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Sharma V, O'Sullivan M, Cassetti O, Winning L, O'Sullivan A, Crowe M. Evaluating the harmonization potential of oral health-related questionnaires in national longitudinal birth and child cohort surveys. J Public Health Dent 2024. [PMID: 38953657 DOI: 10.1111/jphd.12632] [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: 09/07/2023] [Revised: 04/16/2024] [Accepted: 06/14/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND/OBJECTIVES Effective use of longitudinal study data is challenging because of divergences in the construct definitions and measurement approaches over time, between studies and across disciplines. One approach to overcome these challenges is data harmonization. Data harmonization is a practice used to improve variable comparability and reduce heterogeneity across studies. This study describes the process used to evaluate the harmonization potential of oral health-related variables across each survey wave. METHODS National child cohort surveys with similar themes/objectives conducted in the last two decades were selected. The Maelstrom Research Guidelines were followed for harmonization potential evaluation. RESULTS Seven nationally representative child cohort surveys were included and questionnaires examined from 50 survey waves. Questionnaires were classified into three domains and fifteen constructs and summarized by age groups. A DataSchema (a list of core variables representing the suitable version of the oral health outcomes and risk factors) was compiled comprising 42 variables. For each study wave, the potential (or not) to generate each DataSchema variable was evaluated. Of the 2100 harmonization status assessments, 543 (26%) were complete. Approximately 50% of the DataSchema variables can be generated across at least four cohort surveys while only 10% (n = 4) variables can be generated across all surveys. For each survey, the DataSchema variables that can be generated ranged between 26% and 76%. CONCLUSION Data harmonization can improve the comparability of variables both within and across surveys. For future cohort surveys, the authors advocate more consistency and standardization in survey questionnaires within and between surveys.
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Affiliation(s)
- Vinay Sharma
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Michael O'Sullivan
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Oscar Cassetti
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Lewis Winning
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Aifric O'Sullivan
- Institute of Food and Health, Science Centre, South, UCD, Dublin, Ireland
| | - Michael Crowe
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
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van Buuren MMA, Riedstra NS, van den Berg MA, Boel FDEM, Ahedi H, Arbabi V, Arden NK, Bierma-Zeinstra SMA, Boer CG, Cicuttini F, Cootes TF, Crossley K, Felson D, Gielis WP, Heerey J, Jones G, Kluzek S, Lane NE, Lindner C, Lynch JA, Van Meurs J, Mosler AB, Nelson AE, Nevitt M, Oei E, Runhaar J, Tang J, Weinans H, Agricola R. Cohort profile: Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) - an international consortium of prospective cohort studies with individual participant data on hip osteoarthritis. BMJ Open 2024; 14:e077907. [PMID: 38637130 PMCID: PMC11029301 DOI: 10.1136/bmjopen-2023-077907] [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: 07/18/2023] [Accepted: 02/20/2024] [Indexed: 04/20/2024] Open
Abstract
PURPOSE Hip osteoarthritis (OA) is a major cause of pain and disability worldwide. Lack of effective therapies may reflect poor knowledge on its aetiology and risk factors, and result in the management of end-stage hip OA with costly joint replacement. The Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) consortium was established to pool and harmonise individual participant data from prospective cohort studies. The consortium aims to better understand determinants and risk factors for the development and progression of hip OA, to optimise and automate methods for (imaging) analysis, and to develop a personalised prediction model for hip OA. PARTICIPANTS World COACH aimed to include participants of prospective cohort studies with ≥200 participants, that have hip imaging data available from at least 2 time points at least 4 years apart. All individual participant data, including clinical data, imaging (data), biochemical markers, questionnaires and genetic data, were collected and pooled into a single, individual-level database. FINDINGS TO DATE World COACH currently consists of 9 cohorts, with 38 021 participants aged 18-80 years at baseline. Overall, 71% of the participants were women and mean baseline age was 65.3±8.6 years. Over 34 000 participants had baseline pelvic radiographs available, and over 22 000 had an additional pelvic radiograph after 8-12 years of follow-up. Even longer radiographic follow-up (15-25 years) is available for over 6000 of these participants. FUTURE PLANS The World COACH consortium offers unique opportunities for studies on the relationship between determinants/risk factors and the development or progression of hip OA, by using harmonised data on clinical findings, imaging, biomarkers, genetics and lifestyle. This provides a unique opportunity to develop a personalised hip OA risk prediction model and to optimise methods for imaging analysis of the hip.
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Affiliation(s)
- Michiel M A van Buuren
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Noortje S Riedstra
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Myrthe A van den Berg
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Fleur D E M Boel
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Harbeer Ahedi
- Institute for Medical Research, University of Tasmania Menzies, Hobart, Tasmania, Australia
| | - Vahid Arbabi
- Department of Orthopedics, UMC Utrecht, Utrecht, Netherlands
- Orthopaedic-Biomechanics Research Group, Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Nigel K Arden
- Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford Nuffield, Oxford, Oxfordshire, UK
| | | | - Cindy G Boer
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Flavia Cicuttini
- Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - Timothy F Cootes
- Centre for Imaging Sciences, The University of Manchester, Manchester, UK
| | - Kay Crossley
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University School of Allied Health Human Services and Sport, Melbourne, Victoria, Australia
| | - David Felson
- Boston University School of Medicine, Boston, Massachusetts, USA
| | - Willem Paul Gielis
- Department of Orthopedics, UMC Utrecht, Utrecht, Netherlands
- Department of Radiology, UMC Utrecht, Utrecht, Netherlands
| | - Joshua Heerey
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University School of Allied Health Human Services and Sport, Melbourne, Victoria, Australia
| | - Graeme Jones
- Institute for Medical Research, University of Tasmania Menzies, Hobart, Tasmania, Australia
| | - Stefan Kluzek
- Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford Nuffield, Oxford, Oxfordshire, UK
| | - Nancy E Lane
- Department of Medicine, University of California Davis School of Medicine, Sacramento, California, USA
| | - Claudia Lindner
- Centre for Imaging Sciences, The University of Manchester, Manchester, UK
| | - John A Lynch
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - J Van Meurs
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Andrea B Mosler
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University School of Allied Health Human Services and Sport, Melbourne, Victoria, Australia
| | - Amanda E Nelson
- Thurston Arthritis Research Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - M Nevitt
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Edwin Oei
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Jinchi Tang
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Harrie Weinans
- Department of Orthopedics, UMC Utrecht, Utrecht, Netherlands
- Department of Biomechanical Engineering, TU Delft, Delft, Zuid-Holland, Netherlands
| | - Rintje Agricola
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
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Hufstedler H, Roell Y, Peña A, Krishnan A, Green I, Barbosa-Silva A, Kremer A, Blacketer C, Fortier I, Howard K, LeRoy B, Hafeza E, Baorto D, Swertz M, Maxwell L, Jaenisch T. Navigating data standards in public health: A brief report from a data-standards meeting. J Glob Health 2024; 14:03024. [PMID: 38577801 PMCID: PMC10995743 DOI: 10.7189/jogh.14.03024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Affiliation(s)
- Heather Hufstedler
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
| | - Yannik Roell
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Andressa Peña
- Starlz Transplant Institute, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ankur Krishnan
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
| | - Ian Green
- SNOMED, SNOMED International, One Kingdom Street, Paddington Central, London, England, UK
| | - Adriano Barbosa-Silva
- ITTM, i2b2 tranSMART, ITTM S.A. (Information Technology for Translational Medicine), Luxembourg
| | - Andreas Kremer
- ITTM, i2b2 tranSMART, ITTM S.A. (Information Technology for Translational Medicine), Luxembourg
| | - Clair Blacketer
- Janssen Research and Development, Janssen Research & Development, Raritan, New Jersey, USA
| | - Isabel Fortier
- Maelstrom, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | | | | | - Eza Hafeza
- LOINC, Regenstrief Institute, Indianapolis, Indiana, USA
| | - David Baorto
- LOINC, Regenstrief Institute, Indianapolis, Indiana, USA
| | - M Swertz
- EU-CAN-CONNECT, Faculty of Medical Sciences, University of Groningen, Antonius Deusinglaan, the Netherlands
| | - Lauren Maxwell
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas Jaenisch
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
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4
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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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Bhattacharjee T, Kiwuwa-Muyingo S, Kanjala C, Maoyi ML, Amadi D, Ochola M, Kadengye D, Gregory A, Kiragga A, Taylor A, Greenfield J, Slaymaker E, Todd J. INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools. Front Digit Health 2024; 6:1329630. [PMID: 38347885 PMCID: PMC10859396 DOI: 10.3389/fdgth.2024.1329630] [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: 10/31/2023] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Introduction Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources. Methods The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis. Results The INSPIRE datahub's "On-ramp" services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability. Conclusion The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.
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Affiliation(s)
- Tathagata Bhattacharjee
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom
| | | | - Chifundo Kanjala
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom
- UNICEF (Malawi), Lilongwe, Malawi
| | - Molulaqhooa L. Maoyi
- South African Population Research Infrastructure Network (SAPRIN), South African Medical Research Council, Durban, South Africa
| | - David Amadi
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom
| | - Michael Ochola
- African Population and Health Research Center (APHRC), Nairobi, Kenya
| | - Damazo Kadengye
- African Population and Health Research Center (APHRC), Nairobi, Kenya
- Department of Economics and Statistics, Kabale University, Kabale, Uganda
| | - Arofan Gregory
- Committee on Data of the International Science Council (CODATA), Paris, France
| | - Agnes Kiragga
- African Population and Health Research Center (APHRC), Nairobi, Kenya
| | - Amelia Taylor
- Malawi University of Business and Applied Sciences, Blantyre, Malawi
| | - Jay Greenfield
- Department of Economics and Statistics, Kabale University, Kabale, Uganda
| | - Emma Slaymaker
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom
| | - Jim Todd
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom
| | - INSPIRE Network
- Implementation Network for Sharing Population Information from Research Entities (INSPIRE Network), Nairobi, Kenya
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Schröder M, Muller SH, Vradi E, Mielke J, Lim YM, Couvelard F, Mostert M, Koudstaal S, Eijkemans MJ, Gerlinger C. Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart. BIG DATA 2023; 11:399-407. [PMID: 37889577 PMCID: PMC10733752 DOI: 10.1089/big.2022.0178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/29/2023]
Abstract
Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.
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Affiliation(s)
- Megan Schröder
- The Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, Münich, Germany
| | - Sam H.A. Muller
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Eleni Vradi
- Biomedical Data Science II, Bayer AG, Berlin, Germany
| | - Johanna Mielke
- Research and Early Development, Bayer AG, Wuppertal, Germany
| | - Yvonne M.F. Lim
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Institute for Clinical Research, National Institutes of Health, Selangor, Malaysia
| | - Fabrice Couvelard
- Institut de Recherches Internationales SERVIER (I.R.I.S.), Suresnes, France
| | - Menno Mostert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Stefan Koudstaal
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Cardiology, Groene Hart Ziekenhuis, Gouda, The Netherlands
| | - Marinus J.C. Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Christoph Gerlinger
- Clinical Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
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Sexty RE, van der Pal S, Reijneveld SA, Wolke D, Lüchters G, Bakker L, van Buuren S, Bos AF, Bartmann P. Changes in neonatal morbidity, neonatal care practices, and length of hospital stay of surviving infants born very preterm in the Netherlands in the 1980s and in the 2000s: a comparison analysis with identical characteristics definitions. BMC Pediatr 2023; 23:554. [PMID: 37925410 PMCID: PMC10625206 DOI: 10.1186/s12887-023-04354-x] [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: 03/26/2023] [Accepted: 10/10/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND This study evaluates changes in the neonatal morbidity, the neonatal care practices, and the length of hospital stay of surviving very preterm (VP) infants born in the Netherlands in the 1980s and in the 2000s; a period over which historical improvements were introduced into neonatal care. We, herein, also study whether these changes in neonatal morbidity, neonatal care practices and length of hospital stay are associated with sociodemographic, prenatal, and infant characteristics. METHODS Two community-based cohorts from 1983 (POPS) and 2002-03 (LOLLIPOP) have provided the perinatal data for our study. The analysis enrolled 1,228 participants born VP (before the 32nd week of gestation) and surviving to 2 years of age without any severe congenital malformation. A rigorous harmonisation protocol ensured a precise comparison of the cohorts by using identical definitions of the perinatal characteristics. RESULTS In 2003, mothers were older when giving birth, had higher multiple birth rates, and significantly more parents had received higher education. In 2003, less VP infants had severe intraventricular haemorrhage and sepsis and relatively more received continuous positive airway pressure, mechanical ventilation and caffeine therapy than in 1983. Antenatal corticosteroids and surfactant therapy were provided only in 2003. The length of the stay in the neonatal intensive care unit and in hospital had decreased in 2003 by 22 and 11 days, respectively. Differences persisted after adjustment for sociodemographic, prenatal, and infant characteristics. CONCLUSIONS Neonatal morbidities of the surviving VP infants in this study have not increased, and exhibit improvements for various characteristics in two cohorts born 20 years apart with comparable gestational age and birth weight. Our data suggest that the improvements found are associated with more advanced therapeutic approaches and new national protocols in place, and less so with sociodemographic changes. This analysis provides a basis for further comparative analyses of the health and the development of VP children, particularly with regard to long-term outcomes.
