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Early adolescent aggression predicts antisocial personality disorder in young adults: a population-based study.

Alyce M WhippTellervo KorhonenAnu RaevuoriKauko HeikkiläLea PulkkinenRichard J RoseJaakko A KaprioEero Vuoksimaa
Published in: European child & adolescent psychiatry (2018)
Modestly prevalent in the general population (~ 4%), but highly prevalent in prison populations (> 40%), the diagnosis of antisocial personality disorder (ASPD) involves aggression as one of several possible criteria. Using multiple informants, we aimed to determine if general aggression, as well as direct and indirect subtypes, assessed in early adolescence (ages 12, 14) predict young adulthood ASPD in a population-based sample. Using data from a Finnish population-based longitudinal twin cohort study with psychiatric interviews available at age 22 (N = 1347), we obtained DSM-IV-based ASPD diagnoses. Aggression measures from ages 12 (parental and teacher ratings) and 14 (teacher, self, and co-twin ratings) were used to calculate odds ratios (OR) of ASPD from logistic regression models and the area under the curve (AUC) from receiver operating characteristic curve analysis. Analyses were adjusted for sex, age, and family structure. All informants' aggression ratings were significant (p < 0.05) predictors of ASPD (OR range 1.3-1.8; AUC range 0.65-0.72). Correlations between informants ranged from 0.13 to 0.33. Models including two or more aggression ratings, particularly age 14 teacher and self ratings, more accurately predicted ASPD (AUC: 0.80; 95% confidence interval 0.73-0.87). Direct aggression rated by all informants significantly predicted ASPD (OR range 1.4-1.9), whereas only self-rated indirect aggression was significantly associated with ASPD (OR = 1.4). Across different informants, general and direct aggression at ages 12 and 14 predicted ASPD in a population-based sample. Psychiatric, social, and parenting interventions for ASPD prevention should focus on children and adolescents with high aggression levels, with an aim to gather information from multiple informants.
Keyphrases
  • young adults
  • mental health
  • depressive symptoms
  • healthcare
  • cross sectional
  • electronic health record
  • deep learning
  • middle aged
  • social media
  • health information