Quality of Life Research

, Volume 25, Issue 12, pp 3209–3219 | Cite as

Deriving population norms for the AQoL-6D and AQoL-8D multi-attribute utility instruments from web-based data

  • Aimee Maxwell
  • Mehmet Özmen
  • Angelo Iezzi
  • Jeff Richardson
Article

Abstract

Objectives

(i) to demonstrate a method which ameliorates the problem of self-selection in the estimation of population norms from web-based data and (ii) to use the method to calculate population norms for two multi-attribute utility (MAU) instruments, the AQoL-6D and AQoL-8D, and population norms for the sub-scales from which they are constructed.

Methods

A web-based survey administered the AQoL-8D MAU instrument (which subsumes the AQoL-6D questionnaire), to members of the public along with the AQoL-4D which has extant population norms. Age, gender and the AQoL-4D were used as post-stratification auxiliary variables to construct weights to ameliorate the potential effects of self-selection associated with web-based surveys. The weights were used to estimate unbiased population norms. Standard errors from the weighted samples were calculated using Jackknife estimation.

Results

For both AQoL-6D and AQoL-8D, physical health dimensions decline significantly with age. In contrast, for the majority of the psycho-social dimensions there is a significant U-shaped profile. The net effect is a shallow U-shaped relationship between age and both the AQoL-6D and AQoL-8D utilities. This contrasts with the almost monotonic decline in the utilities derived from the AQoL-4D and SF-6D MAU instruments.

Conclusions

Post-stratification weights were used to ameliorate potential bias in the derivation of norms from web-based data for the AQoL-6D and AQoL-8D. The methods may be used generally to obtain norms when suitable auxiliary variables are available. The inclusion of an enlarged psycho-social component in the two instruments significantly alters the demographic profile.

Keywords

CUA Norms AQoL QoL Multi-attribute utility 

Supplementary material

11136_2016_1337_MOESM1_ESM.docx (36 kb)
Supplementary material 1 (DOCX 37 kb)

