Incorporating concerns for equity into health resource allocation:A guide for practitioners

Introduction Unfair differences in health care access, quality or health outcomes exist between and within countries around the world, and improving health equity is an important social objective for many governments and international organizations. This paper summaries the methods for analysing health equity available to policymakers regarding the allocation of health sector resources. Methods We provide an overview of the major tools that have been developed to measure, evaluate and promote health equity, along with the data required to operationalise them. These are organised into four key policy questions facing decision-makers: (i) what is the current level of inequity in health; (ii) does government health expenditure benefit the worst-off; (iii) can government health expenditure more effectively promote equity; and (iv) which interventions provide the best value for money in reducing inequity. Analysis Benefit incidence analysis is identified as the principal tool for estimating the distribution of current public health sector expenditure, with geographical resource allocation formulae and health system reform being the main government policy levers for improving equity. Techniques from the economic evaluation literature, such as extended and distributional cost-effectiveness analysis can be used to identify ‘best buy’ interventions from a health equity perspective. A range of inequality metrics, from gap measures and slope indices to concentration indices and regression analysis, can be applied to these approaches to evaluate changes in equity. Discussion Methods from the economics literature can be used to generate novel evidence on the health equity impacts of resource allocation decisions. They provide policymakers with a toolkit for addressing multiple aspects of health equity, from health outcomes to financial protection, and can be adapted to accommodate data commonly available in either high income or low and middle income settings. However, the quality and reliability of the data are crucial to the validity of all methods.