, Volume 35, Issue 1, pp 111–124 | Cite as

Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs)

  • Billingsley Kaambwa
  • Gang Chen
  • Julie Ratcliffe
  • Angelo Iezzi
  • Aimee Maxwell
  • Jeff Richardson
Original Research Article



Economic evaluation of health services commonly requires information regarding health-state utilities. Sometimes this information is not available but non-utility measures of quality of life may have been collected from which the required utilities can be estimated. This paper examines the possibility of mapping a non-utility-based outcome, the Sydney Asthma Quality of Life Questionnaire (AQLQ-S), onto five multi-attribute utility instruments: Assessment of Quality of Life 8 Dimensions (AQoL-8D), EuroQoL 5 Dimensions 5-Level (EQ-5D-5L), Health Utilities Index Mark 3 (HUI3), 15 Dimensions (15D), and the Short-Form 6 Dimensions (SF-6D).


Data for 856 individuals with asthma were obtained from a large Multi-Instrument Comparison (MIC) survey. Four statistical techniques were employed to estimate utilities from the AQLQ-S. The predictive accuracy of 180 regression models was assessed using six criteria: mean absolute error (MAE), root mean squared error (RMSE), correlation, distribution of predicted utilities, distribution of residuals, and proportion of predictions with absolute errors <0.0.5. Validation of initial ‘primary’ models was carried out on a random sample of the MIC data.


Best results were obtained with non-linear models that included a quadratic term for the AQLQ-S score along with demographic variables. The four statistical techniques predicted models that performed differently when assessed by the six criteria; however, the best results, for both the estimation and validation samples, were obtained using a generalised linear model (GLM estimator).


It is possible to predict valid utilities from the AQLQ-S using regression methods. We recommend GLM models for this exercise.


Root Mean Square Error Generalise Linear Model Ordinary Little Square Validation Sample Mean Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Compliance with Ethical Standards


This work was supported through an Australian National Health and Medical Research Council (NHMRC) project Grant (Grant Number 1006334).

Contribution of authors

Jeff Richardson contributed to the study inception and writing of the NHMRC grant application. Billingsley Kaambwa analysed the data, interpreted the results, and wrote the first draft of the manuscript. Julie Ratcliffe, Gang Chen, Angelo Iezzi, Aimee Maxwell, and Jeff Richardson contributed to the interpretation of results and revision of the manuscript. All authors have read and approved the final manuscript. Billingsley Kaambwa is the guarantor of the manuscript.

Conflict of interest

Billingsley Kaambwa, Gang Chen, Julie Ratcliffe, Angelo Iezzi, Aimee Maxwell, and Jeff Richardson declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval was granted by the MUHREC (CF11/3192–2011001748).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

40273_2016_446_MOESM1_ESM.docx (93 kb)
Supplementary material 1 (DOCX 93 kb)


