Prevalence and Risk Factors of Comorbidities among Hypertensive Patients in China

Hypertension is a severe threat to human being’s health due to its association with many comorbidities. Many research works have explored hypertension’s prevalence and treatment. However, few considered impact of patient’s socioeconomic status and geographical disparities. We intended to fulfill that research gap by analyzing the association of the prevalence of hypertension and three important comorbidities with various socioeconomic and geographical factors. We also investigated the prevalence of those comorbidities if the patient has been diagnosed with hypertension. We obtained a large collection of medical records from 29 hospitals across China. We utilized Bayes’ Theorem, Pearson’s chi-squared test, univariate and multivariate regression methods and geographical detector methods to analyze the association between disease prevalence and risk factors. We first attempted to quantified and analyzed the spatial stratified heterogeneity of the prevalence of hypertension comorbidities by q-statistic using geographical detector methods. We found that the demographic and socioeconomic factors, and hospital class and geographical factors would have an enhanced interactive influence on the prevalence of hypertension comorbidities. Our findings can be leveraged by public health policy makers to allocate medical resources more effectively. Healthcare practitioners can also be benefited by our analysis to offer customized disease prevention for populations with different socioeconomic status.

Hypertension is a severe threat to human being’s health due to its association with many comorbidities. Many research works have explored hypertension’s prevalence and treatment. However, few considered impact of patient’s socioeconomic status and geographical disparities. We intended to fulfill that research gap by analyzing the association of the prevalence of hypertension and three important comorbidities with various socioeconomic and geographical factors. We also investigated the prevalence of those comorbidities if the patient has been diagnosed with hypertension. We obtained a large collection of medical records from 29 hospitals across China. We utilized Bayes’ Theorem, Pearson’s chi-squared test, univariate and multivariate regression methods and geographical detector methods to analyze the association between disease prevalence and risk factors. We first attempted to quantified and analyzed the spatial stratified heterogeneity of the prevalence of hypertension comorbidities by q-statistic using geographical detector methods. We found that the demographic and socioeconomic factors, and hospital class and geographical factors would have an enhanced interactive influence on the prevalence of hypertension comorbidities. Our findings can be leveraged by public health policy makers to allocate medical resources more effectively. Healthcare practitioners can also be benefited by our analysis to offer customized disease prevention for populations with different socioeconomic status.