{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:30:50Z","timestamp":1787567450352,"version":"build-2736575974"},"reference-count":92,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The growing burden of non-communicable diseases (NCDs) in Europe has intensified the need for timely, comparable, and policy-relevant health indicators derived from increasingly heterogeneous health data ecosystems. The European Health Data Space (EHDS) represents a major policy initiative to facilitate the secondary use of health data while preserving privacy, security, and national data sovereignty. In this context, federated analytics can foster international comparisons without requiring the transfer of person-level data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This scoping review aimed to map federated analytical approaches relevant to the production of NCD indicators within the EHDS and to develop a conceptual framework linking distributed statistical methods, privacy-preserving infrastructures, and policy-oriented surveillance requirements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A scoping review was conducted following the PRISMA Extension for Scoping Reviews. Searches were structured into three complementary conceptual domains: (a) federated analytical approaches, (b) distributed statistical inference methods, and (c) governance and health-data infrastructures relevant to the EHDS. Searches were performed in PubMed, Scopus, and IEEE Xplore, for studies published between 2010\u201326. Records were exported with abstracts and full bibliographic metadata. Deduplication and metadata-aware merging were conducted across databases using DOI and normalized-title matching.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We retrieved a total of 2,362 records, of which 1,285 were unique records and 104 were finally retained. The literature revealed a heterogeneous but rapidly expanding ecosystem of distributed analytical approaches. We identified three major domains: (1) distributed regression; (2) federated or distributed analytical infrastructures; and (3) privacy-preserving federated epidemiological analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>An increasing range of solutions for federated analytics is available for policy-grade NCD indicators. Longitudinal and survival models remain methodologically complex because of covariance structures and globally coupled risk sets. A modular approach is needed to incorporate public-health intelligence and a dynamic set of interoperable tools in the EHDS, with the active support of a decentralized network of active stakeholders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Federated analytics is shifting the paradigm from centralized data pooling to computation-to-data architectures. The successful implementation of NCD surveillance in the EHDS require integrating statistics with legal, IT, policy and governance mechanisms. The review outlined a conceptual framework that can couple technical innovation with the best architecture for public-health knowledge production.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1911992","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:14:26Z","timestamp":1787562866000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework"],"prefix":"10.3389","volume":"14","author":[{"given":"Fabrizio","family":"Carinci","sequence":"first","affiliation":[{"name":"Unicamillus International Medical University","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Fava","sequence":"additional","affiliation":[{"name":"Department of Medicine, Mater Dei Hospital of Malta","place":["Msida, Malta"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iztok","family":"\u0160totl","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Diabetes and Metabolic Diseases, University Medical Center Ljubljana","place":["Ljubljana, Slovenia"]},{"name":"Faculty of Medicine, University of Ljubljana","place":["Ljubljana, Slovenia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Nicholson","sequence":"additional","affiliation":[{"name":"Joint Research Center (JRC), European Commission","place":["Ispra, Lombardy, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"B1","unstructured":""},{"key":"B2","unstructured":"A European Union-WHO project"},{"key":"B3","doi-asserted-by":"publisher","first-page":"103304","DOI":"10.1016\/j.eclinm.2025.103304","article-title":"World health Organization's guidance for tracking non-communicable diseases towards sustainable development goals 34: an initiative for facility-based