The Brief Addiction Science Information Source (BASIS)

STASH, Vol. 9(6) - Stages of non-medical prescription opioid use and suicidal ideation and attempts

2013-05-22T12:17:28-04:00

Non-medical prescription opioid use (i.e., use of prescription opioid painkillers without a valid medical reason or prescription) is a public health concern in the United States, in part because it is linked with anxiety, depression, and other harmful consequences (Becker, Sullivan, Tetrault, Desai, Fiellin, 2008). Both anxiety and depression are associated with increased risk of suicide (Goldney, Wilson, Dal Grande, Fisher, McFarlane, 2000), and there is some indication that non-medical prescription opioid use is associated with increased risk for suicidal behavior. This week’s STASH reviews a study that adds to our understanding of opioid misuse and suicide by exploring whether people in various stages of non-medical opioid use (e.g., persistent use versus recent-onset use) have different risk of suicidal ideation or attempt (Kuramoto, Chilcoat, Ko, Martins, 2012).

Methods

This study used data collected as part of the 2009 National Survey on Drug Use and Health (NSDUH; United States Department of Health and Human Services, 2009).
- The NSDUH includes questions that assess past-year suicidal ideation, suicide attempt, and non-medical prescription opioid use.
The sample consisted of the 37,933 participants who completed the survey aged 18 or older.
Researchers separated users into four categories in terms of their non-medical prescription opioid use history.
- Persistent users initiated non-medical use more than two years ago and used within the past year.
- Former users initiated non-medical use more than two years ago but did not use within the past year.
- Recent-onset users initiated non-medical use within the last two years regardless of past-year use.
- Non-users did not report any non-medical prescription opioid use.
Researchers used multiple regressions to determine whether former users, persistent users, or recent onset users were at greater risk for suicidal ideation or suicide attempts compared to non-users.
- Researchers controlled for demographics, other drug use history, and history of major depressive episodes.

Results

Within the sample, 15% reported using non-medical prescription opioid drugs during their lifetime. Five percent reported past-year use.
Both former users and persistent users were at significantly elevated risk of past-year suicidal ideation than non-users. Recent onset users were at elevated risk, but the difference was not statistically significant. See Table 1.
No groups had significantly elevated risk for suicide attempts.

Table 1: Adjusted odds ratios and 95% confidence intervals for the four groups on suicidal ideation.

Non-users

(n = 30,465; 80.3%)

Former Users

(n = 3,769; 9%)

Persistent Users

(n = 2,031; 4%)

Recent Onset Users

(n = 1,668; 2%)

Suicidal Ideation

N/A

(Comparison group)

1.42*

[1.11, 1.81]

1.52*

[1.10, 2.10]

1.27

[0.94, 1.73]

Suicide Attempt

N/A

(Comparison group)

0.78

[0.41, 1.49]

1.73

[0.97, 3.09]

1.52

[0.78, 2.96]

Note: * indicates p < .05
Numbers indicate odds ratios after correcting for demographics, other drug use, and major depressive episode. For example, we can conclude that persistent users were one and a half times more likely than non-users to report suicidal ideation, as the odds ratio is 1.52.

Limitations

The study relies entirely on self-report data.
The NSDUH is cross-sectional by nature. This limits our ability to draw conclusions on causality. It also prevents us from drawing conclusions on the time course of this relationship.
The survey did not collect data on the frequency and dose of opioid use, nor did it allow participants to indicate why they were using the medication. This precludes controlling for factors like chronic pain, which can also influence suicidal ideation.

Conclusions

According to this study, both former and persistent non-medical prescription opioid users show elevated experiences of suicidal ideation, but not suicide attempts. For people who misuse prescription opioids and think about suicide, other factors like chronic pain and depression might play important roles. For example, in one person, chronic pain might cause both depression and opioid use, and depression might cause suicidal thoughts. In another person, depression might cause thoughts about suicide and attempts to self-medicate psychological pain through opioid use. Many different causal chains are possible, and each might require a different kind of treatment plan. This study suggests that clinicians and others who come into regular contact with people who misuse prescription painkillers should provide continued mental health monitoring and support is important, even if a person stops using the opioids.

- Daniel Tao

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References

Becker, W.C., Sullivan, L.E., Tetrault, J.M., Desai, R.A., Fiellin, D.A. (2008). Non-medical use, abuse, and dependence on prescription opioids among U.S. adults: Psychiatric, medical, and substance use correlates. Drug and Alcohol Dependence, 94, 38-47.

Goldney, R.D., Wilson, D., Dal Grande, E., Fisher, L.J., McFarlane, A.C. (2000). Suicidal ideation in a random community sample: Attributable risk due to depression and psychosocial and traumatic events. Australian and New Zealand Journal of Psychiatry, 34, 98-106.

Kuramoto, S.J., Chilcoat, H.D., Ko, J., Martins S.S. (2012). Suicidal ideation and suicide attempt across stages of nonmedical prescription opioid use and presence of prescription opioid disorders among U.S. adults. Journal of Studies on Alcohol and Drugs, 73, 178-184.

United States Department of Health and Human Services. Substance Abuse and Mental Health Services Administration. Office of Applied Studies. National Survey on Drug Use and Health, 2009. ICPSR29621-v3. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2012-11-16. doi:10.3886/ICPSR29621.v3

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

2013-05-15T12:00:00-04:00

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

The DRAM, Vol. 9(5) - A DUI but no diagnosis? How DSM-5 might impact the identification of Alcohol Use Disorders

2013-05-08T16:40:40-04:00

Imagine a man who drinks in dangerous situations, such as when driving, but doesn’t meet any other criteria for alcohol use disorders. According to the DSM-IV, this man is experiencing alcohol abuse. The DSM-IV separates alcohol use disorders (AUDs) into alcohol abuse, characterized by serious negative consequences due to drinking, and alcohol dependence, characterized by physical symptoms related to drinking (APA, 1994); it also requires only one criterion for a diagnosis of alcohol abuse. However, under the new DSM-5 guidelines scheduled to be released this month, our hypothetical man’s diagnostic situation would change. The DSM-5 does not make separate diagnoses for alcohol abuse and dependence but makes a single diagnosis of AUD along a severity continuum and requires a minimum of two criteria. In addition, the DSM-5 substitutes a current criterion related to legal consequences of alcohol for a new criterion related to alcohol craving. This week the DRAM reviews a study that investigated how these potential changes will affect diagnosis and prevalence of AUDs (Agrawal, Heath & Lynsky, 2011).

Methods

Researchers used Wave II data from 34,653 respondents to the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) (Grant et al, 2004).

NESARC used the Alcohol Use Disorders and Associated Disabilities Schedule (AUDADIS-IV) to collect information about both DSM-IV and DSM-5 alcohol use disorder criteria (Grant et al., 2003). Criteria included:

* Each criterion is represented by one to three AUDADIS items
** Includes an item measuring drinking and driving.

Results

· 9.7% of NESARC Wave II respondents qualified for alcohol abuse or alcohol dependence (DSM-IV), while 10.8% of the sample qualified for an AUD as defined by DSM-5.

· As shown in Table 1, 1,033 people who did not qualify for alcohol abuse or alcohol dependence according to DSM-IV criteria qualified for an alcohol use disorder according to DSM-5 criteria. All of these were classified as having a moderate AUD.

o Only 16.1% (n= 166) of these respondents endorsed the new DSM-5 “craving” criterion.

· A total of 659 people who qualified for alcohol abuse or alcohol dependence according to DSM-IV criteria did not qualify for an AUD according to DSM-5 criteria.

o The overwhelming majority of these people (96.5%; n= 636) endorsed the criterion for drinking in hazardous situations, in particular an item measuring drinking and driving.

Table 1. Diagnoses of AUDs According to DSM-IV and DSM-5 in the NESARC Wave II Sample (adapted from Agrawal, Heath & Lynsky, 2011)

Limitations

As with any survey that relies on self-report, it is possible that the answers did not accurately reflect actual behavior or symptoms.

Conclusions

The changes in the DSM-5 had a modest positive effect on the prevalence of AUD , though the addition of the craving criterion did not play a major role in this re-classification. Those who met criteria for AUD under DSM-IV but not under DSM-5 were most likely to endorse the criterion for drinking in hazardous situations. This, combined with the fact that the DSM-5 requires a minimum of two criteria, means that people like the man described above switch from a diagnosis of alcohol abuse to no diagnosis at all. Under DSM-5, these individuals could lose important treatment resources. In addition, there is still debate about whether alcohol abuse and alcohol dependence are two fundamentally different disorders that require different treatment approaches; by condensing them into one, we may lose some important information. Finally, future research might focus on further refining and exploring the divisions of “moderate” and “severe” AUD within DSM-5 diagnoses.

-Katerina Belkin

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References

Agrawal, A., Heath, A.C., Lynsky, M.T. (2011). DSM-IV to DSM-5: the impact of proposed revisions on diagnosis of alcohol use disorders. Addiction, 106, 1935-1943.

American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn, revised. Washington, DC: American Psychiatric Association.

Grant B. F., Dawson D. A., Stinson, F. S., Chou, S. P., Dufour, M. C., Pickering R. P. (2004). The 12-month prevalence and trends in DSM-IV alcohol abuse and dependence: United States, 1991–1992 and 2001–2002. Drug and Alcohol Dependence, 74, 223–34.

Grant B. F., Dawson D. A., Stinson F. S., Chou P. S., 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 a general population sample. Drug and Alcohol Dependence, 71, 7–16.

The WAGER, Vol. 18(5) – Should I sleep on it? How mental fatigue affects risky behavior

2013-05-01T15:32:11-04:00

Factors such as mental fatigue and the outcomes of previous bets might influence how we make decisions about risk. However, previous research reveals inconsistent findings about these factors. Being “cognitively-depleted”—in other words, being mentally fatigued—sometimes promotes riskier behavior and sometimes promotes more conservative decision making (Freeman & Muraven, 2010; Unger & Stahlberg, 2011). Likewise, it is unclear how the outcome of previous bets influences risky decisions (Demareee, Burns, DeDonno et al., 2012). This week’s WAGER reviews a study of the effects of both cognitive depletion and with the outcome of previous bets on risky gambling behavior (Kostek & Ashrafioun, 2013).

Methods

The authors recruited 81 students (average age = 19.1, SD = 1.62, 53% female) from a large Midwestern university to participate in a lab-based gambling study.
The authors randomly assigned participants to one of four groups: Cognitive-depletion/Winning, Cognitive-depletion/Losing, Control/Winning, and Control/Losing.
- The authors first manipulated cognitive depletion using a writing task: those in the cognitive depletion conditions completed a writing task without using the letters ‘a’ or ‘n,’ and those in the control conditions completed a writing task without any letter restrictions.
- Then, the authors manipulated betting outcomes using pre-programmed blackjack hands: those in the winning condition won 75% of blackjack hands played, and those in the losing condition won 25% of blackjack hands played.
The authors measured decisions about risk in two ways:
- Participants completed the Domain-Specific Risk-Taking scale (DOSPERT, Blais & Weber, 2006) which measures the likelihood of engaging in a variety of risky financial behaviors, like ‘Betting a day’s income at the horse races.”
- At the end, participants could wager their five-dollar study compensation one dollar at a time for a chance to win $50. The odds of winning were 1:200 for each dollar wagered.

Results

First, the authors examined participants’ reported likelihood of taking financial risks.
- The authors uncovered an interaction between the two factors. In the control condition, those who won in blackjack said they were more likely to take financial risks than those who lost. But among participants who were cognitively depleted, those who lost in blackjack reported a greater likelihood of risk taking than those who won (Figure 1).
Second, the authors examined how much people bet in the final bet. In this case, the two factors had independent effects on behavior:
- Those in the cognitive depletion condition bet less (mean = $0.76, SD = 1.02) than people than those in the control condition (mean = $1.40, SD = 1.81).
- Those in the winning condition bet significantly more (mean = $1.40, SD = 1.70) than those in the losing condition (mean = $0.76, SD = 1.04).

Figure 1: Likelihood of taking financial risks (mean scores) in the four conditions. Adapted from Kostek & Ashrafioun, 2013.

Note: *group differences significant at p<0.05

Limitations

The study’s sample size is small (n=81), which means we should consider the findings preliminary.
The writing task may have had other effects besides making participants cognitively depleted, such as frustration or boredom. This might have accounted for betting variations.
We do not know if other groups of people, beyond college students, would act the same way.
The 20-hand gambling scenario was brief and might not approximate real-life betting outcomes.

Conclusion

Understanding situational factors could be important to preventing gambling-related problems. In this study, self-reported financial risk varied in a complicated manner-- control subjects reported that they were more likely to engage in risky behavior after doing well at blackjack. In contrast, mentally fatigued participants were more drawn to risk after a series of losses. However, this effect was not apparent at a statistically significant level when the outcome was actual bets—participants bet more when not mentally fatigued and after a series of wins. This highlights the inconsistency between self-reports and actual behavior. Additional research could help to explain this inconsistency.

- Jed Jeng

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References

Blais, A., & Weber, E. (2006). A domain-Specific Risk-Taking (DOSPERT) Scale for Adult Populations. Judgment and Decision Making, 1(1).

Demareee, H., Burns, K., DeDonno, M., Agarwala, E., & Everhart, D. (2012). Risk dishabituation: In repeated gambling, risk is reduced following low-probability "surprising" events (wins or losses). Emotion, 12(3), 495-502.

Freeman, N., & Muraven, M. (2010). Self-Control Depletion Leads to Increased Risk Taking. Social Psychological & Personality Science, 1(2), 175-181.

