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Quality-Adjusted Life Expectancy Norms for the English Population

Open AccessPublished:August 12, 2022DOI:https://doi.org/10.1016/j.jval.2022.07.005

      Highlights

      • In 2022, the National Institute for Health and Care Excellence (NICE) introduced quality-adjusted life-year (QALY) weighting based on the QALY shortfall associated with a condition. Shortfall calculations require estimates of the quality-adjusted life expectancy of healthy individuals.
      • We provide up-to-date estimates of the quality-adjusted life expectancy of the English population by combining national mortality statistics with 5-level version of EQ-5D data from the Health Survey for England mapped to 3-level version of EQ-5D utility values using NICE’s preferred mapping algorithm. We also publish an online tool that enables the QALY shortfall associated with a condition to be simply calculated: https://shiny.york.ac.uk/shortfall.
      • These up-to-date results and the associated tool have the potential to inform NICE appraisals conducted under the new methods.

      Abstract

      Objectives

      The National Institute for Health and Care Excellence in England has implemented severity-of-disease modifiers that give greater weight to health benefits accruing to patients who experience a larger shortfall in quality-adjusted life-years (QALYs) under current standard of care than healthy individuals. This requires an estimate of quality-adjusted life expectancy (QALE) of the general population based on age and sex. Previous QALE population norms are based on nearly 30-year-old assessments of health-related quality of life in the general population. This study provides updated QALE estimates for the English population based on age and sex.

      Methods

      5-level version of EQ-5D data for 14 412 participants from the Health Survey for England (waves 2017 and 2018) were pooled, and health-related quality of life population norms were calculated. These norms were combined with official life tables from the Office for National Statistics for 2017 to 2019 using the Sullivan method to derive QALE estimates based on age and sex. Values were discounted using 0%, 1.5%, and 3.5% discount rates.

      Results

      QALE at birth is 68.24 QALYs for men and 68.21 QALYs for women. These values are significantly lower than previously published QALE population norms based on the older 3-level version of EQ-5D data.

      Conclusion

      This study provides new QALE population norms for England that serve to establish absolute and relative QALY shortfalls for the purpose of health technology assessments.

      Keywords

      Introduction

      The National Institute for Health and Care Excellence (NICE) published revised methods for health technology evaluation in January 2022.
      NICE health technology evaluations: the manual. National Institute for Health and Care Excellence.
      As part of these revisions, NICE has introduced a new severity-of-disease modifier: a mechanism designed to enable their advisory committees to formally, quantitatively, and transparently grant additional weight to incremental quality-adjusted life-years (QALYs) provided to people with more severe health conditions. This mechanism furthers the development introduced through the end-of-life criterion in 2009, in which NICE moved away from the long-held view that QALY gains are equally valuable independent of who they accrue to.
      Appraising life-extending, end of life treatments. National Institute for Health and Clinical Excellence.
      For the purpose of this modifier, NICE defines the severity of a health condition using 2 metrics: absolute QALY shortfall and proportional QALY shortfall. Absolute shortfall is quantified as the absolute number of future QALYs an individual can expect to lose as a result of that condition, given currently available interventions.
      • Arneberg F.
      Measuring the level of severity in pharmacoeconomic analyses: an empirical approach. University of Oslo.
      In contrast, proportional shortfall is quantified as the proportion of future QALYs a person can expect to lose as a result of their condition.
      • Stolk E.A.
      • van Donselaar G.
      • Brouwer W.B.F.
      • Busschbach J.J.V.
      Reconciliation of economic concerns and health policy: illustration of an equity adjustment procedure using proportional shortfall.
      If the magnitude of these shortfall metrics is sufficiently large, any incremental gains in QALYs achieved by a new health technology are assigned weights greater than one, thus increasing the effective cost-effectiveness threshold for these interventions. Outside of England, absolute shortfall is used as severity modifier in Norway,
      • Ottersen T.
      • Førde R.
      • Kakad M.
      • et al.
      A new proposal for priority setting in Norway: open and fair.
      ,
      Guidelines for the submission of documentation for single technology assessment (STA) of pharmaceuticals. Statens Legemiddelverk.
      whereas proportional shortfall is applied in The Netherlands.
      • Reckers-Droog V.T.
      • Van Exel N.J.A.
      • Brouwer W.B.F.
      Looking back and moving forward: on the application of proportional shortfall in healthcare priority setting in the Netherlands.
      ,
      Cost-effectiveness in practice. Zorginstituut Nederland.
      Both shortfall metrics require 2 pieces of information: (1) an estimate of the number of future QALYs people who receive current standard of care can expect to experience in their lifetime and (2) an estimate of the number of future QALYs that individuals with the condition would have experienced had they been healthy. The first of these is a standard output of a cost-utility analysis and so is already available as part of a NICE appraisal conducted using current methods. The second is not routinely calculated as part of a current NICE assessment. In practical terms, this information could be estimated independently by each of the stakeholders making submissions to NICE or appraising evidence on behalf of NICE. Alternatively, reference values (“population norms”) could be established outside the review process to improve consistency across appraisals, reduce the burden on stakeholders, and provide a basis for appraisal-specific modification if warranted.
      Quality-adjusted life expectancy (QALE) population norms for several countries including England have been published by Heijink et al.
      • Heijink R.
      • van Baal P.
      • Oppe M.
      • Koolman X.
      • Westert G.
      Decomposing cross-country differences in quality adjusted life expectancy: the impact of value sets.
      The authors combined mortality data from the Human Mortality Database 2013
      Human mortality database 2013. University of California and Max Planck Institute for Demographic Research.
      and 3-level version of EQ-5D (EQ-5D-3L) data from the 1993 Measuring and Valuing Health (MVH) study
      • Kind P.
      • Hardman G.
      • Macran S.
      UK Population Norms for EQ-5D. Centre for Health Economics, University of York.
      via a life table approach to derive estimates of QALE stratified based on age and sex. In 2020, Briggs et al
      • Briggs A.H.
      • Goldstein D.A.
      • Kirwin E.
      • et al.
      Estimating (quality-adjusted) life-year losses associated with deaths: with application to COVID-19.
      derived QALE estimates for the UK population by combining ONS mortality data for 2016 to 2018 with utility data from Kind et al
      • Kind P.
      • Dolan P.
      • Gudex C.
      • Williams A.
      Variations in population health status: results from a United Kingdom national questionnaire survey.
      reported in Szende et al.
      • Szende A.
      • Janssen B.
      • Cabasés J.M.
      • Ramos Goñi J.M.
      Self-Reported Population Health: An International Perspective Based on EQ-5D.
      More recently, Palmer et al
      • Palmer A.J.
      • Campbell J.A.
      • de Graaff B.
      • et al.
      Population norms for quality adjusted life years for the United States of America, China, the United Kingdom and Australia [published correction appears in Health Econ. 2022. doi: 10.1002/hec.4549].
      used a 2-state Markov model to provide updated QALE estimates based on UK population life tables for the period 2017 to 2019. Each of these studies relies on the rather dated EQ-5D population norms from the 1993 MVH study and focuses on the -3L version of the instrument despite the 5-level version of EQ-5D (EQ-5D-5L) instrument—a newer version of the instrument with a more detailed descriptive system
      • Herdman M.
      • Gudex C.
      • Lloyd A.
      • et al.
      Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L).
      —being rapidly adopted in clinical trials and observational studies. Furthermore, the EQ-5D-3L population norms were based on relatively small samples of individuals, which induces considerable sampling uncertainty in any assessment of severity of disease.
      • Kind P.
      • Hardman G.
      • Macran S.
      UK Population Norms for EQ-5D. Centre for Health Economics, University of York.
      This limits the usefulness of existing QALE population norms for NICE decision making.
      In this article, we report more recent (2017/2018) estimates of the QALE of the English population, drawing on a large data set of quality-of-life measurements collected using the EQ-5D-5L instrument. In parallel, we publish an R-Shiny online tool (https://shiny.york.ac.uk/shortfall) inspired by the iDBC platform of Versteegh et al (https://imta.shinyapps.io/iDBC/). Our tool enables users to combine QALE population norms with the outputs of an economic model to estimate the absolute and proportional QALY shortfall associated with a condition.