monitoring","volume":"85","author":"Quiambao","year":"2025","journal-title":"EClinicalMedicine"},{"key":"B4","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1177\/19322968211005734","article-title":"A Longitudinal Perspective on User Uptake of an Electronic Personal Health Record for Diabetes, With Respect To Patient Demographics","volume":"15","author":"Conway","year":"2021","journal-title":"J Diabetes Sci Technol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"744516","DOI":"10.3389\/fcdhc.2021.744516","article-title":"Making use of comparable health data to improve quality of care and outcomes in diabetes: the EUBIROD review of diabetes registries and data sources in Europe","volume":"2","author":"Carinci","year":"2021","journal-title":"Front Clin Diabetes Healthc"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehad260","article-title":"SCORE2-Diabetes Working Group and the ESC Cardiovascular Risk Collaboration. SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe","author":"Pennells","year":"2023","journal-title":"Eur Heart J"},{"key":"B7","unstructured":""},{"key":"B8","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1038\/s41431-020-0596-x","article-title":"Disruptive and avoidable: GDPR challenges to secondary research uses of data","volume":"28","author":"Peloquin","year":"2020","journal-title":"Eur J Hum Genet"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1136\/medethics-2013-101603","article-title":"Health Research and Systems Governance are at risk: should the right to data protection override health?","author":"Di Iorio","year":"2014","journal-title":"J Med Ethics"},{"key":"B10","doi-asserted-by":"publisher","first-page":"712569","DOI":"10.3389\/fpubh.2021.712569","article-title":"'t Hoen PAC, Nyg\u00e5rd JF, Hoorne B, Fox C, Alagaratnam S. Federated Networks for Distributed Analysis of Health Data","volume":"9","author":"Hallock","year":"2021","journal-title":"Front Public Health"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/116.00000063","article-title":"Federated Analytics: A Survey","volume":"12","author":"Elkordy","year":"2023","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"B12","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1146\/annurev-biodatasci-122220-115746","article-title":"Centralized and Federated Models for the Analysis of Clinical Data","volume":"7","author":"Li","year":"2024","journal-title":"Annu Rev Biomed Data Sci"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e53622","DOI":"10.2196\/53622","article-title":"Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics","volume":"12","author":"Camirand Lemyre","year":"2024","journal-title":"JMIR Med Inform"},{"key":"B14","unstructured":""},{"key":"B15","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.healthpol.2014.11.016","article-title":"Essential levels of health information in Europe: an action plan for a coherent and sustainable infrastructure","volume":"119","author":"Carinci","year":"2015","journal-title":"Health Policy"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1093\/ije\/dyu188","article-title":"DataSHIELD: Taking the analysis to the data, not the data to the analysis","volume":"43","author":"Gaye","year":"2014","journal-title":"Int J Epidemiol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1142\/9789813279827_0004","article-title":"ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites","volume":"2018","author":"Duan","year":"2019","journal-title":"Biocomputing"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-000862","article-title":"Grid Binary LOgistic REgression (GLORE): building shared models without sharing data","author":"Wu","year":"2012","journal-title":"J Am Med Inform Assoc"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv083","article-title":"WEBDISCO: a web service for distributed cox model learning without patient-level data sharing","author":"Lu","year":"2015","journal-title":"J Am Med Inform Assoc"},{"key":"B20","doi-asserted-by":"publisher","first-page":"168494","DOI":"10.3389\/fpubh.2025.1684947","article-title":"Linking registries to deliver standardized NCD indicators in the European Health Data Space: why do we need a Collaborative Health Information European Framework (CHIEF)","volume":"13","author":"Carinci","year":"2025","journal-title":"Front Public Health"},{"key":"B21","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"3","author":"Rieke","year":"2020","journal-title":"NPJ Digit Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"2041","DOI":"10.1093\/jamia\/ocad170","article-title":"Federated and distributed learning applications for electronic health records and structured medical data: a scoping