Predicting coronary heart disease mortality--assessing uncertainties in population forecasts and death probabilities by using Bayesian inference.
BACKGROUND
Predictions concerning people and their health are influenced by many factors and have many sources of uncertainty. Even so predictions can give useful guidelines for health care planning. We present a Bayesian model based on past observations and prior knowledge to predict coronary heart disease (CHD) mortality in selected areas of Finland until the year 2030.


METHODS
CHD mortality data are based on official statistics. The study area consists of one western and two eastern parts of Finland. The modelling of the probability of death follows a Bayesian age-period-cohort model. Two models are used, one assuming that the trend from 1970 to 2002 will continue and the other that mortality will stay at the attained level.


RESULTS
If the observed trend in CHD mortality were to continue, death probabilities would decrease significantly among men aged 50-69 and women aged 50-59. In the older age groups (men aged 70 and women 60 years or more) the changes were found to be negligible. If the trend continues, the number of CHD deaths will decrease from 2002 to 2030 significantly among men [81% decrease; 95% credible interval (95% CI) 54-96%] and women (90%; 67-100%) aged 50-59. In the age group 60-79 the changes will be smaller and non-significant. In the oldest age group (80-99 years) the predicted increase in the number of deaths will be great, from 284 to 1297 (95% CI 474-2620) in men and from 722 to 1970 (717-4017) in women.


CONCLUSIONS
Our predictions emphasize the significance of maintaining the recent decline of CHD mortality among middle-aged adults. Special attention should be paid to CHD mortality among men and women aged 80 and over. Considerable improvements in prevention and treatment are needed to compensate for the effects of ageing of the population.
Huovinen
Elisa
E
National Public Health Institute, Helsinki, Finland.
Härkänen
Tommi
T
Martelin
Tuija
T
Koskinen
Seppo
S
Aromaa
Arpo
A
D000367:Age Factors; D000368:Aged; D001499:Bayes Theorem; D003327:Coronary Disease / Q000401:mortality*; D005260:Female; D005387:Finland / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009026:Mortality / Q000639:trends; D012737:Sex Factors