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Background: Factors associated with tobacco smoking are useful in designing tobacco control programs., Objectives: To estimate the prevalence of and factors associated with tobacco smoking among long-distance drivers., Methods: A cross-sectional study. Stratified cluster sampling approach was used to select drivers based on if they received annual health screening (AHS) or not (non-AHS). We used a structured questionnaire to obtain information and weighted the resulting observations to derive population based estimates. Association between tobacco smoking and socio-demographic factors was explored in multivariate models., Results: A total of 414 male drivers, with a mean age of 43.6 (standard error 0.6) years were studied. Population weighted prevalence of current smoking was 18.9% (95% CI: 14.3-23.4) all drivers, 6.5% (95% CI: 2.6-10.4) of AHS drivers and 19.5 (95% CI: 14.7-24.2) of non-AHS drivers (p<0.001). In multivariate models, having close friends that smoked (OR= 6.36, 95% CI= 2.49 - 16.20) cargo driving (OR= 2.58, 95% CI= 1.29 - 5.15) and lower education levels (OR for post-secondary education vs. Primary education or less= 0.17, 95% CI= 0.04 - 0.81) were associated with current smoking., Conclusion: Prevalence of tobacco smoking is higher among non-AHS compared to AHS drivers. Having close friends that smoked, cargo driving, and lower education levels were associated with current smoking.

For estimating causal effects of treatments, randomized experiments are appropriately considered the gold standard, although they are often infeasible for a variety of reasons. Nevertheless, nonrandomized studies can and should be designed to approximate randomized experiments by using only background information to create subgroups of similar treated and control units, where "similar" here refers to their distributions of background variables. This activity should be conducted without access to any outcome data to assure the objectivity of the design. In many situations, these goals can be accomplished using propensity score methods, as illustrated here in the context of a study on whether nonsmoking Harvard freshmen are influenced by their smoking peers. In that study, propensity score methods were used to create matched groups of treated units (rooming with at least one smoker) and control units (rooming with only non-smokers) who are at least as similar with respect to their distributions of observed background characteristics as if they had been randomized, thereby approximating a randomized experiment with respect to the observed covariates., Statistics