A method for comparing health inequality impact magnitudes, with an illustration for hypothetical treatments of 1,336 diseases

Objectives To compare and communicate magnitudes of health inequality impact across interventions for different diseases. Methods By rescaling the slope index of inequality, we measured health inequality impact as change in the total predicted gap in quality-adjusted life-years (QALYs) between the least and most socially disadvantaged groups. Use of linear regression predictions accounted for effects on intermediate groups. We reported the impact relative to health opportunity cost (HOC) to facilitate comparison across interventions with varying scale and unit costs. We illustrated with aggregate distributional cost-effectiveness analyses of hypothetical treatments for 1,336 diseases in England. We approximated benefit shares for neighbourhood deprivation quintile groups using disease-specific hospital admissions. We tested between-group equality using generalised linear regression and constructed uncertainty intervals using Monte Carlo simulation. We assumed equal HOC and benefit-cost ratio (BCR) of one, with alternative scenarios in sensitivity analysis. Results Health inequality impacts ranged from -33.1% of HOC (inequality-increasing) to +45.3% (inequality-decreasing), and were ≤ -5% for 1.6% of diseases, ≥ 5% for 41.8% and ≥ 20% for 1.6%. The impact was positively associated with the BCR and decreased when the poor were assumed to incur proportionately more HOC. Conclusions Health inequality impacts can be compared using change in the total predicted QALY gap between least and most socially disadvantaged groups, as a proportion of health opportunity cost. For technology appraisal in England, a health inequality impact below 5% of health opportunity cost might be classified as small, 5-10% as medium, and above 10% as large.