三高共管模式下基层社区老年糖尿病患者综合控制情况效果及影响因素研究
Clinical Efficacy of Integrated Hypertension-Hyperglycemia-Hyperlipidemia Management in Elderly Diabetic Patients: A Community-Based Intervention Study
DOI: 10.12677/acm.2025.1561872, PDF,   
作者: 周三敏:青岛大学附属医院内分泌与代谢病科,山东 青岛;平度市东阁街道办事处卫生院全科,山东 青岛;贾宏健, 陈 娇:青岛大学附属医院全科医学科,山东 青岛;张杰涛*:青岛大学附属医院内分泌与代谢病科,山东 青岛;青岛大学附属医院全科医学科,山东 青岛
关键词: 三高共管老年糖尿病基层社区血糖控制慢病管理Integrated 3H Management Elderly Diabetes Primary Care Communities Chronic Disease Management Glycemic Control
摘要: 目的:探讨“三高共管”模式在基层社区老年糖尿病患者中的实施效果,为基层社区老年人慢病管理提供科学依据。方法:采用回顾性队列研究,基于平度市基本公共卫生管理平台及“三高”共管监测平台系统(2023~2024年数据),选取连续2年完成国家基本公共卫生查体及“三高”并发症筛查的561例东阁街道 ≥ 65岁的老年糖尿病患者作为研究对象。收集患者一般信息、血压、实验室检查(空腹血糖、糖化血红蛋白、血脂四项)及生活方式(吸烟、饮酒、锻炼)等指标,以2023年为基线,采用Wilcoxon符号秩检验,McNemar检验进行配对分析;通过多因素多分类Logistic回归分析年龄、性别等因素对血糖、血压、血脂控制的影响。结果:与2023年相比,2024年HbA1c水平明显降低,FPG水平升高,差异均有统计学意义(p < 0.001)。2024年HbA1c、血脂、综合控制达标率分别为82.9%、17.5%、8.2%,显著高于2023年的45.5%、5.0%、0.9%,差异均有统计学意义(p < 0.001)。然而,2024年FPG、BP控制达标率分别为35.3%、30.7%均低于2023年的42.8%、48.0%差异均有统计学意义(p < 0.05)。生活方式各指标、脑血管并发症及用药情况的分布差异均无统计学意义(p > 0.05)。多因素多分类Logistic回归分析显示,居住地是血糖、血压控制变化的影响因素(p < 0.05);与农村居民相比,城镇居民血糖控制变差的风险显著更高[OR (95%CI) = 2.128 (1.120~4.046), p = 0.021],血压控制变差的风险显著更高[OR (95%CI) = 2.981 (1.932~4.600), p < 0.001];性别是血压控制变化的影响因素(p = 0.013),女性血压控制变好的风险显著低于男性[OR (95%CI) = 0.436 (0.249~0.765), p = 0.004]。结论:“三高共管”模式可显著提高老年糖尿病患者的HbA1c、血脂控制达标率。未来需优化政策执行,强化基层医疗资源配置,针对城乡差异、性别差异实施精准干预策略,为老年糖尿病患者打造个性化管理方案。
Abstract: Objective: To explore the implementation effect of the “Integrated 3H Management” model in elderly diabetic patients in primary healthcare Institutions and provide a scientific basis for chronic disease management in the elderly. Methods: The retrospective cohort study was conducted using data from the Pingdu Basic Public Health Management Platform and the “Integrated 3H Management Monitoring System” (2023~2024). This study enrolled 561 elderly diabetic patients aged 65 and above from Dongge Sub-district who had completed both the standardized national basic public health examinations and annual screenings for 3H complications for two consecutive years. Collect general patient information, blood pressure, laboratory tests (fasting blood glucose, glycated hemoglobin, and lipid profile), and lifestyle indicators (smoking, alcohol consumption, and exercise). Baseline data from 2023 were analyzed using Wilcoxon signed-rank tests, McNemar tests for paired comparisons. Multifactorial multicategory logistic regression analysis was used to assess the effects of age, sex, and other factors on the control of blood glucose, blood pressure, and blood lipids. Results: Compared with 2023, the HbA1c levels in 2024 showed a significant decrease, while FPG levels increased, with both differences being statistically significant (p < 0.001). The 2024 control rates for HbA1c, lipids, and comprehensive metrics were 82.9%, 17.5%, and 8.2%, respectively, significantly higher than the 2023 rates (45.5%, 5.0%, and 0.9%; p < 0.001). However, the compliance rates of FPG and BP in 2024 were 35.3% and 30.7% respectively, which were lower than 42.8% and 48.0% in 2023, and the differences were all statistically significant (p < 0.05). There were no statistically significant differences in lifestyle indicators, cerebrovascular complications, or medication regimens (p > 0.05). The results of multifactorial multicategory Logistic regression analysis demonstrated that residential location was a significant influencing factor for changes in blood glucose and blood pressure control (p < 0.05). Compared with rural residents, urban residents had significantly higher odds of worsened blood glucose control [OR (95%CI) = 2.128 (1.120~4.046), p = 0.021] and significantly higher odds of worsened blood pressure control [OR (95%CI) = 2.981 (1.932~4.600), p < 0.001]. Sex was a significant factor influencing blood pressure control changes (p = 0.013); Female patients had significantly lower odds of improved blood pressure control compared to males [OR (95%CI) = 0.436 (0.249~0.765), p = 0.004]. Conclusions: The Integrated 3H Management model significantly improved HbA1c and lipid control rates in elderly patients with diabetes. Future efforts should focus on refining policy implementation, strengthening primary healthcare resource allocation, and developing precise intervention strategies tailored to urban-rural and gender disparities to establish personalized management plans for elderly diabetic patients.
