Lifetime Employment Trajectories and Cancer: A Population-Based Cohort Study.

Working life is associated with lifestyle, screening uptake, and occupational health risks that may explain differences in cancer onset. To better understand the association between working life and cancer risk, we need to account for the entire employment history. We investigated whether lifetime employment trajectories are associated with cancer risk. We used data from 6,809 women and 5,716 men, average age 70 years, from the Survey of Health, Ageing, and Retirement in Europe. Employment history from age 16 to 65 was collected retrospectively using a life calendar and trajectories were constructed using sequence analysis. Associations between employment trajectories and self-reported cancer were assessed using logistic regression. We identified eight employment trajectories for women and two for men. Among women, the risk of cancer was higher in the trajectories "Mainly full-time to home/family", "Full-time or home/family to part-time", "Mainly full-time", and "Other" compared with the "Mainly home/family" trajectory. Among men, the risk of cancer was lower in the "Mainly self-employment" trajectory compared with "Mainly full-time". We could show how employment trajectories were associated with cancer risk, underlining the potential of sequence analysis for life course epidemiology. More research is needed to understand these associations and determine if causal relationships exist.

Selecting qualitative cases using sequence analysis: A mixed-method for in-depth understanding of life course trajectories.

In this paper, we propose a sequence analysis-based method for selecting qualitative cases depending on quantitative results. Inspired by tools developed for cross-sectional analyses, we propose indicators suitable for longitudinal study of the life course in a holistic perspective and a set of corresponding analysis guidelines. Two complementary indicators are introduced, marginality and gain, that allows labeling observations according to both their typicality within their group and their illustrativeness of a given quantitative relationship. These indicators allow selecting a diversity of cases depending on their contributions to a quantitative relationship between trajectories and a covariate or a typology. The computation of the indicators is made available in the TraMineRextras R package. The method and its advantages are illustrated through an original study of the relationships between residential trajectories in the Paris region and residential socialization during childhood. Using the Biographies et Entourage [Event history and entourage] survey and qualitative interviews conducted with a subsample of respondents, the analysis shows the contributions of the method not only to improve the understanding of statistical associations, but also to identify their limitations. Extension and generalization of the method are finally proposed to cover a wider scope of situations.

Residential trajectories across the life course and their association with cognitive functioning in later life.

Previous work has found that later life urban-rural differences in cognitive health can be largely explained by indicators of cognitive reserve such as education or occupation. However, previous research concentrated on residence in limited, specific, periods. This study offers a detailed investigation on the association between urban (vs. rural) residence from birth, and cognitive functioning in older age. Using data from the Survey of Health Ageing and Retirement in Europe we created residential trajectories from birth to survey enrolment with a combination of sequence and cluster analysis. Using mixed-effects models, we investigated the association between residential trajectories in early, mid, and later life and three cognitive functioning outcomes: immediate recall, delayed recall, and verbal fluency. In a sample of 38,165 participants, we found that, even after accounting for differences related to education and occupation, rural (vs. urban) residence in early life remained associated with poorer cognitive performance later in life. This suggests that growing up in rural regions leads to a long-term disadvantage in cognitive functioning. Thus, public health policies should consider that urban-rural inequalities in early life may have long-lasting associations with inequalities in cognitive health in old and very old age.

Sequence analysis: Its past, present, and future.

This article marks the occasion of Social Science Research's 50th anniversary by reflecting on the progress of sequence analysis (SA) since its introduction into the social sciences four decades ago, with focuses on the developments of SA thus far in the social sciences and on its potential future directions. The application of SA in the social sciences, especially in life course research, has mushroomed in the last decade and a half. Using a life course analogy, we examined the birth of SA in the social sciences and its childhood (the first wave), its adolescence and young adulthood (the second wave), and its future mature adulthood in the paper. The paper provides a summary of (1) the important SA research and the historical contexts in which SA was developed by Andrew Abbott, (2) a thorough review of the many methodological developments in visualization, complexity measures, dissimilarity measures, group analysis of dissimilarities, cluster analysis of dissimilarities, multidomain/multichannel SA, dyadic/polyadic SA, Markov chain SA, sequence life course analysis, sequence network analysis, SA in other social science research, and software for SA, and (3) reflections on some future directions of SA including how SA can benefit and inform theory-making in the social sciences, the methods currently being developed, and some remaining challenges facing SA for which we do not yet have any solutions. It is our hope that the reader will take up the challenges and help us improve and grow SA into maturity.

