were female. 
• The interview assessed alcohol and drug abuse, Post-traumatic Stress Disorder (PTSD), and the following traumatic events: sexual assault (SA), physical assault (PA) including severe physical punishment, and witnessed violence (WV).
  • Interview questions that assessed PTSD in the past six months and past year substance abuse used DSM-IV criteria. 
  • Close-ended questions provided measures of SA, PA and WV. Introductory narratives, including a rationale and a behavioral description, illustrated each traumatic event.  

Results:

• Table 1 shows the results for logistic regression analyses used to predict past year alcohol (N = 442, 25.2%) and drug abuse (N=197, 11.2%).
  • Gender was a significant predictor for developing both alcohol and drug abuse (e.g., OR = 2.02 for men vs. female for alcohol abuse, and OR = 4.23 for men vs. female for drug abuse, both p-values < 0.001).  
• Table 2 shows the gender related results for logistic regression analyses to determine whether traumatic events predicted alcohol and drug abuse for each gender separately. 
  • In contrast to the research currently available, traumatic events significantly increase the risk for alcohol and drug abuse in young men as well as in young women. 
  • The results show that young men and young women differ, however, by which traumatic events increase the risk for alcohol and drug abuse. 
  • For young women: SA increases alcohol and drug abuse, WV and PTSD increase alcohol abuse only.  
  • For young men: PA and PTSD increase both alcohol and drug abuse, WV increases alcohol abuse only, and SA was associated with a decrease for alcohol abuse.  

Table 1- Logistic Regression Analyses, Predictions of Past Year Alcohol and Drug Abuse (Adapted from Danielson et al., 2009).

* Click image to enlarge, or adjust your browser's zoom setting.

Table 2- Logistic Regression Analyses, Predictions of Past Year Alcohol and Drug Use by Gender (Adapted from Danielson et al., 2009).

* Click image to enlarge, or adjust your browser's zoom setting.

Limitations:

• The study uses self report.
• The data was collected from telephone interviews; therefore, participation was limited to young adults who reside in a home with a telephone. The results, therefore, might not generalize to other population segments.
• Significant sample attrition occurred between the original 1995 NSA study and this study. Again, this raises questions about the extent to which we can generalize the results.

Discussion:

The results of this study indicate that specific traumatic events increase the likelihood of developing SUDs among males and females. Perhaps this risk disparity between genders emerges from which traumatic events a male or a female is more likely to experience. Some sources suggest that young women are more likely to report experiencing SA (Rentoul & Appleboom, 1997) and young men are more likely to experience PA and/or WV (Office of Justice Programs, 2006). Prevention programs could benefit by shifting their effort to focus on the different traumatic risk factors associated with the development of SUDs among young men and women (e.g., sexually abused women’s programs should focus more on alcohol and drug abuse risk reduction than other possible risks).

-Tasha Chandler

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

References:

Becker, D. F., & Grilo, C. M. (2006). Prediction of drug and alcohol abuse in hospitalized adolescents: Comparisons by gender and substance type. Behaviour Research and Therapy, 44(10), 1431-1440.

Breslau, N., Davis, G. C., & Schultz, L. R. (2003). Posttraumatic stress disorder and the incidence of nicotine, alcohol, and other drug disorders in persons who have experienced trauma. Archives of General Psychiatry, 60, 289-294.

Danielson, C. K., Amstadter, A. B., Dangelmaier, R. E., Resnick, H. S., Saunders, B. E., & Kilpatrick, D. G. (2009). Trauma-related risk factors for substance abuse among male versus female young adults. Addictive Behaviors, 34(4), 395-399.

Kilpatrick, D. G., Acierno, R., Saunders, B., Resnick, H. S., Best, C. L., & Schnurr, P. P. (2000). Risk factors for adolescent substance abuse and dependence: Data from a national sample. Journal of Consulting and Clinical Psychology, 68(1), 19-30.

Lipschitz, D. S., Grilo, C. M., Fehon, D., McGlashan, T. M., & Southwick, S. M. (2000). Gender differences in the associations between posttraumatic stress symptoms and problematic substance use in psychiatric inpatient adolescents. Journal of Nervous and Mental Disease, 188(6), 349-356.

Office of Justice Programs, B. o. J. S. (2006). Victim characteristics.   Retrieved March 31, 2009, from http://www.ojp.usdoj.gov/bjs/cvict_v.htm#gender

Rentoul, L., & Appleboom, N. (1997). Understanding the psychological impact of rape and serious sexual assault of men: a literature review. Journal of Psychiatric and Mental Health Nursing, 4(4), 267-274.

Simpson, T. L., & Miller, W. R. (2002). Concomitance between childhood sexual and physical abuse and substance use problems. A review. Clinical Psychology Review, 22(1), 27-77.

STASH, Vol. 5(2): What were you thinking?

Researchers have used ecological momentary assessment (EMA) devices to collect data about the real time activities, moods, and environments of research participants. This data allows investigators to gain a better understanding of the events that precede episodes of drug use. Participants respond to random prompts programmed to appear on the EMA screens at specific times and also initiate their own entries. This week, we review a study about the use of EMA devices to monitor the changes in activity and mood immediately preceding craving for, and use of, cocaine and heroin. In this study, researchers were able to monitor changes in mood and setting by requesting that participants enter information into the EMA devices at different pre-set time intervals as well as at other random intervals. Because there were few heroin-only related events, we limit our report to the cocaine findings (Epstein et al., 2009).

Methods

• Investigators recruited 130 cocaine- and heroin-using outpatients from a methadone treatment research clinic.
  • Inclusion criteria: (1) age between 18 and 65 years old; (2) evidence of physical dependence on opiates; (3) evidence of cocaine and opiate use.
  • Exclusion criteria: (1) DSM-IV psychotic disorders; (2) current dependence on alcohol or any sedative hypnotic drug; (3) cognitive impairment precluding informed consent; (4) medical illness compromising study participation
• Participants enrolled in a program that included: 
  • Daily methadone injections (target dose 100mg/day), 7 days/week for up to 20 weeks
  • Urine drug tests 3 times/week
  • Abstinence reinforcement in the form of vouchers for weeks 7-18
• At the end of week 3, participants in the program received EMA configured PDAs 
  • Random prompts were timed to occur during participants’ expected waking hours: 5 random prompts per day for 5 weeks, then 2 random prompts per day for 20 weeks
  • Participants also could initiate their own entry when they craved and/or used heroin and/or cocaine
  • At each random prompt or participant initiated entry, participants reported where they were, what they were doing, with whom, and answered questions about their mood
    • Randomly prompted entries assessed craving and lapse triggers
    • Each question began “Within the past hour I felt …”, ended with triggers statements (listed below), and was answered with yes or no
    • Triggers items: “Saw Drug”, “Was Offered Drug”, “Wanted to See What Would Happen”, “Tempted Out of the Blue”, “Handled $10 in Cash”, “Felt Worried”, “Felt Angry”, “Felt Worried”, “Felt Sad”, “Felt Criticized by Others”, “Was in a Good Mood”, and “Felt Uncomfortable”
• Researchers analyzed the randomly prompted entries of 102 participants (those who provided enough data) at each of the 5 hours preceding each event-contingent entry.  Repeated-measures logistic regressions, with hours before use as the within-subject independent variable, determined whether the patterns of reported triggers changed significantly over time. Investigators analyzed the craving and use events separately.

 

Results

• Overall, participants carried PDAs for a total of 14,918 personal-days (mean: 130.9 days per participant) and answered 78.4% of randomly issued prompts (26,969 out of 34,400) and initiated 2,500 event entries (mean: 21.0 per participant). Participants responded to 1,555 randomly prompted entries during the 5 hours before episodes of craving or/and use.
• All triggers were significantly associated with cocaine use (all p < 0.05), but only “Saw Drug”, “Felt Worried”, and “Felt Criticized by Others” were significantly associated with cocaine craving (all p < 0.05).
• A comparison of positive responses to the following putative triggers showed a significant difference between cocaine use and craving (see Figures 1 and 2). “Wanted to See What Would Happen”, “Tempted Out of the Blue”, “Felt Sad”, and “Was in a Good Mood” produced large linear cocaine use increases (all p < 0.001), but not for cocaine craving.

Figure 1: Percent endorsing triggers before cocaine craving event by hours before CRAVING (adapted from Epstein et al., 2009) (all P < 0.001).

Figure 2: Percent endorsing triggers before cocaine use event by hours before USE (adapted from Epstein et al., 2009) (not significant at α=0.05 level).

Limitations
o Researchers did not examine the effects of environment, acquaintances, and activities.
o It’s possible that random prompts from EMA devices had damaging effects.
o The participants, methadone patients, are not representative of most drug users.

Conclusions
The use of ecological momentary assessment (EMA) technology presents possibilities for both research and clinical work. Epstein et al.’s study shows that drug abusers can be trusted with electronic devices (only one PDA was lost or damaged for every 226 person-days of use) and will use them as instructed. EMA device use allows researchers to use the recorded data to better understand the acute daily triggers that lead to drug use and craving. These EMA devices have the potential to change our understanding of pre-use and pre-craving events and lead to the development of better relapse prevention and treatment methods that can target specific precursors to individual drug use. Furthermore, it is possible to use these devices in clinical settings, so the treatment methods can be tailored to patients’ day-to-day lives by analyzing the data from EMA device use over a shorter period of time.

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

References
Epstein, D. H., Willner-Reid, J., Vahabzadeh, M., Mezghanni, M., Lin, J. L., & Preston, K. L. (2009). Real-time electronic diary reports of cue exposure and mood in the hours before cocaine and heroin craving and use. Arch Gen Psychiatry, 66(1), 88-94.