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Sinaci AA, Gencturk M, Alvarez-Romero C, Laleci Erturkmen GB, Martinez-Garcia A, Escalona-Cuaresma MJ, Parra-Calderon CL. Privacy-preserving federated machine learning on FAIR health data: A real-world application. Comput Struct Biotechnol J 2024; 24:136-145. [PMID: 38434250 PMCID: PMC10904920 DOI: 10.1016/j.csbj.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
Objective This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Materials and methods Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets. Results Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%. Discussion The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data. Conclusion This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.
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Affiliation(s)
- A. Anil Sinaci
- SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
| | - Mert Gencturk
- SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Celia Alvarez-Romero
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | | | - Alicia Martinez-Garcia
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | | | - Carlos Luis Parra-Calderon
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
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Barry KM, Avraam D, Cadman T, Elhakeem A, El Marroun H, Jansen PW, Nybo-Andersen AM, Strandberg-Larsen K, Safont LG, Soler-Blasco R, Barreto-Zarza F, Julvez J, Vrijheid M, Heude B, Charles MA, Gomajee AR, Melchior M. Early childcare arrangements and children's internalizing and externalizing symptoms: an individual participant data meta-analysis of six prospective birth cohorts in Europe. THE LANCET REGIONAL HEALTH. EUROPE 2024; 45:101036. [PMID: 39262448 PMCID: PMC11387227 DOI: 10.1016/j.lanepe.2024.101036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/02/2024] [Accepted: 08/06/2024] [Indexed: 09/13/2024]
Abstract
Background Early childcare attendance may be related to children's internalizing and externalizing symptoms throughout childhood and young adolescence, however evidence from Europe is limited. We aimed to assess this association across multiple population-based birth cohorts of children recruited in different European countries. Methods Data come from six parent-offspring prospective birth cohort studies across five European countries within the EU Child Cohort Network. A total of 87,208 parent-child dyads were included in the study. To test associations between childcare attendance (centre-based or informal) anytime between ages 0 and 4 years and children's internalizing and externalizing symptoms in middle childhood and young adolescence (measured at: 5-6 years, 7-9 years, and 10-13 years) a two-stage individual participant data meta-analysis was implemented. Linear regression models were performed in each cohort separately; combined random-effects meta-analysis was then used to obtain overall association estimates. In secondary analyses, we tested interactions between childcare attendance and mother's post-partum depression, low education status, and the child's sex. Findings Compared to children who were exclusively cared for by their parents prior to school entry, those who attended centre-based childcare had lower levels of internalizing symptoms in all age groups [5-6 years: β: -1.78 (95% CI: -3.39, -0.16); 7-9 years: β: -0.55 (95% CI: -0.88, -0.73); 10-13 years: β: -0.76 (95% CI: -1.15, -0.37)]. Children who attended informal childcare appeared to have elevated levels of internalizing symptoms between 7-9 and 10-13 years, respectively [β: 1.65 (95% CI: 1.25, 2.06); β: 1.25 (95% CI: 0.96, 1.54)]. Informal childcare attendance was also associated with increased levels of children's externalizing symptoms between 7-9 and 10-13 years, respectively [β: 2.84 (95% CI: 1.41, 4.26); β: 2.19 (95% CI: 0.54, 3.84)]. Interpretation Early centre-based childcare is associated with decreased levels of children's internalizing symptoms compared to exclusive parental care. For informal childcare, opposite associations were observed. Overall, our results suggest that centre-based childcare attendance may be associated with slight positive impacts on children's emotional development and should be encouraged by public policies. In addition, children from socioeconomically disadvantaged families require special attention, as they may not sufficiently benefit from universal early childhood education and care (ECEC). Funding This research was funded by the ERC Consolidator grant RESEDA (Horizon Europe, 101001420).
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Affiliation(s)
- Katharine M Barry
- Sorbonne Université, Paris, France
- French National Institute of Health and Medical Research (INSERM), Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Equipe de Recherche en Epidémiologie Sociale (ERES), Paris, France
| | - Demetris Avraam
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Tim Cadman
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ahmed Elhakeem
- Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Hanan El Marroun
- Department of Child & Adolescent Psychiatry/Psychology, Erasmus Medical Center (MC), University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Pauline W Jansen
- Department of Child & Adolescent Psychiatry/Psychology, Erasmus Medical Center (MC), University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Anne-Marie Nybo-Andersen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | | | - Llúcia González Safont
- Nursing and Chiropody Faculty of Valencia University, Valencia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Spain
- Joint Research Unit in Epidemiology, Environment and Health (FISABIO-UJI-UV), Valencia, Spain
| | - Raquel Soler-Blasco
- Valencia University, Valencia, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit (FISABIO-Universitat Jaume I-Universitat de Valencia), Valencia, Spain
| | - Florencia Barreto-Zarza
- French National Institute of Health and Medical Research (INSERM), Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Equipe de Recherche en Epidémiologie Sociale (ERES), Paris, France
- Faculty of Psychology, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- Environmental Epidemiology and Child Development Group, Biodonostia Health Research Institute, San Sebastian, Spain
| | - Jordi Julvez
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Pere Virgili Institute for Health Research (IISPV), Universitat Rovira i Virgili, Tarragona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Martine Vrijheid
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Pere Virgili Institute for Health Research (IISPV), Universitat Rovira i Virgili, Tarragona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Barbara Heude
- UMR1153 Center for Research in Epidemiology and Statistics (CRESS), Paris, France
- Early Life Research on Later Health Team (EARoH), Paris, France
| | - Marie-Aline Charles
- Joint ELFE Unit (INSERM), French National Institute for Demographic Studies (INED), Aubervilliers, France
- French Blood Establishment (EFS), Aubervilliers, France
| | - Alexandre Ramchandar Gomajee
- Sorbonne Université, Paris, France
- French National Institute of Health and Medical Research (INSERM), Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Equipe de Recherche en Epidémiologie Sociale (ERES), Paris, France
- French School of Public Health (EHESP), Doctoral Network, Rennes, France
| | - Maria Melchior
- French National Institute of Health and Medical Research (INSERM), Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Equipe de Recherche en Epidémiologie Sociale (ERES), Paris, France
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Li C, Bishop TRP, Imamura F, Sharp SJ, Pearce M, Brage S, Ong KK, Ahsan H, Bes-Rastrollo M, Beulens JWJ, den Braver N, Byberg L, Canhada S, Chen Z, Chung HF, Cortés-Valencia A, Djousse L, Drouin-Chartier JP, Du H, Du S, Duncan BB, Gaziano JM, Gordon-Larsen P, Goto A, Haghighatdoost F, Härkänen T, Hashemian M, Hu FB, Ittermann T, Järvinen R, Kakkoura MG, Neelakantan N, Knekt P, Lajous M, Li Y, Magliano DJ, Malekzadeh R, Le Marchand L, Marques-Vidal P, Martinez-Gonzalez MA, Maskarinec G, Mishra GD, Mohammadifard N, O'Donoghue G, O'Gorman D, Popkin B, Poustchi H, Sarrafzadegan N, Sawada N, Schmidt MI, Shaw JE, Soedamah-Muthu S, Stern D, Tong L, van Dam RM, Völzke H, Willett WC, Wolk A, Yu C, Forouhi NG, Wareham NJ. Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1·97 million adults with 100 000 incident cases from 31 cohorts in 20 countries. Lancet Diabetes Endocrinol 2024; 12:619-630. [PMID: 39174161 DOI: 10.1016/s2213-8587(24)00179-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/04/2024] [Accepted: 06/10/2024] [Indexed: 08/24/2024]
Abstract
BACKGROUND Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches. METHODS This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity. FINDINGS Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7-15) years. Median meat consumption across cohorts was 0-110 g/day for unprocessed red meat, 0-49 g/day for processed meat, and 0-72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 1·10 (95% CI 1·06-1·15) per 100 g/day of unprocessed red meat (I2=61%), 1·15 (1·11-1·20) per 50 g/day of processed meat (I2=59%), and 1·08 (1·02-1·14) per 100 g/day of poultry (I2=68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes. INTERPRETATION The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines. FUNDING The EU, the Medical Research Council, and the National Institute of Health Research Cambridge Biomedical Research Centre.
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Affiliation(s)
- Chunxiao Li
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tom R P Bishop
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Fumiaki Imamura
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Stephen J Sharp
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Matthew Pearce
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Soren Brage
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ken K Ong
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Habibul Ahsan
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Maira Bes-Rastrollo
- University of Navarra, Idisna, Department of Preventive Medicine and Public Health, CIBEROBN-Instituto de Salud Carlos III, Pamplona, Spain
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam UMC, location Vrije Universiteit, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Nicole den Braver
- Department of Epidemiology and Data Science, Amsterdam UMC, location Vrije Universiteit, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Liisa Byberg
- Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Scheine Canhada
- Postgraduate Program in Epidemiology, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hsin-Fang Chung
- Australian Women and Girls' Health Research Centre, School of Public Health, University of Queensland, Brisbane, QLD, Australia
| | - Adrian Cortés-Valencia
- Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Jamaica Plain, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Faculté de Pharmacie, Université Laval, Quebec City, QC, Canada
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bruce B Duncan
- Postgraduate Program in Epidemiology, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Jamaica Plain, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Atsushi Goto
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan; Department of Public Health, School of Medicine, Yokohama City University, Yokohama, Japan
| | - Fahimeh Haghighatdoost
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Tommi Härkänen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Maryam Hashemian
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Frank B Hu
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Till Ittermann
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Ritva Järvinen
- Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Maria G Kakkoura
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nithya Neelakantan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Paul Knekt
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Martin Lajous
- Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico; Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Yanping Li
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Jamaica Plain, MA, USA
| | | | - Reza Malekzadeh
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Miguel A Martinez-Gonzalez
- University of Navarra, Idisna, Department of Preventive Medicine and Public Health, CIBEROBN-Instituto de Salud Carlos III, Pamplona, Spain
| | | | - Gita D Mishra
- Australian Women and Girls' Health Research Centre, School of Public Health, University of Queensland, Brisbane, QLD, Australia
| | - Noushin Mohammadifard
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Gráinne O'Donoghue
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Donal O'Gorman
- School of Health and Human Performance, Dublin City University, Dublin, Ireland
| | - Barry Popkin
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hossein Poustchi
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran; Faculty of Medicine, School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Maria Inês Schmidt
- Postgraduate Program in Epidemiology, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Sabita Soedamah-Muthu
- Centre of Research on Psychological Disorders and Somatic Diseases (CORPS), Department of Medical and Clinical Psychology, Tilburg University, Tilburg, Netherlands; Institute for Food, Nutrition and Health, University of Reading, Reading, UK
| | - Dalia Stern
- CONAHCyT - Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico
| | - Lin Tong
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Walter C Willett
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Nita G Forouhi
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
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Schalk D, Rehms R, Hoffmann VS, Bischl B, Mansmann U. Distributed non-disclosive validation of predictive models by a modified ROC-GLM. BMC Med Res Methodol 2024; 24:190. [PMID: 39210301 PMCID: PMC11363434 DOI: 10.1186/s12874-024-02312-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. METHODS We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivityΔ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. RESULTS In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. CONCLUSIONS The applicability of our algorithms depends on the ℓ 2 sensitivityΔ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with highΔ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy.
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Affiliation(s)
- Daniel Schalk
- Department of Statistics, LMU Munich, Munich, Germany
- DIFUTURE (DataIntegration for Future Medicine, www.difuture.de), LMU Munich, Munich, Germany
- Munich Center for Machine Learning (MCML), LMU Munich, Munich, Germany
| | - Raphael Rehms
- Institute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Munich, Germany
| | - Verena S Hoffmann
- Institute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Munich, Germany
| | - Bernd Bischl
- Department of Statistics, LMU Munich, Munich, Germany
- Munich Center for Machine Learning (MCML), LMU Munich, Munich, Germany
| | - Ulrich Mansmann
- Department of Statistics, LMU Munich, Munich, Germany.
- Institute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Munich, Germany.
- DIFUTURE (DataIntegration for Future Medicine, www.difuture.de), LMU Munich, Munich, Germany.
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5
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Fernandes A, Avraam D, Cadman T, Dadvand P, Guxens M, Binter AC, Pinot de Moira A, Nieuwenhuijsen M, Duijts L, Julvez J, De Castro M, Fossati S, Márquez S, Vrijkotte T, Elhakeem A, McEachan R, Yang T, Pedersen M, Vinther J, Lepeule J, Heude B, Jaddoe VWV, Santos S, Welten M, El Marroun H, Mian A, Andrušaitytė S, Lertxundi A, Ibarluzea J, Ballester F, Esplugues A, Torres Toda M, Harris JR, Lucia Thorbjørnsrud Nader J, Moirano G, Maritano S, Catherine Wilson R, Vrijheid M. Green spaces and respiratory, cardiometabolic, and neurodevelopmental outcomes: An individual-participant data meta-analysis of >35.000 European children. ENVIRONMENT INTERNATIONAL 2024; 190:108853. [PMID: 38963986 DOI: 10.1016/j.envint.2024.108853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/17/2024] [Accepted: 06/25/2024] [Indexed: 07/06/2024]
Abstract
Studies evaluating the benefits and risks of green spaces on children's health are scarce. The present study aimed to examine the associations between exposure to green spaces during pregnancy and early childhood with respiratory, cardiometabolic, and neurodevelopmental outcomes in school-age children. We performed an Individual-Participant Data (IPD) meta-analysis involving 35,000 children from ten European birth cohorts across eight countries. For each participant, we calculated residential Normalized Difference Vegetation Index (NDVI) within a 300 m buffer and the linear distance to green spaces (meters) during prenatal life and childhood. Multiple harmonized health outcomes were selected: asthma and wheezing, lung function, body mass index, diastolic and systolic blood pressure, non-verbal intelligence, internalizing and externalizing problems, and ADHD symptoms. We conducted a two-stage IPD meta-analysis and evaluated effect modification by socioeconomic status (SES) and sex. Between-study heterogeneity was assessed via random-effects meta-regression. Residential surrounding green spaces in childhood, not pregnancy, was associated with improved lung function, particularly higher FEV1 (β = 0.06; 95 %CI: 0.03, 0.09 I2 = 4.03 %, p < 0.001) and FVC (β = 0.07; 95 %CI: 0.04, 0.09 I2 = 0 %, p < 0.001) with a stronger association observed in females (p < 0.001). This association remained robust after multiple testing correction and did not change notably after adjusting for ambient air pollution. Increased distance to green spaces showed an association with lower FVC (β = -0.04; 95 %CI: -0.07, -0.02, I2 = 4.8, p = 0.001), with a stronger effect in children from higher SES backgrounds (p < 0.001). No consistent associations were found between green spaces and asthma, wheezing, cardiometabolic, or neurodevelopmental outcomes, with direction of effect varying across cohorts. Wheezing and neurodevelopmental outcomes showed high between-study heterogeneity, and the age at outcome assessment was only associated with heterogeneity in internalizing problems.. This large European meta-analysis suggests that childhood exposure to green spaces may lead to better lung function. Associations with other respiratory outcomes and selected cardiometabolic and neurodevelopmental outcomes remain inconclusive.
