Spatial analysis of gender variation in the prevalence of hypertension among the middle-aged and elderly population in Zhejiang Province, China. (December 2016)

• Record Type: Journal Article
• Authors: Xu, Li; Lai, Dejian; Fang, Ya
• Is Part Of: BMC public health. Volume 16:Number 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Shared Component Model (SCM) -- Hypertension -- Middle-aged and elderly -- Spatial analysis -- Gender variation; Public health -- Periodicals; 362.105
• DOI: 10.1186/s12889-016-3121-y
• Languages: English
• ISSNs: 1471-2458

Abstract

Abstract Background Previous studies have shown that there may be gender disparities in the prevalence of hypertension; however, these studies do not address the spatial information contained in the sample which may limit the analytical results. Our study extends the existing Shared Component Model (SCM) and compares its utility with a logistic regression model to evaluate the significance of spatial information for identifying risk factors for hypertension and other non-rare diseases. Methods A total of 1267 residents aged 45 years of age and over were included in our study, of which 48.1 % were males. The overall prevalence of hypertension was 33.2 %, with females experiencing a higher prevalence than males (35.5 % vs. 30.6 %). The research variables included body mass index (BMI), Waist -to-Height Ratio (WHtR), smoking status, alcohol consumption etc. The extended SCM is employed to investigate regional gender variations in the risk of hypertension and assess the gender variation in the middle-aged and elderly populations of Zhejiang Province in eastern China and then its performance is compared with that of a traditional multiple logistic regression model. Results Our SCM analysis determined that the spatial pattern of hypertension risk for the middle-aged and elderly populations of Zhejiang Province in eastern China is quite different for males and females. Furthermore, Waist -to-Height Ratio (WHtR) continues to be a simple and effective predictor of hypertension risk for males at the regional level. Conclusions We believe that the extended SCM spatial model is a useful tool for identifying risk factors at the regional level.