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2010—2020年全国县(区)级疾控中心人力资源配置情况分析

Allocation of human resources in Centers for Disease Control and Prevention at the county(district)level in China,2010—2020
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摘要 目的了解2010—2020年我国县(区)级疾控中心人员变化情况,为加强我国县(区)级疾控中心人力资源建设提供参考依据。方法应用R 4.1.3软件进行数据清理与统计分析,运用描述性统计分析方法对人员数量、结构变化进行分析,运用基尼系数分析疾控中心人员公平性变化;采用Joinpoint回归模型进行时间趋势分析。结果2010—2020年我国县(区)级疾控中心在职、在编和专业技术人员数量增长率分别为9.04%、11.66%和15.90%,随年份的增长上述指标呈上升趋势(P<0.05);在编人员和专业技术人员占在职人员比例增长率分别为2.40%和6.29%。2010—2020年我国县(区)级疾控中心人员平均学历、职称分值随时间的变化有所上升;吉林省、黑龙江省和辽宁省平均工作年限随时间的变化有所上升。2010—2020年我国东部地区县(区)级疾控中心在职人员结构进入前10名的省份比例(8/11)高于西部地区(6/13)和中部地区(3/8)。2010—2020年我国县(区)级疾控中心在职人员按人口和GDP配置的基尼系数均小于0.3,呈现公平状态;从人口角度,西部地区县(区)级疾控中心在职人员的均衡性优化程度最高;从GDP角度看,中部地区县(区)级疾控中心在职人员的均衡性最差。结论2010—2020年我国县(区)级疾控中心人力资源数量提升、结构优化、分布较为均衡,但仍需改善需根据政策改善县(区)级疾控中心编制和聘用人员数量,结合辖区人口和GDP发展水平匹配专业技术人员,进一步优化县(区)级疾控中心人力资源的发展。 Objective To investigate the changing trend of personnel in Centers for Disease Control and Prevention(CDCs)at the county(district)level in China from 2010 to 2020,and to provide a reference for further strengthening the construction of human resources in CDCs.Methods R 4.1.3 software was used for data cleaning and statistical analysis;descriptive statistics was used to analyze changes in the number and structure of personnel,and the Gini coefficient was used to analyze changes in the equity of CDCs personnel;the Joinpoint regression model was used to perform a time trend analysis.Results From 2010 to 2020,the numbers of on-the-job personnel,permanent personnel,and professional and technical personnel showed an increase rate of 9.04%,11.66%,and 15.90%,respectively,in CDCs at the country(district)level in China,which showed an increasing trend over years(P<0.05).The proportions of permanent personnel and professional and technical personnel among on-the-job personnel showed an increase rate of 2.40%and 6.29%,respectively.From 2010 to 2020,there were increases in the mean scores of education level and professional title for the personnel in CDCs at the country(district)level in China,and the average years of work experience increased over time in Jilin,Heilongjiang,and Liaoning provinces.From 2010 to 2020,the structure of on-the-job personnel in CDCs at the county(district)level in eastern China was better than those in the western and central regions,with a higher proportion of provinces ranking among top 10 in terms of the structure of on-the-job personnel(8/11 vs 6/13 and 3/8).From 2010 to 2020 in China,the Gini coefficients of on-the-job personnel in CDCs at the country(district)level configured by population and GDP were<0.3,showing equity;from the perspective of population,the western region had the best balance of on-the-job personnel in CDCs at the country(district)level;from the perspective of GDP,the central region had the worst balance of on-the-job personnel in CDCs at the country(district)level.Conclusion The number of human resources in China′s CDCs at the county(district)level increased steadily from 2010 to 2020,with optimized structure and balanced distribution.However,there is still a need to improve the number of permanent and employed personnel in CDCs at the county(district)level in accordance with the policy,and to match the professional and technical staff with the population and GDP development level of the jurisdiction,so as to further optimize the development of human resources in CDCs at the county(district)level.
作者 常诗琳 王静雷 毛凡 赵玉明 司向 石文惠 CHANG Shi-lin;WANG Jing-lei;MAO Fan;ZHAO Yu-ming;SI Xiang;SHI Wen-hui(National Institute of Environmental Health,Chinese Center for Disease Control and Prevention,Beijing 102201,China;Office of Non-Communicable Disease and Aging Health Management,Chinese Center for Disease Control and Prevention;National Center for Chronic and Non-communicable Disease Control and Prevention,Chinese Center for Disease Controland Prevention)
出处 《环境卫生学杂志》 2024年第5期393-399,共7页 JOURNAL OF ENVIRONMENTAL HYGIENE
基金 公共卫生应急反应机制运行(102393220020010000017)。
关键词 县(区)级疾病预防控制中心 人力资源 变化趋势 基尼系数 Centers for Disease Control and Prevention at the county(district)level human resources changing trend Gini coefficient
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