Fibrous Dysplasia in Hospitalized Patients in France: Prevalence and Associated Manifestations from a Health Database

Lucie Levaillant1,2, Régis Coutant1,3, Patrice Rodien2,3,4

  • 1Department of Pediatric Endocrinology and Diabetology, University Hospital of Angers, Endo-ERN Center for Rare Endocrine Conditions, 49933 Angers Cedex 9, France.

Abstract

Insights

This study estimates the prevalence of Fibrous Dysplasia (FD)/McCune-Albright Syndrome (MAS) in France, finding 5.36 cases per million. Treatment data reveals common associated manifestations, highlighting the need for better data collection.

Area of Science:

  • Endocrinology
  • Genetics
  • Public Health

Background:

  • Fibrous dysplasia (FD)/McCune-Albright syndrome (MAS) is a rare genetic disorder affecting bones and endocrine systems.
  • Existing data on FD/MAS prevalence and manifestations are limited, particularly in France.

Purpose of the Study:

  • To estimate the prevalence and incidence of FD/MAS in hospitalized patients in France.
  • To assess the frequency of associated manifestations in FD/MAS patients.

Main Methods:

  • Retrospective cohort study using the French National Health Insurance Database (2012-2024).
  • Identified patients with monostotic FD (MFD) or polyostotic FD/MAS (PFD/MAS) diagnoses.
  • Calculated prevalence and incidence for 2024; analyzed manifestations via diagnoses and drug dispensing data.

Main Results:

  • 2249 patients hospitalized between 2012-2024; 60% had PFD/MAS, 63% female, 26% children.
  • 2024 prevalence: 5.36/million (95% CI: 4.83-5.94); 2024 incidence: 2.58/million (95% CI: 2.21-2.99).
  • Annual treatment rates: bisphosphonates (27%), phosphate supplements (7.4%), aromatase inhibitors (3.3%), pituitary treatments (8%), antithyroid agents (2.2%).

Conclusions:

  • First nationwide estimates of hospitalized FD/MAS prevalence and incidence in France.
  • Hospitalization data likely underestimates monostotic FD frequency.
  • Treatment data provides insights into manifestations; improved outpatient coding and clinical data integration are recommended.

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