      Methods

      To derive QALE population norms, we combined age- and sex-specific EQ-5D-5L utility scores with national life tables of the English population.
      National life tables (pooled for 2017-2019) were taken from the Office for National Statistics.
      National life tables, England, 1980-1982 to 2017-2019. Office for National Statistics.
      We used the Chiang II method to derive crude age- and sex-specific life expectancies (LEs).
      • Chiang C.L.
      The Life Table and Its Applications.
      The LE at the start of age interval i is accordingly estimated by dividing the number of years lived in that and all successive intervals by the number of people alive at the beginning of interval i.
      Information on self-reported EQ-5D-5L health state profiles were retrieved from the 2017 and 2018 waves of the Health Survey for England (HSE), which is a long-running survey of the English population.

      Health survey for England, 2017 [data collection]. NatCen. University College London Department of Epidemiology and Public Health National Centre for Social Research. UK Data Service. https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8488. Accessed August 8, 2022.

      ,

      Health survey for England, 2018 [data collection]. NatCen. University College London Department of Epidemiology and Public Health National Centre for Social Research. UK Data Service. https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8649. Accessed August 8, 2022.

      We used the individual sampling weights to adjust the sample for nonresponders and to make it nationally representative in terms of age, sex, and geography. The HSE is a survey of the noninstitutionalized general population. People in care homes or hospitals, prisoners, and asylum seekers are not included. Thus, the obtained health state profiles likely overestimate the health-related quality of life (HRQoL) of the population. Moreover, the HSE only provides a cross-sectional snapshot of the health of the population in the present. Future health trajectories may differ significantly from the trajectories observed in the HSE data.
      Health states were valued in terms of utilities using Hernandez Alava et al
      • Hernandez Alava M.
      • Pudney S.
      • Wailoo A.
      Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from an English Population Study. Policy Research Unit in Economic Evaluation of Health and Care Interventions.
      crosswalk method, which is the approach recommended by NICE.
      NICE health technology evaluations: the manual. National Institute for Health and Care Excellence.
      This method maps the EQ-5D-5L health states to the EQ-5D-3L value set for the UK. To derive QALEs, mean utility scores based on age and sex were calculated and then combined with LE estimates, using the Sullivan method.
      • Sullivan D.F.
      A single index of mortality and morbidity.
      Age- and sex-specific QALE estimates for the English population are reported for a 1.5% and a 3.5% annual discount rate and undiscounted.
      In secondary analysis, we also construct QALE population norms using the crosswalk method proposed by van Hout et al
      • Van Hout B.
      • Janssen M.F.
      • Feng Y.S.
      • et al.
      Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets.
      applied to the HSE data and using the EQ-5D-3L population norms reported by Kind et al.
      • Kind P.
      • Hardman G.
      • Macran S.
      UK Population Norms for EQ-5D. Centre for Health Economics, University of York.
      We then compare the resulting estimates of QALE at different ages to gauge the effect of the HRQoL weights, holding mortality risks constant.
      For the analysis, we had to make several assumptions. First, although life tables are reported based on single year of age, EQ-5D-5L data were only available according to the grouped age variable in the HSE (16-17 years, 18-19 years, followed by 5-year bands, up until 90+ years). We assumed that HRQoL was constant within each age band. Second, the HSE does not contain any EQ-5D-5L data for children under the age of 16 years. It was assumed that children aged 0 to 15 years had the same HRQoL as those aged 16 to 17 years. Third, the calculation of LEs was based on the assumption that individuals dying at a given year of age had an average survival of 6 months (half-cycle correction). The life table was closed at a maximum age of 100 years.
      To help stakeholders estimate the absolute and proportional QALY shortfall, we developed an interactive web application: the “QALY Shortfall Calculator” is available at https://shiny.york.ac.uk/shortfall. It can be used to compute the difference between the QALE of individuals without and with a particular disease (the estimate for the latter obviously needs to be supplied by the user). The application allows the user to adjust the age and male/female distribution of the patient group and the discount rate that is applied.
      The R source code of the web application, as well as the code used to generate the results reported in this article, is available online at https://github.com/bitowaqr/shortfall.

      Results

      A total of 16 175 individuals participated in the HSE in 2017 (n = 7997) and 2018 (n = 8178). Of these, 1762 respondents (10.9%) did not report their EQ-5D-5L health state and were excluded from the analysis. This left 14 413 individuals for the estimation of utility scores based on age and sex.
      Average EQ-5D-5L utility scores based on age for male and female respondents are presented in Table 1.
      • Hernandez Alava M.
      • Pudney S.
      • Wailoo A.
      Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from an English Population Study. Policy Research Unit in Economic Evaluation of Health and Care Interventions.
      Table 1Mean EQ-5D-5L utility scores based on age group and sex and 95% confidence intervals (based on bootstrapping with 10 000 iterations and Hernandez Alava et al
      • Hernandez Alava M.
      • Pudney S.
      • Wailoo A.
      Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from an English Population Study. Policy Research Unit in Economic Evaluation of Health and Care Interventions.
      crosswalk).
      FemaleMale
      Age, yearsMean (95% CI)nMean (95% CI)n
      16-170.878 (0.870-0.896)1510.918 (0.910-0.935)146
      18-190.856 (0.846-0.895)1220.930 (0.925-0.945)110
      20-240.859 (0.853-0.875)3700.894 (0.889-0.910)298
      25-290.869 (0.864-0.881)5150.895 (0.890-0.907)366
      30-340.869 (0.867-0.883)6690.915 (0.911-0.925)450
      35-390.854 (0.850-0.869)7220.863 (0.853-0.887)465
      40-440.846 (0.842-0.861)6680.872 (0.868-0.887)498
      45-490.806 (0.801-0.820)6930.822 (0.815-0.844)527
      50-540.798 (0.793-0.815)7290.836 (0.831-0.852)529
      55-590.791 (0.787-0.809)7300.809 (0.803-0.826)582
      60-640.776 (0.769-0.797)6080.803 (0.798-0.822)532
      65-690.775 (0.770-0.795)6190.797 (0.792-0.818)568
      70-740.784 (0.779-0.801)6190.801 (0.794-0.818)505
      75-790.730 (0.724-0.755)3990.788 (0.781-0.806)335
      80-840.710 (0.699-0.733)2680.767 (0.760-0.801)233
      85-890.666 (0.657-0.707)1450.727 (0.704-0.764)126
      90+0.666 (0.651-0.721)670.656 (0.635-0.730)49
      CI indicates confidence interval; EQ-5D-5L, 5-level version of EQ-5D.
      The age- and sex-specific period LE and QALE, undiscounted and with a 1.5% and 3.5% discount rate applied, are presented in Table 2.
      • Hernandez Alava M.
      • Pudney S.
      • Wailoo A.
      Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from an English Population Study. Policy Research Unit in Economic Evaluation of Health and Care Interventions.
      At birth, women are expected to live considerably longer lives (+3.7 years) but have similar undiscounted QALE as men (+0.03 QALYs). Over time, the discrepancy in QALE between men and women increases: for example, at age 60 years, women can expect 1.07 more undiscounted QALYs than men.
      Table 2LE and QALE based on age and sex with 0%, 1.5%, and 3.5% discount rates (based on Hernandez Alava et al
      • Hernandez Alava M.
      • Pudney S.
      • Wailoo A.
      Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from an English Population Study. Policy Research Unit in Economic Evaluation of Health and Care Interventions.
      crosswalk utilities and 2017-2019 life tables).
      FemaleMale
      LE, yearsQALE, quality adjusted life yearsLE, yearsQALE, quality adjusted life years
      Age, years0%1.5%3.5%0%1.5%3.5%
      083.3368.2439.9223.6179.6768.2140.6224.36
      182.6367.6039.7823.6279.0267.5940.4724.37
      281.6566.7439.4923.5478.0466.6840.1624.28
      380.6665.8739.2023.4677.0565.7739.8324.18
      479.6765.0038.9023.3776.0664.8639.5024.08
      578.6764.1338.5923.2875.0663.9539.1723.98
      677.6863.2538.2823.1974.0763.0438.8323.87
      776.6862.3837.9723.1073.0862.1338.4823.75
      875.6961.5137.6523.0072.0861.2138.1323.64
      974.6960.6337.3322.9071.0960.3037.7723.52
      1073.7059.7637.0022.7970.0959.3837.4123.39
      1172.7058.8936.6622.6869.0958.4737.0423.26
      1271.7158.0136.3322.5768.1057.5636.6723.13
      1370.7157.1435.9822.4567.1156.6536.2922.99
      1469.7256.2635.6322.3366.1255.7335.9122.85
      1568.7255.3935.2822.2165.1254.8235.5222.70
      1667.7354.5234.9222.0864.1353.9135.1322.55
      1766.7453.6534.5621.9463.1553.0134.7322.39
      1865.7552.7834.1921.8162.1752.1134.3322.23
      1964.7651.9433.8521.6961.1951.2033.9122.05
      2063.7851.0933.4921.5760.2250.2933.4921.87
      2162.7950.2433.1321.4459.2549.4233.1021.72
      2261.8049.3932.7621.3058.2848.5532.7121.57
      2360.8148.5432.3921.1657.3047.6832.3121.41
      2459.8247.6932.0121.0256.3346.8131.9021.24
      2558.8446.8531.6220.8755.3645.9331.4921.07
      2657.8545.9931.2220.7154.3945.0631.0720.90
      2756.8745.1330.8220.5453.4244.1930.6420.71
      2855.8844.2730.4120.3652.4543.3230.2120.52
      2954.9043.4229.9920.1851.4842.4629.7820.33
      3053.9242.5629.5620.0050.5141.5929.3420.13
      3152.9341.7129.1419.8049.5540.7028.8719.90
      3251.9540.8528.7019.6048.5939.8228.3919.67
      3350.9840.0028.2619.4047.6238.9427.9119.42
      3450.0039.1527.8219.1946.6638.0527.4319.17
      3549.0338.3027.3718.9745.7137.1726.9418.91
      3648.0537.4726.9218.7644.7536.3526.4918.70
      3747.0836.6326.4818.5443.8035.5226.0418.48
      3846.1135.8126.0318.3242.8534.7025.5918.26
      3945.1534.9825.5718.0941.9033.8825.1218.03
      4044.1834.1525.1117.8640.9533.0624.6617.79
      4143.2233.3324.6417.6240.0132.2324.1817.54
      4242.2632.5224.1817.3839.0731.4123.6917.27
      4341.3031.7023.7117.1338.1430.5923.2017.00
      4440.3430.8923.2316.8737.2129.7722.7116.73
      4539.3930.0822.7516.6136.2828.9622.2116.45
      4638.4529.3222.3016.3835.3628.2021.7616.21
      4737.5028.5521.8516.1434.4427.4421.3015.96
      4836.5627.7921.4015.9033.5326.6920.8415.71
      4935.6327.0420.9415.6532.6225.9420.3715.45
      5034.6926.2820.4715.3931.7125.2019.9115.19
      5133.7725.5420.0115.1430.8124.4419.4214.91
      5232.8424.8019.5514.8729.9223.6918.9314.61
      5331.9224.0619.0814.6129.0322.9418.4414.32
      5431.0023.3218.6014.3328.1522.2017.9414.01
      5530.0922.5918.1214.0427.2721.4517.4413.70
      5629.1821.8617.6513.7626.3920.7416.9613.40
      5728.2821.1517.1713.4725.5320.0416.4813.10
      5827.3820.4316.6913.1724.6719.3416.0012.80
      5926.4919.7216.2012.8723.8218.6515.5212.49
      6025.6119.0215.7112.5622.9817.9715.0312.18
