The Brief Addiction Science Information Source (BASIS)

STASH Vol. 4(4) – You cannot always get what you need: Racial/Ethnic disparities of opioid analgesic prescribing in U.S. emergency departments

Pain lasting more than 24 hours affects over one quarter of Americans each year (National Center for Health Statistics, 2006) and costs approximately $100 billion in lost productivity, lost income, and health care costs (Stewart, Ricci, Chee, Morganstein, & Lipton, 2003).  Previous research examining U.S. emergency department visits from 1997 to 1999 found racial/ethnic disparities in the prescribing of opioid analgesics for back pain and migraines (Tamayo-Sarver, Hinze, Cydulka, & Baker, 2003).  During 2001, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) established standards of care for the management of pain (Chapman, 2000).  This week’s STASH examines the rates of opioid prescribing before and after the implementation of JCAHO standards of care among visits to the emergency room for the treatment of pain in the U.S. (Pletcher, Kertesz, Kohn, & Gonzales, 2008). 

Pletcher, Kertesz, Kohn, & Gonzales (2008) analyzed prescriptions related to approximately 375,000 emergency room visits over the course of 13 years (1993-2005). The authors utilized data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a publicly available data set that uses a 4-stage probability sample design each year at randomly selected hospitals, excluding federal, military, and Veterans Administration hospitals, across the U.S.  NHAMCS data include patients’ demographics, reasons for visit, diagnoses, and treatment.  The authors analyzed NHAMCS data for patients with pain-related symptoms (e.g. pain, ache, soreness) and conducted logistic regression analyses to estimate the association between a doctor prescribing an opioid and race/ethnicity across time.

Figure 1: Percentage of emergency department pain-related visits for which a doctor prescribed an opioid analgesic by race/ethnicity and survey year (adapted from Pletcher, Kertesz, Kohn, & Gonzales, 2008)

During the study period, the percentage of pain related visits that resulted in an opioid prescription increased from 23% (95% CI, 21%-24%) during 1993 to 37% (95% CI, 34%-39%) during 2005 (significant linear trend, p<.001).  As figure 1 shows, doctors prescribed opioid analgesics to a higher percentage of white patients than non-white patients at all time points.  Compared to white patients, black patients were 36% less likely to receive an opioid analgesic for pain than white patients, Hispanics were 31% less likely and Asians were 27% less likely1.  To offset the differences in the prescribing of opioid analgesics doctors prescribed non-opioid analgesics at a higher rate for non-white (32%) than white (26%) patients.

There are several limitations to this study worth discussing.  First, the NHAMCS does not include substantial clinical information.  Specifically, the survey does not ask if the patient requested an opioid, the ability of patients to pay for a prescription, or in what quantity the doctor prescribed the opioid.  Second, the survey instrument does not record information about drug and alcohol abuse or dependency.  Lastly, hospital staff might have misclassified participant’s race/ethnicity because survey data is based on recording of staff perception of participant’s race/ethnicity rather than the participant’s self-report of race/ethnicity.

Although overall rates of opioid prescribing for pain have increased since hospitals implemented the JCAHO standards of care in 2001, a discrepancy in the rates of opioid prescribing between whites and other races and ethnicities remained constant throughout the study period.  Although it is possible that the discrepancy highlighted in this study is due to an over-prescribing among white-patients who abuse opioids, this does not appear to be the case because researchers found the same discrepancy in children, who are unlikely to be abusing opioids. These results suggest a true racial/ethnic bias among healthcare providers is involved and indicate that JCAHO and other health advocates will need to do more to decrease disparities in health care.  To attempt to explain discrepancies in doctors’ prescribing for pain relief, future versions of the NHAMCS could include information about patients’ alcohol and drug history, opioid prescribing history, and awareness of medical options to treat pain. 

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

Notes

1. Adjusted for age, sex, insurance, type of pain, cancer diagnosis, alcohol intoxication/withdrawal/abuse, alcohol dependence, drug intoxication/withdrawal/abuse, drug dependence, hospital location, owner and setting, and survey year.

References

Chapman, C. R. (2000). New JCAHO Standards for Pain Management: Carpe Diem! APS Bulletin   Retrieved January 24, 2008, from http://www.ampainsoc.org/pub/bulletin/jul00/pres1.htm

National Center for Health Statistics. (2006). Health. Hyattsville, MD: CDC.

Pletcher, M. J., Kertesz, S. G., Kohn, M. A., & Gonzales, R. (2008). Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments. Journal of the American Medical Association, 299(1), 70-78.

Stewart, W. F., Ricci, J. A., Chee, E., Morganstein, D., & Lipton, R. (2003). Lost productive time and cost due to common pain conditions in the US workforce. Journal of the American Medical Association, 290(18), 2443-2454.

Tamayo-Sarver, J. H., Hinze, S. W., Cydulka, R. K., & Baker, D. W. (2003). Racial and ethnic disparities in emergency department analgesic prescription. American Journal of Public Health, 93(12), 2067-2073.

STASH, Vol. 4(3) - OxyContin Use: Prescribed or Recreational?

OxyContin is an opioid agonist. Opioids are among the most potent analgesics; medical doctors prescribe analgesics to reduce or relieve pain (see STASH 1(2) for more information).  Like other opioids (both natural and synthetic), the Drug Enforcement Agency classifies OxyContin as a Schedule II substance (Drug Enforcement Agency, 2008).  Although doctors prescribe OxyContin, evidence suggests illicit use could be widespread among some populations (i.e., Davis and Johnson, 2008).  This week’s BASIS reviews a study by Carise, Leggett, Dugosh, et al. (2007), which explored rates of OxyContin use among a large sample of people seeking addiction treatment.  The data also provide information as to whether use was a regular function of prescribed use or, alternatively, illict and part of a pattern of persisting, multi-substance use problems.

As part of a large standardized intake interview, all admissions to 157 drug treatment programs self-reported information about their lifetime OxyContin use history (The Drug Evaluation Network System; Carise & Gruel, 2003).  The researchers also gathered information about other drug use patterns, past drug treatments, and whether participants took OxyContin for medical purposes or to experience its intoxicating effects recreationally.  The authors defined OxyContin abuse as having taken the drug to “get high or get a buzz,” and regular use as taking the drug more than 3 times per week for more than 1 year.  Three years of assessment interviews at treatment facilities in 22 states yielded 27,816 unique participants: 1,425 (5% of the total sample) used OxyContin, 1,243 (87% of users) regularly used OxyContin, 1,208 (97% of regular users) abused OxyContin, and 300 (24% of regular users) had a prescription.

Table 1.  Differences between treatment seekers who did and did not abuse OxyContin

Table 1 indicates that abusers of OxyContin were more likely than non abusers to be male, white, and to have used heroin, cocaine, and sedatives during their lifetimes.  Half of OxyContin abusers, but only 30% of non abusers, had received psychiatric medication during their lifetimes.  One third of the 300 people prescribed the drug sought additional medication through illicit means, and half (150 of 300) qualified for OxyContin abuse.

The authors might have identified a larger proportion of the sample as OxyContin abusers compared to other studies because their definition of abuse (i.e., use to get high) is less conservative than the more commonly used DSM-IV-TR definition (i.e., repeated impairment, failure to meet responsibilities, or negative consequences; American Psychiatric Association, 2000).   The admission interview did not ask participants which substances prompted their decision to seek treatment, so we do not know if OxyContin was the presenting problem or not.  The data is based on the self-report of participants, so they might have minimized or exaggerated their reports of abuse symptom severity depending upon the perceived value such reports during treatment intake.

Carise et al. offer findings that suggest only a small portion (5%) of substance treatment seekers use OxyContin.  Most patients obtained OxyContin illicitly (75%), and those with a prescription obtained additional medication illicitly (33%) or took the drug to feel intoxicated (56%). These findings suggest that with or without prescription, substance abuse treatment seekers include OxyContin as part of a larger pattern of substance abuse. To address this problem, improved systems for identification and treatment of people who abuse OxyContin is necessary to prevent illict use and unauthorized distribution. 

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

References
American Psychiatric Association. (2000). DSM IV-TR: Diagnostic and statistical manual of mental disorders--Text revision (Fourth ed.). Washington, D.C.: American Psychiatric Association.

Carise, D., & Gurel, O. (2003). Benefits of integrating assessment technology with treatment: The Drug Evaluation Network System project. In J. Sorenson, R. A. Rawson, J. Guydish & J. Zweben (Eds.), Drug abuse treatment through collaboration: Practice and research partnerships that work (pp. 181–195). Washington, DC: American Psychological Association.

