Unraveling multimorbidity patterns of psoriasis using network analysis.

Background: Psoriasis is a chronic disease with a prevalence of 3% in the general population. The high prevalence of psoriasis has prompted the study of its comorbidities in recent decades. However, no studies have ever analyzed comorbidity patterns including all chronic diseases in psoriatic patients.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

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.

Multimorbidity of Psoriasis: A Large-Scale Population Study of Its Associated Comorbidities.

Introduction: Psoriasis is a chronic disease of the skin with a prevalence of 2% in the general population. The high prevalence of psoriasis has prompted the study of its comorbidities in recent decades. We designed a study to determine the prevalence of psoriasis in a large-scale, population-based cohort, to exhaustively describe its comorbidities, and to analyze which diseases are associated with psoriasis.

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.

Background: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets.