New UCSF study: There are 5 different trajectories of youth smoking

Many people think about youth who start smoking as a homogenous group.  Lauren Dutra, Nadra Lisha, Anna Song, and I have found that there are actually four different patterns of youth smoking (in addition to people who never smoke a cigarette).
Our paper, “Beyond experimentation: Five trajectories of cigarette smoking in a longitudinal sample of youth” in PLOS One.  While the largest group of kids starts smoking around 12-13 and plateaus in terms of days smoked per month around age 20, there are also kids who only smoke very occasionally, and others who, while they start around 12-13, their smoking peaks around age 17, then dropps off.  There are also “late escalators” who don’t even start until they are around 20.
Another important observation is that many of these kids never become daily smokers, which is relevant in the current e-cigarette discussions were some are discounting the value of 30-day smoking as a measure of adolescent and young adult smoking behavior.
There are several important implications:

  • Tobacco prevention programs need to recognize that there are different patterns of youth smoking
  • The early established smokers were less likely to be in school and more likely to have kids, meaning that school-based programs will miss this important group
  • A substantial fraction of smokers start after 18, reinforcing the importance of directing tobacco prevention programs to young adults

Here is the abstract:
The first goal of this study was to identify the most appropriate measure of cigarette smoking for identifying unique smoking trajectories among adolescents; the second goal was to describe the resulting trajectories and their characteristics. Using 15 annual waves of smoking data in the National Longitudinal Survey of Youth 1997 (NLSY97), we conducted an exploratory latent class growth analysis to determine the best of four outcome variables for yearly smoking (cigarettes per day on days smoked, days smoked per month, mean cigarettes per day, and total cigarettes per month) among individuals aged 12 to 30 (n = 8,791). Days smoked per month was the best outcome variable for identifying unique longitudinal trajectories of smoking and characteristics of these trajectories that could be used to target different types of smokers for prevention and cessation. Objective statistics were used to identify four trajectories in addition to never smokers (34.1%): experimenters (13.6%), quitters (8.1%), early established smokers (39.0%), and late escalators (5.2%). We identified a quitter and late escalator class not identified in the only other comparable latent class growth analysis. Logistic regressions were used to identify the characteristics of individuals in each trajectory. Compared with never smokers, all trajectories except late escalators were less likely to be black; experimenters were more likely to be out of school and unemployed and drink alcohol in adolescence; quitters were more likely to have a mother with a high school degree/GED or higher (versus none) and to use substances in adolescence and less likely to have ever married as a young adult; early established smokers were more likely to have a mother with a high school diploma or GED, be out of school and unemployed, not live with both parents, have used substances, be depressed, and have peers who smoked in adolescence and to have children as young adults and less likely to be Hispanic and to have ever married as young adults; and late escalators were more likely to be Hispanic, drink alcohol, and break rules in adolescence and less likely to have ever married as young adults. Because of the number of waves of data analyzed, this analysis provided a clearer temporal depiction of smoking behavior and more easily distinguishable smoking trajectories than previous analyses. Tobacco control interventions need to move beyond youth-focused approaches to reach all smokers.
The full citation is:  Dutra LM, Glantz SA, Lisha NE, Song AV (2017) Beyond experimentation: Five trajectories of cigarette smoking in a longitudinal sample of youth. PLoS ONE 12(2): e0171808. doi:10.1371/journal.pone.0171808.  It is available for free here.