Modeling the determinants of smoking behavior among young adults in Khuzestan province: a two-level count regression...

This study investigates the determinants of smoking behavior among young adults in Khuzestan province, southwest Iran, using two-level count regression models. Given the high prevalence of smoking-related diseases and the social impact of smoking, understanding the factors influencing smoking habits is crucial for effective public health interventions.

We conducted a cross-sectional analysis of 1,973 individuals aged 18-35 years, using data from the Daily Smoking Consumption Survey (DSCS) in Khuzestan province collected in 2023. A variety of count regression models, including Poisson, Negative Binomial, Conway-Maxwell Poisson, and their zero-inflated counterparts, were evaluated. The best-fitting model was selected based on goodness-of-fit indices.

Approximately 90% of participants were non-smokers. Among smokers, the prevalence of light, moderate, and heavy smoking was 47.7, 19.0, and 33.3%, respectively. The two-level Zero-Inflated Conway-Maxwell Poisson (ZICMP) model provided the appropriate fit for the data. Key determinants of daily cigarette consumption included gender, age, education, and Body Mass Index (BMI). Men consumed 3.24 times more cigarettes per day than women. Higher education levels were inversely related to smoking intensity, with MSc/PhD holders having significantly lower smoking rates. Age and BMI also significantly influenced smoking behavior, with younger and obese individuals showing lower smoking rates.

The use of advanced count models capable of handling numerous zeros and overdispersion is crucial for accurately analyzing trends in cigarette consumption across different population groups. The results indicate that factors such as older age, lower education levels, and gender differences influence smoking behavior. Therefore, prevention strategies aimed at delaying the onset of smoking, particularly among men, and promoting education among adolescents can effectively reduce smoking rates. However, further research should consider additional socioeconomic variables and encompass a broader age range to enhance the understanding of smoking behavior.