Research on the application of basic public health service database in the prediction model of hypertension among middle-aged and elderly people in China_Chinese Journal of Evidence-Based Medicine

Authors：

TENG Yilin ¹ , SHEN Minghui ² , YUAN Lu ³ , LI Ying ¹ , LI Xingyue ¹ , YANG Huifang ¹ , FENG Wanting ¹ , LYU Liang ¹ ,  TAO Chenglin ¹ , LONG Lu ¹ , LI Jiayuan ¹

1. West China School of Public Health and West China Fourth Hospital of Sichuan University, Chengdu 610041, P. R. China;
2. Health Information Center of Sichuan Province, Chengdu 610041, P. R. China;
3. Community Healthcare Center of Hongguang Street, Pidu District, Chengdu 611730, P. R. China;

Corresponding author：

TAO Chenglin, Email: taotao6906@163.com

Keywords：

Hypertension; Middle-aged and elderly population; Prediction model; Basic public health service project

DOI：

10.7507/1672-2531.202401132

Video：

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Objective To establish a hypertension prediction model for middle-aged and elderly people in China and to use the basic public health service database for performance validation. Methods The literature related to hypertension was retrieved from the internet. Using meta-analysis to assess the effect value of influencing factors. Statistically significant factors, which were also combined in the database, were extracted as the predictors of the models. The predictors’ effect values were logarithmarithm-transformed as the parameters of the Logit function model and the risk score model. Participants who were never diagnosed with hypertension at the physical examination of health service project of Hongguang Town Health Center in Pidu District of Chengdu from January 1, 2017, to January 1, 2022, were considered as the external validation group. Results A total of 15 original studies were involved in the meta-analysis and 11 statistically significant influencing factors for hypertension were identified, including age, female, systolic blood pressure, diastolic blood pressure, BMI, central obesity, triglyceride, smoking, drinking, history of diabetes and family history of hypertension. Of 4997 qualified participants, 684 individuals were identified with hypertension during the five-years follow-up. External validation indicated an AUC of 0.571 for the Logit function model and an AUC of 0.657 for the risk score model. Conclusion In this study, we developed two different prediction models based on the results of meta-analysis. National basic public health service database is used to verify the models. The risk score model has a better prediction performance, which may help quickly stratify the risk class of the community crowd and strengthen the primary-level assistance system.

Citation： TENG Yilin, SHEN Minghui, YUAN Lu, LI Ying, LI Xingyue, YANG Huifang, FENG Wanting, LYU Liang, TAO Chenglin, LONG Lu, LI Jiayuan. Research on the application of basic public health service database in the prediction model of hypertension among middle-aged and elderly people in China. Chinese Journal of Evidence-Based Medicine, 2024, 24(7): 755-761. doi: 10.7507/1672-2531.202401132 Copy

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2.	Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study. J Am Coll Cardiol, 2020, 76(25): 2982-3021.
3.	Luo Y, Xia F, Yu X, et al. Long-term trends and regional variations of hypertension incidence in China: a prospective cohort study from the China health and nutrition survey, 1991-2015. BMJ Open, 2021, 11(1): e042053.
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5.	Deng X, Hou H, Wang X, et al. Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China. Elife, 2021, 10: e66419.
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18.	Staessen JA, Ginocchio G, Thijs L, et al. Conventional and ambulatory blood pressure and menopause in a prospective population study. J Hum Hypertens, 1997, 11(8): 507-514.
19.	Song L, Shen L, Li H, et al. Age at natural menopause and hypertension among middle-aged and older Chinese women. J Hypertens, 2018, 36(3): 594-600.
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21.	Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-W73.
22.	WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health, 2019, 7(10): e1332-e1345.
23.	Feng Y, Kang K, Xue Q, et al. Value of plasma homocysteine to predict stroke, cardiovascular diseases, and new-onset hypertension: a retrospective cohort study. Medicine (Baltimore), 2020, 99(34): e21541.
24.	Fujii M, Ohnishi H, Saitoh S, et al. The combination of abdominal obesity and high-sensitivity C-reactive protein predicts new-onset hypertension in the general Japanese population: the Tanno-Sobetsu study. Hypertens Res, 2015, 38(6): 426-432.
25.	Feng Y, Yang X, Li Y, et al. Metabolic score for visceral fat: a reliable indicator of visceral obesity for predicting risk for hypertension. Nutrition, 2022, 93: 111443.
26.	李国奇, 刘静, 王薇, 等. 中国35−64岁人群15年高血压发生风险预测研究. 中华高血压杂志, 2014, 22(10): 1000.
27.	吕鸿杰. 原发性高血压发病概率预测模型评价研究. 职业与健康, 2011, 27(18): 2051-2053.
28.	Chen Y, Wang C, Liu Y, et al. Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study. J Hum Hypertens, 2016, 30(12): 794-800.

1. Roth GA, Mensah GA, Fuster V. The global burden of cardiovascular diseases and risks: a compass for global action. J Am Coll Cardiol, 2020, 76(25): 2980-2981.
2. Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study. J Am Coll Cardiol, 2020, 76(25): 2982-3021.
3. Luo Y, Xia F, Yu X, et al. Long-term trends and regional variations of hypertension incidence in China: a prospective cohort study from the China health and nutrition survey, 1991-2015. BMJ Open, 2021, 11(1): e042053.
4. 张梅, 吴静, 张笑, 等. 2018年中国成年居民高血压患病与控制状况研究. 中华流行病学杂志, 2021, 42(10): 1780-1789.
5. Deng X, Hou H, Wang X, et al. Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China. Elife, 2021, 10: e66419.
6. Lim NK, Lee JY, Lee JY, et al. The role of genetic risk score in predicting the risk of hypertension in the Korean population: Korean genome and epidemiology study. PLoS One, 2015, 10(6): e0131603.
7. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
8. Parikh NI, Pencina MJ, Wang TJ, et al. A risk score for predicting near-term incidence of hypertension: the Framingham heart study. Ann Intern Med, 2008, 148(2): 102-110.
9. D'Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation, 2008, 117(6): 743-753.
10. 国家卫生计生委. 国家基本公共卫生服务规范(第3版). 北京: 原子能出版社, 2017.
11. 中国高血压防治指南(2018年修订版). 中国心血管杂志, 2019, 24(1): 24-56.
12. 国家卫生和计划生育委员会. 成人体重判定. 2013.
13. Williams C, Sahagún BE, Su MK. Biostatistics and epidemiology principles for the toxicologist: the "testy" test characteristics part ii: positive predictive value and negative predictive value. J Med Toxicol, 2023, 19(3): 298-302.
14. Wu J, Li T, Song X, et al. Prevalence and distribution of hypertension and related risk factors in Jilin province, China 2015: a cross-sectional study. BMJ Open, 2018, 8(3): e020126.
15. Sugiura T, Takase H, Machii M, et al. Central blood pressure predicts the development of hypertension in the general population. Hypertens Res, 2020, 43(11): 1301-1308.
16. Sun JY, Ma YX, Liu HL, et al. High waist circumference is a risk factor of new-onset hypertension: evidence from the China health and retirement longitudinal study. J Clin Hypertens (Greenwich), 2022, 24(3): 320-328.
17. Prabhushankar R, Krueger C, Manrique C. Membrane estrogen receptors: their role in blood pressure regulation and cardiovascular disease. Curr Hypertens Rep, 2014, 16(1): 408.
18. Staessen JA, Ginocchio G, Thijs L, et al. Conventional and ambulatory blood pressure and menopause in a prospective population study. J Hum Hypertens, 1997, 11(8): 507-514.
19. Song L, Shen L, Li H, et al. Age at natural menopause and hypertension among middle-aged and older Chinese women. J Hypertens, 2018, 36(3): 594-600.
20. Wang Z, Chen Z, Zhang L, et al. Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation, 2018, 137(22): 2344-2356.
21. Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-W73.
22. WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health, 2019, 7(10): e1332-e1345.
23. Feng Y, Kang K, Xue Q, et al. Value of plasma homocysteine to predict stroke, cardiovascular diseases, and new-onset hypertension: a retrospective cohort study. Medicine (Baltimore), 2020, 99(34): e21541.
24. Fujii M, Ohnishi H, Saitoh S, et al. The combination of abdominal obesity and high-sensitivity C-reactive protein predicts new-onset hypertension in the general Japanese population: the Tanno-Sobetsu study. Hypertens Res, 2015, 38(6): 426-432.
25. Feng Y, Yang X, Li Y, et al. Metabolic score for visceral fat: a reliable indicator of visceral obesity for predicting risk for hypertension. Nutrition, 2022, 93: 111443.
26. 李国奇, 刘静, 王薇, 等. 中国35−64岁人群15年高血压发生风险预测研究. 中华高血压杂志, 2014, 22(10): 1000.
27. 吕鸿杰. 原发性高血压发病概率预测模型评价研究. 职业与健康, 2011, 27(18): 2051-2053.
28. Chen Y, Wang C, Liu Y, et al. Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study. J Hum Hypertens, 2016, 30(12): 794-800.

Chinese Journal of Evidence-Based Medicine

Research on the application of basic public health service database in the prediction model of hypertension among middle-aged and elderly people in China

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