Determining correlates of the average number of cigarette smoking among college students using count regression models

Abstract

AbstractCollege students, as a large part of young adults, are a vulnerable group to several risky behaviors including smoking and drug abuse. This study aimed to utilize and to compare count regression models to identify correlates of cigarette smoking among college students. This was a cross-sectional study conducted on students of Hamadan University of Medical Sciences. The Poisson, negative binomial, generalized Poisson, exponentiated-exponential geometric regression models and their zero-inflated counterparts were fitted and compared using the Vuong test (α = 0.05). A number of 1258 students participated in this study. The majority of students were female (60.8%) and their average age was 23 years. Most of the students were non-smokers (84.6%). Negative binomial regression was selected as the most appropriate model for analyzing the data (comparable fit and simpler interpretation). The significant correlates of the number of cigarettes smoked per day included gender (male: incident-rate-ratio (IRR = 9.21), birth order (Forth: IRR = 1.99), experiencing a break-up (IRR = 2.11), extramarital sex (heterosexual (IRR = 2.59), homosexual (IRR = 3.13) vs. none), and drug abuse (IRR = 5.99). Our findings revealed that several high-risk behaviors were associated with the intensity of smoking, suggesting that these behaviors should be considered in smoking cessation intervention programs for college students.