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Self-reported impulsivity is related to age at first methamphetamine use
Abstracts / Drug and Alcohol Dependence 171 (2017) e2–e226
Finding a needle in the haystack: Using machine-learning to predict overdose in opioid users Benjamin Sage Crosier 1,∗ , Jacob Borodovsky 2 , Pedro Mateu-Gelabert 3 , Honoria Guarino 4 1
Center for Technology and Behavioral Health, Dartmouth College, West Windsor, VT, United States 2 Psychiatry, Dartmouth College, Hanover, NH, United States 3 NDRI, Inc., New York, NY, United States 4 National Development & Research Institutes, New York, NY, United States Aims: To accurately predict overdose frequency using machine learning and identify key predictive features. Methods: A sample of opioid users (N = 260) was contacted and reported on 1016 variables including demographics, social network info, and drug history. We then used the machine learning technique random forests to (1) attempt to accurately predict a participant’s lifetime opioid overdose status, and (2) the number of times they overdosed. The Gini index was used for feature selection to identify meaningful predictors. Results: Participants were M = 24.28 (SD = 3.11) years old and 66% male. 80% identified as Caucasian. We ran two random forests. The first performed a binary classification to predict lifetime overdose status, with an error rate of 30.25%. The most predictive feature was identified as whether or not that person had ever been arrested. The second model identified the predictors of overdose frequency. This model explained 8% of variance in overdose. The most predictive feature was the number of overdoses in a person’s social network. Conclusions: Two random forests provided a solution to a “needle in the haystack” problem, identifying variables most related to overdose, and predicted overdose with an accuracy much greater than chance. Arrest history and the number of overdoses in a person’s network emerged as the most important predictors of overdose. This information can be used to guide future research, and informs the design of screening tools. Questions asking someone if they have ever been arrested or how many people they have seen overdose could greatly help on-the-ground practitioners by quickly identifying those that are at risk. Machine learning is becoming increasingly important when working with datasets that contain a great number of predictor variables and serve as a powerful non-theoretical method to identify important factors related to substance abuse and other behavioural health problems. Financial Support: NIDA# R01DA035146. http://dx.doi.org/10.1016/j.drugalcdep.2016.08.146 Self-reported impulsivity is related to age at first methamphetamine use Anita Cservenka 2,∗ , Lara Ray 1,2,3 1 Psychology, University of California, Los Angeles, Los Angeles, CA, United States 2 Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States 3 Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
Aims: Methamphetamine (MA) users report higher levels of impulsivity relative to healthy controls, which may result from their substance use. Further, there is some evidence that female stimulant users are more impulsive than male stimulant users. It is
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uncertain however, whether age at first MA use may be explained by self-reported impulsivity, which could thus be a risk factor for initiation of MA use. It was hypothesized that in a community sample of MA users, self-reported impulsivity would be negatively related to age at first MA use in females, controlling for total years of MA use at the time of study visit. Methods: A community sample of MA users was recruited for this study (N = 157; 113 males, 44 females). The Barratt Impulsiveness Scale (BIS-11) was used to assess self-reported impulsivity on three different subscales (Motor, Attention, Nonplanning). Age at first MA use served as dependent variables in a series of multiple regression models with BIS-11 subscales, sex, and their interaction as independent variables, controlling for total years of MA use. Results: While total years of MA use was related to age at first MA use, Attention and Motor impulsivity significantly contributed to age at first MA use when added to the model (Attention: R2 = 0.05, ˇ = −.23, t = −2.69, p = 0.008; Motor: R2 = 0.05, ˇ = −.27, t = −3.06, p = 0.003). However, sex and its interaction with impulsivity, were not significant