Utility Scores for Comorbid Conditions: Methodological Issues and Advances

  • C. N. McIntosh


 Utility scores quantify  health-related quality of life (HRQOL) along a continuum that typically ranges from 0.0 (dead) to 1.0 (full health), and are essential in developing  summary measures of population health (SMPH), as well as performing  cost-effectiveness analysis (CEA) of different treatments and intervention strategies. A key methodological issue is that traditionally, utility scores have been developed primarily for single health conditions, even though comorbidities are common in both general and patient populations.

Inaccuracies in health measurement are likely to occur when  comorbidity is ignored in the estimation of utility scores. In this chapter, methodological issues and advances with regard to deriving utility scores for comorbid health conditions are reviewed.

Direct utility elicitation protocols such as the standard gamble (SG) or time trade-off (TTO) are the most theoretically desirable approaches, but are cognitively burdensome for raters. With population survey data, scores from utility-based HRQOL instruments (e.g., the Health Utilities Index) can often be computed for self-reported comorbidities, but this strategy is often constrained by the limited number of conditions queried, as well as potentially compromised by self-report bias. Another suggested, yet little-used strategy is to map the expected impact of a given comorbidity into the descriptive system of a generic, multiattribute utility instrument, and then compute the corresponding utility score with the scoring algorithm.

Convenient mathematical models (e.g., additive, multiplicative, minimum) for combining single-condition utility scores have also been proposed, but the empirical evidence for their performance is mixed, as well as difficult to assess due to a lack of standardization in utility instrumentation and analytical procedures used. An “encompassing” mathematical model that subsumes traditional models as special cases appears to be more accurate, but has only been examined with respect to directly elicited utilities in the prostate cancer context. A crucial next step is evaluating its performance with respect to a wider variety of health conditions and data sources.

In future work on evaluating and refining methods for obtaining comorbidity-related utilities, cross-study comparability can be enhanced by striving for more consistency in utility instrumentation and analytical techniques.


Utility Score Multiplicative Model Full Health National Population Health Survey Suboptimal Health State 
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.

List of Abbreviations:


