Modelling the progression of illicit substance use patterns from real-world evidence.

Abstract

To investigate an innovative pharmacometrics approach that addresses the challenges of using real-world evidence to model the progression of illicit substance use. The modelling strategy analysed real-world data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth) survey using survival analyses and differential equations. Respondents were categorized into drug-naïve, active users and nonusers. The transitions between categories were modelled using interval-censored parametric survival analysis. The resulting hazard rate functions were used as time-dependent rate constants in a differential equation system. Covariate models for sex and depression status were assessed. AddHealth enrolled 6504 American teenagers (median age 16 years, range 11-21 years); this cohort was followed with five interviews over a 22-year period; the median age at the last interview was 38 years (range 34-45 years). The percentages of illicit drug users at Interviews 1-5 were 7.7%, 5.9%, 15.8%, 21.4% and 0.98%, respectively. The generalized gamma distribution emerged as the preferred model for the survival functions for transitions between categories. Age-dependent prevalence was obtained from the differential equation system. Active drug use was more prevalent in males, increased in adolescence and college years, peaked at 24 years, and decreased to low levels by 35 years. Depression, which was more frequent in females, increased the drug-naïve-active user transition rates but not the active user-nonuser and nonuser-active user transition rates. The evidence did not support an interaction between sex and depression. The model provided a satisfactory approximation for the age-dependent progression of illicit substance use from preadolescence to early middle age.