New Program to Identify Outstanding New Investigators

Background: The early decades of the 21st century are considered to be the era of human resources for health (HRH). The World Health Report (WHR) 2006 launched the Health Workforce Decade (2006–2015), with high priority given for countries to develop effective workforce policies and strategies. In many countries in the Eastern Mediterranean Region (EMR), particularly those classified as Low and Low-Middle Income Countries (LMICs), the limited knowledge about the nature, scope, composition and needs of HRH is hindering health sector reform. This highlights an urgent need to understand the current reality of HRH in several EMR countries. The objectives of this paper are to: (1) lay out the facts on what we know about the HRH for EMR countries; (2) generate and interpret evidence on the relationship between HRH and health status indicators for LMICs and middle and high income countries (MHICs) in the context of EMR; (3) identify and analyze the information gaps (i.e. what we do not know) and (4) provide forward thinking by identifying priorities for research and policy. Methods: The variables used in the analysis were: nurse and physician density, gross national income, poverty, female literacy, health expenditure, Infant Mortality Rate (IMR), Under 5 Mortality Rate (U5MR), Maternal Mortality Rate (MMR) and Life Expectancy (LE). Univariate (charts), bivariate (Pearson correlation) and multivariate analysis (linear regression) was conducted using SPSS 14.0, besides a synthesis of HRH literature. Results: Results demonstrate the significant disparities in physician and nurse densities within the EMR, particularly between LMICs and MHICs. Besides this, significant differences exist in health status indicators within the EMR. Results of the Pearson correlation revealed that physician and nurse density, as well as female literacy in EMR countries were significantly correlated with lower mortality rates and higher life expectancy. Results of the regression analysis for both LMICs and MHICs reveal that physician density is significantly associated with all health indicators for both income groups. Nurse density was found to be significantly associated with lower MMR for the two income groups. Female literacy is notably related to lower IMR and U5MR for both income groups; and only with MMR and LE in LMICs. Health expenditure is significantly associated with lower IMR and U5MR only for LMICs. Based on results, gap analysis and the literature synthesis, information gaps and priorities were identified. Conclusion: The implication of the results discussed in this paper will help EMR countries, particularly LMICs, determine priorities to improve health outcomes and achieve health-related Millenium Development Goals. Published: 23 March 2007 Human Resources for Health 2007, 5:9 doi:10.1186/1478-4491-5-9 Received: 11 May 2006 Accepted: 23 March 2007 This article is available from: http://www.human-resources-health.com/content/5/1/9 © 2007 El-Jardali et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 12 (page number not for citation purposes) Human Resources for Health 2007, 5:9 http://www.human-resources-health.com/content/5/1/9 Background The early decades of the 21st century are considered to be the era of human resources for health (HRH). The health care sector is both labour-intensive and labour-reliant, and the delivery of quality health care services is strongly dependent on having enough well-trained health care workers to meet patient needs and expectations. The World Health Organization (WHO) estimates the current HRH workforce at 59 million and its global shortage at 4.3 million [1]. Health workers are defined as "people engaged in actions whose primary intent is to enhance health" [1]. The World Health Report (WHR), 2006, launched the Health Workforce Decade (2006–2015), with high priority given for countries to develop effective workforce strategies that include three core elements: improving recruitment, helping the existing workforce perform better, and slowing down the rate at which workers leave the health workforce. The report emphasized HRH management and planning as major strategic priorities for achieving this goal with its three core elements. At the global level, many countries are facing critical HRH challenges including worker shortage, skill-mix imbalance, maldistribution, poor work environment, and weak knowledge base [2-4]. In several Low and Low-Middle Income countries (LMICs), the supply of health professionals is being challenged by demographic trends; an aging population; growing shortages; limited education and training capacities; poor recruitment and retention strategies including out-migration of health professionals; skill-mix imbalance; maldistribution; poor HRH planning; absence of a reliable database; poorly informed policy decisions [2,5]; and slow health system reform [5]. In Table 1, we highlight key global HRH