COVID-19 contact-tracing apps:predicted uptake in the Netherlands based on a discrete choice experiment
Background: Smartphone-based contact tracing apps can contribute to reducing COVID-19 transmission rates and thereby support countries emerging from lockdowns as restrictions are gradually eased. Objective: The primary objective of our study is to determine the potential uptake of a contact tracing app in the Dutch population, depending on the characteristics of the app. Methods: A discrete choice experiment was conducted in a nationally representative sample of 900 Dutch respondents. Simulated maximum likelihood methods were used to estimate population average and individual-level preferences... Mehr ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2020 |
Reihe/Periodikum: | Jonker , M , Grob , E , Veldwijk , J , Goossens , L , Bour , S & Rutten-van Mölken , MPMH 2020 , ' COVID-19 contact-tracing apps : predicted uptake in the Netherlands based on a discrete choice experiment ' , JMIR mHealth and uHealth , vol. 8 , no. 10 , e20741 . https://doi.org/10.2196/20741 |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-29209267 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://pure.eur.nl/en/publications/af2a6a51-c185-40dd-8d41-f2fc42dca015 |
Background: Smartphone-based contact tracing apps can contribute to reducing COVID-19 transmission rates and thereby support countries emerging from lockdowns as restrictions are gradually eased. Objective: The primary objective of our study is to determine the potential uptake of a contact tracing app in the Dutch population, depending on the characteristics of the app. Methods: A discrete choice experiment was conducted in a nationally representative sample of 900 Dutch respondents. Simulated maximum likelihood methods were used to estimate population average and individual-level preferences using a mixed logit model specification. Individual-level uptake probabilities were calculated based on the individual-level preference estimates and subsequently aggregated into the sample as well as subgroup-specific contact tracing app adoption rates. Results: The predicted app adoption rates ranged from 59.3% to 65.7% for the worst and best possible contact tracing app, respectively. The most realistic contact tracing app had a predicted adoption of 64.1%. The predicted adoption rates strongly varied by age group. For example, the adoption rates of the most realistic app ranged from 45.6% to 79.4% for people in the oldest and youngest age groups (ie, ≥75 years vs 15-34 years), respectively. Educational attainment, the presence of serious underlying health conditions, and the respondents’ stance on COVID-19 infection risks were also correlated with the predicted adoption rates but to a lesser extent. Conclusions: A secure and privacy-respecting contact tracing app with the most realistic characteristics can obtain an adoption rate as high as 64% in the Netherlands. This exceeds the target uptake of 60% that has been formulated by the Dutch government. The main challenge will be to increase the uptake among older adults, who are least inclined to install and use a COVID-19 contact tracing app.