Literature DB >> 29781627

Triple trajectories of alcohol use, tobacco use, and depressive symptoms as predictors of cannabis use disorders among urban adults.

Jung Yeon Lee1, Judith S Brook1, Wonkuk Kim2.   

Abstract

Heavy cannabis use is associated with a wide array of physical, mental, and functional problems. Therefore, cannabis use disorders (CUDs) may be a major public health concern. Given the adverse health consequences of CUDs, the present study seeks to find possible precursors of CUDs. The current study consisted of 5 waves of data collection from the Harlem Longitudinal Development Study. Among 816 participants, about half are African Americans (52%), and the other half are Puerto Ricans (48%). We used Mplus to obtain the triple trajectories of alcohol use, tobacco use, and depressive symptoms. Logistic regression analyses were then conducted to examine the associations between the trajectory groups and CUDs. The 5 trajectory groups were (1) moderate alcohol use, high tobacco use, and high depressive symptoms (MHH; 12%); (2) moderate alcohol use, high tobacco use, and low depressive symptoms (MHL; 26%); (3) moderate alcohol use, low tobacco use, and low depressive symptoms (MLL; 18%); (4) low alcohol use, no tobacco use, and high depressive symptoms (LNH; 11%); and (5) low alcohol use, no tobacco use, and low depressive symptoms (LNL; 33%). The MHH, MHL, MLL, and LNH trajectory groups were associated with an increased likelihood of having CUDs compared to the LNL trajectory group after controlling for a number of confounding factors (e.g., CUDs in the late 20s). The findings of the current longitudinal study suggest that treatments designed to reduce or quit drinking as well as smoking and to relieve depressive symptoms may reduce the prevalence of CUDs. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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Year:  2018        PMID: 29781627      PMCID: PMC6013376          DOI: 10.1037/adb0000373

Source DB:  PubMed          Journal:  Psychol Addict Behav        ISSN: 0893-164X


  2 in total

1.  Subphenotyping depression using machine learning and electronic health records.

Authors:  Zhenxing Xu; Fei Wang; Prakash Adekkanattu; Budhaditya Bose; Veer Vekaria; Pascal Brandt; Guoqian Jiang; Richard C Kiefer; Yuan Luo; Jennifer A Pacheco; Luke V Rasmussen; Jie Xu; George Alexopoulos; Jyotishman Pathak
Journal:  Learn Health Syst       Date:  2020-08-03

2.  Lifelong smoking trajectories of Northern Finns are characterized by sociodemographic and lifestyle differences in a 46-year follow-up.

Authors:  Petteri Oura; Ina Rissanen; Juho-Antti Junno; Terttu Harju; Markus Paananen
Journal:  Sci Rep       Date:  2020-10-01       Impact factor: 4.379

  2 in total

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