Health-Related Quality of Life

How health-related quality of life describes the effects of health

Health-related quality of life describes how health, illness and treatment affect a person's physical, psychological and social functioning and wellbeing. This page explains how the concept is measured, how different instruments produce different types of information, and how health-related quality-of-life evidence is used in clinical studies, economic evaluation and decision making.

The concept is narrower than overall quality of life because it focuses on aspects that are affected by health or healthcare. Its boundaries remain partly judgmental because factors such as employment, relationships, independence and financial security can be both determinants and consequences of health.

The dimensions represented in a measurement

Health-related quality of life is multidimensional, so no single clinical measure can represent it completely. A measurement approach should state which dimensions it covers and whose perspective the scores represent.

Common dimensions include:

• Physical functioning: A person's ability to move, perform usual activities and complete self-care.
• Symptoms: The presence and severity of pain, fatigue, nausea, breathlessness and other health problems.
• Psychological wellbeing: The effects of health on anxiety, depression, emotional distress and cognitive functioning.
• Social functioning: A person's ability to participate in family, work, education and community life.
• Independence: The extent to which a person can make decisions and manage daily life without assistance.
• Role functioning: The effect of health on responsibilities and activities that are important to the person.

The dimensions included in an instrument determine which changes it can detect. An instrument that gives limited attention to cognition, fatigue or social participation may underrepresent treatment effects in conditions where those dimensions are important.

Choosing between generic and condition-specific instruments

Generic instruments are designed for use across diseases and populations. They support comparisons between conditions and interventions, but they may be less sensitive to changes that are important within a particular disease.

Condition-specific instruments focus on issues experienced by people with a particular disease, symptom or treatment. They may detect clinically meaningful changes that a generic instrument misses, but their scores are usually harder to compare across different conditions.

A study may use both approaches when they serve different purposes. The generic instrument can support comparisons and economic evaluation, while the condition-specific instrument can provide a more detailed account of patient experience.

Profile measures and preference-based measures

A health profile reports scores for one or more dimensions of health-related quality of life. A preference-based measure assigns values to health states using preferences obtained from patients, members of the public or another defined group.

The distinction matters because not every health-related quality-of-life score is a utility value. A symptom score or summary profile cannot be inserted into a quality-adjusted life-year calculation unless it has an appropriate preference-based interpretation or a validated mapping method.

Preference-based instruments generally contain:

• A descriptive system that assigns a person's responses to a health state.
• A value set that converts the health state into a numerical value.
• A stated source population whose preferences were used to estimate the value set.
• A scoring algorithm that links responses to the final value.

Scores from different instruments or national value sets are not automatically interchangeable. Their dimensions, severity levels, valuation methods and reference populations may differ.

Collecting health-related quality-of-life data

Health-related quality of life is usually reported directly by the person experiencing the health state. A proxy respondent may be needed when a patient cannot complete the instrument, but proxy and self-reported scores can differ because they represent different observations and perspectives.

Data collection should specify:

1. Select the instrument according to the population, purpose and intended analysis.
2. Define the assessment schedule so measurements correspond to clinically and analytically meaningful time points.
3. Use a consistent mode of administration unless equivalence between modes has been established.
4. Record who completed the instrument and whether the response was self-reported, assisted or proxy-reported.
5. Document missing items and missed assessments rather than treating them as normal health.
6. Apply the correct scoring algorithm for the instrument, version, language and jurisdiction.
7. Preserve uncertainty and distributional information rather than reporting only an average score.

The timing of measurement can materially affect results. Scores collected during an acute event, immediately after treatment or long after recovery represent different health experiences.

Evaluating whether an instrument is suitable

An instrument should be fit for the population, setting and purpose in which it is used. Familiarity or widespread use does not by itself establish that an instrument measures the relevant changes accurately.

Important measurement properties include:

• Validity: The instrument measures the aspects of health-related quality of life it is intended to measure.
• Reliability: The instrument produces sufficiently consistent results when the underlying health state has not changed.
• Responsiveness: The instrument can detect meaningful change over time.
• Interpretability: The meaning of a score or change can be explained in relation to health experience.
• Feasibility: Respondents can complete the instrument without excessive burden.
• Acceptability: The content and method are appropriate for the population and setting.

