The clustering of longitudinal data sequences is considered using a latent Markovian model (HMTD) combining Gaussian distributions and covariates. The main objective is to evaluate the significance of the estimated parameters. At first, different model specifications are optimized and the one providing the best clustering in terms of BIC is selected. Two different bootstrap procedures are then applied and compared in order to investigate the significance of the parameters of this optimal solution. First, a standard bootstrap procedure is applied using the full original sample and the optimal model with multiple components (clusters) is computed at each iteration. That leads to solutions with different degrees of similarity with the optimal solution and the well-known label-switching problem may occur. An alternative procedure is proposed that consists in applying separate bootstrap procedures on each subsample defined by the optimal clustering. In this case, a single component model is estimated from each bootstrap iteration and for each cluster separately. This method also provides a confidence interval for each parameter and avoids the label-switching problem. The pros and cons of each approach are described and examples based on real data are provided.

Within the social and economic sciences and of particular interest to demographers are life course events. Looking at life sequences we can better understand which states, or life events, precede or are precursors to vulnerability. A tool that has been used for policy evaluation and recently has been gaining ground in life course sequence simulation is dynamic microsimulation. Within this context dynamic microsimulation consists in generating entire life courses from the observation of portions of the trajectories of individuals of different ages. In this work, we aim to use dynamic microsimulation in order to analyse individual professional trajectories with a focus on vulnerability. The primary goal of this analysis is to deepen upon current literature by providing insight from a longitudinal perspective on the signs of work instability and the process of precarity. The secondary goal of this work which is to show how, by using microsimulation, data collected for one purpose can be analysed under a different scope and used in a meaningful way. The data to be used in this analysis are longitudinal and were collected by NCCR-LIVES IP207 under the supervision of Prof. Christian Maggiori and Dr. Gregoire Bollmann. Individuals aged 25 to 55 residing in the German-speaking and French-speaking regions of Switzerland were followed annually for four years. These individuals were questioned regarding, amongst their personal, professional and overall situations and well-being. At the end of the fourth wave, there were 1131 individuals who had participated in all waves. The sample remained representative of the Swiss population with women and the unemployed slightly over represented. Using the information collected from these surveys, we use simulation to construct various longitudinal data modules where each data module represents a specific life domain. We postulate the relationship between these modules and layout a framework of estimation. Within certain data modules a set of equations are created to model the process therein. For every dynamic (time-variant) data module, such as the labour-market module, the transition probabilities between states (ex. labour market status) are estimated using a Markov model and then the possible outcomes are simulated. The benefit of using dynamic microsimulation is that longitudinal sample observations instead of stylised profiles are used to model population dynamics. This is one of the main reasons large-scale dynamic microsimulation models are employed by many developed nations. There has been limited use, however, of such approaches with Swiss data. This work contributes to the analysis of professional trajectories of the active Swiss population by utilising dynamic microsimulation methods.

This is an introduction on discrete-time Hidden Markov models (HMM) for longitudinal data analysis in population and life course studies. In the Markovian perspective, life trajectories are considered as the result of a stochastic process in which the probability of occurrence of a particular state or event depends on the sequence of states observed so far. Markovian models are used to analyze the transition process between successive states. Starting from the traditional formulation of a first-order discrete-time Markov chain where each state is liked to the next one, we present the hidden Markov models where the current response is driven by a latent variable that follows a Markov process. The paper presents also a simple way of handling categorical covariates to capture the effect of external factors on the transition probabilities and existing software are briefly overviewed. Empirical illustrations using data on self reported health demonstrate the relevance of the different extensions for life course analysis.

This research examines the impact of relationship status on self-rated health (SRH) by taking into account intrapersonal and social resources. Data stem from a Swiss-based survey of 1355 participants aged 40-65 years. Three groups are compared: continuously married (n = 399), single divorcees (n = 532) and repartnered divorcees (n = 424). Linear regression models are used to examine the predictive role of relationship status on SRH and to investigate the moderating role of intrapersonal and social resources on SRH. Single divorcees show the lowest SRH scores, whereas their repartnered counterparts reported scores comparable to the continuously married – even after controlling for socio-demographic and economic variables. Although single divorcees reported higher levels of loneliness and agreeableness in addition to lower levels of resilience when compared with the other groups, none of these variables had a significant modification effect on SRH. Our results underscore the positive effect of relationship status on SRH, and contribute new insights on the impact of later-life divorce. Given the growing number of divorcees, related public health challenges are likely to increase.

Background: Centenarian studies from around the world have shown that reaching age 100 typically involves substantial health issues. The present study adds to the existing knowledge from other countries by describing health conditions in German centenarians. Methods: A total of 112 centenarians or their primary contacts provided information on acute and chronic health conditions and pain in the context of the Second Heidelberg Centenarian Study (mean age = 100.45 years, Standard deviation [SD] = 0.47, 89% females). Results: Participants showed high comorbidity, with an average of five illnesses (mean = 5.3; SD = 2.20). Health conditions with highest prevalence were sensory (vision, hearing; 94%), mobility (72%) and musculoskeletal conditions (60%). Cardiovascular conditions (57%) and urinary system ailments (55%) were also common. Pain was experienced often by 30% of the participants. Of those reporting any pain, 36% indicated pain exceeding bearable levels. Conclusion: German centenarians experienced a substantial number of ill nesses, dominated by sensory and mobility conditions. Cardiovascular diseases were the only potentially lethal illnesses with high prevalence. Evidence of unaddressed pain seems alarming, requiring future research. Emerging health profiles indicate that even in very advanced age, quality of life may be improved by enhanced diagnostics and optimal disease management. Mobility limitations may be addressed with preventive efforts.

Perceptions of the future are crucial components of individual well-being. Hopelessness, which is the sense that the future is a dead end, begins with the occurrence of negative life events and develops through the perception of consistent and pervasive negative outcomes. This study investigated the role of the socioeconomic aspects of the context and shared emotions (emotional climates) within a region in reducing or exacerbating hopelessness. Emotional climates have been defined as the emotional relationships constructed among members of a society, and they describe the environmental quality of a particular community. Multilevel modeling with individuals nested into regions (i.e., Swiss cantons) was used to explore the relationship between context and hopelessness. Data from the project “Vulnerability and Growth,” the Swiss Household Panel and official socioeconomic indicators were used. Spatial-weighting methods were applied to estimate depressive and optimistic emotional climates at the canton level. The results show that hopelessness is primarily affected by individual factors such as personality and life events. However, the analyses revealed that socioeconomic conditions and the optimistic and depressive climates that prevail in cantons also affected individuals’ perceptions of hopelessness. Individuals were more likely to feel hopeless in cantons with high unemployment rates and high levels of shared negative emotions. In contrast, positive emotional climates played a protective role against hopelessness. Acknowledgment of the influence of context on individuals’ perceptions of the future and the correlation of their states of anxiety and depression is pivotal for planning effective interventions to prevent depression.

This research explores patterns of family salience based on sociability and solidarity norms according to the institutional context. The data come from the International Social Survey Program (ISSP), from respondents living in four types of welfare regime countries: Mediterranean, corporatist, liberal, and social-democratic. Based on cluster analyses, we found that the salience of family in sociability practices and solidarity norms to a significant extent varies according to welfare regimes. Individuals from a Mediterranean welfare regime more often develop a pattern of sociability by a focus on children or parents. Normatively, they develop a pattern stressing the salience of both family and state. Individuals from a corporatist welfare regime more often develop a pattern of sociability focused on extended kin or feature a lack of sociability. At the normative level, they promote either the patterns of state support or reliance on the self. Individuals from a liberal welfare regime stress patterns of family support or self-reliance at the normative level. They develop a pattern of associational activity or show a lack of sociability. Individuals from social-democratic welfare regimes are more frequently normatively oriented toward state support while promoting participation in associations or showing a lack in their sociability.