Moving beyond the average: a method to measure health related-inequalities within randomised trials

Abstract Background Randomised trials seldom investigate the impacts interventions have on socioeconomic inequality in health, focusing instead on their average treatment effects. Methods We illustrate how standard measures of inequality such as the corrected concentration index can be reported in randomised experiments to quantify the interventions’ socio-economic distribution effect alongside more traditional average treatment effects. We illustrate this with a proof-of-concept example using a study on the impact of information and financial incentives on verified COVID-19 vaccination uptake in rural Ghana ( n = 2,271). We estimate the distributional treatment effects of informational public health messaging, low- and high-cash financial incentives relative to a placebo group and present this information on an achievement plane. Results Health messaging had a pro-rich socioeconomic inequality impact of -0.132 [95% CI: -0.28–0.02] relative to the placebo group, while the low and high cash incentives had impacts of -0.108 [95% CI: -0.29–0.07] and − 0.081 [95% CI: -0.26–0.11] respectively, providing no significant evidence for changes in inequality relative to the placebo group. A low-cash incentive was the only intervention that was shown to increase vaccination uptake. Conclusions A socioeconomic inequality of health distributional indicator could routinely be calculated in experimental results and presented alongside average treatment effects. This would enable the identification of whether there are equality-efficiency trade-offs. The methods presented in this paper can be used to support policy decision making with representative datasets provided there is appropriate statistical power. Trial registration The RCT investigated in this paper was registered with the American Economic Association on January 10, 2022, RCT ID: AEARCTR-0008775.