Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project
Objective To explore factors affecting blood pressure control in chronic disease patients in China′s national basic public health service chronic disease patient management program and to find their relationships with Bayesian network(BN) model, in order to provide a scientific basis for comprehensi...
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Institute of Basic Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences / Peking Union Medical College.
2025-07-01
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| Series: | Jichu yixue yu linchuang |
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| Online Access: | https://journal11.magtechjournal.com/Jwk_jcyxylc/fileup/1001-6325/PDF/1001-6325-2025-45-7-926.pdf |
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| _version_ | 1849710793915891712 |
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| author | LI Danying, GUO Xiaojing, ZHU Xiaolei, SI Xiang, ZHANG Xiaochang, WAN Xia |
| author_facet | LI Danying, GUO Xiaojing, ZHU Xiaolei, SI Xiang, ZHANG Xiaochang, WAN Xia |
| author_sort | LI Danying, GUO Xiaojing, ZHU Xiaolei, SI Xiang, ZHANG Xiaochang, WAN Xia |
| collection | DOAJ |
| description | Objective To explore factors affecting blood pressure control in chronic disease patients in China′s national basic public health service chronic disease patient management program and to find their relationships with Bayesian network(BN) model, in order to provide a scientific basis for comprehensive hypertension management. Methods 5 577 Hypertensive patients were selected from eight provinces(including autonomous regions) covering eastern, central and western parts of China during a survey from 2021 to 2022. Researchers collected individual and community-management data to screen influencing factors by Logistic regression, and to describe factor dependencies and to identify key determinants of blood pressure control with BN in. blood pressure control. Results Logistic regression revealed that urban/rural status, education, alcohol use, exercise, overweight/obesity and community-doctor advice on salt reduction, smoking cessation were significantly associated with blood pressure control(P＜0.05). The BN model identified 22 directed edges showing that urban residence and good hypertension knowledge were more correlated with better control, while community-doctor management and services directly affected patient lifestyle habits but not blood pressure control. Conclusions Research should focus more on urban-rural disparities and hypertension education. Additionally, improving patient habits and community-doctor services is essential for better blood pressure control. |
| format | Article |
| id | doaj-art-dd10af08cf934d6ca9fc5bff5e51fce0 |
| institution | DOAJ |
| issn | 1001-6325 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Institute of Basic Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences / Peking Union Medical College. |
| record_format | Article |
| series | Jichu yixue yu linchuang |
| spelling | doaj-art-dd10af08cf934d6ca9fc5bff5e51fce02025-08-20T03:14:49ZzhoInstitute of Basic Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences / Peking Union Medical College.Jichu yixue yu linchuang1001-63252025-07-0145792693210.16352/j.issn.1001-6325.2025.07.0926Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service ProjectLI Danying, GUO Xiaojing, ZHU Xiaolei, SI Xiang, ZHANG Xiaochang, WAN Xia01. State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences CAMS, School of Basic Medicine PUMC, Beijing 100005;;2. Department of Chronic Disease and Aging Health Management,Chinese Center for Disease Control and Prevention, Beijing 100050, ChinaObjective To explore factors affecting blood pressure control in chronic disease patients in China′s national basic public health service chronic disease patient management program and to find their relationships with Bayesian network(BN) model, in order to provide a scientific basis for comprehensive hypertension management. Methods 5 577 Hypertensive patients were selected from eight provinces(including autonomous regions) covering eastern, central and western parts of China during a survey from 2021 to 2022. Researchers collected individual and community-management data to screen influencing factors by Logistic regression, and to describe factor dependencies and to identify key determinants of blood pressure control with BN in. blood pressure control. Results Logistic regression revealed that urban/rural status, education, alcohol use, exercise, overweight/obesity and community-doctor advice on salt reduction, smoking cessation were significantly associated with blood pressure control(P＜0.05). The BN model identified 22 directed edges showing that urban residence and good hypertension knowledge were more correlated with better control, while community-doctor management and services directly affected patient lifestyle habits but not blood pressure control. Conclusions Research should focus more on urban-rural disparities and hypertension education. Additionally, improving patient habits and community-doctor services is essential for better blood pressure control.https://journal11.magtechjournal.com/Jwk_jcyxylc/fileup/1001-6325/PDF/1001-6325-2025-45-7-926.pdfhypertension|bayesian network|national basic public health service project|hypertension influencing factor |
| spellingShingle | LI Danying, GUO Xiaojing, ZHU Xiaolei, SI Xiang, ZHANG Xiaochang, WAN Xia Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project Jichu yixue yu linchuang hypertension|bayesian network|national basic public health service project|hypertension influencing factor |
| title | Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project |
| title_full | Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project |
| title_fullStr | Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project |
| title_full_unstemmed | Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project |
| title_short | Identifying key factors of hypertension control using Bayesian networks in the 2021—2022 National Basic Public Health Service Project |
| title_sort | identifying key factors of hypertension control using bayesian networks in the 2021 2022 national basic public health service project |
| topic | hypertension|bayesian network|national basic public health service project|hypertension influencing factor |
| url | https://journal11.magtechjournal.com/Jwk_jcyxylc/fileup/1001-6325/PDF/1001-6325-2025-45-7-926.pdf |
| work_keys_str_mv | AT lidanyingguoxiaojingzhuxiaoleisixiangzhangxiaochangwanxia identifyingkeyfactorsofhypertensioncontrolusingbayesiannetworksinthe20212022nationalbasicpublichealthserviceproject |