What is Quality of Life (QoL)?

The World Health Organization defines Quality of Life as:

“An individual’s perception of his/her position in life in the context of the culture and value systems in which he/she lives, and in relation to his/her goals, expectations, standards and concerns. It is a broad-ranging concept, incorporating in a complex way the person’s physical health, psychological state, level of independence, social relationships, and their relationship to salient features of their environment.”

Measurement of this ‘elusive concept’ is problematical. (When concepts are elusive measurement is generally difficult!) To circumvent time consuming (and often fruitless) debate over the question what ‘quality of life’ really is, our approach to this issue is pragmatic. Each AQoL (or other) instrument proposes a set of attributes which is postulated to be of importance for the individual or for policy makers. To the extent to which the descriptive system captures what individuals and policy makers wish to measure, the instrument is successful. To the extent to which it omits important elements, or describes them inadequately, the instrument is defective in a particular context. This pragmatic approach underlies the need for users to assess whether or not a particular instrument satisfactorily meets their needs. There is no perfect instrument.

Because we postulated that ‘utility’ will be most closely related to behaviour in a social context the AQoL suite of instruments adopts the perspective of handicap. However none of the classifications is complete and the AQoL instruments are supplemented, where necessary, with concepts from the other perspectives. For example, pain has an intrinsic disutility in addition to any effect upon a person’s functioning.

AQoL Instrument Developers

Researchers associated with the different instruments are as follows.

AQoL- 8D (developed as PsyQoL) Project team:

Centre for Health Economics

• Prof Jeff Richardson, NHMRC AQoL Grant recipient and Chief Investigator
• Mr Angelo Iezzi, project manager
• Dr Kompal Sinha, analysis
• Ms Aimee Maxwell, project management, website and interviewing
• Dr Munir Khan, analysis

RMIT University

• A/Prof Gerry Elsworth, analysis

The Melbourne Clinic

• Prof Isaac Schweitzer, Chief Investigator

University of Melbourne

• Prof Helen Herrman, Chief Investigator
• A/Prof Graeme Hawthorne, Chief Investigator

Centre for Health Economics in Cancer, British Columbia Cancer Agency

• Dr Stuart Peacock, Chief Investigator

Deakin University

• Ms Cathy Mihalopoulos, Chief Investigator

Associate investigators:

• A/Prof Mal Hopwood, Veterans Psychiatry Unit, Austin Health
• Ms Bridget Organ, St Vincent's Health
• Prof Graham Meadows, Southern Health
• A/Prof Richard Newton, Peninsula Health

Review Team for Construction Item bank (apart from investigators above)
Dr Elise Davies
Professor David Clarke
Dr Carol Harvey
Dr Ingrid Nielsen

AQoL -7D (developed as VisQol) Project team:

Centre for Health Economics

• Prof Jeff Richardson, NHMRC AQoL Grant recipient and Chief Investigator
• Mr Angelo Iezzi, project manager
• Dr Stuart Peacock, analysis and Chief Investigator

Centre for Eye Research Australia

• Dr RoseAnne Misajon, project manager
• Prof Jill Keeffe, Chief Investigator
• Ms Melanie Larizza, admin and interviewing

University of Melbourne

• A/Prof Graeme Hawthorne , analysis and Chief Investigator

AQoL- 6D (developed as AQoL Mark 2) Project team:
Centre for Health Economics

• Prof Jeff Richardson, NHMRC AQoL Grant recipient and Chief Investigator
• Mr Angelo Iezzi, project manager
• Mr Neil Day, analysis
• Dr Stuart Peacock, analysis and Chief Investigator
• Ms Helen McNeil, project manager

University of Melbourne

• A/Prof Graeme Hawthorne, analysis and Chief Investigator

AQoL- 4D (developed as AQoL) Project team:
Centre for Health Economics

• Prof Jeff Richardson, NHMRC AQoL Grant recipient and Chief Investigator
• Ms Helen McNeil, project manager
• A/Prof Graeme Hawthorne, analysis and Chief Investigator

Economic Evaluation, QoL and Quality Adjusted Life Years (QALY)

It has been recognised for a long time that evaluation studies should include the measurement of health outcomes, and a very large number of health status measures have been developed. However, the usefulness of these instruments has been variable. Many have had poor if any evidence of validity or reliability and the purpose for which they were developed has varied. While many of the instruments may have contributed in a general sense to the ‘evaluation of health outcome’, they have often been unsuited to the specific question addressed in economic evaluation, namely, whether or not they indicate a treatment which should be chosen in preference to some other treatment for the same or for some other disease.

The latter question is explicitly addressed by cost-utility analysis. Projects or options are ordered according to the cost per QALY attributable to the project. All else equal, the most desirable options are taken to be those which result in the cheapest QALY. That is, QALYs are the criterion of value in the sense that more are better and, all else equal, projects with more QALYs should be preferred. Despite the recognition of numerous practical problems there appears t be a fairly widespread acceptance of the steps involved in the calculation of QALYs. They are estimated as expected life years times an index of ‘utility’, where this is measured on a 0-1 scale and is taken as quantifying that aspect of the quality of life upon which decisions should be made.

