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样本构成的异质性对基层慢性病管理质量调查结果影响的多重对应研究

Multiple Correspondence Analysis of the Influence of Heterogeneity in Sample Composition on the Investigation Results of Primary Chronic Disease Management Quality
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摘要 背景医疗卫生机构慢性病管理能力评估是慢性病管理中很重要的内容,但研究者在使用相关量表时没有重视样本异质性对调查结果科学性的影响。目的基于成都市基层医疗卫生机构慢性病管理质量的调查,探讨样本构成的异质性对量表评价结果的影响并提出对策建议。方法2022年2月,采用多阶段分层整群抽样从成都市23个地级市、区(县)、46家基层医疗单位选取889名医务人员作为样本,使用中文版慢性病保健评估量表(ACIC)评估医疗单位的慢性病管理质量评分,采用多重对应分析探索性别、工作年限、职称、学历、岗位构成不同带来的样本异质性对评分的影响。结果医务人员慢性病管理能力总评分与其学历呈负相关,与职称呈正相关(P<0.05);研究对象慢性病管理能力决策支持维度评分与工作年限呈负相关(P<0.05);研究对象慢性病管理能力信息系统维度评分与工作年限呈负相关,与学历呈正相关(P<0.05)。多重对应分析结果提示:(1)总体上医务人员学历越高,给出的慢性病管理能力总评分越低;中级职称倾向于给予最高的评分。(2)决策支持评分维度,工作年限越长的医务人员倾向于给予越高的评分。(3)信息系统评分维度,工作年限越短的人员倾向于给予越高的评分;大专/本科学历倾向于给予最高的评分。结论样本构成的异质性对基层慢性病管理质量评价量表的结果会产生确切的影响,这意味着评价会发生偏差,特别是不利于机构间的公正比较,以上提示量表的制订需要严格规范适用范围,量表信效度检验及推广使用中需要重视样本的构成情况,保证其同质性,以确保基层慢性病管理质量评价的客观性、有效性。 Background The assessment of chronic disease management capabilities in healthcare institutions is an important element of chronic disease management,but the researchers did not pay attention to the impact of sample heterogeneity on the scientific validity of survey results when using relevant scales,which requires our attention.Objective To explore the impact of the heterogeneity of sample composition on the evaluation results of the scale and put forward countermeasures based on the investigation of chronic disease management quality in primary medical institutions in Chengdu.Methods In February 2022,a total of 889 medical workers was selected from 46 primary care institutions in 23 prefectures and districts(counties)of Chengdu City by using multi-stage stratified cluster sampling method.The Chinese version of Assessment of Chronic Illness Care(ACIC)was used to assess the quality scores of chronic disease management in the medical institutions.Multiple correspondence analysis was used to explore the impact of sample heterogeneity caused by different genders,years of experience,professional titles,educational levels and job composition on the scale scores.Results The total score of chronic disease management competence of medical workers was negatively correlated with their education level and positively correlated with their professional title(P<0.05).The decision support dimension score of chronic disease management competence was negatively correlated with the working years of subjects(P<0.05),the information system dimension score of chronic disease management competence was negatively correlated with working years and positively correlated with education level(P<0.05).Multiple correspondence analysis showed that the higher educational level was correlated with lower total score of chronic disease management competence,intermediate titles tended to give the highest scores.For the decision support scoring dimension,medical workers with more working years tended to give higher scores.For the information system scoring dimension,medical workers with more working years tended to give higher score,and those with college/bachelor degree tended to give the highest score.Conclusion The heterogeneity of sample composition will have a definite impact on the results of the primary chronic disease management quality evaluation scale,which means that the evaluation will biased,especially not conducive to fair inter-institutional comparisons.The above suggests that the development of the scale needs to strictly regulate the scope of application.The reliability and validity test of the scale and its promotion need to attention to the composition of the sample to ensure its homogeneity,so as to ensure the objectivity and validity of the quality evaluation of primary chronic disease management.
作者 喻国 谌金花 伍悦蕾 刘述益 杜文 肖竹 王意君 YU Guo;CHEN Jinhua;WU Yuelei;LIU Shuyi;DU Wen;XIAO Zhu;WANG Yijun(General Medicine Department of Chengdu Integrated TCM&Western Medicine Hospital,Chengdu 610041,China)
出处 《中国全科医学》 CAS 北大核心 2024年第28期3515-3519,3528,共6页 Chinese General Practice
基金 成都市医学科研课题项目(2021280)。
关键词 慢性病 疾病管理 质量指标 卫生保健 影响因素分析 多重对应分析 Chronic disease Disease management Quality indicators,health care Root cause analysis Multiple correspondence analysis
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