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Which adolescents develop persistent substance dependence in adulthood? Using population-representative longitudinal data to inform universal risk assessment

Published online by Cambridge University Press:  01 December 2015

M. H. Meier ,
W. Hall ,
A. Caspi ,
D. W. Belsky ,
M. Cerdá ,
H. L. Harrington ,
R. Houts ,
R. Poulton  and
T. E. Moffitt
M. H. Meier*
Affiliation:
Department of Psychology, Arizona State University, Tempe, AZ, USA
W. Hall
Affiliation:
University of Queensland Centre for Clinical Research, University of Queensland, Brisbane, Australia National Addiction Centre, King's College, London, UK
A. Caspi
Affiliation:
Department of Psychology and Neuroscience, Duke University, Durham, NC, USA Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, UK
D. W. Belsky
Affiliation:
Department of Medicine, Duke University Medical Center, Durham, NC, USA Social Science Research Institute, Duke University, Durham, NC, USA
M. Cerdá
Affiliation:
Department of Emergency Medicine, Violence Prevention Research Program, University of California Davis, Davis, CA, USA
H. L. Harrington
Affiliation:
Department of Psychology and Neuroscience, Duke University, Durham, NC, USA Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
R. Houts
Affiliation:
Department of Psychology and Neuroscience, Duke University, Durham, NC, USA Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
R. Poulton
Affiliation:
Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
T. E. Moffitt
Affiliation:
Department of Psychology and Neuroscience, Duke University, Durham, NC, USA Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, UK
*
*Address for correspondence: M. H. Meier, PhD., Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ 85287-1104, USA. (Email: madeline.meier@asu.edu)
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Abstract

Background

To our knowledge, there are no universal screening tools for substance dependence that (1) were developed using a population-based sample, (2) estimate total risk briefly and inexpensively by incorporating a relatively small number of well-established risk factors, and (3) aggregate risk factors using a simple algorithm. We created a universal screening tool that incorporates these features to identify adolescents at risk for persistent substance dependence in adulthood.

Method

Participants were members of a representative cohort of 1037 individuals born in Dunedin, New Zealand in 1972–1973 and followed prospectively to age 38 years, with 95% retention. We assessed a small set of childhood and adolescent risk factors: family history of substance dependence, childhood psychopathology (conduct disorder, depression), early exposure to substances, frequent substance use in adolescence, sex, and childhood socioeconomic status. We defined the outcome (persistent substance dependence in adulthood) as dependence on one or more of alcohol, tobacco, cannabis, or hard drugs at ⩾3 assessment ages: 21, 26, 32, and 38 years.

Results

A cumulative risk index, a simple sum of nine childhood and adolescent risk factors, predicted persistent substance dependence in adulthood with considerable accuracy (AUC = 0.80).

Conclusions

A cumulative risk score can accurately predict which adolescents in the general population will develop persistent substance dependence in adulthood.

Keywords

Longitudinalrisk assessmentsubstance dependenceuniversal screen.
Type
Original Articles
Information
Psychological Medicine , Volume 46 , Issue 4 , March 2016 , pp. 877 - 889
Copyright
Copyright © Cambridge University Press 2015 

