Update of the cardiovascular disease policy model to predict cardiovascular events in Argentina.
Pubmed ID: 31829945
Journal: Medicina
Publication Date: Jan. 1, 2019
MeSH Terms: Humans, Male, Adult, Female, Aged, Aged, 80 and over, Cardiovascular Diseases, Risk Factors, Age Factors, Middle Aged, Risk Assessment, Sex Factors, Computer Simulation, Incidence, Forecasting, Mortality, Time Factors, Calibration, Reproducibility of Results, Age Distribution, Sex Distribution, Argentina
Grants: K24 DK102057
Authors: Coxson P, Konfino J, Penko J, Mejía R, Fernández A, Salgado MV, Irazola VE, Gutiérrez L
Cite As: Salgado MV, Coxson P, Konfino J, Penko J, Irazola VE, Gutiérrez L, Fernández A, Mejía R. Update of the cardiovascular disease policy model to predict cardiovascular events in Argentina. Medicina (B Aires) 2019;79(6):438-444.
Studies:
Abstract
Cardiovascular disease (CVD) is the leading cause of death in Argentina. Computer simulation models allow to extrapolate evidence to broader populations than the originally studied, over longer timeframes, and to compare different subpopulations. The Cardiovascular Disease Policy Model (CVDPM) is a computer simulation state transition model used to represent and project future CVD mortality and morbidity in the population 35 years-old and older. The objective of this study was to update Argentina's version of the CVDPM. For this purpose, information from the 2010 National Census, the 2013 National Risk Factor Survey, CESCAS I study, and PrEViSTA study were used to update the dynamics of population size, demographics, and CVD risk factor distributions over time. Model projections were later calibrated by comparing them to actual data on CVD events and mortality in the year 2010 (baseline year) in Argentina. Country statistics for people 35 years-old and older reported for 2010 a total of 41 219 myocardial infarctions (MIs), 58 658 strokes, and 281 710 total deaths. The CVDPM, in turn, predicted 41 265 MIs (difference: 0.11%), 58 584 strokes (difference: 0.13%), and 280 707 total deaths (difference: 0.36%) in the same population. In all cases, the final version of the model predicted the actual number of events with an accuracy superior to 99.5%, and could be used to forecast the changes in CVD incidence and mortality after the implementation of public policies.