U.S. Valuation of the EuroQol EQ-5 Health States

U.S. Valuation of the EuroQol EQ-5D™ Health States

The U.S. Public Health Service (PHS) Panel on Cost-Effectiveness in Health and Medicine recommended in 1996 that efforts be made to collect health states preference values from a nationally representative sample of the U.S. population. Among the generic instruments for measuring health-related quality of life, EQ-5D™ is one of the candidate preference-based instruments identified by the Panel to establish a common metric of health states preference values for use in the so-called "Reference Case" analysis.

This AHRQ research grant project is one of the most significant steps undertaken by the Agency and the research community to support the study of cost-effectiveness in health and medicine in the U.S. since the PHS Panel made their recommendations in 1996. It represents the first effort to establish a preference weighting system specifically for U.S., based on a nationally representative population sample and reflected community preferences for health states on a single common measurement metric. The most important result derived from this project is the scoring algorithm that produces U.S. specific "off the shelf" health states preference indices for use henceforth by all cost-effectiveness analyses of health care interventions or programs which also use EQ-5D™ as part of the outcomes measurement tools.

Furthermore, the wealth of information collected under this project also serves as an invaluable resource for further secondary data analysis to assess, for example, how demographic and socioeconomic factors might affect preference valuation. The release of privacy-compliant data into public domain should help facilitate research in this particular area.

Project Description

Contemporary clinical economic evaluation, specifically the cost-utility analysis of health interventions and programs, is built on a fundamental concept of outcomes—quality-adjusted life years (QALYs)—that attempts to measure the effectiveness of health interventions and programs on the population by quantifying their impacts on both the quantity (i.e., duration) and the quality of life. The quantity and duration of life can be objectively and easily measured by the analysis of mortality events and survival. The quality of life (that can be directly or indirectly attributed to the health conditions and/or interventions), however, represents a theoretical construct that is not directly observable and by definition has a subjective element in all quantifying efforts.

Different measurement approaches can be, and have been, utilized to quantify this "quality modifier" of the duration of life. Some of these measurement approaches rely upon assessment tools and instruments, such as various generic and disease-specific health-related quality of life questionnaires, that generate profiles of different domains/attributes considered to reflect quality of life. Most of these health profile measurement approaches, while informative in clinical applications, do not provide a single numerical value necessary to quantify the preference, utility, or valuation, one attaches to any given health states. Such values generally can only be obtained either via direct measurement using different techniques (e.g., standard gamble, time trade-off) or via preference-based (multi-attribute) health status classification instruments, such as the EQ-5D™. The virtue of these preference-based multi-attribute instruments lies in the empirically derived preference/utility weights that presumably reflect the value or desirability the population attaches to different health states. Among all the prominent and commonly used preference-based multi-attribute instruments, however, there isn't a single preference weighting system that derives its scoring algorithm empirically based on a nationally representative sample of the adult U.S. population.

This AHRQ study fills that void by establishing a U.S. population-based preference weighting system for EQ-5D™ through a series of time trade-off (TTO) exercises and statistical imputations. The EQ-5D™, previously known as the EuroQol instrument, is a generic health-related quality of life measure developed by a group of largely European-based researchers. It is intended to be a simple, self-administered questionnaire that not only contains a descriptive health state classification system but also is capable of generating a composite score or index reflecting the preference value associated with a given health state.

The EQ-5D™ descriptive system consists of five dimensions:

1. Mobility.
2. Self-care.
3. Usual activities.
4. Pain/discomfort.
5. Anxiety/depression.

Each dimension has three levels, reflecting "no health problems," "moderate health problems," and "extreme health problems." A dimension for which there are no problems is said to be at level 1, while a dimension for which there are extreme problems is said to be at level 3. Each unique health state described by the instrument has an associated 5-digit descriptor ranging from 11111 for perfect health to 33333 for the worst possible state. The resulting descriptive system defines 243 health states. In addition, "unconscious" and "immediate death" are included in the EQ-5D™ valuation process but are not a part of the descriptive system. The instrument also includes a 20-centimeter visual analog scale (i.e., EQ-VAS) for the self-assessment of current general health.

Note that not all of the 243 distinct health states derived from the permutations of five health domains and three response levels (i.e., 53 = 243) are logically possible (e.g., confined to bed but have no problems performing usual activities, etc.). Further, there are also cognitive limitations as to how many time trade-off (TTO) exercises between different health states an individual can be expected to effectively and reliably perform. The investigators, in keeping with the original design protocols for the U.K. Measurement and Valuation of Health (MVH) study, selected a subset of 45 health states from the 243 health states for direct measurement via the TTO exercise. Each individual in the face-to-face interview was asked to describe their own current health using the EQ-5D™ descriptive system, and to provide a rank-order as well as visual analog scale rating for a randomly selected set of 15 health states (two anchors—immediate death and no health problem, plus 13 other distinct health states) from the predetermined set of 45 health states. Each individual then performed a series of time trade-off (TTO) exercises containing choices between each of these 13 health states. Once the TTO values for the 45 health states were collected from the face-to-face interviews, statistical imputations were employed to derive at the values for the remaining health states.

Along with the EQ-5D™ and TTO data, personal demographic and socioeconomic information was collected from each respondent during the interview. In addition, respondents were asked to self-complete the 15-item "usual health" Health Utilities Index Mark 2/3 (HUI2/3) and rate their health on a 5-point scale (i.e., excellent to poor). Validity and reliability testing of the measurement data and empirical preference values was undertaken through series statistical analyses. Potential differences or similarities of health states preference values (as measured by and derived from EQ-5D™) between the U.S. and U.K. population as well as those of across ethnic groups (i.e., Hispanics vs. non-Hispanics Blacks vs. non-Hispanics Whites) will be examined via various statistical tests and analyses.

The target population for the study comprised the roughly 210 million civilian, non-institutionalized English- and Spanish-speaking adults, age 18 and older, who resided in the U.S. (50 states plus DC) in 2002. In conjunction with Research Triangle Institute (RTI), a multi-stage probability sample was selected from the target population using a sampling frame based on residential mailing lists and Census demographic data. The two largest minority groups in the U.S., Hispanics and non-Hispanic blacks, were oversampled to ensure adequate numbers of minority respondents. Accounting for household unit occupancy rate, screening rate, and the completion rate, it was projected that the effective yields for the final interview sample would be 4,007 individuals—of which 1,199 (30 percent) would be of Hispanics ethnic origin, 1,229 (31 percent) would be non-Hispanics Blacks, and 1,579 (39 percent) would be non-Hispanics Whites and other ethnic origins. Using a probabilities proportional to size systematic selection algorithm, 60 3-digit Zip Code Tabulation Areas (ZCTAs) were selected from a sampling frame of 883 3-digit ZCTAs formed by collapsing 5-digit ZCTAs to their first 3 digits. Two 5-digit Zip Codes were then selected from each of the 60 ZCTAs. In a third stage, 12,000 addresses ( 100 per Zip Code) were selected for screening and interview. Residents of the 12,000 selected addresses were then located and screened. Seventy-eight eligible addresses not recorded on the mailing list used to select the study sample were identified using a half-open interval linking procedure and added to the sample.

The data collection period began June 8, 2002, and continued through October 31, 2002. Data were collected by 109 field interviewers, including 30 bilingual interviewers. Interviews used a paper-and-pencil format and were administered in English or Spanish. From the list of eligible addresses, 5,237 persons were selected for interview. Completed interviews were obtained from 4,048 participants, yielding an unweighted interview response rate among screened addresses of 77.3 percent. The weighted interview response rate among screened addresses was slightly lower (75.0 percent) due to lower participation among the "other" group. The unweighted interview response rate among all sampled addresses, which accounts for both the screening rate and the interview participation rate, was 59.4 percent. The weighted equivalent was 56.3 percent. Findings from this study are scheduled to be published in the upcoming issue of the journal Medical Care.

The data collection instruments, including both the English version of the EQ-5D™ interview booklet and the English version of the self-administered questionnaire booklet, are released here to facilitate their use by future research projects.

• EQ-5D™ Interview Booklet (Word® file, 500 KB [Plugin Software Help]; PDF file, 828 KB [Plugin Software Help]).
• Self-completion Booklet (Word® file, 97 KB [Plugin Software Help]; PDF file, 40 KB [Plugin Software Help]).

The study data are released here in three separate data formats (SAS, SPSS, and STATA) in zipped files to accommodate the need of researchers who use different statistical analysis software:

• Final SAS U.S. EQ-5D™ Dataset (1 MB).
• Final SPSS U.S. EQ-5D™ Dataset (1.2 MB).
• Final STATA U.S. EQ-5D™ Dataset (1.2 MB).

Erratum: The datasets released in February 2005 contained incorrect variable labels for the chronic conditions (Item 38). The datasets now available have corrected this error. Users are advised to discard the datasets released in February 2005, and substitute the updated datasets for all analyses involving the chronic conditions variables (Item 38).

Current as of January 2012

Internet Citation:

U.S. Valuation of the EuroQol EQ-5D™ Health States.� January 2012. Agency for Healthcare Research and Quality, Rockville, MD.�https://www.ahrq.gov/rice/EQ5Dproj.htm