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Assessing rare diseases prevalence using literature quantification.

Jason Shourick1, Maxime Wack2, Anne-Sophie Jannot2,3

  • 1Department of Medical Informatics, Hôpital Européen Georges Pompidou, AP-HP, 20 Rue Leblanc, 75015, Paris, France. jason.shourick@aphp.fr.

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Summary
This summary is machine-generated.

The number of scientific publications can effectively predict rare disease prevalence. This study demonstrates a strong link between publication volume and disease rarity, offering a valuable tool for healthcare organization.

Keywords:
BibliometricsPrevalenceRare diseases

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Area of Science:

  • Medical Informatics
  • Epidemiology
  • Bibliometrics

Background:

  • Accurate disease prevalence estimation is vital for healthcare planning.
  • Differentiating rare from very rare diseases can be informed by existing literature.
  • The volume of research on a pathology may correlate with its prevalence.

Purpose of the Study:

  • To assess the predictive capability of publication counts for disease prevalence.
  • To determine if literature volume can differentiate rare disease categories.

Main Methods:

  • Queried Orphanet for global prevalence classes of conditions.
  • Cross-referenced Orphanet, MeSH, and OMIM to count publications in PubMed.
  • Evaluated association and predictive performance of publication counts using three query strategies.

Main Results:

  • Prevalence data was available for 3128 conditions.
  • Significant association found between publication numbers and prevalence classes.
  • The best query strategy achieved an Area Under the Curve (AUC) over 94% for prediction.

Conclusions:

  • A strong link exists between publication volume and rare disease prevalence.
  • The number of publications demonstrates excellent predictive performance for disease rarity.
  • This bibliometric approach aids in understanding and organizing rare disease healthcare.