Factors influencing blood glucose control among urban and rural patients with type 2 diabetes mellitus in Nanning City
ZHOU Ji1, YANG Shao-hu1, LYU Ying-nan2
1. Nanning Municipal Center for Disease Control and Prevention, Nanning, Guangxi 530023, China; 2. Center for Genomic and Personalized Medicine of Guangxi Medical University, Nannning, Guangxi 530021, China
Abstract:Objective To understand the factors affecting blood glucose control among urban and rural patients with type 2 diabetes mellitus in Nanning City, and to provide a scientific basis for implementing targeted intervention measures. Methods Using multi-stage random sampling method, questionnaire survey and blood glucose testing were carried out among 300 patients with type 2 diabetes mellitus from one urban district and 200 ones from one country in Nanning City in 2018. SPSS 24.0 software was used for the analysis, and LASSO regression model was used to identify the factors influencing blood glucose control in the patients with type 2 diabetes mellitus. Results The proportion of reaching blood glucose control target was higher in urban patients (45.61%) than in rural ones (29.65%), and the differences were statistically significant (χ2=12.719, P<0.05). The proportions of daily physical exercise, mode of outpatient follow-up, using hypoglycemic medications regularly, reaching blood pressure control target and correctly answering > 6 questions about diabetes prevention knowledge were all higher in the group reaching blood glucose control target than in the group not reaching blood glucose control target, with statistically significant differences (all P<0.05). LASSO regression results showed that whether in cities or rural areas, diabetes duration ≥ 10 years (β=0.30, β=0.07), smoking within one year (β=0.23, β=0.27), using hypoglycemic medications irregularly (β=0.29, β=0.36) and correctly answering ≤ 6 questions about diabetes prevention knowledge (β=0.21, β=0.08) were all the risk factors for blood glucose control in the patients with type 2 diabetes mellitus, whereas the follow-up frequency in one year < 4 times (β=-0.44, β=-0.81) was the protective factor. All medical insurance except urban medical insurance and the frequency of bloodglucose testing less than once a day were the risk factors for blood glucose control in urban patients (β=0.14, β=0.15), but they were the protective factors for rural patients (β=-0.13, β=-0.87). The education level of middle school and above was the protective factor for blood glucose control in urban patients (β=-0.22), but the risk factor for rural patients (β=0.40). In addition, the risk factors for blood glucose control in urban patients included alcohol consumption within one year (β=0.07). The risk factors for blood glucose control in rural patients also included being married (β=0.08), while the protective factors included having no family history of diabetes (β=-1.66). Conclusion Blood glucose control of urban patients with type 2 diabetes mellitus in Nanning City is superior to that of rural patients, and the factors influencing blood glucose control in urban and rural patients are different. Therefore, pertinence measures should be taken according to different urban and rural areas so as to improve the healthy lifestyle and blood glucose control behavior of patients.
周吉, 杨绍湖, 吕应楠. 南宁市城市和农村2型糖尿病患者血糖控制的影响因素分析[J]. 实用预防医学, 2021, 28(7): 785-790.
ZHOU Ji, YANG Shao-hu, LYU Ying-nan. Factors influencing blood glucose control among urban and rural patients with type 2 diabetes mellitus in Nanning City. , 2021, 28(7): 785-790.
[1] 中华医学会糖尿病学分会.中国2型糖尿病防治指南(2017年版)[J].中华糖尿病杂志,2018,10(1):4-67. [2] 秦鸣妍, 梁雅茹, 宫晓. 我国中老年糖尿病患者的血糖控制现状及影响因素研究[J].中国初级卫生保健,2019,33(9):46-50. [3] Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045[J]. Diabetes Res Clin Pract, 2018, 138: 271-281. [4] 李伟芳. 老年2型糖尿病及其慢性并发症的相关研究[D].郑州:郑州大学,2015. [5] Yarkoni T, Westfall J. Choosing prediction over explanation in psychology: lessons from machine learning[J].Perspect Psychol Sci,2017,12(6):1100-1122. [6] Helwig NE. Adding bias to reduce variance in psychological results: a tutorial on penalized regression[J]. Quantitat Method Psychol,2017,13(1):1-19. [7] McNeish DM. Using lasso for predictor selection and to assuage overfitting: a method long overlooked in behavioral sciences[J].Multivariate Behav Res,2015,50(5):471-484. [8] Zou H. The adaptive Lasso and its oracle properties[J]. J American Statistic Associat, 2006, 101(476):1418-1429. [9] Agier L, Portengen L, Chadeau-Hyam M, et al. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations[J].Environ Health Perspect,2016,124(12):1848-1856. [10] Billionnet C, Sherrill D, Annesi-Maesano I. Estimating the health effects of exposure to multi-pollutant mixture[J].Ann Epidemiol,2012,22(2):126-141. [11] 周吉, 吕应楠, 石健. 南宁市城乡2型糖尿病患者血糖控制行为分析[J].中国慢性病预防与控制,2018,26(5):350-353. [12] 刘艺华. 基于队列的基层医疗卫生机构慢性病患者管理项目评估[D].北京:中国疾病预防控制中心,2017. [13] 吴延莉, 李剑虹, 申涛, 等. 北京市朝阳区2型糖尿病患者血糖监测现状及影响因素分析[J].中国健康教育,2019,35(7):601-606. [14] 陈丽丽, 程旻娜, 严青华, 等. 上海市≥35岁自报糖尿病患者血糖控制现状与影响因素分析[J].中国慢性病预防与控制,2018,26(11):818-821. [15] 王芬芬, 卢敏, 王英. 2型糖尿病患者血糖控制及体力活动水平影响因素分析[J].中国公共卫生管理,2020,36(4):541-543. [16] 白莉莉, 邓姝颖, 陈玉玲, 等. 宁夏城乡地区中老年2型糖尿病患者血糖控制的影响因素[J].江苏医药,2020,46(10):996-999. [17] Xu F,He J,Wang Z,et al. The relationship between socio-economic status and diagnosed type 2 diabetes is changing with economic growth in Nanjing, China[J].Diabet Med,2018,35(5):567-575. [18] 薛雨星, 张宁, 盛红艳, 等. 常熟市2型糖尿病患者血糖控制影响因素分析[J].实用预防医学,2016,23(8):917-920. [19] 杨位芳, 田雪莹, 孙喜红. 社区2型糖尿病患者血糖控制效果及影响因素[J].中华行为医学与脑科学杂志,2016,25(6):556-560. [20] 孙中明, 潘恩春, 缪丹丹, 等. 淮安市2型糖尿病患者血糖控制情况与糖尿病家族史的关系[J].中华内分泌代谢杂志,2017,33(7):578-580. [21] 齐敬霄, 王军锋, 王红梅, 等.2016年邢台城乡2型糖尿病患者血糖控制及药物治疗情况[J].实用预防医学,2017,24(11):1321-1323. [22] 韩开益. 山东省城乡糖尿病患者知识、态度和自我管理行为研究[D].济南:山东大学,2018. [23] 张海瑞, 王志玲,门可.强化健康教育对社区老年糖尿病患者生活方式的影响研究[J].实用预防医学,2019,26(10):1159-1161.