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Affiliation(s)
- Amanda Fernandes
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
| | - Demetris Avraam
- Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, UK; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Tim Cadman
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Mònica Guxens
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
| | - Anne-Claire Binter
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Angela Pinot de Moira
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; National Heart and Lung Institute, Imperial College London, London, UK
| | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Liesbeth Duijts
- Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Jordi Julvez
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Clinical and Epidemiological Neuroscience (NeuroÈpia), Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Montserrat De Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Serena Fossati
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Sandra Márquez
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Tanja Vrijkotte
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands
| | - Ahmed Elhakeem
- Population Health Science, Bristol Medical School, Bristol BS8 2BN, United Kingdom; MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2PS, UK
| | - Rosemary McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford Royal Infirmary, Bradford, UK
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford Royal Infirmary, Bradford, UK
| | - Marie Pedersen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Johan Vinther
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Johanna Lepeule
- University Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences, Grenoble, France
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Susana Santos
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Universidade do Porto, Portugal
| | - Marieke Welten
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands; Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Science, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Annemiek Mian
- Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Sandra Andrušaitytė
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Aitana Lertxundi
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Biogipuzkoa Health Research Institute, Environmental Epidemiology and Child Development Group, 20014, San Sebastian, Spain; Department of Preventive Medicine and Public Health, Faculty of Medicine, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Jesús Ibarluzea
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Biogipuzkoa Health Research Institute, Environmental Epidemiology and Child Development Group, 20014, San Sebastian, Spain; Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013 San Sebastian, Spain; Faculty of Psychology of the University of the Basque Country (EHU-UPV), 20018, San Sebastian, Spain
| | - Ferran Ballester
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Department of Nursing, Universitat de València, Valencia, Spain; Epidemiology Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
| | - Ana Esplugues
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Department of Nursing, Universitat de València, Valencia, Spain; Epidemiology Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
| | - Maria Torres Toda
- Unit Medical Expertise and Data Intelligence, Department of Health Protection, National Health Laboratory (LNS), Dudelange, Luxembourg
| | - Jennifer R Harris
- Center for Fertility and Health, The Nowegian Institute of Public Health, Oslo, Norway
| | - Johanna Lucia Thorbjørnsrud Nader
- Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
| | - Giovenale Moirano
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Silvia Maritano
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy; University School for Advanced Studies IUSS Pavia, Pavia, Italy
| | | | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
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6
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Bergeron J, Avraam D, Calas L, Fraser W, Harris JR, Heude B, Mandhane P, Moraes TJ, Muckle G, Nader J, Séguin JR, Simons E, Subbarao P, Swertz MA, Tough S, Turvey SE, Fortier I, Rod NH, Andersen AMN. Stress and anxiety during pregnancy and length of gestation: a federated study using data from five Canadian and European birth cohorts. Eur J Epidemiol 2024; 39:773-783. [PMID: 38805076 PMCID: PMC11344005 DOI: 10.1007/s10654-024-01126-4] [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: 05/12/2023] [Accepted: 04/14/2024] [Indexed: 05/29/2024]
Abstract
While its etiology is not fully elucidated, preterm birth represents a major public health concern as it is the leading cause of child mortality and morbidity. Stress is one of the most common perinatal conditions and may increase the risk of preterm birth. In this paper we aimed to investigate the association of maternal perceived stress and anxiety with length of gestation. We used harmonized data from five birth cohorts from Canada, France, and Norway. A total of 5297 pregnancies of singletons were included in the analysis of perceived stress and gestational duration, and 55,775 pregnancies for anxiety. Federated analyses were performed through the DataSHIELD platform using Cox regression models within intervals of gestational age. The models were fit for each cohort separately, and the cohort-specific results were combined using random effects study-level meta-analysis. Moderate and high levels of perceived stress during pregnancy were associated with a shorter length of gestation in the very/moderately preterm interval [moderate: hazard ratio (HR) 1.92 (95%CI 0.83, 4.48); high: 2.04 (95%CI 0.77, 5.37)], albeit not statistically significant. No association was found for the other intervals. Anxiety was associated with gestational duration in the very/moderately preterm interval [1.66 (95%CI 1.32, 2.08)], and in the early term interval [1.15 (95%CI 1.08, 1.23)]. Our findings suggest that perceived stress and anxiety are associated with an increased risk of earlier birth, but only in the earliest gestational ages. We also found an association in the early term period for anxiety, but the result was only driven by the largest cohort, which collected information the latest in pregnancy. This raised a potential issue of reverse causality as anxiety later in pregnancy could be due to concerns about early signs of a possible preterm birth.
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Affiliation(s)
- Julie Bergeron
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
- Research Institute of the McGill University Health Center, Montreal, Canada.
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - Lucinda Calas
- Centre for Research in Epidemiology and Statistics, INSERM, Paris, France
| | - William Fraser
- Department of Obstetrics and Gynecology, Université de Sherbrooke, Sherbrook, Canada
| | - Jennifer R Harris
- Centre for Fertility and Health, The Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- Centre for Research in Epidemiology and Statistics, INSERM, Paris, France
| | - Piush Mandhane
- Department of Pediatrics, University of Alberta, Edmonton, Canada
| | - Theo J Moraes
- Department of Paediatrics, Hospital for Sick Children and University of Toronto, Toronto, Canada
| | - Gina Muckle
- School of Psychology, Université Laval, Quebec, Canada
| | - Johanna Nader
- Centre for Fertility and Health, The Norwegian Institute of Public Health, Oslo, Norway
| | - Jean R Séguin
- Department of Psychiatry and Addictology, Université de Montréal and CHU Ste-Justine Research Center, Montreal, Canada
| | - Elinor Simons
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, Canada
| | - Padmaja Subbarao
- Department of Paediatrics, Hospital for Sick Children and University of Toronto, Toronto, Canada
| | - Morris A Swertz
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Suzanne Tough
- Department of Paediatrics, University of Calgary, Calgary, Canada
| | - Stuart E Turvey
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, Canada
| | - Isabel Fortier
- Research Institute of the McGill University Health Center, Montreal, Canada
| | - Naja Hulvej Rod
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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7
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Ammon D, Kurscheidt M, Buckow K, Kirsten T, Löbe M, Meineke F, Prasser F, Saß J, Sax U, Stäubert S, Thun S, Wettstein R, Wiedekopf JP, Wodke JAH, Boeker M, Ganslandt T. [Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:656-667. [PMID: 38753022 PMCID: PMC11166738 DOI: 10.1007/s00103-024-03888-4] [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: 12/13/2023] [Accepted: 04/26/2024] [Indexed: 06/01/2024]
Abstract
The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.
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Affiliation(s)
- Danny Ammon
- Datenintegrationszentrum, Universitätsklinikum Jena, Jena, Deutschland
| | - Maximilian Kurscheidt
- GECKO Institut für Medizin, Informatik und Ökonomie, Hochschule Heilbronn, Heilbronn, Deutschland
| | - Karoline Buckow
- TMF - Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V., Berlin, Deutschland
| | - Toralf Kirsten
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Matthias Löbe
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Frank Meineke
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Fabian Prasser
- Berliner Institut für Gesundheitsforschung in der Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Julian Saß
- Berliner Institut für Gesundheitsforschung in der Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Ulrich Sax
- Institut für Medizinische Informatik, Universitätsmedizin Göttingen, Göttingen, Deutschland
| | - Sebastian Stäubert
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Sylvia Thun
- Berliner Institut für Gesundheitsforschung in der Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Reto Wettstein
- Institut für Medizinische Informatik, Universitätsklinikum Heidelberg, Heidelberg, Deutschland
| | - Joshua P Wiedekopf
- Institut für Medizinische Informatik & IT Center for Clinical Research, Universität zu Lübeck, Lübeck, Deutschland
| | - Judith A H Wodke
- Institut für Community Medicine, Medizininformatik, MeDaX, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Martin Boeker
- Institut für Künstliche Intelligenz und Informatik in der Medizin, Klinikum rechts der Isar, Technische Universität München, München, Deutschland
| | - Thomas Ganslandt
- Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058, Erlangen, Deutschland.
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8
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Guimbaud JB, Siskos AP, Sakhi AK, Heude B, Sabidó E, Borràs E, Keun H, Wright J, Julvez J, Urquiza J, Gützkow KB, Chatzi L, Casas M, Bustamante M, Nieuwenhuijsen M, Vrijheid M, López-Vicente M, de Castro Pascual M, Stratakis N, Robinson O, Grazuleviciene R, Slama R, Alemany S, Basagaña X, Plantevit M, Cazabet R, Maitre L. Machine learning-based health environmental-clinical risk scores in European children. COMMUNICATIONS MEDICINE 2024; 4:98. [PMID: 38783062 PMCID: PMC11116423 DOI: 10.1038/s43856-024-00513-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 04/26/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data. METHODS In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions. RESULTS ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health. CONCLUSIONS Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.
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Affiliation(s)
- Jean-Baptiste Guimbaud
- ISGlobal, Barcelona, Spain
- Univ Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622, Villeurbanne, France
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Meersens, Lyon, France
| | - Alexandros P Siskos
- Imperial College London, Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer, London, UK
| | | | - Barbara Heude
- Université Paris Cité, Inserm, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Eduard Sabidó
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Eva Borràs
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Hector Keun
- Imperial College London, Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer, London, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford, UK
- Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Jordi Julvez
- ISGlobal, Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain
| | - Jose Urquiza
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Leda Chatzi
- Department of Preventive Medicine, University of Southern Los Angeles, Los Angeles, CA, USA
| | - Maribel Casas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | - Mónica López-Vicente
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | - Montserrat de Castro Pascual
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | - Nikos Stratakis
- Department of Preventive Medicine, University of Southern Los Angeles, Los Angeles, CA, USA
| | - Oliver Robinson
- Μedical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Mohn Centre for Children's Health and Well-being, School of Public Health, Imperial College London, London, UK
| | | | - Remy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain
| | - Marc Plantevit
- EPITA Research Laboratory (LRE), Kremlin-Bicêtre, France
| | - Rémy Cazabet
- Univ Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622, Villeurbanne, France
| | - Léa Maitre
- ISGlobal, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- CIBER Epidemiología Y Salud Pública (CIBERESP), Madrid, Spain.
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9
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Wirth FN, Abu Attieh H, Prasser F. OHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation. Front Med (Lausanne) 2024; 11:1378866. [PMID: 38818399 PMCID: PMC11137233 DOI: 10.3389/fmed.2024.1378866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations. Methods To decide on a set of relevant document templates, we first analyzed the legal requirements and associated guidelines with a focus on the General Data Protection Regulation (GDPR). Moreover, we analyzed the software architecture of a typical OHDSI stack and related its components to the different general types of concepts and documentation identified. Then, we created those documents for a prototypical OHDSI installation, based on the so-called Broadsea package, following relevant guidelines from Germany. Finally, we generalized the documents by introducing placeholders and options at places where individual institution-specific content will be needed. Results We present four documents: (1) a record of processing activities, (2) an information security concept, (3) an authorization concept, as well as (4) an operational concept covering the technical details of maintaining the stack. The documents are publicly available under a permissive license. Discussion To the best of our knowledge, there are no other publicly available sets of documents designed to simplify the compliance process for OHDSI deployments. While our documents provide a comprehensive starting point, local specifics need to be added, and, due to the heterogeneity of legal requirements in different countries, further adoptions might be necessary.
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Affiliation(s)
| | | | - Fabian Prasser
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany
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10
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Choudhury A, Janssen E, Bongers BC, van Meeteren NLU, Dekker A, van Soest J. Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train. BMC Med Inform Decis Mak 2024; 24:121. [PMID: 38724966 PMCID: PMC11080148 DOI: 10.1186/s12911-024-02526-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVE Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric 'Proof of Principle' study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. MATERIALS AND METHODS Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. RESULTS ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. DISCUSSION AND CONCLUSION PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures.
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Affiliation(s)
- Ananya Choudhury
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
- Clinical Data Science Group, Faculty of Health Medicine and Life Sciences, Maastricht University Medical Center+, Paul-Henri Spaaklaan 1, Maastricht, 6229 GT, Netherlands.
| | - Esther Janssen
- Department of Orthopaedics, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Orthopaedic Surgery, VieCuri Medical Center, Venlo, The Netherlands
| | - Bart C Bongers
- Department of Nutrition and Movement Sciences, Faculty of Health, Medicine and Life Sciences, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, the Netherlands
- Department of Epidemiology, Faculty of Health, Medicine and Life Sciences, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands
| | - Nico L U van Meeteren
- Top Sector Life Sciences and Health (Health∼Holland), the Hague, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Topcare, Leiden, the Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Brightlands Institute for Smart Society (BISS), Faculty of Science and Engineering (FSE), Maastricht University, Heerlen, the Netherlands
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11
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Cadman T, Elhakeem A, Vinther JL, Avraam D, Carrasco P, Calas L, Cardol M, Charles MA, Corpeleijn E, Crozier S, de Castro M, Estarlich M, Fernandes A, Fossatti S, Gruszfeld D, Guerlich K, Grote V, Haakma S, Harris JR, Heude B, Huang RC, Ibarluzea J, Inskip H, Jaddoe V, Koletzko B, Lioret S, Luque V, Manios Y, Moirano G, Moschonis G, Nader J, Nieuwenhuijsen M, Andersen AMN, McEachen R, de Moira AP, Popovic M, Roumeliotaki T, Salika T, Santa Marina L, Santos S, Serbert S, Tzorovili E, Vafeiadi M, Verduci E, Vrijheid M, Vrijkotte TGM, Welten M, Wright J, Yang TC, Zugna D, Lawlor D. Associations of Maternal Educational Level, Proximity to Green Space During Pregnancy, and Gestational Diabetes With Body Mass Index From Infancy to Early Adulthood: A Proof-of-Concept Federated Analysis in 18 Birth Cohorts. Am J Epidemiol 2024; 193:753-763. [PMID: 37856700 PMCID: PMC11367017 DOI: 10.1093/aje/kwad206] [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: 08/01/2022] [Revised: 04/06/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
International sharing of cohort data for research is important and challenging. We explored the feasibility of multicohort federated analyses by examining associations between 3 pregnancy exposures (maternal education, exposure to green vegetation, and gestational diabetes) and offspring body mass index (BMI) from infancy to age 17 years. We used data from 18 cohorts (n = 206,180 mother-child pairs) from the EU Child Cohort Network and derived BMI at ages 0-1, 2-3, 4-7, 8-13, and 14-17 years. Associations were estimated using linear regression via 1-stage individual participant data meta-analysis using DataSHIELD. Associations between lower maternal education and higher child BMI emerged from age 4 and increased with age (difference in BMI z score comparing low with high education, at age 2-3 years = 0.03 (95% confidence interval (CI): 0.00, 0.05), at 4-7 years = 0.16 (95% CI: 0.14, 0.17), and at 8-13 years = 0.24 (95% CI: 0.22, 0.26)). Gestational diabetes was positively associated with BMI from age 8 years (BMI z score difference = 0.18, 95% CI: 0.12, 0.25) but not at younger ages; however, associations attenuated towards the null when restricted to cohorts that measured gestational diabetes via universal screening. Exposure to green vegetation was weakly associated with higher BMI up to age 1 year but not at older ages. Opportunities of cross-cohort federated analyses are discussed.