      6124.7318.3315.2412.2622.1417.3014.5511.86
      6223.8617.6514.7611.9521.3216.6314.0711.54
      6323.0016.9814.2811.6420.5115.9713.5911.22
      6422.1516.3113.8011.3219.7115.3213.1110.89
      6521.3015.6513.3110.9918.9114.6812.6310.56
      6620.4614.9912.8310.6618.1314.0512.1610.23
      6719.6214.3412.3410.3217.3613.4411.699.90
      6818.8013.6911.859.9716.6012.8211.229.56
      6917.9813.0511.369.6215.8612.2210.759.21
      7017.1712.4210.869.2515.1211.6310.288.87
      7116.3811.7810.368.8814.3811.039.818.51
      7215.5811.149.858.4913.6610.449.338.15
      7314.8110.529.358.1012.969.868.867.78
      7414.069.918.857.7112.279.318.407.42
      7513.319.318.357.3211.608.757.947.05
      7612.588.777.906.9710.958.237.506.70
      7711.878.247.466.6210.327.727.076.35
      7811.187.727.036.279.717.226.646.00
      7910.517.226.605.929.126.736.235.65
      809.866.736.185.588.556.265.815.30
      819.226.265.795.258.005.825.434.98
      828.615.825.404.927.465.395.054.66
      838.025.385.024.606.954.984.684.33
      847.464.974.654.286.464.574.324.02
      856.924.564.293.976.004.193.973.71
      866.414.223.983.705.573.853.663.44
      875.943.903.703.465.173.543.383.18
      885.493.603.423.214.793.233.092.93
      895.083.323.172.994.442.942.822.68
      904.683.052.922.774.122.662.562.44
      914.322.792.692.563.802.452.362.26
      923.982.562.472.363.512.252.182.09
      933.672.332.262.173.232.051.991.92
      943.382.112.051.982.981.871.821.76
      953.111.901.851.802.751.691.651.60
      962.881.691.661.612.551.511.491.45
      972.671.481.461.432.381.341.321.30
      982.471.231.221.202.211.131.121.11
      992.280.940.930.922.040.880.870.87
      LE indicates life expectancy; QALE, quality-adjusted life expectancy.
      Appendix Tables 1 and 2 in Supplemental Materials found at https://dx.doi.org/10.1016/j.jval.2022.07.005 provide QALE norms based on the same 2017 to 2019 life tables but using the van Hout et al
      • Van Hout B.
      • Janssen M.F.
      • Feng Y.S.
      • et al.
      Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets.
      crosswalk from -5L to -3L and the original MVH EQ-5D-3L population norms,
      • Kind P.
      • Hardman G.
      • Macran S.
      UK Population Norms for EQ-5D. Centre for Health Economics, University of York.
      respectively. Appendix Table 3 in Supplemental Materials found at https://dx.doi.org/10.1016/j.jval.2022.07.005 shows QALE estimates at selected ages (undiscounted) for all 3 valuation approaches. The 2 crosswalk methods generate very similar QALE estimates. In contrast, using the MVH population norms results in higher estimated QALE for women and, to a lesser degree, for men. For example, QALE at birth for females is estimated to be 71.9 QALYs or nearly 3.7 QALYs more than those estimated using the EQ-5D-5L crosswalk by Hernandez Alava et al
      • Hernandez Alava M.
      • Pudney S.
      • Wailoo A.
      Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from an English Population Study. Policy Research Unit in Economic Evaluation of Health and Care Interventions.
      preferred by NICE. A similar albeit smaller gap of 1.0 QALYs is observed for men.

      Discussion

      NICE has introduced severity-of-disease modifiers that assign greater value to QALY gains for patients with greater absolute or relative expected shortfall in QALE under the current standard of care. This short note provides updated QALE population norms for England based on the EQ-5D-5L instrument that serve to establish the benchmark against which shortfalls can be assessed, thereby complementing recent work by Briggs et al
      • Briggs A.H.
      • Goldstein D.A.
      • Kirwin E.
      • et al.
      Estimating (quality-adjusted) life-year losses associated with deaths: with application to COVID-19.
      and Palmer et al.
      • Palmer A.J.
      • Campbell J.A.
      • de Graaff B.
      • et al.
      Population norms for quality adjusted life years for the United States of America, China, the United Kingdom and Australia [published correction appears in Health Econ. 2022. doi: 10.1002/hec.4549].
      The population norms presented here combine official, full population life tables with HRQoL data obtained from a large, representative sample of the English population and valued using NICE’s newly preferred valuation method. Additional data tables and figures are made available through an interactive website (https://shiny.york.ac.uk/shortfall).
      Existing QALE population norms for England
      • Heijink R.
      • van Baal P.
      • Oppe M.
      • Koolman X.
      • Westert G.
      Decomposing cross-country differences in quality adjusted life expectancy: the impact of value sets.
      ,
      • Briggs A.H.
      • Goldstein D.A.
      • Kirwin E.
      • et al.
      Estimating (quality-adjusted) life-year losses associated with deaths: with application to COVID-19.
      ,
      • Palmer A.J.
      • Campbell J.A.
      • de Graaff B.
      • et al.
      Population norms for quality adjusted life years for the United States of America, China, the United Kingdom and Australia [published correction appears in Health Econ. 2022. doi: 10.1002/hec.4549].
      are based on population norms derived as part of the MVH study.
      • Kind P.
      • Hardman G.
      • Macran S.
      UK Population Norms for EQ-5D. Centre for Health Economics, University of York.
      Our analysis shows that this results in significantly higher QALE population norms and, ceteris paribus, larger estimates of QALY shortfall. This discrepancy may arise for a number of reasons such as changes in population health over the last 3 decades or noise introduced by the crosswalk from the EQ-5D-5L health states to EQ-5D-3L utility scores. Our analysis cannot disentangle these issues, and therefore, we call on NICE to take a position on which QALE population norm should be used in health technology assessments.
      There are 3 main limitations to our study: First, both life tables and HRQoL data reflect the health of current populations, and as a result, our estimates should be interpreted as period QALEs. Medical and societal progress are likely to change both LE and HRQoL for future cohorts of patients, thus creating a need for regular updates of these QALE population norms. Second, approximately 10% of the participants in the HSE did not report their EQ-5D-5L health profiles and were therefore not included in the study. This might have introduced selection bias in our estimates of average HRQoL based on age and sex. Nevertheless, previous work by Love-Koh et al
      • Love-Koh J.
      • Asaria M.
      • Cookson R.
      • Griffin S.
      The social distribution of health: estimating quality-adjusted life expectancy in England.
      found that imputing missing HRQoL data changed QALE estimates by less than 0.01 QALYs. Therefore, we believe that missing data are unlikely to introduce significant bias. Finally, our analysis is based on EQ-5D-5L data being mapped to and valued using the EQ-5D-3L value set (ie, cross-walking), which is consistent with NICE’s current reference case. A new valuation study for the EQ-5D-5L is underway, which will provide health state valuations without the need for crosswalks and associated loss of information. Once published, we plan to update the interactive website to give stakeholders access to QALE estimates based on this new value set.

      Conclusion

      This study provides new QALE population norms for England. These norms serve as an input for the calculation of absolute and relative QALY shortfalls to inform health technology assessment with severity of condition adjustment as applied in England.

      Article and Author Information

      Author Contributions: Concept and design: McNamara, Schneider, Love-Koh, Doran, Gutacker
      Acquisition of data: McNamara, Schneider
      Analysis and interpretation of data: McNamara, Schneider, Love-Koh, Doran, Gutacker
      Drafting of the manuscript: McNamara, Schneider, Love-Koh, Gutacker
      Critical revision of the paper for important intellectual content: McNamara, Schneider, Love-Koh, Doran, Gutacker
      Statistical analysis: Schneider, Love-Koh
      Provision of study materials or patients: McNamara, Schneider, Love-Koh, Doran, Gutacker
      Obtaining funding: McNamara, Schneider, Love-Koh, Doran, Gutacker
      Administrative, technical, or logistic support: McNamara, Schneider, Love-Koh, Gutacker
      Conflict of Interest Disclosures: Dr McNamara is an employee of Lumanity. Dr Schneider reported receiving grants from EuroQol Research Foundation and Wellcome Trust during the conduct of the study. Dr Gutacker reports grants from EuroQol Research Foundation during the conduct of the study and is a member of the EuroQol Group. No other conflicts were reported.
      Funding/Support: This work was supported by the EuroQol Foundation (project number: 123-2020RA), the Wellcome Trust Doctoral Training Centre in Public Health Economics and Decision Science (108903/Z/19/Z), and the University of Sheffield through a PhD scholarship.
      Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; identification, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
      Acknowledgment: The authors are grateful to Matthijs Versteegh for providing comments on an earlier version of this manuscript. This paper reprents the views of the authors alone, and may or may not reflect those of our employers and funders.

      Supplemental Material

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