上海市闵行区2型糖尿病患者社区就医轨迹变化与血糖控制水平的研究  

Study on the trajectories change of visiting community health service centers and blood glucose control level of type 2 diabetes patients in Minhang District,Shanghai

作　　者：何丹丹[1] 周毅彬 许慧琳[1] 梁彤彤 蔡逸舟 俞丹丹 徐晓莉 董琳娟 刘念 刘小华 HE Dan-dan;ZHOU Yi-bin;XU Hui-lin;LIANG Tong-tong;CAI Yi-zhou;YU Dan-dan;XU Xiao-li;DONG Lin-juan;LIU Nian;LIU Xiao-hua(Center for Disease Control and Prevention of Minhang District,Shanghai 201101,China)

机构地区：[1]上海市闵行区疾病预防控制中心,上海201101

出　　处：《复旦学报（医学版）》2024年第6期981-989,共9页Fudan University Journal of Medical Sciences

基　　金：上海市闵行区自然科学研究课题(2021MHZ003)。

摘　　要：目的构建2型糖尿病(type 2 diabetes mellitus,T2DM)患者的社区就医轨迹模型,分析不同轨迹的影响因素,并探讨不同就诊轨迹下的血糖控制水平。方法本研究采用回顾性纵向研究方法,纳入18088例于2006年1月至2009年12月在闵行区各社区卫生服务中心建立居民健康档案并建卡纳入社区随访管理的T2DM患者,在2010年1月1日开始随访,2019年12月31日随访终止,随访信息完整。基于群体的组基轨迹模型(group-based trajectorymodelling,GBTM)来识别、构建糖尿病患者的社区就诊轨迹。利用贝叶斯信息准则(Bayesian information criterion,BIC)、平均验后分组概率(average posterior probability,AvePP)等评价指标选择最佳亚组数的轨迹,比较不同类别轨迹在人口学特征、家族史、居住地、空腹血糖、BMI等方面差异,运用无序多分类Logistic回归模型分析不同就诊轨迹的影响因素。采用Cox比例风险模型分析T2DM不同社区就医轨迹与血糖控制水平的关系。结果运用GBTM分析,构建最优模型Model 4,将社区建卡的18088名T2DM患者分为5个不同轨迹亚组,分别为持续不就诊组(22.29%)、低水平上升组(15.09%)、高水平缓慢下降组(14.18%)、高水平快速下降组(14.90%)、持续规律就诊组(33.54%)。以持续规律就诊组作为对照,性别、年龄、居住地、基线合并高血压、基线空腹血糖水平、BMI对T2DM患者社区就诊轨迹均有影响(P<0.05)。校正了混杂因素后,Cox回归分析结果显示,相对于持续不就诊组,低水平上升组、高水平缓慢下降组和持续规律就诊组血糖控制水平较好,HR分别为0.37(95%CI:0.34~0.39)、0.72(95%CI:0.67~0.78)、0.78(95%CI:0.73~0.84);高水平快速下降组血糖控制水平相当,HR为1.06(95%CI:0.99~1.12)。结论2型糖尿病患者在最优模型下社区就医轨迹呈现不同的动态变化特征;性别、居住地、合并高血压、消瘦影响患者出现不同就医轨迹。定期社区门诊就诊随访可�Objective To construct trajectory models of care-seeking patterns for type 2 diabetes mellitus(T2DM)patients,analyze the influencing factors of different trajectories,and explore the fasting blood glucose control levels of T2DM patients with different trajectories.Methods A retrospective cohort study was carried out on 18088 T2DM patients who had health records and been involved in the diabetic management in Community Health Service Center of Minhang District,Shanghai from 2006 to 2009.Starting from Jan 1,2010,participants were followed up until Dec 31,2019,with complete followup information.Group-based trajectory modelling(GBTM)was employed to identify and construct the fluctuation trajectory of fasting blood glucose in the patients.Bayesian information criterion(BIC),average posterior probability(AvePP)and other evaluation indicators were used to select the optimum subgroup number model.Then the differences in demographic characteristics,health status,family history,fasting blood glucose,BMI,etc were compared among different categories.Multinational logistic regression model was constructed to explore the influencing factors of different fluctuation trajectories.Cox regression analysis was used to examine the relationship between the long-term trajectories of care-seeking patterns and fasting blood glucose control level.Results Using GBTM analysis,we constructed the optimal Model 4 to categorize 18088 T2DM patients with community health records into five distinct trajectory subgroups:continuous non-attendance group(22.29%),low-level increasing group(15.09%),high-level slowly decreasing group(14.18%),high-level rapidly decreasing group(14.90%),and continuous regular attendance group(33.54%).With the continuous regular attendance group serving as the reference,gender,age,place of residence,baseline comorbidity of hypertension,baseline fasting plasma glucose level,and BMI were found to influence the community attendance trajectories of T2DM patients(P<0.05).After adjusting for confounding factors,Cox regression an

关 键 词：糖尿病 社区卫生服务 就医轨迹 组基轨迹模型(GBTM) 血糖 

分 类 号：R179[医药卫生—妇幼卫生保健]