Prevalence and risk factors of comorbidities among hypertensive patients in china

Jiaojiao Wang, Jian James Ma, Jiaqi Liu, Dajun Zeng, Cynthia Song, Zhidong Cao

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)201-212
Number of pages12
JournalInternational Journal of Medical Sciences
Volume14
Issue number3
DOIs
StatePublished - 2017
Externally publishedYes

Fingerprint

Comorbidity
China
Hypertension
Social Class
Bayes Theorem
Public Policy
Health Policy
Administrative Personnel
Research
Medical Records
Public Health
Demography
Delivery of Health Care
Health
Population
Therapeutics

Keywords

  • Bayes’ Theorem
  • Comorbidity
  • Geographical detector
  • Hypertension
  • Prevalence
  • Public health
  • Risk factor

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Prevalence and risk factors of comorbidities among hypertensive patients in china. / Wang, Jiaojiao; Ma, Jian James; Liu, Jiaqi; Zeng, Dajun; Song, Cynthia; Cao, Zhidong.

In: International Journal of Medical Sciences, Vol. 14, No. 3, 2017, p. 201-212.

Research output: Contribution to journalArticle

Wang, Jiaojiao ; Ma, Jian James ; Liu, Jiaqi ; Zeng, Dajun ; Song, Cynthia ; Cao, Zhidong. / Prevalence and risk factors of comorbidities among hypertensive patients in china. In: International Journal of Medical Sciences. 2017 ; Vol. 14, No. 3. pp. 201-212.
@article{1bb5a07ed6694fab8c6030dd066f02f8,
title = "Prevalence and risk factors of comorbidities among hypertensive patients in china",
abstract = "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.",
keywords = "Bayes’ Theorem, Comorbidity, Geographical detector, Hypertension, Prevalence, Public health, Risk factor",
author = "Jiaojiao Wang and Ma, {Jian James} and Jiaqi Liu and Dajun Zeng and Cynthia Song and Zhidong Cao",
year = "2017",
doi = "10.7150/ijms.16974",
language = "English (US)",
volume = "14",
pages = "201--212",
journal = "International Journal of Medical Sciences",
issn = "1449-1907",
publisher = "Ivyspring International Publisher",
number = "3",

}

TY - JOUR

T1 - Prevalence and risk factors of comorbidities among hypertensive patients in china

AU - Wang, Jiaojiao

AU - Ma, Jian James

AU - Liu, Jiaqi

AU - Zeng, Dajun

AU - Song, Cynthia

AU - Cao, Zhidong

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Bayes’ Theorem

KW - Comorbidity

KW - Geographical detector

KW - Hypertension

KW - Prevalence

KW - Public health

KW - Risk factor

UR - http://www.scopus.com/inward/record.url?scp=85014726397&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014726397&partnerID=8YFLogxK

U2 - 10.7150/ijms.16974

DO - 10.7150/ijms.16974

M3 - Article

C2 - 28367080

AN - SCOPUS:85014726397

VL - 14

SP - 201

EP - 212

JO - International Journal of Medical Sciences

JF - International Journal of Medical Sciences

SN - 1449-1907

IS - 3

ER -