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北京大学学报（医学版） ›› 2022, Vol. 54 ›› Issue (3): 450-457. doi: 10.19723/j.issn.1671-167X.2022.03.009

• 论著 • 上一篇    下一篇

基于马尔可夫模型的社区人群糖尿病筛查预防心血管病的效果评价

王佳敏1,刘秋萍1,张明露1,巩超1,刘舒丹1,陈暐烨1,沈鹏2,林鸿波2,高培1,3,*(),唐迅1,*()   

  1. 1. 北京大学公共卫生学院流行病与卫生统计学系, 北京 10019
    2. 宁波市鄞州区疾病预防控制中心, 浙江宁波 3151011
    3. 北京大学临床研究所真实世界证据评价中心, 北京 100191
  • 收稿日期:2022-02-06 出版日期:2022-06-18 发布日期:2022-06-14
  • 通讯作者: 高培,唐迅 E-mail:peigao@bjmu.edu.cn;tangxun@bjmu.edu.cn
  • 基金资助:
    国家自然科学基金(81973132);国家自然科学基金(81961128006);国家重点研发计划(2020YFC2003503)

Effectiveness of different screening strategies for type 2 diabete on preventing cardiovascular diseases in a community-based Chinese population using a decision-analytic Markov model

Jia-min WANG1,Qiu-ping LIU1,Ming-lu ZHANG1,Chao GONG1,Shu-dan LIU1,Wei-ye CHEN1,Peng SHEN2,Hong-bo LIN2,Pei GAO1,3,*(),Xun TANG1,*()   

  1. 1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    2. Yinzhou District Center for Disease Control and Prevention, Ningbo 315101, Zhejiang, China
    3. Center for Real-World Evidence Evaluation, Clinical Research Institute, Peking University, Beijing 100191, China
  • Received:2022-02-06 Online:2022-06-18 Published:2022-06-14
  • Contact: Pei GAO,Xun TANG E-mail:peigao@bjmu.edu.cn;tangxun@bjmu.edu.cn
  • Supported by:
    National Natural Sciences Foundation of China(81973132);National Natural Sciences Foundation of China(81961128006);National Key Research and Development Program of China(2020YFC2003503)

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摘要：

目的: 在中国鄞州电子健康档案研究(Chinese electronic health records research in Yinzhou, CHERRY)的队列人群中，评估国内外不同指南最新推荐的2型糖尿病筛查策略在我国发达地区人群中预防心血管病的效果。方法: 采用马尔可夫(Markov)模型模拟并比较的系统性筛查策略包括：(1) 根据《中国2型糖尿病防治指南(2020年版)》的推荐，在40~70岁人群中筛查(策略1);(2) 根据2022年美国糖尿病学会《糖尿病医学诊疗标准》的推荐，在35~70岁人群中筛查(策略2);(3)根据美国预防服务工作组2021年更新的《2型糖尿病的筛查建议声明》的推荐，在35~70岁且超重或肥胖(体重指数24 kg/m2及以上)的人群中进行筛查(策略3)。根据指南推荐，对筛查阳性(空腹血糖7.0 mmol/L及以上)的人群强化控制血糖以达到目标值(糖化血红蛋白控制在7.0%以下)。马尔可夫模型循环周期设为1年，研究期限设为10年，模拟10个周期，计算的结局指标包括心血管病事件发病数和全因死亡数等结局事件数，以及每预防一例心血管病事件或全因死亡需筛查人数等效果评价指标。马尔可夫模型的参数主要来源于CHERRY队列人群和公开发表的文献。采用单因素敏感性分析探讨筛查方法的灵敏度变化对结果的影响，采用概率敏感性分析探讨糖尿病发病率、筛查方法的灵敏度、强化干预措施的效应强度等参数的不确定性。结果: 研究纳入的289 245名基线无心血管病且未诊断糖尿病的35~70岁人群中，与机会性筛查相比，在40~70岁人群中进行系统性筛查的策略1可预防的心血管病发病数为222[95%不确定性区间(uncertainty interval, UI)：180~264]例，在35~70岁人群中筛查的策略2为227(95%UI: 185~271)例，在35~70岁且超重或肥胖人群中筛查的策略3为131(95%UI: 98~164)例。每预防一例心血管病发病数的需筛查人数在策略1、2和3分别为1 184(95%UI: 994~1 456)人、1 274(95%UI: 1 067~1 564)人和814(95%UI: 649~1 091)人。策略2相比策略1每预防一例心血管病的需筛查人数增加90(95%UI: -197~381)人，但心血管病预防效果相似; 策略3相比策略2的需筛查人数减少460(95%UI: 185~724)人，筛查效率更高。单因素敏感性分析和概率敏感性分析的结果与主分析结果一致。结论: 在我国发达地区人群中，根据现有的最新指南开展糖尿病系统性筛查能够减少心血管病发病和全因死亡，但仅降低筛查起始年龄从40岁到35岁对预防心血管病效果的增益并不明显，如果降低筛查的起始年龄到35岁需要同时考虑超重或肥胖的危险因素以便提高筛查效率。

关键词: 糖尿病, 筛查, 心血管病, 马尔可夫模型

Abstract:

Objective: To evaluate the effectiveness of different screening strategies for type 2 diabetes to prevent cardiovascular disease in a community-based Chinese population from economically developed areas based on the Chinese electronic health records research in Yinzhou (CHERRY) study. Methods: A Markov model was used to simulate different systematic diabetes screening strategies, including: (1) screening among Chinese adults aged 40-70 years recommended by the 2020 Chinese Guideline for the prevention and Treatment of Type 2 Diabetes (Strategy 1); (2) screening among Chinese adults aged 35 to 70 years recommended by the 2022 American Diabetes Association Standard of Medical Care in Diabetes (Strategy 2); and (3) screening among Chinese adults aged 35-70 years with overweight or obesity recommended by the 2021 United States Preventive Services Task Force Recommendation Statement on Screening for Prediabetes and Type 2 Diabetes (Strategy 3). According to the guidelines, individuals who were screened positively (fasting plasma glucose ≥ 7.0 mmol/L) would be introduced to intensive glycemic targets management (glycated hemoglobin < 7.0%).The Markov model simulated different screening scenarios for ten years (cycles) with parameters mainly from the CHERRY study or published literature. Number of cardiovascular disease events or deaths could be prevented and number needed to screen (NNS) were calculated to compare the effectiveness of the different strategies. One-way sensitivity analysis on the sensitivity of screening methods and probabilistic sensitivity analysis on uncertainties of diabetes incidence, the sensitivity of screening methods, and intensive glycemic management effects were conducted. Results: Totally 289 245 Chinese adults aged 35-70 years without cardiovascular diseases or diagnosed diabetes at baseline were enrolled. In terms of the number of cardiovascular disease events could be prevented, Strategy 1 for systematic diabetes screening among the adults aged 35-70 years was 222 (95%UI: 180-264), Strategy 2 for systematic diabetes screening among the adults aged 40-70 years was 227 (95%UI: 185-271), and Strategy 3 for systematic diabetes screening among the adults aged 35-70 years with obesity or overweight (body mass index ≥ 24 kg/m2) was 131 (95%UI: 98-164), compared with opportunistic screening. NNS per cardiovascular disease event for the strategies 1, 2 and 3 were 1 184 (95%UI: 994-1 456), 1 274 (95%UI: 1 067-1 564) and 814 (95%UI: 649-1 091), respectively. Compared with Strategy 1, NNS per cardiovascular disease event for Strategy 2 increased by 90 (95%UI: -197-381) with similar effectiveness of cardiovascular prevention; however, NNS per cardiovascular disease event for Strategy 3 was reduced by 460 (95%UI: 185-724) in contrast to the Strategy 2, suggesting that the Strategy 3 was more efficient. The results were consistent in multiple sensitivity analyses. Conclusion: Systematic screening for diabetes based on the latest guidelines in economically developed areas of China can reduce cardiovascular events and deaths. However, merely lowering the starting age of screening from 40 to 35 years seems ineffective for preventing cardiovascular disease, while screening strategy for Chinese adults aged 35-70 years with overweight or obesity is recommended to improve efficiency.

Key words: Diabetes, Screening, Cardiovascular diseases, Markov model

中图分类号: 

  • R181.3+8

图1

筛查糖尿病预防心血管病的马尔可夫模型状态转换图"

表1

马尔可夫模型中的参数及其来源"

Inputs Value SD Data source
Incidence rate (per 1 000 person years)
  Diabetes 9.46 0.04 Cohort study[11]
  CHD in opportunistic screening for diabetes 6.17 - Estimated from current study
  Stroke in opportunistic screening for diabetes 15.55 - Estimated from current study
All-cause mortality rate (per 1 000 person years)
  Normal blood glucose 6.15 - Estimated from current study
  Opportunistic screening for diabetes 14.95 - Estimated from current study
  Diabetes with CHD 37.10 - Estimated from current study
  Diabetes with stroke 46.01 - Estimated from current study
Screening
  Sensitivity of FPG 0.575 0.03 Cross-sectional study[12]
  Time spent with undiagnosed diabetes/years 6 - Cohort study[9]
HbA1c
  Undiagnosed diabetes/% 7.55 0.02 Estimated from current study
  Opportunistic screening for diabetes/% 7.94 0.02 Estimated from current study
  Systematic screening for diabetes/% 7.00 - Chinese guideline[2]
  Increased HbA1c per year in undiagnosed diabetes/% 0.07 - Estimated from current study
  Increased risk of CHD for 1% increase in HbA1c 0.14 0.06 Cohort study[13]
  Increased risk of stroke for 1% increase in HbA1c 0.12 0.06 Cohort study[13]
  Increased risk of death for 1% increase in HbA1c 0.14 0.06 Cohort study[13]
Utilities
  Diabetes without CVD (undiagnosed) 0.973 - Cross-sectional study[14]
  Diabetes without CVD (opportunistic screening) 0.920 - Cross-sectional study[15]
  Diabetes without CVD (systematic screening) 0.973 - Cross-sectional study[14]
  Diabetes with CHD 0.764 - Cross-sectional study[16]
  Diabetes with stroke 0.740 - Cross-sectional study[15]

表2

研究人群的基线特征"

Characteristics Men(n=138 041) Women(n=151 204) P value
Age/years, ${\bar x}$±s 52.57 ± 9.08 52.17 ± 8.70 < 0.001
Education year ≥ 9, n (%) 27 589 (20.91) 21 545 (14.86) < 0.001
Urban, n (%) 40 997 (29.75) 85 454 (29.59) 0.085
Smoker, n (%) 61 645 (45.06) 4 247 (2.84) < 0.001
Undetected diabetes, n (%) 11 093 (8.04) 8 612 (5.70) < 0.001
Overweight or obesity, n (%) 22 080 (64.20) 21 046 (60.77) < 0.001
BMI/(kg/m2), ${\bar x}$±s 23.43±2.71 23.02±2.93 < 0.001
Waist circumference/cm, ${\bar x}$±s 83.36±7.74 79.12±7.88 < 0.001
FPG/(mmol/L), ${\bar x}$±s 5.54±1.52 5.36±1.18 < 0.001
HbA1c/%, ${\bar x}$±s 6.02±1.27 5.90±1.02 < 0.001
SBP/mmHg, ${\bar x}$±s 132.95±16.83 132.29±18.09 < 0.001
DBP/mmHg, ${\bar x}$±s 81.76±10.30 82.17±9.90 < 0.001

表3

糖尿病筛查的不同策略对心血管病预防效果的比较"

Items Strategy 1 vs. Strategy 0 Strategy 2 vs. Strategy 0 Strategy 3 vs. Strategy 0 Strategy 2 vs. Strategy 1 Strategy 2 vs. Strategy 3
Total number for systematic screening 262 838 289 245 106 589 - -
Total number for treatment 10 551 11 330 5 086 - -
QALY gained 3 159 (2 813, 3 511) 3 342 (2 975, 3 699) 1 541 (1 348, 1 744) 183 (-304, 666) 1 801 (1 403, 2 190)
Life years gained 985 (836, 1 131) 1 002 (852, 1 145) 477 (371, 586) 17 (-166, 199) 525 (370, 674)
Cardiovascular events could be prevented 222 (180, 264) 227 (185, 271) 131 (98, 164) 5 (-45, 57) 96 (54, 141)
  CHD events could be prevented 64 (51, 78) 66 (52, 80) 46 (35, 58) 2 (-15, 18) 20 (5, 34)
  Stroke events could be prevented 158 (126, 190) 161 (129, 195) 85 (59, 110) 3 (-35, 44) 76 (44, 112)
All deaths could be prevented 162 (132, 191) 165 (135, 194) 79 (57, 102) 3 (-33, 39) 86 (55, 115)
NNS per cardiovascular event prevented 1 184 (994, 1 456) 1 274 (1 067, 1 564) 814 (649, 1 091) 90 (-197, 381) 460 (185, 724)
NNS per CHD event prevented 4 107 (3 358, 5 154) 4 383 (3 597, 5 554) 2 317 (1 852, 3 012) 276 (-815, 1 482) 2 066 (1 241, 3 132)
NNS per stroke event prevented 1 664 (1 385, 2 093) 1 797 (1 486, 2 236) 1 254 (969, 1 803) 133 (-317, 573) 543 (11, 946)
NNS per all death prevented 1 622 (1 376, 1 986) 1 753 (1 494, 2 139) 1 349 (1 041, 1 873) 131 (-250, 522) 404 (-60, 800)

图2

筛查方法的灵敏度变化对需筛查人数影响的单因素敏感性分析"

1 Visseren FLJ , Mach F , Smulders YM , et al. 2021 ESC guidelines on cardiovascular disease prevention in clinical practice[J]. Eur Heart J, 2021, 42 (34): 3227- 3337.
doi: 10.1093/eurheartj/ehab484
2 Liss D T , Uchida T , Wilkes CL , et al. General health checks in adult primary care: A review[J]. JAMA, 2021, 325 (22): 2294- 2306.
doi: 10.1001/jama.2021.6524
3 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)[J]. 中华糖尿病杂志, 2021, 13 (4): 315- 409.
4 American Diabetes Association Professional Practice Committee . Classification and diagnosis of diabetes: Standards of medical care in diabetes: 2022[J]. Diabetes Care, 2021, 45 (Suppl 1): S17- S38.
5 US Preventive Services Task Force . Screening for prediabetes and type 2 diabetes: US Preventive Services Task Force recommendation statement[J]. JAMA, 2021, 326 (8): 736- 743.
doi: 10.1001/jama.2021.12531
6 Wang L , Peng W , Zhao Z , et al. Prevalence and treatment of diabetes in China, 2013-2018[J]. JAMA, 2021, 326 (24): 2498- 2506.
doi: 10.1001/jama.2021.22208
7 Lin H , Tang X , Shen P , et al. Using big data to improve cardiovascular care and outcomes in China: A protocol for the Chinese electronic health records research in Yinzhou (CHERRY) study[J]. BMJ Open, 2018, 8 (2): e019698.
doi: 10.1136/bmjopen-2017-019698
8 Kaptoge S , Pennells L , De Bacquer D , et al. World Health Organization cardiovascular disease risk charts: Revised models to estimate risk in 21 global regions[J]. Lancet Global Health, 2019, 7 (10): e1332- e1345.
doi: 10.1016/S2214-109X(19)30318-3
9 Porta M , Curletto G , Cipullo D , et al. Estimating the delay between onset and diagnosis of type 2 diabetes from the time course of retinopathy prevalence[J]. Diabetes Care, 2014, 37 (6): 1668- 1674.
doi: 10.2337/dc13-2101
10 Gillies CL , Lambert PC , Abrams KR , et al. Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis[J]. BMJ, 2008, 336 (7654): 1180- 1185.
doi: 10.1136/bmj.39545.585289.25
11 Quan J , Li TK , Pang H , et al. Diabetes incidence and prevalence in Hong Kong, China during 2006-2014[J]. Diabetic Medicine, 2017, 34 (7): 902- 908.
doi: 10.1111/dme.13284
12 Bao Y , Ma X , Li H , et al. Glycated haemoglobin A1c for diagnosing diabetes in Chinese population: cross sectional epidemiological survey[J]. BMJ, 2010, 340, c2249.
doi: 10.1136/bmj.c2249
13 Stratton IM , Adler AI , Neil HA , et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): Prospective observational study[J]. BMJ, 2000, 321 (7258): 405- 412.
doi: 10.1136/bmj.321.7258.405
14 Zhang Y , Sun J , Pang Z , et al. The impact of new screen-detected and previously known type 2 diabetes on health-related quality of life: A population-based study in Qingdao, China[J]. Qual Life Res, 2014, 23 (8): 2319- 2326.
doi: 10.1007/s11136-014-0674-z
15 Zhang Y , Wu J , Chen Y , et al. EQ-5D-3L decrements by diabetes complications and comorbidities in China[J]. Diabetes Ther, 2020, 11 (4): 939- 950.
doi: 10.1007/s13300-020-00788-z
16 Pan CW , Sun HP , Zhou HJ , et al. Valuing health-related quality of life in type 2 diabetes patients in China[J]. Med Decis Making, 2016, 36 (2): 234- 241.
doi: 10.1177/0272989X15606903
17 Feldman AL , Griffin SJ , Fharm E , et al. Screening for type 2 diabetes: Do screen-detected cases fare better[J]. Diabetologia, 2017, 60 (11): 2200- 2209.
doi: 10.1007/s00125-017-4402-4
18 Simmons RK , Echouffo-Tcheugui JB , Sharp SJ , et al. Screening for type 2 diabetes and population mortality over 10 years (ADDITION-Cambridge): A cluster-randomised controlled trial[J]. Lancet, 2012, 380 (9855): 1741- 1748.
doi: 10.1016/S0140-6736(12)61422-6
19 Kahn R , Alperin P , Eddy D , et al. Age at initiation and frequency of screening to detect type 2 diabetes: A cost-effectiveness analysis[J]. Lancet, 2010, 375 (9723): 1365- 1374.
doi: 10.1016/S0140-6736(09)62162-0
20 Chung S , Azar KMJ , Baek M , et al. Reconsidering the age thresholds for type Ⅱ diabetes screening in the U.S.[J]. Am J Prev Med, 2014, 47 (4): 375- 381.
doi: 10.1016/j.amepre.2014.05.012
21 Wang Y , Zhao L , Gao L , et al. Health policy and public health implications of obesity in China[J]. Lancet Diabetes Endocrinol, 2021, 9 (7): 446- 461.
doi: 10.1016/S2213-8587(21)00118-2
22 Hoerger TJ , Hicks KA , Sorensen SW , et al. Cost-effectiveness of screening for pre-diabetes among overweight and obese U.S. adults[J]. Diabetes Care, 2007, 30 (11): 2874- 2879.
doi: 10.2337/dc07-0885
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[1] . 书讯[J]. 北京大学学报（医学版）, 2007, 39(3): 225 -328 .
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[3] 范少光. 向王志均院士学习[J]. 北京大学学报（医学版）, 2000, 32(4): 300 .
[4] 马红, 高妍, 杨建梅, 惠岩, 陈澜, 王淑凤. 人血浆瘦素水平与肥胖及血浆胰岛素水平的关系[J]. 北京大学学报（医学版）, 2001, 33(6): 559 -561 .
[5] 樊志荣, 卢义侠, 李长龄. 肾病综合征大鼠肾小球血管紧张素Ⅱ受体结合的变化及机制[J]. 北京大学学报（医学版）, 2001, 33(6): 565 .
[6] 范蓉, 张成飞, 高岩, 李斌斌, 王晶. 核因子-κB受体活化因子配体和骨保护素在慢性根尖周炎病损组织中的表达[J]. 北京大学学报（医学版）, 2008, 40(1): 39 -42 .
[7] 章巍, 王广发, 张红, 牟向东, 金哲. 用内科胸腔镜进行滑石粉胸膜固定术治疗恶性胸腔积液27例临床分析[J]. 北京大学学报（医学版）, 2008, 40(6): 600 -602 .
[8] 田清平, 冯雪茹, 庞永正, 唐朝枢, 刘梅林. 血浆皮质醇激素抑制素水平与冠心病的关系[J]. 北京大学学报（医学版）, 2009, 41(5): 537 -540 .
[9] 陈磊, 张豪, 冯海兰, 张凤军. 正常受试者单侧咀嚼运动中的牙合接触模式[J]. 北京大学学报（医学版）, 2009, 41(1): 90 -94 .
[10] 赵英芳, 田新霞, 杜娟, 张云岗, 刘松年, 林杰, 郑杰. 胃癌中p16INK4a和RB基因甲基化状况及其表达[J]. 北京大学学报（医学版）, 2003, 35(4): 382 -385 .
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