review","volume":"30","author":"Li","year":"2023","journal-title":"J Am Med Inform Assoc"},{"key":"B23","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1200\/CCI.19.00047","article-title":"Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care","volume":"4","author":"Zerka","year":"2020","journal-title":"JCO Clin Cancer Inform"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1219128","DOI":"10.3389\/fonc.2023.1219128","article-title":"Quality indicators: completeness, validity and timeliness of cancer registry data contributing to the European Cancer Information System","volume":"13","author":"Giusti","year":"2023","journal-title":"Front Oncol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"7414258","DOI":"10.1155\/2022\/7414258","article-title":"Adherence to General Diabetes and Foot Care Processes, with Prompt Referral, Are Associated with Amputation-Free Survival in People with Type 2 Diabetes and Foot Ulcers: A Scottish National Registry Analysis","volume":"2022","author":"Meza-Torres","year":"2022","journal-title":"J Diabetes Res"},{"key":"B26","article-title":"Methodological development of international measurement of acute myocardial infarction 30-day mortality rates at the hospital level, OECD Health Working Papers No","author":"Padget","year":"2019"},{"key":"B27","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1142\/9789811215636_0061","article-title":"ROBUST-ODAL: learning from heterogeneous health systems without sharing patient-level data","author":"Tong","year":"2020","journal-title":"Pac Symp Biocomput"},{"key":"B29","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1093\/jamia\/ocz199","article-title":"Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm","volume":"27","author":"Duan","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa044","article-title":"Learning from local to global: an efficient distributed algorithm for modeling time-to-event data","author":"Duan","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae313","article-title":"Communication-efficient federated learning of temporal effects on opioid use disorder with data from distributed research networks","author":"Liang","year":"2025","journal-title":"J Am Med Inform Assoc"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae027","article-title":"Learning competing risks across multiple hospitals: one-shot distributed algorithms","author":"Zhang","year":"2024","journal-title":"J Am Med Inform Assoc"},{"key":"B33","doi-asserted-by":"publisher","first-page":"104595","DOI":"10.1016\/j.jbi.2024.104595","article-title":"One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites","volume":"150","author":"Zhang","year":"2024","journal-title":"J Biomed Inform"},{"key":"B34","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/TKDE.2020.2989301","article-title":"VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers","volume":"34","author":"Dai","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3218585","article-title":"WICOX: weight-based integrated cox model for time-to-event data in distributed databases without data-sharing","author":"Park","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10190999","article-title":"Federated survival forests","author":"Archetti","year":"2023"},{"key":"B37","unstructured":"Federated Survival Analysis With Differential-Privacy-Inspired Noise: Single Round Release of Kaplan-Meier Curves, 2025 3rd International Conference on Federated Learning Technologies and Applications (FLTA), Dubrovnik, Croatia, 2025, pp\n          \n          \n            \n              Veeraragavan\n              N\n            \n            \n              Nygard\n              JF"},{"key":"B38","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1093\/ije\/dyq111","article-title":"DataSHIELD: resolving a conflict in contemporary bioscience\u2013performing a pooled analysis of individual-level data without sharing the data","volume":"39","author":"Wolfson","year":"2010","journal-title":"Int J Epidemiol"},{"key":"B39","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1186\/s12874-025-02735-7","article-title":"Federated generalized additive models for location, scale and shape","volume":"25","author":"Swenne","year":"2025","journal-title":"BMC Med Res Methodol"},{"key":"B40","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/s13104-023-06372-5","article-title":"Bishop TRP. dsSurvival 20: privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system","volume":"16","author":"Banerjee","year":"2023","journal-title":"BMC Res Notes"},{"key":"B41","doi-asserted-by":"publisher","first-page":"111025","DOI":"10.1016\/j.isci.2024.111025","article-title":"Federated