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Affiliation(s)
- Réka E Sexty
- Department of Psychology, Health Psychology Unit, University of Graz, Graz, Austria
- University Hospital Bonn, Children's Hospital, Bonn, Germany
| | | | - Sijmen A Reijneveld
- Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Dieter Wolke
- Department of Psychology, University of Warwick, Coventry, UK
| | - Guido Lüchters
- Centre for Development Research (ZEF), Biostatistics, Bonn, Germany
| | | | | | - Arend F Bos
- Department of Paediatrics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Peter Bartmann
- University Hospital Bonn, Children's Hospital, Bonn, Germany.
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Willie TC, Alexander KA, Sharpless L, Zemlak JL, Smith MV, Kershaw TS. Recent Economic Intimate Partner Violence and Posttraumatic Stress Symptoms Among a Racially and Ethnically Diverse Sample of U.S. Women Experiencing Intimate Partner Violence. JOURNAL OF INTERPERSONAL VIOLENCE 2023; 38:11091-11116. [PMID: 37387530 DOI: 10.1177/08862605231178357] [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] [Indexed: 07/01/2023]
Abstract
Posttraumatic stress disorder (PTSD) is a prevalent consequence of physical and sexual intimate partner violence (IPV); however, little is known about the unique contributions of economic IPV. Furthermore, women's economic self-sufficiency may explicate the potential relationship between economic IPV and PTSD symptoms. Guided by the Stress Process Theory and Intersectionality, this study examined associations between economic IPV and women's PTSD symptoms and assessed economic self-sufficiency as a mediator. Participants were 255 adult women experiencing IPV recruited from metropolitan Baltimore, MD, and the state of CT who participated in two different studies. Participants completed surveys on IPV, economic self-sufficiency, and PTSD. Path analyses were conducted to examine direct and indirect associations of economic IPV with economic self-sufficiency and PTSD. Economic IPV was uniquely associated with PTSD symptoms while controlling for other forms of IPV. Economic self-sufficiency significantly partially mediated the association between economic IPV and PTSD symptoms such that economic IPV was associated with PTSD symptoms through economic self-sufficiency. Economic IPV may limit women's ability to make autonomous decisions related to finances, which could be distressing. The mental health impact of economic IPV may be particularly debilitating for women with low economic self-sufficiency as their posttraumatic stress occurs within the context of feeling unable to meet their financial goals and also having a partner control their economic resources. Fostering economic empowerment and asset building may be a strengths-based approach to reduce the PTSD symptomatology among women experiencing IPV.
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Affiliation(s)
- Tiara C Willie
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Megan V Smith
- Yale School of Public Health, New Haven, CT, USA
- Yale School of Medicine, New Haven, CT, USA
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Lawson-Tovey S, Smith SL, Geifman N, Shoop-Worrall S, Ng S, Barnes MR, Wedderburn LR, Hyrich KL. The successes and challenges of harmonising juvenile idiopathic arthritis (JIA) datasets to create a large-scale JIA data resource. Pediatr Rheumatol Online J 2023; 21:70. [PMID: 37438749 PMCID: PMC10339614 DOI: 10.1186/s12969-023-00839-2] [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: 01/19/2023] [Accepted: 06/01/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND CLUSTER is a UK consortium focussed on precision medicine research in JIA/JIA-Uveitis. As part of this programme, a large-scale JIA data resource was created by harmonizing and pooling existing real-world studies. Here we present challenges and progress towards creation of this unique large JIA dataset. METHODS Four real-world studies contributed data; two clinical datasets of JIA patients starting first-line methotrexate (MTX) or tumour necrosis factor inhibitors (TNFi) were created. Variables were selected based on a previously developed core dataset, and encrypted NHS numbers were used to identify children contributing similar data across multiple studies. RESULTS Of 7013 records (from 5435 individuals), 2882 (1304 individuals) represented the same child across studies. The final datasets contain 2899 (MTX) and 2401 (TNFi) unique patients; 1018 are in both datasets. Missingness ranged from 10 to 60% and was not improved through harmonisation. CONCLUSIONS Combining data across studies has achieved dataset sizes rarely seen in JIA, invaluable to progressing research. Losing variable specificity and missingness, and their impact on future analyses requires further consideration.
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Affiliation(s)
- Saskia Lawson-Tovey
- Centre for Genetics and Genomics Versus Arthritis, The University of Manchester, Manchester, UK
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Samantha Louise Smith
- Centre for Genetics and Genomics Versus Arthritis, The University of Manchester, Manchester, UK
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, The University of Surrey, Guildford, UK
| | - Stephanie Shoop-Worrall
- Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Sandra Ng
- Centre for Translational Bioinformatics, William Harvey Research Institute, Bart’s and the London School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - Michael R. Barnes
- Centre for Translational Bioinformatics, William Harvey Research Institute, Bart’s and the London School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - Lucy R. Wedderburn
- Centre for Adolescent Rheumatology Versus Arthritis at UCL, University College London Hospitals and Great Ormond Street Hospital, London, UK
- Infection, Inflammation and Rheumatology, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Kimme L. Hyrich
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK
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10
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Diniz JM, Vasconcelos H, Souza J, Rb-Silva R, Ameijeiras-Rodriguez C, Freitas A. Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review. JMIR Res Protoc 2023; 12:e45823. [PMID: 37335606 DOI: 10.2196/45823] [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: 02/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications. OBJECTIVE The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures. METHODS We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported. RESULTS The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings. CONCLUSIONS We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research. TRIAL REGISTRATION PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/45823.
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Affiliation(s)
- José Miguel Diniz
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- PhD Program in Health Data Science, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Henrique Vasconcelos
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Júlio Souza
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Rita Rb-Silva
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Carolina Ameijeiras-Rodriguez
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Alberto Freitas
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
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11
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Zare Jeddi M, Galea KS, Viegas S, Fantke P, Louro H, Theunis J, Govarts E, Denys S, Fillol C, Rambaud L, Kolossa-Gehring M, Santonen T, van der Voet H, Ghosh M, Costa C, Teixeira JP, Verhagen H, Duca RC, Van Nieuwenhuyse A, Jones K, Sams C, Sepai O, Tranfo G, Bakker M, Palmen N, van Klaveren J, Scheepers PTJ, Paini A, Canova C, von Goetz N, Katsonouri A, Karakitsios S, Sarigiannis DA, Bessems J, Machera K, Harrad S, Hopf NB. FAIR environmental and health registry (FAIREHR)- supporting the science to policy interface and life science research, development and innovation. FRONTIERS IN TOXICOLOGY 2023; 5:1116707. [PMID: 37342468 PMCID: PMC10278765 DOI: 10.3389/ftox.2023.1116707] [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: 12/05/2022] [Accepted: 04/19/2023] [Indexed: 06/23/2023] Open
Abstract
The environmental impact on health is an inevitable by-product of human activity. Environmental health sciences is a multidisciplinary field addressing complex issues on how people are exposed to hazardous chemicals that can potentially affect adversely the health of present and future generations. Exposure sciences and environmental epidemiology are becoming increasingly data-driven and their efficiency and effectiveness can significantly improve by implementing the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship. This will enable data integration, interoperability and (re)use while also facilitating the use of new and powerful analytical tools such as artificial intelligence and machine learning in the benefit of public health policy, and research, development and innovation (RDI). Early research planning is critical to ensuring data is FAIR at the outset. This entails a well-informed and planned strategy concerning the identification of appropriate data and metadata to be gathered, along with established procedures for their collection, documentation, and management. Furthermore, suitable approaches must be implemented to evaluate and ensure the quality of the data. Therefore, the 'Europe Regional Chapter of the International Society of Exposure Science' (ISES Europe) human biomonitoring working group (ISES Europe HBM WG) proposes the development of a FAIR Environment and health registry (FAIREHR) (hereafter FAIREHR). FAIR Environment and health registry offers preregistration of studies on exposure sciences and environmental epidemiology using HBM (as a starting point) across all areas of environmental and occupational health globally. The registry is proposed to receive a dedicated web-based interface, to be electronically searchable and to be available to all relevant data providers, users and stakeholders. Planned Human biomonitoring studies would ideally be registered before formal recruitment of study participants. The resulting FAIREHR would contain public records of metadata such as study design, data management, an audit trail of major changes to planned methods, details of when the study will be completed, and links to resulting publications and data repositories when provided by the authors. The FAIREHR would function as an integrated platform designed to cater to the needs of scientists, companies, publishers, and policymakers by providing user-friendly features. The implementation of FAIREHR is expected to yield significant benefits in terms of enabling more effective utilization of human biomonitoring (HBM) data.
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Affiliation(s)
- Maryam Zare Jeddi
- National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Karen S. Galea
- Institute of Occupational Medicine (IOM), Research Avenue North, Riccarton, United Kingdom
| | - Susana Viegas
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Henriqueta Louro
- National Institute of Health Dr. Ricardo Jorge, Department of Human Genetics, Lisbon and ToxOmics - Centre for Toxicogenomics and Human Health, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Jan Theunis
- VITO HEALTH, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Eva Govarts
- VITO HEALTH, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Sébastien Denys
- SpF— Santé Publique France, Environmental and Occupational Health Division, Saint-Maurice, France
| | - Clémence Fillol
- SpF— Santé Publique France, Environmental and Occupational Health Division, Saint-Maurice, France
| | - Loïc Rambaud
- SpF— Santé Publique France, Environmental and Occupational Health Division, Saint-Maurice, France
| | | | - Tiina Santonen
- Finnish Institute of Occupational Health (FIOH), Helsinki, Finland
| | | | - Manosij Ghosh
- Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Carla Costa
- Department of Environmental Health, National Institute of Health Dr. Ricardo Jorge, Porto, Portugal and EPIUnit—Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | - João Paulo Teixeira
- Department of Environmental Health, National Institute of Health Dr. Ricardo Jorge, Porto, Portugal and EPIUnit—Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | - Hans Verhagen
- Nutrition Innovation Center for Food and Health (NICHE), University of Ulster, Coleraine, United Kingdom
- National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark
- Food Safety and Nutrition Consultancy, Zeist, Netherlands
| | - Radu-Corneliu Duca
- Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Department of Health Protection, Laboratoire National de Santé (LNS), Dudelange, Luxembourg
| | - An Van Nieuwenhuyse
- Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Department of Health Protection, Laboratoire National de Santé (LNS), Dudelange, Luxembourg
| | - Kate Jones
- HSE—Health and Safety Executive, Buxton, United Kingdom
| | - Craig Sams
- HSE—Health and Safety Executive, Buxton, United Kingdom
| | - Ovnair Sepai
- UK Health Security Agency, Radiation, Chemical and Environmental Hazards Division, Chilton, United Kingdom
| | - Giovanna Tranfo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Institute Against Accidents at Work (INAIL), Monte PorzioCatone(RM), Italy
| | - Martine Bakker
- National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Nicole Palmen
- National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Jacob van Klaveren
- National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Paul T. J. Scheepers
- Radboud Institute for Biological and Environmental Sciences, Radboud University, Nijmegen, Netherlands
| | | | - Cristina Canova
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padova, Italy
| | - Natalie von Goetz
- Federal Office of Public Health, Bern, Switzerland
- Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | | | - Spyros Karakitsios
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimosthenis A. Sarigiannis
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Complex Risk and Data Analysis Research Center, University School for Advanced Studies IUSS, Pavia, Italy
| | - Jos Bessems
- VITO HEALTH, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Kyriaki Machera
- Laboratory of Pesticides’ Toxicology, Department of Pesticides Control and Phytopharmacy, Benaki Phytopathological Institute, Kifissia, Greece
| | - Stuart Harrad
- School of Geography, Earth, and Environmental Sciences, University of Birmingham, United Kingdom
| | - Nancy B. Hopf
- Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
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12
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Hao K, Zhang J, Di Narzo A, Zhang X, Hao A, Shan M, Deyssenroth M, Chen J, Zhang Z, Cheng H. Ethnic disparities in ambient air and traffic-related pollution exposure and ethnic-specific impacts on clinical biomarker levels. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162390. [PMID: 36841400 DOI: 10.1016/j.scitotenv.2023.162390] [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: 10/15/2022] [Revised: 02/06/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Although characterizing the inequality in pollution exposure burden across ethnic groups and the ethnic-specific exposure associations is of great social and public health importance, it has not been systematically investigated in large population studies. METHODS The UK Biobank data (N = 485, 806) of individual-level air ambient and traffic-related pollution exposure, biomarkers routinely used in clinical practice, genotype, life-style factors, and socioeconomic status were analyzed. Air pollution exposure estimates were compared among six genetically inferred ethnic groups. We also quantified the association between exposure and biomarkers within and across ethnicities. RESULTS Non-European participants (defined by genetics) disproportionately bear a higher burden of exposure than their European counterparts even after adjusting for covariables including socioeconomic status. For example, exposure to NO2 in people with African ancestry was 30.7 % higher (p = 1.5E-786) than European subjects. Within the genetically defined African group, larger African genetic ancestry proportion (AGAP) was linked to higher ambient air pollutant exposure. Trans-Ethnic analysis identified 32 clinical biomarkers associated with environmental exposure. For 13 biomarkers, the association with exposure was significantly different or even in opposing directions across ethnic groups. CONCLUSIONS Substantial disparities in air pollution exposure was observed among genetically-defined ethnic groups. Most importantly, we show that the impact of exposure on biomarkers varies by ethnicity. Reducing the disproportionally high exposure burden on non-European populations and alleviating the adverse consequences in an ethnic-specific manner are of great urgency and significance.
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Affiliation(s)
- Ke Hao
- Radiology Department, Huadong Hospital, Shanghai, China; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; College of Environmental Science and Engineering, Tongji University, Shanghai, China
| | - Jushan Zhang
- College of Environmental Science and Engineering, Tongji University, Shanghai, China; Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | | | | | - Alice Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Maya Deyssenroth
- Department of Environmental Health Sciences, Columbia University, New York, NY, USA
| | - Jia Chen
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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13
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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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14
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Lipnicki DM, Lam BCP, Mewton L, Crawford JD, Sachdev PS. Harmonizing Ethno-Regionally Diverse Datasets to Advance the Global Epidemiology of Dementia. Clin Geriatr Med 2023; 39:177-190. [PMID: 36404030 PMCID: PMC9767705 DOI: 10.1016/j.cger.2022.07.009] [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] [Indexed: 11/23/2022]
Abstract
Understanding dementia and cognitive impairment is a global effort needing data from multiple sources across diverse ethno-regional groups. Methodological heterogeneity means that these data often require harmonization to make them comparable before analysis. We discuss the benefits and challenges of harmonization, both retrospective and prospective, broadly and with a focus on data types that require particular sorts of approaches, including neuropsychological test scores and neuroimaging data. Throughout our discussion, we illustrate general principles and give examples of specific approaches in the context of contemporary research in dementia and cognitive impairment from around the world.
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Affiliation(s)
- Darren M Lipnicki
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia.
| | - Ben C P Lam
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia
| | - Louise Mewton
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia
| | - John D Crawford
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, Australia
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15
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Abbasizanjani H, Torabi F, Bedston S, Bolton T, Davies G, Denaxas S, Griffiths R, Herbert L, Hollings S, Keene S, Khunti K, Lowthian E, Lyons J, Mizani MA, Nolan J, Sudlow C, Walker V, Whiteley W, Wood A, Akbari A. Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration. BMC Med Inform Decis Mak 2023; 23:8. [PMID: 36647111 PMCID: PMC9842203 DOI: 10.1186/s12911-022-02093-0] [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: 09/27/2022] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. METHODS Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. RESULTS Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. CONCLUSIONS We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.
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Affiliation(s)
- Hoda Abbasizanjani
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.
| | - Fatemeh Torabi
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Stuart Bedston
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Thomas Bolton
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Gareth Davies
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Spiros Denaxas
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Rowena Griffiths
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Laura Herbert
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | | | - Spencer Keene
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Emily Lowthian
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Mehrdad A Mizani
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - John Nolan
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Cathie Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Venexia Walker
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - William Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Angela Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
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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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Krokstad S, Sund ER, Kvaløy K, Rangul V, Næss M. HUNT for better public health. Scand J Public Health 2022; 50:968-971. [PMID: 36113104 PMCID: PMC9578099 DOI: 10.1177/14034948221102309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Aims: The Trøndelag Health Study (HUNT) has collected population data through comprehensive decennial surveys over the last four decades and has so far collected data from 240,000 participants. The participants are identified with the unique Norwegian birth number, which enables them to be followed throughout different life stages, from survey to survey, and to endpoint measures in Norwegian national health registers without attrition bias. Methods: The study design of HUNT offers several advantages: it provides an overview of the public health development in the population over decades, the data can be used in health services research, clinical epidemiology, studies of causation, trajectories, and consequences of diseases, and to study gene × environment interactions. Results: HUNT data have shown major shifts in public health trends, such as decreasing mean blood pressure and resting heart rate among adults, increasing prevalence of obesity, geographical and socioeconomic inequalities in health, increasing mental health distress among adolescents and young adults with an opposite development among the elderly. Data from HUNT have been used in several major international research projects, where data harmonization with several other population cohorts internationally has been done. HUNT has placed great emphasis on safeguarding research ethics, privacy, and data security. The Norwegian authorities established national regulations for the surveys from the time General Data Protection Regulation was introduced in 2018. Conclusions: Researchers can apply for HUNT data access from HUNT Research Centre provided they have obtained project approval from the Regional Committee for Medical and Health Research Ethics. Researchers not affiliated to a Norwegian research institution must collaborate with and apply through a Norwegian principal investigator. Information on the application and conditions for data access is available at www.ntnu.edu/hunt/data.
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Affiliation(s)
- Steinar Krokstad
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Erik R. Sund
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Kirsti Kvaløy
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Vegar Rangul
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Levanger, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Marit Næss
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
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Workflow for building interoperable food and nutrition security (FNS) data platforms. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.03.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Gurugubelli VS, Fang H, Shikany JM, Balkus SV, Rumbut J, Ngo H, Wang H, Allison JJ, Steffen LM. A review of harmonization methods for studying dietary patterns. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2022; 23:100263. [PMID: 35252528 PMCID: PMC8896407 DOI: 10.1016/j.smhl.2021.100263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Data harmonization is the process by which each of the variables from different research studies are standardized to similar units resulting in comparable datasets. These data may be integrated for more powerful and accurate examination and prediction of outcomes for use in the intelligent and smart electronic health software programs and systems. Prospective harmonization is performed when researchers create guidelines for gathering and managing the data before data collection begins. In contrast, retrospective harmonization is performed by pooling previously collected data from various studies using expert domain knowledge to identify and translate variables. In nutritional epidemiology, dietary data harmonization is often necessary to construct the nutrient and food databases necessary to answer complex research questions and develop effective public health policy. In this paper, we review methods for effective data harmonization, including developing a harmonization plan, which common standards already exist for harmonization, and defining variables needed to harmonize datasets. Currently, several large-scale studies maintain harmonized nutrient databases, especially in Europe, and steps have been proposed to inform the retrospective harmonization process. As an example, data harmonization methods are applied to several U.S longitudinal diet datasets. Based on our review, considerations for future dietary data harmonization include user agreements for sharing private data among participating studies, defining variables and data dictionaries that accurately map variables among studies, and the use of secure data storage servers to maintain privacy. These considerations establish necessary components of harmonized data for smart health applications which can promote healthier eating and provide greater insights into the effect of dietary patterns on health.
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Affiliation(s)
| | - Hua Fang
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N Lake Ave, Worcester, 01655, Massachusetts, USA
- Corresponding author. Tel.: +0-508-910-6411;
| | - James M Shikany
- Division of Preventive Medicine, University of Alabama at Birmingham, 1720 University Blvd, Birmingham, 35294, Alabama, USA
| | - Salvador V Balkus
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
| | - Joshua Rumbut
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N Lake Ave, Worcester, 01655, Massachusetts, USA
| | - Hieu Ngo
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
| | - Honggang Wang
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
| | - Jeroan J Allison
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N Lake Ave, Worcester, 01655, Massachusetts, USA
| | - Lyn M. Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, 55455, Minnesota, USA
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20
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Ronkainen J, Nedelec R, Atehortua A, Balkhiyarova Z, Cascarano A, Ngoc Dang V, Elhakeem A, van Enckevort E, Goncalves Soares A, Haakma S, Halonen M, Heil KF, Heiskala A, Hyde E, Jacquemin B, Keikkala E, Kerckhoffs J, Klåvus A, Kopinska JA, Lepeule J, Marazzi F, Motoc I, Näätänen M, Ribbenstedt A, Rundblad A, Savolainen O, Simonetti V, de Toro Eadie N, Tzala E, Ulrich A, Wright T, Zarei I, d’Amico E, Belotti F, Brunius C, Castleton C, Charles MA, Gaillard R, Hanhineva K, Hoek G, Holven KB, Jaddoe VWV, Kaakinen MA, Kajantie E, Kavousi M, Lakka T, Matthews J, Piano Mortari A, Vääräsmäki M, Voortman T, Webster C, Zins M, Atella V, Bulgheroni M, Chadeau-Hyam M, Conti G, Evans J, Felix JF, Heude B, Järvelin MR, Kolehmainen M, Landberg R, Lekadir K, Parusso S, Prokopenko I, de Rooij SR, Roseboom T, Swertz M, Timpson N, Ulven SM, Vermeulen R, Juola T, Sebert S. LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases. Environ Epidemiol 2022; 6:e184. [PMID: 35169663 PMCID: PMC8835657 DOI: 10.1097/ee9.0000000000000184] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 11/14/2021] [Indexed: 11/29/2022] Open
Abstract
The current epidemics of cardiovascular and metabolic noncommunicable diseases have emerged alongside dramatic modifications in lifestyle and living environments. These correspond to changes in our "modern" postwar societies globally characterized by rural-to-urban migration, modernization of agricultural practices, and transportation, climate change, and aging. Evidence suggests that these changes are related to each other, although the social and biological mechanisms as well as their interactions have yet to be uncovered. LongITools, as one of the 9 projects included in the European Human Exposome Network, will tackle this environmental health equation linking multidimensional environmental exposures to the occurrence of cardiovascular and metabolic noncommunicable diseases.
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Affiliation(s)
- Justiina Ronkainen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Rozenn Nedelec
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Angelica Atehortua
- Artificial Intelligence in Medicine Lab (BCN-AIM), University of Barcelona, Barcelona, Spain
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - Zhanna Balkhiyarova
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Bashkir State Medical University, Department of Endocrinology, Ufa, Russian Federation
| | - Anna Cascarano
- Artificial Intelligence in Medicine Lab (BCN-AIM), University of Barcelona, Barcelona, Spain
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - Vien Ngoc Dang
- Artificial Intelligence in Medicine Lab (BCN-AIM), University of Barcelona, Barcelona, Spain
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - Ahmed Elhakeem
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
| | - Esther van Enckevort
- Department of Genetics and Genomics Coordination Center, University of Groningen, Groningen, the Netherlands
| | - Ana Goncalves Soares
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
| | - Sido Haakma
- Department of Genetics and Genomics Coordination Center, University of Groningen, Groningen, the Netherlands
| | - Miia Halonen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Katharina F. Heil
- Artificial Intelligence in Medicine Lab (BCN-AIM), University of Barcelona, Barcelona, Spain
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - Anni Heiskala
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Eleanor Hyde
- Department of Genetics and Genomics Coordination Center, University of Groningen, Groningen, the Netherlands
| | - Bénédicte Jacquemin
- University of Rennes, INSERM, School of Advanced Studies in Public Health (EHESP), Research Institute for Environmental and Occupational Health, UMR_S 1085, Rennes, France
| | - Elina Keikkala
- Finnish Institute for Health and Welfare, Population Health Unit, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Anton Klåvus
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Joanna A. Kopinska
- Department of Social Sciences and Economics, Sapienza University of Rome, Rome, Italy
| | - Johanna Lepeule
- Grenoble Alpes University, INSERM, CNRS, Institute for Advanced Biosciences, Grenoble, France
| | - Francesca Marazzi
- CEIS Tor Vergata, Centre for Economic and International Studies, University of Rome Tor Vergata, Rome, Italy
| | - Irina Motoc
- Amsterdam UMC, Epidemiology and Data Science, University of Amsterdam, Amsterdam Public Health, Amsterdam, the Netherlands
| | - Mari Näätänen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Anton Ribbenstedt
- Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Amanda Rundblad
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Blindern, Oslo, Norway
| | - Otto Savolainen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Department of Biology and Biological Engineering, Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Nina de Toro Eadie
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Evangelia Tzala
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Anna Ulrich
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom
| | - Thomas Wright
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Iman Zarei
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | | | - Federico Belotti
- CEIS Tor Vergata, Centre for Economic and International Studies, University of Rome Tor Vergata, Rome, Italy
- Department of Economics and Finance, University of Rome Tor Vergata, Rome, Italy
| | - Carl Brunius
- Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Marie-Aline Charles
- Center for Research in Epidemiology and Statistics, INSERM, INRAE, University of Paris, Paris, France
- Ined, INSERM, EFS, Elfe Joint Unit, Aubervilliers, France
| | - Romy Gaillard
- The 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
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Biochemistry, University of Turku, Turku, Finland
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Kirsten B. Holven
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Blindern, Oslo, Norway
- National Advisory Unit on Familial Hypercholesterolemia, Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Vincent W. V. Jaddoe
- The 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
| | - Marika A. Kaakinen
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Eero Kajantie
- Finnish Institute for Health and Welfare, Population Health Unit, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Timo Lakka
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Jason Matthews
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Blindern, Oslo, Norway
| | - Andrea Piano Mortari
- CEIS Tor Vergata, Centre for Economic and International Studies, University of Rome Tor Vergata, Rome, Italy
| | - Marja Vääräsmäki
- Finnish Institute for Health and Welfare, Population Health Unit, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | - Marie Zins
- Population-based Epidemiological Cohorts Unit, INSERM UMS 11, Villejuif, France
| | - Vincenzo Atella
- CEIS Tor Vergata, Centre for Economic and International Studies, University of Rome Tor Vergata, Rome, Italy
- Department of Economics and Finance, University of Rome Tor Vergata, Rome, Italy
- Stanford University, Stanford, CA
| | | | - Marc Chadeau-Hyam
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Gabriella Conti
- Department of Economics, University College London, London, United Kingdom
- Social Research Institute, London, United Kingdom
| | - Jayne Evans
- Beta Technology Ltd, Doncaster, United Kingdom
| | - Janine F. Felix
- The 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
| | - Barbara Heude
- Center for Research in Epidemiology and Statistics, INSERM, INRAE, University of Paris, Paris, France
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Marjukka Kolehmainen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), University of Barcelona, Barcelona, Spain
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | | | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom
- UMR 8199-EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille, France
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa, Russian Federation
| | - Susanne R. de Rooij
- Amsterdam UMC, Epidemiology and Data Science, University of Amsterdam, Amsterdam Public Health, Amsterdam, the Netherlands
| | - Tessa Roseboom
- Amsterdam UMC, Epidemiology and Data Science, University of Amsterdam, Amsterdam Public Health, Amsterdam, the Netherlands
- Gynaecology and Obstetrics, Amsterdam Reproduction and Development Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Morris Swertz
- Department of Genetics and Genomics Coordination Center, University of Groningen, Groningen, the Netherlands
| | - Nicholas Timpson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
| | - Stine M. Ulven
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Blindern, Oslo, Norway
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
- Julius Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Teija Juola
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Sylvain Sebert
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Corresponding Author. Address: Faculty of Medicine, Center for Life Course Health Research, University of Oulu, PO Box 5000, FIN-90014, Finland. E-mail: (S. Sebert)
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21
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Temprosa M, Moore SC, Zanetti KA, Appel N, Ruggieri D, Mazzilli KM, Chen KL, Kelly RS, Lasky-Su JA, Loftfield E, McClain K, Park B, Trijsburg L, Zeleznik OA, Mathé EA. COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia. Am J Epidemiol 2022; 191:147-158. [PMID: 33889934 PMCID: PMC8897993 DOI: 10.1093/aje/kwab120] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 04/14/2021] [Accepted: 04/14/2021] [Indexed: 12/13/2022] Open
Abstract
Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ewy A Mathé
- Correspondence to Dr. Ewy Mathé, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD 20850 (e-mail: )
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22
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Li Z, Jiang X, Wang Y, Kim Y. Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg Top Life Sci 2021; 5:765-777. [PMID: 34881778 PMCID: PMC8786302 DOI: 10.1042/etls20210249] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Xiaoqian Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Yejin Kim
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
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23
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Nader JL, López-Vicente M, Julvez J, Guxens M, Cadman T, Elhakeem A, Järvelin MR, Rautio N, Miettunen J, El Marroun H, Melchior M, Heude B, Charles MA, Yang TC, McEachan RRC, Wright J, Polanska K, Carson J, Lin A, Rauschert S, Huang RC, Popovic M, Richiardi L, Corpeleijn E, Cardol M, Mikkola TM, Eriksson JG, Salika T, Inskip H, Vinther JL, Strandberg-Larsen K, Gürlich K, Grote V, Koletzko B, Vafeiadi M, Sunyer J, Jaddoe VWV, Harris JR. Cohort description: Measures of early-life behaviour and later psychopathology in the LifeCycle Project - EU Child Cohort Network. J Epidemiol 2021. [PMID: 34776498 PMCID: PMC10165218 DOI: 10.2188/jea.je20210241] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The EU LifeCycle Project was launched in 2017 to combine, harmonise, and analyse data from more than 250,000 participants across Europe and Australia, involving cohorts participating in the EU-funded LifeCycle Project. The purpose of this cohort description is to provide a detailed overview over the major measures within mental health domains that are available in 17 European and Australian cohorts participating in the LifeCycle Project. METHODS Data on cognitive, behavioural and psychological development has been collected on participants from birth until adulthood through questionnaire and medical data. We developed an inventory of the available data by mapping individual instruments, domain types, and age groups, providing the basis for statistical harmonization across mental health measures. RESULTS The mental health data in LifeCycle contain longitudinal and cross-sectional data for ages 0-18+ years, covering domains across a wide range of behavioural and psychopathology indicators and outcomes (including executive function, depression, ADHD and cognition). These data span a unique combination of qualitative data collected through behavioural/cognitive/mental health questionnaires and examination, as well as data from biological samples and indices in the form of brain imaging (MRI, foetal ultrasound) and DNA methylation data. Harmonized variables on a subset of mental health domains have been developed, providing statistical equivalence of measures required for longitudinal meta-analyses across instruments and cohorts. CONCLUSION Mental health data harmonized through the LifeCycle project can be used to study life course trajectories and exposure-outcome models that examine early life risk factors for mental illness and develop predictive markers for later-life disease.
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Affiliation(s)
- Johanna L Nader
- Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health
| | - Mònica López-Vicente
- ISGlobal, Instituto de Salud Global de Barcelona.,Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam
| | - Jordi Julvez
- ISGlobal, Instituto de Salud Global de Barcelona.,Institut d'Investigació Sanitària Pere Virgili, Hospital Universitari Sant Joan de Reus
| | - Monica Guxens
- ISGlobal, Instituto de Salud Global de Barcelona.,Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam
| | - Tim Cadman
- MRC Integrative Epidemiology Unit at University of Bristol, Population Health Sciences, Bristol Medical School
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at University of Bristol, Population Health Sciences, Bristol Medical School
| | | | - Nina Rautio
- Center for Life Course Health Research, University of Oulu
| | - Jouko Miettunen
- Center for Life Course Health Research, University of Oulu.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu
| | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam.,Department of Pediatrics, University Medical Center Rotterdam.,The Generation R Study Group
| | - Maria Melchior
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, IPLESP
| | - Barbara Heude
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE
| | - Marie-Aline Charles
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE.,Unité mixte Inserm-Ined-EFS Elfe, INED
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust
| | - Kinga Polanska
- Department of Hygiene and Epidemiology, Medical University of Lodz
| | - Jennie Carson
- Telethon Kids Institute, University of Western Australia
| | - Ashleigh Lin
- Telethon Kids Institute, University of Western Australia
| | | | - Rae-Chi Huang
- Telethon Kids Institute, University of Western Australia
| | - Maja Popovic
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte
| | - Eva Corpeleijn
- Department of Epidemiology, University Medical Center Groningen
| | - Marloes Cardol
- Department of Epidemiology, University Medical Center Groningen
| | - Tuija M Mikkola
- Folkhälsan Research Center.,Clinicum, Faculty of Medicine, University of Helsinki
| | - Johan G Eriksson
- Folkhälsan Research Center.,Public Health Promotion Unit, National Institute for Health and Welfare.,Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital.,Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research.,Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore
| | - Theodosia Salika
- Medical Research Council Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton
| | - Hazel Inskip
- Medical Research Council Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton.,NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, University of Southampton
| | | | | | - Kathrin Gürlich
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU
| | - Veit Grote
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete
| | - Jordi Sunyer
- ISGlobal, Instituto de Salud Global de Barcelona
| | - Vincent W V Jaddoe
- Department of Pediatrics, University Medical Center Rotterdam.,The Generation R Study Group
| | - Jennifer R Harris
- Division of Health Data and Digitalization, Center for Fertility and Health and Department of Genetics and Bioinformatics, The Norwegian Institute of Public Health
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24
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Paalanen L, Tolonen H. Experiences from the harmonization of Finnish national population-based health survey data. Scand J Public Health 2021; 50:972-979. [PMID: 34706593 PMCID: PMC9578096 DOI: 10.1177/14034948211052164] [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] [Indexed: 11/15/2022]
Abstract
Aims: There are several advantages to pooling survey data from individual studies over time or across different countries. Our aim is to share our experiences on harmonizing data from 13 Finnish health examination surveys covering the years 1972–2017 and to describe the challenges related to harmonizing different variable types using two questionnaire variables – blood pressure measurement and total cholesterol assessment – as examples. Methods: Data from Finnish national population-based health surveys were harmonized as part of the research project ‘Projections of the Burden of Disease and Disability in Finland – Health Policy Prospects’, including variables from questionnaires, objective health measurements and results from the laboratory analysis of biological samples. The process presented in the Maelstrom Research guidelines for data harmonization was followed with minor adjustments. Results: The harmonization of data from objective measurements and biomarkers was reasonably straightforward, but questionnaire items proved more challenging. Some questions and response options had changed during the covered time period. This concerned, for example, questionnaire items on the availability and use of medication and diet. Conclusions: The long time period – 45 years – made harmonization more complicated. The survey questions or response options had changed for some topics due to changes in society. However, common core variables for topics that were especially relevant for the project, such as lifestyle factors and certain diseases or conditions, could be harmonized with sufficient comparability. For future surveys, the use of standardized survey methods and the proper documentation of data collection are recommended to facilitate harmonization.
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Affiliation(s)
- Laura Paalanen
- Finnish Institute for Health and Welfare (THL), Department of Public Health and Welfare, Helsinki, Finland
| | - Hanna Tolonen
- Finnish Institute for Health and Welfare (THL), Department of Public Health and Welfare, Helsinki, Finland
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25
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Chen D, Jutkowitz E, Iosepovici SL, Lin JC, Gross AL. Pre-statistical harmonization of behavrioal instruments across eight surveys and trials. BMC Med Res Methodol 2021; 21:227. [PMID: 34689753 PMCID: PMC8543796 DOI: 10.1186/s12874-021-01431-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 10/08/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Data harmonization is a powerful method to equilibrate items in measures that evaluate the same underlying construct. There are multiple measures to evaluate dementia related behavioral symptoms. Pre-statistical harmonization of behavioral instruments in dementia research is the first step to develop a statistical crosswalk between measures. Studies that conduct pre-statistical harmonization of behavioral instruments rarely document their methods in a structured, reproducible manner. This is a crucial step which entails careful review, documentation and scrutiny of source data to ensure sufficient comparability between items prior to data pooling. Here, we document the pre-statistical harmonization of items measuring behavioral and psychological symptoms among people with dementia. We provide a box of recommended procedure for future studies. METHODS We identified behavioral instruments that are used in clinical practice, a national survey, and randomized trials of dementia care interventions. We rigorously reviewed question content and scoring procedures to establish sufficient comparability across items as well as item quality prior to data pooling. Additionally, we standardized coding to Stata-readable format, which allowed us to automate approaches to identify potential cross-study differences in items and low-quality items. To ensure reasonable model fit for statistical co-calibration, we estimated two-parameter logistic Item Response Theory models within each of the eight studies. RESULTS We identified 59 items from 11 behavioral instruments across the eight datasets. We found considerable cross-study heterogeneity in administration and coding procedures for items that measure the same attribute. Discrepancies existed in terms of directionality and quantification of behavioral symptoms for even seemingly comparable items. We resolved item response heterogeneity, missingness and skewness, conditional dependency prior to estimation of item response theory models for statistical co-calibration. We used several rigorous data transformation procedures to address these issues, including re-coding and truncation. CONCLUSIONS This study highlights the importance of each aspect involved in the pre-statistical harmonization process of behavioral instruments. We provide guidelines and recommendations for how future research may detect and account for similar issues in pooling behavioral and related instruments.
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Affiliation(s)
- Diefei Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States, 2024 E. Monument Street, Baltimore, MD 21205 USA
| | - Eric Jutkowitz
- Health Services, Policy & Practice, Brown School of Public Health, Providence, RI USA
- Center of Innovation in Long Term Services and Supports, Providence VA Medical Center, Providence, RI USA
| | - Skylar L. Iosepovici
- Health Services, Policy & Practice, Brown School of Public Health, Providence, RI USA
| | - John C. Lin
- Health Services, Policy & Practice, Brown School of Public Health, Providence, RI USA
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States, 2024 E. Monument Street, Baltimore, MD 21205 USA
- Johns Hopkins University Center on Aging and Health, Baltimore, MD USA
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26
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Cai T, Liu M, Xia Y. Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data. J Am Stat Assoc 2021; 117:2105-2119. [PMID: 37975021 PMCID: PMC10653033 DOI: 10.1080/01621459.2021.1904958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 03/05/2021] [Accepted: 03/13/2021] [Indexed: 01/29/2023]
Abstract
Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between-study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Theoretically, SHIR is statistically more efficient than the existing distributed approaches that integrate debiased LASSO estimators from the local sites. Furthermore, the estimation error incurred by aggregating derived data is negligible compared to the statistical minimax rate and SHIR is shown to be asymptotically equivalent in estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of our method is studied and compared with existing approaches via extensive simulation settings. We further illustrate the utility of SHIR to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple chronic disease cohorts.