15-Dimensions Index


 burden of disease


Canadian Community Health Survey


Clinical Classification Category


cost-effectiveness analysis


congestive heart failure


Classification and Measurement System of Functional Health


chronic obstructive pulmonary disease


disutility score


EeuroQol Five Dimensions Index


global burden of disease


health-related quality of life


Health Utilities Index Mark III


International Classification of Diseases-Ninth Revision


Ischemic Heart Disease


Medical Expenditures Panel Survey


Canadian National Population Health Survey


Ontario Diabetes Database


Ontario Health Survey


population attributable risk


Panel on Cost-Effectiveness in Health and Medicine


person trade-off


quality-adjusted life year


Quality of Well-Being Index


Quality of Well-Being Index – Self-Administered


relative risk


Short-Form Six Dimensions Index


socio-economic status


standard gamble


summary measure of population health


time trade-off


utility score


World Health Organization


  1. Andrews G, Sanderson K, Beard J. (1998). Br J Psychiatry. 173: 123–131.PubMedCrossRefGoogle Scholar
  2. Arnesen T, Nord E. (1999). BMJ. 319: 1423–1425.PubMedGoogle Scholar
  3. Austin PC. (2002). Med Decis Making. 22: 152–162.PubMedGoogle Scholar
  4. Basu A, Dale W, Elstein A, Meltzer D. (2008). J. Health Econ. Sep4 [Epub ahead of print] PMID: 18773392.Google Scholar
  5. Begg S, Vos T, Barker B, Stevenson C, Stanley L, Lopez A. (2007). Burden of disease and injury in Australia, 2003. AIHW cat. no. PHE 82. Australian Bureau of Health and Welfare, Canberra.Google Scholar
  6. Bell CM, Chapman RH, Stone PW, Sandberg EA, Neumann PJ. (2001). Med. Decis. Making. 21: 288–294.PubMedGoogle Scholar
  7. Boswell-Purdy J, Flanagan WM, Roberge H, Le Petit C, White KJ, Berthelot JM. (2007). Chron Dis Can. 28: 42–55.Google Scholar
  8. Bowker SL, Pohar SL, Johnson JA. (2006). Health Qual Life Outcomes. 4: 17.PubMedCrossRefGoogle Scholar
  9. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. (2005). JAMA. 294: 716–724.PubMedCrossRefGoogle Scholar
  10. Bravata DM, Nelson LM, Garber AM, Goldstein MK. (2005). Med Decis Making. 25: 158–167.PubMedCrossRefGoogle Scholar
  11. Brazier J, Deverill M, Green C, Harper R, Booth A. (1999). Health Technol. Assess. 3: 1–164.Google Scholar
  12. Broemeling A-M, Watson D, Black C. (2005). Chronic conditions and comorbidity among residents of British Columbia. Centre for Health Services and Policy Research, University of British Columbia, British Columbia.Google Scholar
  13. Brown MM, Brown GC, Sharma S. (2005). Evidence-based to value-based medicine. AMA, Chicago, Illinois.Google Scholar
  14. Clarke P, Gray A, Holman R. (2002). Med Decis Making. 22: 340–349.PubMedGoogle Scholar
  15. Coffey JT, Brandle M, Zhou H, Marriott D, Burke R, Tabaei BP, Engelgau MM, Kaplan RM, Herman WH. (2002). Diabetes Care 25: 2238–2243.PubMedCrossRefGoogle Scholar
  16. Dale W, Basu A, Elstein A, Meltzer D. (2008). Med Decis Making. 28: 102–112.PubMedCrossRefGoogle Scholar
  17. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddardt GL. (2005). Methods for the economic evaluation of health care programmes, 3rd ed. Oxford University Press, Oxford.Google Scholar
  18. Eckman MH, Steere AC, Kalish RA, Pauker SG. (1997). N Engl J Med. 337: 357–363.PubMedCrossRefGoogle Scholar
  19. Eckman MH, Falk RH, Pauker SG. (1998). Arch Intern Med. 158: 1669–1677.PubMedCrossRefGoogle Scholar
  20. Eckman MH, Singh SK, Erban JK, Kao G. (2002). Med Decis Making. 22: 106–124.Google Scholar
  21. Feeny DA. (2005). ISOQoL Res. Newsletter 10: 1 and 8.Google Scholar
  22. Feeny D, Furlong W, Torrance GW, Goldsmith CH, Zhu Z, DePauw S, Denton M, Boyle M. (2002). Med Care 40: 113–128.PubMedCrossRefGoogle Scholar
  23. Flanagan W, McIntosh CN, Le Petit C, Berthelot JM. (2006). PHM. 4: 13.Google Scholar
  24. Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. (2004). Health Qual. Life Outcomes. 20: 51.CrossRefGoogle Scholar
  25. Franci DM, Pathak DV. (2003). Int J Technol. Assess. Health Care 19: 347–361.CrossRefGoogle Scholar
  26. Franks P, Hanmer J, Fryback DG. (2006). Med Care. 44: 478–485.PubMedCrossRefGoogle Scholar
  27. Fryback D, Lawrence W. (1997). Med Decis Making. 17: 276–284.PubMedCrossRefGoogle Scholar
  28. Fu A, Kattan M. (2008). Med Care. 46: 984–990.Google Scholar
  29. Furlong W, Barr RD, Feeny D, Yandow S. (2005). Health Qual. Life Outcomes. 3: 3.PubMedCrossRefGoogle Scholar
  30. Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, Bos GA. van den (2001). J Clin Epidemiol. 54: 661–674.PubMedCrossRefGoogle Scholar
  31. Gold MR, Siegel JE, Russell LB, Weinstein MC. (1996). Cost-Effectiveness in Health and Medicine. Oxford University Press, Oxford.Google Scholar
  32. Hall WH, Jani AB, Ryu JK, Narayan S, Vijayakumar S. (2005). Prostate Cancer Prostatic Dis. 8: 22–30.PubMedCrossRefGoogle Scholar
