目的通过对汕头市、区二级疾控中心的人力资源相关数据进行比较分析,了解疾控中心人力资源配置现状,为汕头市各级疾控中心人才资源配置及专业队伍建设提供参考。方法通过标准表格收集汕头市2022年12月—2023年5月1个市级和7个区(县)疾...目的通过对汕头市、区二级疾控中心的人力资源相关数据进行比较分析,了解疾控中心人力资源配置现状,为汕头市各级疾控中心人才资源配置及专业队伍建设提供参考。方法通过标准表格收集汕头市2022年12月—2023年5月1个市级和7个区(县)疾控中心的人力资源数据,采用描述性方法对疾控中心人员的性别、年龄、职称、学历学位和专业背景等情况进行分析。使用Excel和GraphP ad Prism软件进行数据统计及差异比较分析。结果汕头市、区二级疾控中心人员性别构成较均衡,男女比例分别为48.1%和51.9%,差异无统计学意义(χ^(2)=0.66,P=0.42)。市级疾控中心35岁以下人员占比较区(县)级CDC高(分别为35.5%和18.9%),二级疾控中心人员年龄构成差异有统计学意义(χ^(2)=16.68,P<0.001)。市级疾控中心人员学历以本科、硕士为主,占77.6%;但区(县)疾控中心职工学历严重失衡,以本科、大专为主,硕士人员比例仅为1.7%,二者学历构成差异有统计学意义(χ^(2)=87.43,P<0.001);市级疾控中心人员职称集中在中级、高级职称,占比为55.2%,无职称人员占比为10.3%;区(县)疾控中心人员职称主要集中在中、初级,占比为71.4%,无职称人员占比为21.0%,二者专业技术职称构成差异有统计学意义(χ^(2)=33.99,P<0.001);二级疾控中心职工专业背景均以卫生专业为主(77.4%),二者专业背景构成差异无统计学意义(χ^(2)=1.35,P=0.24)。结论汕头市、区二级疾控中心在编人员总量不足,其中区(县)疾控中心职工总体学历偏低,人才配置不平衡,高层次人才总体缺乏;当地政府应当重视疾控人才队伍建设,提高已有专业人员的专业水平,积极引进高层次人才,切实做好本市各级疾控中心的人力资源规划。展开更多
背景目前,慢性非传染性疾病(以下简称慢性病)成为影响我国人民群众健康的主要疾病。对慢性病防控资源配置的研究多为现况调查或公平性分析,且缺乏代表性强的结果评价指标。目的分析重庆市慢性病防控资源配置适宜程度的变化情况,探讨其...背景目前,慢性非传染性疾病(以下简称慢性病)成为影响我国人民群众健康的主要疾病。对慢性病防控资源配置的研究多为现况调查或公平性分析,且缺乏代表性强的结果评价指标。目的分析重庆市慢性病防控资源配置适宜程度的变化情况,探讨其对慢性病防控效果的影响。方法按照“穷尽”原则,系统收集政府、卫生健康委及相关部门网站、中国知网(CNKI)、Web of Science等公开数据库发布的重庆市2010—2021年慢性病领域所有文献资料,对文献进行摘录,对慢性病资源配置适宜程度进行量化分析。运用课题组前期构建的适宜公共健康体系定量标准,在系统收集信息资料后,分别从慢性病防控资源配置适宜程度的4个二级指标及13个三级指标展开研究,根据文献中资源配置的相关表述,采用“五分度评分”法半定量对其进行严重性评分,再对评分结果进行公式计算,最终得出资源配置的适宜程度。运用相关分析、线性回归分析资源配置适宜程度对慢性病防控效果的影响。结果2010—2021年重庆市慢性病防控资源配置适宜程度不断上升,由6.64%上升到27.57%;人力资源配置适宜程度从36.49%上升到46.59%,财力资源配置适宜程度从41.06%上升到50.28%,物力资源配置适宜程度从41.40%上升至42.96%,信息资源配置适宜程度从5.73%上升到24.09%。其中财力、物力资源配置适宜程度与重庆市慢性病过早死亡率呈显著负相关,相关系数分别为-0.722、-0.586。结论重庆市慢性病防控资源配置适宜程度逐年提高,但总体处于较低水平,信息资源配置程度较低是制约其发展的主要原因;应加快提升资源配置适宜水平来应对慢性病发病人数的迅速上升。展开更多
The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategi...The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.展开更多
