Using Latent Class Analysis to Examine Polysubstance Use Patterns in Adolescents Aged 10-18: A Systematic Review.

Area of Science:

• Psychiatry and Behavioral Sciences
• Public Health
• Data Science

Background:

• Substance use disorder (SUD) frequently originates in adolescence, with over half of cases developing during this critical period.
• Latent class analysis (LCA) is a statistical method used to identify distinct patterns of polysubstance use among adolescents.

Purpose of the Study:

• To systematically review and characterize existing studies on adolescent polysubstance use patterns.
• To provide an updated overview of methodologies and findings in the field of adolescent polysubstance use research.

Main Methods:

• Systematic review focusing on studies with adolescent samples (mean age up to 18 years).
• Analysis of 54 studies, examining variations in sample size, number of substances, and extracted latent classes.
• Assessment of statistical associations between sample characteristics and the number of latent classes identified.

Main Results:

• Most studies were conducted in the United States, with significant heterogeneity in sample sizes, indicator substances, and number of latent classes.
• Larger sample sizes were associated with a greater number of extracted latent classes (Spearman's rho = .274, p = .022).
• The Bayesian Information Criterion was the most frequently used statistical fit index for latent class models.

Conclusions:

• Significant heterogeneity exists in study methodologies, LCA solutions, and statistical approaches for adolescent polysubstance use.
• Studies focusing on specific populations identified patterns involving less common substances.
• Future research should broaden the range of indicator substances and consider geographical influences on polysubstance use patterns.