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
10.3969/j.issn.1672-8467.2024.06.015
- VernacularTitle: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
- Author:
Dan-Dan HE
1
;
Yi-Bin ZHOU
;
Hui-Lin XU
;
Tong-Tong LIANG
;
Yi-Zhou CAI
;
Dan-Dan YU
;
Xiao-Li XU
;
Lin-Juan DONG
;
Nian LIU
;
Xiao-Hua LIU
Author Information
1. 上海市闵行区疾病预防控制中心 上海 201101
- Keywords:
diabetes mellitus;
community health services;
healthcare-seeking behaviors trajectory;
group-based trajectory modelling(GBTM);
blood glucose
- From:
Fudan University Journal of Medical Sciences
2024;51(6):981-989
- CountryChina
- Language:Chinese
-
Abstract:
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 follow-up 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 analysis revealed that compared to the continuous non-attendance group,the low-level increasing group,high-level slowly decreasing group,and continuous regular attendance group had better glycemic control,with HRs of 0.37(95%CI:0.34-0.39),0.72(95%CI:0.67-0.78),and 0.78(95%CI:0.73-0.84),respectively.The glycemic control level in the high-level rapidly decreasing group was comparable,with an HR of 1.06(95%CI:0.99-1.12).Conclusion Based on the optimal model,the community medical treatment trajectories of T2DM patients showed different dynamic characteristics.Factors such as gender,residence,hypertension,and weight loss may influence these varying trajectories.Regular community visits and follow-up may help control blood glucose levels.