  1. 1.
    Braman SS. The global burden of asthma. Chest. 2006;130(1 Suppl):4s–12s.PubMedCrossRefGoogle Scholar
  2. 2.
    Global Initiative for Asthma. Global strategy for asthma management and prevention. Global Initiative for Asthma; 2015.Google Scholar
  3. 3.
    Global Initiative for Asthma. Pocket guide for asthma management and prevention (for adults and children older than 5 years). Global Initiative for Asthma; 2015.Google Scholar
  4. 4.
    Eberhart NK, Sherbourne CD, Edelen MO, Stucky BD, Sin NL, Lara M. Development of a measure of asthma-specific quality of life among adults. Qual Life Res. 2014;23(3):837–48.PubMedCrossRefGoogle Scholar
  5. 5.
    Apfelbacher C, Paudyal P, Bulbul A, Smith H. Measurement properties of asthma-specific quality-of-life measures: protocol for a systematic review. Syst Rev. 2014;3:83.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Apfelbacher CJ, Hankins M, Stenner P, Frew AJ, Smith HE. Measuring asthma-specific quality of life: structured review. Allergy. 2011;66(4):439–57.PubMedCrossRefGoogle Scholar
  7. 7.
    Norman G, Faria R, Paton F, Llewellyn A, Fox D, Palmer S, et al. Omalizumab for the treatment of severe persistent allergic asthma: a systematic review and economic evaluation. Health Technol Assess. 2013;17(52):1–342.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Frew E, Hankins M, Smith HE. Patient involvement in the development of asthma-specific patient-reported outcome measures: a systematic review. J Allergy Clin Immunol. 2013;132(6):1434–6.PubMedCrossRefGoogle Scholar
  9. 9.
    Australian Centre for Asthma Monitoring. Measuring the impact of asthma on quality of life in the Australian population. Canberra: Australian Institute of Health and Welfare; 2004.Google Scholar
  10. 10.
    Buxton MJ. Economic evaluation and decision making in the UK. Pharmacoeconomics. 2006;24(11):1133–42.PubMedCrossRefGoogle Scholar
  11. 11.
    Harris A, Bulfone L. Getting value for money: the Australian experience. In: Jost TS, editor. Health care coverage determinations: an international comparative study. Maidenhead: Open University Press; 2004.Google Scholar
  12. 12.
    National Institute for Health and Care Excellence. Guide to the methods of technology appraisal 2013. London: National Institute for Health and Care Excellence; 2013.Google Scholar
  13. 13.
    Morris S, Devlin N, Parkin D. Economic analysis in health care. Chichester: Wiley; 2007.Google Scholar
  14. 14.
    Drummond MF, Sculpher M, O’Brien B, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005.Google Scholar
  15. 15.
    Brazier J, Ratcliffe J, Salomon J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.Google Scholar
  16. 16.
    Richardson J, McKie J, Bariola E. Multi attribute utility instruments and their use. In: Culyer AJ, editor. Encyclopedia of health economics. San Diego: Elsevier Science; 2014. p. 341–57.CrossRefGoogle Scholar
  17. 17.
    Brazier JE, Yang Y, Tsuchiya A, Rowen DL. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ. 2010;11(2):215–25.PubMedCrossRefGoogle Scholar
  18. 18.
    Marks GB, Dunn SM, Woolcock AJ. A scale for the measurement of quality of life in adults with asthma. J Clin Epidemiol. 1992;45(5):461–72.PubMedCrossRefGoogle Scholar
  19. 19.
    Marks GB, Dunn SM, Woolcock AJ. An evaluation of an asthma quality of life questionnaire as a measure of change in adults with asthma. J Clin Epidemiol. 1993;46(10):1103–11.PubMedCrossRefGoogle Scholar
  20. 20.
    Tsuchiya A, Brazier J, McColl E, Parkin D. Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices. Ref. 02/1. Sheffield Health Economics Group Discussion Paper Series. 2002.Google Scholar
  21. 21.
    Richardson J, Khan MA, Iezzi A, Maxwell A. Measuring the sensitivity and construct validity of six utility instruments in seven disease states. Med Decis Making. 2016;36(2):147–59.PubMedCrossRefGoogle Scholar
  22. 22.
