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血糖稳定性存在错误分类时的贝叶斯影响因素

吴瑶瑶,  程璐瑶,  余小金,  郭海健,  徐金水,  闵捷

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文章导航 > 中华疾病控制杂志  > 2022 >  26(5): 571-575
吴瑶瑶, 程璐瑶, 余小金, 郭海健, 徐金水, 闵捷. 血糖稳定性存在错误分类时的贝叶斯影响因素[J]. 中华疾病控制杂志, 2022, 26(5): 571-575. doi: 10.16462/j.cnki.zhjbkz.2022.05.014
引用本文: 吴瑶瑶, 程璐瑶, 余小金, 郭海健, 徐金水, 闵捷. 血糖稳定性存在错误分类时的贝叶斯影响因素[J]. 中华疾病控制杂志, 2022, 26(5): 571-575. doi: 10.16462/j.cnki.zhjbkz.2022.05.014
shu
WU Yao-yao, CHENG Lu-yao, YU Xiao-jin, GUO Hai-jian, XU Jin-shu, MIN Jie. Bayesian analysis of influencing factors for the misclassification of blood glucose stability[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(5): 571-575. doi: 10.16462/j.cnki.zhjbkz.2022.05.014
Citation: WU Yao-yao, CHENG Lu-yao, YU Xiao-jin, GUO Hai-jian, XU Jin-shu, MIN Jie. Bayesian analysis of influencing factors for the misclassification of blood glucose stability[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(5): 571-575. doi: 10.16462/j.cnki.zhjbkz.2022.05.014
shu

血糖稳定性存在错误分类时的贝叶斯影响因素

doi: 10.16462/j.cnki.zhjbkz.2022.05.014
  • 1.

    210009南京，东南大学公共卫生学院流行病与卫生统计学系

  • 2.

    210009南京，江苏省疾病预防控制中心健康教育所

基金项目: 

国家重点研发计划 2016YFC1305700

江苏省老年医学科技发展基金 JGS2019ZXYY018

详细信息
    通讯作者:

    闵捷，E-mail: minjie93@163.com

  • 中图分类号: R195.1

Bayesian analysis of influencing factors for the misclassification of blood glucose stability

  • 1.

    Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China

  • 2.

    Institute of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China

Funds: 

The National Key Research and Development Program of China 2016YFC1305700

Gerontology Science and Technology Development Fund of Jiangsu Province JGS2019ZXYY018

More Information

    摘要
  • 摘要:   目的  应用贝叶斯Logistic回归分析法探讨糖尿病患者血糖稳定性存在错误分类时的影响因素，并与传统Logistic回归分析法相比较，为临床工作者提供一种结局指标存在错误分类时的研究方法。  方法  采用分层抽样的方法选取江苏省糖尿病并发症监测点上的患者，以糖化血红蛋白(glycosylated hemoglobin, HbA1c)为“金标准”给出问卷调查血糖稳定性的灵敏度与特异度的专家先验，应用贝叶斯方法和传统方法进行单因素与多因素分析，回归模型的选择以偏差信息准则(deviance information criterion, DIC)或赤池信息准则(Akaike information criterion, AIC)小且参数最少为标准。  结果  调查对象共509人，年龄为(62.32±9.29)岁，贝叶斯多因素Logistic回归分析得出糖尿病病程短(OR=0.299，95% CI: 0.093~0.840)、文化程度高(OR=0.348，95% CI: 0.130~0.914)为血糖稳定性的保护因素，食用腌制食物的频率高(OR=4.310，95% CI: 1.369~22.131)、BMI异常(OR=2.912，95% CI: 1.196~9.865)为血糖稳定性的危险因素。传统Logistic回归分析也得出了同样的结果，但每周食用腌制食物的频率和BMI的OR(95% CI)值被低估，分别为1.671(1.100~2.538)、1.650(1.115~2.440)。  结论  贝叶斯方法和传统Logistic回归分析法均得出病程、文化程度、食用腌制食物的频率以及BMI是影响血糖稳定性的因素，且贝叶斯方法可以校正因变量分类错误，得到的结果更可信。
    Abstract:   Objective  The Bayesian Logistic method was used to explore the influencing factors of the misclassification of blood glucose stability in diabetic patients, and compared with the traditional Logistic method, to provide a research method with misclassification of outcome for the clinicians.  Methods  The stratified sampling method was used to select patients at the monitoring sites of diabetes complications in Jiangsu Province. The expert before the sensitivity and specificity of blood glucose stability was given using glycosylated hemoglobin as the gold standard. Univariate and multivariate analyses were carried out using the Bayesian and traditional methods. The selection of the regression model was based on the minimum deviance information criterion or Akaike information criterion and the least parameters.  Results  A total of 509 subjects were investigated, with an age of (62.32±9.29) years old. Bayesian Logistic regression analysis showed that short course of diabetes (OR=0.299, 95% CI: 0.093-0.840) and high education level (OR=0.348, 95% CI: 0.130-0.914) were protective factors for blood glucose stability. High frequency of consumption of preserved food (OR=4.310, 95% CI: 1.369-22.131) and abnormal BMI (OR=2.912, 95% CI: 1.196-9.865) were risk factors for blood glucose stability. Traditional Logistic regression analysis also reached the same result, but the OR (95% CI) values of preserved food consumption and BMI were underestimated, which were 1.671 (1.100-2.538) and 1.650 (1.115-2.440), respectively.  Conclusions  The Bayesian and the traditional Logistic regression method concluded that the course of the disease, educational level, frequency of consumption of preserved food and BMI were the factors affecting blood glucose stability. The Bayesian method could correct the classification error of dependent variables, and the results obtained were more reliable.
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  • 表  1  人口学特征和临床信息

    Table  1.   Demographic characteristics and clinical information

    变量 血糖不稳定[n(%)] 血糖稳定[n(%)] 贝叶斯Logistic法
    OR(95% CI)值    		 传统Logistic法
    OR(95% CI)值
    人口学特征
      年龄(岁)
        ≤50 29(8.33) 25(15.53) 0.303(0.082~1.348) 0.405(0.205~0.801) a
        ＞50~＜61 103(29.60) 49(30.44) 0.957(0.274~6.488) 0.734(0.427~1.264)
        61~＜71 133(38.22) 58(36.02) 1.208(0.340~8.662) 0.801(0.475~1.352)
        71~80 83(23.85) 29(18.01) 1.000 1.000
      性别
        男 151(43.39) 79(49.07) 1.000 1.000
        女 197(56.61) 82(50.93) 2.733(0.759~10.590) 1.257(0.864~1.828)
      地区
        城市 109(31.32) 66(40.99) 1.000 1.000
        农村 239(68.68) 95(59.01) 3.886(1.105~15.040) a 1.523(1.034~2.244) a
      婚姻
        已婚 309(88.79) 145(90.06) 1.393(0.283~4.560) 0.874(0.473~1.616)
        其他 39(11.21) 16(9.94) 1.000 1.000
    人口学特征
      文化程度
        初中及以下 298(85.63) 121(75.16) 1.000 1.000
        高中及以上 50(14.37) 40(24.84) 0.404(0.127~0.842) a 0.508(0.318~0.809) a
      医保类型
        职工医保 57(16.38) 43(26.71) 0.403(0.137~0.850)a 0.537(0.343~0.843) a
        其他 291(83.62) 118(73.29) 1.000 1.000
      BMI
        正常 128(36.78) 81(50.31) 1.000 1.000
        不正常 220(63.22) 80(49.69) 5.015(1.440~19.160) a 1.740(1.192~2.540) a
    生活方式及饮食习惯
      吸烟
        从不 241(69.25) 114(70.81) 1.000 1.000
        曾经 46(13.22) 21(13.04) 1.489(0.364~11.775) 1.036(0.591~1.818)
        现在 61(17.53) 26(16.15) 1.717(0.441~12.833) 1.110(0.666~1.849)
      饮酒
        从不 244(70.11) 116(72.05) 1.000 1.000
        曾经 34(9.77) 15(9.32) 1.609(0.353~14.041) 1.078(0.565~2.057)
        现在 70(20.11) 30(18.63) 1.687(0.469~11.834) 1.109(0.685~1.795)
      体力活动水平
        高 7(2.01) 5(3.11) 1.000 1.000
        中 87(25.00) 40(24.84) 2.149(0.423~13.667) 1.554(0.465~5.195)
        低 254(72.99) 116(72.05) 2.105(0.445~11.519) 1.564(0.486~5.032)
      睡眠质量
        一般及以下 90(25.86) 26(16.15) 1.000 1.000
        较好 71(20.40) 33(20.50) 0.408(0.136~1.111) 0.530(0.322~0.872) a
        好 187(53.74) 102(63.35) 0.733(0.198~5.244) 0.622(0.341~1.133)
      每周有几天吃腌制食物(d)
        E1 214(61.49) 117(72.67) 1.000 1.000
        ≥1 134(38.51) 44(27.33) 5.267(1.252~21.690) a 1.665(1.107~2.504) a
      每周有几天吃甜品(d)
         < 1 339(97.41) 150(93.17) 1.000 1.000
        ≥1 9(2.59) 11(6.83) 0.437(0.079~1.538) 0.362(0.147~0.892) a
    疾病相关因素
      病程(年)
        ＜6 78(22.41) 55(34.16) 0.338(0.104~0.969) a 0.508(0.294~0.877) a
        6~＜12 121(34.77) 54(33.54) 0.984(0.285~5.720) 0.802(0.471~1.365)
        12~＜17 68(19.54) 23(14.29) 2.166(0.464~18.011) 1.058(0.561~1.998)
        17~36 81(23.28) 29(18.01) 1.000 1.000
      是否有并发症
        有 240(68.97) 99(61.49) 3.243(0.917~12.560) 1.392(0.942~2.056)
        无 108(31.03) 62(38.51) 1.000 1.000
      家族史
        无 165(47.41) 76(47.20) 1.000 1.000
        有 174(50.00) 78(48.45) 1.224(0.418~7.022) 0.973(0.665~1.425)
        不清楚 9(2.59) 7(4.35) 0.709(0.123~7.606) 0.576(0.207~1.603)
      用药情况
        无 9(2.59) 8(4.97) 1.000 1.000
        磺脲类 151(43.39) 82(50.93) 1.348(0.315~5.023) 1.637(0.609~4.404)
        胰岛素 41(11.78) 20(12.42) 1.954(0.392~12.379) 1.822(0.611~5.432)
        两者均有 147(42.24) 51(31.68) 4.402(0.940~27.440) 2.562(0.939~6.994)
    注：a OR值的95% CI不包括1.000，说明该变量有统计学意义。
    下载: 导出CSV

    表  2  血糖稳定性的多因素分析

    Table  2.   Multivariate analysis of blood glucose stability

    变量 贝叶斯Logistic法
    OR(95% CI)值    		 传统Logistic法
    OR(95% CI)值
    病程(年)
      ＜6 0.299(0.093~0.840) a 0.471(0.268~0.825) a
      6~＜12 0.642(0.197~2.129) 0.762(0.442~1.314)
      12~＜17 2.035(0.444~16.151) 0.988(0.515~1.896)
      17~36 1.000 1.000
    教育水平
      初中及以下 1.000 1.000
      高中及以上 0.348(0.130~0.914) a 0.538(0.333~0.870) a
    每周有几天吃腌制食物(d)
       < 1 1.000 1.000
      ≥1 4.310(1.369~22.131) a 1.671(1.100~2.538) a
    BMI
      正常 1.000 1.000
      不正常 2.912(1.196~9.865) a 1.650(1.115~2.440) a
    注：a OR值的95% CI不包括1.000，说明该变量有统计学意义。
    下载: 导出CSV
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  • 收稿日期:  2021-07-28
  • 修回日期:  2022-01-18
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