Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women

Silvia Stringhini, Cristian Carmeli, Markus Jokela, Mauricio Avendaño, Peter Muennig, Florence Guida, Fulvio Ricceri, Angelo d'Errico, Henrique Barros, Murielle Bochud, Marc Chadeau-Hyam, Françoise Clavel-Chapelon, Giuseppe Costa, Cyrille Delpierre, Silvia Fraga, Marcel Goldberg, Graham G Giles, Vittorio Krogh, Michelle Kelly-Irving, Richard Layte, Aurélie M Lasserre, Michael G Marmot, Martin Preisig, Martin J Shipley, Peter Vollenweider, Marie Zins, Ichiro Kawachi, Andrew Steptoe, Johan P Mackenbach, Paolo Vineis, Mika Kivimäki, LIFEPATH consortium, Harri Alenius, Mauricio Avendano, Henrique Barros, Murielle Bochud, Cristian Carmeli, Luca Carra, Raphaele Castagné, Marc Chadeau-Hyam, Françoise Clavel-Chapelon, Giuseppe Costa, Emilie Courtin, Cyrille Delpierre, Angelo D'Errico, Pierre-Antoine Dugué, Paul Elliott, Silvia Fraga, Valérie Gares, Graham Giles, Marcel Goldberg, Dario Greco, Allison Hodge, Michelle Kelly Irving, Piia Karisola, Mika Kivimäki, Vittorio Krogh, Thierry Lang, Richard Layte, Benoit Lepage, Johan Mackenbach, Michael Marmot, Cathal McCrory, Roger Milne, Peter Muennig, Wilma Nusselder, Salvatore Panico, Dusan Petrovic, Silvia Polidoro, Martin Preisig, Olli Raitakari, Ana Isabel Ribeiro, Ana Isabel Ribeiro, Fulvio Ricceri, Oliver Robinson, Jose Rubio Valverde, Carlotta Sacerdote, Roberto Satolli, Gianluca Severi, Martin J Shipley, Silvia Stringhini, Rosario Tumino, Paolo Vineis, Peter Vollenweider, Marie Zins, Silvia Stringhini, Cristian Carmeli, Markus Jokela, Mauricio Avendaño, Peter Muennig, Florence Guida, Fulvio Ricceri, Angelo d'Errico, Henrique Barros, Murielle Bochud, Marc Chadeau-Hyam, Françoise Clavel-Chapelon, Giuseppe Costa, Cyrille Delpierre, Silvia Fraga, Marcel Goldberg, Graham G Giles, Vittorio Krogh, Michelle Kelly-Irving, Richard Layte, Aurélie M Lasserre, Michael G Marmot, Martin Preisig, Martin J Shipley, Peter Vollenweider, Marie Zins, Ichiro Kawachi, Andrew Steptoe, Johan P Mackenbach, Paolo Vineis, Mika Kivimäki, LIFEPATH consortium, Harri Alenius, Mauricio Avendano, Henrique Barros, Murielle Bochud, Cristian Carmeli, Luca Carra, Raphaele Castagné, Marc Chadeau-Hyam, Françoise Clavel-Chapelon, Giuseppe Costa, Emilie Courtin, Cyrille Delpierre, Angelo D'Errico, Pierre-Antoine Dugué, Paul Elliott, Silvia Fraga, Valérie Gares, Graham Giles, Marcel Goldberg, Dario Greco, Allison Hodge, Michelle Kelly Irving, Piia Karisola, Mika Kivimäki, Vittorio Krogh, Thierry Lang, Richard Layte, Benoit Lepage, Johan Mackenbach, Michael Marmot, Cathal McCrory, Roger Milne, Peter Muennig, Wilma Nusselder, Salvatore Panico, Dusan Petrovic, Silvia Polidoro, Martin Preisig, Olli Raitakari, Ana Isabel Ribeiro, Ana Isabel Ribeiro, Fulvio Ricceri, Oliver Robinson, Jose Rubio Valverde, Carlotta Sacerdote, Roberto Satolli, Gianluca Severi, Martin J Shipley, Silvia Stringhini, Rosario Tumino, Paolo Vineis, Peter Vollenweider, Marie Zins

Abstract

Background: In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors.

Methods: We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors.

Findings: During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98-1·11) for obesity in men and 2 ·17 (2·06-2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38-1·45 for men; 1·34, 1·28-1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21-1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking.

Interpretation: Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality.

Funding: European Commission, Swiss State Secretariat for Education, Swiss National Science Foundation, the Medical Research Council, NordForsk, Portuguese Foundation for Science and Technology.

Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.

Figures

Figure 1
Figure 1
Mortality for low versus high occupational position in men in 46 cohort studies HRs are adjusted for age, marital status, and race or ethnicity. Pooled HR is represented with a grey diamond and the 95% prediction interval with a black bar. I2 statistic is the percentage of between study heterogeneity; τ2 statistic measures the inter-study variance. The prediction interval provides a predicted range for the true association between occupational position and mortality. HR=hazard ratio.
Figure 2
Figure 2
Mortality for low versus high occupational position in women in 47 cohort studies HRs are adjusted for age, marital status, and race or ethnicity. Pooled HR is represented with a grey diamond and the 95% prediction interval with a black bar. The prediction interval provides a predicted range for the true association between occupational position and mortality. HR=hazard ratio.
Figure 3
Figure 3
Pooled hazard ratios of socioeconomic status and 25 × 25 risk factors for mortality HRs are adjusted for age, marital status, and race or ethnicity. SES=socioeconomic status. BMI=body-mass index.
Figure 4
Figure 4
Pooled hazard ratios of socioeconomic status and 25 × 25 risk factors for all-cause mortality and cause-specific mortality The minimally adjusted models were only adjusted for sex, age, and race or ethnicity; in the mutually adjusted models, SES and the 25 × 25 risk factors are mutually adjusted. BMI=body-mass index. CVD=cardiovascular disease. SES=socioeconomic status.
Figure 5
Figure 5
Population attributable fraction for socioeconomic status and 25 × 25 risk factors Calculations assume risk in the population at the level of the least exposed group. SES=socioeconomic status. PAF=population attributable fraction.
Figure 6
Figure 6
Life expectancy from age 40 years to 85 years and years of life lost due to low socioeconomic status and 25 × 25 risk factors SES=socioeconomic status. BMI=body-mass index.

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