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Meurisse M, Estupiñán-Romero F, González-Galindo J, Martínez-Lizaga N, Royo-Sierra S, Saldner S, Dolanski-Aghamanoukjan L, Degelsegger-Marquez A, Soiland-Reyes S, Van Goethem N, Bernal-Delgado E. Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment. BMC Med Res Methodol 2023; 23:248. [PMID: 37872541 PMCID: PMC10594731 DOI: 10.1186/s12874-023-02068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/11/2023] [Indexed: 10/25/2023] Open
Abstract
INTRODUCTION Causal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data from populations crossing jurisdictional borders, a federated approach (as opposed to a pooling data approach) can be used. Approaching causal inference by re-using routinely collected observational data across different regions in a federated manner, is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to develop studies attempting causal inference using federated cross-national sensitive observational data, is described and showcased within the European BeYond-COVID project. METHODS A framework for approaching federated causal inference by re-using routinely collected observational data across different regions, based on principles of legal, organizational, semantic and technical interoperability, is proposed. The framework includes step-by-step guidance, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a common data model, generating synthetic data, and developing an interoperable and reproducible analytical pipeline for distributed deployment. The conceptual and instrumental phase of the framework was demonstrated and an analytical pipeline implementing federated causal inference was prototyped using open-source software in preparation for the assessment of real-world effectiveness of SARS-CoV-2 primary vaccination in preventing infection in populations spanning different countries, integrating a data quality assessment, imputation of missing values, matching of exposed to unexposed individuals based on confounders identified in the causal model and a survival analysis within the matched population. RESULTS The conceptual and instrumental phase of the proposed methodological framework was successfully demonstrated within the BY-COVID project. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, a common data model, a synthetic dataset and an interoperable analytical pipeline. CONCLUSIONS The framework provides a systematic approach to address federated cross-national policy-relevant causal research questions based on sensitive population, health and care data in a privacy-preserving and interoperable way. The methodology and derived research objects can be re-used and contribute to pandemic preparedness.
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Affiliation(s)
- Marjan Meurisse
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
- IREC - EPID, Université Catholique de Louvain, Brussels, Belgium.
| | - Francisco Estupiñán-Romero
- Data science for Health Services and Policy, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
| | - Javier González-Galindo
- Data science for Health Services and Policy, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
| | - Natalia Martínez-Lizaga
- Data science for Health Services and Policy, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
| | - Santiago Royo-Sierra
- Data science for Health Services and Policy, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
| | - Simon Saldner
- Data Archiving and Networked Services, Royal Netherlands Academy of Arts & Sciences, Amsterdam, The Netherlands
| | | | | | - Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Manchester, UK
- Informatics Institute, Universiteit van Amsterdam, Amsterdam, The Netherlands
| | - Nina Van Goethem
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Enrique Bernal-Delgado
- Data science for Health Services and Policy, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
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Zeitlin J, Philibert M, Estupiñán-Romero F, Loghi M, Sakkeus L, Draušnik Ž, Alcaide AR, Durox M, Cap J, Dimnjakovic J, Misins J, Bernal Delgado E, Thissen M, Gissler M. Developing and testing a protocol using a common data model for federated collection and analysis of national perinatal health indicators in Europe. OPEN RESEARCH EUROPE 2023; 3:54. [PMID: 37830050 PMCID: PMC10565425 DOI: 10.12688/openreseurope.15701.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/01/2023] [Indexed: 10/14/2023]
Abstract
Context: International comparisons of the health of mothers and babies provide essential benchmarks for guiding health practice and policy, but statistics are not routinely compiled in a comparable way. These data are especially critical during health emergencies, such as the coronavirus disease (COVID-19) pandemic. The Population Health Information Research Infrastructure (PHIRI) project aimed to promote the exchange of population data in Europe and included a Use Case on perinatal health. Objective: To develop and test a protocol for federated analysis of population birth data in Europe. Methods: The Euro-Peristat network with participants from 31 countries developed a Common Data Model (CDM) and R scripts to exchange and analyse aggregated data on perinatal indicators. Building on recommended Euro-Peristat indicators, complemented by a three-round consensus process, the network specified variables for a CDM and common outputs. The protocol was tested using routine birth data for 2015 to 2020; a survey was conducted assessing data provider experiences and opinions. Results: The CDM included 17 core data items for the testing phase and 18 for a future expanded phase. 28 countries and the four UK nations created individual person-level databases and ran R scripts to produce anonymous aggregate tables. Seven had all core items, 17 had 13-16, while eight had ≤12. Limitations were not having all items in the same database, required for this protocol. Infant death and mode of birth were most frequently missing. Countries took from under a day to several weeks to set up the CDM, after which the protocol was easy and quick to use. Conclusion: This open-source protocol enables rapid production and analysis of perinatal indicators and constitutes a roadmap for a sustainable European information system. It also provides minimum standards for improving national data systems and can be used in other countries to facilitate comparison of perinatal indicators.
