Quality-adjusted life year

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The quality-adjusted life year or quality-adjusted life-year (QALY) is a measure of disease burden, including both the quality and the quantity of life lived.[1][2] It is used in assessing the value for money of a medical intervention. According to Pliskin et al., The QALY model requires utility independent, risk neutral, and constant proportional tradeoff behaviour.[3]

The QALY is based on the number of years of life that would be added by the intervention. Each year in perfect health is assigned the value of 1.0 down to a value of 0.0 for being dead. If the extra years would not be lived in full health, for example if the patient would lose a limb, or be blind or have to use a wheelchair, then the extra life-years are given a value between 0 and 1 to account for this.[citation needed] Under certain methods, such as the EQ-5D, QALY can be negative number.

Use[edit]

The QALY is often used in cost-utility analysis to calculate the ratio of cost to QALYs saved for a particular health care intervention. This is then used to allocate healthcare resources, with an intervention with a lower cost to QALY saved (incremental cost effectiveness) ratio ("ICER") being preferred over an intervention with a higher ratio.[citation needed]

Meaning[edit]

The concept of the QALY is credited to work by Klarman[4] and later Fanshel and Bush[5] and Torrance [6] who suggested the idea of length of life adjusted by indices of functionality or health.[7] It was officially named the QALY in print in an article by Zeckhauser and Shepard[8] It was later promoted through medical technology assessment conducted by the US Congress Office of Technology Assessment.

Then, in 1980, Pliskin proposed a justification of the construction of the QALY indicator using the multiattribute utility theory: if a set of conditions pertaining to agent preferences on life years and quality of life are verified, then it is possible to express the agent’s preferences about couples (number of life years/health state), by an interval (Neumannian) utility function. This utility function would be equal to the product of an interval utility function on « life years », and an interval utility function on « health state ». Because of these theoretical assumptions, the meaning and usefulness of the QALY is debated.[9][10][11] Perfect health is hard, if not impossible, to define. Some argue that there are health states worse than being dead, and that therefore there should be negative values possible on the health spectrum (indeed, some health economists have incorporated negative values into calculations). Determining the level of health depends on measures that some argue place disproportionate importance on physical pain or disability over mental health.[12] The effects of a patient's health on the quality of life of others (e.g. caregivers or family) do not figure into these calculations.[citation needed]

Calculation[edit]

The QALY is a measure of the value of health outcomes. Since health is a function of length of life and quality of life, the QALY was developed as an attempt to combine the value of these attributes into a single index number. The basic idea underlying the QALY is simple: it assumes that a year of life lived in perfect health is worth 1 QALY (1 Year of Life × 1 Utility value = 1 QALY) and that a year of life lived in a state of less than this perfect health is worth less than 1. In order to determine the exact QALY value, it is sufficient to multiply the utility value associated with a given state of health by the years lived in that state. QALYs are therefore expressed in terms of "years lived in perfect health": half a year lived in perfect health is equivalent to 0.5 QALYs (0.5 years × 1 Utility), the same as 1 year of life lived in a situation with utility 0.5 (e.g. bedridden) (1 year × 0.5 Utility). QALYs can then be incorporated with medical costs to arrive at a final common denominator of cost/QALY. This parameter can be used to develop a cost-effectiveness analysis of any treatment.

Weighting[edit]

The "weight" values between 0 and 1 are usually determined by methods such as:Template:Http://www.sciencedirect.com/science/article/pii/0167629686900202

  • Time-trade-off (TTO): Respondents are asked to choose between remaining in a state of ill health for a period of time, or being restored to perfect health but having a shorter life expectancy.
  • Standard gamble (SG): Respondents are asked to choose between remaining in a state of ill health for a period of time, or choosing a medical intervention which has a chance of either restoring them to perfect health, or killing them.
  • Visual analogue scale (VAS): Respondents are asked to rate a state of ill health on a scale from 0 to 100, with 0 representing being dead and 100 representing perfect health. This method has the advantage of being the easiest to ask, but is the most subjective.

Another way of determining the weight associated with a particular health state is to use standard descriptive systems such as the EuroQol Group's EQ5D questionnaire, which categorises health states according to the following dimensions: mobility, self-care, usual activities (e.g. work, study, homework or leisure activities), pain/discomfort and anxiety/depression.[citation needed]

However, the weight assigned to a particular condition can vary greatly, depending on the population being surveyed. Those who do not suffer from the affliction in question will, on average, overestimate the detrimental effect on quality of life, compared to those who are afflicted.[citation needed]

Debate[edit]

