Healthcare decisions are not only about survival, avoided events, or costs. They also concern the quality of life experienced by patients.
QALYs combine quantity and quality of life
A QALY, or quality-adjusted life year, accounts for both length of life and the health state in which that life is experienced.
This approach is useful when interventions differ not only in survival, but also in symptoms, autonomy, and wellbeing.
Utilities express preferences for health states
Utility scores assign a value to different health states, usually on a scale where 0 represents a state equivalent to death and 1 represents perfect health.
These values allow quality of life to be incorporated into economic models, but they must be selected carefully because they directly influence results.
The source of utility values strongly influences results
Utility data may come from clinical trials, observational studies, databases, published literature, or mapping exercises based on quality-of-life instruments.
Each source involves trade-offs. Trial-based data may be close to the studied population but limited in follow-up, while literature values may be more accessible but less tailored to the context.
Interpretation should remain clinical and human
A QALY gain should not be read as a purely mathematical output. It reflects a combination of time lived, symptoms, functional ability, autonomy, and quality of life.
This is why the analysis should always return to the clinical and human meaning of the results: what does the intervention concretely change for patients and families?
Sensitivity analyses protect credibility
When utilities are uncertain, it is essential to test their influence on cost-utility results. This helps identify whether the conclusion depends on a fragile assumption or remains stable across plausible variations.
This gives decision-makers a more nuanced interpretation and prevents QALYs from being presented as a single definitive truth.
A powerful but sensitive concept
QALYs facilitate comparisons between interventions, but they also raise ethical and methodological questions involving vulnerable populations, rare diseases, disability, and social preferences.
Santicxis supports organizations in selecting, interpreting, and integrating utility data into robust analyses that decision-makers can understand and use.