Background
Health inequalities can be partially addressed through the range of treatments funded by health systems. However, whilst health technology assessment agencies assess the overall balance of health benefits and costs, no quantitative assessment of health inequality impact is consistently undertaken.

Methods
The inequality impact of technologies recommended under the NICE single technology appraisal process from 2012-2014 is assessed using an aggregate distributional cost-effectiveness framework. Data on health benefits, costs and patient populations are extracted from the NICE website. Benefits for each technology are distributed to social groups using the observed socioeconomic distribution of hospital utilisation for the targeted disease. Inequality measures and estimates of cost-effectiveness are compared using the health inequality impact plane and combined using social welfare indices.

Results
Twenty-seven interventions are evaluated. 14 interventions are estimated to increase population health and reduce health inequality, eight to reduce population health and increase health inequality, and five to increase health and increase health inequality. Among the latter five, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact.

Conclusions
The methods proposed offer a way of estimating the health inequality impacts of new health technologies. The methods do not allow for differences in technology-specific utilisation and health benefits, but require less resources and data than conducting full distributional cost-effectiveness analysis. They can provide useful quantitative information to help policy makers consider how far new technologies are likely to reduce or increase health inequalities.

The paper develops and illustrates a new multivariate approach to analysing inequity in health care. We measure multiple inequity in health care relating to multiple equity-relevant variables – including income, gender, ethnicity, rurality, insurance status and others – and decompose the contribution of each variable to multiple inequity. Our approach encompasses the standard bivariate approach as a special case in which there is only one equity-relevant variable, such as income. We illustrate through an application to physician visits in Brazil, using data from the Health and Health Care Supplement of the Brazilian National Household Sample Survey, comprising 391,868 individuals in the year 2008. We find that health insurance coverage and urban location both contribute more to multiple inequity than income. We hope this approach will help researchers and analysts shed light on the comparative size and importance of the many different inequities in health care of interest to decision makers, rather than focus narrowly on income-related inequity.