predictors of age at first MA use. Conclusions: Individuals who report higher impulsivity started using MA at an earlier age, which could suggest that personality factors, such as impulsivity levels during adolescence, may be an important marker for the vulnerability of MA use. These findings indicate that prevention efforts may need to be targeted towards individuals who report high levels of Attention and Motor impulsivity, as they may be at greatest risk for earlier initiation of MA use. Financial Support: National Institute on Drug Abuse (R21 DA029831, PI: Ray; T32 DA024635, PI: London). http://dx.doi.org/10.1016/j.drugalcdep.2016.08.147 Alcohol use among Native Americans compared to whites: Examining the veracity of the ‘Native American elevated alcohol consumption’ belief James K. Cunningham 1,2,∗ , Teshia A. Solomon 1 , Myra L. Muramoto 2 1 Native American Research and Training Center, University of Arizona, Tucson, AZ, United States 2 Family and Community Medicine, University of Arizona, Tucson, AZ, United States
Aims: This study uses national survey data to examine the veracity of the long- standing belief that compared to whites, Native Americans (NA) have elevated alcohol consumption. Methods: The primary data source was the National Survey on Drug Use and Health (NSDUH) from 2009-2013: whites (n = 171,858) and NA (n = 4201). Analyses using logistic regression with demographic covariate adjustment were conducted to assess differences in the odds of NA and whites being alcohol abstinent, light/moderate drinkers (no binge/heavy consumption), binge drinkers (5+ drinks on an occasion 1–4 days), or heavy drinkers (5+ drinks on an occasion 5+ days) in the past month. Complementary alcohol abstinence, light/moderate drinking and excessive drinking analyses were conducted using Behavioral Risk Factor Surveillance System (BRFSS) data from 2011 to 2013: whites (n = 1,130,658) and NA (n = 21,589). Results: In the NSDUH analyses, the majority of NA, 59.9% (95% CI: 56.7–63.1), abstained, whereas a minority of whites, 43.1% (CI: 42.6–43.6), abstained—adjusted odds ratio (AOR): 0.64 (CI: 0.56–0.73). Approximately 14.5% (CI: 12.0–17.4) of NA were light/moderate-only drinkers, versus 32.7% (CI: 32.2–33.3) of whites (AOR: 1.90; CI: 1.51–2.39). NA and white binge drinking estimates were similar—17.3% (CI: 15.0–19.8) and 16.7% (CI:
Finding a needle in the haystack: Using machine-learning to predict overdose in opioid users Benjamin Sage Crosier 1,∗ , Jacob Borodovsky 2 , Pedro Mateu-Gelabert 3 , Honoria Guarino 4 1
Center for Technology and Behavioral Health, Dartmouth College, West Windsor, VT, United States 2 Psychiatry, Dartmouth College, Hanover, NH, United States 3 NDRI, Inc., New York, NY, United States 4 National Development & Research Institutes, New York, NY, United States Aims: To accurately predict overdose frequency using machine learning and identify key predictive features. Methods: A sample of opioid users (N = 260) was contacted and reported on 1016 variables including demographics, social network info, and drug history. We then used the machine learning technique random forests to (1) attempt to accurately predict a participant’s lifetime opioid overdose status, and (2) the number of times they overdosed. The Gini index was used for feature selection to identify meaningful predictors. Results: Participants were M = 24.28 (SD = 3.11) years old and 66% male. 80% identified as Caucasian. We ran two random forests. The first performed a binary classification to predict lifetime overdose status, with an error rate of 30.25%. The most predictive feature was identified as whether or not that person had ever been arrested. The second model identified the predictors of overdose frequency. This model explained 8% of variance in overdose. The most predictive feature was the number of overdoses in a person’s social network. Conclusions: Two random forests provided a solution to a “needle in the haystack” problem, identifying variables most related to overdose, and predicted overdose with an accuracy much greater than chance. Arrest history and the number of overdoses in a person’s network emerged as the most important predictors of overdose. This information can be used to guide future research, and informs the design of screening tools. Questions asking someone if they have ever been arrested or how many people they have seen overdose could greatly help on-the-ground practitioners by quickly identifying those that