difference-in-differences with multiple time periods in DataSHIELD","volume":"27","author":"Huth","year":"2024","journal-title":"iScience"},{"key":"B42","unstructured":"Official website\n          \n          \n            \n              EUBIROD\n              Network"},{"key":"B43","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3414\/ME15-01-0016","article-title":"Defining a European Diabetes Data Dictionary for Clinical Audit and Healthcare Delivery","volume":"55","author":"Cunningham","year":"2016","journal-title":"Methods Inf Med"},{"key":"B44","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1136\/jme.2009.029918","article-title":"Privacy impact assessment in the design of transnational public health information systems: the BIRO project","volume":"35","author":"Di Iorio","year":"2009","journal-title":"J Med Ethics"},{"key":"B45","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1093\/eurpub\/cks043","article-title":"Cross-border flow of health information: is \u201cprivacy by design\u201d enough? Privacy performance assessment in EUBIROD","volume":"23","author":"Di Iorio","year":"2013","journal-title":"Eur J Public Health"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22474-4_4","article-title":"Privacy and health care information systems: where is the balance?","author":"Di Iorio","year":""},{"key":"B47","unstructured":"Best Information through Regional Outcomes: a Shared European Diabetes Information System for Policy and Practice, University of Perugia, Perugia, 2009\n          \n          \n            \n              BIRO\n              Consortium"},{"key":"B48","unstructured":"Statistical Engine, Workpackage 8, The BIRO Project\n          \n          \n            \n              Carinci\n              F\n            \n            \n              Rossi\n              L\n            \n          \n          \n          2009"},{"key":"B49","unstructured":"Carinci\n              F\n            \n            \n              Rossi\n              L\n            \n          \n          \n          2010"},{"key":"B50","unstructured":"NeuBIRO software\n          \n          \n            \n              Gualdi\n              S\n            \n            \n              Carinci\n              F"},{"key":"B51","doi-asserted-by":"publisher","first-page":"231","DOI":"10.5324\/nje.v21i2.1499","article-title":"DataSHIELD - shared individual-level analysis without sharing the data: a biostatistical perspective","volume":"21","author":"Jones","year":"2012","journal-title":"Norsk Epidemiologi"},{"key":"B52","doi-asserted-by":"publisher","first-page":"21","DOI":"10.5334\/dsj-2017-021","article-title":"DataSHIELD\u2014new directions and dimensions","volume":"16","author":"Wilson","year":"2017","journal-title":"Data Sci J"},{"key":"B53","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1159\/000368959","article-title":"DataSHIELD: an ethically robust solution to multiple-site individual-level data analysis","volume":"18","author":"Budin-Lj\u00f8sne","year":"2015","journal-title":"Public Health Genomics"},{"key":"B54","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/1742-7622-10-12","article-title":"Data harmonization and federated analysis of population-based studies: the BioSHaRE project","volume":"10","author":"Doiron","year":"2013","journal-title":"Emerg Themes Epidemiol"},{"key":"B55","doi-asserted-by":"publisher","first-page":"btaf286","DOI":"10.1093\/bioinformatics\/btaf286","article-title":"dsOMOP: bridging OMOP CDM and DataSHIELD for secure federated analysis of standardized clinical data","volume":"41","author":"Sarrat-Gonz\u00e1lez","year":"2025","journal-title":"Bioinformatic"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1109\/BigData62323.2024.10825699","article-title":"Data infrastructure for integrating clinical data in the large-scale international ORCHESTRA cohort: from data import to federated analysis","author":"Puskaric","year":"2024"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.2196\/65681","article-title":"Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study","author":"Ziegler","year":"2025","journal-title":"J Med Internet Res"},{"key":"B58","doi-asserted-by":"publisher","first-page":"vbaf298","DOI":"10.1093\/bioadv\/vbaf298","article-title":"Integrating differential privacy into federated multi-task learning algorithms in dsMTL","volume":"5","author":"Schefzik","year":"2025","journal-title":"Bioinform Adv"},{"key":"B59","article-title":"Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers","author":"Hripcsak","year":"2015","journal-title":"Stud Health Technol