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Affiliation(s)
- Tianxi Cai
- Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, USA
| | - Molei Liu
- Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, USA
| | - Yin Xia
- Department of Statistics, School of Management, Fudan University, Shanghai, China
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27
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Pinot de Moira A, Haakma S, Strandberg-Larsen K, van Enckevort E, Kooijman M, Cadman T, Cardol M, Corpeleijn E, Crozier S, Duijts L, Elhakeem A, Eriksson JG, Felix JF, Fernández-Barrés S, Foong RE, Forhan A, Grote V, Guerlich K, Heude B, Huang RC, Järvelin MR, Jørgensen AC, Mikkola TM, Nader JLT, Pedersen M, Popovic M, Rautio N, Richiardi L, Ronkainen J, Roumeliotaki T, Salika T, Sebert S, Vinther JL, Voerman E, Vrijheid M, Wright J, Yang TC, Zariouh F, Charles MA, Inskip H, Jaddoe VWV, Swertz MA, Nybo Andersen AM. The EU Child Cohort Network's core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables. Eur J Epidemiol 2021; 36:565-580. [PMID: 33884544 PMCID: PMC8159791 DOI: 10.1007/s10654-021-00733-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 02/14/2021] [Indexed: 10/24/2022]
Abstract
The Horizon2020 LifeCycle Project is a cross-cohort collaboration which brings together data from multiple birth cohorts from across Europe and Australia to facilitate studies on the influence of early-life exposures on later health outcomes. A major product of this collaboration has been the establishment of a FAIR (findable, accessible, interoperable and reusable) data resource known as the EU Child Cohort Network. Here we focus on the EU Child Cohort Network's core variables. These are a set of basic variables, derivable by the majority of participating cohorts and frequently used as covariates or exposures in lifecourse research. First, we describe the process by which the list of core variables was established. Second, we explain the protocol according to which these variables were harmonised in order to make them interoperable. Third, we describe the catalogue developed to ensure that the network's data are findable and reusable. Finally, we describe the core data, including the proportion of variables harmonised by each cohort and the number of children for whom harmonised core data are available. EU Child Cohort Network data will be analysed using a federated analysis platform, removing the need to physically transfer data and thus making the data more accessible to researchers. The network will add value to participating cohorts by increasing statistical power and exposure heterogeneity, as well as facilitating cross-cohort comparisons, cross-validation and replication. Our aim is to motivate other cohorts to join the network and encourage the use of the EU Child Cohort Network by the wider research community.
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Affiliation(s)
- Angela Pinot de Moira
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | - Sido Haakma
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Katrine Strandberg-Larsen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Esther van Enckevort
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marjolein Kooijman
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Tim Cadman
- Population Health Science, Bristol Medical School, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2PS, UK
| | - Marloes Cardol
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Eva Corpeleijn
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sarah Crozier
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
- NIHR Applied Research Collaboration Wessex, Southampton Science Park, Innovation Centre, 2 Venture Road, Chilworth, Southampton, SO16 7NP, UK
| | - Liesbeth Duijts
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Ahmed Elhakeem
- Population Health Science, Bristol Medical School, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2PS, UK
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Singapore Institute for Clinical Sciences (SICS), Agency for Science and Technology (A*STAR), Singapore, Singapore
| | - Janine F Felix
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Sílvia Fernández-Barrés
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Rachel E Foong
- Telethon Kids Institute, Perth, WA, Australia
- School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
| | - Anne Forhan
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | - Veit Grote
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital Munich, Munich, Germany
| | - Kathrin Guerlich
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital Munich, Munich, Germany
| | - Barbara Heude
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | | | - Marjo-Riitta Järvelin
- Faculty of Medicine, Center for Life-Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Anne Cathrine Jørgensen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Tuija M Mikkola
- Folkhälsan Research Center, Helsinki, Finland
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna L T Nader
- Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
| | - Marie Pedersen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Maja Popovic
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Nina Rautio
- Faculty of Medicine, Center for Life-Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Justiina Ronkainen
- Faculty of Medicine, Center for Life-Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Theano Roumeliotaki
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Theodosia Salika
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
| | - Sylvain Sebert
- Faculty of Medicine, Center for Life-Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Johan L Vinther
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ellis Voerman
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Faryal Zariouh
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | - Marie-Aline Charles
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
- ELFE Joint Unit, French Institute for Demographic Studies (Ined), French Institute for Medical Research and Health (INSERM), French Blood Agency, Aubervilliers, France
| | - Hazel Inskip
- MRC Lifecourse Epidemiology Unit, 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
| | - Vincent W V Jaddoe
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Morris A 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
| | - Anne-Marie Nybo Andersen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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Kerr DCR, Capaldi DM. Introduction to the special section on the intergenerational transmission of risk for substance use. PSYCHOLOGY OF ADDICTIVE BEHAVIORS 2021; 34:811-817. [PMID: 33271034 DOI: 10.1037/adb0000652] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Six original research papers were submitted to this Special Section to address questions regarding the intergenerational transmission of risk for cannabis and other substance use. Study teams recruited youth in Iowa, Washington, Oregon, New York, and Arizona in the 1980s-1990s, assessed them into adulthood, and recruited their partners and offspring for another study. All of the studies assessed substance use in 2 or more generations. Other strengths in this section include the strong representation of fathers, the demographic diversity of the samples as a whole, and the demonstrations of varied statistical and replication approaches. The findings highlight features of parental histories of cannabis use during adolescence that are associated with their children's risk for cannabis use and factors that explain or weaken intergenerational similarities. Two groups of prevention scholars also offered commentaries on the implications of these studies for prevention and training, and collaboration. It is hoped that the Special Section will stimulate new hypotheses, replications, and communication among etiological and prevention researchers. Furthermore, the papers highlight that the familial transmission of substance use risk should be taken into account more fully in the design of prevention programs to maximize impacts for youth as well as their future offspring. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Marcon Y, Bishop T, Avraam D, Escriba-Montagut X, Ryser-Welch P, Wheater S, Burton P, González JR. Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD. PLoS Comput Biol 2021; 17:e1008880. [PMID: 33784300 PMCID: PMC8034722 DOI: 10.1371/journal.pcbi.1008880] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/09/2021] [Accepted: 03/17/2021] [Indexed: 01/31/2023] Open
Abstract
Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers' ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture ("resources") for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (https://isglobal-brge.github.io/resource_bookdown).
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Affiliation(s)
| | - Tom Bishop
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Xavier Escriba-Montagut
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Patricia Ryser-Welch
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | | | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - 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 (CIBERESP), Barcelona, Spain
- Dept. of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
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Stevens ML, Gupta N, Inan Eroglu E, Crowley PJ, Eroglu B, Bauman A, Granat M, Straker L, Palm P, Stenholm S, Aadahl M, Mork P, Chastin S, Rangul V, Hamer M, Koster A, Holtermann A, Stamatakis E. Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement. BMJ Open Sport Exerc Med 2020; 6:e000874. [PMID: 33408875 PMCID: PMC7768971 DOI: 10.1136/bmjsem-2020-000874] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2020] [Indexed: 01/19/2023] Open
Abstract
Introduction The Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting. Methods (1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6). Results (1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than 'physical activity' for the data we are collecting (Q1); make only minor changes to ProPASS's accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6). Conclusions Thigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS's strategic directions-indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing.
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Affiliation(s)
- Matthew L Stevens
- Musculoskeletal Disorders and Physical Workload, National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Nidhi Gupta
- Musculoskeletal Disorders and Physical Workload, National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Elif Inan Eroglu
- Boden Collaboration for Obesity, Nutrition, Exercise & Eating Disorders, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Patrick Joseph Crowley
- Musculoskeletal Disorders and Physical Workload, National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Barbaros Eroglu
- School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia
| | - Adrian Bauman
- School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia
| | - Malcolm Granat
- School of Health and Society, University of Salford, Salford, UK.,PAL Technologies, Glasgow, UK
| | - Leon Straker
- School of Physiotherapy and Exercise Science, Curtin University, Perth, Western Australia, Australia
| | - Peter Palm
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Sari Stenholm
- Department of Public Health, University of Turku and Turku University Hospital, Turku, Finland
| | - Mette Aadahl
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Paul Mork
- Department of Public Health and Nursing, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sebastien Chastin
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK.,Department of Movement and Sport Sciences, Ghent University, Gent, Belgium
| | - Vegar Rangul
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine, Norwegian University of Science and Technology, Levanger, Norway
| | - Mark Hamer
- Institute Sport Exercise & Health, Faculty of Medical Sciences, University College London, London, UK
| | - Annemarie Koster
- Department of Social Medicine, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Andreas Holtermann
- Musculoskeletal Disorders and Physical Workload, National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Emmanuel Stamatakis
- School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia
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31
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Domingues MAP, Camacho R, Rodrigues PP. CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens. J Biomed Inform 2020; 114:103669. [PMID: 33359111 DOI: 10.1016/j.jbi.2020.103669] [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: 09/16/2020] [Revised: 11/28/2020] [Accepted: 12/16/2020] [Indexed: 11/27/2022]
Abstract
Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.
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Affiliation(s)
| | - Rui Camacho
- Faculty of Engineering of the University of Porto, Portugal; LIAAD-INESC TEC, Porto, Portugal
| | - Pedro Pereira Rodrigues
- CINTESIS - Center for Health Technology and Services Research, Portugal; Faculty of Medicine of the University of Porto, Portugal
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32
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Cai Y, Zijlema WL, Sørgjerd EP, Doiron D, de Hoogh K, Hodgson S, Wolffenbuttel B, Gulliver J, Hansell AL, Nieuwenhuijsen M, Rahimi K, Kvaløy K. Impact of road traffic noise on obesity measures: Observational study of three European cohorts. ENVIRONMENTAL RESEARCH 2020; 191:110013. [PMID: 32805247 DOI: 10.1016/j.envres.2020.110013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Environmental stressors such as transport noise may contribute to development of obesity through increased levels of stress hormones, sleep deprivation and endocrine disruption. Epidemiological evidence supporting an association of road traffic noise with obesity markers is still relatively scant and confined to certain geographical regions. We aimed to examine the cross-sectional associations between road traffic noise and obesity markers in three large European cohorts involving nearly 500,000 individuals. METHODS Three population-based cohorts (UK Biobank, Lifelines, HUNT3) were established between 2006 and 2013 in the UK, the Netherlands and Norway respectively. For all three cohorts, residential 24-h road traffic noise (Lden) for 2009 was modelled from a standardised European noise assessment framework. Residential exposures to NO2 for 2007 and PM2.5 for 2010 were estimated from Europe-wide land use regression models. Obesity markers including body mass index and waist circumference were measured at recruitment. Obesity and central obesity status were subsequently derived. Regression models were fitted in each cohort, adjusting for a harmonised set of demographic and lifestyle covariates, with further adjustments for air pollution in the main model. RESULTS The main analyses included 412,934 participants of UK Biobank, 61,032 of Lifelines and 30,305 of HUNT3, with a mean age of 43-56 years and Lden ranging 42-89 dB(A) across cohorts. In UK Biobank, per 10 dB(A) higher of Lden: BMI was higher by 0.14kg/m2 (95%CI: 0.11-0.18), waist circumference higher by 0.27 cm (95%CI: 0.19-0.35), odds of obesity was 1.06 (95%CI: 1.04-1.08) and of central obesity was 1.05 (95%CI: 1.04-1.07). These associations were robust to most other sensitivity analyses but attenuated by further adjustment of PM2.5 or area-level socioeconomic status. Associations were more pronounced among women, those with low physical activity, higher household income or hearing impairment. In HUNT3, associations were observed for obesity or central obesity status among those exposed to Lden greater than 55 dB(A). In contrast, no or negative associations were observed in the Lifelines cohort. CONCLUSIONS This largest study to date providing mixed findings on impacts of long-term exposure to road traffic noise on obesity, which necessitates future analyses using longitudinal data to further investigate this potentially important epidemiological link.
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Affiliation(s)
- Yutong Cai
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Deep Medicine Programme, Oxford Martin School, University of Oxford, Oxford, UK; MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
| | - Wilma L Zijlema
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Catalonia, Spain
| | - Elin Pettersen Sørgjerd
- Department of Public Health and Nursing, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway; Department of Endocrinology, St.Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Dany Doiron
- Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Susan Hodgson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Bruce Wolffenbuttel
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - John Gulliver
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Catalonia, Spain
| | - Kazem Rahimi
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Deep Medicine Programme, Oxford Martin School, University of Oxford, Oxford, UK
| | - Kirsti Kvaløy
- Department of Public Health and Nursing, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway; Department of Research and Development, Levanger Hospital, Nord-Trøndelag Hospital Trust, Norway
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33
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Wey TW, Doiron D, Wissa R, Fabre G, Motoc I, Noordzij JM, Ruiz M, Timmermans E, van Lenthe FJ, Bobak M, Chaix B, Krokstad S, Raina P, Sund ER, Beenackers MA, Fortier I. Overview of retrospective data harmonisation in the MINDMAP project: process and results. J Epidemiol Community Health 2020; 75:433-441. [PMID: 33184054 PMCID: PMC8053335 DOI: 10.1136/jech-2020-214259] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/09/2020] [Accepted: 07/12/2020] [Indexed: 11/05/2022]
Abstract
Background The MINDMAP project implemented a multinational data infrastructure to investigate the direct and interactive effects of urban environments and individual determinants of mental well-being and cognitive function in ageing populations. Using a rigorous process involving multiple teams of experts, longitudinal data from six cohort studies were harmonised to serve MINDMAP objectives. This article documents the retrospective data harmonisation process achieved based on the Maelstrom Research approach and provides a descriptive analysis of the harmonised data generated. Methods A list of core variables (the DataSchema) to be generated across cohorts was first defined, and the potential for cohort-specific data sets to generate the DataSchema variables was assessed. Where relevant, algorithms were developed to process cohort-specific data into DataSchema format, and information to be provided to data users was documented. Procedures and harmonisation decisions were thoroughly documented. Results The MINDMAP DataSchema (v2.0, April 2020) comprised a total of 2841 variables (993 on individual determinants and outcomes, 1848 on environmental exposures) distributed across up to seven data collection events. The harmonised data set included 220 621 participants from six cohorts (10 subpopulations). Harmonisation potential, participant distributions and missing values varied across data sets and variable domains. Conclusion The MINDMAP project implemented a collaborative and transparent process to generate a rich integrated data set for research in ageing, mental well-being and the urban environment. The harmonised data set supports a range of research activities and will continue to be updated to serve ongoing and future MINDMAP research needs.
