to 0.0001 new cases per person-year. As described before, in epidemiology, rates are reconfigured to

have at least whole numbers so that they are easier to interpret and envision. For this example, you

could express City XYZ’s 2023 adult Type II diabetes incidence rate as 1 new case per 10,000

person-years, or as 10 new cases per 100,000 person-years.

Now imagine another city — City ABC — has a population of 80,0000 adults, and like with City

XYZ, none of them had ever been diagnosed with Type II diabetes. Now, assume that in 2023, 24

adults from City ABC were newly diagnosed with Type II diabetes. City ABC’s 2023 incidence rate

would be calculated as 24 cases in 80,000 individuals in one year, which works out to

or

0.0003 new cases per person-year. To make the estimate comparable to City XYZ’s estimate, let’s

express City ABC’s estimate as 30 new cases per 100,000 person-years. So, the 2023 adult Type II

diabetes incidence rate in City ABC — which is 30 new cases per 100,000 person-years — is three

times as large as the 2023 adult Type II diabetes incidence rate for City XYZ, which is 10 new cases

for 100,000 person-years. (Looks like City ABC’s public health department needs to get advice from

City XYZ!)

Understanding how incidence and prevalence are related

From the definitions and examples in the preceding sections, you see that incidence and prevalence are

two related but distinct concepts. The incidence rate tells you how fast new cases of some condition

arise in a population, and prevalence tells you what fraction of the population has that condition at any

moment.

You may expect that conditions with higher incidence rates would have higher prevalence than

conditions with lower incidence rates. This is true with common chronic conditions, such as

hypertension. But if a condition is acute — including infectious diseases, such as influenza and

COVID-19 — the duration of the condition may be short. In such a scenario, a high incidence rate may

not be paired with a high prevalence. Relatively rare chronic diseases of long duration — such as

dementia — have low yearly incidence rates, but as human health improves and humans live longer on

average, the prevalence of dementia increases.

Analyzing Incidence Rates

The preceding sections show you how to calculate incidence rates and express them in larger units that

are easier to envision. But, as we emphasize in Chapter 10, whenever you report an estimate you’ve

calculated, you should also indicate the level of precision of that estimate. How precise are those

incident rates? And how can you tell when the difference between two incidence rates is statistically

significant? The next sections show you how to calculate standard errors (SEs) and confidence

intervals (CIs) for incidence rates, and how to compare incidence rates between two populations.

Expressing the precision of an incidence rate

The precision of an incidence rate (R) is expressed using a confidence interval (CI). The SE of R

typically is not reported, because the event rate usually isn’t normally distributed. The SE is computed

only as part of the CI calculation.

Random fluctuations in R are attributed entirely to fluctuations in the event count (N). We are assuming

the exposure (described earlier in this chapter as the person-time in the denominator, abbreviated as

E) is known exactly — or at least, much more precisely than N. Therefore, the CI for the event rate is