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

Carbon stored in harvested wood products (HWPs) can affect national greenhouse gas (GHG) inventories, in which the production and end use of HWPs play a key role. The Intergovernmental Panel on Climate Change (IPCC) provides guidance on HWP carbon accounting, which is sensitive to future developments of socioeconomic factors including population, income, and trade. We estimated the carbon stored within HWPs from 1961 to 2065 for 180 countries following IPCC carbon-accounting guidelines, consistent with Food and Agriculture Organization of the United Nations (FAOSTAT) historical data and plausible futures outlined by the shared socioeconomic pathways. We found that the global HWP pool was a net annual sink of 335 Mt of CO₂ equivalent (CO₂e)·y−1 in 2015, offsetting substantial amounts of industrial processes within some countries, and as much as 441 Mt of CO₂e·y−1 by 2030 under certain socioeconomic developments. Furthermore, there is a considerable sequestration gap (71 Mt of CO₂e·y−1 of unaccounted carbon storage in 2015 and 120 Mt of CO₂e·y−1 by 2065) under current IPCC Good Practice Guidance, as traded feedstock is ineligible for national GHG inventories. However, even under favorable socioeconomic conditions, and when accounting for the sequestration gap, carbon stored annually in HWPs is <1% of global emissions. Furthermore, economic shocks can turn the HWP pool into a carbon source either long-term—e.g., the collapse of the USSR—or short-term—e.g., the US economic recession of 2008/09. In conclusion, carbon stored within end-use HWPs varies widely across countries and depends on evolving market forces.

Though Hispanics live long lives, whether a “Hispanic paradox “extends to older-age health remains unclear, as do the social processes underlying racial-ethnic and immigrant-native health disparities. Using data from the Health and Retirement Study (2004–2012; N = 6,581), we assess the health of U.S.- and foreign-born Hispanics relative to U.S.-born whites and blacks and examine the socioeconomic, stress, and behavioral pathways contributing to health disparities. Findings indicate higher disability, depressive, metabolic, and inflammatory risk for Hispanics relative to whites and similar health profiles among Hispanics and blacks. We find limited evidence of a healthy immigrant pattern among foreign-born Hispanics. While socioeconomic factors account for Hispanic-white gaps in inflammation, disparities in other outcomes persist after adjustment for socioeconomic status, due in part to group differences in stress exposure. Hispanics may live long lives, but their lives are characterized by more socioeconomic hardship, stress, and health risk than whites and similar health risks as blacks.

Background Multimorbidity negatively affects health outcomes and impairs health-related quality of life (HRQoL). We assessed the prevalence of multimorbidity in Koreans aged 50 and older, taking into consideration their socioeconomic status, and estimated the loss in HRQoL due to multimorbidity. Methods This study is based on an analysis of data for adults aged 50 and older derived from the cross-sectional nationally representative Korean National Health and Nutrition Examination Survey conducted in 2013–14. The five most prevalent chronic diseases and disease dyads were identified. The impact of the degree of multimorbidity, sex, and socioeconomic status on the European Quality of Life 5 Dimension (EQ-5D) index score were analyzed. Marital status, educational attainment, household income, basic livelihood security benefit, and occupation were considered as socioeconomic factors. Results The analysis included 5996 adults aged 50 years and older with males comprising 46.6%. Two or more chronic diseases were present in 26.8% of the participants aged 50 and older and 37.9% of the participants aged 65 and older. The most prevalent dyadic combination was hypertension and dyslipidemia in the 50 and older group, and hypertension and osteoarthritis in the 65 and older age group. Hypertension dominated the multimorbidity combinations (four of the five most prevalent multimorbidity dyads), while a few conditions such as osteoarthritis had a relatively large influence on quality of life. In addition to the degree of multimorbidity, female and lower socioeconomic status were associated with significantly lower EQ-5D index scores. Conclusions Integrated, holistic healthcare based on a patient-oriented perspective for earlier, more effective intervention, targeting multimorbidity is warranted. Special consideration should be given to patients with low socioeconomic status.

A better socioeconomic development is necessary for environmental sustainability. The current study scrutinizes the asymmetric socioeconomic factors of CO2 emissions in China by using the nonlinear ARDL approach. This study is based on annual data covering the period from 1980 to 2019. Results show that positive change in economic growth is the leading driver of CO2 growth, while a negative change in economic growth is offsetting CO2 emissions in China. Concurrently, positive and negative changes in energy consumption have adverse impacts on CO2 emissions in the long term, while negative shock has a small influence on CO2 emissions compared to the positive shock of energy. Positive years of schooling, shocks are found to be beneficial for fighting against CO2 emissions in China in the long run. The CO2 emissions are asymmetrically affected by the social and economic structure of China. Based on these empirical findings, thereby China should improve its socioeconomic development and standards of CO2 emissions.

Socioeconomic inequalities in health and mortality are well established, but the biological mechanisms underlying these associations are less understood. In parallel, the gut microbiome is emerging as a potentially important determinant of human health, but little is known about its broader environmental and social determinants. We test the association between gut microbiota composition and individual- and area-level socioeconomic factors in a well-characterized twin cohort. In this study, 1672 healthy volunteers from twin registry TwinsUK had data available for at least one socioeconomic measure, existing fecal 16S rRNA microbiota data, and all considered co-variables. Associations with socioeconomic status (SES) were robust to adjustment for known health correlates of the microbiome; conversely, these health-microbiome associations partially attenuated with adjustment for SES. Twins discordant for IMD (Index of Multiple Deprivation) were shown to significantly differ by measures of compositional dissimilarity, with suggestion the greater the difference in twin pair IMD, the greater the dissimilarity of their microbiota. Future research should explore how SES might influence the composition of the gut microbiota and its potential role as a mediator of differences associated with SES.

[This corrects the article DOI: 10.1371/journal.pntd.0007714.].

Abstract Epigenetic clocks are increasingly being used as a tool to assess the impact of a wide variety of phenotypes and exposures on healthy ageing, with a recent focus on social determinants of health. However, little attention has been paid to the sociodemographic characteristics of participants on whom these clocks have been based. Participant characteristics are important because sociodemographic and socioeconomic factors are known to be associated with both DNA methylation variation and healthy ageing. It is also well known that machine learning algorithms have the potential to exacerbate health inequities through the use of unrepresentative samples – prediction models may underperform in social groups that were poorly represented in the training data used to construct the model. To address this gap in the literature, we conducted a review of the sociodemographic characteristics of the participants whose data were used to construct 13 commonly used epigenetic clocks. We found that although some of the epigenetic clocks were created utilizing data provided by individuals from different ages, sexes/genders, and racialized groups, sociodemographic characteristics are generally poorly reported. Reported information is limited by inadequate conceptualization of the social dimensions and exposure implications of gender and racialized inequality, and socioeconomic data are infrequently reported. It is important for future work to ensure clear reporting of tangible data on the sociodemographic and socioeconomic characteristics of all the participants in the study to ensure that other researchers can make informed judgements about the appropriateness of the model for their study population.