title: Future cardiovascular disease in china: markov model and risk factor scenario projections from the coronary heart disease policy model-china.
abstract: BACKGROUND
  The relative effects of individual and combined risk factor trends on future cardiovascular disease in China have not been quantified in detail.
  
  
  RESULTS
  Future risk factor trends in China were projected based on prior trends. Cardiovascular disease (coronary heart disease and stroke) in adults ages 35 to 84 years was projected from 2010 to 2030 using the Coronary Heart Disease Policy Model-China, a Markov computer simulation model. With risk factor levels held constant, projected annual cardiovascular events increased by >50% between 2010 and 2030 based on population aging and growth alone. Projected trends in blood pressure, total cholesterol, diabetes (increases), and active smoking (decline) would increase annual cardiovascular disease events by an additional 23%, an increase of approximately 21.3 million cardiovascular events and 7.7 million cardiovascular deaths over 2010 to 2030. Aggressively reducing active smoking in Chinese men to 20% prevalence in 2020 and 10% prevalence in 2030 or reducing mean systolic blood pressure by 3.8 mm Hg in men and women would counteract adverse trends in other risk factors by preventing cardiovascular events and 2.9 to 5.7 million total deaths over 2 decades.
  
  
  CONCLUSIONS
  Aging and population growth will increase cardiovascular disease by more than a half over the coming 20 years, and projected unfavorable trends in blood pressure, total cholesterol, diabetes, and body mass index may accelerate the epidemic. National policy aimed at controlling blood pressure, smoking, and other risk factors would counteract the expected future cardiovascular disease epidemic in China.
authors.affiliation: Columbia University Medical Center, New York, NY, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Moran
authors.forename: Dongfeng
authors.initials: D
authors.lastname: Gu
authors.forename: Dong
authors.initials: D
authors.lastname: Zhao
authors.forename: Pamela
authors.initials: P
authors.lastname: Coxson
authors.forename: Y Claire
authors.initials: YC
authors.lastname: Wang
authors.forename: Chung-Shiuan
authors.initials: CS
authors.lastname: Chen
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.forename: Kirsten
authors.initials: K
authors.lastname: Bibbins-Domingo
authors.forename: Yu-Ming
authors.initials: YM
authors.lastname: Shen
authors.forename: Jiang
authors.initials: J
authors.lastname: He
authors.forename: Lee
authors.initials: L
authors.lastname: Goldman
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002681:China; D003198:Computer Simulation; D003327:Coronary Disease / Q000453:epidemiology*; D005260:Female; D005544:Forecasting; D006291:Health Policy / Q000639:trends*; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008955:Models, Cardiovascular; D012307:Risk Factors