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Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project

Ditte Mølgaard-Nielsen Orcid Logo, Vera Mitter Orcid Logo, Angela Lupattelli Orcid Logo, Vjola Hoxhaj, Constanza L Andaur Navarro Orcid Logo, Saeed Hayati, Sandra Lopez-Leon, Joan K Morris Orcid Logo, Anja Geldof, Sue Jordan, Maarit K Leinonen Orcid Logo, Visa Martikainen, Marco Manfrini, Luca Cammarota, Amanda Neville, Laia Barrachina-Bonet Orcid Logo, Clara Cavero-Carbonell, Laura García-Villodre, Anthony Caillet, Marie Beslay, Christine Damase-Michel, Marleen M H J van Gelder Orcid Logo, Hedvig Nordeng

BMJ Open, Volume: 15, Issue: 10, Start page: e102343

Swansea University Author: Sue Jordan

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DOI (Published version): 10.1136/bmjopen-2025-102343

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Objective To develop and compare algorithms for identifying gestational diabetes mellitus (GDM) across European electronic healthcare databases and evaluate their impact on the estimated prevalence.Design Multi-national cohort study using routinely collected electronic healthcare dataSetting Nationa...

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ISSN: 2044-6055 2044-6055
Published: BMJ 2025
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In the Nordic countries, GDM prevalence varied only slightly by algorithm; greater variations were observed in other countries. The prevalence ranged from 3.5% (95% CI: 3.5% to 3.5%) to 4.6% (95% CI: 4.5% to 4.7%) in Norway; 12.1% (95% CI: 12.0% to 12.2%) to 15.8% (95% CI: 15.7% to 15.9%) in Finland, where prevalence was much higher than elsewhere. The prevalence ranged from 1.3% (95% CI: 1.3% to 1.3%) to 5.4% (95% CI: 5.3% to 5.5%) in Italy; 1.6% (95% CI: 1.5% to 1.7%) to 6.2% (95% CI: 6.1% to 6.3%) in Spain; and 1.7% (95% CI: 1.6% to 1.8%) to 5.8% (95% CI: 5.7% to 5.9%) in France.Conclusions In this multinational study, GDM prevalence ranged from 1.3% to 15.8% depending on the algorithm and database. 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spelling 2026-02-09T12:36:30.1041142 v2 71345 2026-01-29 Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project 24ce9db29b4bde1af4e83b388aae0ea1 Sue Jordan Sue Jordan true false 2026-01-29 Objective To develop and compare algorithms for identifying gestational diabetes mellitus (GDM) across European electronic healthcare databases and evaluate their impact on the estimated prevalence.Design Multi-national cohort study using routinely collected electronic healthcare dataSetting National and regional databases in five European countries (Norway, Finland, Italy, Spain and France), in primary and/or secondary care.Participants Pregnancy cohorts resulting in stillbirths or live births between 2009 and 2020, comprising 602 897 pregnancies in Norway, 507 904 in Finland, 374 009 in Italy, 193 495 in Spain and 116 762 in France.Primary and secondary outcomes The primary outcome was the prevalence of GDM identified using six algorithms: (1) Only diagnosis; (2) Diagnosis or prescription; (3) Two diagnoses or prescriptions (2DxRx); (4) Diagnosis including unspecified diabetes in pregnancy or prescription (DxRx broad); (5) Diagnosis excluding pre-existing diabetes in pregnancy or prescription; (6) Registration of GDM in a birth registry (BR).Results The strictest algorithm (2DxRx) resulted in the lowest GDM prevalence, while the broadest (DxRx broad) resulted in the highest, except in France where it was BR. In the Nordic countries, GDM prevalence varied only slightly by algorithm; greater variations were observed in other countries. The prevalence ranged from 3.5% (95% CI: 3.5% to 3.5%) to 4.6% (95% CI: 4.5% to 4.7%) in Norway; 12.1% (95% CI: 12.0% to 12.2%) to 15.8% (95% CI: 15.7% to 15.9%) in Finland, where prevalence was much higher than elsewhere. The prevalence ranged from 1.3% (95% CI: 1.3% to 1.3%) to 5.4% (95% CI: 5.3% to 5.5%) in Italy; 1.6% (95% CI: 1.5% to 1.7%) to 6.2% (95% CI: 6.1% to 6.3%) in Spain; and 1.7% (95% CI: 1.6% to 1.8%) to 5.8% (95% CI: 5.7% to 5.9%) in France.Conclusions In this multinational study, GDM prevalence ranged from 1.3% to 15.8% depending on the algorithm and database. Nordic countries showed smaller differences in prevalence between algorithms, while the other countries showed larger variations, likely due to differences in coding practices, healthcare systems and database coverage. Journal Article BMJ Open 15 10 e102343 BMJ 2044-6055 2044-6055 5 10 2025 2025-10-05 10.1136/bmjopen-2025-102343 COLLEGE NANME COLLEGE CODE Swansea University Another institution paid the OA fee The ConcePTION project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 821520. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. 2026-02-09T12:36:30.1041142 2026-01-29T15:17:19.9139904 Faculty of Medicine, Health and Life Sciences School of Health and Social Care - Nursing Ditte Mølgaard-Nielsen 0000-0003-4813-4289 1 Vera Mitter 0000-0002-1483-5020 2 Angela Lupattelli 0000-0002-8787-3183 3 Vjola Hoxhaj 4 Constanza L Andaur Navarro 0000-0002-7745-2887 5 Saeed Hayati 6 Sandra Lopez-Leon 7 Joan K Morris 0000-0002-7164-612x 8 Anja Geldof 9 Sue Jordan 10 Maarit K Leinonen 0000-0002-7631-4749 11 Visa Martikainen 12 Marco Manfrini 13 Luca Cammarota 14 Amanda Neville 15 Laia Barrachina-Bonet 0000-0002-5272-265x 16 Clara Cavero-Carbonell 17 Laura García-Villodre 18 Anthony Caillet 19 Marie Beslay 20 Christine Damase-Michel 21 Marleen M H J van Gelder 0000-0003-4853-4434 22 Hedvig Nordeng 23 71345__36211__07276ca86a274c8697cefdd1285249a8.pdf 71345.VoR.pdf 2026-02-09T12:34:16.7780999 Output 1167848 application/pdf Version of Record true © Author(s) (or their employer(s)) 2025. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license. true eng http://creativecommons.org/licenses/by-nc/4.0/
title Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
spellingShingle Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
Sue Jordan
title_short Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
title_full Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
title_fullStr Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
title_full_unstemmed Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
title_sort Identification of gestational diabetes mellitus in European electronic healthcare databases: insights from the ConcePTION project
author_id_str_mv 24ce9db29b4bde1af4e83b388aae0ea1
author_id_fullname_str_mv 24ce9db29b4bde1af4e83b388aae0ea1_***_Sue Jordan
author Sue Jordan
author2 Ditte Mølgaard-Nielsen
Vera Mitter
Angela Lupattelli
Vjola Hoxhaj
Constanza L Andaur Navarro
Saeed Hayati
Sandra Lopez-Leon
Joan K Morris
Anja Geldof
Sue Jordan
Maarit K Leinonen
Visa Martikainen
Marco Manfrini
Luca Cammarota
Amanda Neville
Laia Barrachina-Bonet
Clara Cavero-Carbonell
Laura García-Villodre
Anthony Caillet
Marie Beslay
Christine Damase-Michel
Marleen M H J van Gelder
Hedvig Nordeng
format Journal article
container_title BMJ Open
container_volume 15
container_issue 10
container_start_page e102343
publishDate 2025
institution Swansea University
issn 2044-6055
2044-6055
doi_str_mv 10.1136/bmjopen-2025-102343
publisher BMJ
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Health and Social Care - Nursing{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Health and Social Care - Nursing
document_store_str 1
active_str 0
description Objective To develop and compare algorithms for identifying gestational diabetes mellitus (GDM) across European electronic healthcare databases and evaluate their impact on the estimated prevalence.Design Multi-national cohort study using routinely collected electronic healthcare dataSetting National and regional databases in five European countries (Norway, Finland, Italy, Spain and France), in primary and/or secondary care.Participants Pregnancy cohorts resulting in stillbirths or live births between 2009 and 2020, comprising 602 897 pregnancies in Norway, 507 904 in Finland, 374 009 in Italy, 193 495 in Spain and 116 762 in France.Primary and secondary outcomes The primary outcome was the prevalence of GDM identified using six algorithms: (1) Only diagnosis; (2) Diagnosis or prescription; (3) Two diagnoses or prescriptions (2DxRx); (4) Diagnosis including unspecified diabetes in pregnancy or prescription (DxRx broad); (5) Diagnosis excluding pre-existing diabetes in pregnancy or prescription; (6) Registration of GDM in a birth registry (BR).Results The strictest algorithm (2DxRx) resulted in the lowest GDM prevalence, while the broadest (DxRx broad) resulted in the highest, except in France where it was BR. In the Nordic countries, GDM prevalence varied only slightly by algorithm; greater variations were observed in other countries. The prevalence ranged from 3.5% (95% CI: 3.5% to 3.5%) to 4.6% (95% CI: 4.5% to 4.7%) in Norway; 12.1% (95% CI: 12.0% to 12.2%) to 15.8% (95% CI: 15.7% to 15.9%) in Finland, where prevalence was much higher than elsewhere. The prevalence ranged from 1.3% (95% CI: 1.3% to 1.3%) to 5.4% (95% CI: 5.3% to 5.5%) in Italy; 1.6% (95% CI: 1.5% to 1.7%) to 6.2% (95% CI: 6.1% to 6.3%) in Spain; and 1.7% (95% CI: 1.6% to 1.8%) to 5.8% (95% CI: 5.7% to 5.9%) in France.Conclusions In this multinational study, GDM prevalence ranged from 1.3% to 15.8% depending on the algorithm and database. Nordic countries showed smaller differences in prevalence between algorithms, while the other countries showed larger variations, likely due to differences in coding practices, healthcare systems and database coverage.
published_date 2025-10-05T05:35:14Z
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