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  Does (Re-)Entering the Labor Market at Advanced Ages Protect Against Cognitive Decline? A Panel-Matching Difference-in-differences ApproachKim, Jung Hyun  ; ; Leist, Anja E-print/Working paper (2022) While prolonged labor market participation becomes increasingly important in ageing societies, evidence of the impacts of entering or exiting work beyond age 65 on cognitive functioning is scarce. We ... [more ▼] While prolonged labor market participation becomes increasingly important in ageing societies, evidence of the impacts of entering or exiting work beyond age 65 on cognitive functioning is scarce. We estimate these effects using panel-matching difference-in-differences with populationrepresentative panel datasets from South Korea and the United States. We compare countries and across socioeconomic characteristics. We find general positive effects of entering the labor market in South Korea, while only individuals with high assets in the US benefit from entering the labor market. Exiting the labor market does not result in changes in cognitive functioning in Korea but is followed by a cognitive decline in individuals with low assets in the US. Findings suggest that the benefits and disincentives from late-life labor status transitions on cognitive functioning vary between South Korea and the US and across socioeconomic groups. [less ▲] Detailed reference viewed: 52 (6 UL) Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciencesLeist, Anja ; Klee, Matthias ; Kim, Jung Hyun et alin Science Advances (2022), 8 Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive ... [more ▼] Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic 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, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may 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. [less ▲] Detailed reference viewed: 42 (2 UL)Symposium: Contextual and life-course determinants of later-life cognitive functioning and dementiaLeist, Anja ; Scientific Conference (2021, June) Detailed reference viewed: 38 (0 UL)Machine learning in the social and health sciencesLeist, Anja ; Klee, Matthias ; Kim, Jung Hyun et alE-print/Working paper (2021) 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 117 (16 UL)Using cohort data to emulate lifestyle interventions: Long-term beneficial effects of initiating physical activity on cognitive decline and dementiaLeist, Anja ; ; in Alzheimer's and Dementia: the Journal of the Alzheimer's Association (2020), 16 Background: Intervention studies have shown beneficial short-term effects of physical activity on cognitive decline and reduced risk of dementia. However, randomized controlled trial data of lifestyle ... [more ▼] Background: Intervention studies have shown beneficial short-term effects of physical activity on cognitive decline and reduced risk of dementia. However, randomized controlled trial data of lifestyle interventions over long time spans are not available due to lack of resources, feasibility or ethical reasons. Drawing from the principles of emulating a ‘target trial’, which apply design principles of randomized trials to the analysis of observational data, cohort data of a large European survey were analyzed to understand the long-term effects of physical activity changes. Method: Biennial assessments of the economic, social, and health situation of respondents aged 50 and older came from the Survey of Health, Ageing and Retirement in Europe (2004-2017). Cognitive functioning (immediate recall, delayed recall, and verbal fluency) and self-reported diagnosis of dementia were assessed at each follow-up. The target trial included sedentary respondents at t1 who, at follow-up (t2), stayed sedentary (“control group”) or newly reported vigorous physical activity more often than once a week (initiators, “treatment group”). Inclusion and exclusion criteria were implemented as close as possible to those of the FINGER trial. Inverse-probability weighting accounted for the probability of initiating physical activity with a large set of predictor variables. Selecting respondents aged 50-85 years old who met the target trial inclusion and exclusion criteria, assessments of cognitive functioning and self-reported diagnosis of dementia were available for 8,781 respondents at t3 (on average 3.02 years later), 3,858 respondents at t4 (5.84 years), and 2,304 respondents at t5 (7.72 years). A total of 304 respondents reported a diagnosis of dementia. Result: Initiators of vigorous physical activity had higher cognitive functioning at two follow-ups compared to non-initiators (t3: “average treatment effect on the treated”, ATET=0.059, CI: 0.028, 0.090), which remained significant after implementing inclusion and exclusion criteria. Initiators had lower risk of dementia compared to non-initiators at all three follow-ups (t3: ATET=-0.009, CI: -0.015, -0.005, relative risk decrease -46.7%), remaining significant after implementing inclusion and exclusion criteria. Conclusion: Emulating a target trial showed long-term benefits of initiating physical activity for cognitive functioning and dementia risk. Multidomain interventions related to nutrition, social, cognitive activities etc. can be similarly emulated. [less ▲] Detailed reference viewed: 182 (2 UL) |
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