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Standardizing the Inclusion of Indirect Medical Costs in Economic Evaluations

Overview of attention for article published in PharmacoEconomics, October 2012
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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blogs
1 blog
policy
5 policy sources
twitter
8 X users

Citations

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71 Dimensions

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78 Mendeley
Title
Standardizing the Inclusion of Indirect Medical Costs in Economic Evaluations
Published in
PharmacoEconomics, October 2012
DOI 10.2165/11586130-000000000-00000
Pubmed ID
Authors

Pieter H.M. van Baal, Albert Wong, Laurentius C.J. Slobbe, Johan J. Polder, Werner B.F. Brouwer, G. Ardine de Wit

Abstract

A shortcoming of many economic evaluations is that they do not include all medical costs in life-years gained (also termed indirect medical costs). One of the reasons for this is the practical difficulties in the estimation of these costs. While some methods have been proposed to estimate indirect medical costs in a standardized manner, these methods fail to take into account that not all costs in life-years gained can be estimated in such a way. Costs in life-years gained caused by diseases related to the intervention are difficult to estimate in a standardized manner and should always be explicitly modelled. However, costs of all other (unrelated) diseases in life-years gained can be estimated in such a way. We propose a conceptual model of how to estimate costs of unrelated diseases in life-years gained in a standardized manner. Furthermore, we describe how we estimated the parameters of this conceptual model using various data sources and studies conducted in the Netherlands. Results of the estimates are embedded in a software package called 'Practical Application to Include future Disease costs' (PAID 1.0). PAID 1.0 is available as a Microsoft® Excel tool (available as Supplemental Digital Content via a link in this article) and enables researchers to 'switch off' those disease categories that were already included in their own analysis and to estimate future healthcare costs of all other diseases for incorporation in their economic evaluations. We assumed that total healthcare expenditure can be explained by age, sex and time to death, while the relationship between costs and these three variables differs per disease. To estimate values for age- and sex-specific per capita health expenditure per disease and healthcare provider stratified by time to death we used Dutch cost-of-illness (COI) data for the year 2005 as a backbone. The COI data consisted of age- and sex-specific per capita health expenditure uniquely attributed to 107 disease categories and eight healthcare provider categories. Since the Dutch COI figures do not distinguish between costs of those who die at a certain age (decedents) and those who survive that age (survivors), we decomposed average per capita expenditure into parts that are attributable to decedents and survivors, respectively, using other data sources.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Switzerland 1 1%
Unknown 76 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Student > Master 17 22%
Researcher 9 12%
Student > Bachelor 5 6%
Lecturer 3 4%
Other 9 12%
Unknown 17 22%
Readers by discipline Count As %
Medicine and Dentistry 19 24%
Economics, Econometrics and Finance 16 21%
Pharmacology, Toxicology and Pharmaceutical Science 6 8%
Business, Management and Accounting 3 4%
Nursing and Health Professions 3 4%
Other 7 9%
Unknown 24 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 15 February 2024.
All research outputs
#1,452,232
of 25,405,598 outputs
Outputs from PharmacoEconomics
#65
of 1,992 outputs
Outputs of similar age
#9,084
of 191,778 outputs
Outputs of similar age from PharmacoEconomics
#10
of 700 outputs
Altmetric has tracked 25,405,598 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,992 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 96% 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 191,778 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 700 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.