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Affiliation(s)
- Tina W Wey
- Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Dany Doiron
- Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Rita Wissa
- Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Guillaume Fabre
- Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Irina Motoc
- Department of Epidemiology and Biostatistics, Amsterdam UMC, VU University Medical Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - J Mark Noordzij
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Milagros Ruiz
- Research Department of Epidemiology and Public Health, University College London, London, UK
| | - Erik Timmermans
- Department of Epidemiology and Biostatistics, Amsterdam UMC, VU University Medical Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Frank J van Lenthe
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands.,Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, Netherlands
| | - Martin Bobak
- Research Department of Epidemiology and Public Health, University College London, London, UK
| | - Basile Chaix
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Nemesis research team, Paris, France
| | - Steinar Krokstad
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway.,Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,McMaster Institute for Research on Aging, McMaster University, Hamilton, Canada.,Labarge Centre for Mobility in Aging, McMaster University, Hamilton, Canada
| | - Erik Reidar Sund
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway.,Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.,Faculty of Nursing and Health Sciences, Nord Universitet-Levanger Campus, Levanger, Norway
| | - Marielle A Beenackers
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Isabel Fortier
- Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada
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34
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Jaddoe VWV, Felix JF, Andersen AMN, Charles MA, Chatzi L, Corpeleijn E, Donner N, Elhakeem A, Eriksson JG, Foong R, Grote V, Haakma S, Hanson M, Harris JR, Heude B, Huang RC, Inskip H, Järvelin MR, Koletzko B, Lawlor DA, Lindeboom M, McEachan RRC, Mikkola TM, Nader JLT, de Moira AP, Pizzi C, Richiardi L, Sebert S, Schwalber A, Sunyer J, Swertz MA, Vafeiadi M, Vrijheid M, Wright J, Duijts L. The LifeCycle Project-EU Child Cohort Network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents. Eur J Epidemiol 2020; 35:709-724. [PMID: 32705500 PMCID: PMC7387322 DOI: 10.1007/s10654-020-00662-z] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/04/2020] [Indexed: 12/15/2022]
Abstract
Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.
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Affiliation(s)
- Vincent W V Jaddoe
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, The Generation R Study Group, (Na 29-18), PO Box 2040, 3000 CA, Rotterdam, The Netherlands. .,Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Janine F Felix
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, The Generation R Study Group, (Na 29-18), PO Box 2040, 3000 CA, Rotterdam, The Netherlands.,Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Anne-Marie Nybo Andersen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Marie-Aline Charles
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France.,ELFE Joint Unit, French Institute for Demographic Studies (Ined), French Institute for Medical Research and Health (INSERM), French Blood Agency, Aubervilliers, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eva Corpeleijn
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nina Donner
- Concentris Research Management GmbH, Fürstenfeldbruck, Germany
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Folkhälsan Research Center, Helsinki, Finland.,Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore.,Singapore Institute for Clinical Sciences (SICS), Agency for Science and Technology (A*STAR), Singapore, Singapore
| | - Rachel Foong
- Telethon Kids Institute, Perth, WA, Australia.,School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
| | - Veit Grote
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU, Munich, Germany
| | - Sido Haakma
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Mark Hanson
- Institute of Developmental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Jennifer R Harris
- Centre for Fertility and Health, The Norwegian Institute of Public Health, Oslo, Norway.,Division of Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | | | - Hazel Inskip
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.,MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
| | - Marjo-Riitta Järvelin
- Center for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK.,Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Berthold Koletzko
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU, Munich, Germany
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Maarten Lindeboom
- Department of Economics, VU University Amsterdam, Amsterdam, The Netherlands
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | | | - Johanna L T Nader
- Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
| | - Angela Pinot de Moira
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Costanza Pizzi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Sylvain Sebert
- Center for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Ameli Schwalber
- Concentris Research Management GmbH, Fürstenfeldbruck, Germany
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Morris A Swertz
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands.,Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Liesbeth Duijts
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, The Generation R Study Group, (Na 29-18), PO Box 2040, 3000 CA, Rotterdam, The Netherlands.,Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Le Sueur H, Bruce IN, Geifman N. The challenges in data integration - heterogeneity and complexity in clinical trials and patient registries of Systemic Lupus Erythematosus. BMC Med Res Methodol 2020; 20:164. [PMID: 32580708 PMCID: PMC7313210 DOI: 10.1186/s12874-020-01057-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 06/19/2020] [Indexed: 11/23/2022] Open
Abstract
Background Individual clinical trials and cohort studies are a useful source of data, often under-utilised once a study has ended. Pooling data from multiple sources could increase sample sizes and allow for further investigation of treatment effects; even if the original trial did not meet its primary goals. Through the MASTERPLANS (MAximizing Sle ThERapeutic PotentiaL by Application of Novel and Stratified approaches) national consortium, focused on Systemic Lupus Erythematosus (SLE), we have gained valuable real-world experiences in aligning, harmonising and combining data from multiple studies and trials, specifically where standards for data capture, representation and documentation, were not used or were unavailable. This was not without challenges arising both from the inherent complexity of the disease and from differences in the way data were captured and represented across different studies. Main body Data were, unavoidably, aligned by hand, matching up equivalent or similar patient variables across the different studies. Heterogeneity-related issues were tackled and data were cleaned, organised and combined, resulting in a single large dataset ready for analysis. Overcoming these hurdles, often seen in large-scale data harmonization and integration endeavours of legacy datasets, was made possible within a realistic timescale and limited resource by focusing on specific research questions driven by the aims of MASTERPLANS. Here we describe our experiences tackling the complexities in the integration of large, diverse datasets, and the lessons learned. Conclusions Harmonising data across studies can be complex, and time and resource consuming. The work carried out here highlights the importance of using standards for data capture, recording, and representation, to facilitate both the integration of large datasets and comparison between studies. Where standards are not implemented at the source harmonisation is still possible by taking a flexible approach, with systematic preparation, and a focus on specific research questions.
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Affiliation(s)
- Helen Le Sueur
- Centre for Health Informatics, Vaughan Housue, Portsmouth St., The University of Manchester, Manchester, M13 9GB, UK
| | - Ian N Bruce
- Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester, UK.,NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Nophar Geifman
- Centre for Health Informatics, Vaughan Housue, Portsmouth St., The University of Manchester, Manchester, M13 9GB, UK. .,The Manchester Molecular Pathology Innovation Centre, The University of Manchester, Manchester, UK.
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36
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Eklund N, Andrianarisoa NH, van Enckevort E, Anton G, Debucquoy A, Müller H, Zaharenko L, Engels C, Ebert L, Neumann M, Geeraert J, T'Joen V, Demski H, Caboux É, Proynova R, Parodi B, Mate S, van Iperen E, Merino-Martinez R, Quinlan PR, Holub P, Silander K. Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events. Biopreserv Biobank 2020; 18:155-164. [PMID: 32302498 PMCID: PMC7310316 DOI: 10.1089/bio.2019.0129] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level. Materials and Methods: The components were defined according to specific scope and use cases by a large group of experts, and through several cycles of reviews, according to the new MIABIS governance model of BBMRI-ERIC (Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium). The guiding principles applied in developing these components included the following terms: model should consider only samples of human origin, model should be applicable to all types of samples and all sample donors, and model should describe the current status of samples stored in a given biobank. Results: A minimal set of standard attributes for defining samples and sample donors is presented here. We added an "event" component to describe attributes that are not directly describing samples or sample donors but are tightly related to them. To better utilize the generic data model, we suggest a procedure by which interoperability can be promoted, using specific MIABIS profiles. Discussion: The MIABIS sample and donor component extensions and the new generic data model complement the existing MIABIS Core 2.0 components, and substantially increase the potential usability of this terminology for better describing biobank samples and sample donors. They also support the use of individual level data about samples and sample donors to obtain accurate and detailed biobank availability queries.
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Affiliation(s)
- Niina Eklund
- THL Biobank, Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Esther van Enckevort
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | | | - Heimo Müller
- Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria
| | | | | | | | - Michael Neumann
- Interdisciplinary Bank of Biomaterials and Data Würzburg, University Hospital Würzburg, Würzburg, Germany
| | - Joachim Geeraert
- Faculty of Medicine and Health Sciences, University of Ghent/University Hospital Ghent, Ghent, Belgium
| | - Veronique T'Joen
- Faculty of Medicine and Health Sciences, University of Ghent/University Hospital Ghent, Ghent, Belgium
| | - Hans Demski
- Helmholtz Zentrum München, Neuherberg, Germany
| | | | | | | | - Sebastian Mate
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Erik van Iperen
- Amsterdam UMC Biobank, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | | | - Philip R Quinlan
- Digital Research Service, University of Nottingham, Nottingham, United Kingdom
| | | | - Kaisa Silander
- THL Biobank, Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
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Mc Dowell CP, Carlin A, Capranica L, Dillon C, Harrington JM, Lakerveld J, Loyen A, Ling FCM, Brug J, MacDonncha C, Herring MP. Associations of self-reported physical activity and anxiety symptoms and status among 7,874 Irish adults across harmonised datasets: a DEDIPAC-study. BMC Public Health 2020; 20:365. [PMID: 32192475 PMCID: PMC7082967 DOI: 10.1186/s12889-020-08481-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 03/09/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Anxiety is an adaptive response to an objective or perceived threat; however, when symptoms become severe and chronic it that can become a maladaptive anxiety disorder. Limited evidence suggests that physical activity may be associated with prevention against anxiety. This study uses data from The Irish Longitudinal Study on Ageing (TILDA) and The Mitchelstown Cohort Study to investigate cross-sectional associations between physical activity and anxiety symptoms and status among Irish adults. METHODS Both datasets were harmonized (n = 7874). The short form International Physical Activity Questionnaire measured physical activity. Participants were classified as meeting World Health Organization physical activity guidelines (≥150 min weekly of moderate intensity physical activity, ≥75 min weekly of vigorous intensity physical activity, or ≥ 600 MET-minutes) or not. They were also divided into three groups based on weekly MET-minutes of moderate-to-vigorous physical activity (Low: 0-599; Moderate: 600-1199; High: ≥1200), and three groups based on weekly minutes of walking (Low: 0-209; Moderate: 210-419; High: 420+). Anxiety symptoms were measured by the Hospital Anxiety and Depression Scale with a score of ≥8 indicating anxiety. Binomial logistic regression, adjusted for relevant confounders examined physical activity-anxiety associations. RESULTS Females had higher rates of anxiety than males (28.0% vs 20.0%; p < 0.001). Following adjustment for relevant covariates, meeting physical activity guidelines was associated with 13.5% (95% CI: 2.0-23.7; p = 0.023) lower odds of anxiety. Moderate and High physical activity were associated with 13.5% (- 11.0-32.6; p = 0.254) and 13.6% (1.4-4.2; p = 0.030) lower odds of anxiety compared to Low physical activity, respectively. Moderate and High walking were associated with 2.1% (- 14.5-16.3; p = 0.789) and 5.1% (- 9.3-17.6; p = 0.467) lower odds of anxiety compared to Low walking, respectively. CONCLUSION Meeting physical activity guidelines is associated with lower odds of anxiety, but the strength of associations did not increase considerably with increased physical activity levels.
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Affiliation(s)
- Cillian P. Mc Dowell
- The Irish Longitudinal Study on Ageing (TILDA), Lincoln Gate, Trinity College Dublin, Dublin 2, Ireland
- Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Angela Carlin
- Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
- Centre for Exercise Medicine, Physical Activity and Health, Sport and Exercise Sciences Research Institute, Ulster University, Northern Ireland, UK
| | - Laura Capranica
- Department of Movement, Human and Health Sciences, University of Rome Foro Italico, Rome, Italy
| | | | | | - Jeroen Lakerveld
- Department of Epidemiology and Biostatistics, Amsterdam UMC, VU University Medical Center, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
- Global Geo Health Data Center, Utrecht University, Utrecht, The Netherlands
| | - Anne Loyen
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Fiona Chun Man Ling
- Department of Sport, Exercise & Rehabilitation, Northumbria University, Newcastle upon Tyne, UK
| | - Johannes Brug
- Amsterdam School for Communication Research, University of Amsterdam, Amsterdam, the Netherlands
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Ciaran MacDonncha
- Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Matthew P. Herring
- Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
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Abstract
Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.
