Lack of Preventive Health Behaviors in the Early Forties: The Role of Earlier Trajectories of Cigarette Smoking From Adolescence to Adulthood

ABSTRACT

Objective: To study the degree to which individuals in different trajectories of cigarette smoking from adolescence to the early forties are similar or different in terms of lack of preventive health behaviors (e.g., underuse of preventive health services, unhealthy eating habits) in early midlife. Methods: Participants came from a community-based random sample of residents in two upstate New York counties (N = 548). Data were collected from adolescence to early midlife (mean age = 43 years, standard deviation [SD] = 2.8) at seven time points. Using growth mixture modeling, we statistically identified the number of smoking trajectories. Logistic regression analysis was used to study the relationship between the probabilities of participants' smoking trajectory group membership and lack of preventive behaviors in early midlife. Results: Five trajectory groups of cigarette smokers were identified. With controls, as compared with the nonsmoker trajectory group, higher probabilities of belonging to the heavy/continuous smoker trajectory group and the late starter trajectory groups were significantly associated with a higher likelihood of lack of preventive health behaviors (adjusted odds ratio [AOR] = 3.49 and 4.02 respectively). In addition, as compared to the quitter/decreaser trajectory group, higher probabilities of belonging to the heavy/continuous smoker trajectory group and the late starter trajectory group were also significantly associated with a higher likelihood of lack of preventive health behaviors (AOR = 3.51 and 4.04 respectively). Conclusions: Intervention programs may consider focusing on heavy/continuous smokers and late starters in programs designed to promote adequate use of preventive health services and healthy general lifestyles in early midlife.

KEYWORDS:

• Trajectory of cigarette smoking
• lack of preventive health behaviors
• underuse of preventive health services
• unhealthy eating habits
• poor sleeping habits
• lack of exercise

Acknowledgments

The Institutional Review Board of the New York University School of Medicine authorized the use of human subjects in this research study. Earlier waves of the study were approved by the Institutional Review Boards of the Mount Sinai School of Medicine and New York Medical College.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Funding

This research was supported by NIH grants DA032603 and 5K05 DA00244 from the National Institute on Drug Abuse, and CA094845 from the National Cancer Institute, awarded to Dr. Judith S. Brook.

Notes

1 GMM is a statistical modeling technique that can be used to identify unobserved differences in growth trajectories. GMM explores qualitative differences in longitudinal growth trajectories, which are based on differences in growth parameter means (e.g., intercept and slope.