Floor effects occur when respondents cluster at the lowest possible score, while ceiling effects occur when they cluster at the highest possible score. Either effect can reduce the ability to distinguish patients or detect change.

Interpreting change over time

A numerical difference does not automatically represent a meaningful improvement or deterioration. Interpretation should consider the instrument's scale, measurement error, baseline score, duration of change and the importance of the affected dimension.

A minimally important difference estimates the smallest change that patients or another defined group may perceive as important. It is context dependent and should not be treated as a universal constant for every population, intervention or analytical purpose.

Group averages can also hide important variation. Two interventions may have similar mean scores while producing different distributions of benefits, harms or responses across patients.

Using health-related quality of life in economic evaluation

Economic evaluations use health-related quality-of-life evidence to represent the effects of illness and treatment on health outcomes. In cost-utility analysis, preference-based values may be combined with time to estimate quality-adjusted life years.

For a period during which health-related quality of life is represented by utility value (u_t), the quality-adjusted life years accrued can be expressed as:

$$ QALY = \sum_{t=1}^{T} u_t \times \Delta t $$

where:

• (u_t) is the utility value applied during period (t).
• (\Delta t) is the duration of that period in years.
• (T) is the number of periods represented.

This calculation assumes that the utility value adequately represents health-related quality of life during the period. Models may require adjustments when health changes within a period, when events cause short-lived effects or when observations are missing.

Translating evidence into model inputs

Health economic models may assign utility values to health states, apply temporary decrements for adverse events or estimate changes associated with treatment. The method should avoid double-counting and should preserve the relationship between the source data and the model structure.

Analysts should examine:

• Whether the instrument and value set are appropriate for the jurisdiction.
• Whether the study population matches the model population.
• Whether values were adjusted for relevant patient characteristics.
• Whether repeated measurements were analysed appropriately.
• Whether adverse-event effects overlap with the utility assigned to a health state.
• Whether mapping was used when a preference-based measure was unavailable.
• Whether uncertainty around utility values is represented in the model.

Mapping estimates preference-based values from another outcome measure using a statistical relationship. It can be useful when direct preference-based data are unavailable, but it adds uncertainty and may perform poorly for severe or unusual health states.

Missing data and survivorship

Missing health-related quality-of-life data can bias results when the probability of missingness is related to illness severity, treatment response or adverse events. Analyses based only on complete responses may overstate health if people in poorer health are less likely to provide follow-up data.

The analysis should report the extent and pattern of missing data, investigate reasons for missingness and test assumptions used to handle it. Death should not be treated as an ordinary missing observation because it is a defined outcome with a specific role in many preference-based analyses.

A simplified example

Suppose a treatment improves a preference-based health-related quality-of-life value from 0.62 to 0.72 for one year. If survival and other outcomes are unchanged, the incremental quality-adjusted life years during that year are:

$$ \Delta QALY = (0.72 - 0.62) \times 1 = 0.10 $$

The result means that the model attributes an additional 0.10 quality-adjusted life years per patient to the improvement during that year. It does not show whether every patient improved by the same amount, whether the benefit continued beyond one year or whether other health effects occurred.

Common misunderstandings

Health-related quality of life is related to health status but is not identical to a diagnosis, clinical outcome or symptom count. It reflects how health affects a person's functioning and wellbeing across the dimensions included in the chosen measure.

Common misunderstandings include:

• Any patient-reported outcome is not automatically a health-related quality-of-life measure.
• A health profile score is not automatically a utility value.
• Different instruments with similar numerical ranges do not necessarily measure the same construct.
• A statistically significant difference is not automatically important to patients.
• A proxy response is not automatically equivalent to a patient's own response.
• A value set from one jurisdiction is not automatically transferable to another.
• Missing observations should not be assumed to represent unchanged or good health.
• Quality-adjusted life years do not capture every aspect of wellbeing, equity or social value.

Interpreting health-related quality-of-life evidence

The meaning of a result depends on the instrument, respondent, timing, scoring method and population. A credible analysis explains why the chosen measure is appropriate and how its scores relate to the health changes being evaluated.

Health-related quality-of-life evidence is most useful when it is interpreted alongside clinical outcomes, adverse events, survival and patient experience. It provides a structured account of how health affects life, but it should not be treated as a complete measure of everything that matters to patients or society.