Carise, D., Leggett Dugosh, K., McLellan, A. T., Camilleri, A., Woody, G. E., & Lynch, K. G. (2007). Prescription OxyContin abuse among patients entering addiction treatment. American Journal of Psychiatry, 164, 1750–1756.

Davis, W.R., Johnson, B.D. (2008). Prescription opoid use, misuse, and diversion among street drug users in New York City. Drug and Alcohol Dependence, 92(1-3), 267-276.

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

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

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

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


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

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

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

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

Note

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

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

References

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

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

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

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

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

STASH Vol. 4 (1) – Running with the (Red) Bulls: Is energy drink consumption fueling caffeine dependence among college students?

Approximately 70% of college students report not getting enough sleep (Engle-Friedman et al., 2003), and many students resort to caffeine consumption to remedy sleep deprivation. Since the late 1990’s, energy drinks like Red Bull have been supplementing coffee and soda as sources of caffeine (Ellis, 2006). According to the Mayo Clinic, many popular energy drinks contain as much or more caffeine as a cup of coffee (Mayo Clinic, 2007).  The increasing popularity of energy drinks with high caffeine levels among college students might lead to an increased number who suffer from the side effects of caffeine consumption (e.g., dehydration, increased blood pressure, irritability, nervousness, and the potential for substance dependence) (SAMHSA, 2007).  In this weeks’ STASH, we examine the prevalence of energy drink consumption among college students, reasons for use of these beverages, and some of the common side effects that result from such use (Malinauskas, Aeby, Overton, Carpenter-Aeby, & Barber-Heidal, 2007).

Malinauskas et al. (2007) recruited 496 college students to participate in a 19 question anonymous self-report survey about energy drink consumption. The respondents volunteered from a state university in the Central Atlantic region of the United States with an undergraduate population of approximately 18,000 students. Researchers varied the time of day and day of the week that they recruited students; they also stationed themselves at various public locations across the campus to diversify the sample. Investigators provided a self-administered survey, which asked for participant age and gender, the average number of energy drinks per month during the current semester, reasons for energy drink consumption, and side effects of energy drink consumption.      

This study does not report the number of students who refused to take part in the survey, consequently, it is not possible to calculate a participation rate.  However, among the 496 college students that did participate, 51% (N = 253) reported drinking more than one energy drink per month during the current semester.  Female college students (53%) were more likely than their male counterparts (42%) to report drinking more than one energy drink per month, χ² (1, N = 496) = 6.46, p = 0.01.  As Figure 1 shows, reasons for consuming energy drinks included: insufficient sleep (67%), to increase energy (65%), and to drink with alcohol (54%).  The most common side effects reported included jolt and crash episodes1 (29% of users), headaches (22%), and heart palpitations (19%).

Figure 1: College students’ reasons for consuming energy drinks according to the average number of drinks per month (Adapted from Malinauskas et al. 2007)

This study has several issues that limit the reliability and validity of the findings.  The survey instrument relied on participants’ retrospective self-report.  Recall makes it difficult for participants to accurately represent the frequency of energy drink consumption or the occurrence of side effects during a semester.   In addition, self-report responses on consumption of energy drinks without an independent confirmation make it impossible to determine the accuracy of the survey.  The survey did not measure other caffeine sources (e.g. soda, coffee, or caffeine pills), which might increase participants’ risk for experiencing side effects or developing tolerance for caffeine which might decrease their risk for side effects.  Researchers asked about participant’s frequency of consuming energy drinks for each of the six reasons listed, treating each as independent of the others; making it impossible to determine exactly how many drinks each participant was consuming per month.  Lastly, there is no mention of response rates, making it impossible to determine how representative the study sample is to all college students.

Despite these important limitations, the results of this study suggest that energy drinks are popular among college students.  Future research could examine daily caffeine consumption, the modes by which students consume caffeine (e.g. coffee, soda, energy drinks, food) and whether gender or grade point average (GPA) impact consumption of energy drinks.  Future research addressing these issues needs to obtain a representative sample.  In addition, survey instruments and methods should be more thorough in examining energy drink consumption, caffeine-related disorders (American Psychiatric Association, 1994), and the side effects of caffeine consumption. 

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

Notes
1. The feeling of increased alertness and energy (the jolt) followed by a sudden drop in energy (the crash) that occurs in response to using energy drinks.

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

Ellis, S. (2006). Energy drinks bubble up. The Christian Science Monitor   Retrieved January 03, 2007, from http://www.csmonitor.com/2006/0607/p13s01-lifo.html

Engle-Friedman, M., Riela, S., Golan, R., Ventuneac, A. M., Davis, C. M., Jefferson, A. D., et al. (2003). The effect of sleep loss on next day effort. Journal of Sleep Research, 12(2), 113-124.

Malinauskas, B. M., Aeby, V. G., Overton, R. F., Carpenter-Aeby, T., & Barber-Heidal, K. (2007). A survey of energy drink consumption patterns among college students. Nutrition Journal, 6(1), 35.

Mayo Clinic. (2007). How much caffeine is in your daily habit? Food and Nutrition   Retrieved January 03, 2008, from http://www.mayoclinic.com/health/caffeine/AN01211

SAMHSA. (2007). Energy Drinks: Power Boosts or Empty Boasts? A Family Guide To Keeping Youth Mentally Healthy & Drug Free   Retrieved January 03, 2007, from http://www.family.samhsa.gov/monitor/energydrinks.aspx

STASH Vol. 3(10) Anabolic steroid use and other risky behaviors: Not just a man’s issue.

During recent years, Centers for Disease Control and Prevention (CDC) researchers have observed an increase in prevalence rates of adolescent females’ self-reports of anabolic steroid use and other risky behaviors. These risky behaviors include, binge drinking, drug use, sexual behavior, and violence in school (Grunbaum et al., 2004).  For decades, researchers, health advocates, and the general public have associated these issues as being a “male problem” (Bahrke, Yesalis, & Wright, 1990; Egan, 2002; Potteiger & Stilger, 1994; "SCOUTING; School Lesson About Steroids", 1985) and targeted interventions for these activities primarily to males (See STASH Vol. 2(5)).  In this weeks’ STASH, we examine the anabolic steroid use of female high school students and its effect on various health behaviors.

Elliot et al. (2007) conducted an analysis of the CDC’s 2003 Youth Risk Behavior Surveillance System (YRBSS) dataset comparing health outcomes of steroid users to non-users among girls (N=7,447) in grades nine through twelve.  The YRBSS sampled participants using a three-stage cluster design (U.S. Region x School x Classroom) drawing a nationally representative sample of students in grades 9-12.  Participants completed a 95 question anonymous self-report survey regarding general health, substance use, violent behavior, and mental health.  Researchers conducted a logistic regression to estimate the association between anabolic steroid use and risky health behaviors; covariates in this analysis included race/ethnicity, grade level, and team sports participation.

About 5% (5.3%) of female U.S. high school students reported using anabolic steroids during their lifetime.  As Table 1 shows, adolescent female anabolic steroid users are more likely than female non-anabolic steroid users to also abuse other substances, engage in risky sexual behavior, attempt to lose weight in unhealthy ways (e.g., not eating for more than 24 hours), and suffer from recent depressive episodes and suicide attempts.  Researchers found participating in team sports to be a protective factor against anabolic steroid use, meaning those who participated in team sports were significantly less likely to use anabolic steroids (OR, 0.52; 95% CI,0.34-0.80).

Table 1: Comparing prevalence estimates of health-related behaviors for female U.S. high school students according to anabolic steroid use (Adapted from Elliot et al., 2007).

This study is not without limitations.  Researchers utilized a cross-sectional design.  A cross-sectional study allows researchers to ask questions about behavior and health outcomes during one point in time, does not allow examination of temporal relationships, and, therefore, limits our ability to understand causal influences.  The YRBSS data is based upon self-report, potentially leading to under-reporting of risky health behaviors and outcomes.  In addition, researchers only included participants who were present in school in their analysis.  Adolescents who do not attend school might engage in the most risky behaviors. If so, then this data evidences conservative estimates of risky health behaviors.

Despite these limitations, the prevalence of risky health behaviors among female students reporting anabolic steroid use raises important pubic health concerns.  These results suggest three things. (1) Female students using steroids might suffer from underlying psychiatric problems, requiring a more extensive treatment program such as counseling.  (2) Those who participated in team sports were less likely to use anabolic steroids, potentially indicating that women’s sports empower girls to make better health choices. These healthy choices might be mediated by increased self-esteem.  (3) It is possible that these results might be due in part to a greater awareness of the dangers of anabolic steroid use among female student athletes.  The benefits related to girls’ participation in team sports deserves further study.  In the meantime, school administrators and health advocates should consider implementing anabolic steroid treatment and prevention programs specific to adolescent females.

