Working conditions and health behavior as causes of educational inequalities in self-rated health: an inverse odds weighting approach

Objective Using a novel mediation method that presents unbiased results even in the presence of exposure– mediator interactions, this study estimated the extent to which working conditions and health behaviors contribute to educational inequalities in self-rated health in the workforce. Methods Respondents of the longitudinal Survey of Health, Ageing, and Retirement in Europe (SHARE) in 16 countries were selected, aged 50–64 years, in paid employment at baseline and with information on education and self-rated health (N=15 028). Education, health behaviors [including body mass index (BMI)] and working conditions were measured at baseline and self-rated health at baseline and two-year follow-up. Causal mediation analysis with inverse odds weighting was used to estimate the total effect of education on self-rated health, decomposed into a natural direct effect (NDE) and natural indirect effect (NIE). Results Lower educated workers were more likely to perceive their health as poor than higher educated workers [relative risk (RR) 1.48, 95% confidence interval (CI) 1.37–1.60]. They were also more likely to have unfavorable working conditions and unhealthy behaviors, except for alcohol consumption. When all working conditions were included, the remaining NDE was RR 1.30 (95% CI 1.15–1.44). When BMI and health behaviors were included, the remaining NDE was RR 1.40 (95% CI 1.27–1.54). Working conditions explained 38% and health behaviors and BMI explained 16% of educational inequalities in health. Including all mediators explained 64% of educational inequalities in self-rated health. Conclusions Working conditions and health behaviors explain over half of the educational inequalities in selfrated health. To reduce health inequalities, improving working conditions seems to be more important than introducing health promotion programs in the workforce

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Equity-specific effects of 26 Dutch obesity-related lifestyle interventions

Context Reducing health inequalities is a policy priority in many developed countries. Little is known about effective strategies to reduce inequalities in obesity and its underlying behaviors. The goal of the study was to investigate differential effectiveness of interventions aimed at obesity prevention, the promotion of physical activity or a healthy diet by SES. Evidence acquisition Subgroup analyses in 2010 and 2011 of 26 Dutch studies funded by The Netherlands Organization for Health Research and Development after 1990 (n=17) or identified by expert contact (n=9). Methodologic quality and differential effects were synthesized in harvest plots, subdivided by setting, age group, intensity, and time to follow-up. Evidence synthesis Seven lifestyle interventions were rated more effective and four less effective in groups with high SES; for 15 studies no differential effects could be demonstrated. One study in the healthcare setting showed comparable effects in both socioeconomic groups. The only mass media campaign provided modest evidence for higher effectiveness among those with high SES. Individually tailored and workplace interventions were either more effective in higher-SES groups (n=4) or no differential effects were demonstrated (n=9). School-based studies (n=7) showed mixed results. Two of six community studies provided evidence for better effectiveness in lower-SES groups; none were more effective in higher-SES groups. One high-intensity community-based study provided best evidence for higher effectiveness in low-SES groups. Conclusions Although for the majority of interventions aimed at obesity prevention, the promotion of physical activity, or a healthy diet, no differential effectiveness could be demonstrated, interventions may widen as well as reduce socioeconomic inequalities in these outcomes. Equity-specific subgroup analyses contribute to needed knowledge about what may work to reduce socioeconomic inequalities in obesity and underlying health behaviors

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives