Signature
U_gp = 0.9508566 + 0.0212126 * male - 0.0002587 * age - 0.0000332 * age^2; Loss = (1 - m) * U_gp
| Inputs | Definition | Unit |
|---|---|---|
male | 1 for men, 0 for women | indicator |
age | Age in completed years | years |
m | Ratio of utility with the condition to utility without it, for example 0.875 for a condition that removes 12.5 per cent | ratio |
U_gp | Mean EQ-5D index for people of a given age and sex | utility |
|---|---|---|
Loss | Baseline utility times one minus the multiplier | utility points |
Function
Utility decrements, their QALY losses and the rules for combining them on a stated baseline
Expresses a harm such as an adverse event, a second chronic condition or an unpleasant treatment as a fall in utility from a stated baseline, turns it into lost QALYs over the harm's duration, and estimates the utility of people with two conditions from single-condition data by the additive, multiplicative or minimum rule of NICE DSU TSD 12. QALYs from periods of constant utility are HE-FM-QALY-001 and the expected QALY loss from adverse events HE-FM-AER-006. Notation follows the Disutility article.
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Implementations
Excel
General population baseline and proportional loss from named cells
With IsMale, AgeYrs and PropMult named, the formulas return the Ara and Brazier baseline and the absolute loss from the proportional decrement, held in GPUtil and PropLoss.
=0.9508566+0.0212126*IsMale-0.0002587*AgeYrs-0.0000332*AgeYrs^2; =(1-PropMult)*GPUtil
Assumptions
Age within the range of the Health Survey for England sample
The regression was fitted to EQ-5D responses from participants aged 16 to 98 in the 2003 and 2006 surveys, so it is used for ages in that range and reflects the population of those years.
Same instrument and value set as the condition utilities
The baseline is the EQ-5D valued with UK general public time trade-off weights, so the multiplier and other utilities in the model need the same instrument and value set.
Worked examples
Baseline and proportional loss for a woman aged 60
The baseline is 0.9508566 minus 0.0002587 times 60 minus 0.0000332 times 3,600, which is 0.8158, and removing 12.5 per cent costs about 0.102, as in the article.
male = 0; age = 60; m = 0.875; U_gp = 0.8158; Loss = 0.102
Baseline and proportional loss for a woman aged 80
At 80 the baseline is 0.7177 and the same proportional decrement costs about 0.090, less than at 60, as in the article.
male = 0; age = 80; m = 0.875; U_gp = 0.7177; Loss = 0.090
Baseline and proportional loss for a man aged 60
For a man aged 60 the baseline is 0.0212 higher, 0.8370, and the proportional loss about 0.105 (computed here for illustration).
male = 1; age = 60; m = 0.875; U_gp = 0.8370; Loss = 0.105
Common errors
Assuming perfect health for people without the modelled condition
TSD 12 states that assuming the baseline is perfect health for people without a condition is inappropriate and records agreement that adjusting for age and gender is an absolute minimum; a woman of 80 has a general population baseline of 0.7177, not 1.
Extrapolating a baseline above general population values
PMG36 section 4.3.7 asks that baselines extrapolated over long horizons reflect the decline in quality of life in the general population and do not exceed general population values at a given age, based on a recent source.
Sources
Ara and Brazier general population EQ-5D regression on age and sex
Ara R, Brazier JE. Populating an economic model with health state utility values: moving toward better practice. Value in Health. 2010;13(5):509-518. doi:10.1111/j.1524-4733.2010.00700.x (coefficients read in the authors' deposited version, HEDS Discussion Paper 09/11, University of Sheffield, full text). Methods and results, pp. 6-7: in the 2003 and 2006 Health Survey for England a random sample of participants aged 16 to 98 completed the EQ-5D (N = 26,679); Model 1, EQ-5D = 0.9508566 + 0.0212126 male minus 0.0002587 age minus 0.0000332 age squared, estimates mean utilities for the general population.
TSD 12 on age and gender adjustment of baseline utilities
Ara R, Wailoo A. NICE DSU Technical Support Document 12: The use of health state utility values in decision models. Sheffield: Decision Support Unit, ScHARR, University of Sheffield; July 2011 (full text read). Executive summary and section 2.1: it is inappropriate to assume the baseline is perfect health if an individual does not have a specific health condition, and adjusting for the effects of age and gender should be conducted as an absolute minimum.
PMG36 on extrapolated baselines and general population values
National Institute for Health and Care Excellence. NICE technology appraisal and highly specialised technologies guidance: the manual (PMG36). London: NICE; 2022, last updated 31 March 2026 (full text of chapter 4 read). Section 4.3.7: when baseline utilities are extrapolated over long time horizons they should be adjusted to reflect decreases in health-related quality of life seen in the general population and should not exceed general population values at a given age; adjustment should be based on a recent source of population health-related quality of life.
Canonical Identity
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