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A method to simulate incentives for cost containment under various cost sharing designs: an application to a first-euro deductible and a doughnut hole

Overview of attention for article published in HEPAC Health Economics in Prevention and Care, November 2016
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

news
1 news outlet
twitter
3 X users
facebook
1 Facebook page

Citations

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3 Dimensions

Readers on

mendeley
19 Mendeley
Title
A method to simulate incentives for cost containment under various cost sharing designs: an application to a first-euro deductible and a doughnut hole
Published in
HEPAC Health Economics in Prevention and Care, November 2016
DOI 10.1007/s10198-016-0843-9
Pubmed ID
Authors

D. Cattel, R. C. van Kleef, R. C. J. A. van Vliet

Abstract

Many health insurance schemes include deductibles to provide consumers with cost containment incentives (CCI) and to counteract moral hazard. Policymakers are faced with choices on the implementation of a specific cost sharing design. One of the guiding principles in this decision process could be which design leads to the strongest CCI. Despite the vast amount of literature on the effects of cost sharing, the relative effects of specific cost sharing designs-e.g., a traditional deductible versus a doughnut hole-will mostly be absent for a certain context. This papers aims at developing a simulation model to approximate the relative effects of different deductible modalities on the CCI. We argue that the CCI depends on the probability that healthcare expenses end up in the deductible range and the expected healthcare expenses given that they end up in the deductible range. Our empirical application shows that different deductible modalities result in different CCIs and that the CCI under a certain modality differs across risk-groups.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 26%
Student > Master 3 16%
Professor > Associate Professor 2 11%
Student > Ph. D. Student 1 5%
Lecturer > Senior Lecturer 1 5%
Other 2 11%
Unknown 5 26%
Readers by discipline Count As %
Economics, Econometrics and Finance 4 21%
Medicine and Dentistry 4 21%
Nursing and Health Professions 2 11%
Business, Management and Accounting 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Other 1 5%
Unknown 5 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 05 October 2017.
All research outputs
#3,240,737
of 25,604,262 outputs
Outputs from HEPAC Health Economics in Prevention and Care
#195
of 1,310 outputs
Outputs of similar age
#50,654
of 313,842 outputs
Outputs of similar age from HEPAC Health Economics in Prevention and Care
#6
of 33 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,310 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done well, scoring higher than 85% 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 313,842 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 83% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.