高血压患者行为危险因素聚集对血压控制的影响

目的 探讨高血压患者行为危险因素聚集对血压控制的影响,为提升血压控制率制定具有成本效益的干预策略提供科学依据。 方法 采用分层多阶段随机抽样方法对云南省8个县(区)≥18岁居民开展问卷调查和体格检查,将其中确诊为高血压的患者作为研究对象。计算存在不同危险因素(吸烟、饮酒、体力活动不足、蔬菜水果摄入不足、静态行为时间过长)个数患者的高血压控制率,多因素非条件logistic回归分析用于检验高血压控制率与行为危险因素聚集的关系。采用敏感性分析探讨其稳健性。 结果 共2 906例高血压患者纳入分析,血压控制率为15.7%。患者中存在≥2项行为危险因素聚集者占比达57.5%,存在0、1、2、3、≥4个行为危险因素的高血压患者血压控制率分别为18.9%、17.5%、15.7%、12.7%、7.2%。多因素非条件logistic回归分析结果显示,高血压控制率随着行为危险因素聚集个数增加而减小(P_趋势<0.05),与存在0个行为危险因素相比,患者存在≥4个行为危险因素会影响血压控制率(OR =2.928,95%CI:1.229～6.976)。敏感性分析结果显示行为危险因素聚集与高血压控制率的关联及趋势稳定。结论 高血压患者中行为危险因素聚集会影响血压控制,应将存在多种危险因素的患者作为干预的重点人群,以期提升血压控制率。

Effects of behavioral risk factor clustering on blood pressure control in hypertensive patients

Objective To assess the relationship between control of blood pressure and clustering of behavioral risk factors among hypertensive patients, and to provide a scientific basis for developing cost-effective intervention strategies to improve rates of blood pressure control. Methods A stratified multistage random sampling method was used to conduct questionnaires and physical examinations on residents aged ≥18 years in eight counties(districts)of Yunnan Province. Patients diagnosed with hypertension were included in the study. The control rates of hypertension of patients with different numbers of risk factors including smoking, alcohol consumption, insufficient physical activity, insufficient intake of vegetables and fruits, and excessive sedentary time were calculated. Multivariate logistic regression models were used to test the relationship between hypertension control and the clustering of behavioral risk factors. Sensitivity analysis was used to explore the robustness of the model. Results There were 2 906 hypertensive patients included in the study, and the control rate of hypertension was 15.7%. Patients with 2 or more behavioral risk factors accounted for 57.5%, and the control rates of hypertensive patients with the presence of 0, 1, 2, 3, and 4 behavioral risk factors were 18.9%, 17.5%, 15.7%, 12.7% and 7.2%, respectively. The results of multivariate logistic regression analysis showed that the rate of hypertension control decreased with the increase in the number of aggregated behavioral risk factors(P_trend<0.05). Compared with patients with no behavioral risk factor, the presence of ≥4 behavioral risk factors in patients affected the rate of blood pressure control(OR=2.928, 95%CI:1.229-6.976). The results of sensitivity analysis showed that the association between aggregation of behavioral risk factors and rate of hypertension control was stable. Conclusion The clustering of behavioral risk factors among hypertensive patients can affect blood pressure control, and patients with multiple risk factors should be targeted as a priority population for intervention with the aim of improving the rate of blood pressure control.