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Prev Med. 1999 Feb;28(2):179-93.

Comparison of risk estimates for selected diseases and causes of death.

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  • 1Cancer Control Research Program, National Cancer Institute, Bethesda, Maryland 20892-7344, USA.



Lifetime risk estimates of disease are limited by long-term data extrapolations and are less relevant to individuals who have already lived a period of time without the disease, but are approaching the age at which the disease risk becomes common. In contrast, short-term age-conditional risk estimates, such as the risk of developing a disease in the next 10 years among those alive and free of the disease at a given age, are less restricted by long-term extrapolation of current rates and can present patients with risk information tailored to their age. This study focuses on short-term age-conditional risk estimates for a broad set of important chronic diseases and nondisease causes of death among white and black men and women.


The Feuer et al. (1993, Journal of the National Cancer Institute) [15] method was applied to data from a variety of sources to obtain risk estimates for select cancers, myocardial infarction, diabetes mellitus, multiple sclerosis, Alzheimer's, and death from motor vehicle accidents, homicide or legal intervention, and suicide.


Acute deaths from suicide, homicide or legal intervention, and fatal motor vehicle accidents dominate the risk picture for persons in their 20s, with only diabetes mellitus and end-stage renal disease therapy (for blacks only) having similar levels of risk in this age range. Late in life, cancer, acute myocardial infarction, Alzheimer's, and stroke become most common. The chronic diseases affecting the population later in life present the most likely diseases someone will face. Several interesting differences in disease and death risks were derived and reported among age-specific race and gender subgroups of the population.


Presentation of risk estimates for a broad set of chronic diseases and nondisease causes of death within short-term age ranges among population subgroups provides tailored information that may lead to better educated prevention, screening, and control behaviors and more efficient allocation of health resources.

[PubMed - indexed for MEDLINE]
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