Inequality-Adjusted ICER: Enhancing Cost-Effectiveness in Health Technology Evaluation

The inequality-adjusted ICER offers a practical way to embed explicit trade-offs between maximising total population health and reducing health inequality directly into cost-effectiveness analysis. By dividing the conventional ICER by an inequality-adjusted ICER, analysts derive a health inequality modifier (HIM) that raises the cost-effectiveness threshold for interventions that narrow inequality gaps and lowers it for those that widen them.

Turning Equity-Efficiency Trade-offs into a Simple Multiplier

This approach unifies direct and indirect equity weighting methods, showing they produce mathematically equivalent decision rules. Indirect weighting adjusts health gains and opportunity costs through an equally distributed equivalent (EDE) derived from a social welfare function. Direct weighting applies explicit equity weights to the proportional distribution of benefits and costs across social groups. The resulting HIM identifies the precise level of inequality aversion at which an intervention crosses an adjusted threshold. In an English simulation covering 1,336 diseases, most conditions displayed pro-poor prevalence, generating HIM values above 1 under a flat opportunity-cost assumption.

Which Diseases Gain or Lose Most from Inequality Adjustment?

Under medium inequality aversion, the inter-percentile range of the HIM spans 0.96–1.18. Only 0.15 % of diseases would face a threshold reduction of 10 % or more, while 10.6 % would qualify for at least a 10 % increase. Conditions such as opioid-use disorders (HIM 1.259) and sickle-cell disease (HIM 1.220) receive the largest upward adjustments. In contrast, pro-rich conditions like melanoma in situ see their thresholds tightened, with modifiers falling as low as 0.73. Higher aversion widens the range to 0.93–1.31, yet even then, adjustments rarely exceed 30 %.

Data Foundations Behind the Inequality-Adjusted ICER

Equity weights are drawn from a social welfare function calibrated to UK public preferences, reflecting aversion to inequality in quality-adjusted life expectancy (QALE) across deprivation quintiles. Baseline QALE ranges from 62.2 years in the most deprived neighbourhoods to 73.3 in the least deprived. Prevalence shares are estimated from Hospital Episode Statistics, corrected for repeat-admission bias and validated against primary-care data. Opportunity costs are assumed flat in the base case, in line with NICE guidance, with sensitivity analyses testing pro-rich and pro-poor gradients. Statistical significance testing prevents unreliable estimates for rare conditions, and all simulation code and results are publicly available.

Strategic Implications for HEOR, Market Access and Reimbursement

The inequality-adjusted ICER and its associated HIM provide a transparent, ICER-compatible tool that fits seamlessly into existing Health Economics and Outcomes Research (HEOR) frameworks. Similar in spirit to NICE’s severity (up to 1.7) and ultra-rare disease (3.33–10) modifiers, the HIM converts complex equity-efficiency trade-offs into a single multiplicative adjustment that decision-makers and manufacturers can readily interpret.

For sponsors developing therapies for strongly pro-poor conditions—such as severe mental illness, substance-use disorders or sickle-cell disease—the framework may support higher justifiable prices. Conversely, interventions targeting pro-rich disease profiles could encounter effectively stricter thresholds. While the overall impact on reimbursement decisions is expected to remain modest, the approach strengthens the equity sensitivity of evaluation processes without discarding established HEOR methods.