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基于ARIMA模型的北京市产前超声筛查人员情况预测 被引量:2

Prediction of Prenatal Ultrasound Screening Staff ’s Situationin Beijing Based on ARIMA Model
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摘要 目的了解北京市产前超声筛查人员情况,对未来产前超声筛查人员数量及内部结构变化趋势进行预测。方法基于北京市2007至2015年产前超声筛查人员资质数据库,从超声筛查人员的地区、年龄、职称等分布情况了解2007至2015年以来产前超声筛查人员构成的变化趋势,并根据现有变化趋势构建预测模型,预测北京市2016至2020年产前超声筛查人员数量及内部结构状况。结果2007至2020年产前超声筛查人员数量呈现上升趋势,预测2020年达到1269人;期间人员学历、职称呈现下降趋势,工作年限逐渐缩短,大部分集中在5年以下,住院医师比例维持在26.6%,人员进修比例逐年增加,到2020年底达到43.2%。结论通过ARIMA模型对超声筛查人力资源变化趋势进行预测,提示在产前超声筛查人员的管理上,应加强对年轻人员的培训,并尽可能为其提供进修学习的机会。 Objective To understand the current situation and predict the trends in number and composition of prenatal ultrasound screening staff in Beijing.Methods We analyzed the region,age,professional title and other characteristics of prenatal ultrasound screening personnel in Beijing during 2007-2015.We then built an ARIMA model basing on the current situation to predict the number and composition of the staff in 2016-2020.Results The number of prenatal ultrasound screening staff showed an upward trend in 2007-2020 and was predicted to reach 1269 in 2020.During this period,the educational achievement and professional title of the staff showed a downward trend,and the working years became shorter,mainly below 5 years.The proportion of resident doctors remained at 26.6%,and that of the staff receiving further education would reach 43.2%by the end of 2020.Conclusion The prediction under ARIMA model suggests that efforts should be made to strengthen the training of young doctors and provide them opportunities for further study.
作者 徐宏燕 刘雅倩 刘凯波 齐庆青 冯星淋 XU Hongyan;LIU Yaqian;LIU Kaibo;QI Qingqing;FENG Xinglin(Department of Perinatal Health,Beijing Obstetrics and Gynecology Hospital,Capital Medical University,Beijing Maternal and Child Health Care Hospital,Beijing 100026,China;Department of Health Policy and Management,School of Public Health,Peking University,Beijing 100191,China)
出处 《中国医学科学院学报》 CAS CSCD 北大核心 2021年第4期513-520,共8页 Acta Academiae Medicinae Sinicae
基金 国家自然科学基金(71761130083)。
关键词 产前超声筛查 卫生人力资源 预测分析 ARIMA模型 prenatal ultrasound screening health human resource prediction analysis ARIMA model
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