基于健康生态学模型的福建省居民高血压患病的影响因素
Factors associated with the prevalence of hypertension among residents in Fujian Province:based on the health ecological model
常华靖 1林晨晗 2黄婧如 3林黛茜 4潘忞 5陈纯娴 2许昌声 6谢良地 5韩英5
作者信息
- 1. 福建医科大学公共卫生学院,福建福州 350122
- 2. 福建医科大学第一临床学院
- 3. 福建中医药大学中西结合学院
- 4. 福建医科大学附属第一医院综合病房一科
- 5. 福建医科大学附属第一医院综合病房二科;福建医科大学附属第一医院滨海院区国家区域医疗中心老年科;福建省高血压研究所;福建省老年高血压疾病临床研究中心;国家老年疾病临床医学研究中心福建分中心
- 6. 福建省高血压研究所
- 折叠
摘要
目的 基于健康生态学模型,探究福建省成人高血压患病的影响因素,为制定高血压的干预策略提供科学依据.方法 2020年8月至2021年4月,采用多阶段分层随机抽样方法抽取福建省18岁及以上常住居民9 725人,通过问卷调查、体格检查和实验室检查收集相关资料,并筛选其中健康生态学模型涉及的个人特质层、行为特征层、人际关系层、生活和工作条件层、政策环境层5个维度的变量,采用单因素及多因素logistic回归分析探究影响福建省居民高血压患病的影响因素;构建可视化列线图预测模型,并通过受试者工作特征(ROC)曲线的曲线下面积(AUC)、校准曲线和决策曲线对其预测能力进行验证与评估.结果 福建省18岁及以上居民高血压患病率为34.2％,年龄标准化患病率为35.9％.多因素logistic回归分析结果显示,个人特质层中男性、年龄较大、超重或肥胖、中心性肥胖、高血压家族史、糖尿病、血脂异常、高尿酸血症与高血压患病呈高风险相关;行为特征层中无体力活动、饮酒与高血压患病呈高风险相关;人际关系层中现居地为农村与高血压患病呈高风险相关;生活和工作条件层中受教育程度较高、职业为非农民、自评经济状况较高与高血压患病呈低风险相关(均P＜0.05).依据以上因素构建列线图,内部验证显示训练集和验证集的AUC(95％CI)分别为0.835(0.826～0.844)和0.843(0.825～0.860),校准曲线显示该模型校准度较好,决策曲线验证该模型具有临床适用性.结论 福建省居民高血压患病率仍处于较高水平,健康生态学模型的多个维度中均有成人高血压患病的影响因素,本研究构建的高血压患病列线图预测模型可帮助医疗卫生工作者早期发现高风险人群,从多维度、个人防治与环境防治相结合角度制定有针对性的高血压综合防治策略与措施.
Abstract
Objective To explore the influencing factors of adult hypertension in Fujian Province based on the health ecological model(HEM)and to provide the scientific basis for formulating intervention strategies for hypertension.Methods From August 2020 to April 2021,a multi-stage stratified random sampling method was used to select 9 725 permanent residents aged 18 and above in Fujian Province.Individual information was collected through ques-tionnaires,physical examinations,and laboratory examinations,and the variables involved in health ecological model were filtrated into the five dimensions:personal traits,behavioral characteristics,interpersonal relationships,living and working conditions,and policy environment.Single-factor and multi-factor logistic regression analyses were used to explore the influencing factors of hypertension among residents in Fujian Province.A visual nomogram prediction model was constructed and its prediction ability was verified and evaluated through the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,calibration curve,and decision curve.Results The preva-lence of hypertension among residents aged 18 and above in Fujian Province was 34.2％,and the age-standardized prevalence rate was 35.9％.The results of multivariable logistic regression analysis showed that in the layer of per-sonal traits,males,older age,overweight or obesity,central obesity,family history of hypertension,diabetes,dys-lipidemia,and hyperuricemia were associated with a higher risk of hypertension;in the layer of behavioral character-istics,non-physical activity and drinking alcohol were associated with a higher risk of hypertension;in the layer of interpersonal relationship,current residence in a rural area was associated with a higher risk of hypertension;in the layer of living and working conditions,higher education,non-farmer,and higher self-assessed economic status were associated with a lower risk of hypertension(all P＜0.05).A nomogram was constructed based on the above fac-tors.Internal validation showed that the AUC values of the training dataset and validation dataset were 0.835(95％CI:0.826-0.844)and 0.843(95％CI:0.825-0.860)respectively.The calibration curve showed that the model had good calibration,and the decision curve verified that the model had clinical applicability.Conclusions The prevalence of hypertension among residents in Fujian Province is still at a high level.Most dimen-sions of HEM contain factors are related to the prevalence of adult hypertension.The nomogram prediction model for hypertension constructed in this study can help medical staff identify high-risk groups at an early stage and for-mulate targeted comprehensive prevention and treatment strategies for hypertension from a multi-dimensional per-spective that combines personal prevention and environmental prevention.
关键词
高血压/患病率/影响因素/健康生态学模型/多因素logistic回归/列线图Key words
hypertension/prevalence/influencing factors/health ecological model/multi-factor logistic re-gression/nomogram引用本文复制引用
基金项目
国家卫生健康委员会项目(NHC2020-609)
福建省财政补助卫生专项(BPB-HY2021)
出版年
2024
NETL
NSTL
万方数据