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

Reference

Bahrke, M. S., Yesalis, C. E., 3rd, & Wright, J. E. (1990). Psychological and behavioural effects of endogenous testosterone levels and anabolic-androgenic steroids among males. A review. Sports Medicine, 10(5), 303-337.

Egan, T. (2002). Body-Conscious Boys Adopt Athletes' Taste for Steroids [Electronic Version]. The New York Times. Retrieved 12/5/2007 from http://query.nytimes.com/gst/fullpage.html?res=9E00E3DF1239F931A15752C1A9649C8B63&sec=&spon=&pagewanted=all#.

Elliot, D. L., Cheong, J., Moe, E. L., & Goldberg, L. (2007). Cross-sectional study of female students reporting anabolic steroid use. Archives of Pediatrics & Adolescent Medicine, 161(6), 572-577.

Grunbaum, J. A., Kann, L., Kinchen, S., Ross, J., Hawkins, J., Lowry, R., et al. (2004). Youth risk behavior surveillance--United States, 2003. MMWR Surveill Summ, 53(2), 1-96.

Potteiger, J. A., & Stilger, V. G. (1994). Anabolic Steroid Use in the Adolescent Athlete. Journal of Athletic Training, 29(1), 60-64.

SCOUTING; School Lesson About Steroids. (1985).   Retrieved December 5, 2007, from http://query.nytimes.com/gst/fullpage.html?sec=health&res=9A0DE3D91430F936A35755C0A960948260&n=Top/News/Health/Diseases,%20Conditions,%20and%20Health%20Topics/Steroids

STASH Vol. 3(9) - The Tortoise or the Hare: Comparing Physiological & Subjective Effects of Methamphetamine and Cocaine

Conventional wisdom asserts that drugs of abuse have different patterns of action and that a drug of choice among abusers is the drug that best fits abusers’ preferred course of action (Khantzian & Shaffer, 1981; Mathias, 2001; Shaffer & Simoneau, 2001). However, empirical evidence about the matter also indicates that an abuser’s drug of use also depends on its ease of access, ingestion, and surrounding social circumstances (Harford, 1978). In this issue of STASH, we discuss Newton, De La Garza II, Kalechstein, and Nestor’s (2005) comparison of the subjective and physiologic effects of cocaine and methamphetamine. 

In this single-blind experiment, researchers administered saline placebo and 40mg of cocaine to 14 non-treatment seeking cocaine dependent volunteers and saline placebo and 30mg of methamphetamine to 11 non-treatment seeking methamphetamine dependent volunteers. Researchers had detoxed patients for 3 days prior to the study. They measured how “high” the subjects were by obtaining their subjective ratings of adjectives. The researchers recorded cardiovascular responses by automatically monitoring heart rate, and systolic and diastolic blood pressure (BP). Both the physiological and subjective measures were obtained at -15, 3, 6, 10, 15, 20, and 30 minutes after injection of saline placebo followed by the active drug of choice. Heart rate also was measured at 60 minutes post injection. Researchers used repeated measures analyses of variance (ANOVA) and Huynh-Feldt sphericity correction to calculate time and group by time effects from the observed data. 

Figure 1 shows that the participants’ description of “highs” correlated with heart rates; this was evidenced for both cocaine (red lines) and methamphetamine (blue lines). Methamphetamine produced a later onset and longer surge (square markers) than cocaine (diamond markers). Methamphetamine’s cardiovascular effects were felt at five minutes, peaked at 10 minutes, and remained relatively high for an extended period of time. Cocaine, on the other hand, caused an immediate reaction that peaked after10 minutes for heart rate. However, after the peak, the effects of cocaine diminished rapidly and the users returned to almost their initial, baseline state.

Figure 1: Cardiovascular and Subjective Effects: Methamphetamine vs. Cocaine (Adapted from: Newton et al, (2005))

This study has some limitations. First, the cocaine and methamphetamine groups might not be representative of the cocaine and methamphetamine dependent populations. (Substance Abuse and Mental Health Services Administration, 2007a, 2007b; Winslow, 2007). Second, researchers administered both drugs intravenously, which can result in a different rate of absorption than other routes of administration for these users. Users often have several routes of administration (e.g., oral, inhaled, intravenous). Third, laboratory settings are very different from the in vivo contexts within which users administrate their drugs. Research has shown that the social setting influences subjective state more than most observers recognize (Zinberg, 1984).

Despite these limitations, the mean heart rate changes and the participants’ descriptions of subjective effects indicate a correlation between substance user perceptions, drug induced physiological effects, and the need that these circumstances create for drug use. An understanding of the preferred type or attributes of drug effect could help researchers and practitioners to better redirect craving and find healthier substitutes. For example, a methamphetamine user could redirect addictive tendencies towards another stimulating activity (e.g., cross country running), which somewhat mimics, but is healthier, than stimulant use.

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

References

Harford, R. J. (1978). Drug Preferences of multiple drug abusers. Journal of Consulting and Clinical Psychology, 46(5), 908-912.

Khantzian, E. J. (1975). Self selection and progression in drug dependence. Psychiatry Digest, 36, 19-22.

Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231-244.

Mathias, R. (2001). Even Modest Cocaine Use May Cause Brain Changes That Could Contribute to Addiction. NIDA Notes, 16(3).

Newton, T. F., De La Garza II, R., Kalechstein, A.D. , Nestor, L. (2005). Cocaine and methamphetamine produce different patterns of subjective and cardiovascular effects. Pharmacology, Biochemistry and Behavior, 82(1), 90-97.

Simon, S. L., Richardson, K., Dacey, J, Glynn, S, Domier, C.P., Rawson, R.A., et al. (2002). A comparison of patterns of methamphetamine and cocaine use. Journal Addictive Disorders, 21(1), 35-44.

Substance Abuse and Mental Health Services Administration. (2007a). The NSDUH Report: Demographic and Geographic Variations in Injection Drug Use.Rockville.

Substance Abuse and Mental Health Services Administration. (2007b). The NSDUH Report: Worker Substance Use, by Industry Category.Rockville.

Winslow, B. T., Voorhees, K.I, Pehl K.A. (2007). Methamphetamine Abuse. American Family Physician, 76(8), 1169-1174.

Zinberg, N. E. (1984). Drug, Set, and Setting: The Basis for Controlled Intoxicant Use. New Haven: Yale University Press.

STASH Vol. 3(8) - Does Homelessness contribute to Drug Use? Analyzing the Social Adaptation Model

Research shows higher lifetime and six month prevalence of substance use disorders among the homeless than found among non-homeless populations (Fischer, Shapiro, Breakey, Anthony, & Kramer, 1986; Koegel, Burnam, & Farr, 1988).  The social adaptation model (Stark, 1987) suggests that homelessness might be a risk factor for substance use.  This week’s STASH reviews a study that examines the relationship between homelessness and substance use.

Using a multistage probability design, trained interviewers from the University of Illinois at Chicago Survey Research Laboratory surveyed English speaking adults between the ages of 18 and 40 living in the city of Chicago between June 2001 and January 2002 (Johnson & Fendrich, 2007).  Participants (n=627, response rate = 40%) reported their lifetime and most recent substance use (i.e., tobacco, alcohol, marijuana, cocaine, crack, heroin, hallucinogens, inhalants, stimulants, tranquilizers, sedatives, and pain relievers), frequency and age of onset of substance use, and onset and recency of homelessness.  Researchers used bivariate and multivariate statistics to analyze the association between early homelessness (i.e., before age 19) and recent substance use.

Table 1: Association between First Homeless Experience and Recent Drug Use 



Adapted from (Johnson and Fendrich, 2007)

Analysis revealed that 66.7% of participants who had experienced homelessness prior to 19 years of age (n=45) also had used substances within the past year. In comparison, only 31% of those lacking early homeless experiences (n=583) reported substance use within the past year (χ₂=23.76, df=1, p<.001; cf. Table 1). Multivariate analyses, controlling for the age of first substance use, also yielded a significant link between early homelessness and recent substance use (unstandardized coefficient=0.23, standard error=0.08, p<0.01).This study has several limitations. First, self-report often results in participants underestimating their socially undesirable behaviors (e.g., substance use, homelessness). Second, as a result of the low response rate (40%) and exclusion of currently homeless people, the participants might not accurately represent the Chicago homeless population.

