Project outline:

Smoking and depression commonly co-occur in the general population. That is, individuals with depression are more likely to be smokers and smoke more heavily compared to individuals without depression, and smokers are more likely to report depressive symptoms compared to nonsmokers. In adolescents, there is also evidence of this relationship. It has been shown that depression and other mood disorders (e.g., anxiety) in adolescents increase the likelihood of experimental smoking and smoking initiation. Others have demonstrated that tobacco smoking among adolescents leads to increase depressive symptoms, which remit following cessation. Regardless of directionality, it is well-known that smokers with elevated depressive symptoms experience more difficulties when quitting smoking. Furthermore, concurrent depressive symptoms and tobacco use may interact synergistically to produce greater health risks than either disorder alone, especially for heart disease.

Better characterizing the developmental (longitudinal) relationship between these variables, particularly among adolescents, would be a significant contribution to the existing literature and provide evidence for the diversity in comorbidity of adolescent smoking and depression. This is an important area as both smoking and depression are related to increased use of other drugs and illicit substances, mental health distress, and physical health problems. Recently, much research has focused on modelling patterns in substance abuse and mood disorders across early developmental periods, but smoking and depression among adolescents have yet to be modelled in a dual trajectory analysis. Smoking trajectories for this cohort have been previously established and the data lend themselves to modelling trajectories of the development and early course of depression as well. The ALSPAC cohort presents an ideal dataset to be analysed as dual trajectories to model the comorbid development of smoking and depression in adolescents. Further, we can examine sociodemographic predictors and mental, behavioural, and physical outcomes of group membership.

Aims:

1. Examine the co-occurrence of smoking and depression among adolescents using dual trajectory modelling.

2. Explore outcomes between different trajectory groups, including:

a. mood symptoms (e.g., anxiety),

b. substance use (e.g., cannabis), and

c. biological markers (e.g., lung function).

We will use longitudinal latent class analysis to generate trajectories for adolescent smoking and depression, both separately and then in conjunction. Adolescent smoking will be based on self-reported smoking status. Adolescent depression will be based on the self-report MFQ.

Hypotheses:

We hypothesize that severity of tobacco use will be related to severity of depression (to be examined by cross tabulation). More specifically, we expect that adolescents who smoke will endorse higher levels of depressive symptoms, and that logical dual trajectories of adolescent smoking and depression will emerge. We also expect that there will be significant group differences, both in predictor variables (sociodemographics) and outcome variables (substance use, mood symptoms, and biological markers).

Variables:

Exposure variables. Smoking status will be determined by self-report during the clinical interview and depression will be determined by self-reported on the MFQ. These variables will then be modelled by dual trajectories which will elicit specific smoking x depression groups.

Outcome variables. Other substance use (e.g., cannabis), mood symptoms (e.g., anxiety), and biological markers (e.g., lung function). These outcome variables will be applied to both the groups that emerge from the individual trajectory models (smoking and depression) as well as those from the smoking x depression dual trajectory model.

Confounding variables. Smoking status and depression have also been shown to be differentially associated with gender, race, age, education and SES (housing tenure, crowding status and maternal educational attainment). Therefore these covariates will be included in our models. Another variable that has garnered attention recently is traumatic events in childhood, so we will explore the effect of early life stressors (e.g., abuse, deaths) on both smoking and depression.