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Calculating the U.S. Population-based EQ-5D Index Score

Research Initiative in Clinical Economics

Research

Calculating the U.S. Population-based EQ-5D™ Index Score


The EQ-5D™ has two parts. The first part is a descriptive system that classifies respondents into one of 243 distinct health states. The descriptive system consists of the following five dimensions:

  1. Mobility (MO).
  2. Self-care (SC).
  3. Usual activities (UA).
  4. Pain/discomfort (PD).
  5. Anxiety/depression (AD).

Each dimension has three possible levels (i.e., 1, 2, or 3), representing "no problems," "some problems," and "extreme problems," respectively. Respondents are asked to choose one level that reflects their "own health state today" for each of the five dimensions. Once the data have been collected and a database created, a scoring function can be used to assign a value (i.e., EQ-5D™ index score) to self-reported health states from a set of population-based preference weights.

The second part is a 20-cm visual analog scale (EQ-VAS) that has endpoints labeled "best imaginable health state" and "worst imaginable health state" anchored at 100 and 0, respectively. Respondents are asked to indicate how they rate their own health by drawing a line from an anchor box to that point on the EQ-VAS which best represents their own health on that day. Hence, the EQ-5D™ produces three types of data for each respondent:

  1. A profile indicating the extent of problems on each of the five dimensions represented by a five-digit descriptor (e.g., 11121, 33211)
  2. A population preference-weighted health index score based on the descriptive system.
  3. A self-reported assessment of health status based on the EQ-VAS.

To calculate the EQ-5D™ index score based on the U.S. population-based preference weights, a scoring algorithm has been created and operationalized in SAS, Stata, SPSS, and Excel. A downloadable file is available on this website for each program.

To use the SAS-, Stata-, and SPSS-based scoring algorithms, the analytical dataset must include the dimension variables named as follows:

DimensionVariable Name
MobilityMO
Self-careSC
Usual activitiesUA
Pain/discomfortPD
Anxiety/depressionAD

The possible range for each of the dimension variables is 1 to 3, where 1 = no problems, 2 = moderate problems, and 3 = extreme problems. Missing values should be left blank. The index score will not be calculated when responses are missing for one or more of the dimensions. Once the analytical dataset has appropriately named dimension variables, the SAS, Stata, or SPSS program code can be run to generate the EQ-5D™ index scores.

To use the Excel® file to generate the EQ-5D™ index scores, data for the five dimensions (MO, SC, UA, PD, AD) is pasted into the first five columns in the spreadsheet. Once calculated, the EQ-5D™ index score appears in the last column.

For the U.S. general population, the possible EQ-5D™ index scores range from -0.11 (i.e., 33333) to 1.0 (i.e., 11111) on a scale where 0.0 = death and 1.0 = perfect health. The scoring algorithm was based on the modeling work presented in the following article:

Shaw JW, Johnson JA , Coons SJ. U.S. valuation of the EQ-5D™ health states: development and testing of the D1 valuation model. Medical Care. In press.

The scoring algorithm are released here in four separate formats (SAS, SPSS, STATA and Excel) to accommodate the need of researchers who use different statistical analysis software:

Current as of August 2005


Internet Citation:

Calculating the U.S. Population-based EQ-5D™ Index Score.� August 2005. Agency for Healthcare Research and Quality, Rockville, MD.�http://www.ahrq.gov/rice/EQ5Dscore.htm


 

Page last reviewed February 2005
Internet Citation: Calculating the U.S. Population-based EQ-5D Index Score: Research Initiative in Clinical Economics. February 2005. Agency for Healthcare Research and Quality, Rockville, MD. https://archive.ahrq.gov/professionals/clinicians-providers/resources/rice/EQ5Dscore.html

 

The information on this page is archived and provided for reference purposes only.

 

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