References

  1. 1.
    Simmons, C. A., & Lehmann, P. (2013). Tools for strengths-based assessment and evaluation. New York: Springer.Google Scholar
  2. 2.
    Bowling, A. (2005). Measuring health: A review of quality of life measurement scales (3rd ed.). Maidenhead, Berkshire: Open University Press.Google Scholar
  3. 3.
    McDowell, I. (2006). Measuring health: A guide to rating scales and questionnaires. Oxford: Oxford University Press.CrossRefGoogle Scholar
  4. 4.
    Brazier, J., Ratcliffe, J., Salomon, J., & Tsuchiya, A. (2007). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.Google Scholar
  5. 5.
    Richardson, J., McKie, J., & Bariola, E. (2014). Multi attribute utility instruments and their use. In A. J. Culyer (Ed.), Encyclopedia of health economics (pp. 341–357). San Diego: Elsevier Science.CrossRefGoogle Scholar
  6. 6.
    EuroQol Group. (1990). EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 16, 199–208.CrossRefGoogle Scholar
  7. 7.
    Dolan, P., Gudex, C., Kind, P., Williams, A. (1995). A social tariff for EuroQoL: Results from a UK general population survey. Discussion Paper No 138. York: Centre for Health Economics, University of York.Google Scholar
  8. 8.
    Torrance, G., Feeny, D., Furlong, W., Barr, R., Zhang, Y., & Wang, Q. (1996). Multiattribute utility function for a comprehensive health status classification system: Health utilities index mark II. Medical Care, 34(7), 702–722.CrossRefPubMedGoogle Scholar
  9. 9.
    Feeny, D., Furlong, W., Torrance, G., Goldsmith, C., Zhu, Z., DePauw, S., et al. (2002). Multi attribute and single attribute utility functions for the health utilities index mark 3 system. Medical Care, 40(2), 113–128.CrossRefPubMedGoogle Scholar
  10. 10.
    Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21, 271–292.CrossRefPubMedGoogle Scholar
  11. 11.
    Brazier, J., Roberts, J., Tsuchiya, A., & Busschbach, J. (2004). A comparison of the EQ-5D and SF-6D across seven patient groups. Health Economics, 13, 873–884.CrossRefPubMedGoogle Scholar
  12. 12.
    Sintonen, H., & Pekurinen, M. (1989). A generic 15 dimensional measure of health-related quality of life (15D). Journal of Social Medicine, 26, 85–96.Google Scholar
  13. 13.
    Hawthorne, G., Richardson, J., & Osborne, R. (1999). The Assessment of Quality of Life (AQoL) instrument: A psychometric measure of health related quality of life. Quality of Life Research, 8, 209–224.CrossRefPubMedGoogle Scholar
  14. 14.
    Kaplan, R., Bush, J., & Berry, C. (1976). Health status: Types of validity and the index of wellbeing. Health Services Research, 11(4), 478–507.PubMedPubMedCentralGoogle Scholar
  15. 15.
    Misajon, R., Hawthorne, G., Richardson, J., Barton, J., Peacock, S., Iezzi, A., & Keeffe, J. (2005). Vision and quality of life: The development of a utility measure. Investigative Ophthalmology and Visual Science, 46(11), 4007–4015.CrossRefPubMedGoogle Scholar
  16. 16.
    Richardson, J., Iezzi, A., Peacock, S., Sinha, K., Misajon, R., & Keeffe, J. (2012). Utility weights for the vision related Assessment of Quality of Life (AQoL) 7D instrument. Ophthalmic Epidemiology, 19(3), 172–182.CrossRefPubMedGoogle Scholar
  17. 17.
    Richardson, J., Elsworth, G., Iezzi, A., Khan, M. A., Mihalopoulos, C., Schweitzer, I., Herrman, H. (2011). Increasing the sensitivity of the AQoL inventory for evaluation of interventions affecting mental health. Research Paper 61. Melbourne: Centre for Health Economics, Monash University.Google Scholar
  18. 18.
    Richardson, J., Sinah, K., Iezzi, A., Khan, M. A. (2014). Modelling utility weights for the Assessment of Quality of Life (AQoL)-8D. Quality of Life Research, 23, 2395–2404.Google Scholar
  19. 19.
    Chen, G., Khan, M. A., Iezzi, A., Ratcliffe, J., & Richardson, J. (2016). Mapping between 6 multi attribute utility instruments. Medical Decsion Making, 36(2), 160–175.CrossRefGoogle Scholar
  20. 20.
    Richardson, J., Khan, M. A., Iezzi, A., Maxwell, A. (2015). Measuring the sensitivity and construct validity of six utility instruments in seven disease states. Medical Decision Making, Accepted 22 Sep 2015.Google Scholar
  21. 21.
    Richardson, J., Khan, M. A., Iezzi, A., & Maxwell, A. (2015). Comparing and explaining differences in the content, sensitivity and magnitude of incremental utilities predicted by the EQ-5D, SF-6D, HUI 3, 15D, QWB and AQoL-8D multi attribute utility instruments’. Medical Decision Making, 35(3), 276–291.CrossRefPubMedGoogle Scholar
  22. 22.
    Richardson, J., Chen, G., Khan, M. A., & Iezzi, A. (2015). Can multi attribute utility instruments adequately account for subjective well-being? Medical Decision Making, 35(3), 292–304. doi:10.1177/0272989X14567354
  23. 23.
    Campbell, J. A., Palmer, A. J., Venn, A., Sharman, M., Otahal, P., Neil, A. (2016). A head-to-head comparison of the EQ-5D-5L and AQoL-8D multi-attribute utility instruments in patients who have previously undergone bariatric surgery. The Patient—Patient-Centered Outcomes Research, 2016 1–12. doi:10.1007/s40271-015-0157-5
  24. 24.
    Hawthorne, G., Osborne, R., Sansoni, J., & Taylor, A. (2007). The SF-36 version 2: critical analysis of population weights, scoring algorithms and population norms. Quality of Life Research, 16(4), 661–673.CrossRefPubMedGoogle Scholar
  25. 25.
    ABS. (1995). Austalian Bureau of Statistics, National Health Survey SF-36 Population Norms Australia ABS Catalogue No. 4399. Canberra: http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4399.01995?OpenDocument. Accessed 19 Apr 2012.
  26. 26.
    Slade, T., Johnston, A., Oakley Brown, M. A., Adnrews, G., & Whitefor, H. (2009). National survey of mental health and wellbeing: Methods and key findings. Australian and New Zealand Journal of Psychiatry, 43(7), 594–605.CrossRefPubMedGoogle Scholar
  27. 27.
    Hawthorne, G., Herrman, H., & Murphy, B. (2006). Interpreting the WHOQoL-Brèf: Preliminary population norms and effect size. Social Indicators Research, 77, 37–59.CrossRefGoogle Scholar
  28. 28.
    Cummins, R. A., Knapp, T. M., Woerner, J., Walter, J., Page, K. (2005). The personal Wellbeing of Australians living within federal electoral divisions. Report No: 13.1. Melbourne: Deakin University.Google Scholar
  29. 29.
    Norman, R., Church, J., van den Berg, B., & Goodall, S. (2013). Australian health-related quality of life population norms derived from the SF-6D. Australian and New Zealand Journal of Public Health, 37(1), 17–23.CrossRefPubMedGoogle Scholar
  30. 30.
    Hawthorne, G., Korn, S., & Richardson, J. (2013). Population norms for the AQoL derived from the 2007 Australian National Survey of Mental Health and Wellbeing. Australian and New Zealand Journal of Public Health, 37(1), 17–23.CrossRefGoogle Scholar
  31. 31.
    Hawthorne, G., & Osborne, R. (2005). Population norms and meaningful differences for the Assessment of Quality of Life (AQoL) measure. Australian and New Zealand Journal of Public Health, 29(2), 136–142.CrossRefPubMedGoogle Scholar
  32. 32.
    ABS. (2013). Australian demographic statistics, population by age and sex, Cat 3201.0. Canberra: Australian Bureau of Statistics http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3201.0Jun%202010?OpenDocument. Accessed 12 Aug 2013.
  33. 33.
    Richardson, J., Iezzi, A., Khan, M. A., Chen, G. (2014). Interim population norms for the AQoL-6D and AQoL-8D multi attribute utility instruments. Research Paper 87. Melbourne: Centre for Health Economics, Monash University.Google Scholar
  34. 34.
    Meade, A. W., & Craig, B. S. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455.CrossRefPubMedGoogle Scholar
  35. 35.
    Gatz, D. F., & Smith, L. (1995). The standard error of a weighted mean concentration-I: Bootstrapping vs other methods. Atmospheric Environment, 29(11), 1185–1193.CrossRefGoogle Scholar
  36. 36.
    AQoL. (2016) Assessment of Quality of Life (AQoL). http://www.aqol.com.au.
  37. 37.
    Hawthorne, G. (2009). Assessing utility where short measures are required: Development of the short assessment of quality of life-8 (AQoL-8) instrument. Value in Health, 12(6), 948–957.CrossRefPubMedGoogle Scholar
  38. 38.
    Frijters, P., & Beatton, T. (2012). The mystery of the U-shaped relationship between happiness and age. Journal of Economic Behavior and Organization, 82, 525–542.CrossRefGoogle Scholar
  39. 39.
    Richardson, J., Iezzi, A., & Khan, M. A. (2015). Why do multi attribute utility instruments produce different utilities: The relative importance of the descriptive systems, scale and ‘micro utility’ effects. Quality of Life Research. doi:10.1007/s11136-015-0926-6.PubMedCentralGoogle Scholar
  40. 40.
    Iezzi, A., & Richardson, J. (2016). A comparison of AQoL-4D, AQoL-6D, AQoL-7D and AQoL-8D multi attribute utility instruments. Research Paper 93. Melbourne: Centre for Health Economics, Monash University.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aimee Maxwell
    • 1
  • Mehmet Özmen
    • 2
  • Angelo Iezzi
    • 1
  • Jeff Richardson
    • 1
  1. 1.Centre for Health Economics, Monash Business SchoolMonash UniversityMelbourneAustralia
  2. 2.Department of Econometrics and Business Statistics, Monash Business SchoolMonash UniversityMelbourneAustralia

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