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Affiliation(s)
- Jennifer Zeitlin
- Université Paris Cité, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Obstetrical, Perinatal and Pediatric Epidemiology Research Team, EPOPé, INSERM, Paris, 75004, France
| | - Marianne Philibert
- Université Paris Cité, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Obstetrical, Perinatal and Pediatric Epidemiology Research Team, EPOPé, INSERM, Paris, 75004, France
| | - Francisco Estupiñán-Romero
- Data Sciences for Health Services and Policy Research, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain
| | - Marzia Loghi
- Directorate for Social Statistics and Welfare, Italian Statistical Institute (ISTAT), Rome, Italy
| | - Luule Sakkeus
- Estonian Institute for Population Studies, Tallin University, Tallin, Estonia
| | | | | | - Mélanie Durox
- Université Paris Cité, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Obstetrical, Perinatal and Pediatric Epidemiology Research Team, EPOPé, INSERM, Paris, 75004, France
| | - Jan Cap
- National Health Information Center, Bratislava, Slovakia
| | | | - Janis Misins
- Centre for Disease Prevention and Control of Latvia, Riga, Latvia
| | - Enrique Bernal Delgado
- Data Sciences for Health Services and Policy Research, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain
| | - Martin Thissen
- Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
| | - Mika Gissler
- Department of Knowledge Brokers, THL Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | - Euro-Peristat Research Group
- Université Paris Cité, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Obstetrical, Perinatal and Pediatric Epidemiology Research Team, EPOPé, INSERM, Paris, 75004, France
- Data Sciences for Health Services and Policy Research, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain
- Directorate for Social Statistics and Welfare, Italian Statistical Institute (ISTAT), Rome, Italy
- Estonian Institute for Population Studies, Tallin University, Tallin, Estonia
- Croatian Institute of Public Health, Zagreb, Croatia
- University of Alcala, Madrid, Spain
- National Health Information Center, Bratislava, Slovakia
- Centre for Disease Prevention and Control of Latvia, Riga, Latvia
- Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
- Department of Knowledge Brokers, THL Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
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González-García J, Estupiñán-Romero F, Tellería-Orriols C, González-Galindo J, Palmieri L, Faragalli A, Pristās I, Vuković J, Misinš J, Zile I, Bernal-Delgado E. Correction to: Coping with interoperability in the development of a federated research infrastructure: achievements, challenges and recommendations from the JA-InfAct. Arch Public Health 2022; 80:116. [PMID: 35410393 PMCID: PMC8996578 DOI: 10.1186/s13690-022-00877-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Juan González-García
- Biocomputing Unit, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain
| | - Francisco Estupiñán-Romero
- Data Sciences for Health Services and Policy Research, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain
| | | | - Javier González-Galindo
- Data Sciences for Health Services and Policy Research, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain
| | - Luigi Palmieri
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità-ISS, Rome, Italy
| | - Andrea Faragalli
- Center of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | | | | | - Janis Misinš
- Centre for Disease Prevention and Control, Riga, Latvia
| | - Irisa Zile
- Centre for Disease Prevention and Control, Riga, Latvia
| | - Enrique Bernal-Delgado
- Data Sciences for Health Services and Policy Research, Institute for Health Sciences in Aragon (IACS), Zaragoza, Spain.
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