Cost Utility Analysis (CUA)

Cost utility analysis (CUA) is a form of economic evaluation that compares the costs of health care interventions with their outcomes expressed in quality adjusted life years (QALYs), capturing both the quantity and quality of life in a single measure.^[2][3] CUA is technically a subtype of cost effectiveness analysis, but it is distinguished by its use of preference based utility weights to adjust survival time for health related quality of life.^[3] By standardizing outcomes in QALYs, CUA enables comparison of cost effectiveness across different diseases, interventions, and populations, making it the preferred method for health technology assessment agencies in many countries.

CUA addresses a fundamental limitation of standard cost effectiveness analysis: different interventions produce different types of health outcomes (blood pressure reduction, seizure prevention, pain relief), making direct comparison impossible when outcomes are measured in natural units. QALYs solve this problem by creating a universal currency for health. CUA results, expressed as cost per QALY gained, can be compared against a willingness to pay threshold to determine whether an intervention represents acceptable value for money. This framework underlies coverage decisions for new drugs, devices, and technologies in the United Kingdom, Canada, Australia, and numerous other countries.

CUA follows the same analytical framework as cost effectiveness analysis but replaces disease specific outcome measures with QALYs. Researchers model the health states that patients may experience over a defined time horizon, assign utility values (anchored at 0 for death and 1 for perfect health, with negative values possible for states valued as worse than death) to each state using validated instruments such as the EQ 5D, and calculate the expected QALYs for each treatment alternative by multiplying the time spent in each state by its utility and summing.^[2] Costs are estimated by tracking resource use and applying unit prices. The primary result is the incremental cost utility ratio (ICUR), calculated as the incremental cost divided by the incremental QALYs.^[1] Decision analytic models (decision trees, Markov models, microsimulations) are typically used to project costs and QALYs over appropriate time horizons. Probabilistic sensitivity analysis characterizes uncertainty by varying the uncertain model parameters simultaneously, with results often presented as cost effectiveness acceptability curves.^[1]

Use CUA when the interventions being compared affect both the length and quality of life, when you need a standardized outcome measure that permits cross disease comparison, or when the evaluation will inform decisions by HTA agencies that require QALY based submissions. CUA is appropriate for evaluating pharmaceuticals, surgical procedures, screening programs, rehabilitation interventions, and public health initiatives. It is the reference case analysis for submissions to NICE and Canada's Drug Agency (formerly CADTH).

Using utility values from inappropriate sources (for example, clinician ratings instead of patient or public valuations) introduces bias. Another mistake is selecting a model structure that does not reflect the natural history of the disease, leading to inaccurate QALY estimates. Researchers sometimes use a time horizon that is too short to capture all relevant costs and health effects, underestimating the long term value of preventive interventions. Failing to conduct probabilistic sensitivity analysis is a major shortcoming because deterministic results do not convey the decision uncertainty. Finally, CUA cannot capture all value dimensions; equity concerns, the severity of the condition, the availability of alternatives, and broader societal benefits may need to be considered alongside the cost per QALY ratio.

Researchers conduct a CUA comparing three management strategies for newly diagnosed type 2 diabetes: lifestyle modification alone, metformin monotherapy, and combination therapy (metformin plus an SGLT2 inhibitor). Using a microsimulation model calibrated to the UKPDS outcomes model and projecting over a 40 year time horizon, they estimate that combination therapy produces 14.2 QALYs per patient versus 13.4 for metformin alone and 12.8 for lifestyle modification alone. Total discounted lifetime costs are $125,000 for combination therapy, $98,000 for metformin, and $78,000 for lifestyle modification. The ICUR for combination therapy versus metformin is $33,750 per QALY, which is below the $50,000 per QALY threshold used in the analysis. Probabilistic sensitivity analysis shows a 68% probability of cost effectiveness at a $50,000 per QALY threshold.