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Impact of Pre-Adolescent Substance Familiarity on Subsequent Use: Longitudinal Analysis of Risk by Latent Classes in the Adolescent Brain Cognitive Development Sample

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posted on 2024-09-16, 04:40 authored by Andrew Moore, Ben Lewis, Hugh Farrior, Jesse Hinckley, Sara Jo Nixon, Devika Bhatia

Predicting substance use in adolescence is a difficult yet important task in developing effective prevention. We aim to extend previous findings on the linear associations between familiarity with (knowledge of) substances in childhood and subsequent substance use in adolescence through a latent class analysis (LCA) to create risk profiles based on substance familiarity.

Using the ABCD Study® sample, we conducted an LCA using 18 binary substance familiarity variables (n = 11,694 substance-naïve youth). Complementary analyses investigated the relationship between LCA groups and (1) longitudinal use, (2) use initiation, and (3) early use.

The optimal LCA resulted in a four-class solution: Naïve, Common, Uncommon, and Rare, with each group increasing in both the number and rarity of known substances. Analysis 1 revealed an increased risk in use over time among both the Uncommon and Rare groups (ORs = 2.08 and 5.55, respectively, ps < 0.001) compared to the Common group. Analysis 2 observed a decreased risk for initiation between the Naïve and Common groups (OR = 0.61, p = 0.009); however, the Uncommon and Rare groups were at an increased risk compared to the Common group (ORs = 2.08 and 3.42, respectively, ps < 0.001). Analysis 3 found an increased risk of early use between the Common and Uncommon groups (OR = 1.92, p < 0.001) with a similar trend between the Common and Rare groups (OR = 1.90, p = 0.06).

These results highlight distinct risk profiles for adolescent substance use based on substance familiarity in middle childhood. Current work could be applied as an early screening tool for clinicians to identify those at risk for adolescent substance use.

Funding

The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. Andrew Moore was supported by NIAAA of the NIH under award number F31AA031435. Ben Lewis was supported by a NIAAA of the NIH under award number K01AA026893. Devika Bhatia was supported by a postdoctoral training grant, Grant Number T32 MH015442.

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