title: Coronary heart disease and stroke attributable to major risk factors is similar in Argentina and the United States: the Coronary Heart Disease Policy Model.
abstract: BACKGROUND
  Cardiovascular disease is the leading cause of death in Argentina and the U.S. Argentina is 92% urban, with cardiovascular disease risk factor levels approximating the U.S.
  
  
  METHODS
  The Coronary Heart Disease (CHD) Policy Model is a national-scale computer model of CHD and stroke. Risk factor data were obtained from the Cardiovascular Risk Factor Multiple Evaluation in Latin America Study (2003-04), Argentina National Risk Factor Survey (2005) and U.S. national surveys. Proportions of cardiovascular events over 2005-2015 attributable to risk factors were simulated by setting risk factors to optimal exposure levels [systolic blood pressure (SBP) 115 mm Hg, low-density lipoprotein cholesterol (LDL) 2.00 mmol/l (78 mg/dl), high-density lipoprotein cholesterol (HDL) 1.03 mmol/l (60 mg/dl), absence of diabetes, and smoking]. Cardiovascular disease attributable to body mass index (BMI) >21 kg/m² was assumed mediated through SBP, LDL, HDL, and diabetes.
  
  
  RESULTS
  Cardiovascular disease attributable to major risk factors was similar between Argentina and the U.S., except for elevated SBP in men (CHD 8% points higher in Argentine men, 6% higher for stroke). CHD attributable to BMI >21 kg/m² was substantially higher in the U.S. (men 10-11% points higher; women CHD 13-14% higher).
  
  
  CONCLUSIONS
  Projected cardiovascular disease attributable to major risk factors appeared similar in Argentina and the U.S., though elevated BMI may be responsible for more of U.S. cardiovascular disease. A highly urbanized middle-income nation can have cardiovascular disease rates and risk factor levels comparable to a high income nation, but fewer resources for fighting the epidemic.
authors.affiliation: Division of General Medicine, Columbia University Medical Center, New York, USA. aem35@columbia.edu
authors.forename: Andrew
authors.initials: A
authors.lastname: Moran
authors.forename: Vincent
authors.initials: V
authors.lastname: Degennaro
authors.forename: Daniel
authors.initials: D
authors.lastname: Ferrante
authors.forename: Pamela G
authors.initials: PG
authors.lastname: Coxson
authors.forename: Walter
authors.initials: W
authors.lastname: Palmas
authors.forename: Raul
authors.initials: R
authors.lastname: Mejia
authors.forename: Eliseo J
authors.initials: EJ
authors.lastname: Perez-Stable
authors.forename: Lee
authors.initials: L
authors.lastname: Goldman
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001118:Argentina / Q000453:epidemiology; D003198:Computer Simulation / Q000639:trends; D003327:Coronary Disease / Q000453:epidemiology* / Q000209:etiology; D048909:Diabetes Complications / Q000150:complications / Q000453:epidemiology; D005260:Female; D006291:Health Policy / Q000639:trends; D006306:Health Surveys / Q000379:methods; D006801:Humans; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects / Q000453:epidemiology; D020521:Stroke / Q000453:epidemiology* / Q000209:etiology; D014481:United States / Q000453:epidemiology