Inform"},{"key":"B60","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1002\/pds.5587","article-title":"Federated analyses of multiple data sources in drug safety studies","volume":"32","author":"Gedeborg","year":"2023","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"B61","doi-asserted-by":"publisher","first-page":"e70177","DOI":"10.1002\/pds.70177","article-title":"Core Concepts in Pharmacoepidemiology: Multi-Database Distributed Data Networks","volume":"34","author":"Haber","year":"2025","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"B62","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1093\/jamia\/ocad235","article-title":"CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data","volume":"31","author":"Mullie","year":"2024","journal-title":"J Am Med Inform Assoc"},{"key":"B63","doi-asserted-by":"publisher","first-page":"e40035","DOI":"10.2196\/40035","article-title":"A Hybrid Architecture (CO-CONNECT) to Facilitate Rapid Discovery and Access to Data Across the United Kingdom in Response to the COVID-19 Pandemic: Development Study","volume":"24","author":"Jefferson","year":"2022","journal-title":"J Med Internet Res"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1145\/3696593.3696627","article-title":"A Data Space infrastructure supporting the integration of clinical data nodes and cancer registries to improve personalized medicine","author":"Hamiti","year":"2024"},{"key":"B65","doi-asserted-by":"publisher","first-page":"262","DOI":"10.3390\/life14020262","article-title":"A Federated Database for Obesity Research: An IMI-SOPHIA Study","volume":"14","author":"Delfin","year":"2024","journal-title":"Life (Basel)"},{"key":"B66","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.radonc.2019.11.019","article-title":"Distributed learning on 20,000+ lung cancer patients - The Personal Health Train","volume":"144","author":"Deist","year":"2020","journal-title":"Radiother Oncol"},{"key":"B67","article-title":"VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange","author":"Moncada-Torres","year":"2021","journal-title":"AMIA Annu Symp Proc"},{"key":"B68","doi-asserted-by":"publisher","first-page":"vbaf046","DOI":"10.1093\/bioadv\/vbaf046","article-title":"DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform","volume":"5","author":"Avraam","year":"2025","journal-title":"Bioinform Ad"},{"key":"B69","doi-asserted-by":"publisher","first-page":"13","DOI":"10.2196\/59685","article-title":"Federated Analysis With Differential Privacy in Oncology Research: Longitudinal Observational Study Across Hospital Data Warehouses","author":"Ryffel","year":"2025","journal-title":"J Med Internet Res"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-016-0316-1","article-title":"et al. Secure multi-party computation Grid LOgistic REgression (SMAC-GLORE)","author":"Shi","year":"2016","journal-title":"BMC Med Inform Decis Mak"},{"key":"B71","doi-asserted-by":"publisher","first-page":"e2304415120","DOI":"10.1073\/pnas.2304415120","article-title":"Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption","volume":"120","author":"Geva","year":"2023","journal-title":"Proc Natl Acad Sci USA"},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.5220\/0012753900003767","article-title":"A framework for federated analysis of health data using multiparty homomorphic encryption","author":"Puskaric","year":"2024"},{"key":"B73","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s12911-025-03328-6","article-title":"Privacy-preserving data quality assessment for federated health data networks","volume":"26","author":"Tom\u00e1\u0161ik","year":"2026","journal-title":"BMC Med Inform Decis Mak"},{"key":"B74","doi-asserted-by":"publisher","first-page":"e102401","DOI":"10.1136\/bmjopen-2025-102401","article-title":"Real-world data and patient-reported outcomes in diabetes in Emilia-Romagna (REWINDER): protocol of a federated cohort study for the regional evaluation of quality of care during and after COVID-19","volume":"15","author":"Carinci","year":"2025","journal-title":"BMJ Open"},{"key":"B75","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1016\/j.envres.2016.09.014","article-title":"Road traffic noise, blood pressure and heart rate: Pooled analyses of harmonized data from 88,336 participants","volume":"151","author":"Zijlema","year":"2016","journal-title":"Environ Res"},{"key":"B76","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.jaci.2022.01.023","article-title":"Associations of early-life pet ownership with asthma and allergic sensitization: A meta-analysis of more than 77,000 children from the EU Child Cohort Network","volume":"150","author":"Pinot de