Explanation of Disease Rate Statistics

When comparing incidence or mortality rates of two or more groups, one group is assigned the rate of 1.00 and the other groups are compared to it. These are called disease rate ratios or odds rate ratios.


Group A 1.00
Group B 1.35

Group B has a 35% higher rate of the disease than Group A.

Group A 1.00
Group B .85

Group B has a 15% lower rate of the disease than Group A.

In addition to the rates, there has to be a test to determine if the rates are different enough not to be due merely to random chance (also known as statistical significance). Statistical significance for a disease rate is usually expressed by way of a 95% confidence interval (CI). This is done by giving a lower and upper limit for the interval. If 1.00 does not fall between the two numbers (i.e., within the interval), then the finding is significant and not due to random chance.

Example 1:

.85 (.75, .95)

The finding is statistically significant because 1.00 falls outside the 95% CI.

Example 2:

.85 (.65, 1.05)

The finding is not statistically significant because 1.00 falls inside the 95% CI.

Sometimes, p-values are given rather than confidence intervals. In these cases, a p-value of less than .05 means the finding is statistically significant.


When disease rates are adjusted, it means they are changed to account for variables that might affect them.

For example, say a study finds that smoking is related to cancer and that drinking is also related to cancer. But many of the people in the study both smoke and drink, so you don't know whether it was the smoking or the drinking (or both) that is actually related to the cancer. By adjusting, you can look at the different levels of drinking taking into account how much the subjects smoked, and get a number for drinking that isn't influenced as much by smoking.

Most studies adjust for more than one variable at a time. They often adjust for all the variables that, in the non-adjusted analysis, had a significant relationship to the disease.

What often happens is that a variable loses its significance once the results are adjusted. For example, a study of people aged from 20 to 60 years old will likely correlate the likelihood of having a heart attack with having gray hair. But once you adjust for the age of the participants, the correlation with gray hair will fall away and we can then assume gray hair doesn't cause heart attacks.

Well-designed studies allow researchers to consider adjusted results in their calculations. Frequently, these adjusted results are easier to draw conclusions from. The articles on VeganHealth.org use adjusted rates unless otherwise noted.