title: The future impact of population growth and aging on coronary heart disease in China: projections from the Coronary Heart Disease Policy Model-China.
abstract: BACKGROUND
  China will experience an overall growth and aging of its adult population in coming decades. We used a computer model to forecast the future impact of these demographic changes on coronary heart disease (CHD) in China.
  
  
  METHODS
  The CHD Policy Model is a validated state-transition, computer simulation of CHD on a national scale. China-specific CHD risk factor, incidence, case-fatality, and prevalence data were incorporated, and a CHD prediction model was generated from a Chinese cohort study and calibrated to age-specific Chinese mortality rates. Disability-adjusted life years (DALYs) due to CHD were calculated using standard methods. The projected population of China aged 35-84 years was entered, and CHD events, deaths, and DALYs were simulated over 2000-2029. CHD risk factors other than age and case-fatality were held at year 2000 levels. Sensitivity analyses tested uncertainty regarding CHD mortality coding, the proportion of total deaths attributable to CHD, and case-fatality.
  
  
  RESULTS
  We predicted 7.8 million excess CHD events (a 69% increase) and 3.4 million excess CHD deaths (a 64% increase) in the decade 2020-2029 compared with 2000-2009. For 2030, we predicted 71% of almost one million annual CHD deaths will occur in persons >or=65 years old, while 67% of the growing annual burden of CHD death and disability will weigh on adults <65 years old. Substituting alternate CHD mortality assumptions led to 17-20% more predicted CHD deaths over 2000-2029, though the pattern of increases in CHD events and deaths over time remained.
  
  
  CONCLUSIONS
  We forecast that absolute numbers of CHD events and deaths will increase dramatically in China over 2010-2029, due to a growing and aging population alone. Recent data suggest CHD risk factor levels are increasing, so our projections may underestimate the extent of the potential CHD epidemic in China.
authors.affiliation: Division of General Internal Medicine, Columbia University Medical Center, New York, USA. aem35@columbia.edu
authors.forename: Andrew
authors.initials: A
authors.lastname: Moran
authors.forename: Dong
authors.initials: D
authors.lastname: Zhao
authors.forename: Dongfeng
authors.initials: D
authors.lastname: Gu
authors.forename: Pamela
authors.initials: P
authors.lastname: Coxson
authors.forename: Chung-Shiuan
authors.initials: CS
authors.lastname: Chen
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
authors.forename: Jiang
authors.initials: J
authors.lastname: He
authors.forename: Lee
authors.initials: L
authors.lastname: Goldman
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002681:China / Q000453:epidemiology; D003198:Computer Simulation; D003327:Coronary Disease / Q000453:epidemiology* / Q000401:mortality*; D017281:Cost of Illness; D006233:Persons with Disabilities / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011157:Population Dynamics; D011158:Population Growth; D015995:Prevalence; D016016:Proportional Hazards Models; D019057:Quality-Adjusted Life Years; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors