Like many countries, the COVID-19 pandemic has forced Statistics Netherlands to make changes in its fieldwork strategy. Since mid-March 2020, there have been limited opportunities to conduct face-to-face interviews. Therefore, from September 2020, CAPI sampled people are offered the opportunity to respond by telephone. For this purpose, face-to-face interviewers are instructed to persuade the potential respondent at the doorway. When people refuse a face-to-face interview, interviewers ask for a telephone number and try to make an appointment to conduct the interview by telephone. The aim of our study was to investigate the effects of conducting the interview by telephone instead of face-to-face on important survey outcome variables. We were particularly interested in whether differences are due to selection effects or caused by mode-specific measurement errors. Because we did not have the time or capacity to set up a controlled experiment, we performed regression analyses to decompensate the differences between selection effects and mode-specific measurement errors. We used data of the Labour Force Survey (LFS) and the Housing Survey (WoON). Our analysis showed that there were differences in important target variables, for both LFS and WoON. These differences were, however, mainly caused by selection effects – which can be taken into account for during weighting – and were less likely to be caused by mode specific measurement errors. Although there are important limitations and caveats, these findings are supportive to further implement this field strategy.
Thirty to sixty per cent of older patients experience functional decline after hospitalisation, associated with an increase in dependence, readmission, nursing home placement and mortality. First step in prevention is the identification of patients at risk. The objective of this study is to develop and validate a prediction model to assess the risk of functional decline in older hospitalised patients.
Loneliness among young adults is a growing concern worldwide, posing serious health risks. While the human ecological framework explains how various factors such as socio-demographic, social, and built environment characteristics can affect this feeling, still, relatively little is known about the effect of built environment characteristics on the feelings of loneliness that young people experience in their daily life activities. This research investigates the relationship between built environment characteristics and emotional state loneliness in young adults (aged 18–25) during their daily activities. Leveraging the Experience Sampling Method, we collected data from 43 participants for 393 personal experiences during daily activities across different environmental settings. The findings of a mixed-effects regression model reveal that built environment features significantly impact emotional state loneliness. Notably, activity location accessibility, social company during activities, and walking activities all contribute to reducing loneliness. These findings can inform urban planners and municipalities to implement interventions that support youngsters’ activities and positive experiences to enhance well-being and alleviate feelings of loneliness in young adults. Specific recommendations regarding the built environment are (1) to create spaces that are accessible, (2) create spaces that are especially accessible by foot, and (3) provide housing with shared facilities for young adults rather than apartments/studios.
Zijn data-analyse en bio-informatica de sleutel naar voorspellingen over de invloed van giftige stoffen op de gezondheid van mensen? Het project DART Pathfinder is een vervolgonderzoek naar een dierproefvrije testmethode. Met moderne ICT-technieken proberen we die voorspellingen te doen.Doel Het doel van dit project is om gegevens over giftige stoffen uit verschillende data bronnen samen te brengen. In het onderzoek gebruiken we technieken uit de bio-informatica. Zo willen we de eigenschappen van giftige stoffen beter in kaart brengen en (nadelige) effecten van soortgelijke stoffen kunnen voorspellen. Veel bedrijven maken producten of stoffen, die getest moeten worden of ze veilig zijn. Met dit project helpen we bedrijven om o.b.v. bestaande gegevens een betere keuze te maken welke testen ze hiervoor het beste kunnen gebruiken. Resultaten Kennis over computer modellen die voorspellingen doen, zoals machine learning, regression tree-based models; Nieuwe algoritmen (instructies om berekeningen uit te voeren) Inzicht in nieuwe biologische mechanismen obv data science Nieuwe statische methoden om data te analysen en voorspellingen te doen. Looptijd 01 februari 2018 - 01 februari 2022 Aanpak Met de gegevens uit het onderzoek maken we een computermodel dat voorspelt of giftige stoffen invloed hebben op de voortplanting en ontwikkeling van mensen. Die voorspelling gebeurt via machine learning, algoritmen en statistische methoden. Voor dit model wordt informatie uit publieke databases over fysische en chemische eigenschappen van mogelijk gevaarlijke stoffen samengevoegd met de gegevens over de invloed van deze stoffen op levende organismen. Net als in het eerste onderzoek (PreDART) werken we met rondwormen (C.elegans) en embryo's van zebravissen, met als doel geen proeven meer met ratten en konijnen te hoeven doen.