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Affiliation(s)
- Tim Cadman
- Correspondence to Dr. Tim Cadman, Section of Epidemiology, Øster Farimagsgade 5, DK-1353 Copenhagen K, Denmark (e-mail: )
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12
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Schmid K, Sehring J, Németh A, Harter PN, Weber KJ, Vengadeswaran A, Storf H, Seidemann C, Karki K, Fischer P, Dohmen H, Selignow C, von Deimling A, Grau S, Schröder U, Plate KH, Stein M, Uhl E, Acker T, Amsel D. DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification. Brain Pathol 2024; 34:e13228. [PMID: 38012085 PMCID: PMC11007060 DOI: 10.1111/bpa.13228] [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: 10/29/2023] [Accepted: 11/10/2023] [Indexed: 11/29/2023] Open
Abstract
The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.
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Affiliation(s)
- Kai Schmid
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Jannik Sehring
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Attila Németh
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Patrick N. Harter
- Neurological Institute (Edinger Institute)University Hospital FrankfurtFrankfurtGermany
- Present address:
Center for Neuropathology and Prion ResearchUniversity Hospital of MunichMunichGermany
| | - Katharina J. Weber
- Neurological Institute (Edinger Institute)University Hospital FrankfurtFrankfurtGermany
- German Cancer Consortium (DKTK)HeidelbergGermany
- German Cancer Research Center (DKFZ)HeidelbergGermany
- Frankfurt Cancer Institute (FCI)FrankfurtGermany
- University Cancer Center (UCT) FrankfurtFrankfurtGermany
| | - Abishaa Vengadeswaran
- Medical Informatics Group (MIG), Goethe University FrankfurtUniversity Hospital FrankfurtFrankfurt am MainGermany
| | - Holger Storf
- Medical Informatics Group (MIG), Goethe University FrankfurtUniversity Hospital FrankfurtFrankfurt am MainGermany
| | | | - Kapil Karki
- DIZ MarburgPhillips University MarburgMarburgGermany
| | - Patrick Fischer
- Institute for Medical InformaticsJustus‐Liebig UniversityGiessenGermany
- Department of Neuropathology, German Cancer Research Center (DKFZ)Universitätsklinikum Heidelberg, and CCU NeuropathologyHeidelbergGermany
| | - Hildegard Dohmen
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Carmen Selignow
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | | | - Stefan Grau
- Department of NeurosurgeryHospital FuldaFuldaGermany
| | - Uwe Schröder
- Department of NeurosurgeryMVZ Frankfurt/OderFrankfurtGermany
| | - Karl H. Plate
- Neurological Institute (Edinger Institute)University Hospital FrankfurtFrankfurtGermany
| | - Marco Stein
- Department of NeurosurgeryUniversity Hospital Giessen und Marburg Location GiessenGiessenGermany
| | - Eberhard Uhl
- Department of NeurosurgeryUniversity Hospital Giessen und Marburg Location GiessenGiessenGermany
| | - Till Acker
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Daniel Amsel
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
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13
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Aguilar-Lacasaña S, Fontes Marques I, de Castro M, Dadvand P, Escribà X, Fossati S, González JR, Nieuwenhuijsen M, Alfano R, Annesi-Maesano I, Brescianini S, Burrows K, Calas L, Elhakeem A, Heude B, Hough A, Isaevska E, W V Jaddoe V, Lawlor DA, Monaghan G, Nawrot T, Plusquin M, Richiardi L, Watmuff A, Yang TC, Vrijheid M, F Felix J, Bustamante M. Green space exposure and blood DNA methylation at birth and in childhood - A multi-cohort study. ENVIRONMENT INTERNATIONAL 2024; 188:108684. [PMID: 38776651 DOI: 10.1016/j.envint.2024.108684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/21/2024] [Accepted: 04/21/2024] [Indexed: 05/25/2024]
Abstract
Green space exposure has been associated with improved mental, physical and general health. However, the underlying biological mechanisms remain largely unknown. The aim of this study was to investigate the association between green space exposure and cord and child blood DNA methylation. Data from eight European birth cohorts with a total of 2,988 newborns and 1,849 children were used. Two indicators of residential green space exposure were assessed: (i) surrounding greenness (satellite-based Normalized Difference Vegetation Index (NDVI) in buffers of 100 m and 300 m) and (ii) proximity to green space (having a green space ≥ 5,000 m2 within a distance of 300 m). For these indicators we assessed two exposure windows: (i) pregnancy, and (ii) the period from pregnancy to child blood DNA methylation assessment, named as cumulative exposure. DNA methylation was measured with the Illumina 450K or EPIC arrays. To identify differentially methylated positions (DMPs) we fitted robust linear regression models between pregnancy green space exposure and cord blood DNA methylation and between cumulative green space exposure and child blood DNA methylation. Two sensitivity analyses were conducted: (i) without adjusting for cellular composition, and (ii) adjusting for air pollution. Cohort results were combined through fixed-effect inverse variance weighted meta-analyses. Differentially methylated regions (DMRs) were identified from meta-analysed results using the Enmix-combp and DMRcate methods. There was no statistical evidence of pregnancy or cumulative exposures associating with any DMP (False Discovery Rate, FDR, p-value < 0.05). However, surrounding greenness exposure was inversely associated with four DMRs (three in cord blood and one in child blood) annotated to ADAMTS2, KCNQ1DN, SLC6A12 and SDK1 genes. Results did not change substantially in the sensitivity analyses. Overall, we found little evidence of the association between green space exposure and blood DNA methylation. Although we identified associations between surrounding greenness exposure with four DMRs, these findings require replication.
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Affiliation(s)
- Sofia Aguilar-Lacasaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain; Universitat de Barcelona, Barcelona, Spain.
| | - Irene Fontes Marques
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Xavier Escribà
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Serena Fossati
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Juan R González
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Rossella Alfano
- Centre for Environmental Sciences, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium
| | - Isabella Annesi-Maesano
- Desbrest Institute of Epidemiology and Public Health (IDESP), Montpellier University and Inserm, Montpellier, Service des Maladies Allergiques et Respiratoires, CHU, Montpellier, France
| | - Sonia Brescianini
- Centre for Behavioural Science and Mental Health, Istituto Superiore di Sanità, Rome, Italy
| | - Kimberley Burrows
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucinda Calas
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France
| | - Ahmed Elhakeem
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France
| | - Amy Hough
- Born in Bradford, Wolfson Centre for Applied Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Elena Isaevska
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Medical Sciences, University of Turin, CPO-Piemonte, Turin, Italy
| | - Vincent W V Jaddoe
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Genevieve Monaghan
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tim Nawrot
- Centre for Environmental Sciences, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium; Department of Public Health, Leuven University (KU Leuven), Leuven, Belgium
| | - Michelle Plusquin
- Centre for Environmental Sciences, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium
| | - Lorenzo Richiardi
- Department of Medical Sciences, University of Turin, CPO-Piemonte, Turin, Italy
| | - Aidan Watmuff
- Born in Bradford, Wolfson Centre for Applied Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Janine F Felix
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
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14
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Baumgartner M, Kreiner K, Lauschensky A, Jammerbund B, Donsa K, Hayn D, Wiesmüller F, Demelius L, Modre-Osprian R, Neururer S, Slamanig G, Prantl S, Brunelli L, Pfeifer B, Pölzl G, Schreier G. Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses. Front Med (Lausanne) 2024; 11:1301660. [PMID: 38660421 PMCID: PMC11039786 DOI: 10.3389/fmed.2024.1301660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes. Materials and methods Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network. Results In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested. Discussion The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.
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Affiliation(s)
- Martin Baumgartner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Karl Kreiner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Aaron Lauschensky
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Bernhard Jammerbund
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Klaus Donsa
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Dieter Hayn
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Fabian Wiesmüller
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Lea Demelius
- Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
- Know-Center GmbH, Graz, Austria
| | | | - Sabrina Neururer
- Department of Clinical Epidemiology, Tyrolean Federal Institute for Integrated Care, Tirol Kliniken GmbH, Innsbruck, Austria
- Division for Digital Health and Telemedicine, UMIT TIROL—Private University for Health Sciences and Technology, Hall in Tyrol, Austria
| | | | | | - Luca Brunelli
- Department of Internal Medicine III, Cardiology and Angiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Bernhard Pfeifer
- Division for Digital Health and Telemedicine, UMIT TIROL—Private University for Health Sciences and Technology, Hall in Tyrol, Austria
- Tyrolean Federal Institute for Integrated Care, Tirol Kliniken GmbH, Innsbruck, Austria
| | - Gerhard Pölzl
- Department of Internal Medicine III, Cardiology and Angiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Günter Schreier
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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15
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Li S, Dragan I, Tran VDT, Fung CH, Kuznetsov D, Hansen MK, Beulens JWJ, Hart LM‘, Slieker RC, Donnelly LA, Gerl MJ, Klose C, Mehl F, Simons K, Elders PJM, Pearson ER, Rutter GA, Ibberson M. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study. Front Endocrinol (Lausanne) 2024; 15:1350796. [PMID: 38510703 PMCID: PMC10951062 DOI: 10.3389/fendo.2024.1350796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/22/2024] [Indexed: 03/22/2024] Open
Abstract
Introduction Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.
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Affiliation(s)
- Shiying Li
- Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Van Du T. Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Chun Ho Fung
- Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Joline W. J. Beulens
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
| | - Leen M. ‘t Hart
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Roderick C. Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands
| | - Louise A. Donnelly
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | | | | | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Petra J. M. Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC–location VUmc, Amsterdam, Netherlands
| | - Ewan R. Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Guy A. Rutter
- Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
- Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom
- Lee Kong Chian School of Medicine, Nan Yang Technological University, Singapore, Singapore
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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16
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Cadman T, Strandberg-Larsen K, Calas L, Christiansen M, Culpin I, Dadvand P, de Castro M, Foraster M, Fossati S, Guxens M, Harris JR, Hillegers M, Jaddoe V, Lee Y, Lepeule J, El Marroun H, Maule M, McEachen R, Moccia C, Nader J, Nieuwenhuijsen M, Nybo Andersen AM, Pearson R, Swertz M, Vafeiadi M, Vrijheid M, Wright J, Lawlor DA, Pedersen M. Urban environment in pregnancy and postpartum depression: An individual participant data meta-analysis of 12 European birth cohorts. ENVIRONMENT INTERNATIONAL 2024; 185:108453. [PMID: 38368715 DOI: 10.1016/j.envint.2024.108453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND Urban environmental exposures associate with adult depression, but it is unclear whether they are associated to postpartum depression (PPD). OBJECTIVES We investigated associations between urban environment exposures during pregnancy and PPD. METHODS We included women with singleton deliveries to liveborn children from 12 European birth cohorts (N with minimum one exposure = 30,772, analysis N range 17,686-30,716 depending on exposure; representing 26-46 % of the 66,825 eligible women). We estimated maternal exposure during pregnancy to ambient air pollution with nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10), road traffic noise (Lden), natural spaces (Normalised Difference Vegetation Index; NDVI, proximity to major green or blue spaces) and built environment (population density, facility richness and walkability). Maternal PPD was assessed 3-18 months after birth using self-completed questionnaires. We used adjusted logistic regression models to estimate cohort-specific associations between each exposure and PPD and combined results via meta-analysis using DataSHIELD. RESULTS Of the 30,772 women included, 3,078 (10 %) reported having PPD. Exposure to PM10 was associated with slightly increased odds of PPD (adjusted odd ratios (OR) of 1.08 [95 % Confidence Intervals (CI): 0.99, 1.17] per inter quartile range increment of PM10) whilst associations for exposure to NO2 and PM2.5 were close to null. Exposure to high levels of road traffic noise (≥65 dB vs. < 65 dB) was associated with an OR of 1.12 [CI: 0.95, 1.32]. Associations between green spaces and PPD were close to null; whilst proximity to major blue spaces was associated with increased risk of PPD (OR 1.12, 95 %CI: 1.00, 1.26). All associations between built environment and PPD were close to null. Multiple exposure models showed similar results. DISCUSSION The study findings suggest that exposure to PM10, road traffic noise and blue spaces in pregnancy may increase PPD risk, however future studies should explore this causally.