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Walhovd KB, Fjell AM, Westerhausen R, Nyberg L, Ebmeier KP, Lindenberger U, Bartrés-Faz D, Baaré WF, Siebner HR, Henson R, Drevon CA, Strømstad Knudsen GP, Ljøsne IB, Penninx BW, Ghisletta P, Rogeberg O, Tyler L, Bertram L. Healthy minds 0–100 years: Optimising the use of European brain imaging cohorts (“Lifebrain”). Eur Psychiatry 2020; 50:47-56. [DOI: 10.1016/j.eurpsy.2017.12.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/26/2022] Open
Abstract
AbstractThe main objective of “Lifebrain” is to identify the determinants of brain, cognitive and mental (BCM) health at different stages of life. By integrating, harmonising and enriching major European neuroimaging studies across the life span, we will merge fine-grained BCM health measures of more than 5000 individuals. Longitudinal brain imaging, genetic and health data are available for a major part, as well as cognitive and mental health measures for the broader cohorts, exceeding 27,000 examinations in total. By linking these data to other databases and biobanks, including birth registries, national and regional archives, and by enriching them with a new online data collection and novel measures, we will address the risk factors and protective factors of BCM health. We will identify pathways through which risk and protective factors work and their moderators. Exploiting existing European infrastructures and initiatives, we hope to make major conceptual, methodological and analytical contributions towards large integrative cohorts and their efficient exploitation. We will thus provide novel information on BCM health maintenance, as well as the onset and course of BCM disorders. This will lay a foundation for earlier diagnosis of brain disorders, aberrant development and decline of BCM health, and translate into future preventive and therapeutic strategies. Aiming to improve clinical practice and public health we will work with stakeholders and health authorities, and thus provide the evidence base for prevention and intervention.
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Cohen JA, Trojano M, Mowry EM, Uitdehaag BMJ, Reingold SC, Marrie RA. Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment. Mult Scler 2020; 26:23-37. [PMID: 31778094 PMCID: PMC6950891 DOI: 10.1177/1352458519892555] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 11/10/2019] [Indexed: 12/21/2022]
Abstract
Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome measures during patient encounters; imaging and biospecimen analyses; and data from wearable devices increase dataset utility. However, observational studies utilizing these data are susceptible to many potential sources of bias, creating barriers to acceptance by regulatory agencies and the medical community. Therefore, data standardization and validation within datasets, harmonization across datasets, and application of appropriate analysis methods are important considerations. We review approaches to improve the scope, quality, and analyses of real-world data to advance understanding of multiple sclerosis and its treatment, as an example of opportunities to better support patient care and research.
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Affiliation(s)
- Jeffrey A Cohen
- Department of Neurology, Mellen Center for
Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland
Clinic, Cleveland, OH, USA
| | - Maria Trojano
- Department of Basic Medical Sciences,
Neurosciences and Sense Organs, University of Bari “Aldo Moro,” Bari,
Italy
| | - Ellen M Mowry
- Department of Neurology, School of Medicine, The
Johns Hopkins University, Baltimore, MD, USA
| | - Bernard MJ Uitdehaag
- Department of Neurology, Amsterdam University
Medical Center, Amsterdam, The Netherlands
| | | | - Ruth Ann Marrie
- Departments of Internal Medicine (Neurology) and
Community Health Sciences, Rady Faculty of Health Sciences, Max Rady College
of Medicine, University of Manitoba, Winnipeg, MB, Canada
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41
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Patrick CJ, Iacono WG, Venables NC. Incorporating neurophysiological measures into clinical assessments: Fundamental challenges and a strategy for addressing them. Psychol Assess 2019; 31:1512-1529. [PMID: 30896211 PMCID: PMC6754804 DOI: 10.1037/pas0000713] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Recent scientific initiatives have called for increased use of neurobiological variables in clinical and other applied assessments. However, the task of incorporating neural measures into psychological assessments entails significant methodological challenges that have not been effectively addressed to date. As a result, neurophysiological measures remain underutilized in clinical and applied assessments, and formal procedures for integrating such measures with report-based measures are lacking. In this article, we discuss major methodological issues that have impeded progress in this direction, and propose a systematic research strategy for integrating neurophysiological measures into psychological assessment protocols. The strategy we propose is an iterative psychoneurometric approach that provides a means to establish multimethod (MM) measurement models for core biobehavioral traits that influence functioning across diverse areas of life. We provide a detailed illustration of a MM model for one such trait, inhibitory control (inhibition-disinhibition), and highlight work being done to develop counterpart models for other biobehavioral traits (i.e., threat sensitivity, reward sensitivity, affiliative capacity). We discuss how these measurement models can be refined and extended through use of already existing data sets, and outline steps that can be taken to establish norms for MM assessments and optimize the feasibility of their use in everyday practice. We believe this model-oriented strategy can provide a viable pathway toward effective use of neurophysiological measures in routine clinical assessments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Overview of Federated Facility to Harmonize, Analyze and Management of Missing Data in Cohorts. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9194103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Cohorts are instrumental for epidemiologically oriented observational studies. Cohort studies usually observe large groups of individuals for a specific period of time to identify the contributing factors to a specific outcome (for instance an illness) and create associations between risk factors and the outcome under study. In collaborative projects, federated data facilities are meta-database systems that are distributed across multiple locations that permit to analyze, combine, or harmonize data from different sources making them suitable for mega- and meta-analyses. The harmonization of data can increase the statistical power of studies through maximization of sample size, allowing for additional refined statistical analyses, which ultimately lead to answer research questions that could not be addressed while using a single study. Indeed, harmonized data can be analyzed through mega-analysis of raw data or fixed effects meta-analysis. Other types of data might be analyzed by e.g., random-effects meta-analyses or Bayesian evidence synthesis. In this article, we describe some methodological aspects related to the construction of a federated facility to optimize analyses of multiple datasets, the impact of missing data, and some methods for handling missing data in cohort studies.
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Filatova S, Gyllenberg D, Sillanmäki L, Suominen A, Hinkka-Yli-Salomäki S, Kaljonen A, Kerkelä M, Keski-Säntti M, Ristikari T, Lagström H, Hurtig T, Miettunen J, Surcel HM, Veijola J, Gissler M, Sourander A. The Finnish psychiatric birth cohort consortium (PSYCOHORTS) - content, plans and perspectives. Nord J Psychiatry 2019; 73:357-364. [PMID: 31271336 DOI: 10.1080/08039488.2019.1636135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Background: Psychiatric disorders tend to be developmental, and longitudinal settings are required to examine predictors of psychiatric phenomena. Replicating and combining data and results from different birth cohorts, which are a source of reliable data, can make research even more valuable. The Finnish Psychiatric Birth Cohort Consortium (PSYCOHORTS) project combines birth cohorts in Finland. Aim: The aim of this paper is to introduce content, plans and perspectives of the PSYCOHORTS project that brings together researchers from Finland. In addition, we illustrate an example of data harmonization using available data on causes of death. Content: PSYCOHORTS includes eight Finnish birth cohorts. The project has several plans: to harmonize different data from birth cohorts, to incorporate biobanks into psychiatric birth cohort research, to apply multigenerational perspectives, to integrate longitudinal patterns of marginalization and inequality in mental health, and to utilize data in health economics research. Data on causes of death, originally obtained from Finnish Cause of Death register, were harmonized across the six birth cohorts using SAS macro facility. Results: Harmonization of the cause of death data resulted in a total of 21,993 observations from 1965 to 2015. For example, the percentage of deaths due to suicide and the sequelae of intentional self-harm was 14% and alcohol-related diseases, including accidental poisoning by alcohol, was 13%. Conclusions: PSYCOHORTS lays the foundation for complex examinations of psychiatric disorders that is based on compatible datasets, use of biobanks and multigenerational approach to risk factors, and extensive data on marginalization and inequality.
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Affiliation(s)
- S Filatova
- a Centre for Child Psychiatry, University of Turku , Turku , Finland
| | - D Gyllenberg
- a Centre for Child Psychiatry, University of Turku , Turku , Finland.,b National Institute of Health and Welfare , Helsinki , Finland.,c Department of Adolescent Psychiatry, University of Helsinki and Helsinki University Central Hospital , Helsinki , Finland
| | - L Sillanmäki
- a Centre for Child Psychiatry, University of Turku , Turku , Finland
| | - A Suominen
- a Centre for Child Psychiatry, University of Turku , Turku , Finland.,d Turku University Central Hospital , Turku , Finland
| | | | - A Kaljonen
- e Department of Biostatistics, Faculty of Medicine, University of Turku , Finland
| | - M Kerkelä
- b National Institute of Health and Welfare , Helsinki , Finland.,f Medical Research Center, University of Oulu and University Hospital of Oulu , Finland
| | - M Keski-Säntti
- b National Institute of Health and Welfare , Helsinki , Finland
| | - T Ristikari
- b National Institute of Health and Welfare , Helsinki , Finland
| | - H Lagström
- g Department of Public Health, University of Turku and Turku University Hospital , Turku , Finland
| | - T Hurtig
- h Research Unit of Clinical Neuroscience, Psychiatry University of Oulu , Finland.,i PEDEGO Research Unit, Child Psychiatry, University of Oulu , Finland.,j Clinic of Child Psychiatry, University Hospital of Oulu , Finland
| | - J Miettunen
- k Centre for Life Course Health Research, University of Oulu , Finland
| | - H-M Surcel
- l Biobank Borealis, University of Oulu , Finland.,m Faculty of Medicine, University of Oulu , Finland
| | - J Veijola
- f Medical Research Center, University of Oulu and University Hospital of Oulu , Finland.,n University Hospital of Oulu , Finland
| | - M Gissler
- a Centre for Child Psychiatry, University of Turku , Turku , Finland.,b National Institute of Health and Welfare , Helsinki , Finland.,o Department of Neurobiology, Care Sciences and Society, Karolinska Institute , Stockholm , Sweden
| | - A Sourander
- a Centre for Child Psychiatry, University of Turku , Turku , Finland.,p INVEST Research Flagship, University of Turku , Finland.,q Turku University Hospital , Turku , Finland
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Sanchez-Niubo A, Egea-Cortés L, Olaya B, Caballero FF, Ayuso-Mateos JL, Prina M, Bobak M, Arndt H, Tobiasz-Adamczyk B, Pająk A, Leonardi M, Koupil I, Panagiotakos D, Tamosiunas A, Scherbov S, Sanderson W, Koskinen S, Chatterji S, Haro JM. Cohort Profile: The Ageing Trajectories of Health - Longitudinal Opportunities and Synergies (ATHLOS) project. Int J Epidemiol 2019; 48:1052-1053i. [PMID: 31329885 PMCID: PMC6693815 DOI: 10.1093/ije/dyz077] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/01/2019] [Indexed: 11/14/2022] Open
Affiliation(s)
- Albert Sanchez-Niubo
- Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Laia Egea-Cortés
- Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Beatriz Olaya
- Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Francisco Félix Caballero
- Department Preventive Medicine and Public Health, Universidad Autónoma de Madrid/Idipaz, Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, CIBERESP, Madrid, Spain
| | - Jose L Ayuso-Mateos
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain
- Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS Princesa), Madrid, Spain
| | - Matthew Prina
- Social Epidemiology Research Group. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Global Health Institute, King's College London, London, UK
| | - Martin Bobak
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | - Beata Tobiasz-Adamczyk
- Department of Medical Sociology, Department of Epidemiology, Chair of Epidemiology and Preventive Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Andrzej Pająk
- Department of Epidemiology and Population Studies, Faculty of Health Sciences, Jagienllonian University Medical College, Krakow, Poland
| | | | - Ilona Koupil
- Department of Public Health Sciences, Centre for Health Equity Studies, Stockholm University, Stockholm, Sweden
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Sergei Scherbov
- International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria
- Austrian Academy of Science, Vienna Institute of Demography, Vienna, Austria
- Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russian Federation
| | - Warren Sanderson
- International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria
- Department of Economics, Stony Brook University, Stony Brook, NY, USA
| | - Seppo Koskinen
- National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Somnath Chatterji
- Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Josep Maria Haro
- Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
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Harville EW, Jacobs M, Shu T, Breckner D, Wallace M. Comparison of reproductive history gathered by interview and by vital records linkage after 40 years of follow-up: Bogalusa Babies. BMC Med Res Methodol 2019; 19:114. [PMID: 31164081 PMCID: PMC6549375 DOI: 10.1186/s12874-019-0758-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 05/21/2019] [Indexed: 11/17/2022] Open
Abstract
Background To examine the consistency and likely degree of bias in a study of cardiovascular health, linked with reproductive data over 40 years. Methods Linkage of vital statistics data of births to female Bogalusa Heart Study participants was compared to interviewing of female participants. The characteristics of participants, the agreement, and demographic, study-related, and medical predictors of discrepancy were analyzed, using kappa statistics, mean and median differences, and logistic regression. Results Overall, 3944 (66.7%) of participants were located by one or both sources. The strongest predictor of either linkage or interview was recent and/or frequent participation in the parent study. Agreement between the two sources was generally good (kappa > 0.9 for birthweight and 0.8 for gestational age). Black race, older age, and time since pregnancy were associated with greater discrepancy in reporting of outcomes, but cardiovascular risk factors generally were not. Conclusions Combining information from multiple sources to increase sample size and outcome ascertainment may be valid, which will increase population health sciences’ ability to leverage the many existing, large-scale sources to answer previously unexplored questions, even those that the data were not initially collected to answer. Electronic supplementary material The online version of this article (10.1186/s12874-019-0758-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Emily W Harville
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal ST STE 2000, New Orleans, LA, 70112, USA.