    Richardson J, Iezzi A, Khan MA, Maxwell A. Cross-national comparison of twelve quality of life instruments. MIC paper 1: background, questions, instruments. Research paper 76. Melbourne, VIC: Centre for Health Economics, Monash University; 2012.Google Scholar
  23. 23.
    Petrou S, Rivero-Arias O, Dakin H, Longworth L, Oppe M, Froud R, et al. The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: explanation and elaboration. Pharmacoeconomics. 2015;33(10):993–1011.PubMedCrossRefGoogle Scholar
  24. 24.
    Spilker B. Quality of life and pharmacoeconomics in clinical trials. Philadelphia: Lippincott-Raven; 1996.Google Scholar
  25. 25.
    Katz PP, Eisner MD, Henke J, Shiboski S, Yelin EH, Blanc PD. The Marks Asthma Quality of Life Questionnaire: further validation and examination of responsiveness to change. J Clin Epidemiol. 1999;52(7):667–75.PubMedCrossRefGoogle Scholar
  26. 26.
    Ware JE Jr, Kemp JP, Buchner DA, Singer AE, Nolop KB, Goss TF. The responsiveness of disease-specific and generic health measures to changes in the severity of asthma among adults. Qual Life Res. 1998;7(3):235–44.PubMedCrossRefGoogle Scholar
  27. 27.
    Bayliss MS, Espindle DM, Buchner D, Blaiss MS, Ware JE. A new tool for monitoring asthma outcomes: the ITG Asthma Short Form. Qual Life Res. 2000;9(4):451–66.PubMedCrossRefGoogle Scholar
  28. 28.
    Hawthorne G, Richardson J, Osborne R. The Assessment of Quality of Life (AQoL) instrument: a psychometric measure of health-related quality of life. Qual Life Res. 1999;8(3):209–24.PubMedCrossRefGoogle Scholar
  29. 29.
    Richardson J, Hawthorne G. The Australian quality of life (AQoL) instrument: psychometric properties of the descriptive system and inital validation. Aust Stud Health Service Adm. 1998;85:315–42.Google Scholar
  30. 30.
    Richardson J, Khan MA, Chen G, Iezzi A, Maxwell A. Population norms and Australian profile using the Assessment of Quality of Life (AQoL) 8D Utility Instrument. Melbourne: Centre for Health Economics, Monash University; 2012.Google Scholar
  31. 31.
    Hawthorne G, Korn S, Richardson J. Population norms for the AQoL derived from the 2007 Australian National Survey of Mental Health and Wellbeing. Aust NZ J Public Health. 2013;37(1):7–16.CrossRefGoogle Scholar
  32. 32.
    Richardson J, Sinha K, Iezzi A, Khan MA. Modelling utility weights for the Assessment of Quality of Life (AQoL)-8D. Qual Life Res. 2014;23(8):2395–404.PubMedCrossRefGoogle Scholar
  33. 33.
    Richardson J, Iezzi A, Khan MA, Maxwell A. Validity and reliability of the Assessment of Quality of Life (AQoL)-8D multi-attribute utility instrument. Patient. 2014;7(1):85–96.PubMedCrossRefGoogle Scholar
  34. 34.
    Richardson J, Khan MA, Iezzi A, Maxwell A. Comparing and explaining differences in the magnitude, content, and sensitivity of utilities predicted by the EQ-5D, SF-6D, HUI 3, 15D, QWB, and AQoL-8D multiattribute utility instruments. Med Decis Making. 2015;35(3):276–91.PubMedCrossRefGoogle Scholar
  35. 35.
    Richardson J, Chen G, Khan MA, Iezzi A. Can multi attribute utility instruments adequately account for subjective wellbeing? Med Decis Making. 2015;35(3):292–304.PubMedCrossRefGoogle Scholar
  36. 36.
    Cheung K, Oemar M, Oppe M, Rabin R. EQ-5D user guide: basic information on how to use EQ-5D—Version 2.0. Rotterdam: EuroQoL Group; 2009.Google Scholar
  37. 37.
    Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727–36.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Devlin N, Shah K, Feng Y, Mulhern B, Hout B. Valuing health-related quality of life: an EQ-5D-5L value set for England. Research paper 16/01. Office of Health Economics; 2016.Google Scholar
  39. 39.
    Kind P, Hardman G, Macran S. UK population norms for EQ-5D: discussion paper 172. York: University of York, Centre for Health Economics; 1999.Google Scholar
  40. 40.
    Janssen M, Pickard A, Golicki D, Gudex C, Niewada M, Scalone L, et al. Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study. Qual Life Res. 2013;22(7):1717–27.PubMedCrossRefGoogle Scholar
