The Brief Addiction Science Information Source (BASIS)

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

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

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[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.

STASH, Vol. 9(4) - Did I or didn’t I? The effect of placebo versus marijuana on impulsivity and risk taking

Marijuana use is associated with risky behaviors, such as risky sexual behavior; and some dangerous outcomes, such as motor vehicle accidents (Drummer et al., 2004; Fernandez et al., 2004). However, it is not clear how individuals’ expectations about the experiential effects of marijuana use might play a role in elevated risk taking behavior; expectations in general exert powerful effects on behavior (e.g., Testa et al., 2006). This week’s STASH reviews a study that investigates the separate effects of true marijuana taking and marijuana expectancy on risk behavior and impulsivity (Metrik et al., 2012).

Methods

• Researchers recruited 136 marijuana smokers through flyers, newspaper advertisements and social media websites to participate in a marijuana use study.
• At baseline participants were randomly assigned to one of 4 conditions:
  • 1) Told THC /Received THC; 2) Told Placebo/Received Placebo; 3) Told THC/Received Placebo; 4) Told Placebo/Received THC.
  • Researchers told participants in the “Told THC” conditions  that they would be smoking an active marijuana cigarette that contains THC (i.e., delta-9-tetrahydrocannabinol). Researcher told participants in the “told Placebo” conditions that they would be smoking placebo cigarette without any THC.
  • Participants in the “Received THC” conditions smoked a cigarette that contained 2.8% of THC. Participants in the “Received placebo” conditions smoked a cigarette that contained no THC until the ash reached 10 mm from the end.
• At baseline, participants also completed several tasks including following:
  • Impulsive Disinhibition: Participants completed the Stroop Color-Word task. In the Stroop task, longer response times to stimuli on a computer screen indicate greater difficulty suppressing an instinctive behavior.. In this study the researchers measured reaction time as well as the number of correct responses to color-congruent trials (e.g., the word “blue” written in blue font) and color-incongruent trials (e.g., the word “blue” written in red font).
  • Impulsive Decision-Making: Participants completed the Experiential Discounting Task (Reynolds & Schiffbauer, 2004). In the EDT, participants decide whether to accept a series of monetary rewards that vary in their size and delay. Some people devalue, or discount, larger rewards which are further away in time as compared to smaller, immediate rewards; this delay discounting is believed to mark impulsivity (Reynolds & Schiffbauer, 2004). The outcome variable based on participants’ decisions varied from 0 (i.e., greatest delay discounting) to 1 (i.e., no delay discounting).
  • Ninety minutes after smoking, participants completed the tasks again as well as a manipulation check that asked about suspicions about THC content.  For the EDT, the researchers subtracted baseline score from the post-smoking score. More negative difference scores indicate a larger increase in impulsivity after smoking.
• The researchers also measured subjective intoxication with the ARCI-Marijuana scale (Chait, Fischman, & Schuster, 1985) 12 minutes after smoking and following the impulsivity tasks.

Results

• Two participants (6%) reported suspicions about THC content in the Told THC/Received Placebo condition and seven (21%) in the Told Placebo/Received THC condition. [i]
• Both consuming THC (F (1, 125) = 77.23, p < .001) and having the expectation of consuming THC (F (1, 125) = 6.32, p < .05) increased subjective intoxication.
• The researchers used linear regression to test the effects of pharmacology and expectations on the Stroop Color-Word and the EDT task. They entered the baseline value in the first step, followed by the main effects of pharmacology and expectancies on the second step, followed by the pharmacology by expectancy interaction on the third step.
• For the Stroop Color-Word task, the researchers observed no main effect of expectancy and no interaction between pharmacology and expectancy. Pharmacology had a significant main effect on the number of correct responses for color-incongruent trials(Β = -1.28, p < 0.05), but not for color-congruent trials. Participants who consumed THC made more errors on color-incongruent trials. Pharmacology had no effect on response time.
• A different pattern emerged for the EDT; pharmacology did not exert an effect but expectations did (Β = 0.09, p < 0.05). Those in “Told THC” conditions discounted delayed rewards less (i.e., made less impulsive choices) than those in “Told Placebo” conditions. Figure 1 presents the EDT difference scores among the four conditions.

Figure 1. Difference scores (Post-smoking minus baseline) in delayed discounting task  as a function of pharmacology and expectations 

Limitations

• The researchers used computerized tasks to assess impulsivity. As with any well controlled experiment,  the generalizability beyond these measurements may be questionable due to limited external validity .
• For ethical reasons, participants in this study only included current marijuana smokers. The effect of marijuana on occasional smokers is not clear.
• Again, for ethical reasons and also to conceal the placebo, the experimenters had to give only small doses of THC. The effect of larger doses is unclear.

Conclusions

In this study, taking placebo increased subjective feeling of intoxication. This demonstrates that expectancy may play a substantial role in individuals’ perception of the drug effect. Further, expectations influenced some aspects of impulsive behavior, but in a surprising way. Individuals who believed they consumed THC were more cautious in their decisions.  Specifically, they discounted delayed reward less then did those participants who believed they used placebo. This signals a compensatory effect (Bates & Blakely, 1999);  individuals who believe they are under influence of drug might deliberately try to compensate for its potential effects and practice more caution.

Consuming THC did not influence the main impulsivity outcome. This implies that the effect of marijuana on impulsivity is not as strong as the effect of other drugs (Fillmore, 2003). This result, as with all research,  should not be automatically generalized beyond the particular setting and interpreted as a proof of no effect of the drug on impulsivity and risk perception.

- Julia Braverman

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

References

Bates, M. N., & Blakely, T. A. (1999). Role of cannabis in motor vehicle crashes. Epidemiol Rev, 21(2), 222-232.

Chait, L. D., Fischman, M. W., & Schuster, C. R. (1985). 'Hangover' effects the morning after marijuana smoking. Drug and Alcohol Dependence, 15(3), 229-238.

Drummer, O. H., Gerostamoulos, J., Batziris, H., Chu, M., Caplehorn, J., Robertson, M. D., & Swann, P. (2004). The involvement of drugs in drivers of motor vehicles killed in Australian road traffic crashes. Accident analysis and prevention, 36(2), 239-248.

Fernandez, M. I., Collazo, J. B., Hernandez, N., Bowen, G. S., Varga, L. M., Vila, C. K., . . . Perrino, T. (2004). Predictors of HIV risk among Hispanic farm workers in South Florida: women are at higher risk than men. AIDS Behav, 8(2), 165-174. doi: 10.1023/b:aibe.0000030247.00140.62

Fillmore, M. T. (2003). Drug abuse as a problem of impaired control: current approaches and findings. Behavioral and Cognitive Neuroscience Reviews, 2(3), 179-197. doi: 10.1177/1534582303257007

Metrik, J., Kahler, C. W., Reynolds, B., McGeary, J. E., Monti, P. M., Haney, M., . . . Rohsenow, D. J. (2012). Balanced placebo design with marijuana: Pharmacological and expectancy effects on impulsivity and risk taking. Psychopharmacology, 223(4), 489-499. doi: 10.1007/s00213-012-2740-y

Reynolds, B., & Schiffbauer, R. (2004). Measuring state changes in human delay discounting: an experiential discounting task. Behavioral Processes, 67(3), 343-356. doi: 10.1016/j.beproc.2004.06.003

Testa, M., Fillmore, M. T., Norris, J., Abbey, A., Curtin, J. J., Leonard, K. E., . . . Hayman, L. W., Jr. (2006). Understanding alcohol expectancy effects: revisiting the placebo condition. Alcoholism: Clinical and Experimental Research, 30(2), 339-348. doi: 10.1111/j.1530-0277.2006.00039.x

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[i] For the EDT, we are reporting the results excluding participants who reported suspicions about placebo content. 

STASH, Vol. 9(3) - Re-thinking Special K: Using ketamine to treat treatment resistant depression

Ketamine (street name “Special K”) is an anesthetic drug that some people use recreationally at sub- anesthetic doses to induce a dissociative state. Notably, some research suggests that intravenous ketamine provides rapid relief from depressive symptoms among people with major depressive disorder (MDD). Ketamine might therefore be a useful option for people who have not responded to traditional anti-depressives (Berman et al. 2000). This week’s STASH reviews meta-analysis describing studies of ketamine for the treatment of MDD and bipolar disorder (BD) (aan het Rot, Zarate Jr., Charney, &  Mathew, 2012).

Methods

• To identify studies, the researchers searched for relevant terms in scientific databases and examined reference lists.
  • Inclusion criteria included:
    • Patients met clinical criteria for a mood disorder.
    • Patients received ketamine to study its antidepressant effects.
    • Patients were clinically assessed for at least 230 minutes after they received ketamine.
• The search yielded 10 case studies (total N  = 14), 10 open-label investigations (total N = 83), and 5 controlled trials (total N = 66). Here we focus on the 2 open-label investigations and 5 controlled trials that measured response up to 72 hours post infusion.
  • Patients in different studies had different levels of resistance to depression treatment; however, no studies included patients using ketamine as an initial treatment for depression. 
  • The researchers defined a “response” to ketamine as a 50% or greater improvement on at least one of these self-report measures of depression:
    • Antidepressant Treatment History Form (ATHF)^[1]
    • Beck depression Inventory (BDI) ^[2]
    • Hamilton Depressive Rating Scale (HDRS)^[3]
    • Montgomery-Asberg Depression Rating Scale (MADRS)^[4]
• Using this definition, the researchers identified the percentage of participants classified as responders in each study at each time point (i.e. 3 to 4, 24, and 72 hours after infusion).

Results

• Most studies used one dose of .5mg/kg of ketamine administered intravenously (IV) over 40-60 min with a placebo of saline in a randomized order.
• Figure 1 shows IV ketamine response among participants in open-label and controlled studies. Ketamine had rapid, but transient, antidepressant effects and effects varied considerably from study to study.
• At 3-4 hours, the percent of patients responding ranged from 0-100%. At 72 hours, the percent of patients responding ranged from 14-70%.
• Adverse effects during administration included dissociation, dizziness, and blood pressure changes. 

Figure 1: Percent of patients who responded to IV ketamine over time in open-label (square markers) and controlled (triangle markers) trials organized by disorder treated, type of trial, and number of doses (Adapted from aan het Rot et al., 2012)

Limitations

• The total number of patients is small.
• Inter-study variations in ketamine amount and administration methods, as well as outcome data, make it difficult to compare outcomes of the different studies.
• Outcome assessments were very short in duration; the longest follow-up period was only 72 hours.
• This study did not clearly outline the selection criteria of patients included in the various studies.

Conclusions

Despite its misuse potential in recreational settings, there seem to be benefits in using ketamine in controlled settings for people who do not respond to anti-depressants. These data suggest fast-acting but somewhat transient effects of IV ketamine; future studies are needed to determine whether ketamine can provide long-lasting effects, potentially using other delivery modes (e.g., oral, intranasal, intramuscular).  Patients varied a great deal in their response to treatment, suggesting the need for additional research on predictors of response. Lastly, researchers are using animal models and brainwave recordings to gather important information on the neurobiological mechanisms underlying ketamine’s effects (e.g., Duman & Aghajanian, 2012).

--Emily Shoov 

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

References

aan het Rot, M., Zarate Jr., C. A., Charney, D.S., Mathew, S.J. (2012). Ketamine for depression: Where do we go from here? Biological Psychiatry 72(7), 537–547.

Berman, R. M., Cappiello, A., Anand, A., et al. (2000). Antidepressant effects of ketamine in depressed patients. Biological Psychiatry, 47, 351-354.

DiazGranados N, Ibrahim L, Brutsche NE, Newberg A, Kronstein P, Khalife S, et al. (2010): A randomized add-on trial of an N-methyl-D-aspartate antagonist in treatment-resistant bipolar depression. Archives of General Psychiatry, 67, 793– 802. 

