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;
TAO Chenglin, Email: taotao6906@163.com
Hypertension; Middle-aged and elderly population; Prediction model; Basic public health service project
10.7507/1672-2531.202401132
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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.