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See detailWhich demographic and socio-economic factors are associated with vaccination willingness and beliefs towards vaccination? Rapid report with first results
Leist, Anja UL; Klee, Matthias UL; Paccoud, Ivana UL et al

Report (2021)

In the framework of the CoVaLux project on vaccination and long COVID in Luxembourg, the project “Socio-economic determinants of long COVID and vaccination, and economic consequences with focus on labour ... [more ▼]

In the framework of the CoVaLux project on vaccination and long COVID in Luxembourg, the project “Socio-economic determinants of long COVID and vaccination, and economic consequences with focus on labour market and health care” aims to triangulate evidence from different data sources such as social security and general population data, the national cohort CON-VINCE as well as national health surveys. We seek to arrive at robust assessments of how socio-economic determinants shape vaccination willingness, occurrence, severity and persistence of long COVID, and economic consequences of long COVID in Luxembourg. [less ▲]

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See detailInvestigating the associations of trajectories of depressive symptoms and self-perceived health and incident dementia : an unsupervised machine learning approach.
Klee, Matthias UL; Leist, Anja UL

Poster (2021, July)

Background: Risk factors for dementia show inter-individually varying trajectories over the lifespan. However, risk factors have been mainly investigated with one time-point assessments. New research ... [more ▼]

Background: Risk factors for dementia show inter-individually varying trajectories over the lifespan. However, risk factors have been mainly investigated with one time-point assessments. New research suggests that certain risk factor trajectories are associated with increased risk of adverse cognitive outcomes (Demnitz et al., 2020 [https://doi.org/10.1101/2020.05.20.20106963]; Singh-Manoux et al., 2018 [https://doi.org/10.1016/j.jalz.2017.06.2637]). However, it remains unclear how sequential and simultaneous changes of risk factors alter the individual risk for developing dementia. Testing the joint contribution of trajectories of depressive symptoms and self-perceived health on incidence of dementia, we hypothesized that consistently poor as well as deteriorating trajectories increase the risk for incident dementia, and explored possible interactions of the trajectories. Method: A total of 5,326 respondents to the SHARE survey, mean age 73.9 years, and 6 complete follow-ups spanning ~13 years, answered the EURO-D depression scale, self-perceived health (SPH) (t1-t5), and self-reported dementia diagnosis at last follow-up (t6). To investigate the predictive ability of distinct longitudinal trajectories, we applied unsupervised statistical learning methods (K-means cluster modelling). Clusters indicated distinct risk factor trajectories, which were used as exposures in stepwise logistic regressions to predict incident dementia, controlling for age, gender, education, and country. Result: Cluster analysis revealed five distinct trajectories each for SPH and EURO-D, with varying dementia incidence. In stepwise logistic regressions, respondents with trajectories “consistently poor health” and “consistently high depression” showed elevated risk of dementia (OR = 4.02 [1.39, 14.75] and OR = 2.26 [1.03, 4.95], respectively) compared to the combined baseline risk for “consistently low depression” and “consistently good health”. Interactions were not significant. However, respondents with the combination of “consistently high depression” and “consistently poor health” showed increased risk (N = 246; 6.1% dementia). Conclusion: Applying unsupervised machine learning is helpful to incorporate longitudinal information on depressive symptoms and self-perceived health and model these risk factors longitudinally to test their contribution to explain incidence of dementia. The predictive ability of the trajectories of depressive symptoms and self-perceived health for dementia indicates the potential for improving the identification of people at risk for developing dementia in late life by exploiting trajectory information readily accessible through regular medical check-ups in old age. [less ▲]

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See detailMachine learning in the social and health sciences
Leist, Anja UL; Klee, Matthias UL; Kim, Jung Hyun UL et al

E-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 ▲]

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