Assessment of Inequality Alongside Policy-oriented Trials: An Empirical Framework Based on the Case of Screening

Despite the dual objectives of many health care systems of improving total health and reducing health inequality, trial designs seem to ignore the assessment of inequality effects. Our study aimed to illustrate an empirical framework for the assessment of inequality effects alongside policy-oriented trials to inform a possible efficiency versus equality trade-off.

We measured inequality in the concentrations of all-cause and disease-related mortality and hospital admissions across ranks of socioeconomic status in a randomized controlled trial that tested the efficacy of general population screening of men for vascular disease. We used alternative definitions of inequality (relative/absolute, in attainment/shortfall, ranked by education/income), and supplemented the classical "frequentist" approach to statistical inference with Bayesian posterior probabilities. Equality contours for health improvement that leave inequality unaffected are illustrated graphically. We used bootstrapping for interpretation.

We estimated the posterior probability of screening increasing inequality to be between 0.21 and 0.93 depending on the inequality definition. Income-ranked inequality appeared to be generally higher than education-ranked inequality but less affected by screening. For the shortfall-relative index based on education-rank, the mean health improvement of a 7% relative reduction in all-cause mortality generated by screening incurred a mean relative increase in inequality of 28%. For the income-based indices, there was no evidence of a trade-off.

We illustrated how decision uncertainty can be reduced by explicit assessment of inequality alongside trials and found some evidence of a possible equity-efficiency trade-off in the context of screening, although this depended on the definition of equality.