RESUMEN
BACKGROUND: Digital technological development in the last 20 years has led to significant growth in digital collection, use, and sharing of health data. To maintain public trust in the digital society and to enable acceptable policy-making in the future, it is important to investigate people's preferences for sharing digital health data. OBJECTIVE: The aim of this study is to elicit the preferences of the public in different Northern European countries (the United Kingdom, Norway, Iceland, and Sweden) for sharing health information in different contexts. METHODS: Respondents in this discrete choice experiment completed several choice tasks, in which they were asked if data sharing in the described hypothetical situation was acceptable to them. Latent class logistic regression models were used to determine attribute-level estimates and heterogeneity in preferences. We calculated the relative importance of the attributes and the predicted acceptability for different contexts in which the data were shared from the estimates. RESULTS: In the final analysis, we used 37.83% (1967/5199) questionnaires. All attributes influenced the respondents' willingness to share health information (P<.001). The most important attribute was whether the respondents were informed about their data being shared. The possibility of opting out from sharing data was preferred over the opportunity to consent (opt-in). Four classes were identified in the latent class model, and the average probabilities of belonging were 27% for class 1, 32% for class 2, 23% for class 3, and 18% for class 4. The uptake probability varied between 14% and 85%, depending on the least to most preferred combination of levels. CONCLUSIONS: Respondents from different countries have different preferences for sharing their health data regarding the value of a review process and the reason for their new use. Offering respondents information about the use of their data and the possibility to opt out is the most preferred governance mechanism.
RESUMEN
Kidney stone disease is a complex disorder with a strong genetic component. We conducted a genome-wide association study of 28.3 million sequence variants detected through whole-genome sequencing of 2,636 Icelanders that were imputed into 5,419 kidney stone cases, including 2,172 cases with a history of recurrent kidney stones, and 279,870 controls. We identify sequence variants associating with kidney stones at ALPL (rs1256328[T], odds ratio (OR)=1.21, P=5.8 × 10(-10)) and a suggestive association at CASR (rs7627468[A], OR=1.16, P=2.0 × 10(-8)). Focusing our analysis on coding sequence variants in 63 genes with preferential kidney expression we identify two rare missense variants SLC34A1 p.Tyr489Cys (OR=2.38, P=2.8 × 10(-5)) and TRPV5 p.Leu530Arg (OR=3.62, P=4.1 × 10(-5)) associating with recurrent kidney stones. We also observe associations of the identified kidney stone variants with biochemical traits in a large population set, indicating potential biological mechanism.