文章引用:周三敏, 贾宏健, 陈娇, 张杰涛. 三高共管模式下基层社区老年糖尿病患者综合控制情况效果及影响因素研究[J]. 临床医学进展, 2025, 15(6): 1452-1461. https://doi.org/10.12677/acm.2025.1561872

参考文献

[1] 中华人民共和国卫生健康委等. 关于印发健康中国行动——心脑血管疾病防治行动实施方案(2023-2030年)的通知[EB/OL].
https://www.gov.cn/zhengce/zhengceku/202311/content_6915365.htm, 2024-11-30.
[2] Ko, S., Han, K.D., Park, Y., Yun, J., Kim, K., Bae, J., et al. (2023) Diabetes Mellitus in the Elderly Adults in Korea: Based on Data from the Korea National Health and Nutrition Examination Survey 2019 to 2020. Diabetes & Metabolism Journal, 47, 643-652. [Google Scholar] [CrossRef] [PubMed]
[3] Yan, Y., Wu, T., Zhang, M., Li, C., Liu, Q. and Li, F. (2022) Prevalence, Awareness and Control of Type 2 Diabetes Mellitus and Risk Factors in Chinese Elderly Population. BMC Public Health, 22, Article No. 1382. [Google Scholar] [CrossRef] [PubMed]
[4] Fang, M., Wang, D., Coresh, J. and Selvin, E. (2021) Trends in Diabetes Treatment and Control in U.S. Adults, 1999-2018. New England Journal of Medicine, 384, 2219-2228. [Google Scholar] [CrossRef] [PubMed]
[5] Li, Y., Teng, D., Shi, X., Qin, G., Qin, Y., Quan, H., et al. (2020) Prevalence of Diabetes Recorded in Mainland China Using 2018 Diagnostic Criteria from the American Diabetes Association: National Cross Sectional Study. BMJ, 369, m997. [Google Scholar] [CrossRef] [PubMed]
[6] 陈伟标, 张艳, 袁雪丽, 等. 深圳市老年人“三高”共病现状及关联因素分析[J]. 中国慢性病预防与控制, 2023, 31(1): 51-55.
[7] 关昌荣, 王存库, 田冶, 等. 2022年海南省老年人“三高”共病现状及影响因素研究[J]. 现代预防医学, 2024, 51(15): 2719-2725.
[8] 金梦龙, 秦晓英, 马力亚∙阿米提, 等. 新疆哈萨克族血脂异常、高血压和糖尿病共病现状及影响因素研究[J]. 中国全科医学, 2024, 27(12): 1438-1444.
[9] 青岛市人民政府. 青岛市人民政府关于2023年重点办好城乡建设和改善人民生活方面16件实事的通知[EB/OL].
http://www.qingdao.gov.cn/zwgk/xxgk/bgt/gkml/gwfg/202301/t20230106_6595841.shtml, 2025-01-02.
[10] 青岛市人民政府. 青岛市人民政府关于2024年重点办好城乡建设和改善人民生活方面15件实事的通知[EB/OL].
http://www.qingdao.gov.cn/zwgk/xxgk/bgt/gkml/gwfg/202401/t20240131_7819394.shtml, 2025-01-02.
[11] 国家老年医学中心, 中华医学会老年医学分会, 中国老年保健协会糖尿病专业委员会. 中国老年糖尿病诊疗指南(2024版) [J]. 中华糖尿病杂志, 2024, 16(2): 147-189.
[12] American Diabetes Association Professional Practice Committee (2025) Summary of Revisions: Standards of Care in Diabetes-2025. Diabetes Care, 48, S6-S13.
[13] 中国老年学和老年医学学会. 老年2型糖尿病慢病管理指南[J]. 中西医结合研究, 2023, 15(4): 239-253.
[14] 吴平生. 2018美国血胆固醇管理指南要点[J]. 中国循环杂志, 2019, 34(S1): 108-110.
[15] 许敏锐, 潘英姿, 石素逸, 等. 常州市武进区2型糖尿病患者糖化血红蛋白控制状况及其影响因素研究[J]. 现代预防医学, 2021, 48(18): 3434-3438, 3443.
[16] 刘杰, 顾天伟, 李平, 等. 南京市社区2型糖尿病患者代谢指标达标和微血管并发症患病现况及影响因素分析[J]. 中国糖尿病杂志, 2020, 28(1): 23-28.
[17] 范文瑜, 张世龙, 王海鹏, 等. 2019-2022年青岛市城阳区“三高共管 六病同防”慢病管理模式下患者血压、血糖、血脂控制变化趋势及影响因素分析[J]. 中国公共卫生, 2025, 41(2): 148-153.
[18] 李梦宇, 连隽, 廖子锐, 等. 国家基本公共卫生服务老年人健康体检的异常检出率分析[J]. 中国全科医学, 2023, 26(22): 2756-2762.
[19] 贾伟平. 血糖监测技术的进步与展望[J]. 上海交通大学学报(医学版), 2022, 42(9): 1171-1175.
[20] 纪立农, 王潇雨. 治疗糖尿病 从控制血糖到改善结局[EB/OL]. 健康报: 2019-10-22.
https://faxing.jkb.com.cn/home/index/menu.html?goods=1&item=711613&page=138371334&name=jkb, 2025-03-20.
[21] 中华医学会老年医学分会, 中国医疗保健国际交流促进会高血压病分会. 老年高血压特点及临床诊治流程专家共识(2024) [J]. 中华老年医学杂志, 2024, 43(3): 257-268.
[22] “三高”共管规范化诊疗中国专家共识(2023版)专家组. “三高”共管规范化诊疗中国专家共识(2023版) [J]. 中华心血管病杂志(网络版), 2023, 6(1): 1-11.
[23] Christensen, J.R., Laursen, D.H., Lauridsen, J.T., Hesseldal, L., Jakobsen, P.R., Nielsen, J.B., et al. (2022) Reversing Type 2 Diabetes in a Primary Care-Anchored eHealth Lifestyle Coaching Programme in Denmark: A Randomised Controlled Trial. Nutrients, 14, Article 3424. [Google Scholar] [CrossRef] [PubMed]
[24] 马兴丽, 张高辉, 张世龙, 等. 2019-2022年青岛市城阳区“三高共管 六病同防”慢病管理模式对老年患者健康行为变化趋势及影响因素分析[J]. 中国公共卫生, 2025, 41(2): 144-147.
[25] 蔡淳, 关颖, 刘月星, 等. 上海市松江区农村和城乡结合部糖尿病综合管理效果评价[J]. 中华糖尿病杂志, 2019, 11(8): 518-523.
[26] 黄艳丽, 叶静雪, 刘鸿源. 中美英基层医疗服务质量评价框架及“两病”质量指标对比研究[J]. 中国全科医学, 2021, 24(31): 3929-3941.
[27] Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., et al. (2023) Artificial Intelligence in Diabetes Management: Advancements, Opportunities, and Challenges. Cell Reports Medicine, 4, Article ID: 101213. [Google Scholar] [CrossRef] [PubMed]
[28] 范文瑜, 马兴丽, 张世龙, 等. 医防融合服务中家庭医生团队协作水平及影响因素研究[J]. 中国全科医学, 2025, 28(16): 1966-1972.
[29] 章炜颖, 朱虹玮, 马程乘, 等. 社区医防融合数智健康管理路径建设的思考[J]. 中华全科医师杂志, 2024, 23(5): 520-524.
[30] 程晓冉, 张笑天, 李明月, 等. 医防融合背景下慢性病随访对高血压和糖尿病患者健康行为及血压/血糖控制的影响研究[J]. 中国全科医学, 2023, 26(28): 3482-3488.