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Affiliation(s)
- Tim Cadman
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands; Department of Social Medicine, School of Medicine, University of Crete, Greece.
| | - Katrine Strandberg-Larsen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Lucinda Calas
- Inserm, UMR1153 Center for Research in Epidemiology and Statistics (CRESS), Early Life Research on Later Health Team (EARoH), Paris, France
| | - Malina Christiansen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Iryna Culpin
- MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, United Kingdom
| | - Payam Dadvand
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain
| | - Montserrat de Castro
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain
| | - Maria Foraster
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain
| | - Serena Fossati
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain
| | - Mònica Guxens
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain; Department of Child and Adolescent Psychiatry, University Medical Center, Erasmus MC, Rotterdam, the Netherlands
| | - Jennifer R Harris
- Center for Fertility and Health, Norwegian Institute of Public Health, Olso, Norway
| | - Manon Hillegers
- Department of Child and Adolescent Psychiatry, University Medical Center, Erasmus MC, Rotterdam, the Netherlands
| | - Vincent Jaddoe
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Yunsung Lee
- Center for Fertility and Health, Norwegian Institute of Public Health, Olso, Norway
| | - Johanna Lepeule
- Université Grenoble Alpes INSERM CNRS Institute for Advanced Biosciences Team of Environmental Epidemiology Applied to Development and Respiratory Health, F-38700 La Tronche, France
| | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, University Medical Center, Erasmus MC, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Milena Maule
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy
| | - Rosie McEachen
- Bradford Institute for Health Research, Bradford BD9 6RJ, United Kingdom
| | - Chiara Moccia
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy
| | - Johanna Nader
- Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain
| | - Anne-Marie Nybo Andersen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Rebecca Pearson
- MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, United Kingdom; Manchester Metropolitan University, All Saints Building, All Saints, Manchester, United Kingdom
| | - Morris Swertz
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marina Vafeiadi
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Pompeu Fabra University, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford BD9 6RJ, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, United Kingdom
| | - Marie Pedersen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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Mullie L, Afilalo J, Archambault P, Bouchakri R, Brown K, Buckeridge DL, Cavayas YA, Turgeon AF, Martineau D, Lamontagne F, Lebrasseur M, Lemieux R, Li J, Sauthier M, St-Onge P, Tang A, Witteman W, Chassé M. CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data. J Am Med Inform Assoc 2024; 31:651-665. [PMID: 38128123 PMCID: PMC10873779 DOI: 10.1093/jamia/ocad235] [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: 06/26/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVES Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. MATERIALS AND METHODS We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. RESULTS The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. DISCUSSION AND CONCLUSION The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
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Affiliation(s)
- Louis Mullie
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
| | - Jonathan Afilalo
- Department of Medicine, Jewish General Hospital, Montréal, H3T 1E4, Canada
| | - Patrick Archambault
- Department of Emergency Medicine and Family Medicine, Université Laval, Québec, G1V 0A6, Canada
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Rima Bouchakri
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Kip Brown
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - David L Buckeridge
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University Health Centre, Montréal, H3A 1G1, Canada
| | | | - Alexis F Turgeon
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - Denis Martineau
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - François Lamontagne
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Martine Lebrasseur
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Renald Lemieux
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Jeffrey Li
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Michaël Sauthier
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Pediatrics, Université de Montréal and CHU Sainte-Justine Research Centre, Montréal, H3C 3J7, Canada
| | - Pascal St-Onge
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - An Tang
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Radiology, Centre Hospitalier de l’Université de Montréal, Montréal, H2X 3E4, Canada
| | - William Witteman
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Michaël Chassé
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
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18
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Delfin C, Dragan I, Kuznetsov D, Tajes JF, Smit F, Coral DE, Farzaneh A, Haugg A, Hungele A, Niknejad A, Hall C, Jacobs D, Marek D, Fraser DP, Thuillier D, Ahmadizar F, Mehl F, Pattou F, Burdet F, Hawkes G, Arts ICW, Blanch J, Van Soest J, Fernández-Real JM, Boehl J, Fink K, van Greevenbroek MMJ, Kavousi M, Minten M, Prinz N, Ipsen N, Franks PW, Ramos R, Holl RW, Horban S, Duarte-Salles T, Tran VDT, Raverdy V, Leal Y, Lenart A, Pearson E, Sparsø T, Giordano GN, Ioannidis V, Soh K, Frayling TM, Le Roux CW, Ibberson M. A Federated Database for Obesity Research: An IMI-SOPHIA Study. Life (Basel) 2024; 14:262. [PMID: 38398771 PMCID: PMC10890572 DOI: 10.3390/life14020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/12/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.
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Affiliation(s)
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Juan Fernandez Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Femke Smit
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Daniel E. Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Ali Farzaneh
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - André Haugg
- Global Biostatistics & Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach, Germany
| | - Andreas Hungele
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Christopher Hall
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | - Daan Jacobs
- Nederlandse Obesitas Kliniek, Huis Ter Heide, 3712 BA Utrecht, The Netherlands
| | - Diana Marek
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Diane P. Fraser
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Dorothee Thuillier
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Fariba Ahmadizar
- Data Science and Biostatistics Department, Julius Global Health, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Francois Pattou
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Frederic Burdet
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Gareth Hawkes
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Ilja C. W. Arts
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Jordi Blanch
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- ISV-Girona Research Group, Research Unit in Primary Care, Primary Care Services, Catalan Institute of Health (ICS), 08908 Barcelona, Spain
| | - Johan Van Soest
- Brightlands Institute for Smart Society (BISS), Faculty of Science and Engineering, Maastricht University, 6229 EN Maastricht, The Netherlands
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Center, 6229 EN Maastricht, The Netherlands
| | - José-Manuel Fernández-Real
- Nutrition, Eumetabolism and Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Av. França 30, 17007 Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Av. França, s/n, 17007 Girona, Spain
| | - Juergen Boehl
- Global Biostatistics & Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach, Germany
| | - Katharina Fink
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Marleen M. J. van Greevenbroek
- Department of Internal Medicine and CARIM School of Cardiovascular Diseases, Maastricht University, 6229 EN Maastricht, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Michiel Minten
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Nicole Prinz
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | | | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Rafael Ramos
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
- Research in Vascular Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Parc Hospitalari Martí i Julià, Edifici M2, 17190 Salt, Spain
| | - Reinhard W. Holl
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Scott Horban
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Van Du T. Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Violeta Raverdy
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Yenny Leal
- Nutrition, Eumetabolism and Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Av. França 30, 17007 Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Av. França, s/n, 17007 Girona, Spain
| | | | - Ewan Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | | | - Giuseppe N. Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Vassilios Ioannidis
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Keng Soh
- Novo Nordisk A/S, 2860 Søborg, Denmark
| | - Timothy M. Frayling
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, 1 Rue Michel-Servet, CH-1211 Geneva, Switzerland
| | - Carel W. Le Roux
- Diabetes Complications Research Centre, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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19
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Tomasoni D, Lombardo R, Lauria M. Strengths and limitations of non-disclosive data analysis: a comparison of breast cancer survival classifiers using VisualSHIELD. Front Genet 2024; 15:1270387. [PMID: 38348453 PMCID: PMC10859452 DOI: 10.3389/fgene.2024.1270387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface.
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Affiliation(s)
- Danilo Tomasoni
- Fondazione the Microsoft Research–University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Mario Lauria
- Fondazione the Microsoft Research–University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Povo, Italy
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20
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Ades AE, Welton NJ, Dias S, Phillippo DM, Caldwell DM. Twenty years of network meta-analysis: Continuing controversies and recent developments. Res Synth Methods 2024. [PMID: 38234221 DOI: 10.1002/jrsm.1700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/19/2024]
Abstract
Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.
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Affiliation(s)
- A E Ades
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Nicky J Welton
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Sofia Dias
- Centre for Reviews and Dissemination, University of York, York, UK
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21
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Zöller D, Haverkamp C, Makoudjou A, Sofack G, Kiefer S, Gebele D, Pfaffenlehner M, Boeker M, Binder H, Karki K, Seidemann C, Schmeck B, Greulich T, Renz H, Schild S, Seuchter SA, Tibyampansha D, Buhl R, Rohde G, Trudzinski FC, Bals R, Janciauskiene S, Stolz D, Fähndrich S. Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: an approach based on federated learning. Respir Res 2024; 25:38. [PMID: 38238846 PMCID: PMC10797985 DOI: 10.1186/s12931-023-02607-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 11/14/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is an inflammatory multisystemic disease caused by environmental exposures and/or genetic factors. Inherited alpha-1-antitrypsin deficiency (AATD) is one of the best recognized genetic factors increasing the risk for an early onset COPD with emphysema. The aim of this study was to gain a better understanding of the associations between comorbidities and specific biomarkers in COPD patients with and without AATD to enable future investigations aimed, for example, at identifying risk factors or improving care. METHODS We focused on cardiovascular comorbidities, blood high sensitivity troponin (hs-troponin) and lipid profiles in COPD patients with and without AATD. We used clinical data from six German University Medical Centres of the MIRACUM (Medical Informatics Initiative in Research and Medicine) consortium. The codes for the international classification of diseases (ICD) were used for COPD as a main diagnosis and for comorbidities and blood laboratory data were obtained. Data analyses were based on the DataSHIELD framework. RESULTS Out of 112,852 visits complete information was available for 43,057 COPD patients. According to our findings, 746 patients with AATD (1.73%) showed significantly lower total blood cholesterol levels and less cardiovascular comorbidities than non-AATD COPD patients. Moreover, after adjusting for the confounder factors, such as age, gender, and nicotine abuse, we confirmed that hs-troponin is a suitable predictor of overall mortality in COPD patients. The comorbidities associated with AATD in the current study differ from other studies, which may reflect geographic and population-based differences as well as the heterogeneous characteristics of AATD. CONCLUSION The concept of MIRACUM is suitable for the analysis of a large healthcare database. This study provided evidence that COPD patients with AATD have a lower cardiovascular risk and revealed that hs-troponin is a predictor for hospital mortality in individuals with COPD.
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Affiliation(s)
- Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany.
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany.
| | - Christian Haverkamp
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Ghislain Sofack
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Saskia Kiefer
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Denis Gebele
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Michelle Pfaffenlehner
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Martin Boeker
- Institute of Artificial Intelligence and Informatics in Medicine, Medical Centre Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
- Freiburg Centre for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Kapil Karki
- Data Integration Centre, Medical Faculty, Philipps-University Marburg, Marburg, Germany
| | - Christian Seidemann
- Data Integration Centre, Medical Faculty, Philipps-University Marburg, Marburg, Germany
| | - Bernd Schmeck
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Marburg, Germany
- Department of Medicine, Pulmonary and Critical Care Medicine, University Hospital Giessen and Marburg, Philipps-University Marburg, Marburg, Germany
- German Centres for Lung Research (DZL) and for Infectious Disease Research (DZIF), SYNMIKRO Centre for Synthetic Microbiology, Philipps-University Marburg, Marburg, Germany
| | - Timm Greulich
- Department of Medicine, Pulmonary and Critical Care Medicine, University Hospital Giessen and Marburg, Philipps-University Marburg, Marburg, Germany
- German Centres for Lung Research (DZL) and for Infectious Disease Research (DZIF), SYNMIKRO Centre for Synthetic Microbiology, Philipps-University Marburg, Marburg, Germany
| | - Harald Renz
- Institute of Laboratory Medicine, German Centre for Lung Research (DZL) and the Universities of Giessen and Marburg Lung Centre (UGMLC), Philipps-University Marburg, Marburg, Germany
| | - Stefanie Schild
- Medical Centre for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Susanne A Seuchter
- Medical Centre for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Dativa Tibyampansha
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Centre of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Roland Buhl
- Pulmonary Department, University Medical Centre of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Gernot Rohde
- Department of Respiratory Medicine, Medical Clinic I, Goethe University Frankfurt, University Hospital, Frankfurt/Main, Germany
| | - Franziska C Trudzinski
- Department of Pneumology and Critical Care Medicine, German Centre for Lung Research (DZL), Translational Lung Research Centre Heidelberg (TLRC-H), University of Heidelberg, Thoraxklinik, Heidelberg, Germany
| | - Robert Bals
- Department of Internal Medicine V - Pulmonology, Allergology, Critical Care Medicine, Saarland University Medical Centre, Saarland University Hospital, 66421, Homburg/Saar, Germany
| | - Sabina Janciauskiene
- Department of Pulmonary and Infectious Diseases and BREATH German Centre for Lung Research (DZL), Hannover Medical School, Hannover, Germany
| | - Daiana Stolz
- Department of Pneumology, University Medical Centre Freiburg, Freiburg, Germany
| | - Sebastian Fähndrich
- Department of Pneumology, University Medical Centre Freiburg, Freiburg, Germany
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22
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Welten S, Weber S, Holt A, Beyan O, Decker S. Will it run?-A proof of concept for smoke testing decentralized data analytics experiments. Front Med (Lausanne) 2024; 10:1305415. [PMID: 38259836 PMCID: PMC10801058 DOI: 10.3389/fmed.2023.1305415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/14/2023] [Indexed: 01/24/2024] Open
Abstract
The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.
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Affiliation(s)
- Sascha Welten
- Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Sven Weber
- Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Adrian Holt
- Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Oya Beyan
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Fraunhofer Institute for Applied Information Technology FIT, St. Augustin, Germany
| | - Stefan Decker
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Fraunhofer Institute for Applied Information Technology FIT, St. Augustin, Germany
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23
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Bernier A, Molnár-Gábor F, Knoppers BM, Borry P, Cesar PMDG, Devriendt T, Goisauf M, Murtagh M, Jiménez PN, Recuero M, Rial-Sebbag E, Shabani M, Wilson RC, Zaccagnini D, Maxwell L. Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory. Eur J Hum Genet 2024; 32:69-76. [PMID: 37322132 PMCID: PMC10267538 DOI: 10.1038/s41431-023-01403-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/26/2023] [Accepted: 05/24/2023] [Indexed: 06/17/2023] Open
Abstract
The coming-into-force of the EU General Data Protection Regulation (GDPR) is a watershed moment in the legal recognition of enforceable rights to informational self-determination. The rapid evolution of legal requirements applicable to data use, however, has the potential to outstrip the capabilities of networks of biomedical data users to respond to the shifting norms. It can also delegitimate established institutional bodies that are responsible for assessing and authorising the downstream use of data, including research ethics committees and institutional data custodians. These burdens are especially pronounced for clinical and research networks that are of transnational scale, because the legal compliance burden for outbound international data transfers from the EEA is especially high. Legislatures, courts, and regulators in the EU should therefore implement the following three legal changes. First, the responsibilities of particular actors in a data sharing network should be delimited through the contractual allocation of responsibilities between collaborators. Second, the use of data through secure data processing environments should not trigger the international transfer provisions of the GDPR. Third, the use of federated data analysis methodologies that do not provide analysis nodes or downstream users access to identifiable personal data as part of the outputs of those analyses should not be considered circumstances of joint controllership, nor lead to the users of non-identifiable data to be considered controllers or processors. These small clarifications of, or modifications to, the GDPR would facilitate the exchange of biomedical data amongst clinicians and researchers.
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Affiliation(s)
- Alexander Bernier
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain.
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain.
- Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences, Montréal, QC, Canada.
| | - Fruzsina Molnár-Gábor
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- Heidelberg Academy of Sciences and Humanities, Heidelberg University, Heidelberg, Germany
| | - Bartha M Knoppers
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences, Montréal, QC, Canada
| | - Pascal Borry
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Priscilla M D G Cesar
- Institute on Ethics & Policy for Innovation (IEPI), McMaster University, Hamilton, ON, Canada
- RECODID: Reconciliation of Cohort Data in Infectious Diseases, Heidelberg, Germany
| | - Thijs Devriendt
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Melanie Goisauf
- ELSI Services & Research, BBMRI-ERIC, Graz, Austria
- CINECA: Common Infrastructure for International Cohorts in Europe, Canada, and Africa, Heidelberg, Germany
| | - Madeleine Murtagh
- EUCAN-Connect: Federated, FAIR Platform Enabling Large-Scale Analysis of High-Value Cohort Data Connecting Europe and Canada in Personalized Health, Groningen, the Netherlands
- School of Social and Political Studies, University of Glasgow, Glasgow, Scotland, UK
| | - Pilar Nicolás Jiménez
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- EuCanImage: A European Cancer Image Platform Linked to Biological and Health Data for Next Generation Artificial Intelligence and Precision Medicine in Oncology, Barcelona, Spain
- Social and Legal Sciences Applied to the New Technosciences Research Group, Faculty of Law, University of the Basque Country, Bilbao, Spain
| | - Mikel Recuero
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- EuCanImage: A European Cancer Image Platform Linked to Biological and Health Data for Next Generation Artificial Intelligence and Precision Medicine in Oncology, Barcelona, Spain
- Social and Legal Sciences Applied to the New Technosciences Research Group, Faculty of Law, University of the Basque Country, Bilbao, Spain
| | - Emmanuelle Rial-Sebbag
- CINECA: Common Infrastructure for International Cohorts in Europe, Canada, and Africa, Heidelberg, Germany
- CERPOP, Inserm, Toulouse Paul Sabatier University, Toulouse, France
| | - Mahsa Shabani
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Metamedica, Faculty of Law and Criminology, Ghent University, Ghent, Belgium
| | - Rebecca C Wilson
- EUCAN-Connect: Federated, FAIR Platform Enabling Large-Scale Analysis of High-Value Cohort Data Connecting Europe and Canada in Personalized Health, Groningen, the Netherlands
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Davide Zaccagnini
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Lynkeus S.R.L, Roma, Italy
| | - Lauren Maxwell
- RECODID: Reconciliation of Cohort Data in Infectious Diseases, Heidelberg, Germany
- Heidelberg Institute for Global Health, Heidelberg University, Im Neuenheimer Feld 130/3, 69120, Heidelberg, Germany
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24
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Guerlich K, Avraam D, Cadman T, Calas L, Charles MA, Elhakeem A, Fernández-Barrés S, Guxens M, Heude B, Ibarluzea J, Inskip H, Julvez J, Lawlor DA, Murcia M, Salika T, Sunyer J, Tafflet M, Koletzko B, Grote V, Plancoulaine S. Sleep duration in preschool age and later behavioral and cognitive outcomes: an individual participant data meta-analysis in five European cohorts. Eur Child Adolesc Psychiatry 2024; 33:167-177. [PMID: 36749392 PMCID: PMC10805899 DOI: 10.1007/s00787-023-02149-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/22/2023] [Indexed: 02/08/2023]
Abstract
Short sleep duration has been linked to adverse behavioral and cognitive outcomes in schoolchildren, but few studies examined this relation in preschoolers. We aimed to investigate the association between parent-reported sleep duration at 3.5 years and behavioral and cognitive outcomes at 5 years in European children. We used harmonized data from five cohorts of the European Union Child Cohort Network: ALSPAC, SWS (UK); EDEN, ELFE (France); INMA (Spain). Associations were estimated through DataSHIELD using adjusted generalized linear regression models fitted separately for each cohort and pooled with random-effects meta-analysis. Behavior was measured with the Strengths and Difficulties Questionnaire. Language and non-verbal intelligence were assessed by the Wechsler Preschool and Primary Scale of Intelligence or the McCarthy Scales of Children's Abilities. Behavioral and cognitive analyses included 11,920 and 2981 children, respectively (34.0%/13.4% of the original sample). In meta-analysis, longer mean sleep duration per day at 3.5 years was associated with lower mean internalizing and externalizing behavior percentile scores at 5 years (adjusted mean difference: - 1.27, 95% CI [- 2.22, - 0.32] / - 2.39, 95% CI [- 3.04, - 1.75]). Sleep duration and language or non-verbal intelligence showed trends of inverse associations, however, with imprecise estimates (adjusted mean difference: - 0.28, 95% CI [- 0.83, 0.27] / - 0.42, 95% CI [- 0.99, 0.15]). This individual participant data meta-analysis suggests that longer sleep duration in preschool age may be important for children's later behavior and highlight the need for larger samples for robust analyses of cognitive outcomes. Findings could be influenced by confounding or reverse causality and require replication.
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Affiliation(s)
- Kathrin Guerlich
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital Munich, Lindwurmstr. 4, 80337, Munich, Germany
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tim Cadman
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucinda Calas
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), 75004, Paris, France
| | - Marie-Aline Charles
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), 75004, Paris, France
- Ined, Inserm, Joint unit Elfe, Aubervilliers, France
| | - Ahmed Elhakeem
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Silvia Fernández-Barrés
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Mònica Guxens
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, The Netherlands
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), 75004, Paris, France
| | - Jesús Ibarluzea
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Biodonostia Health Research Institute, Group of Environmental Epidemiology and Child Development, 20014, San Sebastian, Spain
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013, San Sebastian, Spain
- Faculty of Psychology of the University of the Basque Country, 20018, San Sebastian, Spain
| | - Hazel Inskip
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Jordi Julvez
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Clinical and Epidemiological Neuroscience Group (NeuroÈpia), Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus (Tarragona), Catalonia, Spain
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Mario Murcia
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
- Servicio de Análisis de Sistemas de Información Sanitaria, Conselleria de Sanitat, Generalitat Valenciana, Valencia, Spain
| | - Theodosia Salika
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, UK
| | - Jordi Sunyer
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Parc de Salut Mar, Barcelona, Catalonia, Spain
| | - Muriel Tafflet
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), 75004, Paris, France
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital Munich, Lindwurmstr. 4, 80337, Munich, Germany
| | - Veit Grote
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital Munich, Lindwurmstr. 4, 80337, Munich, Germany.
| | - Sabine Plancoulaine
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), 75004, Paris, France.
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25
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Dellacasa C, Ortali M, Rossi E, Abu Attieh H, Osmo T, Puskaric M, Rinaldi E, Prasser F, Stellmach C, Cataudella S, Agarwal B, Mata Naranjo J, Scipione G. An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation. Digit Health 2024; 10:20552076241248922. [PMID: 38766364 PMCID: PMC11100396 DOI: 10.1177/20552076241248922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
Background The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR). Methods The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation. Results The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2. Conclusions Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.
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Affiliation(s)
- Chiara Dellacasa
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Maurizio Ortali
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Elisa Rossi
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Hammam Abu Attieh
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Osmo
- Département Archivage et Services aux Données (DASD), Centre Informatique National de l'Enseignement Supérieur (CINES), Montpellier, France
| | - Miroslav Puskaric
- High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany
| | - Eugenia Rinaldi
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Caroline Stellmach
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Bhaskar Agarwal
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
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26
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Reinikainen J, Kuulasmaa K, Oskarsson V, Amouyel P, Biasch K, Brenner H, De Ponti R, Donfrancesco C, Drygas W, Ferrieres J, Grassi G, Grimsgaard S, Iacoviello L, Jousilahti P, Kårhus LL, Kee F, Linneberg A, Luksiene D, Mariño J, Moitry M, Palmieri L, Peters A, Piwonska A, Quarti-Trevano F, Salomaa V, Sans S, Schmidt CO, Schöttker B, Söderberg S, Tamosiunas A, Thorand B, Tunstall-Pedoe H, Vanuzzo D, Veronesi G, Woodward M, Lekadir K, Niiranen T. Regional and temporal differences in the associations between cardiovascular disease and its classic risk factors: An analysis of 49 cohorts from 11 European countries. Eur J Prev Cardiol 2023:zwad359. [PMID: 37976098 DOI: 10.1093/eurjpc/zwad359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/07/2023] [Accepted: 11/16/2023] [Indexed: 11/19/2023]
Abstract
AIMS The regional and temporal differences in the associations between cardiovascular disease (CVD) and its classic risk factors are unknown. The current study examined these associations in different European regions over a 30-year period. METHODS The study sample comprised 553818 individuals from 49 cohorts in 11 European countries (baseline: 1982-2012) who were followed up for a maximum of 10 years. Risk factors (sex, smoking, diabetes, non-HDL [high-density lipoprotein] cholesterol, systolic blood pressure [BP], and body mass index [BMI]) and CVD events (coronary heart disease or stroke) were harmonized across cohorts. Risk factor-outcome associations were analysed using multivariable-adjusted Cox regression models, and differences in associations were assessed using meta-regression. RESULTS The differences in the risk factor-CVD associations between central Europe, northern Europe, southern Europe, and the United Kingdom were generally small. Men had a slightly higher hazard ratio (HR) in southern Europe (p = 0.043 for overall difference) and those with diabetes had a slightly lower HR in central Europe (p = 0.022 for overall difference) compared with the other regions. Of the six CVD risk factors, minor HR decreases per decade were observed for non-HDL cholesterol (7% per mmol/L; 95% confidence interval [CI], 3-10%) and systolic BP (4% per 20 mmHg; 95% CI, 1-8%), while a minor HR increase per decade was observed for BMI (7% per 10 kg/m2; 95% CI, 1-13%). CONCLUSION The results demonstrate that all classic CVD risk factors are still relevant in Europe, irrespective of regional area. Preventive strategies should focus on risk factors with the greatest population attributable risk.
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Affiliation(s)
- Jaakko Reinikainen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Kari Kuulasmaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Viktor Oskarsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | | | - Katia Biasch
- Department of Epidemiology and Public Health, University of Strasbourg, Strasbourg, France
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Roberto De Ponti
- Research center in Epidemiology and Preventive Medicine (EPIMED), Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Chiara Donfrancesco
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy
| | - Wojciech Drygas
- Department of Epidemiology, Cardiovascular Disease Prevention and Heart Promotion, National Institute of Cardiology, Warsaw, Poland
- Lazarski University, Warsaw, Poland
| | - Jean Ferrieres
- Department of Cardiology, Toulouse University School of Medicine, Rangueil Hospital, INSERM UMR 1027, Toulouse Cedex 9, France
| | - Guido Grassi
- Clinica Medica, University of Milano-Bicocca, Milan, Italy
| | - Sameline Grimsgaard
- Department of Community Medicine, UiT the Arctic University of Norway, Tromsø, Norway
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy
- Research Center in Epidemiology and Preventive Medicine-EPIMED, Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Line L Kårhus
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Frank Kee
- Centre for Public Health, The Queen's University of Belfast, Northern Ireland
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dalia Luksiene
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Joany Mariño
- Unit Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marie Moitry
- Department of Epidemiology and Public Health, University of Strasbourg, Strasbourg, France
| | - Luigi Palmieri
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Cardiovascular Disease Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Aleksandra Piwonska
- Department of Epidemiology, Cardiovascular Disease Prevention and Heart Promotion, National Institute of Cardiology, Warsaw, Poland
| | | | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Susana Sans
- Catalan Department of Health, Barcelona, Spain
| | - Carsten Oliver Schmidt
- Unit Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Stefan Söderberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Abdonas Tamosiunas
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hugh Tunstall-Pedoe
- Cardiovascular Epidemiology Unit, Institute of Cardiovascular Research, University of Dundee, Dundee, Scotland, UK
| | | | - Giovanni Veronesi
- Research center in Epidemiology and Preventive Medicine (EPIMED), Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Mark Woodward
- The George Institute for Global Health, School of Public Health, Imperial College London, London UK
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Karim Lekadir
- Artifcial Intelligence in Medicine Lab (BCN AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
- Department of Internal Medicine, University of Turku and Turku University Hospital, Turku, Finland
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27
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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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Maxwell L, Shreedhar P, Dauga D, McQuilton P, Terry RF, Denisiuk A, Molnar-Gabor F, Saxena A, Sansone SA. FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries. Lancet Digit Health 2023; 5:e712-e736. [PMID: 37775189 PMCID: PMC10552001 DOI: 10.1016/s2589-7500(23)00129-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/27/2023] [Accepted: 07/05/2023] [Indexed: 10/01/2023]
Abstract
Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis.
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Affiliation(s)
- Lauren Maxwell
- Heidelberger Institut für Global Health, Universitätsklinikum Heidelberg, Heidelberg, Germany.
| | - Priya Shreedhar
- Heidelberger Institut für Global Health, Universitätsklinikum Heidelberg, Heidelberg, Germany
| | | | - Peter McQuilton
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Robert F Terry
- TDR, the Special Programme for Research and Training in Tropical Diseases, WHO, Geneva, Switzerland
| | - Alisa Denisiuk
- Faculty of Chemistry, Georg-August-Universität Göttingen, Göttingen, Germany
| | | | | | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
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29
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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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30
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Tahar K, Martin T, Mou Y, Verbuecheln R, Graessner H, Krefting D. Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments. Methods Inf Med 2023; 62:71-89. [PMID: 36596461 PMCID: PMC10462432 DOI: 10.1055/a-2006-1018] [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: 07/15/2022] [Accepted: 11/10/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. OBJECTIVES The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. METHODS We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. RESULTS Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. CONCLUSION We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.
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Affiliation(s)
- Kais Tahar
- Department of Medical Informatics, University Medical Center Göttingen, Georg-August-University, Göttingen, Germany
| | - Tamara Martin
- Centre for Rare Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Yongli Mou
- Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany
| | - Raphael Verbuecheln
- Medical Data Integration Center, University Hospital Tübingen, Tübingen, Germany
| | - Holm Graessner
- Centre for Rare Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Georg-August-University, Göttingen, Germany
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31
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Bender U, Norris CM, Dreyer RP, Krumholz HM, Raparelli V, Pilote L. Impact of Sex- and Gender-Related Factors on Length of Stay Following Non-ST-Segment-Elevation Myocardial Infarction: A Multicountry Analysis. J Am Heart Assoc 2023; 12:e028553. [PMID: 37489737 PMCID: PMC10492965 DOI: 10.1161/jaha.122.028553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/30/2023] [Indexed: 07/26/2023]
Abstract
Background Gender-related factors are psycho-socio-cultural characteristics and are associated with adverse clinical outcomes in acute myocardial infarction, independent of sex. Whether sex- and gender-related factors contribute to the substantial heterogeneity in hospital length of stay (LOS) among patients with non-ST-segment-elevation myocardial infarction remains unknown. Methods and Results This observational cohort study combined and analyzed data from the GENESIS-PRAXY (Gender and Sex Determinants of Cardiovascular Disease: From Bench to Beyond Premature Acute Coronary Syndrome study), EVA (Endocrine Vascular Disease Approach study), and VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI [Acute Myocardial Infarction] Patients study) cohorts of adults hospitalized across Canada, the United States, Switzerland, Italy, Spain, and Australia for non-ST-segment-elevation myocardial infarction. In total, 5219 participants were assessed for eligibility. Sixty-three patients were excluded for missing LOS, and 2938 were excluded because of no non-ST-segment-elevation myocardial infarction diagnosis. In total, 2218 participants were analyzed (66% women; mean±SD age, 48.5±7.9 years; 67.8% in the United States). Individuals with longer LOS (51%) were more likely to be White race, were more likely to have diabetes, hypertension, and a lower income, and were less likely to be employed and have completed secondary education. No univariate association between sex and LOS was observed. In the adjusted multivariable model, age (0.62 d/10 y; P<0.001), unemployment (0.63 days; P=0.01), and some of countries included relative to Canada (Italy, 4.1 days; Spain, 1.7 days; and the United States, -1.0 days; all P<0.001) were independently associated with longer LOS. Medical history mediated the effect of employment on LOS. No interaction between sex and employment was observed. Longer LOS was associated with increased 12-month all-cause mortality. Conclusions Older age, unemployment, and country of hospitalization were independent predictors of LOS, regardless of sex. Individuals employed with non-ST-segment-elevation myocardial infarction were more likely to experience shorter LOS. Sociocultural factors represent a potential target for improvement in health care expenditure and resource allocation.