| | - Marni Jacobs
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal ST STE 2000, New Orleans, LA, 70112, USA.,Division of Biostatistics and Study Methodology, Children's National Health System, Washington, DC, 20010, USA
| | - Tian Shu
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal ST STE 2000, New Orleans, LA, 70112, USA
| | - Dorothy Breckner
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal ST STE 2000, New Orleans, LA, 70112, USA
| | - Maeve Wallace
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal ST STE 2000, New Orleans, LA, 70112, USA.,Department of Global Community Health and Behavior, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA
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Simell BA, Törnwall OM, Hämäläinen I, Wichmann HE, Anton G, Brennan P, Bouvard L, Slimani N, Moskal A, Gunter M, Zatloukal K, Minion JT, Soini S, Mayrhofer MT, Murtagh MJ, van Ommen GJ, Johansson M, Perola M. Transnational access to large prospective cohorts in Europe: Current trends and unmet needs. N Biotechnol 2019; 49:98-103. [PMID: 30342241 DOI: 10.1016/j.nbt.2018.10.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 10/15/2018] [Accepted: 10/15/2018] [Indexed: 01/15/2023]
Abstract
Biobank samples and data from studies of large prospective cohorts (LPC) represent an invaluable resource for health research. Efficient sharing and pooling of samples and data is a central pre-requisite for new advances in biomedical science. This requirement, however, is not compatible with the present scattered and traditional access governance structures, where legal and ethical frameworks often form an obstacle for effective sharing. Moreover, the EU General Data Protection Regulation (GDPR) is demanding increasingly rigorous administration from all those organisations processing personal data. The BBMRI-LPC project (Biobanking and Biomolecular Research Infrastructure - Large Prospective Cohorts) assembled 21 LPCs from 10 countries and two EU-wide multinational cohort networks with a key objective to promote collaborative innovative transnational research proposed by external researchers on the broad field of common chronic diseases, and analyze the gaps and needs involved. BBMRI-LPC organized three scientific calls to offer European investigators an opportunity to gain free of charge transnational access to research material available in the participating cohorts. A total of 11 high-quality research proposals involving multiple prospective cohorts were granted, and the access process in the individual projects carefully monitored. Divergent access governance structures, complex legal and ethical frameworks and heterogeneous procedures were identified as currently constituting substantial obstacles for sample and data transfer in Europe. To optimize the scientific value and use of these research resources, practical solutions for more streamlined access governance in collaborative projects are urgently needed. A number of infrastructure developments could be made to improve time-efficiency in access provision.
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Affiliation(s)
- Birgit A Simell
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Biomedicum 2U, Tukholmankati 8, 00290, Helsinki, Finland.
| | - Outi M Törnwall
- Biobanking and Biomolecular Resources Research Infrastructure, European Research Infrastructure Consortium (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6 8010 Graz, Austria
| | - Iiro Hämäläinen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Biomedicum 2U, Tukholmankati 8, 00290, Helsinki, Finland; National Institute for Health and Welfare (THL), Biomedicum 1, Haartmaninkatu 8, 00290, Helsinki, Finland
| | - H-Erich Wichmann
- Helmholtz Zentrum Munich, Institute of Epidemiology, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany; Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig Maximilians University, Geschwister-Scholl-Platz 1, 80539, Munich, Germany; Institute of Medical Statistics and Epidemiology, Technical University Munich, Arcisstraße 21, 80333, Munich, Germany
| | - Gabriele Anton
- Helmholtz Zentrum Munich, Institute of Epidemiology, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany; Institute of Medical Statistics and Epidemiology, Technical University Munich, Arcisstraße 21, 80333, Munich, Germany
| | - Paul Brennan
- International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France
| | - Laurene Bouvard
- International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France
| | - Nadia Slimani
- International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France
| | - Aurelie Moskal
- International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France
| | - Marc Gunter
- International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France; Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
| | - Kurt Zatloukal
- Medical University of Graz, Auenbruggerpl. 2, 8036, Graz, Austria
| | | | - Sirpa Soini
- National Institute for Health and Welfare (THL), Biomedicum 1, Haartmaninkatu 8, 00290, Helsinki, Finland
| | - Michaela T Mayrhofer
- Biobanking and Biomolecular Resources Research Infrastructure, European Research Infrastructure Consortium (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6 8010 Graz, Austria
| | | | - Gert-Jan van Ommen
- Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands
| | - Mattias Johansson
- International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France
| | - Markus Perola
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Biomedicum 2U, Tukholmankati 8, 00290, Helsinki, Finland; National Institute for Health and Welfare (THL), Biomedicum 1, Haartmaninkatu 8, 00290, Helsinki, Finland; University of Tartu, Estonian Genome Center, Riia 23b, 51010, Tartu, Estonia
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47
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Benet M, Albang R, Pinart M, Hohmann C, Tischer CG, Annesi-Maesano I, Baïz N, Bindslev-Jensen C, Lødrup Carlsen KC, Carlsen KH, Cirugeda L, Eller E, Fantini MP, Gehring U, Gerhard B, Gori D, Hallner E, Kull I, Lenzi J, McEachan R, Minina E, Momas I, Narduzzi S, Petherick ES, Porta D, Rancière F, Standl M, Torrent M, Wijga AH, Wright J, Kogevinas M, Guerra S, Sunyer J, Keil T, Bousquet J, Maier D, Anto JM, Garcia-Aymerich J. Integrating Clinical and Epidemiologic Data on Allergic Diseases Across Birth Cohorts: A Harmonization Study in the Mechanisms of the Development of Allergy Project. Am J Epidemiol 2019; 188:408-417. [PMID: 30351340 DOI: 10.1093/aje/kwy242] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 10/16/2018] [Indexed: 12/27/2022] Open
Abstract
The numbers of international collaborations among birth cohort studies designed to better understand asthma and allergies have increased in the last several years. However, differences in definitions and methods preclude direct pooling of original data on individual participants. As part of the Mechanisms of the Development of Allergy (MeDALL) Project, we harmonized data from 14 birth cohort studies (each with 3-20 follow-up periods) carried out in 9 European countries during 1990-1998 or 2003-2009. The harmonization process followed 6 steps: 1) organization of the harmonization panel; 2) identification of variables relevant to MeDALL objectives (candidate variables); 3) proposal of a definition for each candidate variable (reference definition); 4) assessment of the compatibility of each cohort variable with its reference definition (inferential equivalence) and classification of this inferential equivalence as complete, partial, or impossible; 5) convocation of a workshop to agree on the reference definitions and classifications of inferential equivalence; and 6) preparation and delivery of data through a knowledge management portal. We agreed on 137 reference definitions. The inferential equivalence of 3,551 cohort variables to their corresponding reference definitions was classified as complete, partial, and impossible for 70%, 15%, and 15% of the variables, respectively. A harmonized database was delivered to MeDALL investigators. In asthma and allergy birth cohorts, the harmonization of data for pooled analyses is feasible, and high inferential comparability may be achieved. The MeDALL harmonization approach can be used in other collaborative projects.
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Affiliation(s)
- Marta Benet
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
| | | | - Mariona Pinart
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Cynthia Hohmann
- Institute for Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Christina G Tischer
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
| | - Isabella Annesi-Maesano
- Epidemiology of Allergic and Respiratory Diseases Department, Institut Pierre Louis d’Epidémiologie et de Santé Publique, Institut National de la Santé et de la Recherche Médicale, Paris, France
- Saint-Antoine Medical School, Université Pierre et Marie Curie, Paris, France
| | - Nour Baïz
- Epidemiology of Allergic and Respiratory Diseases Department, Institut Pierre Louis d’Epidémiologie et de Santé Publique, Institut National de la Santé et de la Recherche Médicale, Paris, France
- Saint-Antoine Medical School, Université Pierre et Marie Curie, Paris, France
| | - Carsten Bindslev-Jensen
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, Odense, Denmark
| | - Karin C Lødrup Carlsen
- Department of Paediatric Allergy and Pulmonology, Division of Paediatric and Adolescent Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Kai-Hakon Carlsen
- Department of Paediatric Allergy and Pulmonology, Division of Paediatric and Adolescent Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Lourdes Cirugeda
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
| | - Esben Eller
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, Odense, Denmark
| | - Maria Pia Fantini
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum–University of Bologna, Bologna, Italy
| | - Ulrike Gehring
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | | | - Davide Gori
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum–University of Bologna, Bologna, Italy
| | - Eva Hallner
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
| | - Inger Kull
- Sachs’ Children and Youth Hospital, South General Hospital Stockholm, Stockholm, Sweden
- Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden
| | - Jacopo Lenzi
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum–University of Bologna, Bologna, Italy
| | - Rosemary McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | | | - Isabelle Momas
- Université Paris Descartes, Sorbonne Paris Cité, EA 4064 Epidémiologie Environnementale, Paris, France
- Mairie de Paris, Direction de l’Action Sociale de l’Enfance et de la Santé, Cellule Cohorte, Paris, France
| | - Silvia Narduzzi
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Emily S Petherick
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom
| | - Daniela Porta
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Fanny Rancière
- Université Paris Descartes, Sorbonne Paris Cité, EA 4064 Epidémiologie Environnementale, Paris, France
| | - Marie Standl
- Institute of Epidemiology I, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Maties Torrent
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
- Servei de Salut de les Illes Balears, Area de Salut de Menorca, Spain
| | - Alet H Wijga
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - Manolis Kogevinas
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
- National School of Public Health, Athens, Greece
| | - Stefano Guerra
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona
| | - Jordi Sunyer
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Thomas Keil
- Institute for Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jean Bousquet
- Contre les Maladies Chroniques pour un Vieillissement Actif en France, European Innovation Partnership on Active and Healthy Ageing Reference Site, Montpellier, France
- Institut National de la Santé et de la Recherche Médicale, Unité Mixte de Recherche 1168
| | | | - Josep M Anto
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Judith Garcia-Aymerich
- ISGlobal
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Barcelona, Spain
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48
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Kourou KD, Pezoulas VC, Georga EI, Exarchos TP, Tsanakas P, Tsiknakis M, Varvarigou T, De Vita S, Tzioufas A, Fotiadis DI. Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective. IEEE Rev Biomed Eng 2019; 12:303-318. [DOI: 10.1109/rbme.2018.2855055] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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49
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Pezoulas VC, Exarchos TP, Andronikou V, Varvarigou T, Tzioufas AG, De Vita S, Fotiadis DI. Towards the Establishment of a Biomedical Ontology for the Primary Sjögren's Syndrome. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4089-4092. [PMID: 30441255 DOI: 10.1109/embc.2018.8513349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Primary Sjögren's Syndrome (pSS) has been characterized as a hypersensitivity reaction type II systemic autoimmune chronic disease causing exocrine gland dysfunction mainly affecting women near the menopausal age. pSS patients exhibit dryness of the main mucosal surfaces and are highly prone to lymphoma development. This paper presents a first biomedical ontology for pSS based on a reference model which was determined by pSS clinical experts. The ensuing ontology constitutes the fundamental basis for mapping pSS-related ontologies from international cohorts to a common ontology. The ontology mapping (i.e., schematic interlinking) procedure is, in fact, a preliminary step to harmonize heterogeneous medical data obtained from various cohorts.
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van Veen EB. Observational health research in Europe: understanding the General Data Protection Regulation and underlying debate. Eur J Cancer 2018; 104:70-80. [PMID: 30336359 DOI: 10.1016/j.ejca.2018.09.032] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 09/27/2018] [Indexed: 01/26/2023]
Abstract
Insights into the incidence and survival of cancer, the influence of lifestyle and environmental factors and the interaction of treatment regimens with outcomes are hugely dependent on observational research, patient data derived from the healthcare system and from volunteers participating in cohort studies, often non-selective. Since 25th May 2018, the European General Data Protection Regulation (GDPR) applies to such data. The GDPR focusses on more individual control for data subjects of 'their' data. Yet, the GDPR was preceded by a long debate. The research community participated actively in that debate, and as a result, the GDPR has research exemptions as well. Some of those apply directly; other exemptions need to be implemented into national law. Those exemptions will be discussed together with a general outline of the GDPR. I propose a substantive definition of research-absent in the GDPR-which can warrant its special status in the GDPR. The debate is not over yet. Most legal texts exhibit ambiguity and are interpreted against a background of values. In this case, those could be subsumed under informational self-determination versus solidarity and the deeper meaning of autonomy. Values will also guide national implementation and their interpretation. The value of individual control or informational self-determination should be balanced by nuanced visions about our mutual dependency in healthcare, as an ever-learning system, especially in the European solidarity-based healthcare systems. Good research governance might be a way forward to escape the consent or anonymise dichotomy.
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Affiliation(s)
- Evert-Ben van Veen
- MLC Foundation, Dagelijkse Groenmarkt 2, 2513 AL Den Haag, the Netherlands.
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