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.