Identifying the Smoking and Smokeless Tobacco-Related Predictors on Frequencies of Heavy Vehicle Traffic Accidents in Bangladesh: Linear and Binary Logistic Regression-Based Approach. (24th January 2023)

• Record Type: Journal Article
• Authors: Uddin, Md. Anwar; Debnath, Mithun; Roy, Sumit; Adiba, Saima; Talukder, Mohammad Mahbub Alam
• Other Names: Li Gen Academic Editor.
• Is Part Of: Advances in civil engineering. Volume 2023(2023)
• Publication Date: 2023-01-24
• Subjects: Civil engineering -- Periodicals; Civil engineering; Periodicals; 624
• DOI: 10.1155/2023/7116057
• Languages: English
• ISSNs: 1687-8086

Abstract

Abstract : Smoking is responsible for ninety percent of all premature deaths worldwide. Its prevalence is increasing in developing countries such as Bangladesh. Road traffic accidents (RTAs) have risen dramatically in recent years, with tobacco use accounting for 4–5 million fatalities each year. This trend will likely continue as more bus and truck drivers smoke in Bangladesh. Therefore, our study attempts to identify predictors that may be directly related to the frequency of RTAs and smoking. The study included 424 bus and truck drivers and ten key informant interviews (KIIs). Then, a linear regression (LR) analysis model was used to determine how various smoking-related predictors contribute to the frequency of accidents. Furthermore, a binary logistic regression (BLR) model was used to examine the likelihood of a driver being involved in an accident related to various smoking-related predictors. This study demonstrates a strong association between the incidence of accidents and the number of times a person smokes, smokes while driving, and uses smokeless tobacco (SLT) daily. The result has been taken from the second BLR model, which fits with the data more than the LR model. According to that model, a driver is more likely to be in an accident if the number of days per year that he smokes cigarettes increases and if he smokes while driving. Additionally, it stresses the need for more research to make a more accurate forecast.