Third, other factors known to be related to homelessness (e.g., childhood abuse and history of mental illness; Koegel, Melamid, & Burnam, 1995), were not measured; without accounting for these risk factors, the study cannot measure the independent effect of early homelessness on substance use.

Despite the limitations, this study does indicate an association between homelessness and substance use. Perhaps homeless people start using drugs to better deal with their daily struggles on the street or they might be introduced to drugs within homeless shelters. Alternatively, excessive substance use might lead to financial ruin or involvement in other illegal activities, resulting in homelessness.  Whatever the reasons for the association between drug use and homelessness, findings from this study suggest that public health interventions should include efforts to address both the potential for homelessness among drug users and the potential for drug use among homeless people.

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

References

Fischer, P. J., Shapiro, S., Breakey, W. R., Anthony, J. C., & Kramer, M. (1986). Mental health and social characteristics of the homeless:  a survey of mission users. American Journal of Public Health, 76, 519-524.

Johnson, T. P., & Fendrich, M. (2007). Homelessness and Drug Use:  Evidence from a community sample. American Journal of Preventative Medicine, 32(6S), S211-S218.

Koegel, P., Burnam, A., & Farr, R. K. (1988). The prevalence of specific psychiatric disorders among homeless individuals in the inner city of Los Angeles. Archives of General Psychiatry, 45, 1085-1092.

Koegel, P., Melamid, E., & Burnam, A. (1995). Childhood risk factors for homelessness among homeless adults. American Journal of Public Health, 85, 1642-1649.

Stark, L. (1987). A century of alcohol and homelessness:  demographics and stereotypes. Alcohol Health and Research World, 11, 8-13.

STASH Vol. 3(7) - How Marijuana use among American Indian Youth Stacks Up

In a previous issue of STASH 2(6), we reported University of Michigan’s Monitoring the Future (MTF) study showing an elevated rate of marijuana use among Latino adolescents compared to non-Latino adolescents in the United States (US).  This week we will compare US trends in marijuana use among American Indian (AI) adolescents with non-AI adolescents. 

Beauvais, Jumper-Thurman, Helm, Plested, and Burnside (2004) report on an annual survey of  7th-12th grade AI youth living on or near a reservation from 1975 to 2000. During school, researchers surveyed between 1,500 and 2,500 adolescents annually about lifetime marijuana use. Beauvais et al. used MTF data about lifetime marijuana use among a nationally representative sample of about 16,000 12th graders annually as a non-AI comparison group. The authors did not conduct statistical tests to determine whether there were significant differences in marijuana use between the AI and non-AI samples.

Figure 1.  Prevalence (%) of 7-12th grade American Indian (AI) and non-AI 12th graders who have ever used marijuana

Figure 1 shows that although the AI group is younger, their use of marijuana is higher than the non-AI sample. Trends in marijuana use from 1975 to 2000 are similar for both groups. There is an increase in marijuana use until the early 1980s, a decline in marijuana use until the early 1990s and an increase again during the early 1990s. 

This study has some limitations. Findings describing AI marijuana use among this sample might not be representative of all AIs. Grouping all AIs together ignores large differences between various subgroups and cultures.  Therefore this statistical representation might ignore considerable variation within the AI population.  Both the AI and non-AI samples might have lower than typical marijuana use rates because this study used self-report measures and collected this information only from adolescents attending school. Self-reports in school might have biased the extent of marijuana use reports; further, dropouts and absentees might have been more likely to use marijuana. 

Despite these limitations, this evidence seems to show reliably that AI youth report higher rates of lifetime marijuana use than the non-AI population. Higher lifetime marijuana use might be due to single, ritualistic practices among AIs, more permissive attitudes toward marijuana use than non-AIs, or more accessibility to marijuana use for AI living in some areas.  Future research could explore AI use rates by more specific factors such as tribe, cultural affiliation, or geographic location to inform the development of public health interventions specific to AI populations.

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

References

Beauvais, F., Jumper-Thurman, P., Helm, H., Plested, B., & Burnside, M. (2004). Surveillance of drug use among American Indian adolescents: Patterns over 25 years. Journal of Adolescent Health, 34, 493-500.

Johnston, L. D., O'Malley, P.M., Bachman, J.G., Schulenberg, J.E. (2007). Monitoring the Future national survey results on drug use, 1975-2006. Volume I: Secondary school students (No. NIH Publication No. 07-6205). Bethesda, MD: National Institute on Drug Abuse.

The STASH Vol. 3(6) - Sufferin’ Succulents!? Lack of Residual Psychological Effects from Peyote Use

Peyote is a small, spineless cactus containing the hallucinogen mescaline. The Native American Church (NAC) expects members to abstain from any drug or alcohol use except peyote, which is taken only during religious ceremonies (Stewart, 1987). This circumstance creates a unique opportunity to study the psychological effects of long-term peyote use, without confounding effects from additional drug use. This week’s STASH reviews just such a study conducted on a sample of Navajo Native Americans.

Halpern, Sherwood, Hudson, Yurgelun-Todd, and Pope (2005) recruited 61 Navajo NAC members with a history of extensive ritual use of peyote (i.e., ingested peyote at least 100 times) and 79 Navajos who report minimal use of peyote, alcohol or other substance use. Researchers excluded people with a history of psychological disorders or head injury, those who self-reported illicit substance use other than peyote, and people with other markers of severe illness. Researchers use multiple linear regression to adjust for age and gender so they could properly compare the peyote users to the control group on non-users. Table 1 presents the comparison for measures from the Rand Mental Health Inventory.

   

There were no differences between study groups on demographic characteristics. The mean age of all study participants was approximately 30 years, and the majority of study participants were female and middle class. More than half of the sample had a high school education or less. Of the people contacted for the peyote and control groups, 12% did not meet inclusion criteria. The peyote group evidenced significantly more general positive affect and psychological well-being than the control group. For none of the measures were the peyote users less mentally healthy than the control group.

This study has some limitations. First, retrospective self-report of drug use is subject to self-presentation biases. For example, some participants might have underreported prior drug or alcohol use for fear of legal sanctions. Second, the peyote users belonged to a religious community and the control group did not. Therefore the increase in metal health of the peyote group might have been due to the effects of belonging to such a community. Furthermore, members of the community who might have experienced negative psychological effects from peyote might have been more likely to stop using it or not to enroll in the study; thus, these people were not included in the experimental group Also, all study participants were recruited with the assistance of a Navajo case finder so they might not be representative of this Navajo community. Third, because of the unique cultural characteristics of this sample, researchers cannot generalize study findings to other populations of peyote users or users of other types of hallucinogens (e.g., LSD).

Despite these limitations, this study demonstrates an absence of any peyote-related adverse psychological effects among a sample of Navajo Native Americans who were taking the drug within the context of religious ceremony.

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

References

Halpern, J. H., Sherwood, A. R., Hudson, J. I., Yurgelun-Todd, D., & Pope, H. G. (2005). The psychological and cognitive effects of long-term peyote use among Native Americans. Biological Psychiatry, 58, 624-631.

Stewart, O. (1987). Peyote religion: A history. Tulsa: University of Oklahoma Press.

STASH Vol. 3(5) - Internet Based Home-Drug Tests: Parents Use Questionable Means to Test Their Teens

The Internet is an unregulated marketplace for drugs and drug-testing products. In the last issues of STASH 3(3), 3(4), we reviewed studies examining Internet accessibility of illicit drugs (e.g., opioids, ecstasy), and Internet information about these illicit drugs. Internet sites also make available products that claim to test illicit drug use. This week’s STASH reports a study that examined whether Internet based home-drug testing sites provide complete and accurate information for parents who decide to test their teenagers’ for drug use (Levy, Van Hook, & Knight, 2004).

Levy, Van Hook, and Knight (2004) conducted Internet searches for home drug-testing and selected for further analyses eight sites containing parental advice sections. Two of the authors reviewed the content of each site and noted the product claims, the possibility of inaccuracy, as well as advice to the parents concerning follow-up procedures.

Table 1 shows that all eight of the home drug testing kits claimed that their products would reveal whether a child used drugs, but only three discussed the potential for false negative or false positive findings. The majority of the sites claimed that home drug testing can reduce the effects of peer pressure. One site explicitly warned parents not to test their children without consent. Half of the sites stressed the importance of parents consulting a medical professional about test results and/or teen behavior.

Table 1. Content of Internet based Home Drug-testing Sites (Adapted from Levy, Van Hook, & Knight (2004, p. 724)

This study has some limitations. The current study only reviews home drug-testing websites with specific parental advice sections; consequently, these sites might not be representative of all of Internet home drug-testing websites. Although this study employed a small sample, the findings suggest that many Internet-based home drug-testing kits do not provide parents with essential information, for example, about the risks of false positive and false negative tests, specimen collection procedures, or the importance of consulting a health professional.

Given the complex nature of drug testing, health professionals should advise parents of the limitations and risks associated with these tests. Similarly, parents who choose to purchase these products should use extreme caution. Finally the results of this study suggest a need to regulate these and similar sites that claim to provide health related information and resources.

What do you think? Let us know. Comments can be sent to Erinn Walsh.

References

Levy, S., Van Hook, S., & Knight, J. (2004). A Review of Internet-Based Home Drug-Testing Products for Parents. Pediatrics, 113(4), 720-726.

STASH Vol. 3(4) - Addiction and Technology - Websites about Ecstasy: Which Sources of Information are Drug Users Using?

The Internet is a plentiful source of information about illicit drugs. In the last issue of STASH (3(3)), we reported a study which examined Internet sites offering to sell non-prescription opioids. The Internet also can be a source of information about illicit drugs. This week’s STASH reviews a study of Methylenedioxymethamphetamine (MDMA, i.e., ecstasy) users’ perceptions about the accuracy of available sources of educational MDMA information. Investigating information that might influence a decision to take an illicit drug can shed light on how best to create effective public health interventions to prevent and minimize harms associated with such drug use.

Falck, Carlson, Wang, and Siegal (2004) employed a snowball sampling method to generate the study sample. To be eligible for the study, participants were 18 to 30 years of age, not currently enrolled in drug treatment, and had used MDMA at least once during the past six months. Identified eligible participants then named acquaintances who were added to the pool of potential participants. Research assistants conducted person-to-person structured interviews asking participants about their sociodemographic characteristics and their perceived accuracy of six main sources of information about MDMA (i.e., US government sources, drug abuse treatment programs, physicians, parents, friends, and other drug users). The authors used descriptive statistics to rank order the sources of information according to accuracy; they used analysis of variance (ANOVA) to test the relationships between participants’ characteristics and Internet use.

Adapted from Falck et al. 2004

The sample of 304 participants was predominantly white (81.6%), with a mean age of 21.2 (SD=2.8) years, and 50.3% had at least some college education. Half of the sample reported using the Internet as an informational resource to learn about MDMA. Younger and more educated people tended to seek information using the Internet  more than older (F2302=5.47, P=0.005) or less educated (F2302=3.13 P=0.004) participants. Of those participants using the Internet, more than four times as many participants used non-government websites (e.g., http://erowid. com/, http://dancesafe.org/) compared to government websites (e.g., www.NIDA.gov, 28% verses 6.6%). As shown in Figure 1, participants rated friends (45.7%) and Drug Treatment Programs (37%) as very accurate sources of information about MDMA and rated government related resources (10.2%) and parents (5.9%) as the least accurate sources of information about MDMA.

This study has several limitations. First, the authors’ main focus was sources of information about MDMA rather than specifically the nature of information for which the subjects were looking. For example, someone could have been looking for information about the drug experience, others about possible risks. Second, snowball sampling creates a potentially biased sample because people are more likely to invite acquaintances with similar demographic characteristics. Therefore, the study findings might not generalize to all MDMA users. Third, study participants might not report using resources that create cognitive dissonance with their current drug use patterns such as parents and government sources.

The study found that the resource MDMA users perceived as most accurate (i.e., friends) is the source least likely to provide complete or accurate information about MDMA. Friends might be a resource for descriptions of MDMA experiences (e.g., where to get MDMA), but not for reporting adverse cognitive consequences. The sources considered as the next most accurate (i.e. drug treatment programs and physicians) inform users as to the adverse consequences of MDMA. This information derives from experience treating people who use MDMA. Because government sources do not claim to have direct experience treating users, and certainly not experience using MDMA, those seeking information might not trust the accuracy of this source. These findings suggest that effective public health efforts to minimize or prevent harms associated with MDMA should include some information based on the personal experiences of those treating users or taking the drug. More research will be necessary to clarify the efficacy of this strategy.

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

References

Falck, R. S., Carlson, R. G., Wang, J., & Siegal, H. A. (2004). Sources of information about MDMA (3,4­methylenedioxymethamphetamine): perceived accuracy, importance, and implications for prevention among young adult users. Drug and Alcohol Dependence, 74, 45-54.

STASH Vol. 3(3) - Addiction and Technology: Websites Selling Opioids without a Prescription

The National Drug Use and Health Study (Substance Abuse and Mental Health Services Administration 2004), the Drug Abuse Warning Network (Substance Abuse and Mental Health Services Administration 2005), and the Monitoring the Future Study (Johnston, O'Malley, and Bachman 2005) note an increase in opioid use (e.g., Codeine, Vicotin, OxyContin) among young people during the last five years. The reason for this increase in opioid use is unknown. In the last issue of ASHES 3(3), we reviewed a study showing that minors are able to purchase cigarettes over the Internet. It is important to examine whether the increased use of other illicit substances might be due to increased accessibility via the internet (Working Group on Unlawful Conduct on the Internet 1999) To pursue this point, this week’s STASH examines the prevalence of websites selling opioids on the Internet without a prescription.

Forman, Woody, McLellan, and Lynch (2006) conducted Google searches during August 2004 using 27 opioid search terms (i.e., generic and brand name opioids) with and without the prefix “no prescription.” Two coders independently examined the first 20 links (i.e., the links most likely to be influential to Internet users) generated through these searches and categorized each link as: 1) no prescription website (NPW) if it sold opioids directly or through a portal website without a prescription; 2) addiction information websites (AIW) if it contained information about opioids, drug use or drug treatment (e.g., www.nida.nih.gov); or, 3) other if it was a legitimate link to an online pharmacy, a broken link or a different type of link (e.g., name of the rock group Codeine). Discrepancies in classification between the coders were reconciled by having both raters look at the link together and discuss the classification (i.e., inter-rater reliability was not calculated).

Adapted from (Forman et al. 2006)

As shown in Figure 1, the proportion of NPWs or AIWs varies across the opioid search terms. Some search terms such as no prescription hydrocodone, no prescription Vicodin, and no prescription OxyContin yielded more NPWs than AIWs. Alternatively, some search terms such as no prescription buprenorphine and no prescription Fentanyl yielded more AIWs than NPWs.

This study has some limitations. This is an exploratory study: it is not based on a priori hypotheses. Therefore, the information provided is simply descriptive. It is important to keep in mind that researchers made no attempt to buy opioids from these websites, so we do not know if it really is possible to purchase opioids from these sites without a prescription.

Despite these limitations, this is the first published study that conducts an examination of the prevalence of websites reporting that they sell opioids without a prescription. Findings from this study suggest that Internet users can obtain opioids illegally via the Internet and that NPWs often are more prevalent than websites providing educational information about these drugs. These findings are particularly worrisome because young people might have an easier time purchasing opioids over the Internet (e.g., using their parents’ credit card) than they would purchasing such drugs in person at legal venues (e.g., a pharmacy). To address this growing public health concern, there is a need to strengthen existing legal and medical regulations to prevent illegal selling of opioids via the Internet and improve dissemination of literature to educate the public about the dangers of using opioids without medical supervision.

What do you think? Comments can be sent to Allyson Peller.

References

Forman, Robert F., George E. Woody, Thomas McLellan, and Kevin G. Lynch. 2006. The Availability of Web Sites Offering to Sell Opioid Medications Without Prescriptions. American Journal of Psychiatry 163 (7):1233-1238.

Johnston, Lloyd D., Patrick M. O'Malley, and Jerald G. Bachman. 2005. Monitoring the Future: National Results on Adolescent Drug Use. Bethesda, MD: U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health.

Substance Abuse and Mental Health Services Administration. National Survey on Drug Use and Health 2004 [cited February 14, 2006. Available from http://oas.samhsa.gov/nsduh.htm#NSDUHinfo.

Substance Abuse and Mental Health Services Administration. Drug Abuse Warning Network 2005 [cited. Available from https://dawninfo.samhsa.gov/default.asp.

Working Group on Unlawful Conduct on the Internet. The White House Executive Order 13133 1999 [cited. Available from http://nodis3.gsfc.nasa.gov/displayEO.cfm?id=EO_13133_.

STASH Vol. 3(2) - Cocaine Use during Pregnancy: What are the Long-Term Effects on the Child?