Duman, R. S. & Aghajanian, G. K. (2012). Synaptic dysfunction in depression: Potential therapeutic targets. Science, 338 (6103), 68-72.

Mathew, S. J., Murrough J. W., aan het Rot, M., Collins, K A., Reich, D. L., & Charney, D. S. (2010). Riluzole for relapse prevention following intravenous ketamine in treatment-resistant depression: A pilot randomized, placebo-controlled continuation trial. International Journal of Neuropsychopharmacology, 13, 71-82.

Valentine GW, Mason GF, Gomez R, Fasula M, Watzl J, Pittman B, et al. (2011): The antidepressant effect of ketamine is not associated with changes in occipital amino acid neurotransmitter content as measured by [(1)H]-MRS. Psychiatry Research, 191, 122–127.

Zarate CA, Brutsche NE, Ibrahim L, Franco-Chaves J, DiazGranados N, Cravchik A, et al. (2012): Replication of ketamine’s antidepressant efficacy in bipolar depression: A randomized controlled add-on trial. Biological Psychiatry, 71, 939 –946

Zarate CA Jr, Singh JB, Carlson PJ, Brutsche NE, Ameli R, Luckenbaugh DA, et al. (2006): A randomized trial of an N-methyl-D-aspartate antagonist in treatment-resistant major depression. Archives of General Psychiatry, 63, 856–864.

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[1] An instrument designed to assess adequacy of medication trials for major depression by rating them on a scale of 1-5,  now in the form of  a computer-based algorithm (Oquendo, 2003)

[2] A 21-item self-report of depressive ideation (Beck, 1961)

[3] A multi-item questionnaire used to provide an indication of depression (Hamilton, 1960).

[4] A ten-item diagnostic questionnaire used to measure severity of depressive episodes in patients with mood disorders. (Montgomery and Asberg, 1979)

STASH, Vol. 9(2) - Lag time: Barriers to treatment in a national sample of people with substance disorders

Introduction

Wait time is one of the most commonly cited barriers to substance abuse treatment (Claus, 2002; Festinger, 1995). To date, no national study has collected data about factors that might influence wait time. This week we review a study that identified program and client characteristics associated with longer wait time for entry in a substance abuse treatment program (Andrews, 2013).

Methods

• The researchers used data from the National Treatment Improvement Evaluation Study (Gerstein, 1995), a national longitudinal study of outpatient substance abuse treatment programs serving underserved clients such as minorities, youth, and those in the criminal justice system. The sample was made up of 2,920 clients, racially and ethnically diverse, attending 57 substance abuse treatment programs.
• The researchers gathered client data (e.g., gender, race, treatment payment source, referral source, severity of substance abuse, HIV status) through computer-assisted, personal interviews. For wait time, respondents answered the question  “How long ago was your name put in for treatment this time?” using 6 categorical response options (i.e., 1 day, 2-6 days, 1-4 weeks, 1-3 months, 4-12 months, more than one year). Researchers collected program characteristics (e.g., financial resources, organizational structure, types of treatment provided) through interviews conducted with program administrators and clinical directors. 
• The researchers identified the proportion of clients who waited over one month to receive substance abuse treatment.
• Using generalized linear modeling, they examined program characteristics and client characteristics associated with wait time of over one month before entering treatment.

Results

• 28% of clients reported waiting over one month to enter treatment
• Odds ratios differed significantly for selected client-specific factors:
  • Clients of African-American ethnicity were 1.40 times more likely, 95% Confidence Interval (CI) [1.04, 1.88], compared to clients of white ethnicity, to report waiting for over a month before entering treatment, p < .05     
  • Clients who were referred through a criminal justice path were 1.70 times, 95% CI [1.18, 2.43], more likely than clients referred through social services or self-referred clients to report waiting for over a month before entering treatment, p < .01
  • Clients with a diagnosis of HIV/AIDS were less likely than clients without a diagnosis of HIV/AIDS to report waiting over a month to enter treatment, (0.38%, 95% CI [ 0.19, 0.77]), p < .01.
  • Those with relatively high severity of substance use were less likely than those with low severity to wait more than one month (0.99%, 95% CI [0.98, 1.0]), p < .01 †
  • Methadone treatment programs were significantly more likely to admit clients to treatment within one month (3.90%, 95% CI [1.0, 15.17]), p < .05. No other program-specific factors were significantly associated with wait time. 

Table 1: Odds ratios (95% confidence intervals) for selected associations between client and program-specific factors and waiting more than one month before substance use treatment. Adapted from Andrews, 2013.

	Variable	OR	CI lower	CI upper
Client characteristics	African-American versus White	1.40 *	1.04	1.88
	Latino versus White	1.25	0.85	1.84
	Referred through criminal justice versus self-referral or social services	1.70 **	1.18	2.43
	Referred through social services versus self-referral or criminal justice	0.94	0.58	1.52
	HIV/AIDS diagnosis versus no diagnosis	0.38 **	0.19	0.77
	Physical health problems versus no physical health problems	1.25	0.98	1.60
	High severity of substance use versus low severity	0.99 **	0.98	1.00
Program characteristics	Methadone program versus other type of program	3.90 *	1.00	15.17
	High program revenue versus low program revenue	0.93	0.33	2.81

* p < 0.05;
** p < 0.01

Limitations

• The data were collected in 1992-1994 and therefore do not reflect and more recent national trends in drug use behavior and the availability of outpatient treatment.  
• As with any measure based on retrospective self-report, the wait time data could be inaccurate.
• The response option for treatment wait times might be problematic, particularly the options “1-4 weeks” and “1-3 months”, which appear to overlap. This might further limit the reliability of wait time data.
• The results of the study only pertain to a limited sample—people who eventually enter treatment despite a longer waiting time—so it is impossible to account for selective attrition.

Discussion

This study analyzed factors associated with long wait time before entering substance abuse treatment. The results indicate that some treatment-seekers involuntarily wait longer than others to enter treatment for substance abuse.