  41. 41.
    Conner-Spady BL, Marshall DA, Bohm E, Dunbar MJ, Loucks L, Khudairy AA, et al. Reliability and validity of the EQ-5D-5L compared to the EQ-5D-3L in patients with osteoarthritis referred for hip and knee replacement. Qual Life Res. 2015;24(7):1775–84.PubMedCrossRefGoogle Scholar
  42. 42.
    Golicki D, Niewada M, Buczek J, Karlinska A, Kobayashi A, Janssen MF, et al. Validity of EQ-5D-5L in stroke. Qual Life Res. 2015;24(4):845–50.PubMedCrossRefGoogle Scholar
  43. 43.
    Horsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003;1:54.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Feeny D, Furlong W, Torrance GW, Goldsmith CH, Zhu Z, DePauw S, et al. Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Med Care. 2002;40(2):113–28.PubMedCrossRefGoogle Scholar
  45. 45.
    Moy ML, Fuhlbrigge AL, Blumenschein K, Chapman RH, Zillich AJ, Kuntz KM, et al. Association between preference-based health-related quality of life and asthma severity. Ann Allergy Asthma Immunol. 2004;92(3):329–34.PubMedCrossRefGoogle Scholar
  46. 46.
    McTaggart-Cowan HM, Marra CA, Yang Y, Brazier JE, Kopec JA, FitzGerald JM, et al. The validity of generic and condition-specific preference-based instruments: the ability to discriminate asthma control status. Qual Life Res. 2008;17(3):453–62.PubMedCrossRefGoogle Scholar
  47. 47.
    Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I: conceptual framework and item selection. Med Care. 1992;30(6):473–83.PubMedCrossRefGoogle Scholar
  48. 48.
    Brazier JE, Ratcliffe J, Salomon J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.Google Scholar
  49. 49.
    Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002;21(2):271–92.PubMedCrossRefGoogle Scholar
  50. 50.
    Harrison M, Davies L, Bansback N, McCoy M, Verstappen S, Watson K, et al. The comparative responsiveness of the EQ-5D and SF-6D to change in patients with inflammatory arthritis. Qual Life Res. 2009;18(9):1195–205.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Kontodimopoulos N, Pappa E, Papadopoulos A, Tountas Y, Niakas D. Comparing SF-6D and EQ-5D utilities across groups differing in health status. Qual Life Res. 2009;18(1):87–97.PubMedCrossRefGoogle Scholar
  52. 52.
    Goncalves Campolina A, Bruscato Bortoluzzo A, BosiFerraz M, Mesquita Ciconelli R. Validity of the SF-6D index in Brazilian patients with rheumatoid arthritis. Clin Exp Rheumatol. 2009;27(2):237–45.PubMedGoogle Scholar
  53. 53.
    Sintonen H. The 15D instrument of health-related quality of life: properties and applications. Ann Med. 2001;33(5):328–36.PubMedCrossRefGoogle Scholar
  54. 54.
    Sintonen H. The 15D-measure of health-related quality of life. I: reliability, validity and sensitivity of its health state descriptive system. Melbourne: National Centre for Health Program Evaluation; 1994.Google Scholar
  55. 55.
    Haapaniemi TH, Sotaniemi KA, Sintonen H, Taimela E. The generic 15D instrument is valid and feasible for measuring health related quality of life in Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2004;75(7):976–83.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Linde L, Sorensen J, Ostergaard M, Horslev-Petersen K, Hetland ML. Health-related quality of life: validity, reliability, and responsiveness of SF-36, 15D, EQ-5D [corrected] RAQoL, and HAQ in patients with rheumatoid arthritis. J Rheumatol. 2008;35(8):1528–37.PubMedGoogle Scholar
  57. 57.
    StataCorp LP. Intercooled Stata 131 for windows. College Station: StataCorp LP; 2014.Google Scholar
  58. 58.
    Rumsey DJ. Statistics II for dummies. Hoboken: Wiley Publishing, Inc; 2009.Google Scholar
  59. 59.
    Gray AM, Rivero-Arias O, Clarke PM. Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med Decis Making. 2006;26(1):18–29.PubMedCrossRefGoogle Scholar
  60. 60.
    Long JS. Regression models for categorical and limited dependent. A volume in the Sage Series for Advanced Quantitative Techniques. Thousand Oaks: Sage Publications; 1997.Google Scholar
