Dimensions of Social Stratification and Their Relation to Mortality : A Comparison Across Gender and Life Course Periods in Finland

Differences in mortality between groups with different socioeconomic positions (SEP) are well-established, but the relative contribution of different SEP measures is unclear. This study compares the correlation between three SEP dimensions and mortality, and investigates differences between gender and age groups (35-59 vs. 60-84). We use an 11% random sample with an 80% oversample of deaths from the Finnish population with information on education, occupational class, individual income, and mortality (n=496,658; 274,316 deaths between 1995 and 2007). We estimate bivariate and multivariate Cox proportional hazard models and population attributable fractions. The total effects of education are substantially mediated by occupation and income, and the effects of occupation is mediated by income. All dimensions have their own net effect on mortality, but income shows the steepest mortality gradient (HR 1.78, lowest vs. highest quintile). Income is more important for men and occupational class more important among elderly women. Mortality inequalities are generally smaller in older ages, but the relative importance of income increases. In health inequality studies, the use of only one SEP indicator functions well as a broad marker of SEP. However, only analyses of multiple dimensions allow insights into social mechanisms and how they differ between population subgroups.Peer reviewe

Statistical methods for causal analysis in life course research: an illustration of a cross-lagged structural equation model, a latent growth model, and an autoregressive latent trajectories model

We present three statistical methods for causal analysis in life course research that are able to take into account the order of events and their possible causal relationship: a cross-lagged model, a latent growth model (LGM), and a synthesis of the two, an autoregressive latent trajectories model (ALT). We apply them to a highly relevant causality question in life course and health inequality research: does socioeconomic status (SES) affect health (social causation) or does health affect SES (health selection)? Using retrospective survey data from SHARELIFE covering life courses from childhood to old age, the cross-lagged model suggests an equal importance of social causation and health selection; the LGM stresses the effect of education on health growth; whereas the ALT model confirms no causality. We discuss examples, present short and non-technical introduction of each method, and illustrate them by highlighting their relative strengths for causal life course analysis

What causes health inequality? : a systematic review on the relative importance of social causation and health selection

First published online 18 June 2015. The social gradient in health is one of the most reliable findings in public health research. The two competing hypotheses that try to explain this gradient are known as the social causation and the health selection hypothesis. There is currently no synthesis of the results of studies that test both hypotheses. We provide a systematic review of the literature that has addressed both the health selection and social causation hypotheses between 1994 and 2013 using seven databases following PRISMA rules. The search strategy resulted in 2952 studies, of which, we included 34 in the review. The synthesis of these studies suggests that there is no general preference for either of the hypotheses (12 studies for social causation, 10 for health selection). However, both a narrative synthesis as well as meta-regression results show that studies using indicators for socio-economic status (SES) that are closely related to the labor market find equal support for health selection and social causation, whereas indicators of SES like education and income yield results that are in favor of the social causation hypothesis. High standards in statistical modeling were associated with more support for health selection. The review highlights the fact that the causal mechanisms behind health inequalities are dependent on whether or not the dimension being analyzed closely reflects labor market success. Additionally, further research should strive to improve the statistical modeling of causality, as this might influence the conclusions drawn regarding the relative importance of health selection and social causation

The long arm of childhood circumstances on health in old age: Evidence from SHARELIFE

Socioeconomic status (SES) and health during childhood have been consistently observed to be associated with health in old age in many studies. However, the exact mechanisms behind these two associations have not yet been fully understood. The key challenge is to understand how childhood SES and health are associated. Furthermore, data on childhood factors and life course mediators are sometimes unavailable, limiting potential analyses. Using SHARELIFE data (N = 17230) we measure childhood SES and health circumstances, and examine their associations with old age health and their possible pathways via education, adult SES, behavioural risks, and labour market deprivation. We employ structural equation modelling to examine the mechanism of the long lasting impact of childhood SES and health on later life health, and how mediators partly contribute to these associations. The results show that childhood SES is substantially associated with old age health, albeit almost fully mediated by education and adult SES. Childhood health and behavioural risks have a strong effect on old age health, but they do not mediate the association between childhood SES and old age health. Childhood health in contrast retains a strong association with old age health after taking adulthood characteristics into account. This paper discusses the notion of the ‘long arm of childhood’ and concludes that it is a lengthy, mediated, incremental progression rather than a direct effect. Policies should certainly focus on childhood, especially when it comes to addressing childhood health conditions, but our results suggest other important entry points for improving old age health when it comes to socioeconomic determinants

Faunistic remarkable beetle records from flowering areas in Schleswig-Holstein

In Schleswig-Holstein werden seit einigen Jahren Blühflächen zur Förderung des Rebhuhns angelegt, deren Arthropodenfauna von 2016 bis 2019 mit unterschied- lichen Fangmethoden untersucht wurde. Dabei gelangen bemerkenswerte Nachweise von 37 Käferarten die hier publiziert werden. Bei ausgewählten Arten werden weitere Informationen und Fundumstände dokumentiert und diskutiert.Wildflower fields were sown from 2016 until 2019 for the Grey Partridge, whose ar - thropod assemblages have been studied with different trapping methods. Overall, remarkable records of 37 beetle species have been found and its circumstances will be discussed

First record of Anisodactylus signatus (Panzer, 1797) (Coleoptera: Carabidae) in Schleswig-Holstein

Anisodactylus signatus (Panzer, 1797) konnte erstmals für Schleswig-Holstein nachgewiesen werden. Das Weibchen wurde auf einer Blühfläche nördlich von Neumünster mit Bodenfallen gefangen. Es werden Angaben zum Fundort, Verbreitung und Habitatansprüchen geliefert.Anisodactylus signatus (Panzer, 1797) was discovered in the province of Schleswig-Holstein (Germany) for the first time. The female was caught on a sown wildflower field north of Neumünster using pitfall traps. Details of the locality, distribution and habitat requirements are given

Umweltfolgen des Straßenverkehrs

Series: IIR-Discussion Paper

Der Unfallatlas - Eine interaktive Kartenanwendung der Statistischen Ämter des Bundes und der Länder: Hintergrund, Funktionalitäten und Analysepotenzial

Der Unfallatlas der Statistischen Ämter des Bundes und der Länder basiert auf georeferenzierten Unfalldaten. Die interaktive Kartenanwendung bietet den Nutzerinnen und Nutzern die Möglichkeit, für eine Vielzahl von Bundesländern Unfälle mit Personenschaden koordinatenscharf zu erkunden. Im Sommer 2020 wurde der Unfallatlas für das Berichtsjahr 2019 aktualisiert und damit um die Unfalldaten der Bundesländer Nordrhein-Westfalen und Thüringen erweitert. Die interaktive Kartenanwendung mit ihrer georeferenzierten Datenbasis bietet vielfältige Analysepotenziale. Diese beziehen sich nicht nur auf die Analyse der Verteilung von Unfällen in verschiedenen Kategorien. Aufgrund von erhobenen Eigenschaften der Unfälle sowie der kartographischen Verortung lassen sich auch mögliche Ursachen analysieren