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A randomized clinical trial of a behavioral intervention to reduce opioid overdose risk behavior
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CPDD 77th Annual Meeting Abstracts (2015) / Drug and Alcohol Dependence 156 (2015) e2–e101
A computation opponent process model describes and predicts cocaine self-administration among naïve rats Georgiy Bobashev 1,∗ , Nicole Seider 2 , Serge Ahmed 3 1
RTI International, Durham, NC, United States Psychology, UNC Chapel Hill, Chapel Hill, NC, United States 3 University of Bordeaux, Bordeaux, France 2
Aims: To develop, calibrate and validate a computational opponent process model to cocaine self-administration data in rats. Methods: Outcomes. Numbers of injections per session and time between injections. Data. We used data from two 30-day rat cocaine self administration experiments (Mihindou et al., 2011, 2013). Daily sessions lasted for 6 h in one and 3 h in another experiment. Model. We used a control theory computational model (4 difference equations) that simulates self-administration controlled by an adaptive self-stimulating threshold (Newlin et al., 2012). The threshold is modeled as a function of both: the drug effect and the delayed opponent process (allostatic adjustment). Calibration and analysis. We calibrated the model to a time series based on 6-h sessions. We used a non-linear fit algorithm and mean square error (between the data and the model-generated trajectories) as the goodness of fit criterion. We used this calibrated model to predict the numbers of injections in the 3-h session time series. We examined variance of times between the injections among 3-h and 6-h sessions. Results: The model calibrated to the 6-h session almost perfectly predicted long-term (after 5 days) numbers of injections in 3-h sessions. In the first 5 days, experimental data showed qualitative differences in stabilization of the variances of times between injections between the 3-h the 6-h sessions. The patterns of increase in daily numbers of injections in the first 5 days were also qualitatively different (concave vs. convex). This difference could be potentially attributed to the longer time the rats needed to reach stable allostatic process (learn the effect of the drug). Model improvement is proposed to capture these differences. Conclusions: A computational model of self-administration well describes and predicts long-term cocaine self-administration of initially naïve rats. Accounting for the initial learning of drug effects (first 1–7 days) is the next step in model improvement. Financial support: Supported in part by a NIDA grant R01DA025163. http://dx.doi.org/10.1016/j.drugalcdep.2015.07.977 A randomized clinical trial of a behavioral intervention to reduce opioid overdose risk behavior Amy S. Bohnert 1,3,∗ , Frederic Blow 1,3 , Rebecca Cunningham 1 , Laura Thomas 1 , Mark K. Greenwald 2 , S. Chermack 1,3 , Erin E. Bonar 1 , Maureen Walton 1 1
University of Michigan, Ann Arbor, MI, United States 2 Psychiatry, Wayne State University, Detroit, MI, United States 3 VA Center for Clinical Management Research, Ann Arbor, MI, United States Aims: Prescription opioid overdose represents a significant public health problem, but strategies to prevent overdose risk behavior have not been studied. This study compared a motivational enhancement-based brief intervention to an educational
enhanced usual care condition in a clinical trial conducted in the emergency department (ED). Methods: Participants were approached and screened via tablet computer while waiting in the ED and were eligible if they reported prescription opioid misuse in the prior 3 months; individuals with a prior overdose experience were purposely over-sampled (76%). In total, 204 patients were randomized, and 86% were retained at 6 months follow-up. The intervention was delivered during the ED visit by master’s-level therapists. The two primary outcomes were composite measures of prescription opioid misuse and overdose risk behaviors. Poisson regression was used to account for the outcome measure distributions. Results: Patients in the intervention condition reported lower levels of overdose risk behaviors at follow-up compared to controls; incidence rate ratio (IRR) = 0.78, 95% CI: 0.65–0.94. The intervention condition was also associated with significantly lower levels of prescription opioid misuse at follow-up compared to controls; IRR = 0.85, 95% CI: 0.74–0.99). Conclusions: This study represents the first clinical trial of a behavioral overdose prevention intervention and indicates that a motivational enhancement-based approach can reduce prescription opioid overdose risk behavior. Because the effect sizes were relatively modest, future research should explore methods to amplify the impact of the intervention. Financial support: CDC grant R49 CE002099. http://dx.doi.org/10.1016/j.drugalcdep.2015.07.978 Associations between age and cannabis use problems among medical cannabis patients Kipling Bohnert 1,2,∗ , Mark A. Ilgen 1,2 1
Psychiatry, University of Michigan, Ann Arbor, MI, United States 2 VA Ann Arbor, Ann Arbor, MI, United States Aims: 23 States and the District of Columbia have passed legislation allowing for the use of cannabis for those with qualifying medical conditions. Despite the increasing number of States legalizing medical cannabis in recent years, understanding of medical cannabis patients is limited, including the extent to which patients experience problems related to cannabis use. Moreover, young people may be particularly vulnerable for such problems. In this pilot study, we estimate the prevalence of problems related to cannabis use among medical cannabis patients, as well as examine potential age differences associated with the report of specific problems. Methods: This study includes adults 18 years and older from a convenience sample of patients from one medical cannabis clinic in Michigan. Of the 370 individuals who were approached in the waiting area of the clinic, 348 (94.1%) consented to participate. This analysis includes the 288 (82.8%) participants who reported cannabis use in the past 3 months. Problems related to past 3-month cannabis use were assessed via a modified version of the World Health Organization’s Alcohol, Smoking and Substance Involvement Screening Test (ASSIST). Bivariate logistic regression models were used to evaluate associations between age and specific problems related to cannabis use in the past 3 months. Results: Prevalence estimates of problems related to cannabis use in the past 3 months ranged from 9.3% for failing to control, cut down or stop using cannabis to 80.2% for having a strong desire to use cannabis. Significant age associations were detected for the problems of cannabis use leading to health, social, financial, or legal problems (Odds Ratio (OR) = 0.96, 95% Confidence Interval (CI) = 0.92, 0.99) and failure to do what was normally expected because of cannabis use (OR = 0.96, 95% CI = 0.93, 0.99), such that
CPDD 77th Annual Meeting Abstracts (2015) / Drug and Alcohol Dependence 156 (2015) e2–e101
A computation opponent process model describes and predicts cocaine self-administration among naïve rats Georgiy Bobashev 1,∗ , Nicole Seider 2 , Serge Ahmed 3 1
RTI International, Durham, NC, United States Psychology, UNC Chapel Hill, Chapel Hill, NC, United States 3 University of Bordeaux, Bordeaux, France 2
Aims: To develop, calibrate and validate a computational opponent process model to cocaine self-administration data in rats. Methods: Outcomes. Numbers of injections per session and time between injections. Data. We used data from two 30-day rat cocaine self administration experiments (Mihindou et al., 2011, 2013). Daily sessions lasted for 6 h in one and 3 h in another experiment. Model. We used a control theory computational model (4 difference equations) that simulates self-administration controlled by an adaptive self-stimulating threshold (Newlin et al., 2012). The threshold is modeled as a function of both: the drug effect and the delayed opponent process (allostatic adjustment). Calibration and analysis. We calibrated the model to a time series based on 6-h sessions. We used a non-linear fit algorithm and mean square error (between the data and the model-generated trajectories) as the goodness of fit criterion. We used this calibrated model to predict the numbers of injections in the 3-h session time series. We examined variance of times between the injections among 3-h and 6-h sessions. Results: The model calibrated to the 6-h session almost perfectly predicted long-term (after 5 days) numbers of injections in 3-h sessions. In the first 5 days, experimental data showed qualitative differences in stabilization of the variances of times between injections between the 3-h the 6-h sessions. The patterns of increase in daily numbers of injections in the first 5 days were also qualitatively different (concave vs. convex). This difference could be potentially attributed to the longer time the rats needed to reach stable allostatic process (learn the effect of the drug). Model improvement is proposed to capture these differences. Conclusions: A computational model of self-administration well describes and predicts long-term cocaine self-administration of initially naïve rats. Accounting for the initial learning of drug effects (first 1–7 days) is the next step in model improvement. Financial support: Supported in part by a NIDA grant R01DA025163. http://dx.doi.org/10.1016/j.drugalcdep.2015.07.977 A randomized clinical trial of a behavioral intervention to reduce opioid overdose risk behavior Amy S. Bohnert 1,3,∗ , Frederic Blow 1,3 , Rebecca Cunningham 1 , Laura Thomas 1 , Mark K. Greenwald 2 , S. Chermack 1,3 , Erin E. Bonar 1 , Maureen Walton 1 1
