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Substance use trajectories from early adolescence through the college years
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Abstracts / Drug and Alcohol Dependence 146 (2015) e202–e284
Opioid use disorders: Trends and correlates Martin J. Dennis, Michael L. Dennis, Rodney R. Funk Chestnut Health Systems, Normal, IL, United States Aims: The aims of this poster are to (1) explore the trends in opioid use disorders overall, by age and geography and (2) examine differences in the liability for addiction based on age of onset. Methods: Data are from the 2011 National Survey on Drug Use and Heath (NSDUH), 2011 Drug Abuse Warning Network (DAWN), and 2011 Treatment Episode Data Set (TEDS). Results: From 2002 to 2011 the number of people with opioid use disorders rose 38%. The number of people with prescription opioid use disorders is 3 times higher than heroin use disorders, but the increase of heroin use disorders was higher (104%). Over half the people with prescription opioid use disorders were over the age of 25, but the rate of growth was highest (+55%) among those age 18–25. From 2004 to 2010, this has been a 42% rise in drug related emergency room admissions, with the rates particularly high for prescription opioid alone (132%), prescription opioid + other illicit drugs (139%), prescription opioid + alcohol (+63%) or prescription opioid + illicit drugs + Alcohol (+94%). From 2000 to 2010, there has been a 44% increase in opioid related treatment admissions overall, including 352% for prescription opioids. The rate of growth of prescription Opioid Treatment admissions are growing even more rapidly for young adults ages 18–24 (+489%). Conclusions: The rapid growth in opioid use disorders is being driven by prescription opioid use disorders. While most people are adults, the rate of growth among young adults is much faster and also raises the potential for years of problems ahead. Geographic variation in the rates of disorders suggest that availability and local practices are significant, and that it may be feasible to stop the further rates of disorder or even reverse it. The return of opioids also has important implications for the need to have detoxification and medication assisted treatment services available as part of health care reform. Financial support: NIDA Grant no. R37 DA011323. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.178 Ecological momentary assessment to predict the risk of relapse Michael L. Dennis, Christy K. Scott, Rodney R. Funk Chestnut Health Systems, Normal and Chicago, IL, United States Aims: To demonstrate the feasibility and accuracy of EMA collected by smart phone to predict the risk of relapse within the next 7 days. Methods: Data were collected from 52 clients post treatment who where 48% Female, 65% Black, 4% Hispanic, 56% adolescent, 4% weekly alcohol users, and 23% weekly other drug users. Participants received a smartphone and data plan for 6 weeks. They completed 90% of 6 EMA/day at 6 random times (4860 total observations). Each 2 min EMA was about the past 30 min and asked who the participant was with, where they were, what else they were doing, and what they were feeling. Participants were asked to rate the extent which area made them want to use alcohol/drugs, or supported their recovery. They were also asked whether they were using and to rate other factors related to relapse including withdrawal, craving, physical pain, ability to resist using, and exposure to drugs and alcohol. The combined ratings (alpha = .93) were used to predict the risk of subsequent alcohol or drug use based on self-report in
the next 42 EMA or weekly urine tests. CHAID was used to check individual items that could improve the prediction model. Results: The analysis identify 5 main risk groups: (1) Current use or withdrawal (381, 14% of observation), (2) High risk (145, 5%), (3) Moderate risk (836, 31%), (4) Low risk (560, 20%) and (5) Denial (804, 30%). Relative to the low risk group (8% subsequent use OR = 1.0), the moderate risk (26%, OR = 3.9) and high risk (66%, OR = 20.5) were significantly more likely to use in the next 7 days. Regardless of their scale score, those who had used or experienced withdrawal symptoms in the past 30 min were even more likely to use in the next week (95%, OR = 32.3). The denial group, who perceived literally no risks and complete support, where actually at moderate risk of relapse (35%, OR = 5.8). Conclusions: The feasibility and effectiveness of using smart phones to monitor and predict the risk of relapse in the near future creates an opportunity to intervene prior to use and improve the effectiveness of recovery management. Financial support: NIDA DA011323, DA021174. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.179 Substance use trajectories from early adolescence through the college years Karen J. Derefinko 1 , Richard J. Charnigo 1 , Richard Milich 1 , Donald R. Lynam 2 1 2