The researchers speculate that HIV positive status and high addiction severity might have given clients priority entry to treatment; it is also possible, however, that some people with these characteristics dropped off wait lists more quickly, so that the pool of those who stayed and entered treatment was made up of people who experienced shorter wait times. The researchers further speculate that people referred through criminal justice were more motivated to enter treatment despite longer wait times because they were under supervision. They note also, that prior research indicates that African-Americans are more likely than Whites to report long wait lists as barriers to alcohol and substance abuse treatment (Grant, 1997).  Although the reasons for this are unclear, this corroborates previous findings that disparities exist. It is likely that longer wait times are more common for populations considered to be socially or financially disadvantaged, and that this exists in a constellation of factors such as ability to pay and location of outpatient treatment.

For future research looking at wait time and program admission, it would be helpful to examine a wider sample of people—this means finding means of gathering data from people who did not enter programs as well as those who persevered. In light of the changes to access to treatment resulting from the Affordable Care Act (ACA), these investigations will be more important than ever.

-Kat Belkin

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

References

 Andrews, C. M., Shin, H., Marsh, J.C., Cao, D. (2013). Client and program characteristics associated with wait time to substance abuse entry. American Journal of Drug and Alcohol Abuse, 39(1), 61-68.

Claus, R. E., Kindleberger, L.R. . (2002). Engaging substance abusers after centralized assessment: predictors of treatment entry and dropout. Journal of Psychoactive Drugs, 34(1), 25-31.

Festinger, D. S., Lamb, R.J., Kountz, M.R., Kirby, K.C., Marlowe, D. (1995). Pretreatment dropout as a function of treatment delay and client variables. Addictive Behaviors, 20(1), 111-115.

Gerstein, D. R., Atta, A.R., Ingels, J.S., Johnson, R.A., Rasinski, K.A., Schildlaus, S., Talley, K., Jordan, K., Phillips, D.B., Anderson, D.W., Condelli, W.G., Collins, J.S. (1995). NTIES: National Treatment Improvement Evaluation Study. Rockville, Maryland: Center for Substance Abuse Treatment.

Grant, B.F. (1997). Barriers to alcoholism treatment: reasons for not seeking treatment in a general population study. Journal of Studies on Alcohol and Drugs, 58, 365-371.

† The researchers operationalized severity of substance use as the number of days of use of one of the five most common drugs in the past month. 

 

STASH, Vol. 9(1) - Not so different after all? Sex-specific patterns of psychiatric comorbidity among men and women with SUD

People who experience substance-use disorders (SUD) also tend to experience a range of comorbid psychiatric conditions (e.g., Kessler et al., 1997). Some evidence suggests that the profile of comorbid conditions differs for men and women with SUD (e.g., Kessler et al., 1997). However, past estimates of sex-specific comorbid associations have not been adjusted for sociodemographic characteristics and the presence of other co-occurring psychiatric conditions. This week’s STASH reviews a study that examines sex-specific comorbid associations with SUD while controlling for these additional variables (Goldstein, Dawson, Chou, & Grant, 2012).

Methods

• The researchers used data from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), which gathered data on SUD, other psychiatric conditions, and sociodemographic characteristics using face-to-face interviews during 2004-2005 (n=34,653) (Grant, Kaplan, Moore, & Kimball, 2007).
• Interviewers assessed SUD and other psychiatric conditions using the Alcohol Use Disorders and Associated Disabilities Interview Schedule-DSM-IV version (AUDADIS-IV; Grant, Dawson, & Hasin, 2004). The AUDADIS-IV measures DSM-IV criteria for abuse and dependence of 10 drug categories. All conditions were measured on a lifetime basis.
• Goldstein and colleagues used sex-specific multivariate logistic regressions to estimate associations of drug use disorders with other psychiatric disorders¹,first controlling only for sociodemographic characteristics and then controlling for both sociodemographic characteristics and all other Axis I and II disorders.² 

Results

• Men were more likely than women to report drug abuse and dependence (both χ² (1) > 36; both p < .001).
• In the analysis controlling for only sociodemographic characteristics, drug abuse was positively associated with all examined psychiatric disorders except bipolar II (risk only higher among women) and dependent personality disorder (risk only higher among men).
  • The patterns between men and women were largely similar; the only odds ratios that differed between men and women were for any alcohol use disorder and nicotine dependence (both higher among women).
• As displayed in Figure 1, when researchers also controlled for the presence of other Axis I and II disorders, drug use disorders remained significantly and positively associated in both sexes with any mood disorder, bipolar I disorder, any alcohol use disorder, alcohol abuse, nicotine dependence, any personality disorder, and antisocial personality disorder; in men with ADHD and major depression; and in women with borderline personality disorder.
  • Odds ratios differed significantly between men and women only for bipolar I disorder and alcohol abuse (risk higher among women) and any personality disorder (risk higher among men).

Figure 1: Odds ratios (95% confidence intervals) for selected associations between drug use disorders and other psychiatric disorders among NESARC respondents, as a function of sex. Adapted from Goldstein et al. (2012).

Notes: * Odds ratios are significantly different (at p < .05) between women and men

† Odds ratios are significantly different (at p < .05) than 1 for both women and men.

†† Odds ratio is significantly different (at p < .05) than 1 for men only

Limitations

• Because this study used a cross-sectional design, it is impossible to identify causal mechanisms, such as a mood disorder causing drug abuse.
• Although the NESARC used strict confidentiality guarantees, self-reports of drug abuse—like self-reports of all stigmatized behaviors--are susceptible to self-presentation biases.

Conclusions

This study adds to a growing body of evidence indicating that people who experience drug abuse are at higher risk for other psychiatric disorders. This pattern highlights the need for careful assessment and integrated treatment of mental health disorders during SUD treatment. A novel contribution of this study is the adjustment for sociodemographic characteristics and other psychiatric conditions. With this adjustment, the associations between SUD and other psychiatric conditions are weaker than those previously reported (Kessler et al., 1997), potentially because much of the comorbidity between SUD and other specific psychiatric conditions reflects high rates of the co-occurrence of other conditions. When this artifact is considered, gender differences in the profile of comorbid conditions tend to fall away.   

