The Washington State Occupational Mortality Database has been developed to track worker mortality and cause of death.

This can be used as a resource when developing materials for training or statistical data pertaining to incidents.  

To search, find "Firefighters and Fire Protection workers (850)" under the Male occupation box and then select "Malignant Neoplasms (total)" under "Cause of death"  to give you a number of deaths by age group (in 10 year increments).  This can further be limited by specific disease (ie prostate, brain, etc.).  Play with different causes to see the statistics..

The other columns list incidents observed vs expected compared to general population.

PMR is the proportional mortality ratio and any number >100 is greater than 100% incidents compared to expected.

The link to the page is https://fortress.wa.gov/doh/occmort/OCCMORT/default.aspx

A proportional mortality ratio is the proportion of deaths in a specific occupation that are due to a specific cause-of-death divided by the proportion of deaths in all occupations that are due to that specific cause of death. For example, among male school teachers there were a total of 7,778 deaths, and 332 of these were caused by lung cancer. There were a total of 811,256 deaths to males of all occupations, of which 46,802 were caused by lung cancer. So the crude PMR for lung cancer among male school teachers is (332/7778)/(46,802/811,256) X 100 = 74. (In the data reported on this website, crude PMRs are not used, instead, the PMRs are age-adjusted to account for different age distributions in different occupations.) A PMR greater than 100 indicates that members of that occupation were more likely than average to die of that cause-of-death, while a PMR of less than 100 indicates that they were less likely than average to die of that cause-of-death.

PMRs may also be calculated by dividing the observed number of deaths by the expected number of deaths in an occupation and cause-of-death group.

The greatest value of proportional mortality ratios is that they can be used in studies where mortality rates cannot be calculated, because of a lack of information on the population at risk. The greatest weakness of PMRs is that they say nothing about the overall force of mortality.⁶ A proportional excess in one cause of death may mean either an excess in the rate for that cause of death, or a deficit in the rates of other causes. In a healthy cohort, a deficit of cardiovascular diseases can cause a proportional excess of some cancers, even when there is no excess in the absolute rate of those cancers. Several researchers have compared the performance of PMRs to that of standardized mortality ratios (SMRs), and studied the effect of the `healthy worker effect' on PMRs. Decouflé and colleagues show that a cause-specific PMR is equivalent to the cause-specific SMR multiplied by the overall SMR for the cohort.⁷ Therefore, the PMR will either over-estimate or under-estimate the corresponding SMR if the overall SMR is either less than or greater than 100.

Correct interpretation of the results of PMR studies, therefore, requires an awareness of the weaknesses of the technique, and a consideration of the epidemiologic factors operating on each occupation.

The number of deaths that actually occurred in a specific occupation and cause-of-death group.

The age-adjusted number of deaths that would have occurred in a specific occupation and cause-of-death group, if that occupation had the same mortality experience as the entire cohort.

A measure of the statistical significance of the PMR. All the p-values in this analysis are two-sided.