Increases in wellbeing in the transition to retirement for the unemployed: catching up with formerly employed persons

This paper examines the extent to which wellbeing levels change in the transition to retirement depending on transitioning from being employed, unemployed or economically inactive. Whereas transitioning from employment to unemployment has been found to cause a decrease in subjective wellbeing with more time spent in unemployment, it is not clear how transitioning from unemployment to retirement affects wellbeing levels. We use the Survey of Health, Ageing and Retirement in Europe to monitor the life satisfaction of respondents who retire in between two waves. We portray wellbeing scores before and after retirement and then identify the change in life satisfaction during the retirement transition using a First Difference model. Results indicate that being unemployed before retirement is associated with an increase in life satisfaction, but presents mainly a catching-up effect compared to employed persons transitioning to retirement. These results are still significant if we control for selection into unemployment and country differences. Retirement from labour market inactivity does not lead to significant changes in wellbeing. As the wellbeing of unemployed persons recovers after transitioning to retirement, especially the currently unemployed population should be supported to prevent detrimental consequences of economically unfavourable conditions and lower wellbeing

Does (re-)entering the labour market at advanced ages protect against cognitive decline?:A matching difference-in-differences approach

peer reviewedBACKGROUND: While prolonged labour market participation becomes increasingly important in ageing societies, evidence on the impacts of entering or exiting work beyond age 65 on cognitive functioning is scarce. METHODS: We use data from two large population-representative data sets from South Korea and the USA to investigate and compare the effects of the labour market (re-)entry and exit by matching employment and other confounder trajectories prior to the exposure. We chose the Korean Longitudinal Study of Aging (N=1872, 2006-2020) for its exceptionally active labour participation in later life and the Health and Retirement Study (N=4070, 2006-2020) for its growing inequality among US older adults in labour participation. We use the matching difference-in-differences (DID) method, which allows us to make causal claims by reducing biases through matching. RESULTS: We find general positive effects of entering the labour market in South Korea (DID estimate: 0.653, 95% CI 0.167 to 1.133), while in the USA such benefit is not salient (DID estimate: 0.049, 95% CI -0.262 to 0.431). Exiting the late-life labour market leads to cognitive decline in both South Korea (DID estimate: -0.438, 95% CI -0.770 to -0.088) and the USA (DID estimate: -0.432, 95% CI -0.698 to -0.165). CONCLUSIONS: Findings suggest that Korean participants cognitively benefited from late-life labour market participation, while US participants did not. Differences in participant characteristics and reasons for labour market participation may have led to the differential findings. We found the negative effects of exiting the late-life labour force in both countries

Novel Approaches and Architecture for Survivable Optical Internet

Any unexpected disruption to WDM (Wavelength Division Multiplexing) based optical networks which carry data traffic at tera-bit per second may result in a huge loss to its end-users and the carrier itself. Thus survivability has been well-recognized as one of the most important objectives in the design of optical Internet. This thesis proposes a novel survivable routing architecture for the optical Internet. We focus on a number of key issues that are essential to achieve the desired service scenarios, including the tasks of (a) minimizing the total number of wavelengths used for establishing working and protection paths in WDM networks; (b) minimizing the number of affected working paths in case of a link failure; (c) handling large scale WDM mesh networks; and (d) supporting both Quality of Service (QoS) and best-effort based working lightpaths. To implement the above objectives, a novel path based shared protection framework namely Group Shared protection (GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy, and optimization is performed on these PGs. To the best of our knowledge this is the first work done in the area of group based WDM survivable routing approaches where not only the resource sharing is conducted among the PGs to achieve the best possible capacity efficiency, but also an integrated survivable routing framework is provided by incorporating the above objectives. Simulation results show the effectiveness of the proposed schemes

Machine learning in the social and health sciences

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research

Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.// METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.// RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.// DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention

Retrograde procedural memory is impaired in people with Parkinson’s disease with freezing of gait

BackgroundFreezing of gait (FOG), is associated with impairment of different cognitive functions. Previous studies hypothesized that FOG may be due to a loss of automaticity.Research questionTo explore whether FOG is associated with impairment in cognitive functions, focusing on retrograde procedural memory, the memory responsible for the automatic, implicit stored procedures that have been acquired in earlier life stages.MethodsIn this cross-sectional, case–control study, 288 people with typical Parkinson’s disease (PD) from the Luxembourg Parkinson’s Study were assigned to Freezers (FOG+) and non-Freezers (FOG−) based on the MDS-UPDRS 2.13 (self-reported FOG episodes) and 3.11 (FOG evaluated by clinicians during gait assessment). Both groups were matched on age, sex and disease duration. Global cognition (MoCA), retrograde procedural memory and visuo-constructive abilities (CUPRO), psychomotor speed and mental flexibility (TMT) were assessed. Furthermore, we repeated our analyses by additionally controlling for depression (BDI-I).ResultsBesides lower global cognition (MoCA; p = 0.007) and mental flexibility (TMT-B and Delta-TMT; p < 0.001), FOG+ showed a lower performance in retrograde procedural memory (CUPRO-IS1; p < 0.001) compared to FOG−. After controlling additionally for depression, our main outcome variable CUPRO-IS1 remained significantly lower in FOG+ (p = 0.010).ConclusionOur findings demonstrated that besides lower global cognition and mental flexibility scores, FOG+ showed lower performance in retrograde procedural memory compared to matched FOG-control patients, even when accounting for factors such as age, sex, disease duration or depression.SignificanceIn the context of limited treatment options, especially for non-invasive therapeutic approaches, these insights on procedural memory and FOG may lead to new hypotheses on FOG etiology and consequently the development of new treatment options

Do economic recessions during early and mid-adulthood influence cognitive function in older age?

Background Fluctuations in the national economy shape labour market opportunities and outcomes, which in turn may influence the accumulation of cognitive reserve. This study examines whether economic recessions experienced in early and mid-adulthood are associated with later-life cognitive function. Method Data came from 12 020 respondents in 11 countries participating in the Survey of Health, Ageing and Retirement in Europe (SHARE). Cognitive assessments in 2004/2005 and 2006/2007 were linked to complete work histories retrospectively collected in 2008/2009 and to historical annual data on fluctuations in Gross Domestic Product per capita for each country. Controlling for confounders, we assessed whether recessions experienced at ages 25–34, 35–44 and 45–49 were associated with cognitive function at ages 50–74. Results Among men, each additional recession at ages 45–49 was associated with worse cognitive function at ages 50–74 (b=−0.06, CI −0.11 to −0.01). Among women, each additional recession at ages 25–44 was associated with worse cognitive function at ages 50–74 (b25–34=−0.03, CI −0.04 to −0.01; b35–44=−0.02, CI −0.04 to −0.00). Among men, recessions at ages 45–49 influenced risk of being laid-off, whereas among women, recessions at ages 25–44 led to working part-time and higher likelihood of downward occupational mobility, which were all predictors of worse later-life cognitive function. Conclusions Recessions at ages 45–49 among men and 25–44 among women are associated with later-life cognitive function, possibly through more unfavourable labour market trajectories. If replicated in future studies, findings indicate that policies that ameliorate the impact of recessions on labour market outcomes may promote later-life cognitive function