Monitoring and evaluation of human resources for health: an international perspective - Gupta Neeru; Zurn Pascal; Diallo Khassoum; Dal Poz Mario (BMC, 2003)
Abstract Background Despite the undoubted importance of human resources to the functions of health systems, there is little consistency between countries in how human resource strategies are monitored and evaluated. This paper presents an integrated approach for developing an evidence base on human resources for health (HRH) to support decision-making, drawing on a framework for health systems performance assessment. Methods Conceptual and methodological issues for selecting indicators for HRH monitoring and evaluation are discussed, and a range of primary and secondary data sources that might be used to generate indicators are reviewed. Descriptive analyses are conducted drawing primarily on one type of source, namely routinely reported data on the numbers of health personnel and medical schools as covered by national reporting systems and compiled by the World Health Organization. Regression techniques are used to triangulate a given HRH indicator calculated from different data sources across multiple countries. Results Major variations in the supply of health personnel and training opportunities are found to occur by region. However, certain discrepancies are also observed in measuring the same indicator from different sources, possibly related to the occupational classification or to the sources' representation. Conclusion Evidence-based information is needed to better understand trends in HRH. Although a range of sources exist that can potentially be used for HRH assessment, the information that can be derived from many of these individual sources precludes refined analysis. A variety of data sources and analytical approaches, each with its own strengths and limitations, is required to reflect the complexity of HRH issues. In order to enhance cross-national comparability, data collection efforts should be processed through the use of internationally standardized classifications (in particular, for occupation, industry and education) at the greatest level of detail possible.