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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.)

Background: The American Heart Association developed the Life's Simple 7 metric for defining cardiovascular health. Little is known about the association of co-occurring social risk factors on ideal cardiovascular health., Methods: Using data on 11,467 adults aged ≥25 years from the National Health and Nutrition Examination Survey 1999-2006, we examined the association between cumulative social risk and ideal cardiovascular health in US adults. A cumulative risk score (range 0 to 3 or 4) was created by summing four social risk factors (low family income, low education level, minority race, and single-living status). Ideal levels for each component in Life's Simple 7 (blood pressure, cholesterol, glucose, BMI, smoking, physical activity, and diet) were used to create an ideal Life's Simple 7 score [0-1 (low), 2, 3, 4, and 5-7 (high)]., Results: Adults with low income (odds ratio [OR]=0.30 [95% CI 0.23-0.39]), low education [0.22 (0.16-0.28)], who are non-white (0.44 [0.36-0.54]) and single-living [0.79 (0.67-0.95)] were less likely to have 5-7 versus 0 ideal Life's Simple 7 scores after adjustment for age and sex. Adults were less likely to attain 5-7 versus 0 ideal Life's Simple 7 scores as exposure to the number of social risk factors increased [OR (95% CI) of 0.58 (0.49-0.68); 0.27 (0.21-0.35); and 0.19 (0.14-0.27) for cumulative social risk scores of 1, 2, and 3 or 4, respectively, each versus 0]., Conclusions: US adults with an increasing number of socially risk factors, were progressively less likely to attain ideal levels of cardiovascular health factors., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Background: Ecological and cross-sectional studies have indicated that conservative political ideology is associated with better health. Longitudinal analyses of mortality are needed because subjective assessments of ideology may confound subjective assessments of health, particularly in cross-sectional analyses., Methods: Data were derived from the 2008 General Social Survey-National Death Index data set. Cox proportional analysis models were used to determine whether political party affiliation or political ideology was associated with time to death. Also, we attempted to identify whether self-reported happiness and self-rated health acted as mediators between political beliefs and time to death., Results: In this analysis of 32,830 participants and a total follow-up time of 498,845 person-years, we find that political party affiliation and political ideology are associated with mortality. However, with the exception of independents (adjusted HR (AHR)=0.93, 95% CI 0.90 to 0.97), political party differences are explained by the participants' underlying sociodemographic characteristics. With respect to ideology, conservatives (AHR=1.06, 95% CI 1.01 to 1.12) and moderates (AHR=1.06, 95% CI 1.01 to 1.11) are at greater risk for mortality during follow-up than liberals., Conclusions: Political party affiliation and political ideology appear to be different predictors of mortality., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: IQ is thought to explain social gradients in mortality. IQ scores are based roughly equally on Verbal IQ (VIQ) and Performance IQ tests. VIQ tests, however, are suspected to confound true verbal ability with socioeconomic status (SES), raising the possibility that associations between SES and IQ scores might be overestimated. We examined, first, whether two of the most common types of VIQ tests exhibited differential item functioning (DIF) favouring persons of higher SES and/or majority race/ethnicity. Second, we assessed what impact, if any, this had on estimates of the extent to which VIQ explains social gradients in mortality., Methods: Data from the General Social Survey-National Death Index cohort, a US population representative dataset, was used. Item response theory models queried social-factor DIF on the Thorndike Verbal Intelligence Scale and Wechsler Adult Intelligence Scales, Revised Similarities test. Cox models examined mortality associations among SES and VIQ scores corrected and uncorrected for DIF., Results: When uncorrected for DIF, VIQ was correlated with income, education, occupational prestige and race, with correlation coefficients ranging between |0.12| and |0.43|. After correcting for DIF, correlations ranged from |0.06| to |0.16|. Uncorrected VIQ scores explained 11-40% of the Relative Index of Inequalities in mortality for social factors, while DIF-corrected scores explained 2-29%., Conclusions: Two of the common forms of VIQ tests appear confound verbal intelligence with SES. Since these tests appear in most IQ batteries, circumspection may be warranted in estimating the amount of social inequalities in mortality attributable to IQ., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: C-reactive protein, homocysteine, cholesterol, and fibrinogen are known to vary by socioeconomic status (SES). Using a nationally representative study, we examined whether these factors vary independently of all other known risk factors, such as diet, exercise, and genetic predisposition., Methods: We analyzed the 1999-2002 National Health Examination and Nutrition Survey using logistic regression models., Results: We found that high-density lipoprotein cholesterol blood levels increase with income and educational attainment after controlling all known risk factors for elevated cholesterol (e.g., diet, exercise, and family history). Blood levels of C-reactive protein are inversely associated with income and education. Homocysteine blood levels are inversely associated with income even after controlling for blood folate level. A non-significant inverse relationship between homocysteine levels and educational attainment was also observed. Blood levels of low-density lipoprotein cholesterol and fibrinogen were not significantly associated with income or education., Conclusions: Levels of "good" (high density lipoprotein) cholesterol increase with income and education even after controlling for factors known to place people at risk of high cholesterol. Stress differences by social class may play a role.

The burden of disease associated with being African-American in the US, and the contribution of socio-economic status (SES) to that burden have not been quantified. We derived burden of disease estimates for African-Americans and whites by age group, with and without adjustment for SES (income and education). We used (1) EQ-5D Index scores from the 2000 US Medical Expenditure Panel Survey to derive quality-adjusted life year (QALY) compatible estimates of health-related quality of life (HRQL); (2) 1990-1992 US National Health Interview Survey data linked to National Death Index data through 1995 to derive mortality risks; and (3) 2000 US mortality data from the National Center for Health Statistics to derive current mortality estimates for the US population. We found that relative to whites, African-Americans suffer 67,000 more deaths annually, resulting in 2.2 million years of life lost, and 1.1 million years after SES adjustment. Total QALYs lost (HRQL and mortality) dropped from 2.3 million to 902,000 after SES adjustment. SES differences between African-Americans and whites appear to explain all the HRQL disparity but only half the mortality disparity. Better understanding of the disparate effects of SES may inform interventions to address health disparities adversely affecting African-Americans.