, Volume 29, Issue 3, pp 175–187 | Cite as

Standardizing the Inclusion of Indirect Medical Costs in Economic Evaluations

  • Pieter H.M. van BaalEmail author
  • Albert Wong
  • Laurentius C.J. Slobbe
  • Johan J. Polder
  • Werner B.F. Brouwer
  • G. Ardine de Wit
Leading Article Standardizing Indirect Medical Costs


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 lifeyears 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.


Economic Evaluation Health Expenditure Healthcare Expenditure Supplemental Digital Content Hospital Expenditure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The study was supported by a grant from the Dutch National Institute for Public Health and the Environment Strategic Research Fund (SOR S/260186/01/FU) and the Dutch Ministry of Health, Welfare and Sports, with full freedom of research and publication. The authors have no conflicts of interest that are directly relevant to the content of this article.


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Copyright information

© Springer International Publishing AG 2011

Authors and Affiliations

  • Pieter H.M. van Baal
    • 1
    • 2
    Email author
  • Albert Wong
    • 1
    • 3
  • Laurentius C.J. Slobbe
    • 4
  • Johan J. Polder
    • 3
    • 4
  • Werner B.F. Brouwer
    • 2
  • G. Ardine de Wit
    • 5
    • 6
  1. 1.Expertise Centre for Methodology and Information ServicesNational Institute for Public Health and the Environment (RIVM)Bilthoventhe Netherlands
  2. 2.Erasmus University Rotterdam, Institute of Health Policy & Management and Institute for Medical Technology AssessmentRotterdamthe Netherlands
  3. 3.Tilburg University, Department TranzoTilburgthe Netherlands
  4. 4.National Institute for Public Health and the Environment, Centre for Public Health ForecastingBilthoventhe Netherlands
  5. 5.National Institute for Public Health and the Environment, Centre for Prevention and Health Services ResearchBilthoventhe Netherlands
  6. 6.Julius Center for Health Sciences and Primary Care, University Medical CenterUtrechtthe Netherlands

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