Cocaine use during pregnancy results in exposure to a toxic substance during a key time in the neurolbiogical development of the fetus. The full effects of this exposure on, for example, the subsequent cognitive ability are unknown until the child is capable of more complex intellectual skills (Thadani, 1995; Vorhees, 1986) Therefore, a longitudinal study is necessary to isolate these effects. However, isolating the effects of cocaine exposure is difficult because the same exposure carries with it correlated problems within the child’s care-giving environment that also might interact with cognitive functioning (Richardson, 1996; Singer, 2001). This week’s STASH discusses a longitudinal study that examined the cognitive development and quality of the care-giving environment for cocaine exposed children from birth to four years of age.

Singer and colleagues’ (Singer, Minnes, & Short, 2004) conducted a longitudinal study that enrolled 415 participants pregnant women (281 cocaine positive and 197 cocaine negative) from an initial cohort of 647 identified by the researchers as at high-risk for cocaine use. Some women did not participate in the study because complete information could not be collected (n = 54), or they refused to participate (n = 155) or they did not show for initial interviews (n = 23). Researchers identified infants as cocaine exposed or not cocaine exposed based on biological specimens (e.g., urine screens for the mother, meconium samples for the infant) collected immediately after birth and maternal self-report of cocaine use during pregnancy. The researchers compared the demographics of the mothers of both groups of children, and information obtained at four years of age on measures of intelligence from the Wechsler Preschool and Primary Scales of Intelligence Test and measures of the quality of the care-giving environment obtained from interviews with the mothers or custodians using the Home Observation of the Environment: Preschool Version (HOME). Chi square tests and multivariate analyses tested the significance of the differences in cognitive ability and care-giving environment between groups defined by prenatal cocaine exposure (i.e., exposed versus not exposed). Researchers obtained assessments from 93% of the study participants at the 4-year follow-up period.

Mothers of the cocaine exposed children were less likely to be married, to have completed high school, or to have prenatal care; they were more likely to already have more children before they had the target child of the study. As Table 1 shows, there was no significant difference between overall cognitive ability and care-giving environment between the cocaine exposed and non-exposed children at four years of age. However, cocaine exposed children had significantly lower cognitive ability on three subscales (i.e., arithmetic, object assembly, and information skills).

Table 1. Care-giving Environment and Cognitive Ability Scores for Children at 4 years of age (adapted from Singer et al.)

* Home Observation of the Environment: Preschool Version score (HOME) ** Wechsler Preschool and Primary Scales of Intelligence Test scores
† adjusted for HOME score, parity and caregiver characteristics

The authors made some conclusions about the effects the care-giving environment had on IQ scores of the children by comparing both groups of children foster or adoptive care to those living with their mothers. However, the number of children in this study placed in non-relative foster or adoptive care is small, so it is not possible to draw definitive conclusions about the effects of foster or adoptive care on the cognitive development of cocaine exposed children.

There were some limitations to this study. Many women who use cocaine also use other licit and illicit substances, and many at-risk mothers abuse other substances but not cocaine. For example, there is significant evidence that prenatal exposure to alcohol causes significant impairment in exposed children’s cognitive ability like fetal alcohol syndrome (Steinhausen, 1998). Therefore it is difficult for researchers to isolate the effects of cocaine without the contributing effects of exposure to other substances.

Despite these limitations, this study prospectively examined the long-term effects of prenatal cocaine exposure among a large cohort of high-risk children. Findings suggest that prenatal cocaine exposure is not associated with significantly lower overall cognitive ability at 4 years of age but is associated with significantly lower scores on three subscales of cognitive ability. This study did not examine the effects of support services for this high risk population. Future studies could examine effective early interventions and community programs designed to support biological mothers and foster/adoptive caregivers and their drug exposed children.

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

References

Richardson, G. A., Controy, M.L., Day, N.L. (1996). Prenatal cocaine exposure: effects on the development of school age children. Neurotoxical Teratol, 18, 627-634.

Singer, L. T. (2001). General Issues in Infant Assessment and Development. In L. T. Singer, Zeskind, P.S. (Ed.), Biobehavioral Assessment of the Infant (pp. 15-40). New York, NY: Guilford Press.

Singer, L. T., Minnes, S., & Short, S., et. al. (2004). Cognitive Outcomes of Preschool Children with Prenatal Cocaine Exposure. JAMA, 291(20), 2448-2455.

Steinhausen, H.-C., and Spohr, Hans-Ludwig. (1998). Long-term outcome of children with fetal alcohol syndrome: Psychopathology, behavior, and intelligence. Alcoholism: Clinical and Experimental Research, 22(2), 334-338.

Thadani, P. V. (1995). Biological Mechanisms and Prenatal Exposure to Drugs. Rockville, MD: National Institute on Drug Abuse.

Vorhees, C. V. (1986). Principles of Behavioral Tertiology. In E. P. Riley, Vorhees, C.V. (Ed.), Handbook of Behavioral Tertiology (pp. 23-48). New York, NY: Plenum.

STASH Vol. 3(1) - The transition from drug use to drug dependence: The bridge to more troubled waters

At some point in life, most of us have used, perhaps even repeatedly, substances with the potential for dependence. Many people at least try drugs such as alcohol, marijuana, or cocaine. However, few people progress from being users to becoming abusers or drug dependent.

This issue of STASH presents a series of related reports investigating the transition from drug use to drug dependence for alcohol, marijuana, and cocaine. The reports provide probabilities for drug use and drug dependence (Anthony, Warner, & Kessler, 1994), compare the time lag between initiation of use and development of drug dependence across the different drugs (Wagner & Anthony, 2002), and examine whether there are gender differences in these transition patterns (Wagner & Anthony, 2007).

Each of these reports used data from the National Comorbidity Survey (NCS) 1990-1992, a large-scale study designed to estimate prevalence and comorbidity of psychiatric disorders in the United States general population. Researchers used the total sample of 8,098 participants between the ages of 15 and 54 in the analyses of probabilities for drug use and drug dependence. For analyzing gender differences, researchers referred to a subsample of 6,792 participants between the ages of 15 and 45 years (Chen, Wagner, & Anthony, 2002; Wagner & Anthony, 2007). In the NCS, each participant completed a face-to-face structured interview yielding DSM-III-R diagnostic criteria for dependence on alcohol, marijuana, and cocaine (American Psychiatric Association, 1987). For each drug, age at drug use initiation and age at onset of drug dependence were assessed by standardized questions. Analyses estimated the probabilities of initiation of use and calculated risks of transitions from first use to dependence.

Among the 8,098 participants in the total sample, there were 7,485 alcohol users, 3,940 marijuana users, and 1,337 cocaine users. Of the persons who tried alcoholic beverages at least once, 15-16% eventually developed alcohol dependence. The rate was similar for cocaine users, 16%-17%, and lower, 9%, for marijuana users.

The probabilities of initiation of drug use peaked at age 18 for alcohol and marijuana, and at age 20 for cocaine (Figure 1). The risks of developing drug dependence among drug users showed a peak during the teen years for alcohol and marijuana, and at age 23 to 25 for cocaine (Figure 2). A closer look at the shapes of the risk curves revealed additional differences across types of drugs: the risk of cocaine dependence occurred early and explosively after first cocaine use and continued to be high for about a decade after first cocaine use. Alcohol and marijuana dependence emerged somewhat slower. While the risk of developing marijuana dependence leveled off, the risk of developing alcohol dependence persisted for decades. The researchers found gender differences among the risks of drug dependence for marijuana but not for alcohol or cocaine. Male marijuana users were roughly twice as likely as female marijuana users to become dependent in the 2 to 5 years after first use.

Figure 1: Probabilities of drug use initiation, by age (adapted from Wagner & Anthony, 2002).

Figure 2: Probabilities of drug dependence among drug users, by age (adapted from Wagner & Anthony, 2002).

Limitations of the analyses include the use of a cross-sectional data set, which precludes obtaining longitudinal observations, and the use of slightly different samples between the studies, which limits direct comparisons. A focal issue that needs to be considered when interpreting the findings is that all analyses are based mainly on two variables, namely age of onset of first drug use and age of onset of drug dependence. These two measures rely on accurate participant recall for the times and dates of past events, a challenging cognitive task for both younger and older adults. The measures potentially are subject to underreporting regarding the use of illicit substances or alcohol drinking as minors because participants might minimize their reports of illegal activities in the past. Also, these two measures represent composite indices that do not differentiate factors that might moderate the risk of drug dependence (e.g., distinguishing occasional use from regular use, quantities and frequencies of use, dosage forms such as beer vs. liquors, or means of drug administration such as oral, nasal, or inhaling).