文摘目的通过对汕头市、区二级疾控中心的人力资源相关数据进行比较分析,了解疾控中心人力资源配置现状,为汕头市各级疾控中心人才资源配置及专业队伍建设提供参考。方法通过标准表格收集汕头市2022年12月—2023年5月1个市级和7个区(县)疾控中心的人力资源数据,采用描述性方法对疾控中心人员的性别、年龄、职称、学历学位和专业背景等情况进行分析。使用Excel和GraphP ad Prism软件进行数据统计及差异比较分析。结果汕头市、区二级疾控中心人员性别构成较均衡,男女比例分别为48.1%和51.9%,差异无统计学意义(χ^(2)=0.66,P=0.42)。市级疾控中心35岁以下人员占比较区(县)级CDC高(分别为35.5%和18.9%),二级疾控中心人员年龄构成差异有统计学意义(χ^(2)=16.68,P<0.001)。市级疾控中心人员学历以本科、硕士为主,占77.6%;但区(县)疾控中心职工学历严重失衡,以本科、大专为主,硕士人员比例仅为1.7%,二者学历构成差异有统计学意义(χ^(2)=87.43,P<0.001);市级疾控中心人员职称集中在中级、高级职称,占比为55.2%,无职称人员占比为10.3%;区(县)疾控中心人员职称主要集中在中、初级,占比为71.4%,无职称人员占比为21.0%,二者专业技术职称构成差异有统计学意义(χ^(2)=33.99,P<0.001);二级疾控中心职工专业背景均以卫生专业为主(77.4%),二者专业背景构成差异无统计学意义(χ^(2)=1.35,P=0.24)。结论汕头市、区二级疾控中心在编人员总量不足,其中区(县)疾控中心职工总体学历偏低,人才配置不平衡,高层次人才总体缺乏;当地政府应当重视疾控人才队伍建设,提高已有专业人员的专业水平,积极引进高层次人才,切实做好本市各级疾控中心的人力资源规划。
文摘背景目前,慢性非传染性疾病(以下简称慢性病)成为影响我国人民群众健康的主要疾病。对慢性病防控资源配置的研究多为现况调查或公平性分析,且缺乏代表性强的结果评价指标。目的分析重庆市慢性病防控资源配置适宜程度的变化情况,探讨其对慢性病防控效果的影响。方法按照“穷尽”原则,系统收集政府、卫生健康委及相关部门网站、中国知网(CNKI)、Web of Science等公开数据库发布的重庆市2010—2021年慢性病领域所有文献资料,对文献进行摘录,对慢性病资源配置适宜程度进行量化分析。运用课题组前期构建的适宜公共健康体系定量标准,在系统收集信息资料后,分别从慢性病防控资源配置适宜程度的4个二级指标及13个三级指标展开研究,根据文献中资源配置的相关表述,采用“五分度评分”法半定量对其进行严重性评分,再对评分结果进行公式计算,最终得出资源配置的适宜程度。运用相关分析、线性回归分析资源配置适宜程度对慢性病防控效果的影响。结果2010—2021年重庆市慢性病防控资源配置适宜程度不断上升,由6.64%上升到27.57%;人力资源配置适宜程度从36.49%上升到46.59%,财力资源配置适宜程度从41.06%上升到50.28%,物力资源配置适宜程度从41.40%上升至42.96%,信息资源配置适宜程度从5.73%上升到24.09%。其中财力、物力资源配置适宜程度与重庆市慢性病过早死亡率呈显著负相关,相关系数分别为-0.722、-0.586。结论重庆市慢性病防控资源配置适宜程度逐年提高,但总体处于较低水平,信息资源配置程度较低是制约其发展的主要原因;应加快提升资源配置适宜水平来应对慢性病发病人数的迅速上升。
基金funded by the Center of Advanced Systems Understanding(CASUS)which is financed by Germany's Federal Ministry of Education and Research(BMBF)by the Saxon Ministry for Science,Culture and Tourism(SMWK)with tax funds on the basis of the budget approved by the Saxon State Parliament.
文摘The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.
