The emergence of co-occurring adolescent polysubstance use and depressive symptoms: A latent growth modeling approach

Abstract

This study tests competing models of the relation between depression and polysubstance use over the course of adolescence. Participants included a nationwide sample of adolescents (N = 3,604), ages 12 to 17 at study Wave 1, assessed annually for 3 years. Models were tested using cohort-sequential latent growth curve modeling to determine whether depressive symptoms at baseline predicted concurrent and age-related changes in drug use, whether drug use at baseline predicted concurrent and age-related changes in depressive symptoms, and whether initial levels of depression predicted changes in substance use significantly better than vice versa. The results suggest a transactional model such that early polysubstance use promotes early depressive symptoms, which in turn convey elevated risk for increasing polysubstance use over time, which in turn conveys additional risk for future depressive symptoms, even after accounting for gender, ethnicity, and household income. In contrast, early drug use did not portend risk for future depressive symptoms. These findings suggest a complicated pattern of interrelations over time and indicate that many current models of co-occurring polysubstance use and depressive symptoms may not fully account for these associations. Instead, the results suggest a developmental cascade, in which symptoms of one disorder promote symptoms of the other across intrapersonal domains.

Figures

Figure 1.

Cohort-sequential latent growth curve model for depressive symptoms. Variables represent baseline symptom levels at age 12 (intercept) and age-related increases from 12 to 19 (slope). Solid age boxes represent collected data, whereas dashed lines reflect data missing by design. Asterisks indicate that pathways were allowed to vary freely, with the exception that regression weights at each age are equal across cohorts. The intercept (age 12) is based on data from all ages across all cohorts, as notated by the “1s.” An identical procedure was used to model the polysubstance use variable. Cov, covariance; D, variance; y.o., year old.

Figure 2.

Depressive symptoms and polysubstance use endorsement between ages 12 and 19, across three cohorts in the National Survey of Adolescents. Values reflect mean number of symptoms endorsed (depression; left vertical axis) and mean number of substances used (polysubstance use; right vertical axis). Error bars reflect standard errors.

Figure 3.

Cohort-sequential latent growth models. Model (a) tests the negative affect regulation model, with depression intercept predicting polysubstance use intercept and slope, and depression slope predicting polysubstance use slope. Model (b) tests the opponent process model, with polysubstance use intercept predicting depression intercept and slope, and polysubstance use slope predicting depression slope. Variables represent baseline symptom levels at age 12 (intercept) and age-related increases from 12 to 19 (slope). Single-headed arrow values reflect standardized b weights; double-headed arrow values reflect correlations (r). Regression pathways for both models are all significant at p ≤ .004 unless marked nonsignificant (ns, all ps ≥ .17). Gender, ethnicity, and household income are included in both models but not depicted for readability. Values in parentheses reflect standard error. Adapted from “Depression and Delinquence Covaration in an Accelerated Longitudinal Sample of Adolescents,” by M. J. Kofler, M. R. McCart, K. Zajac, K. J. Ruggiero, B. E. Saunders, and D. G. Kilpatrick, 2011, Journal of Consulting and Clinical Psychology, 79, 458–469. Copyright 2011 by American Psychological Association. Adapted with permission.

Figure 4.

Final developmental cascade model of depressive symptoms and polysubstance use. Variables represent baseline symptom levels at age 12 (intercept) and age-related increases from ages 12 to 19 (slope). Single-headed arrow values reflect standardized β weights; double-headed arrow values reflect correlations (r). Regression pathways are significant at p ≤ .0005 unless marked nonsignificant (ns, p>.05). Indirect effects: POLY_Intercept → DEP_Intercept → POLY_Slope: αβ = 0.12, p= .008; DEP_Intercept → POLY_Slope → DEP_Slope: αβ= 0.17, p=.008.