首页> 外文期刊>BMC Health Services Research >Detecting the priority areas for health workforce allocation with LISA functions: an empirical analysis for China
【24h】

Detecting the priority areas for health workforce allocation with LISA functions: an empirical analysis for China

机译:用丽莎职能检测健康劳动力分配优先领域:中国的实证分析

获取原文
           

摘要

Health workforce misdistribution leads to severe inequity and low-efficiency in health services in the developing countries. Targeting at China, this research aims to reveal, visualize and compare the geographical distribution patterns of different subtypes of urban and rural health workforce and identify the priority regions for health workforce planning and allocation policies designing. The health workforce density (workforce-to-population ratio) is adopted to represent the accessibility to health workforce in each geographical unit. Besides a descriptive geography of health workforce as a whole, the local indicators of spatial association (LISA) are used to explore the spatial clusters of different subtypes of health workforce, which are visualized by geographical tools. Results reveal that regional disparities and spatial clusters exist in China's health workforce distribution, with different types of workforce exhibiting relatively different spatial distribution characteristics. Besides, huge urban-rural disparities are found in the distribution of health workforce in China. Unexpectedly but intriguingly, most of the high-high and high-low cluster area of urban health workforce are concentrated in the western China (Xinjiang, Xizang etc.), indicating the relative abundant stock of urban health workforce in these units, while the low-low and low-high cluster area of different types of urban health workforce are mainly distributed in middle China. Regarding the rural health workforce, there is an obvious and similar low-low and low-high clustering pattern in western provinces (Sichuan, Yunnan) for the licensed doctors, pharmacists, technologists, which play a critical role in health services delivery. Different types of health workforce displayed distinct spatial distribution patterns, while the misdistribution of rural health workforce imposed more challenges to the Chinese health sector due to its poorer stock and more disadvantaged positions of backward regions (i.e., low-low and low-high cluster area). Subtype-specific and region-oriented health workforce planning and allocation policies are suggested to be made, aiming at the urban and rural health workforce respectively, by prioritizing the identified low-low and low-high cluster areas.
机译:卫生劳动力误解措施导致发展中国家的卫生服务严重不公平和低效率。本研究旨在揭示,可视化和比较城乡卫生劳动力不同亚型的地理分布格局,并确定卫生劳动力规划和分配政策设计的优先区。卫生劳动力密度(劳动力与人口比率)被采用在每个地理单位中代表健康劳动力的可访问性。除了整体健康劳动力的描述性地理外,空间协会(LISA)的当地指标用于探索不同卫生劳动力的不同亚型的空间集群,由地理工具可视化。结果表明,在中国的健康劳动力分布中存在区域差异和空间集群,不同类型的劳动力表现出相对不同的空间分布特征。此外,巨大的城乡差异在中国的卫生劳动力分销中被发现。出乎意料但有趣的是,大多数都是城市健康劳动力的大多数高高和高低集群区域都集中在中国西部(新疆,Xizang等)中,表明在这些单位中的城市健康劳动力的相对丰富的股票,而低 - 不同类型的城市健康劳动力的流量和低矮的聚类区域主要分布在中部。关于农村卫生劳动力,西部省份(四川,云南)在持牌医生,药剂师,技术人员中存在明显而相似的低矮的聚类模式,在卫生服务交付中发挥着关键作用。不同类型的健康劳动力展示了不同的空间分布模式,而由于其较差的股票和倒退地区(即低低级和低群集区域),农村卫生劳动力的误解对中国卫生部门的挑战造成更多挑战)。通过优先考虑所确定的低低和低群集地区,分别瞄准城市和农村卫生劳动力的亚型特定和地区的卫生劳动力规划和分配政策。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号