--Heather Gray

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

References

Goldstein, R. B., Dawson, D. A., Chou, S. P., & Grant, B. F. (2012). Sex differences in prevalence and comorbidity of alcohol and drug use disorders: results from wave 2 of the national epidemiologic survey on alcohol and related conditions. Journal of Studies on Alcohol and Drugs, 73(6), 938-950.

Grant, B. F., Dawson, D. A., & Hasin, D. (2004). The Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions Alcohol Use Disorders and Associated Disabilities Interview Schedule-DSM-IV Version. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism.

Grant, B. F., Kaplan, K., Moore, T., & Kimball, J. (2007). 2004-2005 Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions: Source and accuracy statement. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism.

Kessler, R. C., Crum, R. M., Warner, L. A., Nelson, C. B., Schulenberg, J., & Anthony, J. C. (1997). Lifetime co-occurrence of DSM-III-R alcohol abuse and dependence with other psychiatric disorders in the National Comorbidity Survey. Archives of General Psychiatry, 54, 313-321.

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¹ The researchers performed the same set of analyses for alcohol use disorders, but we are not reporting those results here.

² For example, the fully adjusted model exploring comorbidity between drug abuse and social phobia adjusted for sociodemographic characteristics plus any mood disorder, any anxiety disorder except social phobia, any alcohol use disorder, nicotine dependence, ADHD, and any personality disorder.

STASH, Vol. 8(10) – A mile higher: Do medical marijuana laws predict marijuana use?

The legal landscape of marijuana legislation has changed drastically in last decade with many states passing medical marijuana laws, decriminalizing small quantities of marijuana, or outright legalizing the use of marijuana. It is unclear how these laws might affect marijuana use and related problems. This week’s STASH reviews a study that examines how marijuana use and abuse/dependence differ in states with and without medical marijuana laws (Cerda, Wall, Keyes et al., 2012).

Figure 1. States with legalized medical marijuana laws in 2004[i]

Methods

• The primary exposure variable was whether states had legalized medical marijuana use by 2004 (Figure 1).
• For outcome data, the researchers used Wave 2 data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) which gathered data on marijuana usage and problems using face-to-face interviews during 2004-2005 (n=34,653) (Grant, Stinson, Dawson et al., 2004).
  • Researchers administered the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version (AUDADIS-IV) which covers past-year non-medical marijuana use[ii] and symptoms of abuse/dependence  (Grant, Dawson, Stinson et al., 2003).
  • The three outcome variables were (1) prevalence of past-year non-medical marijuana use among the general population, (2) prevalence of past-year marijuana abuse/dependence[iii] among the general population, and (3) prevalence of marijuana abuse/dependence among past-year users.
• The researchers used a state-level linear regression model predicting the prevalence of marijuana use and abuse/dependence from medical marijuana laws, adjusting for demographic variables (e.g., proportions of residents who are male, without a high school diploma, and under age 30).
• They also used a multilevel logistic regression model predicting individual marijuana outcomes from both individual covariates and state-level covariates (including medical marijuana law).

Results

• Figure 2 represents the findings of the state-level analysis.
  • The rate of past-year non-medical marijuana use was higher in states with medical marijuana use laws (7.13%, 95% CI = 6.02, 8.24) than in states without these laws (3.57%, 95% CI = 3.10, 4.03)(p<0.01)
  • The prevalence of marijuana abuse/dependence was also higher in states with medical marijuana use laws (2.61%, 95% CI = 1.96, 3.25) than in states without these laws (1.27%, 95% CI = 1.00, 1.54) (p<0.01).
  • However, marijuana abuse/dependence among past-year users did not differ significantly between states with and without medical marijuana use laws (37.68% and 35.34%).
  • The multilevel analysis revealed that residents in states with medical marijuana laws were 1.92 (95% CI = 1.49, 2.47) times more likely to have used marijuana in the past year and 1.81 (95% CI = 1.22, 2.67) times more likely to experience marijuana abuse/dependence. However, living in a state with legalized medical marijuana did not increase risk for marijuana abuse/dependence among past-year users (OR = 1.03, 95% CI = 0.67, 1.60).

Figure 2: Regression-adjusted mean prevalence and 95% confidence intervals for past-year outcomes in states with and without legalized medical marijuana.

Limitations

• The study does not examine the causal relationship between the passage of medical marijuana legislation and marijuana use behavior. While it is possible that medical marijuana legislation encourages non-medical marijuana use, another possibility is that higher rates of marijuana use contribute to the passage of medical marijuana legislation. Other causal pathways are possible.
• This study does not consider how marijuana use changes over the span of time after the laws are enacted.

Conclusions

The study shows that the states with medical marijuana laws have a higher rate of marijuana use and abuse/dependence among the general population than other states. However, these relationships were not evident among past-year users.  This suggests that the higher rates of marijuana abuse/dependence in states that have legalized medical marijuana can be explained by a higher rate of marijuana use. The passage of medical marijuana laws might contribute to increased use by changing norms about the safety and acceptability of marijuana use. A longitudinal study that included measures of attitudes and beliefs, not just marijuana use, would be needed to address this possibility.

-Jed Jeng

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References

Cerda, M., Wall, M., Keyes, K. M., Galea, S., & Hasin, D. (2012). Medical marijuana laws in 50 states: Investigating the relationship between state legalization of medical marijuana and marijuana use, abuse and dependence. Drug and Alcohol Dependence, 120(1-3), 22-27.

Grant, B. F., Dawson, D. A., Stinson, F. S., Chou, S. P., Ward, K., & Pickering, R. P. (2003). The Alcohol Use Disorder and AssociatedDisabilitiesInterviewSchedule-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(1), 7-16.

Grant, B. F., Stinson, F. S., Dawson, D. A., Chou, S. P., Dufour, M. C., Compton, W., et al. (2004). Prevalence and Co-occurrence of Substance Use Disorders and IndependentMood and Anxiety Disorders. Archives of General Psychiatry, 61(8), 807-816.

