Editorial

A QALY is a QALY is a QALY — Or is it?

Clinical decision support and knowledge-enhanced health and healthcare, as they have evolved over a more than six decades, have a number of purposes, as delineated in the previous chapter. For the various purposes, a number of methods may be useful. The methods have themselves had a long history of exploration and application. These are reviewed in terms of their historical context and current relevance.

Durch den Marktzugang hochpreisiger neuer Arzneimittel werden die finanzielle Belastungsfähigkeit des Gesundheitssystems sowie die Angemessenheit von Arzneimittelpreisen und die Funktionalität etablierter Preisbildungsverfahren vielfach angezweifelt. Ist es also Zeit für eine Erweiterung der bislang auf klinische Nutzendaten fokussierenden Bewertungsperspektive in Deutschland? Sollten zukünftig Kosten und Nutzenaspekte neuer Technologien gleichsam in der Bewertung über Marktzugang und Preise eine Rolle spielen? Die Erfahrungen bei europäischen Nachbarn und der Ständigen Impfkommission in der Bewertung neuer Impfungen und Impfstrategien mit Kosten-Nutzen-Analysen sind ermutigend. Eine Einführung als zusätzliches Entscheidungskriterium in der Preisfindung neuer Technologien schafft selbstverständlich Mehraufwand für die beteiligten Akteure, bietet jedoch auch die Chance, transparente und nachvollziehbare Antworten auf die Frage zu liefern, was eine gesundheitliche Verbesserung wert ist – eine Frage, die angesichts zunehmender Finanzierungsherausforderungen der gesetzlichen Krankenversicherung weiter an Bedeutung gewinnen wird.

Due to market access of high-priced new drugs, the financial burden on the health care system and the appropriateness of drug prices are often doubted. Is it time for a broader perspective of health technology assessment in Germany, which has so far focused on clinical value? Should cost and benefit aspects of new technologies be given equal weight in future assessments of market access for new technologies? The experiences of European neighbors and the Standing Committee on Vaccination with cost-benefit analyses are encouraging. Introducing cost-benefit analyses as a further decision criterion in the pricing of new technologies naturally creates additional work for the players involved but also offers the opportunity to provide transparent and understandable answers to the question of what a health improvement is worth. In view of the increasing funding challenges facing the statutory health insurance system in Germany, this question will continue to gain importance.

This study aimed to explore quality-adjusted life-year (QALY) and subsequent cost-effectiveness estimates based on the more physical health–focused EQ-5D 5-level version (EQ-5D-5L) value set for England or cross-walked EQ-5D 3-level version UK value set scores or more mental health recovery-focused Recovering Quality of Life Utility Index (ReQoL-UI), when using alternative within-trial statistical methods. We describe possible reasons for the different QALY estimates based on the interaction between item scores, health state profiles, preference-based scores, and mathematical and statistical methods chosen.

QALYs are calculated over 8 weeks from a case study 2:1 (intervention:control) randomized controlled trial in patients with anxiety or depression. Complete case and with missing cases imputed using multiple-imputation analyses are conducted, using unadjusted and regression baseline-adjusted QALYs. Cost-effectiveness is judged using incremental cost-effectiveness ratios and acceptability curves. We use previously established psychometric results to reflect on estimated QALYs.

A total of 361 people (241:120) were randomized. EQ-5D-5L crosswalk produced higher incremental QALYs than the value set for England or ReQoL-UI, which produced similar unadjusted QALYs, but contrasting baseline-adjusted QALYs. Probability of cost-effectiveness <£30 000 per QALY ranged from 6% (complete case ReQoL-UI baseline-adjusted QALYs) to 64.3% (multiple-imputation EQ-5D-5L crosswalk unadjusted QALYs). The control arm improved more on average than the intervention arm on the ReQoL-UI, a result not mirrored on the EQ-5D-5L nor condition-specific (Patient-Health Questionnaire-9, depression; Generalized Anxiety Disorder-7, anxiety) measures.

ReQoL-UI produced contradictory cost-effectiveness results relative to the EQ-5D-5L. The EQ-5D-5L’s better responsiveness and “anxiety/depression” and “usual activities” items drove the incremental QALY results. The ReQoL-UI’s single physical health item and “personal recovery” construct may have influenced its lower 8-week incremental QALY estimates in this patient sample.

