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Incidence Rates of Autoimmune Diseases in European Healthcare Databases: A Contribution of the ADVANCE Project

Author

Listed:
  • Corinne Willame

    (Julius Global Health, University Medical Center Utrecht)

  • Caitlin Dodd

    (Julius Global Health, University Medical Center Utrecht)

  • Lieke van der Aa

    (Sciensano)

  • Gino Picelli

    (Epidemiological Information for Clinical Research from an Italian Network of Family Paediatricians (Pedianet))

  • Hanne-Dorthe Emborg

    (Statens Serum Institut)

  • Johnny Kahlert

    (Aarhus University Hospital)

  • Rosa Gini

    (Agenzia regionale di sanità della Toscana)

  • Consuelo Huerta

    (Spanish Agency of Medicines and Medical Devices-AEMPS)

  • Elisa Martín-Merino

    (Spanish Agency of Medicines and Medical Devices-AEMPS)

  • Chris McGee

    (University of Surrey
    Royal College of General Practitioners, Research and Surveillance Centre)

  • Simon Lusignan

    (University of Surrey
    Royal College of General Practitioners, Research and Surveillance Centre)

  • Giuseppe Roberto

    (Agenzia regionale di sanità della Toscana)

  • Marco Villa

    (Ospedale Val Padana)

  • Daniel Weibel

    (VACCINE.GRID
    Erasmus University Medical Center)

  • Lina Titievsky

    (Pfizer)

  • Miriam C. J. M. Sturkenboom

    (Julius Global Health, University Medical Center Utrecht
    VACCINE.GRID
    P-95)

Abstract

Introduction The public–private ADVANCE collaboration developed and tested a system to generate evidence on vaccine benefits and risks using European electronic healthcare databases. In the safety of vaccines, background incidence rates are key to allow proper monitoring and assessment. The goals of this study were to compute age-, sex-, and calendar-year stratified incidence rates of nine autoimmune diseases in seven European healthcare databases from four countries and to assess validity by comparing with published data. Methods Event rates were calculated for the following outcomes: acute disseminated encephalomyelitis, Bell’s palsy, Guillain–Barré syndrome, immune thrombocytopenia purpura, Kawasaki disease, optic neuritis, narcolepsy, systemic lupus erythematosus, and transverse myelitis. Cases were identified by diagnosis codes. Participating organizations/databases originated from Denmark, Italy, Spain, and the UK. The source population comprised all persons registered, with at least 1 year of data prior to the study start, or follow-up from birth. Stratified incidence rates were computed per database over the period 2003 to 2014. Results Between 2003 and 2014, 148,947 incident cases of nine autoimmune diseases were identified. Crude incidence rates were highest for Bell’s palsy [23.8/100,000 person-years (PYs), 95% confidence interval (CI) 23.6–24.1] and lowest for Kawasaki disease (0.7/100,000 PYs, 95% CI 0.6–0.7). Specific patterns were observed by sex, age, calendar time, and data sources. Rates were comparable with published estimates. Conclusion A range of autoimmune events could be identified in the ADVANCE system. Estimation of rates indicated consistency across selected European healthcare databases, as well as consistency with US published data.

Suggested Citation

  • Corinne Willame & Caitlin Dodd & Lieke van der Aa & Gino Picelli & Hanne-Dorthe Emborg & Johnny Kahlert & Rosa Gini & Consuelo Huerta & Elisa Martín-Merino & Chris McGee & Simon Lusignan & Giuseppe Ro, 2021. "Incidence Rates of Autoimmune Diseases in European Healthcare Databases: A Contribution of the ADVANCE Project," Drug Safety, Springer, vol. 44(3), pages 383-395, March.
  • Handle: RePEc:spr:drugsa:v:44:y:2021:i:3:d:10.1007_s40264-020-01031-1
    DOI: 10.1007/s40264-020-01031-1
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    Cited by:

    1. Andrea Spini & Giulia Hyeraci & Claudia Bartolini & Sandra Donnini & Pietro Rosellini & Rosa Gini & Marina Ziche & Francesco Salvo & Giuseppe Roberto, 2021. "Real-World Utilization of Target- and Immunotherapies for Lung Cancer: A Scoping Review of Studies Based on Routinely Collected Electronic Healthcare Data," IJERPH, MDPI, vol. 18(14), pages 1-21, July.
    2. Likeng Liang & Jifa Hu & Gang Sun & Na Hong & Ge Wu & Yuejun He & Yong Li & Tianyong Hao & Li Liu & Mengchun Gong, 2022. "Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources," Drug Safety, Springer, vol. 45(5), pages 511-519, May.

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