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References

APA (1987). Diagnostic and Statistical Manual of Mental Disorders, 3rd edn, revised. American Psychiatric Association: Washington, DC.Google Scholar
APA (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association: Washington, DC.Google Scholar
Brook, JS, Lee, JY, Brown, EN, Finch, SJ, Brook, DW (2011). Developmental trajectories of marijuana use from adolescence to adulthood: personality and social role outcomes. Psychological Reports 108, 339–357.Google Scholar
Carter, A, Hall, W (2011). Addiction Neuroethics: The Promises and Perils of Neuroscience Research on Addiction. Cambridge University Press: Cambridge.CrossRefGoogle Scholar
Chassin, L, Flora, DB, King, KM (2004). Trajectories of alcohol and drug use and dependence from adolescence to adulthood: the effects of familial alcoholism and personality. Journal of Abnormal Psychology 113, 483–498.Google Scholar
Chung, T, Smith, GT, Donovan, JE, Windle, M, Faden, VB, Chen, CM, Martin, CS (2012). Drinking frequency as a brief screen for adolescent alcohol problems. Pediatrics 129, 205–212.Google Scholar
Clark, DΒ, Chung, T, Martin, C (2006). Alcohol use frequency as a screen for alcohol use disorders in adolescents. International Journal of Adolescent Medicine and Health 18, 181–188.Google Scholar
Cook, RJ, Sackett, DL (1995). The number needed to treat: a clinically useful measure of treatment effect. British Medical Journal 310, 452–454.Google Scholar
Copeland, WE, Angold, A, Shanahan, L, Dreyfuss, J, Dlamini, I, Costello, EJ (2012). Predicting persistent alcohol problems: a prospective analysis from the Great Smoky Mountain Study. Psychological Medicine 42, 1925–1935.CrossRefGoogle ScholarPubMed
Costello, A, Edelbrock, C, Kalas, R, Kessler, M, Klaric, S (1982). Diagnostic Interview Schedule for Children (DISC). National Institute of Mental Health: Rockville, MD.Google Scholar
D'Agostino, RB, Vasan, RS, Pencina, MJ, Wolf, PA, Cobain, M, Massaro, JM, Kannel, WB (2008). General cardiovascular risk profile for use in primary care - the Framingham Heart Study. Circulation 117, 743–753.Google Scholar
Elley, W, Irving, J (1976). Revised socio-economic index for New Zealand. New Zealand Journal of Educational Studies 11, 25–36.Google Scholar
Evans, GW, Li, D, Whipple, SS (2013). Cumulative risk and child development. Psychological Bulletin 139, 1342–1396.Google Scholar
Felitti, VJ, Anda, RF, Nordenberg, D, Williamson, DF, Spitz, AM, Edwards, V, Koss, MP, Marks, JS (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults - the Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine 14, 245–258.Google Scholar
Fergusson, DM, Horwood, LJ, Ridder, EM (2007). Conduct and attentional problems in childhood and adolescence and later substance use, abuse and dependence: results of a 25-year longitudinal study. Drug and Alcohol Dependence 88, S14–S26.Google Scholar
Grant, BF, Dawson, DA (1998). Age of onset of drug use and its association with DSM-IV drug abuse and dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse 10, 163–173.Google Scholar
Grant, BF, Goldstein, RB, Saha, TD, Chou, SP, Jung, J, Zhang, H, Pickering, RP, Ruan, WJ, Smith, SM, Huang, B (2015). Epidemiology of DSM-5 alcohol use disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry 72, 757–766.Google Scholar
Grant, BF, Hasin, DS, Chou, SP, Stinson, FS, Dawson, DA (2004). Nicotine dependence and psychiatric disorders in the United States - Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of General Psychiatry 61, 1107–1115.CrossRefGoogle ScholarPubMed
Hall, WD, Carter, A, Yücel, M (2015). Ethical issues in the neuroprediction of addiction risk and treatment response. In Handbook of Neuroethics, (ed. Clausen, J. and Levy, N.), pp. 1025–1044. Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
Heyman, GM (2013). Quitting drugs: quantitative and qualitative features. Annual Review of Clinical Psychology 9, 29–59.CrossRefGoogle ScholarPubMed
Hussong, AM, Jones, DJ, Stein, GL, Baucom, DH, Boeding, S (2011). An internalizing pathway to alcohol use and disorder. Psychology of Addictive Behaviors 25, 390–404.Google Scholar
Kendler, KS, Jacobson, KC, Prescott, CA, Neale, MC (2003). Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. American Journal of Psychiatry 160, 687–695.Google Scholar
Kendler, KS, Myers, J, Damaj, MI, Chen, XG (2013). Early smoking onset and risk for subsequent nicotine dependence: a monozygotic co-twin control study. American Journal of Psychiatry 170, 408–413.CrossRefGoogle ScholarPubMed
Kirisci, L, Reynolds, M, Carver, D, Tarter, R (2013). Quick screen to detect current substance use disorder in adolescents and the likelihood of future disorder. Drug and Alcohol Dependence 128, 116–122.Google Scholar
Krueger, RF, Hicks, BM, Patrick, CJ, Carlson, SR, Iacono, WG, McGue, M (2002). Etiologic connections among substance dependence, antisocial behavior, and personality: modeling the externalizing spectrum. Journal of Abnormal Psychology 111, 411–424.CrossRefGoogle ScholarPubMed
Levy, S, Weiss, R, Sherritt, L, Ziemnik, R, Spalding, A, Van Hook, S, Shrier, LA (2014). An electronic screen for triaging adolescent substance use by risk levels. JAMA Pediatrics 168, 822–828.Google Scholar
McGue, M, Iacono, WG, Krueger, R (2006). The association of early adolescent problem behavior and adult psychopathology: a multivariate behavioral genetic perspective. Behavior Genetics 36, 591–602.Google Scholar
