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Journal of Peking University (Health Sciences) ›› 2022, Vol. 54 ›› Issue (3): 450-457. doi: 10.19723/j.issn.1671-167X.2022.03.009

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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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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

CLC Number: 

  • R181.3+8

Figure 1

Markov model state-transition diagram of diabetes screening for preventing cardiovascular disease S, status; CVD, cardiovascular disease; CHD, coronary heart disease."

Table 1

Parameters and data sources in the Markov model"

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]

Table 2

Baseline characteristics of study population"

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

Table 3

Comparisons of effectiveness for preventing cardiovascular disease by different strategies of diabeles screening"

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)

Figure 2

One-way sensitivity analyses on number needed to screen per cardiovascular disease event prevented by different sensitivity of screening test Strategy 1, systematic diabetes screening in Chinese adults aged 40-70 years; Strategy 2, systematic diabetes screening in Chinese adults aged 35 to 70 years; Strategy 3, systematic diabetes screening in Chinese adults aged 35-70 years with overweight or obesity (BMI ≥ 24 kg/m2). FPG, fasting plasma glucose; HbA1c, glycated hemoglobin."

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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