Factors Influencing the Generation of Evidence from Simple Data Held in International Rare Disease Patient Registries

Pharmaceut Med. 2020 Feb;34(1):31-38. doi: 10.1007/s40290-019-00316-w.

Abstract

Background: Rare diseases (defined as affecting < 1 in 2000 Europeans) may collectively affect up to approximately 8% of the population. The low prevalence of individual diseases limits patient studies and data collection is a key challenge; international rare disease patient registries are essential for optimal data collection and research. Registry data achieves value when research conducted on them are published-this is termed evidence generation.

Objective: The aim of this study was to examine selected factors and their association with evidence generation, via scientific publication, from international rare disease patient registry data.

Methods: All international rare disease patient registries listed in the Orphanet 2018 report were analysed. Rates of scientific publications were compared by funding stream, disease area and registry size using multivariable regression analyses. Publication characteristics, such as novelty of findings, were also compared by registry funding stream, disease area and duration of operation.

Results: Privately funded registries had approximately two to four times higher rates of scientific publication compared with publically funded registries, with adjusted rate ratios of 1.85 (95% confidence interval [CI] 1.07-3.22) and 4.18 (95% CI 2.54-6.87) for private not-for-profit and private for-profit funding, respectively. The inclusion of outcomes, use of pharmaceutical medicines, novel findings and citation rate for publications generated from patient registries with any private funding was not significantly different from those attributed to only publicly funded registries.

Conclusion: The results of this study indicate that privately funded international rare disease patient registries produce significantly more evidence than their publicly funded counterparts. Examination of the quality indicators of these publications showed they were of the same high quality as those generated from publicly funded patient registry data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Collection / methods*
  • Evidence-Based Medicine
  • Humans
  • Rare Diseases*
  • Registries