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[i] modified from Map of US state cannabis laws by Lokal_Profil (CC BY-SA 2.5)

[ii] The AUDADIS-IV covers cannabis use, but the authors note that the proportion of non-marijuana cannabis use is very small (~1%)

[iii] Researchers combined marijuana abuse and dependence diagnoses into one outcome variable

STASH, Vol. 8(9) - It’s ten o’clock, do you know where your children’s friends’ parents are? Downstream effects of parenting style on adolescent substance use

Research has established that both peer groups and parenting styles shape adolescents’ substance use. Adolescents are more likely to use drugs if their friends use drugs and if their parents are relatively neglectful (Steinberg, 2001). However, few studies have examined the influence of parents of friends on adolescents’ behavior. In this week’s STASH, we review a study that examined the extent to which adolescents are influenced by their friends’ parents’ behavior (Shakya, Christakis & Fowler, 2012).

Methods

• The study used data from a longitudinal survey of adolescent health and social networks (Add Health); 90,118 adolescents across the United States were interviewed at school and at home across four waves (Harris, 2007). The study used data from the first two waves, Wave I (1994-1995) and Wave II (1996), with a total sample of 2,790 adolescents.

• Adolescents named their friends and answered questions about their parents. Using these data, the authors were able to form an index of each adolescents’ (1) mothers’ parenting styles and (2) friends’ parents’ parenting styles.

• The authors categorized parenting styles using a validated 4-cluster system: 

                o        authoritative (high warmth, high control)
                o        authoritarian (low warmth, high control)
                o        permissive (high warmth, low control)
                o        neglectful (low warmth, low control)

• Adolescents reported whether they (1) had participated in binge drinking in the last year; (2) gotten drunk in the last year; (3) smoked cigarettes in the last month; and (4) used marijuana in the last month.

• The authors used generalized estimating equation procedures to account for multiple observations of the same adolescent (i.e., adolescent + friend 1, adolescent + friend 2).

Results 

• As shown in Figure 1, when an adolescents’ friends’ parents used an authoritative parenting style, as compared to a neglectful parenting style, that adolescent was less likely to binge drink, smoke, drink to the point of drunkenness, and use marijuana. †

• When an adolescents’ friends’ parents used an authoritarian parenting style, as compared to a neglectful parenting style, that adolescent was less likely to engage in binge drinking  (95% CI, 6%-54%).  †

Figure 1: Risk ratios for association between friend’s parent’s style and adolescent risk behavior

a This is a bivariate association
* Indicates statistically significant odds ratio

Limitations

• The results of this study are not necessarily generalizable to the rest of non-adolescents.
• Self-report of drug use can be unreliable. The adolescents in the study may have over- or under-reported actual drug use.
• Because the results of this study are observational, they cannot fully explain the directionality of the association. Parents could be influenced by their children’s friends’ drug use to act differently.

Conclusion

The results of this study suggest that social network influences, particularly those pertaining to substance use, may extend beyond peer-peer or child-parent relationships. This study confirmed and extended previous work which found authoritative parenting to be a positive influence on adolescents, suggesting that this parenting style might influence not only parents’ own children but also their peers.   More research needs to be done on the timing and directionality of this influence.

-Kat Belkin

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References

Harris, Kathleen Mullan and Hedwig Lee. 2007. “The National Longitudinal Study of Adolescent Health” in International Encyclopedia of the Social Sciences, 2nd Edition, edited byWilliam A. Darity. New York, NY: Macmillan Reference.

Shakya, H.B., Christakis, N.A., & Fowler, J.H. (2012). Parental influence on substance use in adolescent social networks. Arch Pediatric Adolescent Medicine. Published online October 8, 2012. www.archpediatrics.com, doi:10.1001/archpediatrics.2012.1372

Steinberg L. (2001). We know some things: parent-adolescent relationships in retrospect and prospect. J Res Adolesc, 11(1):1-19.

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† = This effect was found while controlling for the parenting style of the adolescent's own mother, school-level fixed effects, and demographics.

STASH, Vol. 8(8) - Are mothers who use cocaine more aggressive toward their children?

Previous studies have shown that children exposed prenatally to cocaine are more aggressive later on than other children (Sood et al, 2005). It is possible that a mother’s cocaine use during her pregnancy is associated with her aggressive behavior toward her child later on, and that this maternal aggression in turn plays a role in later childhood aggression. This week’s STASH reviews a study that investigates the association between prenatal cocaine use and maternal aggression during the early years of a child’s life and the early relationship between maternal and childhood aggression (Eiden, Schuetze, Colder & Veira, 2011). 

Methods

• Researchers recruited new mothers from two hospitals serving a mainly low-income population. The researchers determined maternal cocaine use status by a combination of self-report, hair-sample and urine toxicology 4 – 8 weeks after delivery.
  • Laboratory tests revealed that 119 mothers used cocaine during pregnancy.
  • For every cocaine-positive mother, the researchers recruited one non-cocaine using mother matched on maternal education, maternal race/ethnicity, and infant gender.
  • The final sample consisted of 220 mother-child dyads, 119 cocaine positive and 101 cocaine negative. Seventy-four percent of mothers were African American and 60% were single. Forty-nine percent of infants were males.
• Trained observers blind to cocaine status coded mother-child behavior for key variables on two occasions:
  • 13 months after delivery, researchers observed mother-child 10 min. free play and coded maternal negative affect by rating variables such as hostile tone of voice, criticism, and angry mood on 1 – 5 scale.
  • 24 months after the initial assessment, researchers coded maternal and child aggression during multiple 5 – 10 minutes sessions (e.g., free play, structured play, cleaning the room, eating snack etc.). The coderes rated duration, intensity (1 – 4 scale) and number of aggressive episodes that included physical and verbal aggression toward a person or an object.

