The Effects of Marijuana Use on Transitions through Stages of Alcohol Involvement for Men and Women in the NESARC I and II

Abstract

Background: With the changing context of marijuana use, it is critical to identify effects of use. We extend previous work by examining whether marijuana use influences progression and remission through alcohol involvement stages for men and women. Methods: Data come from Waves I and II of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, n = 34,432). We assess the potential influence of marijuana use at Wave 1 on transitions across three latent statuses of alcohol involvement between waves. We apply propensity score weighting to account for shared risk factors. Results: Marijuana use was associated cross-sectionally and longitudinally with alcohol involvement statuses for both sexes. After propensity score adjustment, men with marijuana histories were 3.50 times as likely as men without such histories to transition from no to severe problems across waves relative to staying in the same status (p < .001). Women with marijuana histories were 1.74 times as likely as women without such histories to transition from no problems at Wave 1 to moderate problems at Wave 2 (p = .030) and 0.13 times as likely as women without such histories to transition from severe problems to no problems (p = .006). Conclusions: Results suggest that marijuana use impacts progression to more serious stages of alcohol involvement for both men and women, as well as hinders remission among women. Findings point to the importance of screening those with marijuana histories for alcohol problems, as well as the need to understand the mechanism of why marijuana use may increase the risk of alcohol problems.

Keywords:

• Alcohol progression
• alcohol remission
• cannabis effects
• latent transition analyses
• longitudinal data
• population-based data
• sex differences

Notes

1 While the LCA and LTA portions are similar to previously published work, they vary slightly since the sample size for each analysis changes based on missing data for the covariates in the model.

Additional information

Funding

This work was supported by the National Institutes of Health grant 2R01AA016346-05A1. Dr. Green also acknowledge support from the Eunice Kennedy Shriver National Center for Child Health and Human Development grant P2C-HD041041, Maryland Population Research Center.