QALYs are constructed from two distinct parts, the quantity and quality of life. Measuring the QoL also has two distinct parts: (i) describing the health state that is of interest; and (ii) measuring the ‘utility’ (strength of preference) for that health state. This second step may be thought of as attaching an ‘importance weight’ to the length of life where ‘importance’ in this instance means ‘strength of preference’.

There are numerous “scaling” techniques used to measure utility directly. These involve the use of category rating or a Rating Scale (RS), also known as Visual Analogue Scale (VAS), the Standard Gamble (SG), the Time Trade-off (TTO), and the Person Trade Off (PTO). For example, relative to full health, which has a numerical value of unity (1), patients with diabetes may have a utility value of 0.85.

The Centre for Health Economics at Monash is developing a new measuring technique, the Relative Social Willingness to Pay (RS-WTP) which, like the PTO, adopts a societal perspective but uses dollars for the evaluation task.

Wholistic Measurement: Each technique involves the presentation of a health state description to interviewees and the eliciting of their preferences for this state relative to some reference states, usually full health and death. The utility revealed by these techniques is taken as having an interval property. Thus, for example, the difference between utility values of 0.2 and 0.4 is treated as being quantitatively equivalent to the difference between 0.6 and 0.8. The interval property is required for the valid summation of utilities.

Multi Attribute Utility Instruments

A second, indirect approach to measuring QALYs requires the prior establishment of a multi-attribute utility scale which may be applied to any health state.

Generic multi-attribute utility (MAU) instruments (like the first approach to measurement) have two distinct parts. The first concerns the description of the health state (the descriptive instrument). The second concerns the utility weights to be attached to the health state described by the descriptive instrument. The descriptive instrument is constructed in the same way as other generic profile instruments. A series of multi-response items are combined in a questionnaire. This represents the ‘descriptive system’ which should be capable of representing the health state being evaluated. The utility (strength of preference) for each health state descriptions is converted into a numerical value using one of the  ‘scaling’ techniques described above, either the Standard Gamble, Time Trade-Off, Person Trade-Off or Rating Scale described below.

In all but the simplest instruments, the number of possible health states defined by the instrument is far too great for direct observation of the utility value of every state in the descriptive instrument and, as a consequence, a limited number of utility values are observed and the remainder of the health states are inferred from these using some form of model..

Use of MAU Instruments: Researchers may use an MAU instrument either by circulating the descriptive instrument to patients before and after a medical intervention or, more generally and less accurately, by having an expert (such as a doctor) fill in the (believed) health state of typical patients before and after an intervention. In either case, the researcher may subsequently estimate the utility score of the health states using the MAU scores or scoring algorithm.

Once a generic MAU instrument has been constructed, it permits the fairly rapid calculation of utility scores, but it has limitations. Its descriptive system is necessarily limited. Instruments may describe health state with greater or lesser accuracy and, generally, the smaller the instrument the less sensitive it will be to differences in health states. For example, the simple Rosser-Kind instrument cannot distinguish the many sources of distress, such as distress arising from the social context of the problem and distress arising from the medical context of the problem, even though both may be of considerable importance for patient wellbeing. None of the MAU instruments constructed so far takes account of temporal aspects of people’s preferences: a health state such as paraplegia may be intolerable in the short term but less so in the longer term as the result of ‘adaptation’. Conversely, a problem such as pain may not be too troublesome in the short run but become increasingly unacceptable in the long run because of ‘saturation’.

Range of MAU Instruments.

Five MAU instruments have been commonly used, and a simple generic utility instrument has been used by the World Bank to measure the burden of disease with a QALY-based measure (the Disability Adjusted Life Year (DALY)) the earliest American instrument, the Quality of Well Being (QWB) was used in the famous – or infamous – Oregon experiment in which all Medicaid procedures were subject to cost utility analysis. The first UK instrument constructed by Rosser and Kind employed only two dimensions (distress and disability) and, including death, defined only 29 health states. Because of its simplicity, it is the only instrument for which all health states were directly evaluated. However, it simplicity and insensitivity have attracted widespread criticism: many UK commentators equated the QALY method with the use of this instrument and criticised the approach on the basis of this instrument’s shortcomings. Other MAU instruments include the Canadian Health Utilities Index (HUI) Mark I II and III, the Finnish 15D and the European EuroQoL (EQ5D). The development of the Australian AQoL was commenced in 1997, and the World Health Organization’s WHOQoL may be converted into a generic utility instrument . The Organization for Economic Cooperation and Development (OECD) has also considered the construction of such an instrument.

Applications of MAU Instruments.

There are three potential applications for each of the instruments. Importantly, as generic instruments, they are applicable to all public health and clinical interventions.

1. Each instrument can be used as a simple additive health related quality of life (HRQoL) measure, providing profile scores on the different dimensions or items of the descriptive systems.

2. When utilities are computed, instruments can provide an overall index of HRQoL.

3. When utilities are computed, these instruments can be used in economic evaluation, specifically in cost-utility analysis requiring the computation of quality-adjusted life years (QALYs).