Moira","year":"2022","journal-title":"J Allergy Clin Immunol"},{"key":"B77","doi-asserted-by":"publisher","first-page":"11540","DOI":"10.1038\/s41598-023-38457-3","article-title":"A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health","volume":"13","author":"Azizi","year":"2023","journal-title":"Sci Rep"},{"key":"B78","doi-asserted-by":"publisher","first-page":"e41021","DOI":"10.1097\/MD.0000000000041021","article-title":"Assessing the relationship between sex, gender, and hypertension: a federated analysis of european and canadian public health surveys","volume":"104","author":"Azizi","year":"2025","journal-title":"Medicine"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1093\/eurpub\/ckae036","article-title":"PHIRI: lessons for an extensive reuse of sensitive data in federated health research","author":"Gonz\u00e1lez-Garc\u00eda","year":"2024","journal-title":"Eur J Public Health"},{"key":"B80","doi-asserted-by":"publisher","first-page":"105708","DOI":"10.1016\/j.ijmedinf.2024.105708","article-title":"Developing a prototype for federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data","volume":"195","author":"Eradat Oskoui","year":"2025","journal-title":"Int J Med Inform"},{"key":"B81","doi-asserted-by":"publisher","DOI":"10.3233\/SHTI210166","article-title":"Lo-Ciganic WH, Lyu T, Tao C, Merkel PA, Kranzler HR, Bian J, Chen Y. Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network","author":"Tong","year":"2021","journal-title":"AMIA Annu Symp Proc"},{"key":"B82","doi-asserted-by":"publisher","DOI":"10.1016\/j.jid.2025.07.036","article-title":"Implementing Federated Analysis Using DataSHIELD in the DREAM TO TREAT Atopic Dermatitis Registries Collaboration","author":"Krogh","year":"2026","journal-title":"J Invest Dermatol"},{"key":"B83","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.111084","article-title":"Developing federated time-to-event scores using heterogeneous real-world survival data","author":"Li","year":"2025","journal-title":"Comput Biol Med"},{"key":"B84","doi-asserted-by":"publisher","first-page":"106394","DOI":"10.1016\/j.cmpb.2021.106394","article-title":"A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data","volume":"211","author":"Khalid","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.1097\/PHH.0000000000001408","article-title":"A Governance Framework to Integrate Longitudinal Clinical and Community Data in a Distributed Data Network: The Childhood Obesity Data Initiative","author":"Kraus","year":"2022","journal-title":"J Public Health Manag Pract"},{"key":"B86","doi-asserted-by":"publisher","first-page":"00168","DOI":"10.1183\/23120541.00168-2022","article-title":"Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future","volume":"8","author":"Kroes","year":"2022","journal-title":"ERJ Open Res"},{"key":"B87","doi-asserted-by":"publisher","first-page":"S45","DOI":"10.1097\/MLR.0b013e3181d9919f","article-title":"Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care","volume":"48","author":"Brown","year":"2010","journal-title":"Med Care"},{"key":"B88","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1093\/jamia\/ocu023","article-title":"Feasibility and utility of applications of the common data model to multiple, disparate observational health databases","volume":"22","author":"Voss","year":"2015","journal-title":"J Am Med Inform Assoc"},{"key":"B89","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1136\/amiajnl-2014-002747","article-title":"Launching PCORnet, a national patient-centered clinical research network","volume":"21","author":"Fleurence","year":"2014","journal-title":"J Am Med Inform Assoc"},{"key":"B90","article-title":"Technological Enablers for Privacy Preserving Data Sharing and Analysis","author":"Hurtado Ram\u00edrez","year":"2023"},{"key":"B91","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1056\/NEJMp1401111","article-title":"Learning from big health care data","volume":"370","author":"Schneeweiss","year":"2014","journal-title":"N Engl J Med"},{"key":"B92","article-title":"The cathedral & the bazaar: musings on Linux and open source by an accidental revolutionary","author":"Raymond","year":"2001"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1911992\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:14:29Z","timestamp":1787562869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1911992\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,21]]},"references-count":92,"alternative-id":["10.3389\/fpubh.2026.1911992"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1911992","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,21]]},"article-number":"1911992"}}