University of Kentucky, Lexington, KY, United States Purdue University, West Lafayette, IN, United States
Aims: The transition from adolescence into adulthood is an important period for the development of alcohol, marijuana, and hard drug use. Prior research has explored patterns of substance use in adolescents to assist in understanding how use develops (Schulenberg and Maggs, 2008). However, little is known about changes in patterns across high school and college, and whether individual factors affect these changes. The aims of this study were to identify distinct patterns of substance use and to explore the impulsive trait correlates of these trajectories. Methods: Participants were 451 college freshmen (48% male). At the first assessment, participants reported on substance use from age 13 until present, and completed a measure of multifaceted impulsivity, the UPPS (Whiteside and Lynam, 2001). Participants were then followed longitudinally for two additional years to assess substance use. Results: Group-based trajectory modeling, performed using the PROC TRAJ application, was used to estimate developmental trajectory groups. Trajectories were estimated using ten data points per individual representing average weekly use for each year from age 13 to 22. Visual inspection of the trajectories indicated increases in substance use immediately upon college entry. Results supported a five-group model of alcohol use, a four-group model of marijuana use, and a two-group model of hard drug use. Across all substances, the probability of escalating in trajectory class was characterized by sensation seeking, whereas continued heavy alcohol and hard drug use into adulthood are characterized by impulsivity under the condition of negative emotions. Conclusions: These results suggest confirm that the start of college is an important developmental transition in terms of polysubstance use. In addition, although the trait of sensation seeking appears to relate to trends in the early stages of use, other traits of impulsivity become relevant when determining which individuals go on to chronic, problematic use. Financial support: This research was supported by grants from the National Institute on Drug Abuse (DA005312 and DA007304). http://dx.doi.org/10.1016/j.drugalcdep.2014.09.180
Abstracts / Drug and Alcohol Dependence 146 (2015) e202–e284
Opioid use disorders: Trends and correlates Martin J. Dennis, Michael L. Dennis, Rodney R. Funk Chestnut Health Systems, Normal, IL, United States Aims: The aims of this poster are to (1) explore the trends in opioid use disorders overall, by age and geography and (2) examine differences in the liability for addiction based on age of onset. Methods: Data are from the 2011 National Survey on Drug Use and Heath (NSDUH), 2011 Drug Abuse Warning Network (DAWN), and 2011 Treatment Episode Data Set (TEDS). Results: From 2002 to 2011 the number of people with opioid use disorders rose 38%. The number of people with prescription opioid use disorders is 3 times higher than heroin use disorders, but the increase of heroin use disorders was higher (104%). Over half the people with prescription opioid use disorders were over the age of 25, but the rate of growth was highest (+55%) among those age 18–25. From 2004 to 2010, this has been a 42% rise in drug related emergency room admissions, with the rates particularly high for prescription opioid alone (132%), prescription opioid + other illicit drugs (139%), prescription opioid + alcohol (+63%) or prescription opioid + illicit drugs + Alcohol (+94%). From 2000 to 2010, there has been a 44% increase in opioid related treatment admissions overall, including 352% for prescription opioids. The rate of growth of prescription Opioid Treatment admissions are growing even more rapidly for young adults ages 18–24 (+489%). Conclusions: The rapid growth in opioid use disorders is being driven by prescription opioid use disorders. While most people are adults, the rate of growth among young adults is much faster and also raises the potential for years of problems ahead. Geographic variation in the rates of disorders suggest that availability and local practices are significant, and that it may be feasible to stop the further rates of disorder or even reverse it. The return of opioids also has important implications for the need to have detoxification and medication assisted treatment services available as part of health care reform. Financial support: NIDA Grant no. R37 DA011323. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.178 Ecological momentary assessment to predict the risk of relapse Michael L. Dennis, Christy K. Scott, Rodney R. Funk Chestnut Health Systems, Normal and Chicago, IL, United States Aims: To demonstrate the feasibility and accuracy of EMA collected by smart phone to predict the risk of relapse within the next 7 days. Methods: Data were collected from 52 clients post treatment who where 48% Female, 65% Black, 4% Hispanic, 56% adolescent, 4% weekly alcohol users, and 23% weekly other drug users. Participants received a smartphone and data plan for 6 weeks. They completed 90% of 6 EMA/day at 6 random times (4860 total observations). Each 2 min EMA was about the past 30 min and asked who the participant was with, where they were, what else they were doing, and what they were feeling. Participants were asked to rate the extent which area made them want to use alcohol/drugs, or supported their recovery. They were also asked whether they were using and to rate other factors related to relapse including withdrawal, craving, physical pain, ability to resist using, and exposure to drugs and alcohol. The combined ratings (alpha = .93) were used to predict the risk of subsequent alcohol or drug use based on self-report in
the next 42 EMA or weekly urine tests. CHAID was used to check individual items that could improve the prediction model. Results: The analysis identify 5 main risk groups: (1) Current use or withdrawal (381, 14% of observation), (2) High risk (145, 5%), (3) Moderate risk (836, 31%), (4) Low risk (560, 20%) and (5) Denial (804, 30%). Relative to the low risk group (8% subsequent use OR = 1.0), the moderate risk (26%, OR = 3.9) and high risk (66%, OR = 20.5) were significantly more likely to use in the next 7 days. Regardless of their scale score, those who had used or experienced withdrawal symptoms in the past 30 min were even more likely to use in the next week (95%, OR = 32.3). The denial group, who perceived literally no risks and complete support, where actually at moderate risk of relapse (35%, OR = 5.8). Conclusions: The feasibility and effectiveness of using smart phones to monitor and predict the risk of relapse in the near future creates an opportunity to intervene prior to use and improve the effectiveness of recovery management. Financial support: NIDA DA011323, DA021174. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.179 Substance use trajectories from early adolescence through the college years Karen J. Derefinko 1 , Richard J. Charnigo 1 , Richard Milich 1 , Donald R. Lynam 2 1 2
University of Kentucky, Lexington, KY, United States Purdue University, West Lafayette, IN, United States
Aims: The transition from adolescence into adulthood is an important period for the development of alcohol, marijuana, and hard drug use. Prior research has explored patterns of substance use in adolescents to assist in understanding how use develops (Schulenberg and Maggs, 2008). However, little is known about changes in patterns across high school and college, and whether individual factors affect these changes. The aims of this study were to identify distinct patterns of substance use and to explore the impulsive trait correlates of these trajectories. Methods: Participants were 451 college freshmen (48% male). At the first assessment, participants reported on substance use from age 13 until present, and completed a measure of multifaceted impulsivity, the UPPS (Whiteside and Lynam, 2001). Participants were then followed longitudinally for two additional years to assess substance use. Results: Group-based trajectory modeling, performed using the PROC TRAJ application, was used to estimate developmental trajectory groups. Trajectories were estimated using ten data points per individual representing average weekly use for each year from age 13 to 22. Visual inspection of the trajectories indicated increases in substance use immediately upon college entry. Results supported a five-group model of alcohol use, a four-group model of marijuana use, and a two-group model of hard drug use. Across all substances, the probability of escalating in trajectory class was characterized by sensation seeking, whereas continued heavy alcohol and hard drug use into adulthood are characterized by impulsivity under the condition of negative emotions. Conclusions: These results suggest confirm that the start of college is an important developmental transition in terms of polysubstance use. In addition, although the trait of sensation seeking appears to relate to trends in the early stages of use, other traits of impulsivity become relevant when determining which individuals go on to chronic, problematic use. Financial support: This research was supported by grants from the National Institute on Drug Abuse (DA005312 and DA007304). http://dx.doi.org/10.1016/j.drugalcdep.2014.09.180