José Pulido1, Fernando Vallejo2, Ignacio Alonso-López3, Enrique Regidor4, Fernando Villar5, Luis de la Fuente6, Antonia Domingo-Salvany7, Gregorio Barrio8. 1. National School of Public Health, Carlos III Health Institute, Avenida Monforte de Lemos 5, E-28029 Madrid, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Avenida Monforte de Lemos 5, E-28029 Madrid, Spain; Department of Preventive Medicine and Public Health, Madrid Complutense University, Ciudad Universitaria s/n, E-28040 Madrid, Spain. Electronic address: jpulido@isciii.es. 2. National School of Public Health, Carlos III Health Institute, Avenida Monforte de Lemos 5, E-28029 Madrid, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Avenida Monforte de Lemos 5, E-28029 Madrid, Spain. Electronic address: fvallejo@isciii.es. 3. National School of Public Health, Carlos III Health Institute, Avenida Monforte de Lemos 5, E-28029 Madrid, Spain. Electronic address: ialopez@isciii.es. 4. Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Avenida Monforte de Lemos 5, E-28029 Madrid, Spain; Department of Preventive Medicine and Public Health, Madrid Complutense University, Ciudad Universitaria s/n, E-28040 Madrid, Spain. Electronic address: enriqueregidor@hotmail.com. 5. National School of Public Health, Carlos III Health Institute, Avenida Monforte de Lemos 5, E-28029 Madrid, Spain. Electronic address: fvillar@isciii.es. 6. Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Avenida Monforte de Lemos 5, E-28029 Madrid, Spain; National Epidemiology Center, Carlos III Health Institute, Avenida Monforte de Lemos 5, E-28029 Madrid, Spain. Electronic address: lfuente@isciii.es. 7. IMIM, Institut Hospital del Mar d'Investigacions Mèdiques, Carrer del Dr. Aiguader, 88, E-08003 Barcelona, Spain. Electronic address: adomingo@imim.es. 8. National School of Public Health, Carlos III Health Institute, Avenida Monforte de Lemos 5, E-28029 Madrid, Spain. Electronic address: gbarrio@isciii.es.
Abstract
AIMS: To assess disparities in directly alcohol-attributable (DAA) mortality by industry/occupation in Spain during 2002-2011 and the contribution of different socio-demographic factors, including socioeconomic position, to explain such disparity. METHODS: Nationwide cohort study covering 16 million economically active people living in Spain in 2001. Deaths at age 25-64 were analyzed. Subjects were classified by employment status, industry and occupation at baseline. Poisson regression models were built, calculating rate ratios (RRs) compared to all employees or those in the education sector. RESULTS: DAA mortality was much higher in the unemployed than in employees (Crude RR: 2.4; 95% CI: 2.3-2.6) and varied widely across industries/occupations. Crude RRs>3.0 (p<0.05) compared to teachers were found in employees in extractive industries/fishing, agriculture/livestock, construction, catering/accommodation and protective services. Socio-demographic factors, especially age, gender and educational attainment contributed more to explain risk disparities than other factors or potential selection bias. However, after exhaustive sociodemographic adjustment, including education attainment and material wealth, a RR>1.33 (p<0.05) remained in unemployed, catering/accommodation employees and unskilled construction workers. RRs were significantly larger in women than men (p<0.05) among mineworkers/fishworkers/sailors (RR=8.6 vs. 1.2) and drivers (RR=3.7 vs. 1.0). CONCLUSIONS: The results could be extrapolated to all alcohol-attributable mortality since disparities for other strongly alcohol-related deaths, although smaller, were in the same direction. Given the wide occupational disparities in alcohol-attributable mortality, implementation of special measures to reduce this mortality in the highest risk groups is fully justified. Future research should better characterize the explanatory factors of disparities and their role in the causal chain.
AIMS: To assess disparities in directly alcohol-attributable (DAA) mortality by industry/occupation in Spain during 2002-2011 and the contribution of different socio-demographic factors, including socioeconomic position, to explain such disparity. METHODS: Nationwide cohort study covering 16 million economically active people living in Spain in 2001. Deaths at age 25-64 were analyzed. Subjects were classified by employment status, industry and occupation at baseline. Poisson regression models were built, calculating rate ratios (RRs) compared to all employees or those in the education sector. RESULTS:DAA mortality was much higher in the unemployed than in employees (Crude RR: 2.4; 95% CI: 2.3-2.6) and varied widely across industries/occupations. Crude RRs>3.0 (p<0.05) compared to teachers were found in employees in extractive industries/fishing, agriculture/livestock, construction, catering/accommodation and protective services. Socio-demographic factors, especially age, gender and educational attainment contributed more to explain risk disparities than other factors or potential selection bias. However, after exhaustive sociodemographic adjustment, including education attainment and material wealth, a RR>1.33 (p<0.05) remained in unemployed, catering/accommodation employees and unskilled construction workers. RRs were significantly larger in women than men (p<0.05) among mineworkers/fishworkers/sailors (RR=8.6 vs. 1.2) and drivers (RR=3.7 vs. 1.0). CONCLUSIONS: The results could be extrapolated to all alcohol-attributable mortality since disparities for other strongly alcohol-related deaths, although smaller, were in the same direction. Given the wide occupational disparities in alcohol-attributable mortality, implementation of special measures to reduce this mortality in the highest risk groups is fully justified. Future research should better characterize the explanatory factors of disparities and their role in the causal chain.