RESUMO
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: This was 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: 414 male drivers, mean age 43.6 (standard error 0.6) years. Population weighted prevalence of current smoking was 18.9% (95% CI: 14.3-23.4) of 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.
Assuntos
Condução de Veículo/estatística & dados numéricos , Fumar/epidemiologia , Fumar Tabaco , Adulto , Estudos Transversais , Humanos , Masculino , Pessoa de Meia-Idade , Nigéria/epidemiologia , Prevalência , Fatores Socioeconômicos , Inquéritos e Questionários , NicotianaRESUMO
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.