Confirming previous research, these studies found the highest probabilities for first use of alcohol, marijuana, or cocaine before the age of 25 years. Extending previous findings, the studies revealed that the peak risks for developing dependence on alcohol, marijuana, or cocaine also occurred before the age of 25 years. Thus, drug dependence is a psychiatric disorder that typically begins to evolve during adolescence and young adulthood regardless of the target drug. These findings suggest that public health interventions to prevent drug dependence should target individuals under the age of 25.

What do you think? Comments can be addressed to Anja Schumann.

References

American Psychiatric Association (Ed.). (1987). Diagnostic and statistical manual of mental disorders (DSM-III-R). Washington, DC: American Psychiatric Association.

Anthony, J. C., Warner, L. A., & Kessler, R. C. (1994). Comparative epidemiology of dependence on tobacco, alcohol, controlled substances, and inhalants: Basic findings from the National Comorbidity Survey. Experimental and Clinical Psychopharmacology, 2, 244-268.

Chen, C. Y., Wagner, F. A., & Anthony, J. C. (2002). Marijuana use and the risk of Major Depressive Episode. Epidemiological evidence from the United States National Comorbidity Survey. Social Psychiatry and Psychiatric Epidemiology, 37(5), 199-206.

Wagner, F. A., & Anthony, J. C. (2002). From first drug use to drug dependence; developmental periods of risk for dependence upon marijuana, cocaine, and alcohol. Neuropsychopharmacology, 26(4), 479-488.

Wagner, F. A., & Anthony, J. C. (2007). Male-female differences in the risk of progression from first use to dependence upon cannabis, cocaine, and alcohol. Drug & Alcohol Dependence, 86(2-3), 191-198.

STASH Vol. 2(11) - Exploring the Gateway to Illicit Drug Use

The “gateway hypothesis” asserts that the illicit drug use develops from a sequence of events that begins with licit substance use (e.g., tobacco and alcohol), continues to the use of illicit drugs (e.g., marijuana), and finally to the use of less common illicit drugs (e.g., cocaine and heroin). According to this hypothesis, factors specific to each drug determine the progressive transition from one drug to the next; marijuana use is often specified as the gateway to further illicit drug use (Morral, McCaffrey, & Paddock, 2002). Alternatively, some suggest a reverse gateway effect in which social or individual factors (e.g., conduct problems, peer influence) common to all substances explain the reasons for illicit drug use (Lessem et al., 2006). This week’s STASH reviews a study that examines whether the gateway hypothesis has support for certain risk factors relating to the transition from licit to illicit drugs (Tarter, Vanyukov, Kirisci, Reynolds, & Clark, 2006).

Tarter and colleagues recruited 224 males aged 10 to 12 to participate in a study that would assess them at ages 12 to 14, 16, 19, and finally at 22. Study subjects were the offspring of men recruited to participate in another study, which had included subjects with a history of illicit substance use. During each study period, researchers administered the Structural Clinical Interview for DSM-III-R (SCID) to assess criteria for substance use disorders. In addition, participants self-administered the Drug Use Screening Inventory (i.e., a self-report assessment of past month drug use), and took urine drug tests and alcohol breath tests for physiological evidence of recent drug use. In addition, the investigators measured 35 variables that evaluated psychological, family, peer, school, and neighborhood characteristics. Researchers compared responses to the 35 variables by grouping three different sequences of tobacco, alcohol, and marijuana use; they analyzed this data using one-way analyses of variance (ANOVA) between pairs of these three groups. The three different sequences of using tobacco, alcohol, and marijuana were: (1) subjects who used alcohol/tobacco at baseline, but did not use marijuana by age 22; (2) subjects who used alcohol/tobacco and then went on to use marijuana (gateway effect); and (3) subjects who used marijuana and then went on to use alcohol/tobacco (reverse gateway effect).

The statistical comparisons between pairs of groups showed that not all participants progressed as the gateway hypothesis predicts. Table 1 shows differences between participants who used alcohol or tobacco only and participants who used alcohol or tobacco followed by marijuana use. Participants whose drug use pattern supported the gateway hypothesis (i.e., transitioned from licit drugs to marijuana use) were more behaviorally deviant, were affiliated with deviant peers, had friends who were not approved by parents, were less involved with school, and were more inclined to desist substance abuse than participants who used licit drugs only up to age 22.

Table 1. Differences between adolescents who used licit drugs only and adolescents who progressed from licit drug use to marijuana use (Adapted from Tarter et al., 2006)


Table 2 shows that three variables distinguished participants evidencing the gateway sequence of drug use from participants with the reverse gateway sequence. Participants with the reverse gateway sequence lived in a poorer neighborhood, had greater exposure to drugs, and had more negligent parents. The magnitude of effect sizes for these differences is small, ranging from 0.05 to 0.08, indicating that these statistically significant differences are modest.

Table 2. Differences between adolescents who went from licit drug use to marijuana use and adolescents who went from marijuana use to licit drug use (Adapted from Tarter et al., 2006)


This study has some limitations. Participants’ drug use is based on self-report. Self-report among young people is subject to bias. For example, some will under report their illicit drug use because they fear legal sanctions and others will over estimate their use because they want to dramatize their use. In addition, these findings might not generalize to other segments of the youth population because this study included predominantly Caucasian, male participants. The authors also admit that it is possible that this study might not have measured all factors associated with the transition to illicit drug use or use of specific drugs.

Despite these limitations, this study suggests that illicit drug use does not always occur in the sequence specified in the gateway hypothesis, and that risk factors are not necessarily drug-specific. Rather, these findings suggest that illicit drug use is the result of general social risk factors (e.g., neighborhood characteristics and parental influence). Public policy makers could use these findings to create prevention strategies that target risk factors specific to drug use among youth.

What do you think? Comments can be addressed to Sarbani Hazra.

References

Lessem, J. M., Hopfer, C. J., Haberstick, B. C., Timberlake, D., Ehringer, M. A., Smolen, A., et al. (2006). Relationship between Adolescent Marijuana Use and Young Adult Illicit Drug Use. Behavior Genetics, 36(4), 498-506.

Morral, A. R., McCaffrey, D. F., & Paddock, S. M. (2002). Reassessing the marijuana gateway effect. Addiction, 97, 1493-1504.

Tarter, R., Vanyukov, M., Kirisci, L., Reynolds, M., & Clark, D. (2006). Predictors of Marijuana Use in Adolescents Before and After Licit Drug Use: Examination of the Gateway Hypothesis. American Journal of Psychiatry, 163(12), 2134-2140.

STASH Vol. 2(10) - Prescription Pain Relievers: Friend or Foe?

Prescription pain reliever abuse (e.g., opioids: RxO) is a rapidly growing public health concern in the United States. Use of RxOs is a medical treatment to help individuals manage pain; however, nonmedical use and abuse of RxOs is increasing (Paulozzi, Budnitz, & Yongli, 2006). According to National Survey of Drug Use and Health (NSDUH) data, between 1992 and 1998, 4 to 5 million Americans used RxO for nonmedical purposes during each of those years. These users increased in number to 6.5 million during 1999 and to 8.4 million by 2001 (Substance Abuse and Mental Health Services Administration, 2006). This week’s STASH reviews Birnbaum et al.’s (2006) study, which examined the healthcare costs associated with RxO abuse.

Using Treatment Episode Data Sets (TEDS), collected from state-licensed treatment facilities, researchers observed that there were approximately 50,000 individuals admitted for treatment of RxO abuse during 2001. To assess healthcare costs, researchers considered both treatment costs and excess costs. Treatment costs are any costs related to treatment for RxO abuse. Excess costs are medical costs, other than treatment, that are due to RxO abuse.

To calculate treatment costs, the researchers used publicly available information about federal, state, and private expenditures on substance abuse treatment. They then multiplied those substance abuse treatment expenditure by the ratio of RxO treatment admissions to all substance abuse admissions (from the TEDS data sets) to estimate the amount attributable to RxO treatment costs.

To calculate excess medical costs, the researchers first used a large private insurance database to determine the non-treatment costs incurred by identified RxO abusers in that data set compared to those incurred by other insured individuals. They then multiplied that per person excess cost estimate by the number of privately insured RxO abusers (estimated using TEDS). They used publicly available information about Medicaid and uninsured costs (compared to private insurance costs) and TEDS information to obtain similar estimates for publicly insured and uninsured RxO abusers.

Researchers estimated the total healthcare costs of RxO abuse to be $2.6 billion. Total treatment costs during 2001 were more than $126 million. Of these treatment costs, public funds (i.e., from federal and state/local spending) covered approximately 64%. The excess medical costs due to RxO abuse were more than $2.4 billion. Of these excess costs, researchers estimated that public funds (i.e., publicly-insured and uninsured) covered 66% of these costs. (See Table 1).

Table 1. RxO Abuse Excess Healthcare Cost Estimates, 2001, United States (Adapted from Birnbaum et al., 2006)

The datasets that researchers employed in this study had potential limitations. The TEDS dataset might overestimate RxO abuse treatment admissions because of clinician reporting bias. For example, recent press attention about RxO abuse might lead clinicians to increasingly label admissions as RxO abuse. Alternatively, these data might (1) lead to underestimates of RxO abuse and (2) not be representative of the larger population because the TEDS data only represents individuals seeking treatment at state licensed facilities. Finally, the number of assumptions researchers had to make when calculating estimates may have caused inaccuracies in the results due to the differences between the populations and datasets.

Despite these limitations, this study represents the first estimate of treatment costs for RxO abuse in the United States. Because the number of opioid prescriptions have increased in the past decade, many more people have access and exposure to opioids than ever before (Gilson, Ryan, & Jornson, 2004). This increase in access might lead to an increase in RxO abuse and therefore an increase in excess healthcare costs. Future studies should examine the efficacy of various strategies to prevent RxO abuse and provide cost-effective treatment for people who abuse RxOs. Finally, despite the negative consequences that can result from RxO abuse, when used properly, opioids allow individuals with chronic pain to experience a higher quality of life. Therefore, when trying to curb RxO abuse, it is important to recognize the risks and benefits of RxOs and ensure that individuals who truly need these drugs still have access to them.

What do you think? Comments can be addressed to Juan Molina.

References

Birnbaum, H. G., White, A. W., Reynolds, J. L., Greenberg, P. E., Zhang, M., Vallow, S., et al. (2006). Estimated Costs of Prescription Opioid Analgesic Abuse in the United States in 2001. Clinical Journal of Pain, 22(8), 667-676.

Gilson, A. M., Ryan, K. M., & Jornson, D. E. (2004). A reassessment of trends in the medical use and abuse of opioid analgesics and implications for diversion control:1997-2002. Journal of Pain Symptom Management, 28, 176-188.

Paulozzi, L. J., Budnitz, D. S., & Yongli, X. (2006). Increasing Deaths from opioid analgesics in the United States. Pharmacoepidemiology and Drug Safety, 15, 618-627.

Substance Abuse and Mental Health Services Administration. (2006). Office of Applied Studies. Detailed Tables for The National Survey on Drug Use and Health. Retrieved November 13, 2006, from
http://www.oas.samhsa.gov/WebOnly.htm#NHSDAtabs

STASH Vol. 2(9) - Eating Disorders and Substance Use: A Double Danger?

Some research suggests a frequent co-occurrence of specific eating disorders and substance use. For example, studies of clinical populations found that bulimics and binge eaters, in particular, relative to anorexic patients or the general population, are more likely to abuse several substances.(Blinder, Cumella, & Sanathara, 2006; Conason, Brunstein Klomek, & Sher, 2006). It is unclear whether this elevated risk also is evident in non-clinical populations, such as community samples. In this week’s STASH, we examine Piran and Robinson’s (2006) study assessing substance use and eating disordered behaviors among female college students.

Five hundred and twenty six female undergraduate students volunteered to participate in the study. Participants were from various racial backgrounds: Caucasian (54%), Asian (29%), another ethnicity (11%), African (3%), Latin/Hispanic (2%), and Native/Aboriginal (1%). Participants completed items from The Women’s Health Survey, which includes items from the Eating Disorder Interview Schedule, Questionnaire for Eating Disorder Diagnoses, and the Young Canadians’ Alcohol and Other Drug Use Survey. The survey also included lifetime specific questions about dieting because of body size or weight concerns, purging (e.g., laxative use or vomiting to control weight or to get rid of food), and eating binges (i.e., eating an unusually large amount of food in a short time and feeling that eating is out of control).

The investigators conducted chi square tests to assess the relationship between eating disorders and substance use, and used the Fisher Exact Probability Test for subsamples less than five. The researchers compared substance use and abuse among participants according to clusters of co-occurring behavior patterns: (1) control (participants who never dieted, purged, or binged); (2) participants who dieted and purged, but never binged; (3) participants who dieted and binged but never purged; and (4) participants who binged, dieted, and purged. (1)

Table 1 Percentage of lifetime substance use/abuse among female undergraduate participants with clusters of eating disordered behaviors (N = 359) (Adapted from Piran et al., 2006)

The three most commonly used substances were marijuana (42%), prescription painkillers (25%), and hallucinogens/heroin (14%). According to the study’s results, 28% of the participants reported some form of binging while 37% of the participants reported some dieting. Table 1 shows that participants in the diet, purge, and no binge group reported significant cocaine and stimulant/amphetamine use. Compared to the other groups, the binge, diet, and no purge group reported more antidepressant and sleeping pill abuse (See Table 1). Lastly, participants in the binge, diet, and purge group reported the most cocaine use in comparison to the control group and the other two eating disordered groups.

There are several limitations in the study. Because the data is based on self-report, participants might have underreported both their substance use and eating behaviors, due to the stigma associated with these behaviors. In addition, the results might not be generalizable to all people with eating disorders because this was a female undergraduate sample. There might be other confounding factors involved in this relationship, like peer pressure and college related variables (e.g., age, substance accessibility, stress related to schoolwork), that might have contributed to eating disorders and substance use among this study population. Another limitation is that the study did not uniformly assess criteria for both substance use and substance abuse for all of the substances described in the analyses. For example, researchers assessed abuse of prescription painkiller but only assessed use of substances such as cocaine.

This study suggests that women who have disordered eating behaviors are at risk for substance use and prescription drug abuse. Furthermore, certain eating disorders co-occur with specific types of substance use and abuse. For instance, women who binge and diet may be vulnerable to prescription drug abuse, while women who diet and purge may be vulnerable to amphetamine and stimulant use. Perhaps women who are severe dieters are more at risk to misuse diet related amphetamines and diet pills. Further research about this relationship should closely examine these differences and the motivations behind them. There’s also a need for more research about the severity of these behaviors among other populations. For example, studies should assess whether this relationship exists among women in different age groups or men suffering from eating disorders. More research can better inform intervention programs, for both eating disorders and substance use, about individuals most susceptible to this dangerous co­occurrence of disordered behaviors. Similarly, treatment programs should consider this comorbidity when providing services to clients with eating disorders and substance abuse problems.

What do you think? Comments can be addressed to Sarbani Hazra.

Notes

1. The researchers also compared substance use among participants grouped according to eating behaviors: control (participants who never binged, dieted, or purged), binge only (participants who binged but never dieted or purged), and severe diet only (participants dieted daily for at least a month but never binged or purged).

References

Blinder, B. J., Cumella, E. J., & Sanathara, V. A. (2006). Psychiatric Comorbidities of Female Inpatients With Eating Disorders. Psychosomatic Medicine, 68(3), 454-462.

Conason, A. H., Brunstein Klomek, A., & Sher, L. (2006). Recognizing alcohol and drug abuse in patients with eating disorders. Quarterly Journal of Medicine, 99(5), 335-339.

Piran, N., & Robinson, S. R. (2006). Associations between disordered eating behaviors and licit and illicit substance use and abuse in a university sample. Addictive Behaviors, 31(10), 1761-1775.

STASH Vol. 2(8) - Ecstasy: Is What You See Really What You Get?

Recreational use of the illegal drug 3,4-methylenedioxymethamphetamine (MDMA), commonly referred to as ecstasy, can result in dangerous consequences. The drug effects have included accelerated heartbeat, jaw clenching, grinding of teeth, nystagmus (i.e., rolling or flickering of the eyes), hallucinations, and death (Kalant, 2001; National Institute on Drug Abuse, 2006; Zervogiannis, 2005).

As Table 1 shows, colors and logos are distinguishing traits of ecstasy tablets. However, because these drugs are trafficked illegally, there is a lack of public knowledge about these identifying features and whether they are indicative of the actual contents of ecstasy tablets. With increased trafficking and seizures of ecstasy tablets in Taiwan during recent years, researchers in Taiwan have been investigating ecstasy use among this population (Cheng, 2003; Ministry of Justice, March 2005). This week’s STASH reviews Teng et al.’s study examining the association between the appearance of ecstasy tablets (i.e., color and logo) and the contents of tablets confiscated in Taiwan (Teng, Wu, Liu, Li, & Chien, 2006).

Table 1: Ecstasy Tablets Confiscated in Taiwan Classified by Logo and MDMA Contents (adapted from Teng et al., 2006)