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Affiliation(s)
- Uri Bender
- Department of Medicine, McGill University and Centre for Outcomes Research and EvaluationResearch Institute, McGill University Health CentreMontrealQuebecCanada
| | - Colleen M. Norris
- Faculties of Nursing, Medicine and School of Public HealthUniversity of AlbertaEdmontonCanada
| | - Rachel P. Dreyer
- Department of Emergency MedicineYale School of MedicineNew HavenCTUSA
- Center for Outcomes Research and EvaluationYale New Haven HospitalNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Section of Cardiovascular Medicine, Department of Internal MedicineYale School of MedicineNew HavenCTUSA
- Department of Health Policy and ManagementYale School of Public HealthNew HavenCTUSA
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal MedicineYale School of MedicineNew HavenCTUSA
- Department of Health Policy and ManagementYale School of Public HealthNew HavenCTUSA
| | - Valeria Raparelli
- Department of Translational MedicineUniversity of FerraraItaly
- University Center for Studies on Gender MedicineUniversity of FerraraItaly
| | - Louise Pilote
- Department of Medicine, McGill University and Centre for Outcomes Research and EvaluationResearch Institute, McGill University Health CentreMontrealQuebecCanada
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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, Sedlmayr M. Ten Topics to Get Started in Medical Informatics Research. J Med Internet Res 2023; 25:e45948. [PMID: 37486754 PMCID: PMC10407648 DOI: 10.2196/45948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.
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Affiliation(s)
- Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
| | - Najia Ahmadi
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kai Fitzer
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
| | - Sophia Grummt
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kilian-Ludwig Heine
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ian-C Jung
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Goettingen, Germany
| | - Andreas Kühn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Tobias Schmidt
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Paul Schmücker
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Christina Schüttler
- Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
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33
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Zuber S, Bechtiger L, Bodelet JS, Golin M, Heumann J, Kim JH, Klee M, Mur J, Noll J, Voll S, O’Keefe P, Steinhoff A, Zölitz U, Muniz-Terrera G, Shanahan L, Shanahan MJ, Hofer SM. An integrative approach for the analysis of risk and health across the life course: challenges, innovations, and opportunities for life course research. DISCOVER SOCIAL SCIENCE AND HEALTH 2023; 3:14. [PMID: 37469576 PMCID: PMC10352429 DOI: 10.1007/s44155-023-00044-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/26/2023] [Indexed: 07/21/2023]
Abstract
Life course epidemiology seeks to understand the intricate relationships between risk factors and health outcomes across different stages of life to inform prevention and intervention strategies to optimize health throughout the lifespan. However, extant evidence has predominantly been based on separate analyses of data from individual birth cohorts or panel studies, which may not be sufficient to unravel the complex interplay of risk and health across different contexts. We highlight the importance of a multi-study perspective that enables researchers to: (a) Compare and contrast findings from different contexts and populations, which can help identify generalizable patterns and context-specific factors; (b) Examine the robustness of associations and the potential for effect modification by factors such as age, sex, and socioeconomic status; and (c) Improve statistical power and precision by pooling data from multiple studies, thereby allowing for the investigation of rare exposures and outcomes. This integrative framework combines the advantages of multi-study data with a life course perspective to guide research in understanding life course risk and resilience on adult health outcomes by: (a) Encouraging the use of harmonized measures across studies to facilitate comparisons and synthesis of findings; (b) Promoting the adoption of advanced analytical techniques that can accommodate the complexities of multi-study, longitudinal data; and (c) Fostering collaboration between researchers, data repositories, and funding agencies to support the integration of longitudinal data from diverse sources. An integrative approach can help inform the development of individualized risk scores and personalized interventions to promote health and well-being at various life stages.
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Affiliation(s)
- Sascha Zuber
- Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC Canada
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland
| | - Laura Bechtiger
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | | | - Marta Golin
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | - Jens Heumann
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | - Jung Hyun Kim
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Matthias Klee
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jure Mur
- University of Edinburgh, Edinburgh, Scotland
| | - Jennie Noll
- Pennsylvania State University, State College, PA USA
| | - Stacey Voll
- Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC Canada
| | - Patrick O’Keefe
- Department of Neurology, Oregon Health & Science University, Portland, OR USA
| | - Annekatrin Steinhoff
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Ulf Zölitz
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | | | - Lilly Shanahan
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
- Department of Psychology, University of Zürich, Zürich, Switzerland
| | - Michael J. Shanahan
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
- Department of Sociology, University of Zürich, Zürich, Switzerland
| | - Scott M. Hofer
- Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC Canada
- Department of Neurology, Oregon Health & Science University, Portland, OR USA
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García-Closas M, Ahearn TU, Gaudet MM, Hurson AN, Balasubramanian JB, Choudhury PP, Gerlanc NM, Patel B, Russ D, Abubakar M, Freedman ND, Wong WSW, Chanock SJ, Berrington de Gonzalez A, Almeida JS. Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research. Am J Epidemiol 2023; 192:995-1005. [PMID: 36804665 PMCID: PMC10505418 DOI: 10.1093/aje/kwad040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 11/28/2022] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
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Affiliation(s)
- Montserrat García-Closas
- Correspondence to Dr. Montserrat García-Closas, Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850 (e-mail: )
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Winkler EC, Jungkunz M, Thorogood A, Lotz V, Schickhardt C. Patient data for commercial companies? An ethical framework for sharing patients' data with for-profit companies for research. JOURNAL OF MEDICAL ETHICS 2023:jme-2022-108781. [PMID: 37230744 DOI: 10.1136/jme-2022-108781] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 04/29/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Research using data from medical care promises to advance medical science and improve healthcare. Academia is not the only sector that expects such research to be of great benefit. The research-based health industry is also interested in so-called 'real-world' health data to develop new drugs, medical technologies or data-based health applications. While access to medical data is handled very differently in different countries, and some empirical data suggest people are uncomfortable with the idea of companies accessing health information, this paper aims to advance the ethical debate about secondary use of medical data generated in the public healthcare sector by for-profit companies for medical research (ReuseForPro). METHODS We first clarify some basic concepts and our ethical-normative approach, then discuss and ethically evaluate potential claims and interests of relevant stakeholders: patients as data subjects in the public healthcare system, for-profit companies, the public, and physicians and their healthcare institutions. Finally, we address the tensions between legitimate claims of different stakeholders in order to suggest conditions that might ensure ethically sound ReuseForPro. RESULTS We conclude that there are good reasons to grant for-profit companies access to medical data if they meet certain conditions: among others they need to respect patients' informational rights and their actions need to be compatible with the public's interest in health benefit from ReuseForPro.
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Affiliation(s)
- Eva C Winkler
- Section for Translational Medical Ethics, Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Martin Jungkunz
- Section for Translational Medical Ethics, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | | | - Vincent Lotz
- Section for Translational Medical Ethics, Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Christoph Schickhardt
- Section for Translational Medical Ethics, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
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36
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Maier D, Vehreschild JJ, Uhl B, Meyer S, Berger-Thürmel K, Boerries M, Braren R, Grünwald V, Hadaschik B, Palm S, Singer S, Stuschke M, Juárez D, Delpy P, Lambarki M, Hummel M, Engels C, Andreas S, Gökbuget N, Ihrig K, Burock S, Keune D, Eggert A, Keilholz U, Schulz H, Büttner D, Löck S, Krause M, Esins M, Ressing F, Schuler M, Brandts C, Brucker DP, Husmann G, Oellerich T, Metzger P, Voigt F, Illert AL, Theobald M, Kindler T, Sudhof U, Reckmann A, Schwinghammer F, Nasseh D, Weichert W, von Bergwelt-Baildon M, Bitzer M, Malek N, Öner Ö, Schulze-Osthoff K, Bartels S, Haier J, Ammann R, Schmidt AF, Guenther B, Janning M, Kasper B, Loges S, Stilgenbauer S, Kuhn P, Tausch E, Runow S, Kerscher A, Neumann M, Breu M, Lablans M, Serve H. Profile of the multicenter cohort of the German Cancer Consortium's Clinical Communication Platform. Eur J Epidemiol 2023; 38:573-586. [PMID: 37017830 PMCID: PMC10073785 DOI: 10.1007/s10654-023-00990-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/09/2023] [Indexed: 04/06/2023]
Abstract
Treatment concepts in oncology are becoming increasingly personalized and diverse. Successively, changes in standards of care mandate continuous monitoring of patient pathways and clinical outcomes based on large, representative real-world data. The German Cancer Consortium's (DKTK) Clinical Communication Platform (CCP) provides such opportunity. Connecting fourteen university hospital-based cancer centers, the CCP relies on a federated IT-infrastructure sourcing data from facility-based cancer registry units and biobanks. Federated analyses resulted in a cohort of 600,915 patients, out of which 232,991 were incident since 2013 and for which a comprehensive documentation is available. Next to demographic data (i.e., age at diagnosis: 2.0% 0-20 years, 8.3% 21-40 years, 30.9% 41-60 years, 50.1% 61-80 years, 8.8% 81+ years; and gender: 45.2% female, 54.7% male, 0.1% other) and diagnoses (five most frequent tumor origins: 22,523 prostate, 18,409 breast, 15,575 lung, 13,964 skin/malignant melanoma, 9005 brain), the cohort dataset contains information about therapeutic interventions and response assessments and is connected to 287,883 liquid and tissue biosamples. Focusing on diagnoses and therapy-sequences, showcase analyses of diagnosis-specific sub-cohorts (pancreas, larynx, kidney, thyroid gland) demonstrate the analytical opportunities offered by the cohort's data. Due to its data granularity and size, the cohort is a potential catalyst for translational cancer research. It provides rapid access to comprehensive patient groups and may improve the understanding of the clinical course of various (even rare) malignancies. Therefore, the cohort may serve as a decisions-making tool for clinical trial design and contributes to the evaluation of scientific findings under real-world conditions.
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Affiliation(s)
- Daniel Maier
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jörg Janne Vehreschild
- University Hospital Frankfurt, Frankfurt, Germany.
- Department of Internal Medicine I, University Hospital of Cologne, Cologne, Germany.
- German Centre for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany.
| | - Barbara Uhl
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sandra Meyer
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Karin Berger-Thürmel
- University Hospital Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Boerries
- Faculty of Medicine, Institute of Medical Bioinformatics and Systems Medicine, Medical Center, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rickmer Braren
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Medicine, Technical University Munich, Munich, Germany
| | - Viktor Grünwald
- West German Cancer Center, University Hospital Essen, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Boris Hadaschik
- West German Cancer Center, University Hospital Essen, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Palm
- West German Cancer Center, University Hospital Essen, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Susanne Singer
- University Medical Center of the Johannes Gutenberg University, Mainz, Germany
- German Cancer Consortium (DKTK), Partner Site Mainz and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Stuschke
- West German Cancer Center, University Hospital Essen, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Juárez
- German Cancer Research Center (DKFZ), Federated Information Systems, Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pierre Delpy
- German Cancer Research Center (DKFZ), Federated Information Systems, Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mohamed Lambarki
- German Cancer Research Center (DKFZ), Federated Information Systems, Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hummel
- Charité Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cäcilia Engels
- Charité Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefanie Andreas
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nicola Gökbuget
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristina Ihrig
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Susen Burock
- Charité Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dietmar Keune
- Charité Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Angelika Eggert
- Charité Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrich Keilholz
- Charité Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hagen Schulz
- University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Büttner
- University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Steffen Löck
- University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mechthild Krause
- University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mirko Esins
- West German Cancer Center, University Hospital Essen, Essen, Germany
| | - Frank Ressing
- West German Cancer Center, University Hospital Essen, Essen, Germany
| | - Martin Schuler
- West German Cancer Center, University Hospital Essen, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Brandts
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel P Brucker
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Gabriele Husmann
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Oellerich
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patrick Metzger
- Faculty of Medicine, Institute of Medical Bioinformatics and Systems Medicine, Medical Center, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik Voigt
- Faculty of Medicine, Institute of Medical Bioinformatics and Systems Medicine, Medical Center, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anna L Illert
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine I, Faculty of Medicine, Medical Center, University of Freiburg, Freiburg, Germany
| | - Matthias Theobald
- University Medical Center of the Johannes Gutenberg University, Mainz, Germany
- German Cancer Consortium (DKTK), Partner Site Mainz and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Kindler
- University Medical Center of the Johannes Gutenberg University, Mainz, Germany
- German Cancer Consortium (DKTK), Partner Site Mainz and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ursula Sudhof
- University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Achim Reckmann
- University Medical Center of the Johannes Gutenberg University, Mainz, Germany
- German Cancer Consortium (DKTK), Partner Site Mainz and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Schwinghammer
- University Hospital Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Nasseh
- University Hospital Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wilko Weichert
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Medicine, Technical University Munich, Munich, Germany
| | - Michael von Bergwelt-Baildon
- University Hospital Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Bitzer
- Center for Personalized Medicine, Eberhard-Karls University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tübingen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nisar Malek
- Center for Personalized Medicine, Eberhard-Karls University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tübingen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Öznur Öner
- Center for Personalized Medicine, Eberhard-Karls University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tübingen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus Schulze-Osthoff
- Center for Personalized Medicine, Eberhard-Karls University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tübingen and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Bartels
- University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jörg Haier
- Comprehensive Cancer Center Hannover (Claudia von Schilling-Zentrum), Hannover Medical School, Hannover, Germany
| | - Raimund Ammann
- Comprehensive Cancer Center Hannover (Claudia von Schilling-Zentrum), Hannover Medical School, Hannover, Germany
| | - Anja Franziska Schmidt
- Comprehensive Cancer Center Hannover (Claudia von Schilling-Zentrum), Hannover Medical School, Hannover, Germany
| | - Bernd Guenther
- Comprehensive Cancer Center Hannover (Claudia von Schilling-Zentrum), Hannover Medical School, Hannover, Germany
| | - Melanie Janning
- DKFZ-Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
- Mannheim University Medical Center, University of Heidelberg, Mannheim, Germany
- Department of Personalized Medical Oncology (A420), DKFZ German Cancer Research Center, Heidelberg, Germany
| | - Bernd Kasper
- Mannheim University Medical Center, University of Heidelberg, Mannheim, Germany
| | - Sonja Loges
- DKFZ-Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
- Mannheim University Medical Center, University of Heidelberg, Mannheim, Germany
- Department of Personalized Medical Oncology (A420), DKFZ German Cancer Research Center, Heidelberg, Germany
| | | | - Peter Kuhn
- Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
| | | | | | | | | | - Martin Breu
- University Hospital of Würzburg, Würzburg, Germany
| | - Martin Lablans
- German Cancer Research Center (DKFZ), Federated Information Systems, Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hubert Serve
- University Hospital Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute, Frankfurt, Germany
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Fortier I, Wey TW, Bergeron J, Pinot de Moira A, Nybo-Andersen AM, Bishop T, Murtagh MJ, Miočević M, Swertz MA, van Enckevort E, Marcon Y, Mayrhofer MT, Ornelas JP, Sebert S, Santos AC, Rocha A, Wilson RC, Griffith LE, Burton P. Life course of retrospective harmonization initiatives: key elements to consider. J Dev Orig Health Dis 2023; 14:190-198. [PMID: 35957574 DOI: 10.1017/s2040174422000460] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.
