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What explains the distribution of doctors and nurses in different countries, and does it matter for health outcomes?

国内生产总值 人均 基尼系数 发展中国家 读写能力 人口 代理(统计) 分布(数学) 世界发展指标 健康素养 变量 医学 医疗保健 经济增长 环境卫生 经济 统计 不平等 数学分析 数学 经济不平等
作者
Roy Carr‐Hill,Elizabeth Currie
出处
期刊:Journal of Advanced Nursing [Wiley]
卷期号:69 (11): 2525-2537 被引量:15
标识
DOI:10.1111/jan.12138
摘要

Abstract Aims To re‐examine the form of the relationships between the global distribution of health professionals (physicians and nurses), gross national product per capita, female literacy, and infant and under‐5 mortality rates reported in three papers by Robinson and Wharrad using more recent data. The secondary aim was to explore prior assumptions about the quality of the data, the homogeneity of the sample, and the form of the relationship. Background The analyses by Robinson and Wharrad did not take account of differences between developing and developed countries. Furthermore, the intervening years have seen changes in healthcare professional roles and their global distribution. Re‐examination of these relationships is therefore timely. Design A secondary analysis of routinely collected data from international databases. Methods A database was constructed from Organisation for Economic Co‐operation and Development, United Nations and World Bank sources on 177 countries for around 2005. Regression analyses were performed first with number of physicians and of nurses per 1000 population as dependent variables and gross national product per capita, female literacy rates, and the Gini coefficient as independent variables; and second with all those variables as independent variables and infant mortality, under‐5 mortality rates, and maternal mortality rates as dependent variables. Results There were clear and interesting differences between richer (Organisation for Economic Co‐operation and Development) countries and developing countries in the coefficients and in the power of the equations. Conclusions The importance of understanding the implications of carrying out cross‐country analysis and the urgent need for standardization of definitions in datasets are emphasized.
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