  61. 61.
    Chay KY, Powell JL. Semiparametric censored regression models. J Econ Perspect. 2001;15(4):29–42.CrossRefGoogle Scholar
  62. 62.
    Johnston J, DiNardo J. Econometric methods. London: The McGraw-Hill Companies, Inc; 1997.Google Scholar
  63. 63.
    McCullagh P, Nelder JA. Generalized linear models. 2nd ed. London: Chapman & Hall; 1989.CrossRefGoogle Scholar
  64. 64.
    Manning WG. The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ. 1998;17(3):283–95.PubMedCrossRefGoogle Scholar
  65. 65.
    Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.Google Scholar
  66. 66.
    Ospina R, Ferrari SLP. A general class of zero-or-one inflated beta regression models. Comput Stat Data Anal. 2012;56(6):1609–23.CrossRefGoogle Scholar
  67. 67.
    Khan I, Morris S. A non-linear beta-binomial regression model for mapping EORTC QLQ-C30 to the EQ-5D-3L in lung cancer patients: a comparison with existing approaches. Health Qual Life Outcomes. 2014;12(1):1–16.CrossRefGoogle Scholar
  68. 68.
    Longworth L, Yang Y, Young T, Mulhern B, Hernández Alava M, Mukuria C, et al. Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. Health Technol Assess. 2014;18(9):1–224.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Brennan D, Spencer AJ. Mapping oral health related quality of life to generic health state values. BMC Health Serv Res. 2006;6(1):96.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Sauerland S, Weiner S, Dolezalova K, Angrisani L, Noguera CM, Garcia-Caballero M, et al. Mapping utility scores from a disease-specific quality-of-life measure in bariatric surgery patients. Value Health. 2009;12(2):364–70.PubMedCrossRefGoogle Scholar
  71. 71.
    Bansback N, Marra C, Tsuchiya A, Anis A, Guh D, Hammond T, et al. Using the health assessment questionnaire to estimate preference-based single indices in patients with rheumatoid arthritis. Arthritis Rheum. 2007;57(6):963–71.PubMedCrossRefGoogle Scholar
  72. 72.
    Daniel WW, Terrell JC. Business statistics. Boston: Houghton Mifflin Company; 1995.Google Scholar
  73. 73.
    Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev. 2014;7(3):1247–50.CrossRefGoogle Scholar
  74. 74.
    Cheung YB, Thumboo J, Gao F, Ng GY, Pang G, Koo WH, et al. Mapping the English and Chinese versions of the Functional Assessment of Cancer Therapy-General to the EQ-5D utility index. Value Health. 2009;12(2):371–6.PubMedCrossRefGoogle Scholar
  75. 75.
    Kaambwa B, Billingham L, Bryan S. Mapping utility scores from the Barthel index. Eur J Health Econ. 2013;14(2):231–41.PubMedCrossRefGoogle Scholar
  76. 76.
    Dakin H, Petrou S, Haggard M, Benge S, Williamson I. Mapping analyses to estimate health utilities based on responses to the OM8-30 Otitis Media Questionnaire. Qual Life Res. 2010;19(1):65–80.PubMedCrossRefGoogle Scholar
  77. 77.
    Brazier J, Rowen D. Alternatives to EQ-5D for generating health state utility values. Contract no. 11. Sheffield: Decision Support Unit, School of Health and Related Research, University of Sheffield; 2011.Google Scholar
  78. 78.
    Khan KA, Petrou S, Rivero-Arias O, Walters SJ, Boyle SE. Mapping EQ-5D utility scores from the PedsQL generic core scales. Pharmacoeconomics. 2014;32(7):693–706.PubMedCrossRefGoogle Scholar
  79. 79.
    Pinedo-Villanueva RA, Turner D, Judge A, Raftery JP, Arden NK. Mapping the Oxford hip score onto the EQ-5D utility index. Qual Life Res. 2013;22(3):665–75.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Billingsley Kaambwa
    • 1
  • Gang Chen
    • 2
  • Julie Ratcliffe
    • 1
  • Angelo Iezzi
    • 2
  • Aimee Maxwell
    • 2
  • Jeff Richardson
    • 2
  1. 1.Flinders Health Economics GroupFlinders University, A Block, Repatriation General HospitalAdelaideAustralia
  2. 2.Centre for Health Economics, Building 75, 15 Innovation WalkMonash UniversityClaytonAustralia

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