Meier, MH, Caspi, A, Houts, R, Slutske, WS, Harrington, H, Jackson, KM, Belsky, DW, Poulton, R, Moffitt, TE (2013). Prospective developmental subtypes of alcohol dependence from age 18 to 32 years: implications for nosology, etiology, and intervention. Development and Psychopathology 25, 785–800.Google Scholar
Milne, BJ, Caspi, A, Crump, R, Poulton, R, Rutter, M, Sears, MR, Moffitt, TE (2009a). The validity of the family history screen for assessing family history of mental disorders. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics 150B, 41–49.Google Scholar
Milne, BJ, Caspi, A, Harrington, H, Poulton, R, Rutter, M, Moffitt, TE (2009b). Predictive value of family history on severity of illness: the case for depression, anxiety, alcohol dependence, and drug dependence. Archives of General Psychiatry 66, 738–747.Google Scholar
Moffitt, T, Caspi, A, Rutter, M, Silva, P (2001). Sex differences in Antisocial Behaviour: Conduct Disorder, Delinquency, and Violence in the Dunedin Longitudinal Study. Cambridge University Press: Cambridge, UK.Google Scholar
Moffitt, TE, Caspi, A, Taylor, A, Kokaua, J, Milne, BJ, Polanczyk, G, Poulton, R (2010). How common are common mental disorders? Evidence that lifetime prevalence rates are doubled by prospective versus retrospective ascertainment. Psychological Medicine 40, 899–909.CrossRefGoogle ScholarPubMed
Odgers, CL, Caspi, A, Nagin, DS, Piquero, AR, Slutske, WS, Milne, BJ, Dickson, N, Poulton, R, Moffitt, TE (2008). Is it important to prevent early exposure to drugs and alcohol among adolescents? Psychological Science 19, 1037–1044.Google Scholar
Odgers, CL, Milne, BJ, Caspi, A, Crump, R, Poulton, R, Moffitt, TE (2007). Predicting prognosis for the conduct-problem boy: can family history help? Journal of the American Academy of Child and Adolescent Psychiatry 46, 1240–1249.CrossRefGoogle ScholarPubMed
Pardini, D, White, HR, Stouthamer-Loeber, M (2007). Early adolescent psychopathology as a predictor of alcohol use disorders by young adulthood. Drug and Alcohol Dependence 88, S38–S49.Google Scholar
Pepe, MS, Janes, H, Longton, G, Leisenring, W, Newcomb, P (2004). Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 159, 882–890.Google Scholar
Poulton, R, Hancox, R, Milne, B, Baxter, J, Scott, K, Wilson, N (2006). The Dunedin Multidisciplinary Health and Development Study: are its findings consistent with the overall New Zealand population? New Zealand Medical Journal 119, 45–55.Google Scholar
Poulton, R, Moffitt, TE, Silva, PA (2015). The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future. Soc Psychiatry Psychiatr Epidemiol 50, 679–693.CrossRefGoogle ScholarPubMed
Rice, ME, Harris, GT (2005). Comparing effect sizes in follow-up studies: ROC area, Cohen's d, and r. Law and Human Behavior 29, 615–620.CrossRefGoogle Scholar
Robins, L, Cottler, L, Bucholz, K, Compton, W (1995). Diagnostic Interview Schedule for DSM-IV. Washington University School of Medicine: St. Louis.Google Scholar
Robins, LN, Helzer, JE, Croughan, J, Ratcliff, KS (1981). National Institute of Mental Health Diagnostic Interview Schedule - its history, characteristics, and validity. Archives of General Psychiatry 38, 381–389.Google Scholar
Rutter, M (1981). Stress, coping and development - some issues and some questions. Journal of Child Psychology and Psychiatry and Allied Disciplines 22, 323–356.Google Scholar
Sameroff, AJ, Seifer, R, Barocas, R, Zax, M, Greenspan, S (1987). Intelligence quotient scores of 4-year-old children - social-environmental risk-factors. Pediatrics 79, 343–350.Google Scholar
Stone, AL, Becker, LG, Huber, AM, Catalano, RF (2012). Review of risk and protective factors of substance use and problem use in emerging adulthood. Addictive Behaviors 37, 747–775.Google Scholar
Subramaniam, GA, Volkow, ND (2014). Substance misuse among adolescents: to screen or not to screen? Jama Pediatrics 168, 798–799.CrossRefGoogle ScholarPubMed
Vanyukov, MM, Kirisci, L, Moss, L, Tarter, RE, Reynolds, MD, Maher, BS, Kirillova, GP, Ridenour, T, Clark, DB (2009). Measurement of the risk for substance use disorders: phenotypic and genetic analysis of an index of common liability (vol 39, pg 233, 2009). Behavior Genetics 39, 596–596.Google Scholar
Ware, JH (2006). Statistics and medicine - the limitations of risk factors as prognostic tools. New England Journal of Medicine 355, 2615–2617.Google Scholar
Weissman, MM, Wickramaratne, P, Adams, P, Wolk, S, Verdeli, H, Olfson, M (2000). Brief screening for family psychiatric history - the family history screen. Archives of General Psychiatry 57, 675–682.Google Scholar
Whelan, R, Watts, R, Orr, CA, Althoff, RR, Artiges, E, Banaschewski, T, Barker, GJ, Bokde, AL, Buchel, C, Carvalho, FM, Conrod, PJ, Flor, H, Fauth-Buhler, M, Frouin, V, Gallinat, J, Gan, G, Gowland, P, Heinz, A, Ittermann, B, Lawrence, C, Mann, K, Martinot, JL, Nees, F, Ortiz, N, Paillere-Martinot, ML, Paus, T, Pausova, Z, Rietschel, M, Robbins, TW, Smolka, MN, Strohle, A, Schumann, G, Garavan, H, the IC (2014). Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512, 185–189.Google Scholar
Wilson, PWF, D'Agostino, RB, Levy, D, Belanger, AM, Silbershatz, H, Kannel, WB (1998). Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847.Google Scholar
Wright, BRE, Caspi, A, Moffitt, TE, Miech, RA, Silva, PA (1999). Reconsidering the relationship between SES and delinquency: causation but not correlation. Criminology 37, 175–194.Google Scholar
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