Eleanor Hyde

Summary: Extensive human health data from cohort studies, national registries, and biobanks can reveal lifecourse risk factors impacting health. Combining these sources offers increased statistical power, rare outcome detection, replication of findings, and extended study periods. Traditionally, this required data transfer to a central location or separate partner analyses with pooled summary statistics, posing ethical, legal, and time constraints. Federated analysis—which involves remote data analysis without sharing individual-level data—is a promising alternative. One promising solution is DataSHIELD (https://datashield.org/), an open-source R based implementation. To enable federated analysis, data owners need a user-friendly way to install the federated infrastructure and manage users and data. Here, we present MOLGENIS Armadillo: a lightweight server for federated analysis solutions such as DataSHIELD. Availability and implementation: Armadillo is implemented as a collection of three packages freely available under the open source licence LGPLv3: two R packages downloadable from the Comprehensive R Archive Network (CRAN) (“MolgenisArmadillo” and “DSMolgenisArmdillo”) and one Java application (“ArmadilloService”) as jar and docker images via Github (https://github.com/molgenis/molgenis-service-armadillo).

Background: Social inequalities in child mental health are an important public health concern. Whilst previous studies have examined inequalities at a single time point, very few have used repeated measures outcome data to describe how these inequalities emerge. Our aims were to describe social inequalities in child internalising and externalising problems across multiple countries and to explore how these inequalities change as children age. Methods: We used longitudinal data from eight birth cohorts containing participants from twelve countries (Australia, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Poland, Norway, Spain and the United Kingdom). The number of included children in each cohort ranged from N = 584 (Greece) to N = 73,042 (Norway), with a total sample of N = 149,604. Child socio‐economic circumstances (SEC) were measured using self‐reported maternal education at birth. Child mental health outcomes were internalising and externalising problems measured using either the Strengths and Difficulties Questionnaire or the Child Behavior Checklist. The number of data collection waves in each cohort ranged from two to seven, with the mean child age ranging from two to eighteen years old. We modelled the slope index of inequality (SII) using sex‐stratified multi‐level models. Results: For almost all cohorts, at the earliest age of measurement children born into more deprived SECs had higher internalising and externalising scores than children born to less deprived SECs. For example, in Norway at age 2 years, boys born to mothers of lower education had an estimated 0.3 (95% CI 0.3, 0.4) standard deviation higher levels of internalising problems (SII) compared to children born to mothers with high education. The exceptions were for boys in Australia (age 2) and both sexes in Greece (age 6), where we observed minimal social inequalities. In UK, Denmark and Netherlands inequalities decreased as children aged, however for other countries (France, Norway, Australia and Crete) inequalities were heterogeneous depending on child sex and outcome. For all countries except France inequalities remained at the oldest point of measurement. Conclusions: Social inequalities in internalising and externalising problems were evident across a range of EU countries, with inequalities emerging early and generally persisting throughout childhood.

Purpose: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency–funded project (EUPAS39322), defined a set of metadata to describe real‐world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct.Methods: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe.Results: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype.Conclusions: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.