How Welfare Regimes Moderate the Associations Between Cognitive Aging, Education, and Occupation.

OBJECTIVES: Previous studies have shown the importance of individual markers of cognitive reserve, such as education and occupation, for cognitive health in old age. However, there has been only little investigation so far on how this relationship varies across contexts. METHODS: We analyzed data from the Survey of Health, Ageing, and Retirement in Europe, using second-order latent growth models, to assess the moderating role of welfare regimes on the relationship between education and occupation skill level in explaining overall cognitive functioning and decline in old age. Our sample includes 13 European countries using data from 5 regular waves of the survey (2004-2007 and 2011-2015) and 2 retrospective ones (2008-2009 and 2017). Cognitive functioning was modeled as a latent variable measured by immediate and delayed recall, verbal fluency, and numeracy. RESULTS: 74,193 participants were included from the survey. Our analysis showed that the association of education with cognition was weaker overall in Scandinavian countries, but stronger in Southern European countries, relative to Bismarckian ones. However, educational differences in the decline of cognition were more pronounced only in Scandinavian compared to Bismarckian countries. Additionally, higher-skilled occupations in Scandinavian countries had better overall functioning compared to the same occupations in Bismarckian countries, but there was no difference in the decline in cognitive functioning. DISCUSSION: Our findings indicate that the associations of cognitive functioning and its decline with individuals' cognitive reserve markers (education and occupational skill level) vary according to welfare regimes, showing the importance of contextual factors in cognitive aging processes.

Validating Sequence Analysis Typologies Using Parametric Bootstrap.

In this article, the author proposes a methodology for the validation of sequence analysis typologies on the basis of parametric bootstraps following the framework proposed by Hennig and Lin (2015). The method works by comparing the cluster quality of an observed typology with the quality obtained by clustering similar but nonclustered data. The author proposes several models to test the different structuring aspects of the sequences important in life-course research, namely, sequencing, timing, and duration. This strategy allows identifying the key structural aspects captured by the observed typology. The usefulness of the proposed methodology is illustrated through an analysis of professional and coresidence trajectories in Switzerland. The proposed methodology is available in the WeightedCluster R library.

Holistic analysis of the life course: Methodological challenges and new perspectives.

We survey state-of-the-art approaches to study trajectories in their entirety, adopting a holistic perspective, and discuss their strengths and weaknesses. We begin by considering sequence analysis (SA), one of the most established holistic approaches. We discuss the inherent problems arising in SA, particularly in the study of the relationship between trajectories and covariates. We describe some recent developments combining SA and Event History Analysis, and illustrate how weakening the holistic perspective-focusing on sub-trajectories-might result in a more flexible analysis of life courses. We then move to some model-based approaches (included in the broad classes of multistate and of mixture latent Markov models) that further weaken the holistic perspective, assuming that the difficult task of predicting and explaining trajectories can be simplified by focusing on the collection of observed transitions. Our goal is twofold. On one hand, we aim to provide social scientists with indications for informed methodological choices and to emphasize issues that require consideration for proper application of the described approaches. On the other hand, by identifying relevant and open methodological challenges, we highlight and encourage promising directions for future research.

Origins of nonlocality near the neutrality point in graphene.

We present an experimental study of nonlocal electrical signals near the Dirac point in graphene. The in-plane magnetic field dependence of the nonlocal signal confirms the role of spin in this effect, as expected from recent predictions of the Zeeman spin Hall effect in graphene, but our experiments show that thermo-magneto-electric effects also contribute to nonlocality, and the effect is sometimes stronger than that due to spin. Thermal effects are seen to be very sensitive to sample details that do not influence other transport parameters.