Kostek, J., & Ashrafioun, L. (2013). Tired Winners: The Effects of Cognitive Resources and Prior Winning on Risky Decision Making. Journal of Gambling Studies, Advance online publication.

Unger, A., & Stahlberg, D. (2011). Ego-Depletion and Risk Behavior. Social Psychology, 42(1), 28-38.

STASH, Vol. 9(5) – Maybe problems don’t last forever: An alternative view of remission from drug dependence

2013-04-24T16:18:38-04:00

Many people believe that substance abuse and dependence are necessarily progressive illnesses, and that the probability of quitting drugs decreases with each successive year of heavy drug use. This might be a good way to summarize some individuals’ experience with drug use. However, new research using data from a national survey suggests that the number of people who quit using drugs increases steadily over time, regardless of their time spent dependent on drugs. This week’s STASH reviews an article exploring this phenomenon (Heyman, 2013).

Methods

Heyman (2013) explored the rates of remission from cocaine, marijuana, alcohol and cigarette dependence using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; Grant and Dawson, 2006) and Lopez-Quintero et al. (2011). For instance, a person is considered as having remitted from cocaine dependence if he describes himself as having been met diagnostic criteria for cocaine dependence at some point in his lifetime but not in the past year.
The two key pieces of information were (1) the cumulative probability of remission (i.e., the proportion of participants who were currently remitted) and (2) the amount of time in years since the onset of dependence. [1]
Heyman’s (2013) contribution was to determine the simplest possible mathematical relationship between years of drug dependence and cumulative remission rates. In doing so, Heyman found formulas for the percentage of eventual “remitters” and the percent of additional people who remit each year.

Results

Figure 1 illustrates the cumulative probability of remission from cocaine, marijuana, alcohol, and cigarette dependence as a function of time since the onset of dependence. For all four drugs, the data fit the equations for exponential decay. That is, each year the number of people who quit drugs was a constant proportion of those currently dependent, regardless of how long they had been dependent.
The chances that a person would eventually quit using if given enough time were 98% for cocaine, 94% for marijuana, 95% for alcohol, and 100% for cigarettes. [2]
However, the time to remission varied considerably by drug. The half life for cocaine dependence—the time it took for half of people to stop being dependent – was 4 years. The half lives for marijuana, alcohol, and cigarettes were 6, 16, and 30 years, respectively.

Figure 1. Cumulative probabilities of remission from each drug within one, five, ten, and twenty years of initial onset of dependence. Adapted from Heyman (2013).

Limitations

Substance use data were self-reported and therefore might be biased if respondents misremembered their past history or exaggerated or underrepresented important facts or details.
Remission may have been temporary in some cases. Participants might have had relapses outside the scope of the study. They would count as individuals in remission when they actually should not. This could mean that the real rates of remission may be lower than what the data show.

Conclusions

One of the more surprising findings from this study was the constant rate of remission regardless of time spent dependent. Heyman (2013) suggests that this is consistent with the view of addiction as “a steady by fragile state that can abruptly shift to a new state.” For example, moving to a new city or making a new set of friends might change one’s relationship with an object of addiction, regardless of time spent addicted. Overall, it took longer for people to quit using alcohol and cigarettes than cocaine and marijuana, and Heyman suggests that the efforts it takes to obtain the illegal substances may explain some of the differences. That is, it may be easier to quit something when it is harder to get. Still, whatever substance a person might have problems with, according to this “maturing out” model, there is always a chance of being able to put these problems in the past.

– Matthew Tom

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References

Grant, B. F., & Dawson, D. A. (2006). Introduction to the national epidemiologic survey on alcohol and related conditions (NESARC). Alcohol Health & Research World, 29(2), 74.

Heyman, G. M. (2013). Quitting Drugs: Quantitative and Qualitative Features. Annual Review of Clinical Psychology, 9(1), 29–59. doi:10.1146/annurev-clinpsy-032511-143041

Lopez-Quintero, C., Hasin, D. S., de Los Cobos, J. P., Pines, A., Wang, S., Grant, B. F., & Blanco, C. (2010). Probability and predictors of remission from life-time nicotine, alcohol, cannabis or cocaine dependence: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Addiction (Abingdon, England), 106(3), 657–669. doi:10.1111/j.1360-0443.2010.03194.x

[1] Lopez-Quintero et al. restricted the data set to individuals with lifetime DSM-IV diagnosis of dependence on any of the four substances (cocaine: n=408, marijuana: n=530, alcohol: n=4,781, and cigarettes: n=6,937).

[2] While the results imply that everyone eventually stops smoking, they say nothing about the circumstances under which people stop smoking. For example, some people might quit smoking in their old age because of a change in living circumstances, such as being hospitalized.