Health-related quality of life (HRQoL)
1000minds is used for research into health-related quality of life (HRQoL), such as creating social value sets for the EQ-5D-5L, SF-6Dv2 and other health descriptive systems.
Social value sets for New Zealand are available for download.
What is HRQoL?
Health-related quality of life (HRQoL) is the perceived quality of a person’s life with respect to the physical, mental and emotional aspects of their health, such as their experience of pain, disability, depression, etc.
HRQoL is measured by quantifying how a person’s health affects their health-related quality of life, where their health is represented on the various dimensions included in the particular health descriptive system used for representing HRQoL. Well-known health descriptive systems in use globally are surveyed later below.
HRQoL values are represented as index values ranging from 1 = perfect health – i.e. no problems on any of the dimensions included the health descriptive system – to 0 = dead, and with negative values for health states considered worse than dead.
For example, imagine a person who has slight problems with their mobility, self-care and usual activities and who is in moderate pain and severely anxious or depressed. Based on these ratings on the five health dimensions (i.e. for the EQ-5D-5L system discussed below), their HRQoL would be scored as a specific value: e.g. 0.433.
In other words, this health state has a HRQoL value equivalent to 43.3% of the value of perfect health (no problems on any health dimensions), which has a value of 1. One year spent in this health state is equivalent to 0.433 of a year in full health.
Different health states, as represented on the particular health descriptive system being used, have different values, of course. For example, extreme problems with mobility, self-care, usual activities, pain and anxiety/depression is likely to have a negative value: e.g. -0.830, i.e. worse than dead, which has a value of 0.
What is HRQoL used for?
HRQoL is used for measuring and valuing changes in people’s health status at both the individual and population levels.
Such HRQoL valuations are used for Cost-Utility Analysis (CUA), a common type of economic evaluation for prioritizing “health technologies” for funding, such as pharmaceuticals, medical procedures, devices and equipment.
Government agencies such as New Zealand’s Pharmaceutical Management Agency (Pharmac), the UK’s National Institute for Health and Care Excellence (NICE) and Canada’s Drug Agency (CDA), for example, use CUA to help decide which pharmaceuticals to fund – known as health technology assessment or prioritization.
HRQoL plays a key role in CUA by enabling analysts to compare the benefits delivered by the various health technologies being considered in terms of how well patients live in terms of their HRQoL as well as how long they live. These comparisons, combining both HRQoL and life expectancy, involve the calculation of Quality-Adjusted Life Years (QALYs).
Another important application of HRQoL is Patient-Reported Outcome Measures (PROMs), which, as the name implies, are used for directly capturing patients’ own perspectives on their health and HRQoL (without the need for interpretation by clinicians or others). PROMs are used for evaluating the performance of health care providers, such as hospitals.
HRQoL health descriptive systems
A veritable global industry has grown up around the creation of systems for describing and valuing people’s HRQoL.
Well-known health descriptive systems include:
- 15D (15 Dimensions)
- AQoL (Assessment of Quality of Life)
- EQ-5D (EuroQoL, 5 Dimensions)
- EQ-HWB (EuroQoL, Health and Wellbeing)
- HUI (Health Utilities Index)
- PROMIS (Patient-Reported Outcomes Measurement Information System)
- SF-6D (Short Form, 6 Dimensions)
The two most widely used systems worldwide are the EQ-5D and SF-6D.
EQ-5D
The EQ-5D represents HRQoL on five dimensions:
- mobility
- self-care
- usual activities
- pain/discomfort
- anxiety/depression
The EQ-5D’s most recent version, the EQ-5D-5L, has five levels of problems on each dimension: e.g. no, slight, moderate, severe and extreme problems. Thus, the EQ-5D-5L is capable of representing 3125 (55) health states.
SF-6D
The SF-6D represents HRQoL on six dimensions:
- physical functioning
- role limitations
- social functioning
- pain
- mental health
- vitality
The SF-6D’s most recent version, the SF-6Dv2, has six levels on the pain dimension and five levels on the five other dimensions. Thus, the SF-6Dv2 represents 18,750 (6×55) health states.
What are HRQoL value sets?
A value set consists of HRQoL index values for all health states representable by the particular descriptive system used, such as:
- 3125 values for the EQ-5D-5L’s 3125 states
- 18,750 values for the SF-6Dv2’s 18,750 states
Health state values are anchored at 1 = perfect health, i.e. no problems on any of the dimensions included the health descriptive system, to 0 = dead, and with negative values for HRQoL worse than dead.
Value sets at the population level – social value sets – are used by policy-makers and researchers to support decision-making about the allocation of health care resources, such as for CUA and PROMs, as discussed earlier.
1000minds tool for creating HRQoL value sets
A specialized 1000minds tool is available for creating both personal and social value sets – i.e. for each individual participant and populations overall – for any of the health descriptive systems above (and, potentially, others).
Try a demonstration of the participant experience:
These tools were used to create EQ-5D-5L and SF-6Dv2 value sets for New Zealand; both value sets are available below. The video shown to participants for the SF-6Dv2 is shown in Figure 1.
How does the 1000minds HRQoL valuation tool work?
The 1000minds tool implements the PAPRIKA method, a type of adaptive discrete choice experiment (DCE), and a binary search algorithm to identify any health states worse than dead. The tool also includes extensive checks of the quality of each participant’s data.
The simplicity of PAPRIKA’s DCE questions (see Figure 2) ensures they are relatively quick and easy for people to think about and have confidence in their answers – and so researchers can have confidence in the validity and reliability of the HRQoL data generated.
Another benefit of the PAPRIKA method is that a personal value set is created for each participant, in contrast to most other DCE-based methods which generate aggregate data. Individual-level data enables the heterogeneity of health state preferences to be explored as well as by sub-groups, e.g. by ethnicity, age, health status, etc.
Potentially 1000s of people can be surveyed to obtain a representative sample of a country’s population. Overall, compared to other HRQoL valuation methods, the 1000minds tool significantly reduces the cost and time involved in creating and analyzing value sets.
The 1000minds valuation tool can also support CUA and PROMs at the individual patient level by incorporating the patient’s preferences into treatment decisions in “real time”. For example, the tool could be available on computer tablets in doctor waiting rooms or as a mobile app for patients to quickly create their own personal value sets.
EQ-5D-5L and SF-6Dv2 value sets
Two social value sets for New Zealand have been created. They are available and explained here, with references to peer-reviewed articles and downloads of the value sets:
How significant are correlations between HRQoL value sets?
In addition to HRQoL-valuation research (as above), a custom 1000minds software module was used for simulations and analysis to investigate the validity of high correlation coefficients reported in the academic literature for value sets derived from different samples across countries or using different valuation techniques.
Such high correlation coefficients are conventionally interpreted as evidence that the people in the respective samples have similar HRQoL preferences.
However, as we demonstrate and correct for in the article below, value sets contain many inherent rankings of health state values by design.
Therefore, the observed “high” correlations are artifacts of these inherent rankings and so they are in fact spurious (rather than constituting evidence for the people in the respective samples having similar HRQoL preferences).
Based on millions of simulations, we created the following tool to let you look up the statistical significance of correlation coefficients for EQ-5D-3L, EQ-5D-5L and SF-6Dv2 value sets:
Lookup correlation significance
If you would like to test significance for a different tool, we may be able to generate significance tables for you.
Peer-reviewed article
F Ombler, M Albert & P Hansen (2018), “How significant are ‘high’ correlations between EQ-5D value sets?”, Medical Decision Making 38, 635-45