challenges that are also relevant to LMICs. The HRH challenges listed in Table 1 mostly affect LMICs that suffer from poor health outcomes, such as rising death rates and decreasing life expectancies at birth [2]. This is critical in the context of the Eastern-Mediterranean Region (EMR), where the World Bank classified most (61%) of its 22 countries as Low or Low-Middle Income Countries [6]. In addition, EMR has the second lowest HRH density (per 1000 population), right after Africa, among the six administrative regions of the WHO (See Table 2) [1]. Evidence from several research studies shows that health worker density is directly correlated with population-based health indicators such as Maternal Mortality Rate (MMR), Infant Mortality Rate (IMR) and Under-5 Mortality Rate (U5MR) [7,8]. While these studies used global data to test the relationship between worker's density and health outcome indicators, none has used the most recent data to test this relationship in LMICs versus Middle and High Income countries (MHICs). While HRH density might be equally important for both LMICs and MHICs, examining the relationship for each of these two groups is critical for determining priorities for these countries to improve health outcomes and achieve the Millennium Development Goals (MDG). Currently, many EMR countries are either implementing health reform plans or in the process of doing so. Evidence suggests that successful health system reform in any country depends on the provision of effective, efficient, assessable, sustainable and high quality services by a health workforce that is sufficient in number, appropriately-trained and equitably-distributed [9]. For several EMR countries, a limited understanding of HRH issues, challenges and priorities may hinder sustainable health sector reform [2,10]. Many developed countries have researched the nature and scope of HRH planning and management, particularly its problems, needs, gaps and impacts on health status. Yet for many EMR countries, almost nothing is known. This highlights an urgent need to understand the current reality of HRH in the EMR. In this paper, we make use of the most recent and available data (both global and regional) to generate and analyze evidence on HRH in the context of EMR. HRH in EMR is an underdeveloped field where evidence base has to be established. This paper will help several EMR countries determine priorities for improving population health outcomes; one of those priorities is HRH. Study objectives The objectives of this paper are to: 1. lay out the facts on what we know about the HRH in EMR countries; 2. generate and interpret evidence on the relationship between HRH and health status indicators for LMICs and MHICs in the context of EMR; 3. identify and analyze the knowledge gaps; 4. provide forward thinking by identifying priorities for research and policy. The first objective will be achieved using univariate and bivariate (Pearson correlation) analysis of the most recent regional data for the 22 EMR countries. The second objective will be realized through multivariate analysis techniques (linear regression) of the most recent global data. The remaining two objectives will be achieved by reviewing and analyzing published HRH literature in developed and developing countries. This literature includes major health reports on the EMR, published by researchers, stakeholder organizations and agencies including the WHO. To our knowledge, this study is among very few research papers that investigate HRH issues and analyze Page 2 of 12 (page number not for citation purposes) Human Resources for Health 2007, 5:9 http://www.human-resources-health.com/content/5/1/9 and interpret the global HRH data in the context of the EMR. Methods Study variables and sources Our analysis comprises the following 5 independent variables: 1. Physician and nurse densities: they collectively account for the majority of healthcare providers in most countries [7]; 2. Gross national income (GNI): it captures a multitude of factors that affect mortality rates such as nutrition, access to safe water, sanitation, housing, etc. [7]; 3. Percentage of the population living below the poverty line of $1 per day: higher poverty rates are associated with higher mortality rates [7]; 4. Female adult literacy: it is known to reflect behaviour and lifestyle which in turn influence mortality rates [7]; 5. Total expenditure on health: it represents the resources spent on health, which may influence health outcomes [11]. The dependent variables are: IMR; U5MR; MMR; and Life expectancy (LE). These variables were selected since evidence shows that they can be influenced by HRH densities [1] and other socioeconomic factors. Data for both the independent and dependent variables was retrieved from the sources listed in Table 3. Table 1: HRH challenges Challenges for HRH Global LMICs Health worker shortages (particularly nurses and physicians) Poor working conditions and remuneration