Measuring inequalities in the distribution of health workers: the case of Tanzania

Michael A Munga,Ottar Mæstad

doi:10.1186/1478-4491-7-4

Abstract

BackgroundThe overall human resource shortages and the distributional inequalities in the health workforce in many developing countries are well acknowledged. However, little has been done to measure the degree of inequality systematically. Moreover, few attempts have been made to analyse the implications of using alternative measures of health care needs in the measurement of health workforce distributional inequalities. Most studies have implicitly relied on population levels as the only criterion for measuring health care needs. This paper attempts to achieve two objectives. First, it describes and measures health worker distributional inequalities in Tanzania on a per capita basis; second, it suggests and applies additional health care needs indicators in the measurement of distributional inequalities.MethodsWe plotted Lorenz and concentration curves to illustrate graphically the distribution of the total health workforce and the cadre-specific (skill mix) distributions. Alternative indicators of health care needs were illustrated by concentration curves. Inequalities were measured by calculating Gini and concentration indices.ResultsThere are significant inequalities in the distribution of health workers per capita. Overall, the population quintile with the fewest health workers per capita accounts for only 8% of all health workers, while the quintile with the most health workers accounts for 46%. Inequality is perceptible across both urban and rural districts. Skill mix inequalities are also large. Districts with a small share of the health workforce (relative to their population levels have an even smaller share of highly trained medical personnel. A small share of highly trained personnel is compensated by a larger share of clinical officers (a middle-level cadre) but not by a larger share of untrained health workers. Clinical officers are relatively equally distributed. Distributional inequalities tend to be more pronounced when under-five deaths are used as an indicator of health care needs. Conversely, if health care needs are measured by HIV prevalence, the distributional inequalities appear to decline.ConclusionThe measure of inequality in the distribution of the health workforce may depend strongly on the underlying measure of health care needs. In cases of a non-uniform distribution of health care needs across geographical areas, other measures of health care needs than population levels may have to be developed in order to ensure a more meaningful measurement of distributional inequalities of the health workforce.

Highlights

The overall human resource shortages and the distributional inequalities in the health workforce in many developing countries are well acknowledged
One way to capture this information would be to build more comprehensive measures of health care needs than we have been able to do in this paper, by measuring differences in the disease burden across different parts of the country and how these differences translate into health care needs
Superimposing concentration curves for the cadre-specific distribution provides an illuminating way to analyse the relationships between distributional inequalities in the total health workforce and skill mix inequalities

Summary

Introduction

The overall human resource shortages and the distributional inequalities in the health workforce in many developing countries are well acknowledged. This paper attempts to achieve two objectives It describes and measures health worker distributional inequalities in Tanzania on a per capita basis; second, it suggests and applies additional health care needs indicators in the measurement of distributional inequalities. Inequalities in the distribution of health workers are often described by comparing the number of health workers per capita across districts or other local administrative units [6,7,8]. While several existing studies confine themselves to the distribution of a single cadre, such as general practitioners or nurses [5,7,9], we describe the distribution both at the aggregate level and at the cadre level In this way, we are able to study, for instance, whether districts that have relatively few physicians are "compensated" by having relatively more lower-cadre workers

Methods

Results

Discussion

Conclusion