Education as risk factor of Mild Cognitive Impairment - the role of the gut microbiome

Poster (2023, July)

Education as risk factor of Mild Cognitive Impairment - the role of the gut microbiome.

Poster (2023, January)

Individual factors and beliefs determining COVID-19 vaccination willingness

Poster (2022, April)

Background: High vaccination coverage rates are necessary to reduce infections and transmissions of the SARS-CoV-2 virus causing COVID-19 and to allow successful mitigation of the current pandemic. To ... [more ▼]

Background: High vaccination coverage rates are necessary to reduce infections and transmissions of the SARS-CoV-2 virus causing COVID-19 and to allow successful mitigation of the current pandemic. To date, we are still lacking information to explain the hesitancy in Luxembourg towards uptake of the available COVID-19 vaccines. The present study explored motivations for and against vaccination in a population-representative sample of residents across Luxembourg to identify hesitant groups and develop strategies to increase population immunity against SARS-CoV-2. Methods: In the framework of the nationwide, representative longitudinal CON-VINCE study, a sample of 1589 respondents (49.6% women, 84.3% Luxembourg nationality) ranging from 18-84 years, participated in the survey in spring 2021. The protocol of the CON-VINCE study has been described in detail elsewhere (Snoeck et al. 2020). Results: 52% of the respondents had at least partial vaccination at time of assessment between April to June 2021. The most common reasons for vaccination of those willing to be vaccinated (81.2%) were altruistic motivations. Prevalent reasons against vaccination for those undecided (8.7%) or reluctant (10.2%) to be vaccinated were that the vaccine had not been tested sufficiently and the fear of long-term vaccine side effects. Only very few of the vaccination-hesitant or -reluctant respondents reported that they did not believe in vaccination in general. Conclusion: The present study identified motivations for and against COVID-19 vaccination and determined demographic and socio-economic factors associated with vaccination willingness. To increase vaccination rates, public health communication needs to target those unsure or unwilling to be vaccinated. We will continue to study the vaccination uptake in the Luxembourg population, as CON-VINCE is now part of the H2020-funded international ORCHESTRA project (https://orchestra-cohort.eu), research into comparing these results on a Pan-European level. [less ▲]

Which demographic and socio-economic factors are associated with vaccination willingness and beliefs towards vaccination? Rapid report with first results

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 ▲]

Machine learning in the social and health sciences

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 ▲]