The method of ranking interventions on grounds of their cost per QALY gained ratio (or ICER) is controversial because it implies a quasi-utilitarian calculus to determine who will or will not receive treatment.[13] However, its supporters argue that since health care resources are inevitably limited, this method enables them to be allocated in the way that is approximately optimal for society, including most patients. Another concern is that it does not take into account equity issues such as the overall distribution of health states - particularly since younger, healthier cohorts have many times more QALYs than older or sicker individuals. As a result, QALY analysis may undervalue treatments which benefit the elderly or others with a lower life expectancy. Also, many would argue that all else being equal, patients with more severe illness should be prioritised over patients with less severe illness if both would get the same absolute increase in utility.[14] Recently the results of a European Commission Project,ECHOUTCOME recommended to not use QALYs in health decision making after surveying 1361 subjects in the UK, Belgium, France and Italy and establishing that the four theoretical assumptions underlying QALYs are invalid (quality of life should be measured in consistent intervals; life years and QOL should be independent; people should be neutral about risk; and willingness to sacrifice life years should be constant over time), explaining why divergent QALY results can be generated using the same dataset.[15][16]

See also[edit]

References[edit]

  1. ^ "Measuring effectiveness and cost effectiveness: the QALY". National Institute for Health and Clinical Excellence. 
  2. ^ "Entry for "QALY"". Bandolier Journal Glossary. Oxford University. 
  3. ^ Pliskin, J. S.; Shepard, D. S.; Weinstein, M. C. (1980). "Utility Functions for Life Years and Health Status". Operations Research 28: 206–24. doi:10.1287/opre.28.1.206. JSTOR 172147. 
  4. ^ Klarman, Herbert E.; Francis, John O'S; Rosenthal, Gerald D. (1968). "Cost effectiveness analysis applied to the treatment of chronic renal disease". Medical care 6 (1): 48–54. doi:10.1097/00005650-196801000-00005. Retrieved 2014-05-06. 
  5. ^ Fanshel, Sol; Bush, J.W. (1970). "A health-status index and its application to health-services outcomes". Operations Research 18 (6): 1021–66. doi:10.1287/opre.18.6.1021. Retrieved 2014-05-06. 
  6. ^ Torrance, G W; Thomas, W.H.; Sackett, D.L. (1972). "A utility maximization model for evaluation of health care programs". Health services research 7 (2): 118–133. ISSN 0017-9124. PMC 1067402. PMID 5044699. 
  7. ^ Kaplan, Robert M. (1995). "Utility assessment for estimating quality-adjusted life years". Valuing health care: Costs, benefits, and effectiveness of pharmaceuticals and other medical technologies: 31–60. Retrieved 2014-05-06. 
  8. ^ Zeckhauser, Richard; Shepard, Donald (1976). "Where Now for Saving Lives?". Law and Contemporary Problems 40 (4): 5–45. doi:10.2307/1191310. ISSN 0023-9186. 
  9. ^ Prieto, Luis; Sacristán, José A (2003). "Problems and solutions in calculating quality-adjusted life years (QALYs)". Health and Quality of Life Outcomes 1: 80. doi:10.1186/1477-7525-1-80. PMC 317370. PMID 14687421. 
  10. ^ Schlander, Michael (2007-07-09). "Lost in Translation? Over-Reliance on QALYs May Lead to Neglect of Relevant Evidence". Copenhagen, Denmark: Institute for Innovation & Valuation in Health Care. Retrieved 2008-11-14. 
  11. ^ Mortimer, D.; Segal, L. (2007). "Comparing the Incomparable? A Systematic Review of Competing Techniques for Converting Descriptive Measures of Health Status into QALY-Weights". Medical Decision Making 28 (1): 66–89. doi:10.1177/0272989X07309642. PMID 18263562. 
  12. ^ Dolan, P (January 2008). "Developing methods that really do value the ‘Q’ in the QALY" (PDF). Health Economics, Policy and Law 3 (1): 69–77. doi:10.1017/S1744133107004355. 
  13. ^ Schlander, Michael (2010-05-23), "Measures of efficiency in healthcare: QALMs about QALYs?", Institute for Innovation & Valuation in Health Care, retrieved 2010-05-23 
  14. ^ Nord, Erik; Pinto, Jose Luis; Richardson, Jeff; Menzel, Paul; Ubel, Peter (1999). "Incorporating societal concerns for fairness in numerical valuations of health programmes". Health Economics 8 (1): 25–39. doi:10.1002/(SICI)1099-1050(199902)8:1<25::AID-HEC398>3.0.CO;2-H. PMID 10082141. 
  15. ^ Dreaper, J. (24 January 2013). "Researchers claim NHS drug decisions are flawed". BBC News. 
  16. ^ Holmes D (March 2013). "Report triggers quibbles over QALYs, a staple of health metrics". Nat. Med. 19 (3): 248. doi:10.1038/nm0313-248. PMID 23467219.