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Two-part models identifying predictors of cigarette, e-cigarette, and cannabis use and change in use over time among young adults in the US.
Wang, Yan; Romm, Katelyn F; Edberg, Mark C; Bingenheimer, Jeffrey B; LoParco, Cassidy R; Cui, Yuxian; Berg, Carla J.
Afiliação
  • Wang Y; Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA.
  • Romm KF; George Washington Cancer Center, George Washington University, Washington, District of Columbia, USA.
  • Edberg MC; TSET Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
  • Bingenheimer JB; Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
  • LoParco CR; Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA.
  • Cui Y; Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA.
  • Berg CJ; Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA.
Am J Addict ; 2024 Apr 29.
Article em En | MEDLINE | ID: mdl-38685757
ABSTRACT
BACKGROUND AND

OBJECTIVES:

Limited longitudinal research has examined differential interpersonal and intrapersonal correlates of young adult use and use frequency of cigarettes, e-cigarettes, and cannabis. This study aimed to address these limitations.

METHODS:

We analyzed five waves of longitudinal data (2018-2020) among 3006 US young adults (Mage = 24.55, 44% male, 32% sexual minority, ~30% racial/ethnic minority). Two-part latent growth models examined likelihood of past-month cigarette, e-cigarette, and cannabis use (binary part) and days used (continuous part) and identified predictors (depressive symptoms, personality traits, adverse childhood experiences [ACEs], parental use) of baseline use and changes over time.

RESULTS:

Regarding baseline past-month use (27% cigarettes, 38% e-cigarettes, 39% cannabis), depressive symptoms, ACEs, and parental substance use predicted use outcomes (i.e., likelihood, frequency) for each product; extraversion predicted cigarette and e-cigarette use outcomes; openness predicted e-cigarette and cannabis use outcomes; conscientiousness negatively predicted cigarette and cannabis use outcomes; and agreeableness negatively predicted cannabis use frequency. Regarding longitudinal changes, conscientiousness predicted accelerated increase of cigarette use frequency at later timepoints; depressive symptoms predicted increases in likelihood of e-cigarette use but the association weakened over time; and parental cannabis use predicted decreased cannabis use frequency but the association weakened over time. DISCUSSION AND

CONCLUSIONS:

Young adult substance use interventions should target high-risk subgroups and focus on distinct factors impacting use, including chronic, escalating, and decreasing use. SCIENTIFIC

SIGNIFICANCE:

This study advances the literature regarding distinct predictors of different substance use outcomes and provides unique data to inform interventions targeting young adult cigarette, e-cigarette, and cannabis use.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article