To quantify savings of loss-of-QALE (quality-adjusted life expectancy) and lifetime medical costs from prevention of different cancers.

We collected nation-wide data on 808,700 new cancer cases of 14 different organ systems and followed them from 1998 to 2014 in Taiwan. We also collected 13,005 cancer patients from a medical center and 47,320 repeated measurements of quality of life (QoL) of EQ-5D to obtain utility values and multiplied them with the corresponding survival rates to calculate QALE. With Kaplan–Meier estimation to survival function to the end of follow-up, we extrapolated to lifetime through a rolling over algorithm on the logit transform of the survival ratio between the index cohort and age-, sex, and calendar year matched referents simulated from vital statistics. Lifetime costs for each cancer were estimated by multiplying survival with average monthly costs after adjustment with annual discount rate. The loss-of-QALE was estimated by the difference in QALE between the index cancer cohort and corresponding referents.

The dynamic changes and weighted averages of the QoL utility values of 14 different cancers ranged from 0.82 to 0.95. Successful prevention of liver, lung, esophagus, or nasopharynx cancer would save more than 10 quality-adjusted life years and more than 21,000 USD per case for both genders. Since the saving of loss-of-QALE was adjusted for different age, sex, and calendar-year distributions, it could be used in cost effectiveness evaluation.

Savings of loss-of-QALE and lifetime costs could be used for comparison of prevention, diagnosis, treatment and rehabilitation from a lifetime horizon.

Decision-making frameworks that draw on economic evaluations increasingly use equity weights to facilitate a more equitable and fair allocation of healthcare resources. These weights can be attached to health gains or reflected in the monetary threshold against which the incremental cost-effectiveness ratios of (new) health technologies are evaluated. Currently applied weights are based on different definitions of disease severity and do not account for age-related preferences in society. However, age has been shown to be an important equity-relevant characteristic. This study examines the willingness to pay (WTP) for health-related quality of life (QOL) gains in relation to the disease severity and age of patients, and the outcome of the disease.

We obtained WTP estimates by applying contingent-valuation tasks in a representative sample of the public in The Netherlands (n = 2023). We applied random-effects generalized least squares regression models to estimate the effect of patients’ disease severity and age, size of QOL gains, disease outcome (full recovery/death 1 year after falling ill), and respondent characteristics on the WTP.

Respondents’ WTP was higher for more severely ill and younger patients and for larger-sized QOL gains, but lower for patients who died. However, the relations were nonlinear and context dependent. Respondents with a lower age, who were male, had a higher household income, and a higher QOL stated a higher WTP for QOL gains.

Our results suggest that—if the aim is to align resource-allocation decisions in healthcare with societal preferences—currently applied equity weights do not suffice.

Public health policy has two primary aims: promoting population health and reducing health inequalities. When these aims conflict, policy-makers must determine the relative importance to place on each in decision-making. We conducted a computer-based, face-to-face, choice-experiment to explore how the UK-public think government should act in these situations; and to explore how “inequality-aversion” may differ depending on the groups between which a health inequality exists and type of health an intervention provides. We tested three hypotheses: (1) the UK-public are more averse to inequalities in health between socioeconomic groups than they are to inequalities in health between neutrally labelled groups; (2) this difference is, at least in part, driven by the role non-health information plays in determining aversion to inequalities in health between socioeconomic groups; and (3) the UK-public are more willing to prioritise groups with lower lifetime health over groups with higher lifetime health if an intervention improves life-expectancy than if it improves quality-of-life. Eighty people participated in Sheffield and Hull in May/June 2019. Each participant completed three Person-Trade-Off exercises between interventions that would improve population health and reduce health inequalities, or improve population health by a larger amount but increase health inequalities. Participants were randomised to exercises involving scenarios with socioeconomic groups or neutrally-labelled groups, and each answered questions about three health-benefit types: increased life-expectancy; pain-relief; and mobility-improvement. Following the exercises, participants provided rationales for their selections. Respondents were (1) more averse to inequalities in health between socioeconomic groups than neutrally labelled groups. Participant rationales suggest (2) this divergence is partly motivated by factors other than health: for example, financial inequality between socioeconomic groups. The sample was also (3) more willing to prioritise neutrally labelled groups with lower lifetime health if an intervention improves life-expectancy rather than if it improves quality-of-life.