独立自然村老年高血压人群流行病学数据获取方法的分析

Background: In China, imperfections in the early detection, real-time monitoring and effective control of chronic diseases make it impossible to accurately obtain the epidemiological characteristics of hypertension and other chronic diseases, negatively influencing chronic disease management and residents' health. Artificial intelligence(AI)technologies significantly improve health records management. Exploring methods that can accurately obtain epidemiological characteristics is conducive to the management of chronic diseases. Objective: To compare the reliability of three methods used for accurately obtaining epidemiological data of hypertensive elderly people in an isolated natural village, providing a reference for bettering the prevention and treatment of hypertension in primary care. Methods: This study was conducted in an isolated natural village with a population of 2 603 as of June 2019 in Menjiazhuang Township, Jizhou District, Hengshui City, Hebei Province. For obtaining the epidemiological data of hypertension in villagers aged over 65 years, three methods were used, with different numbers of participants. The first method was by searching the data stored in the local health information management system, the number of villagers aged over 65 years was determined〔n=516, accounting for 20.76% of the total villagers registered by the village physician since January 2016 (n=2 485, accounting for 95.47% of the total village residents)〕, and the number of diagnosed hypertensive cases registered was also collected, then the hypertension prevalence was estimated. The second method was by use of a household survey(consisting of completing a blood pressure questionnaire and blood pressure measurement) conducted between June and July 2019, the number of respondents was determined as 416〔accounting for 80.62% of the total surveyed villagers aged over 65 years(n=516)〕, and the hypertension prevalence, awareness level, treatment rate and control rate of hypertension in the respondents were obtained according to the survey results. And the third method was searching the health management data in an AI-assisted diagnosis and treatment system, which has been used since December 2017, and had stored 9 856 visits of 2 460 villagers as of June 2019, including 337 villagers aged over 65 years. By analyzing the healthcare-seeking data of older villagers, the hypertension prevalence, awareness level, treatment rate and control rate of hypertension were obtained. The merits and limitations of these three methods were compared. Results: No significant differences were found in the mean age and sex ratio across the three participant groups(P>0.05). According to the statistics of hypertension registration, the prevalence of hypertension was 43.80%(226/516). According to the household survey, the prevalence of hypertension was 75.24%(313/416);260 cases had a history of definite hypertension, and 53 were newly found with hypertension;the awareness rate of hypertension was 83.07%(260/313);238 received anti-hypertensive medication treatment in the past two weeks, with a treatment rate of 76.04%(238/313);74 had a blood pressure reading of<140/90 mm Hg(1 mm Hg =0.133 kPa), with a hypertension control rate of 23.64%(74/313). According to the analysis of the data in the AI-assisted diagnosis and treatment system, the hypertension prevalence was 52.23%(176/337);172 had a hypertension history;the awareness rate of hypertension was 97.73%(172/176);168 received anti-hypertensive medication treatment in the past two weeks, with a treatment rate of 96.45%(168/176);118 had blood pressure measurement records, and 32 of them had a blood pressure reading of<140/90 mm Hg, with a hypertension control rate of 27.12%(32/118). Conclusion: The use of AI-assisted diagnosis and treatment system and household surveys can both better obtain the prevalence rate, awareness rate, treatment rate and other epidemiological characteristics of hypertension. In particular, using the AI-assisted diagnosis and treatment system can obtain data more comprehensively and dynamically, which facilitates the collection of epidemiological data and chronic disease management.