↓ Skip to main content

The quality of economic evaluations of ultra-orphan drugs in Europe – a systematic review

Overview of attention for article published in Orphanet Journal of Rare Diseases, July 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

policy
1 policy source
twitter
12 X users
facebook
2 Facebook pages

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
180 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The quality of economic evaluations of ultra-orphan drugs in Europe – a systematic review
Published in
Orphanet Journal of Rare Diseases, July 2015
DOI 10.1186/s13023-015-0305-y
Pubmed ID
Authors

Y. Schuller, C. E. M. Hollak, M. Biegstraaten

Abstract

An orphan disease is defined in the EU as a disorder affecting less than 1 in 2 000 individuals. The concept of ultra-orphan has been proposed for diseases with a prevalence of less than 1:50 000. Drugs for ultra-orphan diseases are amongst the most expensive medicines on a cost-per-patient basis. The extremely high prices have prompted initiatives to evaluate cost-effectiveness and cost-utility in EU-member states. The objective of this review was to evaluate the quality of cost-effectiveness and cost-utility studies on ultra-orphan drugs. We searched 2 databases and the reference lists of relevant systematic reviews. Studies reporting on full economic evaluations, or at least aiming at such evaluation, were eligible for inclusion. Quality was assessed with the use of the Consensus on Health Economic Criteria (CHEC)-list. Two-hundred-fifty-one studies were identified. Of these, 16 fitted our inclusion criteria. A study on enzyme replacement and substrate reduction therapies for lysosomal storage disorders did not perform a full economic evaluation due to the high drug costs and the lack of a measurable effect on either clinical or health-related quality of life outcomes. Likewise, a cost-effectiveness analysis of laronidase for mucopolysaccharidosis type 1 was considered unfeasible due to lack of clinical effectiveness data, while in the same study a crude model was used to estimate cost-utility of enzyme replacement therapy (ERT) for Fabry disease. Three additional studies, one on ERT for Fabry disease, one on ERT for Gaucher disease and one on eculizumab for paroxysmal nocturnal haemoglobinuria, used an approach that was too simplistic to lead to a realistic estimate of the incremental cost-effectiveness (ICER) or cost-utility ratio (ICUR). In all other studies (N = 11) more sophisticated pharmacoeconomic models were used to estimate cost-effectiveness and cost-utility of the specific drug, mostly ERT or drugs indicated for pulmonary arterial hypertension (PAH). Seven studies used a Markov-state-transition model. Other models used were patient-level simulation models (N = 3) and decision trees (N = 1). Only 4 studies adopted a societal perspective. All but 2 studies discounted costs and effects appropriately. Drugs for metabolic diseases appeared to be significantly less cost-effective than drugs indicated for PAH, with ICERs ranging from €43 532 (Gaucher disease) to €3 282 252 (Fabry disease). Quality of studies using a Markov-state-transition or patient-level simulation model is in general good with 14-19 points on the CHEC-list. We therefore conclude that economic evaluations of ultra-orphan drugs are feasible if pharmacoeconomic modelling is used. Considering the need for modelling of several disease states and the small patient groups, a Markov-state-transition model seems to be most suitable type of model. However, it should be realised that ultra-orphan drugs will usually not meet the conventional criteria for cost-effectiveness. Nevertheless, ultra-orphan drugs are often reimbursed. Further discussion on the use of economic evaluations and their consequences in case of ultra-orphan drugs is therefore warranted.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 180 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 <1%
Brazil 1 <1%
Unknown 178 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 16%
Student > Master 28 16%
Other 22 12%
Student > Ph. D. Student 19 11%
Student > Bachelor 12 7%
Other 29 16%
Unknown 41 23%
Readers by discipline Count As %
Medicine and Dentistry 39 22%
Pharmacology, Toxicology and Pharmaceutical Science 22 12%
Economics, Econometrics and Finance 12 7%
Biochemistry, Genetics and Molecular Biology 10 6%
Agricultural and Biological Sciences 10 6%
Other 34 19%
Unknown 53 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 May 2020.
All research outputs
#2,949,184
of 25,342,911 outputs
Outputs from Orphanet Journal of Rare Diseases
#387
of 3,066 outputs
Outputs of similar age
#35,718
of 269,554 outputs
Outputs of similar age from Orphanet Journal of Rare Diseases
#10
of 35 outputs
Altmetric has tracked 25,342,911 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,066 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,554 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.