University of Michigan, Ann Arbor, MI, United States 2 Psychiatry, Wayne State University, Detroit, MI, United States 3 VA Center for Clinical Management Research, Ann Arbor, MI, United States Aims: Prescription opioid overdose represents a significant public health problem, but strategies to prevent overdose risk behavior have not been studied. This study compared a motivational enhancement-based brief intervention to an educational
enhanced usual care condition in a clinical trial conducted in the emergency department (ED). Methods: Participants were approached and screened via tablet computer while waiting in the ED and were eligible if they reported prescription opioid misuse in the prior 3 months; individuals with a prior overdose experience were purposely over-sampled (76%). In total, 204 patients were randomized, and 86% were retained at 6 months follow-up. The intervention was delivered during the ED visit by master’s-level therapists. The two primary outcomes were composite measures of prescription opioid misuse and overdose risk behaviors. Poisson regression was used to account for the outcome measure distributions. Results: Patients in the intervention condition reported lower levels of overdose risk behaviors at follow-up compared to controls; incidence rate ratio (IRR) = 0.78, 95% CI: 0.65–0.94. The intervention condition was also associated with significantly lower levels of prescription opioid misuse at follow-up compared to controls; IRR = 0.85, 95% CI: 0.74–0.99). Conclusions: This study represents the first clinical trial of a behavioral overdose prevention intervention and indicates that a motivational enhancement-based approach can reduce prescription opioid overdose risk behavior. Because the effect sizes were relatively modest, future research should explore methods to amplify the impact of the intervention. Financial support: CDC grant R49 CE002099. http://dx.doi.org/10.1016/j.drugalcdep.2015.07.978 Associations between age and cannabis use problems among medical cannabis patients Kipling Bohnert 1,2,∗ , Mark A. Ilgen 1,2 1
Psychiatry, University of Michigan, Ann Arbor, MI, United States 2 VA Ann Arbor, Ann Arbor, MI, United States Aims: 23 States and the District of Columbia have passed legislation allowing for the use of cannabis for those with qualifying medical conditions. Despite the increasing number of States legalizing medical cannabis in recent years, understanding of medical cannabis patients is limited, including the extent to which patients experience problems related to cannabis use. Moreover, young people may be particularly vulnerable for such problems. In this pilot study, we estimate the prevalence of problems related to cannabis use among medical cannabis patients, as well as examine potential age differences associated with the report of specific problems. Methods: This study includes adults 18 years and older from a convenience sample of patients from one medical cannabis clinic in Michigan. Of the 370 individuals who were approached in the waiting area of the clinic, 348 (94.1%) consented to participate. This analysis includes the 288 (82.8%) participants who reported cannabis use in the past 3 months. Problems related to past 3-month cannabis use were assessed via a modified version of the World Health Organization’s Alcohol, Smoking and Substance Involvement Screening Test (ASSIST). Bivariate logistic regression models were used to evaluate associations between age and specific problems related to cannabis use in the past 3 months. Results: Prevalence estimates of problems related to cannabis use in the past 3 months ranged from 9.3% for failing to control, cut down or stop using cannabis to 80.2% for having a strong desire to use cannabis. Significant age associations were detected for the problems of cannabis use leading to health, social, financial, or legal problems (Odds Ratio (OR) = 0.96, 95% Confidence Interval (CI) = 0.92, 0.99) and failure to do what was normally expected because of cannabis use (OR = 0.96, 95% CI = 0.93, 0.99), such that