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  • Healthcare Value Assessment: Beyond QALYs and Towards Holistic Outcome Measures

    Healthcare Value Assessment: Beyond QALYs and Towards Holistic Outcome Measures
    Healthcare Value Assessment: Beyond QALYs and Towards Holistic Outcome Measures

    Quality-Adjusted Life Years (QALY) is an important concept in health technology assessment since it provides a comprehensive assessment of the impact of diverse healthcare interventions by combining the length and quality of life into a single measure. QALY is measured by integrating life expectancy and quality of life (QoL). Various methods are employed to assess QoL, including structured questionnaires (such as generic questionnaires like EQ-5D, or disease-specific questionnaires), and preference-based interviews like time trade-off (TTO), standard gamble (SG), or visual analog scale (VAS).[1]

    The extensive use of QALYs is a result of several advantages. QALYs offer a standardized metric for comparing the effectiveness of various health interventions used in the management of diverse health conditions. This common scale helps policymakers and healthcare professionals prioritize interventions within a shared framework. The use of QALY can also promote objectivity in decision-making by treating individuals equally and avoiding biases related to age or health status. Additionally, it is possible to evaluate changes in health status over time by using QALYs, acknowledging the dynamic nature of health conditions.[1,2]

    QALY essentially involves evaluation and quantification of QoL through various means: since this results exclusively from a patient’s subjective assessment of the outcome of an intervention, QALYs also represent a patient-centric view of treatment outcomes, contrasting to the physician-centered efficacy and safety measures. This inclusion ensures a broader and more comprehensive perspective, aligning with the principle that public funding drives new treatments. QALY also contributes immensely to cost-utility analysis by allowing the calculation of cost-per-QALY, thereby helping policymakers navigate the complex landscape of resource allocation; this enables the maximization of health outcomes within budget constraints.[1,2]

    Despite these advantages, the QALY approach is not without its criticisms. The value dependency of QALY introduces subjectivity, with the choice of perspective (patient, service provider, or general public) impacting the analysis. Critics argue that disease-non-specific (generic) QoL tools like EQ-5D, while having the advantage of enabling comparison between diverse conditions, may oversimplify health states, especially in mental health cases, potentially neglecting important aspects valued by patients. Additionally, concerns about equity arise as QALY assumes equal social value for all, potentially leading to inequitable weighting based on characteristics such as sex and socioeconomic standing.[3,4]

    The age neutrality of QALY and its focus primarily on health service costs are additional drawbacks. These limitations raise questions about their universality and applicability across diverse healthcare scenarios. The discounting of future benefits also sparks ethical concerns regarding the fair distribution of healthcare resources over time. Decision-makers must grapple with these challenges, balancing the advantages and disadvantages of the QALY approach when employing it as a metric for healthcare decision-making.[3,4]

    In response to the acknowledged limitations of the QALY approach, several alternative metrics have been proposed, each presenting a distinct set of advantages and limitations. These alternatives aim to offer more comprehensive and nuanced evaluations of healthcare interventions, recognizing the diverse aspects of health outcomes. The willingness to Pay (WTP) approach serves as an alternative by incorporating individual preferences, and addresses a criticism of QALY for overlooking individual values. However, the WTP exercise is better suited for economic evaluations than for pure QoL analyses. Conducting a comprehensive WTP interview demands additional expertise in economic modeling, surveys, and analysis. WTP questionnaires also often involve more items and scale points, contributing to implementation complexities. These factors collectively make WTP a time and resource-intensive method.[5,6]

    Another metric seeking to address QALY’s limitations by focusing on a different aspect of well-being is Quality-Adjusted Time Without Symptoms or Toxicity (Q-TWIST); however, this approach has challenges related to complexity and applicability, potentially restricting its application to a narrower range of healthcare interventions. Wellbeing-Adjusted Life Year (WELLBY) takes a broader approach by encompassing happiness and life satisfaction alongside health considerations, thereby addressing the limitation of QALY’s narrow focus on health outcomes; on the flip side, WELLBY faces challenges related to subjectivity and standardization, which could impact its reliability and consistency across evaluations.[5,6]

    Disability-Adjusted Life Year (DALY) offers a comprehensive evaluation by considering both premature mortality and years lived with disability, presenting a holistic perspective on disease burden. However, DALY’s criticisms include complexities in measuring disability, thereby potentially not capturing overall well-being satisfactorily, akin to looking at a ‘negative’ aspect to capture a ‘positive’ measure. Next, the Multi-Criteria Decision Analysis (MCDA) is a measure that evaluates options with conflicting criteria and selects the best solution accommodating complexity and stakeholder involvement. However, MCDA is often considered subjective and inconsistent and is also quite resource-intensive. Finally, the equal value of Life Years Gained (evLYG) strives for equitable assessments by considering age and baseline health, addressing QALY’s potential age bias; however, there are challenges in determining equal value across diverse health conditions.[5,6]

    The choice of a specific metric to measure QoL and QALY hinges on evaluation goals, contextual nuances, and stakeholder values. The ongoing evolution of healthcare value assessment methodologies reflects the dynamic nature of the field, with researchers and policymakers continually exploring and refining approaches to better capture the complexity of healthcare outcomes and their societal implications.[5,6]

    While numerous alternatives are available, QALY is undoubtedly the most frequently used metric despite its drawbacks and criticisms. The advantages of QALY, such as its comprehensive measurement, comparative analysis capabilities, and standardized metric, make it a practical tool for assessing the value of healthcare interventions. However, the subjectivity in quality assessment and limited applicability in certain scenarios might prompt consideration of alternative metrics discussed above. Ultimately, the quest for a complete tool for value assessment requires ongoing research, collaboration, and a commitment to refining existing methodologies to better align with the diverse and complex nature of healthcare interventions and outcomes.

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    References

    1. Whitehead SJ, Ali S. Health outcomes in economic evaluation: the QALY and utilities. British medical bulletin. 2010 Dec 1;96(1):5-21.
    2. Prieto L, Sacristán JA. Problems and solutions in calculating quality-adjusted life years (QALYs). Health Qual Life Outcomes. 2003 Dec 19;1:80.
    3. Brazier J, Tsuchiya A. Improving cross-sector comparisons: going beyond the health-related QALY. Applied health economics and health policy. 2015 Dec;13:557-65.
    4. Pettitt DA, Raza S, Naughton B, Roscoe A, Ramakrishnan A, Ali A, Davies B, Dopson S, Hollander G, Smith J, Brindley D. The limitations of QALY: a literature review. Journal of Stem Cell Research and Therapy. 2016;6(4).
    5. Carlson JJ, Brouwer ED, Kim E, Wright P, McQueen RB. Alternative Approaches to Quality-Adjusted Life-Year Estimation Within Standard Cost-Effectiveness Models: Literature Review, Feasibility Assessment, and Impact Evaluation. Value Health. 2020 Dec;23(12):1523-1533.
    6. Gafni A. Alternatives to the QALY measure for economic evaluations. Supportive care in cancer. 1997 Mar;5:105-11.

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  • ‘Bolt-On’s for Utility Instruments: How Should They be Developed and Used?

    ‘Bolt-On’s for Utility Instruments: How Should They be Developed and Used?

    Health utilities are cardinal values in identifying an individual’s preference for different health outcomes. Health utilities are transformed to Quality Adjusted Life Years (QALYs) to enable comparisons between health interventions in cost-utility analysis in Pharmacoeconomics. Health utilities are measured using various utility ‘instruments’, which can be either generic (that is, can be applied to all diseases) or disease-specific.[1] Utility instruments are most of the times well-crafted questionnaires designed to capture and score the utility and thereby calculate the overall quality of life. Since the comparison of utility gains are often used in healthcare policy setting, it is essential that the instruments used to measure the utilities are rigorously validated.

    Many of the popular and repeatedly validated utility instruments such as EQ-5D (EuroQol 5-dimension domain) and HUI (Health Utilities Index) are ‘generic’ and can be applied to most health conditions. Despite their popularity, the fact remains that these generic instruments are unable to capture some disease-specific factors that might determine the quality of life in patients suffering from the disease. For example, in a disease like psoriasis, the quality of life is determined to a large extent by disease-specific factors such as itching and embarrassment, which are not captured by EQ-5D.[2] To rectify this inadequacy, the existing generic measures are modified by adding disease-specific dimensions as ‘bolt-ons’, with an aim to comprehensively describe and value health.

    Bolt-ons are dimensions or measures added to the parent instrument/Questionnaire in order to compensate/improve the inadequacy faced while applying it for a specific group of population or a particular health state.[3] The use of Bolt-ons ensures that not only the validity of the original utility instrument is retained, but also improves the sensitivity of the instrument in the therapeutic area where it is deficient. The wording and development of Bolt-ons in research are selected based on their statistically significant values and consistency by exploring various studies. This also depends on the topic of interest by the researcher. There is no public or patient involvement in the development of Bolt-on descriptions.[4]

    For example, a ‘cognitive function’ bolt-on was added to the EQ-5D, and the resulting ‘EQ-6D’ was found to be more sensitive in capturing the overall health status of the population.[5] Likewise, two additional dimensions, namely ‘skin irritation’ and ‘self-confidence’ were bolted on to the EQ-5D so that the resulting questionnaire (the EQ-PSO bolt-on questionnaire) was more suitable to assess utilities in patients with psoriasis.[2] An enhanced version of EQ-5D with a vision bolt-on was found to be more discriminative while assessing quality of life in patients with visual problems than the original version of EQ-5D.[6] Next, while assessing the quality of life loss due to respiratory problems, a team from the Netherlands explored the impact of adding two ‘respiratory’ bolt-ons (‘limitations in physical activity due to shortness of breath’ and ‘breathing problems’) to EQ-5D-5L, and concluded that both the bolt-ons, especially the ‘ later, was associated with an improved discrimination of declining quality of life, when compared to the original instrument.[7] However, not all bolt-ons can be expected to enhance the parent utility measure. For example, adding a ‘sleep’ bolt-on to EQ-5D did not improve the validity or sensitivity of EQ-5D in calculating the quality of life, probably because the existing items in EQ-5D themselves captured the impact of sleep on overall quality of life.[8]

    Though the Bolt-on dimensions enhance the health care utility values, there are some challenging issues while designing the Bolt-on descriptors and its implementation. This is because the population recruited is usually heterogenous. The terminology used should be subjected to Qualitative interviews prior to its use in research. However, additional information on the bolt-on descriptors can be obtained via quantitative research. Bolt-ons may not be necessary when the intervention involved removal of the intervention (E.g. from spectacles to laser eye surgery). Also, the impact of the Bolt-on dimensions depends on the severity states of EQ-5D.

    The research on implementation of bolt-on dimensions is still in the infant stage which necessitates research in larger group of population to identify whether more complex models are necessary. The impact of addition of the bolt-on dimension should be rigorously explored in comparison with the original instrument. The validity and sensitivity of the modified measure should be evaluated in heterogeneous populations using appropriate psychometric testing to establish the bolt-on instrument as a viable tool for research. Care should be taken that the addition of the bolt-on is not compromising the validity of the instrument, and at the same time, the bolt-on is not providing spurious information.

    With the rapid mushrooming of health utilities, developing new instruments entail careful validation and psychometric testing with notable gaps involving considerable time and resources. Bolt-on dimensions bridge the gap by being synergistic to the existing utility instruments and making them more responsive rather than just being additive.

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    References:

    1. What are health utilities? 2nd ed. Newmarket: Hayward Medical Communications; 2014 [cited 26 August 2020]. Available from: http://www.whatisseries.co.uk/
    2. Swinburn P, Lloyd A, Boye K, Edson-Heredia E, Bowman L, Janssen B. Development of a Disease-Specific Version of the EQ-5D-5L for Use in Patients Suffering from Psoriasis: Lessons Learned from a Feasibility Study in the UK. Value in Health. 2013;16(8):1156-1162.
    3. Longworth L, Yang Y, Young T, 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. Southampton (UK): NIHR Journals Library; 2014 Feb. (Health Technology Assessment, No. 18.9.) Chapter 4, Developing ‘bolt-on’ items to EQ-5D.Available from: https://www.ncbi.nlm.nih.gov/books/NBK261620/
    4. Stevens K. How Well Do the Generic Multi-attribute Utility Instruments Incorporate Patient and Public Views Into Their Descriptive Systems?. The Patient – Patient-Centered Outcomes Research. 2015;9(1):5-13
    5. Hoeymans N, van Lindert H, Westert GP. The health status of the Dutch population as assessed by the EQ-6D. Qual Life Res. 2005;14(3):655-663. doi:10.1007/s11136-004-1214-z
    6. Luo N, Wang X, Ang M, et al. A Vision “Bolt-On” Item Could Increase the Discriminatory Power of the EQ-5D Index Score. Value Health. 2015;18(8):1037-1042.
    7. Hoogendoorn M, Oppe M, Boland MRS, Goossens LMA, Stolk EA, Rutten-van Molken MPMH. Exploring the Impact of Adding a Respiratory Dimension to the EQ-5D-5L. Med Decis Making. 2019;39(4):393-404.
    8. Yang Y, Brazier J, Tsuchiya A. Effect of adding a sleep dimension to the EQ-5D descriptive system: a “bolt-on” experiment. Med Decis Making. 2014;34(1):42-53.

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  • Use of QALY in Healthcare Decision-Making – The Controversy Continues

    Use of QALY in Healthcare Decision-Making – The Controversy Continues

    In many parts of the world, the value of medicines is measured by a unit called ‘Quality-Adjusted Life Year’ (QALY), a metric that health economists and others use to quantify the health benefits generated by a particular treatment. QALYs are often used by state-run health systems in many countries to help decide which drugs to cover.

    QALYs measure the amount of time patients live after receiving a treatment, and the quality of their health. They provide a convenient yardstick for measuring and comparing health effects of varied interventions across diverse diseases and conditions. They represent the effects of a health intervention in terms of the gains or losses in time spent, in a series of “quality-weighted” health states. Some government-run health systems set rough caps on the amount, they are willing to pay per QALY.

    However, use of QALYs can be controversial, as some critics feel they amount to putting a price on life. Drug makers have been among the metric’s biggest critics and a few even opine that there are well-documented disadvantages of using QALYs to assess the value of a therapy. The 2010 Affordable Care Act explicitly bans the government from using a cost-per-QALY yardstick, or any similar measure, “as a threshold to determine coverage” under Medicare, a provision for which the pharmaceutical industry lobbies. Spain is the latest addition to the list, after Germany and USA, banning the use of QALY in healthcare decision making, after considering that this approach methodologically and ethically lacks robustness.

    The 2010 Patient Protection and Affordable Care Act (ACA) created a Patient-Centered Outcomes Research Institute (PCORI) to conduct comparative-effectiveness research (CER) but prohibited this institute from developing or using cost-per-QALY thresholds. The ACA specifically forbids the use of cost per QALY “as a threshold.” The precise intent and consequences of this language are unclear. One might interpret it to mean that the PCORI, or its contractors or grantees, can still calculate cost-per-QALY ratios as long as they are not compared with a threshold (e.g., $100,000 per QALY) or used to make a recommendation based on such a threshold. Comparisons of cost-per-QALY ratios across interventions could still be useful to decision makers even without the invocation of an explicit threshold. However, the ACA suggests a broader ban on the use of cost-utility analyses, which could eventually have a chilling effect on the field.

    When asked how they would like to allocate society’s health resources, researchers tend not to favor QALY “optimization” strategies. Instead, they tend to believe that equally ill people should have the same right to treatment, regardless of whether the treatment effect (that is, the QALY gain) is large. Moreover, QALYs do not distinguish the aggregation of modest benefits to large numbers of people from a substantial benefit going to a few people. QALYs might not adequately capture preferences about the amount of time experienced in a health state, or the order in which health states are experienced.

    Alternatives metrics to QALYs have been suggested, although all have limitations. Healthy-year equivalents (which measure preferences for life health profiles rather than discrete states) have been proposed, but their feasibility has been questioned, and the metric has not gained traction. Many health economists favor willingness-to-pay (WTP) metrics that involve asking people directly what they would be willing to pay for health improvements. However, such metrics require assigning monetary value to health benefits, which others find objectionable.

    Finally, analysts could simply calculate separately the costs and health consequences of different strategies (sometimes called “cost-consequences analyses”) and leave decision makers to decide if any particular treatment is “worth it.” However, the method would sidestep explicit discussions about value and provides no guidance for allocating resources fairly or efficiently across treatments. A number of government health authorities, including those in Australia, the United Kingdom, and Canada, have incorporated cost-effectiveness considerations explicitly into coverage and pricing decisions about drugs and other technologies. Although few currently require QALYs in economic evaluations, there is a clear preference for them in these and other countries. Hence, the flexible use of QALYs could be beneficial.

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