Results

• Mothers who used cocaine during pregnancy displayed more negative affect 13 months after delivery (M = 4.66, SD = .80) than mother who did not use cocaine (M = 4.36, SD = .54; F = 7.84, p <.01). 
• As shown in Table 1, cocaine-using mothers were also more aggressive toward their children two years later (F = 2.93, p <.05). These results did not change when controlled for education and other substance use or when children in foster care were excluded. 
• The effect of cocaine during pregnancy on maternal aggression two years later was mediated by maternal negative affect coded at the 13-month session (B = .36, p <. 05).
• Maternal and child aggression 24 months after delivery were strongly linked (r = .53; p < .05).
• However, cocaine status group was not associated with child aggression two years later. 

Table 1. Mean (SD) group differences in maternal aggression toward 24-month-olds.
 	

 
Non-cocaine group
Cocaine group
F
Duration of maternal aggressive episodes (in seconds)*
47.74 (75.44)
89.20 (109.70)
7.86
Number of maternal aggressive  episodes*
5.04 (4.19)
7.34 (6.05)
8.23
Rated intensity of aggressive episodes (1 – 4 scale) *
1.63 (1.09)
2.09 (1.34)
5.9

* p < .01

Limitations

• The authors use multiple methods to determine prenatal cocaine use, however none of this method guarantees actual use 4 – 8 weeks before.
• It is not possible to conclude that cocaine use caused maternal negative affect at 13 months and maternal aggression at 24 months because cocaine use could be a marker for other variables linked to aggression not measured in this study.
• The observers assessed mother-child interactions in an artificial context. Mothers might have attempted to alter their behavior for the coders.

Conclusion

Mothers who used cocaine during pregnancy displayed more negative affect toward their children 13 months later. This in turn, was positively associated with duration, number of episodes, and intensity of aggression toward their 24-month old toddlers. In addition, the results showed a significant positive association between mother and child aggression. Further studies should explore the direction of influence for child and mother aggression. This study has demonstrated that it is important to assess mother’s aggression and affect and include them as potential variables in models that predict the effects of cocaine use on childhood aggression.

Also, on a practical level, cocaine use during pregnancy may be used as an indicator of need for child abuse prevention efforts.

-Julia Braverman

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References

Sood, B. G., Nordstorm B. B., Covington, C. Sokol, R. J.,Ager, J., Janisse, J. et al. (2005). Gender and alcohol moderate caregiver reported child behavior after prenatal cocaine. Neurotoxicology Teratoloty, 27, 191-201

Eiden, R. D., Schuetze, P., Colder, C.R., & Veira, Y. (2011). Maternal cocaine use and mother-toddler aggression. Neurotoxicology and Teratology, 33, 360-369.

STASH, Vol. 8(7) - In the moment: Proximal antecedents of marijuana use

The Addiction Syndrome (Shaffer, LaPlante, & Nelson, 2012a; Shaffer et al., 2012b) model suggests that addictive behavior develops, in part, over time as a consequence of specific distal and proximal antecedents -- factors that create longer-term and shorter-term risk, respectively. Although researchers have given a great deal of scientific attention to longer-term risk factors, they have paid less attention to the proximal antecedents of addictive behavior (Najavits, 2012). This week’s STASH reviews analyses of ecological momentary assessments from marijuana users to identify common proximal events for marijuana use (Buckner, Crosby, Silgado, Wonderlich, & Schmidt, 2012).

Methods

• Researchers recruited 49 18-22 year old college students (39% female) who used marijuana during the past 3 months.
• Students used Personal Digital Assistants (PDA) for two continuous weeks to complete brief survey items (i.e., ecological momentary assessments (EMA)) in as close to real time as possible (i.e., ecological momentary assessments; EMA) under three conditions:
  • Signal contingent (i.e., random survey request initiated by PDA alarms)
  • Interval contingent (i.e., survey as pre-determined points, like bedtime)
  • Event contingent (i.e., surveys completed each time a participant was about to use marijuana)
• The surveys assessed a range of health-related behaviors, including marijuana use patterns, craving, state anxiety, and situational context (e.g., alone or with friends).

Results

• Mixed effects linear models (Figure) of “pre” and “post” marijuana use patterns for craving and anxiety suggested that:
  • Craving increased significantly prior to marijuana use, F (1, 1409.75) = 60.3, p < .001;
  • Craving decreased significantly following marijuana use, F (1, 1412.33) = 109.83, p < .001;
  • Anxiety did not increase significantly prior to marijuana use, F (1, 1410.34) = .04, p > .1;
  • Anxiety did not decrease significantly prior to marijuana use, F (1,1413.73) = 2.91, p > .05.
• Nearly 80% of reported marijuana use occurred during social situations.

Figure 1: Momentary relationships between craving and anxiety, and marijuana use

Limitations

• This study included a very small sample size.
• The nature of the sample consisted of college students enrolled in psychology classes. Although college students are at high risk for marijuana use, this study should be repeated using other populations.
• The study advances knowledge because the EMA reduces the likelihood of memory effects on self-report; however, self-presentation effects might have affected students’ reports.

Concluding Thoughts

Few studies have examined real-time precipitants of addictive behavior, so this study advances what we know about in-the-moment initiation of risky behavior. Specifically, this study suggests that craving and social situations elevate the risk for marijuana initiation among recent marijuana users. Contrary to popular opinion, marijuana use had a weak momentary relationship with anxiety. To advance the study of proximal antecedents, researchers should consider expanding the diversity of factors related to smoking marijuana, and look at the specificity of these factors for marijuana use. It is unclear whether the factors are specific to marijuana use or something more general.

-Debi LaPlante

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References

Buckner, J. D., Crosby, R. D., Silgado, J., Wonderlich, S. A., &  Schmidt, N. B. (2012). Immediate antecedents of marijuana use: An analysis from ecological momentary assessment. Journal of Behavior Therapy and Experimental Psychiatry, 43, 647-655.

Najavits, L. M. (2012). Proximal Influences on Addiction. In H. J. Shaffer, D. A. LaPlante & S. E. Nelson (Eds.), The APA Addiction Syndrome Handbook (Vol. 1. Foundations, Influences, and Expressions of Addiction, pp. 3-25). Washington, DC: American Psychological Association Press.