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Affiliation(s)
- Isabel Fortier
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Tina W Wey
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Julie Bergeron
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | | | | | - Tom Bishop
- Epidemiology Unit, University of Cambridge, England, UK
| | - Madeleine J Murtagh
- School of Social and Political Sciences, University of Glasgow, Scotland, UK
| | - Milica Miočević
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Morris A Swertz
- University Medical Center Groningen, University of Groningen, Netherlands
| | - Esther van Enckevort
- Department of Genetics, University Medical Center Groningen, University of Groningen, Netherlands
| | | | | | - Jos Pedro Ornelas
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | | | - Ana Cristina Santos
- Department of Epidemiology, Institute of Public Health of the University of Porto, Portugal
| | - Artur Rocha
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Rebecca C Wilson
- Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, England, UK
| | - Lauren E Griffith
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, England, UK
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Gedeborg R, Igl W, Svennblad B, Wilén P, Delcoigne B, Michaëlsson K, Ljung R, Feltelius N. Federated analyses of multiple data sources in drug safety studies. Pharmacoepidemiol Drug Saf 2023; 32:279-286. [PMID: 36527437 DOI: 10.1002/pds.5587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/30/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE Studies of rare side effects of new drugs with limited exposure may require pooling of multiple data sources. Federated Analyses (FA) allow real-time, interactive, centralized statistical processing of individual-level data from different data sets without transfer of sensitive personal data. METHODS We review IT-architecture, legal considerations, and statistical methods in FA, based on a Swedish Medical Products Agency methodological development project. RESULTS In a review of all post-authorisation safety studies assessed by the EMA during 2019, 74% (20/27 studies) reported issues with lack of precision in spite of mean study periods of 9.3 years. FA could potentially improve precision in such studies. Depending on the statistical model, the federated approach can generate identical results to a standard analysis. FA may be particularly attractive for repeated collaborative projects where data is regularly updated. There are also important limitations. Detailed agreements between involved parties are strongly recommended to anticipate potential issues and conflicts, document a shared understanding of the project, and fully comply with legal obligations regarding ethics and data protection. FA do not remove the data harmonisation step, which remains essential and often cumbersome. Reliable support for technical integration with the local server architecture and security solutions is required. Common statistical methods are available, but adaptations may be required. CONCLUSIONS Federated Analyses require competent and active involvement of all collaborating parties but have the potential to facilitate collaboration across institutional and national borders and improve the precision of postmarketing drug safety studies.
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Affiliation(s)
- Rolf Gedeborg
- Department of Efficacy and Safety 1, Division of Licensing, Medical Products Agency, Uppsala, Sweden
| | - Wilmar Igl
- Statistics Group, Department of Efficacy and Safety 2, Division of Licensing, Medical Products Agency, Uppsala, Sweden
| | - Bodil Svennblad
- Department of Surgical Sciences, Unit of Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Peter Wilén
- Department of Legal Affairs, Medical Products Agency, Uppsala, Sweden
| | - Bénédicte Delcoigne
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Sweden
| | - Karl Michaëlsson
- Department of Surgical Sciences, Unit of Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Rickard Ljung
- Division of Use and Information, Medical Products Agency, Uppsala, Sweden
| | - Nils Feltelius
- Division of Use and Information, Medical Products Agency, Uppsala, Sweden
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Vinther JL, Cadman T, Avraam D, Ekstrøm CT, I. A. Sørensen T, Elhakeem A, Santos AC, Pinot de Moira A, Heude B, Iñiguez C, Pizzi C, Simons E, Voerman E, Corpeleijn E, Zariouh F, Santorelli G, Inskip HM, Barros H, Carson J, Harris JR, Nader JL, Ronkainen J, Strandberg-Larsen K, Santa-Marina L, Calas L, Cederkvist L, Popovic M, Charles MA, Welten M, Vrijheid M, Azad M, Subbarao P, Burton P, Mandhane PJ, Huang RC, Wilson RC, Haakma S, Fernández-Barrés S, Turvey S, Santos S, Tough SC, Sebert S, Moraes TJ, Salika T, Jaddoe VWV, Lawlor DA, Nybo Andersen AM. Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies. PLoS Med 2023; 20:e1004036. [PMID: 36701266 PMCID: PMC9879424 DOI: 10.1371/journal.pmed.1004036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 12/19/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence. METHODS AND FINDINGS We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries. CONCLUSIONS This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term.
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Affiliation(s)
- Johan L. Vinther
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Tim Cadman
- Population Health Science, Bristol Medical School, Bristol, United Kingdom
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Claus T. Ekstrøm
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I. A. Sørensen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ahmed Elhakeem
- Population Health Science, Bristol Medical School, Bristol, United Kingdom
| | - Ana C. Santos
- EPIUnit–Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal
| | - Angela Pinot de Moira
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Carmen Iñiguez
- Department of Statistics and Operational Research, Universitat de València, València, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- FISABIO—Universitat Jaume I—Universitat de València Epidemiology and Environmental Health Joint Research Unit, València, Spain
| | - Costanza Pizzi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Elinor Simons
- Section of Allergy and Immunology, Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, Canada
- The Children’s Hospital Research Institute of Manitoba (CHRIM), Winnipeg, Canada
| | - Ellis Voerman
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC–Sophia Children’s Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Eva Corpeleijn
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Faryal Zariouh
- Ined, Inserm, EFS, joint unit Elfe, Aubervilliers Cedex, France
| | - Gilian Santorelli
- Born In Bradford, Bradford Institute for Health Research, Bradford, United Kingdom
| | - Hazel M. Inskip
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, United Kingdom
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Henrique Barros
- EPIUnit–Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal
| | - Jennie Carson
- Telethon Kids Institute, Perth, Australia
- University of Western Australia, School of Population and Global Health, Perth, Australia
| | - Jennifer R. Harris
- Center for Fertillity and Health, The Norwegian Institute of Public Health, Oslo, Norway
| | - Johanna L. Nader
- Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
| | - Justiina Ronkainen
- Center for Life-course Health research, University of Oulu, Oulu, Finland
| | | | - Loreto Santa-Marina
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Biodonostia Health Research Institute, San Sebastian, Spain
- Health Department of Basque Government, Subdirectorate of Public Health of Gipuzkoa, San Sebastian, Spain
| | - Lucinda Calas
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Luise Cederkvist
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Maja Popovic
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | | | - Marieke Welten
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC–Sophia Children’s Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Martine Vrijheid
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Meghan Azad
- Section of Allergy and Immunology, Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, Canada
- Developmental Origins of Chronic Diseases in Children Network (DEVOTION), Children’s Hospital, Winnipeg, Canada
- Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, Canada
| | - Padmaja Subbarao
- Translational Medicine Program, Department of Pediatrics, The Hospital for Sick Children, Toronto, Canada
- Department of Medicine, McMaster University, Hamilton, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | | | - Rae-Chi Huang
- Telethon Kids Institute, Perth, Australia
- Edith Cowan University, School of Medicine and Health Sciences, Joondalup, Australia
| | - Rebecca C. Wilson
- Institute of Population Health, University of Liverpool, Liverpool, United Kingdom
| | - Sido Haakma
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Sílvia Fernández-Barrés
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Stuart Turvey
- Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, Canada
| | - Susana Santos
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC–Sophia Children’s Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Suzanne C. Tough
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Sylvain Sebert
- Center for Life-course Health research, University of Oulu, Oulu, Finland
| | - Theo J. Moraes
- Translational Medicine Program, Department of Pediatrics, The Hospital for Sick Children, Toronto, Canada
| | - Theodosia Salika
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC–Sophia Children’s Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Deborah A. Lawlor
- Population Health Science, Bristol Medical School, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
| | - Anne-Marie Nybo Andersen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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40
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Fleischer T, Ulke C, Ladwig KH, Linkohr B, Johar H, Atasoy S, Speerforck S, Kruse J, Zöller D, Binder H, Otten D, Brähler E, Beutel ME, Tibubos AN, Grabe HJ, Schomerus G. [Sex- and Regionalspecific Differences in Child Abuse and Violence Before the German Reunification. Results from GESA, a Multi-Cohort Study]. Psychother Psychosom Med Psychol 2022; 72:550-557. [PMID: 36195099 DOI: 10.1055/a-1926-7428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Results from a population-based study suggest sex-specific patterns of self-reported child maltreatment, more frequently reported in former West than East Germany. Aim of the current study was to examine these patterns in two regional samples of the former East- (SHIP, 2008 - 2012) and West German (KORA, 2013 - 2014) population. Child maltreatment was assessed using the Childhood Trauma Screener (CTS). Overall, child maltreatment was less often reported in the East German sample, compared to the West German sample. The most prominent differences were identified in self-rated emotional violence (east 6.1%, west 8.7%), physical violence (east 5.7%, west 10.3%) and physical neglect (east 10.0%, west 19.2%). However, we could not find differences in sex-specific patterns between the East and West German samples. Results were discussed within a historical context, since the events took place before the German reunification in two oppose political systems.
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Affiliation(s)
- Toni Fleischer
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Christine Ulke
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Karl-Heinz Ladwig
- Institut für Epidemiologie, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany.,Psychosomatische Medizin und Psychotherapie, Klinikum rechts der Isar der Technischen Universitat Munchen, Munchen, Germany
| | - Birgit Linkohr
- Institut für Epidemiologie, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany
| | - Hamimatunnisa Johar
- Institut für Epidemiologie, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany.,Psychosomatische Medizin und Psychotherapie, Klinikum rechts der Isar der Technischen Universitat Munchen, Munchen, Germany.,Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia
| | - Seryan Atasoy
- Institut für Epidemiologie, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany.,Psychosomatische Medizin und Psychotherapie, Klinikum rechts der Isar der Technischen Universitat Munchen, Munchen, Germany.,Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Gießen Marburg, Gießen, Germany
| | - Sven Speerforck
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Johannes Kruse
- Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Gießen Marburg, Gießen, Germany
| | - Daniela Zöller
- Institut für Medizinische Biometrie und Statistik, Albert-Ludwigs-Universitat Freiburg Medizinische Fakultat, Freiburg, Germany.,Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Harald Binder
- Institut für Medizinische Biometrie und Statistik, Albert-Ludwigs-Universitat Freiburg Medizinische Fakultat, Freiburg, Germany.,Freiburger Zentrum für Datenanalyse und Modellbildung, Albert-Ludwigs-Universitat Freiburg, Freiburg im Breisgau, Germany
| | - Danielle Otten
- Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Elmar Brähler
- Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Manfred E Beutel
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Ana N Tibubos
- Pflegewissenschaft, Diagnostik in der Gesundheitsversorgung und E-Health, Universität Trier, Trier, Germany.,Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Hans Jörgen Grabe
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Georg Schomerus
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsklinikum Leipzig, Leipzig, Germany
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41
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Escribà-Montagut X, Marcon Y, Avraam D, Banerjee S, Bishop TRP, Burton P, González JR. Software Application Profile: ShinyDataSHIELD—an R Shiny application to perform federated non-disclosive data analysis in multicohort studies. Int J Epidemiol 2022; 52:315-320. [PMCID: PMC9908040 DOI: 10.1093/ije/dyac201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 10/10/2022] [Indexed: 07/13/2024] Open
Abstract
Motivation DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals’ information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD. Implementation ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure. General features The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration). Availability ShinyDataSHIELD is publicly hosted online [https://datashield-demo.obiba.org/ ], the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under ‘Commons Clause’ License Condition v1.0. Docker images are also available [https://hub.docker.com/r/brgelab/shiny-data-shield ].
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Affiliation(s)
- Xavier Escribà-Montagut
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | | | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Soumya Banerjee
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tom R P Bishop
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Juan R González
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública, Barcelona, Spain
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42
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Jannasch F, Dietrich S, Bishop TRP, Pearce M, Fanidi A, O'Donoghue G, O'Gorman D, Marques-Vidal P, Vollenweider P, Bes-Rastrollo M, Byberg L, Wolk A, Hashemian M, Malekzadeh R, Poustchi H, Luft VC, de Matos SMA, Kim J, Kim MK, Kim Y, Stern D, Lajous M, Magliano DJ, Shaw JE, Akbaraly T, Kivimaki M, Maskarinec G, Le Marchand L, Martínez-González MÁ, Soedamah-Muthu SS, Wareham NJ, Forouhi NG, Schulze MB. Associations between exploratory dietary patterns and incident type 2 diabetes: a federated meta-analysis of individual participant data from 25 cohort studies. Eur J Nutr 2022; 61:3649-3667. [PMID: 35641800 PMCID: PMC9464116 DOI: 10.1007/s00394-022-02909-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 05/09/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE In several studies, exploratory dietary patterns (DP), derived by principal component analysis, were inversely or positively associated with incident type 2 diabetes (T2D). However, findings remained study-specific, inconsistent and rarely replicated. This study aimed to investigate the associations between DPs and T2D in multiple cohorts across the world. METHODS This federated meta-analysis of individual participant data was based on 25 prospective cohort studies from 5 continents including a total of 390,664 participants with a follow-up for T2D (3.8-25.0 years). After data harmonization across cohorts we evaluated 15 previously identified T2D-related DPs for association with incident T2D estimating pooled incidence rate ratios (IRR) and confidence intervals (CI) by Piecewise Poisson regression and random-effects meta-analysis. RESULTS 29,386 participants developed T2D during follow-up. Five DPs, characterized by higher intake of red meat, processed meat, French fries and refined grains, were associated with higher incidence of T2D. The strongest association was observed for a DP comprising these food groups besides others (IRRpooled per 1 SD = 1.104, 95% CI 1.059-1.151). Although heterogeneity was present (I2 = 85%), IRR exceeded 1 in 18 of the 20 meta-analyzed studies. Original DPs associated with lower T2D risk were not confirmed. Instead, a healthy DP (HDP1) was associated with higher T2D risk (IRRpooled per 1 SD = 1.057, 95% CI 1.027-1.088). CONCLUSION Our findings from various cohorts revealed positive associations for several DPs, characterized by higher intake of red meat, processed meat, French fries and refined grains, adding to the evidence-base that links DPs to higher T2D risk. However, no inverse DP-T2D associations were confirmed.
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Affiliation(s)
- Franziska Jannasch
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. .,NutriAct Competence Cluster Nutrition Research Potsdam-Berlin, Nuthetal, Germany. .,German Center for Diabetes Research, Munich-Neuherberg, Germany.
| | - Stefan Dietrich
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,Department of Food Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Tom R P Bishop
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Matthew Pearce
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Anouar Fanidi
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Gráinne O'Donoghue
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Donal O'Gorman
- School of Health and Human Performance, Dublin City University, Dublin, Ireland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Office BH10-642, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Office BH10-642, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Maira Bes-Rastrollo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,CIBERobn, Instituto de Salud Carlos III, Madrid, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Liisa Byberg
- Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Alicja Wolk
- Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden.,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Maryam Hashemian
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Biology Department, School of Arts and Sciences, Utica College, Utica, NY, USA
| | - Reza Malekzadeh
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Poustchi
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Vivian C Luft
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | | | - Jihye Kim
- Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, South Korea
| | - Mi Kyung Kim
- Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, South Korea
| | - Yeonjung Kim
- Division of Health and Nutrition Survey and Analysis, Korea Disease Control Prevention Agency, Seoul, South Korea
| | - Dalia Stern
- CONACyT-Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
| | - Martin Lajous
- CONACyT-Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Tasnime Akbaraly
- Inserm U 1018, Université Paris-Saclay, UVSQ, Villejuif, Maison des Sciences de l'Homme - SUD, Montpellier, France.,Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | | | - Miguel Ángel Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,CIBERobn, Instituto de Salud Carlos III, Madrid, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.,Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA
| | - Sabita S Soedamah-Muthu
- Center of Research On Psychological and Somatic Disorders (CORPS), Department of Medical and Clinical Psychology, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.,Institute for Food, Nutrition and Health, University of Reading, Reading, RG6 6AR, UK
| | | | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,NutriAct Competence Cluster Nutrition Research Potsdam-Berlin, Nuthetal, Germany.,German Center for Diabetes Research, Munich-Neuherberg, Germany
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43
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Cao H, Zhang Y, Baumbach J, Burton PR, Dwyer D, Koutsouleris N, Matschinske J, Marcon Y, Rajan S, Rieg T, Ryser-Welch P, Späth J, Herrmann C, Schwarz E. dsMTL - a computational framework for privacy-preserving, distributed multi-task machine learning. Bioinformatics 2022; 38:4919-4926. [PMID: 36073911 DOI: 10.1093/bioinformatics/btac616] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multi-cohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources. RESULTS Here, we describe the development of "dsMTL", a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of these methods and the relevant machine learning workflows to ensure the validity of the software implementation. Second, we implement dsMTL as a library for the R programming language, building on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. Third, we demonstrate the applicability of dsMTL for comorbidity modeling in distributed data. We show that comorbidity modeling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation of multiple models built on the distributed datasets individually. The application of dsMTL was computationally efficient and highly scalable when applied to moderate-size (n < 500), real expression data given the actual network latency. AVAILABILITY dsMTL is freely available at https://github.com/transbioZI/dsMTLBase (server-side package) and https://github.com/transbioZI/dsMTLClient (client-side package). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Youcheng Zhang
- Health Data Science Unit, Medical Faculty Heidelberg & BioQuant, Heidelberg, 69120, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Paul R Burton
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany
| | - Julian Matschinske
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Sivanesan Rajan
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Thilo Rieg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Patricia Ryser-Welch
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Julian Späth
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Carl Herrmann
- Health Data Science Unit, Medical Faculty Heidelberg & BioQuant, Heidelberg, 69120, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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O'Connor M, Spry E, Patton G, Moreno-Betancur M, Arnup S, Downes M, Goldfeld S, Burgner D, Olsson CA. Better together: Advancing life course research through multi-cohort analytic approaches. ADVANCES IN LIFE COURSE RESEARCH 2022; 53:100499. [PMID: 36652217 DOI: 10.1016/j.alcr.2022.100499] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 06/22/2022] [Accepted: 07/15/2022] [Indexed: 06/17/2023]
Abstract
Longitudinal cohorts can provide timely and cost-efficient evidence about the best points of health service and preventive interventions over the life course. Working systematically across cohorts has the potential to further exploit these valuable data assets, such as by improving the precision of estimates, enhancing (or appropriately reducing) confidence in the replicability of findings, and investigating interrelated questions within a broader theoretical model. In this conceptual review, we explore the opportunities and challenges presented by multi-cohort approaches in life course research. Specifically, we: 1) describe key motivations for multi-cohort work and the analytic approaches that are commonly used in each case; 2) flag some of the scientific and pragmatic challenges that arise when adopting these approaches; and 3) outline emerging directions for multi-cohort work in life course research. Harnessing their potential while thoughtfully considering limitations of multi-cohort approaches can contribute to the robust and granular evidence base needed to promote health and wellbeing over the life span.
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Affiliation(s)
- Meredith O'Connor
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia.
| | - Elizabeth Spry
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
| | - George Patton
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia
| | - Margarita Moreno-Betancur
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia
| | - Sarah Arnup
- Murdoch Children's Research Institute, Parkville, Australia
| | - Marnie Downes
- Murdoch Children's Research Institute, Parkville, Australia
| | - Sharon Goldfeld
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Royal Children's Hospital, Centre for Community Child Health, Parkville, Australia
| | - David Burgner
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Royal Children's Hospital, Department of General Medicine, Parkville, Australia; Monash University, Department of Pediatrics, Clayton, Australia
| | - Craig A Olsson
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
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Field M, I Thwaites D, Carolan M, Delaney GP, Lehmann J, Sykes J, Vinod S, Holloway L. Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer. J Biomed Inform 2022; 134:104181. [PMID: 36055639 DOI: 10.1016/j.jbi.2022.104181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/29/2022] [Accepted: 08/20/2022] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia
| | - Martin Carolan
- Illawarra Cancer Care Centre, Wollongong, NSW, Australia
| | - Geoff P Delaney
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Joerg Lehmann
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia; Department of Radiation Oncology, Calvary Mater Newcastle, NSW, Australia
| | - Jonathan Sykes
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia; Blacktown Haematology and Oncology Cancer Care Centre, Blacktown Hospital, Blacktown, NSW, Australia; Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, NSW, Australia
| | - Shalini Vinod
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia
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Eva G, Liese G, Stephanie B, Petr H, Leslie M, Roel V, Martine V, Sergi B, Mette H, Sarah J, Laura RM, Arnout S, Morris A S, Jan T, Xenia T, Nina V, Koert VE, Sylvie R, Greet S. Position paper on management of personal data in environment and health research in Europe. ENVIRONMENT INTERNATIONAL 2022; 165:107334. [PMID: 35696847 DOI: 10.1016/j.envint.2022.107334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
Management of datasets that include health information and other sensitive personal information of European study participants has to be compliant with the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679). Within scientific research, the widely subscribed'FAIR' data principles should apply, meaning that research data should be findable, accessible, interoperable and re-usable. Balancing the aim of open science driven FAIR data management with GDPR compliant personal data protection safeguards is now a common challenge for many research projects dealing with (sensitive) personal data. In December 2020 a workshop was held with representatives of several large EU research consortia and of the European Commission to reflect on how to apply the FAIR data principles for environment and health research (E&H). Several recent data intensive EU funded E&H research projects face this challenge and work intensively towards developing solutions to access, exchange, store, handle, share, process and use such sensitive personal data, with the aim to support European and transnational collaborations. As a result, several recommendations, opportunities and current limitations were formulated. New technical developments such as federated data management and analysis systems, machine learning together with advanced search software, harmonized ontologies and data quality standards should in principle facilitate the FAIRification of data. To address ethical, legal, political and financial obstacles to the wider re-use of data for research purposes, both specific expertise and underpinning infrastructure are needed. There is a need for the E&H research data to find their place in the European Open Science Cloud. Communities using health and population data, environmental data and other publicly available data have to interconnect and synergize. To maximize the use and re-use of environment and health data, a dedicated supporting European infrastructure effort, such as the EIRENE research infrastructure within the ESFRI roadmap 2021, is needed that would interact with existing infrastructures.
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Affiliation(s)
- Govarts Eva
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium.
| | - Gilles Liese
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Bopp Stephanie
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Matalonga Leslie
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Vermeulen Roel
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Vrijheid Martine
- ISGlobal, Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Beltran Sergi
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Spain
| | - Hartlev Mette
- Faculty of Law, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Standaert Arnout
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Swertz Morris A
- Department of Genetics & Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Theunis Jan
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Trier Xenia
- European Environment Agency (EEA), Copenhagen, Denmark
| | - Vogel Nina
- German Environment Agency (UBA), Berlin, Germany
| | | | - Remy Sylvie
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Schoeters Greet
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
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Banerjee S, Bishop TRP. dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system. BMC Res Notes 2022; 15:230. [PMID: 35761417 PMCID: PMC9235208 DOI: 10.1186/s13104-022-06111-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. RESULTS We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures.
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Affiliation(s)
- Soumya Banerjee
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Tom R P Bishop
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
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Banerjee S, Sofack GN, Papakonstantinou T, Avraam D, Burton P, Zöller D, Bishop TRP. dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD. BMC Res Notes 2022; 15:197. [PMID: 35659747 PMCID: PMC9166323 DOI: 10.1186/s13104-022-06085-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/24/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but an analytic workflow involving local analysis undertaken at individual studies hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. RESULTS We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.
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Affiliation(s)
- Soumya Banerjee
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
| | - Ghislain N Sofack
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Breisgau, Germany
- Freiburg Center for Data Analysis and Modelling, University of Freiburg, Breisgau, Germany
| | - Thodoris Papakonstantinou
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Breisgau, Germany
- Freiburg Center for Data Analysis and Modelling, University of Freiburg, Breisgau, Germany
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Daniela Zöller
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Breisgau, Germany
- Freiburg Center for Data Analysis and Modelling, University of Freiburg, Breisgau, Germany
| | - Tom R P Bishop
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
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Does social support prevent suicidal ideation in women and men? Gender-sensitive analyses of an important protective factor within prospective community cohorts. J Affect Disord 2022; 306:157-166. [PMID: 35304236 DOI: 10.1016/j.jad.2022.03.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/18/2022] [Accepted: 03/10/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Suicidal ideation and behavior constitute important public mental health issues. In this study, we examined whether social integration prevents suicidal ideation over time and whether gender modifies this association. METHODS Data from the Gutenberg Health Study (population-based representative community sample in midwest Germany) and the Study of Health in Pomerania (population-based cohort study in northeast Germany) were used. Participants reporting low social support were compared to those receiving middle or high social support. Within a longitudinal study design, we calculated multiple logistic regression models including interaction terms and relevant covariates to test whether gender modified the association of social support and suicidal ideation. RESULTS Suicidal ideation was present in 7.4% (N = 982) of the pooled cohorts' 13,290 participants. More women (8.6%, N = 565) than men (6.2%, N = 417) reported suicidal ideation. Middle or high social support was associated with a lower probability to report suicidal ideation five years later after controlling for sociodemographic factors, living situation, and cohort (OR = 0.42, 95%-CI = 0.34-0.52). Male gender was negatively related to suicidal ideation, but no statistically significant interaction of gender and social support was found (ratio of ORs = 1.00, 95%-CI = 0.73-1.35). LIMITATIONS The number of people reporting suicidal ideation in the SHIP study was small, especially for men. Suicidal ideation was measured using a single item. CONCLUSIONS Social support is an important protective factor in preventing suicidal ideation for both women and men. Future research should further clarify gender-specific effects of family variables in suicidal ideation and test similar predictive models of suicidal behavior.
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Privacy-preserving federated neural network learning for disease-associated cell classification. PATTERNS 2022; 3:100487. [PMID: 35607628 PMCID: PMC9122966 DOI: 10.1016/j.patter.2022.100487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/14/2022] [Accepted: 03/14/2022] [Indexed: 11/21/2022]
Abstract
Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, PriCell, for complex models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions’ input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a published state-of-the-art convolutional neural network architecture in a decentralized and privacy-preserving manner. Our solution achieves an accuracy comparable with the one obtained with the centralized non-secure solution. PriCell guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data. We enable collaborative and privacy-preserving model training between institutions Training under encryption does not degrade the utility of the data We apply our solution to the single-cell analysis in a federated setting Our method is generalizable to other machine learning tasks in the healthcare domain
High-quality medical machine learning models will benefit greatly from collaboration between health care institutions. Yet, it is usually difficult to transfer data between these institutions due to strict privacy regulations. In this study, we propose a solution, PriCell, that relies on multiparty homomorphic encryption to enable privacy-preserving collaborative machine learning while protecting via encryption the institutions' input data, the model, and any value exchanged between the institutions. We show the maturity of our solution by training a published state-of-the-art convolutional neural network in a decentralized and privacy-preserving manner. We compare the accuracy achieved by PriCell with the centralized and non-secure solutions and show that PriCell guarantees privacy without reducing the utility of the data. The benefits of PriCell constitute an important landmark for real-world applications of collaborative training while preserving privacy.
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