摘要
目的探讨昆山高新区2016年高血压患病率及其影响因素,为临床管理提供参考。方法于2016年在昆山高新区对18~69岁常住居民进行问卷调查与体格检查,共调查1 206人。以Logistic回归分析诸因素与高血压患病关联;用2010年第六次全国人口普查中年龄构成计算年龄标化高血压患病率。结果高血压粗患病率为16.67%,男性和女性患病率差异无统计学意义(18.54%vs.14.89%,χ~2=2.89,P=0.089 1);年龄标化患病率在总人群、男性和女性人群分别为22.43%、23.83%和21.15%。调整混杂因素之后,多因素分析提示,年龄每递增1岁(OR=1.07,95%CI:1.05~1.08)、初中以下文化程度(OR=1.70,95%CI:1.12~2.58)、有高血压家族史(OR=1.57,95%CI:1.11~2.24)、脉压差每递增1个分位数(OR=1.46,95%CI:1.22~1.73)、肥胖(OR=2.89,95%CI:1.80~4.64)以及腰臀比每递增1个分位数(OR=1.57,95%CI:1.09~2.28)与高血压患病有关(P<0.05)。结论昆山市2016年18~69岁常住居民中高血压患病率较高,急需加强对可改变行为因素的干预,特别是肥胖对象。
Objective To examine the prevalence and associated factors of hypertension in Kunshan HiTech Zone, Jiangsu province, in 2016. Methods Totally 1 206 residents aged 18-69 in Kunshan Hitech Zone were investigated with questionnaire and physical examinations. Logistic regession was used to examine the factors of hypertension, and the sixth Chinese population census in 2010 was used to calculate age-standardized prevalance of hypertension. Results The prevalence of hypertension was16.67% in the total population, and there was no significant difference between male and female(18.54% vs. 14.89%, χ~2=2.89, P=0.089 1). Age-adjusted prevalence was 22.43% in the total population, 23.83% in the male population and 21.15% in the female population. After adjusting confounding factors, advaneced age(OR=1.07, 95% CI: 1.05-1.08), low educational levels(OR=1.70, 95% CI:1.12-2.58), family history of hypertension(OR=1.57, 95%CI: 1.11-2.24), advanced pulse pressure(OR=1.46, 95%CI: 1.22-1.73), obesity(OR=2.89, 95%CI: 1.80-4.64) and higher waist-to-hips ratio(OR=1.57, 95%CI: 1.09-2.28) were associated with prevalence of hypertension. Conclusion Hypertension is highly prevalent among the permanent residents aged 18-69. Therefore, necessary actions including detection and prevention measures should be taken for the modifiable behavior factors, especially in the obesity population.
出处
《慢性病学杂志》
2018年第4期383-386,共4页
Chronic Pathematology Journal
基金
昆山市社会发展科技专项(KS1525)
关键词
高血压
患病率
多因素分析
肥胖
Hypertension
Prevalence
Logistic regression
Obesity