Shaffer, H. J., LaPlante, D. A., & Nelson, S. E. (Eds.). (2012). The APA Addiction Syndrome Handbook (Vol. 1. Foundations, Influences, and Expressions of Addiction). Washington, D.C.: American Psychological Association Press.

Shaffer, H. J., LaPlante, D. A., & Nelson, S. E. (Eds.). (2012). The APA Addiction Syndrome Handbook (Vol. 2. Recovery, Prevention, and Other Issues). Washington, D.C.: American Psychological Association Press. 

 

STASH, Vol. 8(6) - Something to cry about: Harsh physical punishment and future risk for drug and alcohol use disorders

Children who experience corporal punishment are at higher risk for later aggression, delinquency, and overall poor mental health, including substance abuse/dependence (e.g., Gershoff, 2002; MacMillan, Boyle, Wong, Duku, Fleming & Walsh, 1999). However, due to measurement issues, it is unclear whether these associations are found outside the context of extreme childhood maltreatment (Baumrind, Larzelere, & Cowan, 2002). This week’s STASH reviews a nationally representative survey investigating the associations between childhood physical punishment in the absence of extreme maltreatment and a range of adult mental health conditions, including drug and alcohol use disorders (Afifi, Mota, Dasiewicz, MacMillan & Sareen, 2012).

Method

• Participants were 34,653 adults (age ≥20) who responded to the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC: Grant et al., 2005). The data were collected between 2004 and 2005.
  • Participants answered questions about their demographics, childhood experience, mental health, and other topics.
  • The physical punishment question was, “As a child, how often were you ever pushed, grabbed, shoved, slapped or hit by your parents or any adult living in your house?” Responses options ranged from “never” to “very often.”
  • Afifi and colleagues classified participants as having experienced harsh physical punishment if they responded “sometimes,” “fairly often,” or “very often”.
• To distinguish between physical punishment and extreme childhood maltreatment, Afifi and colleagues excluded participants who reported experiencing severe child maltreatment (e.g., severe physical abuse, sexual or emotional abuse).
• The NESARC researchers assessed lifetime Axis I and Axis II mental health conditions, including alcohol abuse/dependence and drug abuse/dependence, using a fully structured clinical interview
• Afifi and colleagues conducted logistic regression to examine if reports of physical punishment were associated with lifetime Axis I and Axis II disorders. They included gender, age, marital status, race/ethnicity, education, income, and history of family dysfunction¹as covariates in the model presented here. Those who experienced no physical punishment served as the reference group.

Results

• Six percent of participants (N = 1,258) reported experiencing physical punishment without more extreme childhood maltreatment. The remaining 19,349 experienced no physical punishment.²
• Of these 20,607 total respondents, 5,976 (29%) had a lifetime diagnosis of alcohol abuse/dependence and 1,518 (7%) had a lifetime diagnosis of any drug abuse/ dependence.
• Compared to those who did not report experiencing physical punishment, those who reported experiencing physical punishment as children were more likely to report experiencing later alcohol abuse/dependence (odds ratio = 1.59, 99.9%; confidence interval = 1.21 – 2.08, p ≤ 0.001) and later drug abuse/dependence (odds ratio = 1.53, 99.9% confidence interval = 1.06-2.20, p ≤ 0.001).
• Figure 1 presents the proportion of respondents in each group with lifetime diagnoses of any mood disorder, any anxiety disorder, any alcohol abuse/dependence, and any drug use/dependence:

Figure 1. Proportion of respondents in physical punishment and no physical punishment group.

Limitations

• The wording of the question that assessed physical punishment does not include punishment; it rather refers to “being pushed, grabbed or hit” in general. It is not clear if respondents interpreted the question as related to punishment exclusively.
• This is a retrospective study that assessed punishment by self-report memories, which might be faulty or biased.
• Because this study used a correlational design, conclusions about causality remain speculative.

Conclusion

This study demonstrated that harsh physical punishment experienced as a child, even in the absence of extreme maltreatment, is associated with alcohol and drug dependence/abuse later in life, as well as other mental health conditions. These findings inform parents as well as health care providers who give recommendation to parents about physical punishment. Future studies are necessary to examine the prospective association between physical punishment and these conditions, including potential underlying mechanisms such as chronic stress and the often-resulting physiological and emotional dysregulation that can result from such stress. While interpreting these results, the reader should take note that this study examined physical punishment that includes physical force more harsh than “customary” physical punishment (i.e., spanking).

-Julia Braverman

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

References

Afifi T. O, Mota N. P, Dasiewicz P., Macmillan H.L., Sareen J. (2012) Physical Punishment and Mental Disorders: Results From a Nationally Representative US Sample. Pediatrics 130, 1 -8

Baumrind, D. L., Cowan, R. E. & Phili, A. (2002) Ordinary physical punishment: Is it harmful? Psychological Bulletin, 128, 580-589.

Gershoff E.T. (2002) Corporal punishment by parents and associated child behaviours and experiences:a meta-analytic and theretical review. Psychological Bulletin, 128, 539-579.

Grant B. F, Hasin D. S, Stinson F. S, Dawson, D.A., June Ruan W, Goldstein R. B, Smith S. M, Saha T. D, Huang B. (2005) Prevalence, correlates, co-morbidity, and comparative disability of DSM-IV generalized anxiety disorder in the USA: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychological Medicine. 35, 1747–1759

MacMillan, H. L., Boyle, M. H., Wong, M. Y., Duku, E. K., Fleming, J. E., & Walsh, C. A. (1999). Slapping and spanking in childhood and its association with lifetime prevalence of psychiatric disorders in a general population sample. CMAJ: Canadian Medical Association Journal, 161, 805-809.

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¹ Family dysfunction is defined as whether a parent or other adult in the household had 1 or more of the following: (1) had a problem with alcohol or drugs; (2) went to jail or prison; (3) was treated or hospitalized for a mental illness; (4) attempted suicide; and/or (5) died by suicide.

² The numbers do not sum up into the overall sample size, because they include only responders to the specific questions and exclude those who experienced severe physical, sexual or emotional childhood maltreatment.