Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-26T16:50:04.317Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of drug abuse recurrence: a Swedish National Study

Published online by Cambridge University Press:  10 October 2017

K. S. Kendler ,
H. Ohlsson ,
K. Sundquist  and
J. Sundquist
K. S. Kendler*
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
H. Ohlsson
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden
K. Sundquist
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden
J. Sundquist
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden
*
Author for correspondence: K. S. Kendler, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Relapse from drug abuse (DA) is common, but has rarely been studied in general population samples using a wide range of objective predictors.

Method

Using nationwide registries, we ascertained 44 523 subjects first registered for DA between the ages of 15 and 40 in 1998 to 2004 and followed for 8 years. We predicted relapse in subjects defined as a second DA registration. We also predicted DA relapse in relative pairs concordant for DA but discordant for relapse.

Results

In multivariate regression analyses, the strongest predictors for relapse were prior criminal behavior, male sex, being on social welfare, low school achievement, prior alcoholism, and a high-risk father. A risk index trained from these analyses on random split-halves demonstrated a risk ratio of 1.11 [95% confidence intervals (CIs) 1.10–1.11] per decile and an ROC value of 0.70 (0.69–0.71). Co-relative analyses indicated that a modest proportion of this association was causal, with the remainder arising from familial confounders. A developmental structural equation model revealed a complex interviewing of risk pathways to DA with three key mediational hubs: low educational attainment, early age at first registration, and being on social welfare.

Conclusions

In a general population sample, using objective registry information, DA relapse is substantially predictable. However, the identified risk factors may not be valid targets for interventions because many index familial risk and may not impact causally on probability of relapse. Risk for DA relapse may reflect an inter-weaving, over developmental time, of genetic–temperamental vulnerability, indices of externalizing behaviors and social factors reflecting deprivation.

Keywords

Co-relative analysisdrug abuseprognosisrecurrencerelapse
Type
Original Articles
Information
Psychological Medicine , Volume 48 , Issue 8 , June 2018 , pp. 1367 - 1374
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H (1987) Factor analysis and AIC. Psychometrika 52, 317–332.CrossRefGoogle Scholar
Altman, DG, Deeks, JJ and Sackett, DL (1998) Odds ratios should be avoided when events are common. British Medical Journal 317, 1318.CrossRefGoogle ScholarPubMed
Biederman, J, et al. (1998) Does attention-deficit hyperactivity disorder impact the developmental course of drug and alcohol abuse and dependence? Biological Psychiatry 44, 269–273.CrossRefGoogle ScholarPubMed
Brandon, TH, Vidrine, JI and Litvin, EB (2007) Relapse and relapse prevention. Annual Reviews of Clinical Psychology 3, 257–284.CrossRefGoogle ScholarPubMed
Branigan, AR, McCallum, KJ and Freese, J (2013) Variation in the heritability of educational attainment: an international meta-analysis. Social Forces 92, 109–140.CrossRefGoogle Scholar
Brownell, KD, et al. (1986) Understanding and preventing relapse. American Psychologist 41, 765–782.CrossRefGoogle ScholarPubMed
Calabria, B, et al. (2010) Systematic review of prospective studies investigating “remission” from amphetamine, cannabis, cocaine or opioid dependence. Addiction Behavior 35, 741–749.CrossRefGoogle ScholarPubMed
Farmer, RF, et al. (2015) Natural course of cannabis use disorders. Psychological Medicine 45, 63–72.CrossRefGoogle ScholarPubMed
Farmer, RF, et al. (2016) Association of comorbid psychopathology with the duration of cannabis use disorders. Psychology of Addiction Behavior 30, 82–92.CrossRefGoogle ScholarPubMed
Feingold, D, et al. (2015) Natural outcome of cannabis use disorder: a 3-year longitudinal follow-up. Addiction 110, 1963–1974.CrossRefGoogle ScholarPubMed
Florez-Salamanca, L, et al. (2013) Probability and predictors of cannabis use disorders relapse: results of the National Epidemiologic Survey on alcohol and related conditions (NESARC). Drug Alcohol and Dependence 132, 127–133.CrossRefGoogle ScholarPubMed
Hibell, B, et al. (2000) The 1999 ESPAD Report: Alcohol and Other Drug Use Among Students in 30 European Countries. The Swedish Council for Information on Alcohol and Other Drugs (Pompidou Group), Modin Tryck AB: Stockholm, Sweden.Google Scholar
Hser, YI, et al. (2007 a). Trajectories of heroin addiction: growth mixture modeling results based on a 33-year follow-up study. Evaluation Review 31, 548–563.CrossRefGoogle ScholarPubMed
Hser, YI, Longshore, D and Anglin, MD (2007 b). The life course perspective on drug use: a conceptual framework for understanding drug use trajectories. Evaluation Review 31, 515–547.CrossRefGoogle ScholarPubMed
Kendler, KS, et al. (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.CrossRefGoogle ScholarPubMed
Kendler, KS, et al. (2015 a). A Swedish national twin study of criminal behavior and its violent, white-collar and property subtypes. Psychological Medicine 45, 2253–2262.CrossRefGoogle ScholarPubMed
Kendler, KS, et al. (2015 b). A population-based Swedish twin and sibling study of cannabis, stimulant and sedative abuse in men. Drug Alcohol and Dependence 149, 49–54.CrossRefGoogle ScholarPubMed
Kendler, KS, et al. (2012) Genetic and familial environmental influences on the risk for drug abuse: a national Swedish adoption study. Archives of General Psychiatry 69, 690–697.CrossRefGoogle ScholarPubMed
Krapohl, E, et al. (2014) The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proceedings of the National Academy of Sciences of the United States of America 111, 15273–15278.CrossRefGoogle Scholar
Kraus, L, et al. (2003) Estimating prevalence of problem drug use at national level in countries of the European Union and Norway. Addiction 98, 471–485.CrossRefGoogle ScholarPubMed
Kringlen, E, Torgersen, S and Cramer, V (2001) A Norwegian psychiatric epidemiological study. American Journal of Psychiatry 158, 1091–1098.CrossRefGoogle ScholarPubMed
Lopez-Quintero, C, et al. (2011) Probability and predictors of remission from life-time nicotine, alcohol, cannabis or cocaine dependence: results from the National Epidemiologic Survey on alcohol and related conditions. Addiction 106, 657–669.CrossRefGoogle ScholarPubMed
McLellan, AT, et al. (1983) Predicting response to alcohol and drug abuse treatments. Role of psychiatric severity. Archives of General Psychiatry 40, 620–625.CrossRefGoogle ScholarPubMed
Muthén, LK and Muthén, BO (2015) Mplus User's Guide: 1998–2015, 7th edn. Muthén & Muthén: Los Angeles, CA.Google Scholar
R Development Core Team (2014) The R Project for Statistical Computing; R: A Language and Environment for Statistical Computing. [3.1.2]. R Foundation for Statistical Computing: Vienna, Austria.Google Scholar
Sarvet, AL and Hasin, D (2016) The natural history of substance use disorders. Current Opinions in Psychiatry 29, 250–257.CrossRefGoogle ScholarPubMed
SAS Institute I (2011) SAS/STAT User's Guide, Version 9.3. SAS Institute Inc.: Cary, NC.Google Scholar
Tsuang, MT, et al. (1996) Genetic influences on abuse of illicit drugs: a study of 3,297 twin pairs. American Journal of Medical Genetics 67, 473–477.3.0.CO;2-L>CrossRefGoogle Scholar
van der Pol, P, et al. (2015) Three-year course of cannabis dependence and prediction of persistence. European Addiction Research 21, 279–290.CrossRefGoogle ScholarPubMed
White, AM, et al. (2004) Predictors of relapse during treatment and treatment completion among marijuana-dependent adolescents in an intensive outpatient substance abuse program. Substance Abuse 25, 53–59.CrossRefGoogle Scholar
Zou, G (2004) A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology 159, 702–706.CrossRefGoogle ScholarPubMed
Supplementary material: File

Kendler et al supplementary material

Appendix

Download Kendler et al supplementary material(File)
File 241.2 KB