Inequality in Human Resources for Health: Measurement Issues

Abstract

This article discusses options to allow comparative analysis of inequalities in the distribution of health workers (HWs) across and within countries using a single summary measure of the distribution. Income inequality generally is measured across individuals, but inequalities in the dispersion of HWs must use geographical areas or population groupings as units of analysis. The article first shows how this change of observational unit creates a resolution problem for various inequality indices and then tests how sensitive a simple ratio measure of the distribution of HWs is to changes in resolution. This ratio of inequality is illustrated first with the global distribution of HWs and then with its distributions within Indonesia. The resolution problem is not solved through this new approach, and indicators of inequalities of access to HWs or health services more generally appear not to be comparable across countries. Investigating geographical inequalities over time in one setting is possible but only if the units of analysis remain the same over time.

Subnational regional inequality in access to improved drinking water and sanitation in Indonesia: results from the 2015 Indonesian National Socioeconomic Survey (SUSENAS)

“…A resolution problem emerges when inequality measures are calculated using variable numbers of subunits (i.e. districts) [21]. As a result, provinces with larger numbers of districts will tend to show larger levels of inequality than provinces with fewer districts.…”

“…Within each province, the mean difference from mean (MDM) and the weighted index of disparity (IDIS – W) were calculated to measure absolute and relative inequality, respectively, between the districts in that province. These summary measures are appropriate to measure subnational regional health inequalities, especially with larger numbers of subnational regions; they have advantages over other types of summary measures (like variance and Theil index), including intuitiveness and the ability to calculate analogous absolute and relative measures [21]. MDM shows the average absolute difference between each district and the province average.…”

Evaluating health service coverage in Ghana’s Volta Region using a modified Tanahashi model

“…Few studies provide a comprehensive overview of health systems barriers to service delivery and access to quality care at regional, district, and sub-district levels, despite the fact that service delivery in LMICs is often decentralized and focused at lower levels of care. Understanding peripheral level health systems rather than those at global or national scale is notably important in sub-Saharan Africa where regional variations within the same country can be extensive [5]. Like many countries, Ghana's approach to UHC includes several key policy initiatives that prioritize the needs of underserved groups [6].…”

Spatial illustration of health‐care workforce accessibility index in China: How far has our 2009 health‐care reform brought us?

“…The traditional non‐population density‐based indices cannot be used for comparing data across different regions, but the PHCWA index makes comparisons feasible and meaningful . This is due to the fact that the traditional indices do not consider the fact that health‐care teams have to cover significantly larger areas in regions with extremely low population density.…”