are at risk. Machine learning is becoming increasingly important when working with datasets that contain a great number of predictor variables and serve as a powerful non-theoretical method to identify important factors related to substance abuse and other behavioural health problems. Financial Support: NIDA# R01DA035146. http://dx.doi.org/10.1016/j.drugalcdep.2016.08.146 Self-reported impulsivity is related to age at first methamphetamine use Anita Cservenka 2,∗ , Lara Ray 1,2,3 1 Psychology, University of California, Los Angeles, Los Angeles, CA, United States 2 Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States 3 Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
Aims: Methamphetamine (MA) users report higher levels of impulsivity relative to healthy controls, which may result from their substance use. Further, there is some evidence that female stimulant users are more impulsive than male stimulant users. It is
e49
uncertain however, whether age at first MA use may be explained by self-reported impulsivity, which could thus be a risk factor for initiation of MA use. It was hypothesized that in a community sample of MA users, self-reported impulsivity would be negatively related to age at first MA use in females, controlling for total years of MA use at the time of study visit. Methods: A community sample of MA users was recruited for this study (N = 157; 113 males, 44 females). The Barratt Impulsiveness Scale (BIS-11) was used to assess self-reported impulsivity on three different subscales (Motor, Attention, Nonplanning). Age at first MA use served as dependent variables in a series of multiple regression models with BIS-11 subscales, sex, and their interaction as independent variables, controlling for total years of MA use. Results: While total years of MA use was related to age at first MA use, Attention and Motor impulsivity significantly contributed to age at first MA use when added to the model (Attention: R2 = 0.05, ˇ = −.23, t = −2.69, p = 0.008; Motor: R2 = 0.05, ˇ = −.27, t = −3.06, p = 0.003). However, sex and its interaction with impulsivity, were not significant predictors of age at first MA use. Conclusions: Individuals who report higher impulsivity started using MA at an earlier age, which could suggest that personality factors, such as impulsivity levels during adolescence, may be an important marker for the vulnerability of MA use. These findings indicate that prevention efforts may need to be targeted towards individuals who report high levels of Attention and Motor impulsivity, as they may be at greatest risk for earlier initiation of MA use. Financial Support: National Institute on Drug Abuse (R21 DA029831, PI: Ray; T32 DA024635, PI: London). http://dx.doi.org/10.1016/j.drugalcdep.2016.08.147 Alcohol use among Native Americans compared to whites: Examining the veracity of the ‘Native American elevated alcohol consumption’ belief James K. Cunningham 1,2,∗ , Teshia A. Solomon 1 , Myra L. Muramoto 2 1 Native American Research and Training Center, University of Arizona, Tucson, AZ, United States 2 Family and Community Medicine, University of Arizona, Tucson, AZ, United States
Aims: This study uses national survey data to examine the veracity of the long- standing belief that compared to whites, Native Americans (NA) have elevated alcohol consumption. Methods: The primary data source was the National Survey on Drug Use and Health (NSDUH) from 2009-2013: whites (n = 171,858) and NA (n = 4201). Analyses using logistic regression with demographic covariate adjustment were conducted to assess differences in the odds of NA and whites being alcohol abstinent, light/moderate drinkers (no binge/heavy consumption), binge drinkers (5+ drinks on an occasion 1–4 days), or heavy drinkers (5+ drinks on an occasion 5+ days) in the past month. Complementary alcohol abstinence, light/moderate drinking and excessive drinking analyses were conducted using Behavioral Risk Factor Surveillance System (BRFSS) data from 2011 to 2013: whites (n = 1,130,658) and NA (n = 21,589). Results: In the NSDUH analyses, the majority of NA, 59.9% (95% CI: 56.7–63.1), abstained, whereas a minority of whites, 43.1% (CI: 42.6–43.6), abstained—adjusted odds ratio (AOR): 0.64 (CI: 0.56–0.73). Approximately 14.5% (CI: 12.0–17.4) of NA were light/moderate-only drinkers, versus 32.7% (CI: 32.2–33.3) of whites (AOR: 1.90; CI: 1.51–2.39). NA and white binge drinking estimates were similar—17.3% (CI: 15.0–19.8) and 16.7% (CI: