Prediction of Long-Term Outcomes in Young Adults with a History of Adolescent Alcohol-Related Hospitalization

Alcohol Alcohol. 2016 Jan;51(1):47-53. doi: 10.1093/alcalc/agv072. Epub 2015 Jun 25.

Abstract

Aims: Empirical data concerning the long-term psychosocial development of adolescents admitted to inpatient treatment with alcohol intoxication (AIA) are lacking. The aim of this study was to identify the factors that, at the time of admission, predict future substance use, alcohol use disorders (AUD), mental health treatment, delinquency and life satisfaction.

Methods: We identified 1603 cases of AIA treated between 2000 and 2007 in one of five pediatric departments in Germany. These former patients were invited to participate in a telephone interview. Medical records were retrospectively analyzed extracting potential variables predicting long-term outcomes.

Results: Interviews were conducted with 277 individuals, 5-13 [mean 8.3 (SD 2.3)] years after treatment, with a response rate of 22.7%; of these, 44.8% were female. Mean age at the interview was 24.4 (SD 2.2) years. Logistic and linear regression models revealed that being male, using illicit substances and truancy or runaway behavior in adolescence predicted binge drinking, alcohol dependence, use of illicit substances and poor general life satisfaction in young adulthood, explaining between 13 and 24% of the variance for the different outcome variables.

Conclusions: This naturalistic study confirms that known risk factors for the development of AUD also apply to AIA. This finding facilitates targeted prevention efforts for those cases of AIA who need more than the standard brief intervention for aftercare.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Alcoholic Intoxication / epidemiology*
  • Alcoholism / epidemiology*
  • Binge Drinking / epidemiology*
  • Female
  • Germany / epidemiology
  • Hospitalization / statistics & numerical data*
  • Humans
  • Juvenile Delinquency / statistics & numerical data*
  • Linear Models
  • Logistic Models
  • Male
  • Mental Health Services / statistics & numerical data*
  • Personal Satisfaction*
  • Risk Factors
  • Sex Factors
  • Substance-Related Disorders / epidemiology
  • Young Adult