A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk.
BACKGROUND
We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective.


METHODS
The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined.


RESULTS
Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier.


CONCLUSIONS
The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective.
Department of Epidemiology & Preventive Medicine, Monash Medical School, Alfred Hospital, Commercial Road, Prahran 3181, Victoria, Australia. john.mcneil@med.monash.edu.au
J J
JJ
McNeil
A
A
Peeters
D
D
Liew
S
S
Lim
T
T
Vos
D000206:Actuarial Analysis; D000328:Adult; D000368:Aged; D001315:Australia / Q000453:epidemiology; D003327:Coronary Disease / Q000453:epidemiology* / Q000401:mortality / Q000517:prevention & control; D003362:Cost-Benefit Analysis; D005260:Female; D005544:Forecasting; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011322:Primary Prevention / Q000191:economics; D015203:Reproducibility of Results; D012306:Risk; D016019:Survival Analysis