Forecasting the Global Shortage of Physicians: An Economic-And Needs-Based approach/Prevoir la Penurie Mondiale De Medecins: Une Demarche Economique Fondee Sur Les besoins/Prevision De la Escasez Mundial De Medicos Mediante Un Modelo Economico Y Basado En Las Necesidades

Article excerpt

Introduction

The world health report 2006: working together for health has brought renewed attention to the global human resources required to produce health. (1) It estimated that 57 countries have an absolute shortage of 2.3 million physicians, nurses and midwives. These shortages suggest that many countries have insufficient numbers of health professionals to deliver essential health interventions, such as skilled attendance at birth and immunization programmes. However, these estimates do not take into account the ability of countries to recruit and retain these workers, nor are they specific enough to inform policy-makers about how, and to what extent, health workforce investment should be channelled into training of different professions.

This paper focuses on physicians, who serve a key role in health-care provision. Using the most updated information on the supply of physicians over a 20-year period, we project the size of the future global need for, demand for and supply of physicians to year 2015, the target date for the Millennium Development Goals (MDGs). (2) Needs-based estimates use an exogenous health benchmark to judge the adequacy of the number of physicians required to meet MDG targets. Demand estimates are based on a country's economic growth and the increase in health-care spending that results from it, which primarily goes towards worker salaries. We then compare the needs-based and demand-based estimates to the projected supply of physicians, extrapolated based on historical trends. Our results point to dramatic shortages of physicians in the WHO African Region by 2015. We provide estimates of shortages by country in Africa and discuss their implications for different workforce policy choices.

Methods

For illustrative purposes, we provide a stylized version of the conceptual framework we employed for forecasting physician numbers in Fig. 1. First, we project the supply in the per capita number of physicians (S) based on historical data on physician numbers for each country; this serves as a baseline against which different forecasts can be evaluated. We employ two forecasting methods. The forecast for the needs-based estimate (N) is determined by calculating the number of physicians that would be required to reach The world health report 2006 goal of having 80% of live births attended by a skilled health worker. (3) The second forecasting method reflects the demand for physicians in each country as determined by economic growth ([D.sub.1] and [D.sub.2]). With these different estimates, shortages or surpluses can be calculated. For example, by year 8, about 3.5 physicians per 1000 population will be needed compared to the projected supply of 3.0 per 1000, producing a 0.5 per 1000 shortage. In comparison, 4.0 per 1000 will be demanded according to the scenario represented by [D.sub.1], resulting in a demand-based shortage of about 1.0 physicians per 1000. A different scenario can arise if supply exceeds demand, as represented by [D.sub.2], resulting in a surplus. We can then multiply this estimated shortage by projected population numbers to calculate the absolute deficit of the numbers of physicians. In this illustrative case, the needs-based shortage exceeds the demand-based shortage. This framework can be applied at the country, regional and global levels of analyses, depending on the level of aggregation of physician numbers.

We now describe our estimation procedures more formally. First, baseline supply projections to the year 2015 were estimated using the historical growth rate of physician densities in each country. The following regression equation was run for each country for time t = {1980, ... 2001}:

ln(physicians per 1000 [population.sub.t]) = [[alpha].sub.0] + [[alpha].sub.1] x [year.sub.t] + [[epsilon].sub.t]

where [[epsilon].sub.t] is the random disturbance term, and [[alpha].sub.0] and [[alpha].sub.1] are unknown parameters to be estimated from the model. …