Co-RESPOND: a federated network of cohorts on mental health and adversity during the COVID-19 pandemic. Challenges, solutions and recommendations for retrospective data harmonization.
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| Title: | Co-RESPOND: a federated network of cohorts on mental health and adversity during the COVID-19 pandemic. Challenges, solutions and recommendations for retrospective data harmonization. |
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| Alternate Title: | Co-RESPOND: Una red federada de cohortes sobre salud mental y adversidad durante la pandemia COVID-19. Desafíos, soluciones y recomendaciones para la armonización de datos retrospectivos. |
| Authors: | Petri-Romão, Papoula (AUTHOR), Stoffers-Winterling, Jutta (AUTHOR), Doerschner, Charlotte (AUTHOR), Jurgeit, Jocelyne (AUTHOR), Gödde, Philipp (AUTHOR), Hecker, Irwin (AUTHOR), Melchior, Maria (AUTHOR), Czepiel, Diana (AUTHOR), Witteveen, Anke (AUTHOR), van der Ven, Els (AUTHOR), Sijbrandij, Marit (AUTHOR), Mediavilla, Roberto (AUTHOR), Ayuso-Mateos, José Luis (AUTHOR), Smith, Pierre (AUTHOR), Lorant, Vincent (AUTHOR), Monistrol Mula, Anna (AUTHOR), Haro Abad, Josep Maria (AUTHOR), Gémes, Katalin (AUTHOR), Mittendorder-Rutz, Ellenor (AUTHOR), Monzio Compagnoni, Matteo (AUTHOR) |
| Source: | European Journal of Psychotraumatology. Dec2025, Vol. 16 Issue 1, p1-23. 23p. |
| Subjects: | Mental health, COVID-19, Cohort analysis, Psychological resilience, Internetworking, Emotional trauma, Data harmonization |
| Abstract (English): | Background: The SARS-Cov-2 pandemic was associated with a substantial rise in trauma and stressor exposure. The Co-RESPOND consortium (part of the EU horizon 2020-funded RESPOND project) has been initiated to study the impact on mental health, using longitudinal data of separate international cohorts. Aims: The Co-RESPOND initiative aims to retrospectively harmonize mental health and resilience data of ongoing longitudinal cohort studies at the individual participant level; to create an interoperable network of cohorts within a secure environment; to manage these data along with harmonization products (e.g. transformation procedures and variable dictionaries) according to the FAIR principles; and to keep this network live in order to add new data waves or to be joined by new cohorts. Methods: Data were harmonized retrospectively according to the Maelstrom guidance. A federated data network (FDN) was created using the OBiBa software suite. Results: To date, Co-RESPOND consists of nine European cohorts and one global cohort, including 50,885 individual participants. This paper presents Co-RESPOND as a case study for retrospective harmonization of decentralized data where teams collected and transformed data without prior coordination, facing methodological as well as regulatory challenges. The process of this project is outlined in detail, so it could be applied by other researchers for future projects. Its outcomes and the resulting data harmonization products are presented. Conclusions and outlook: The harmonized data are now ready to be shared with external partners for analyses, and Co-RESPOND is open for more partners to join. Lessons learned throughout the project will be reported, and established classification standards will be recommended for use to generate data sets that are available for joint analyses from the start. Trial registration: ClinicalTrials.gov identifier: NCT04556565. HIGHLIGHTS: Longitudinal cohort data collected during the COVID-19 pandemic hold an extraordinary opportunity to study the impact of elevated trauma and stressor prevalence on trauma and mental health. This project aims to retrospectively harmonize, i.e. transform, already collected data of originally separate cohorts in a way that it can be analysed jointly on the individual-participant level, and to manage data sustainably in a fair (findable, accessible, interoperable, and reusable) way. Ten cohort studies covering mental health outcomes of more than 50.000 individuals have been harmonized so far. The data sets are available within a federated data network and can be accessed upon request for further analyses. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | Antecedentes: La pandemia de SARS-Cov-2 se asoció con un alza importante de la exposición a trauma y factores estresantes. El consorcio Co-RESPOND (parte del proyecto RESPOND financiado por la UE en el marco del programa horizonte 2020) se ha puesto en marcha para estudiar el impacto en la salud mental, utilizando datos longitudinales de distintas cohortes internacionales. Objetivo: La iniciativa Co-RESPOND tiene como objetivo armonizar retrospectivamente los datos de salud mental y resiliencia de los estudios de cohortes longitudinales en curso a nivel de participante individual; crear una red interoperable de cohortes dentro de un ambiente seguro; gestionar estos datos junto con los productos de armonización (ej., procedimientos de transformación y diccionarios de variables) de acuerdo a los principios FAIR; y mantener esta red activa para agregar nuevas oleadas de datos o para que se unan a nuevas cohortes. Métodos: Los datos se armonizaron retrospectivamente de acuerdo con la guía Maelstrom. Se creo una red de datos federada (FDN por sus siglas en ingles) utilizando el paquete de software OBiBa. Resultados: Hasta la fecha, Co-RESPOND consta de nueve cohortes europeas y una cohorte mundial, con 50.885 participantes. Este artículo presenta Co-RESPOND como un caso estudio de armonización retrospectiva de datos descentralizados, donde los equipos recopilaron y transformaron los datos sin coordinación previa, afrontando desafíos metodológicos y regulatorios. El proceso de este proyecto se describe en detalle para que otros investigadores puedan aplicarlo en proyectos futuros. Se presentan sus resultados y los productos de armonización de datos resultantes. Conclusiones y perspectivas: Los datos armonizados ya están listos para ser compartidos con pares externos para su análisis y Co-RESPOND está abierto a la incorporación de más socios. Se informará sobre las lecciones aprendidas a lo largo del proyecto y se recomendarán los estándares de clasificación establecidos para generar conjuntos de datos disponibles para análisis conjuntos desde el inicio. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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