  33. Harris R, Nease R. (1997). J Health Econ. 16: 113–120.PubMedCrossRefGoogle Scholar
  34. Hazen G. (2004). Decis Analysis. 1: 205–216.CrossRefGoogle Scholar
  35. Johnston JA, Brill-Edwards P, Ginsberg JS, Pauker SG, Eckman MH. (2005). Am J Med. 118: 503–514.PubMedCrossRefGoogle Scholar
  36. Kao TC, Cruess DF, Garner D, Foley J, Seay T, Friedrichs P, Thrasher JB, Mooneyhan RD, McLeod DG, Moul JW. (2000). J Urol. 163: 858–864.PubMedCrossRefGoogle Scholar
  37. Katzmarzyk PT, Janssen I. (2004). Appl. Physiol. Nutr. Metab. 29: 90–115.CrossRefGoogle Scholar
  38. Knight SJ, Nathan DP, Siston AK, Kattan MW, Elstein AS, Collela. (2002). Clin Prostate Cancer 1:105–14.PubMedGoogle Scholar
  39. Laux G, Kuehlein T, Rosemann T, Szecsenyi J. (2008). BMC Health Serv Res. 8: 14.PubMedCrossRefGoogle Scholar
  40. Maddigan SL, Feeny DH, Johnson JA. (2005). Qual Life Res. 14: 1311–1320.PubMedCrossRefGoogle Scholar
  41. Maddigan SL, Feeny DH, Majumdar SR, Farris KB, Johnson JA. (2006). Am J Public Health. 96: 1649–1655.PubMedCrossRefGoogle Scholar
  42. Manuel DG, Schultz SE. (2004). PHM 2: 4.Google Scholar
  43. Manuel DG, Schultz SE, Kopec JA. (2002). J Epidemiol Community Health 56: 843–850.PubMedCrossRefGoogle Scholar
  44. Mas-Colell A, Whinston MD, Green JR. (1995). Microeconomic theory. Oxford University Press, New York.Google Scholar
  45. Mathers CD, Iburg KM, Begg SB. (2006). PHM 4:4.Google Scholar
  46. Mathers CD, Sadana R, Salomon JA, Murray CJL, Lopez AD. (2001). Lancet. 357: 1685–1691.PubMedCrossRefGoogle Scholar
  47. Mathers C, Vos T, Stevenson C. (1999). The burden of disease and injury in Australia. Australian Institute of Health and Welfare, AIHW cat. no. PHE 17. Australian Bureau of Health and Welfare, Canberra.Google Scholar
  48. McIntosh CN, Gorber SC, Bernier J, Berthelot JM. (2007). Chron Dis Can. 28: 29–41.Google Scholar
  49. Murray CJL. (1996). In: Murray CJL, Lopez AD (ed). The global burden of disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020, Volume 1. Harvard University Press, Boston, pp. 1–98.Google Scholar
  50. Murray CJL, Lopez AD. (2000). Health Econ. 9: 69–82.PubMedCrossRefGoogle Scholar
  51. Murray CJL, Salomon JA, Mathers CD, Lopez AD (eds). (2002). Summary measures of population health: Concepts, ethics, measurement and applications. World Health Organization, Geneva.Google Scholar
  52. Naglie G, Krahn MD, Naimark D, Redelmeier DA, Detsky AS. (1997). Med Decis Making. 17: 136–141.PubMedCrossRefGoogle Scholar
  53. Quan H, Parsons GA, Ghali WA. (2002). Med Care. 40: 675–685.PubMedCrossRefGoogle Scholar
  54. Ritvo P, Irvine J, Naglie G, Tomlinson G, Bezjak A, Matthew A, Trachtenberg J, Krahn M. (2005). J Clin Epidemiol. 58: 466–474.PubMedCrossRefGoogle Scholar
  55. Rutten-van-Mölken MPMH, Oostenbrink JB, Tashkin, DP, Burkhart D, Monz BU. (2006). Chest. 130: 1117–1128.PubMedCrossRefGoogle Scholar
  56. Salomon JA, Murray CJL. (2004). Health Econ. 13: 281–290.PubMedCrossRefGoogle Scholar
  57. Saarni S, Härkänen T, Sintonen H, Suvisaari J, Koskinen S, Aromaa A, Lönnqvist J. (2006). Qual. Life Res. 15: 1403–1414.PubMedCrossRefGoogle Scholar
  58. Schultz SE, Kopec JA. (2003). Health Rep. 14: 41–53.PubMedGoogle Scholar
  59. Statistics Canada. (2002–2003). National Population Health Survey: Health Institutions Component Cycle 5.
  60. Stewart ST, Lenert L, Bhatnagar V, Kaplan RM. (2005). Med Care. 43: 347–355.PubMedCrossRefGoogle Scholar
  61. Stouthard MEA, Essink-Bot ML, Bonsel GJ, Barendregt JJM, Kramers PGN, van de Water HPA, Gunning-Schepers LJ, van der Maas PJ. (1997). Disability weights for diseases in the Netherlands. Department of Public Health, Erasmus University, Rotterdam.Google Scholar
  62. Sullivan PW, Ghushchyan V. (2006). Med Decis Making. 26: 410–420.PubMedCrossRefGoogle Scholar
  63. Sullivan PW, Lawrence WF, Ghushchyan V. (2005). Med Care. 43: 736–749.PubMedCrossRefGoogle Scholar
  64. Tengs T, Wallace A. (2000). Med Care. 38: 583–637.PubMedCrossRefGoogle Scholar
  65. Tunis S, Stryer DB, Clancy CM. (2003). JAMA. 290: 1624–1632.PubMedCrossRefGoogle Scholar
  66. US Bureau of the Census. (1992). International Population Reports, an Aging World II. P25, 92–3. US Government Printing Office, Washington DCGoogle Scholar
  67. van Baal PHM, Hoeymans N, Hoogenveen RT, de Wit GA, Westert GP. (2006). PHM 4: 1Google Scholar
  68. Vos T, Begg S. (2000). The Victorian Burden of Disease Study: Morbidity. Public Health Division, Department of Human Services, Melbourne.Google Scholar
  69. Wu SY, Green A. (2000). Projection of Chronic Illness Prevalence and Cost Inflation. RAND Health, Washington DC.Google Scholar

Copyright information

© Springer Science+Business Media LLC 2010

Authors and Affiliations

  • C. N. McIntosh

There are no affiliations available

Personalised recommendations