FAQ

A "crude" rate is a number that measures the total number of cases in a given time period and population. It is called "crude" because it does not take into account factors like age that can make one population different from another and can also affect rates of disease that are more common in older or younger people.

A crude rate is calculated by taking the number of times an event occurred in a given time period and dividing it by the number of people in the population at that time. Because rates are often small and populations are usually large, we will often multiply the rate by a constant like 100,000 and then describe the rate as "per 100,000 population" so that it is easier to understand.

For example, to calculate the crude rate of COVID-19 cases in New Castle County in a given year, you would take the total number of cases of COVID-19 that were reported and divide it by the total number of people who lived in New Castle county that year, and then multiply that number by 100,000.

For example, if there were 500 deaths in a population of 10,000 individuals, the crude mortality rate would be:

An "age-adjusted" rate is a number that measures the total number of cases in a given time period and population, taking into account the fact that some populations have more people that are older, some have more people that are younger, and many diseases are more common in older or younger people. Age-adjusted rates are used so that you can more fairly compare two groups to each other and reduce the effect that age has on the differences between the groups.

For example, say you are looking at a disease like COVID-19, which has been shown to cause more severe illness in people over 65 years old. If you have one neighborhood that is mostly retirees who are over 65, there may appear to be a very high rate of COVID-19 compared with a neighborhood of younger people and families with children. If you used age-adjusted rates to compare, it would minimize the effect of the different ages in the neighborhoods so that you can compare the rates of COVID-19 more fairly. The rate of COVID-19 may still be higher in the neighborhood of retirees, but the comparison would be more fair between the two.

Let's consider cases of a specific disease, such as cancer, in two different regions: Region X and Region Y.

The crude incidence rate of cancer in Region X is 40 cases per 100,000 people, while in Region Y, it is 30 cases per 100,000 people. Initially, it may seem that Region X has a higher incidence of cancer than Region Y because 40 (Region X) is higher than 30 (Region Y).

However, when we look a little closer, we see that Region X has a significantly older population compared to Region Y, where the population is relatively younger.

To account for this age difference and make a fair comparison, we calculate the age-adjusted incidence rate. After adjusting for age, we find that the age-adjusted incidence rate in Region X is 35 cases per 100,000 individuals, while in Region Y, it is 40 cases per 100,000 individuals.

Now, with the age-adjusted rates, we can make a more accurate comparison. Despite the lower crude incidence rate in Region Y, when we account for age, we see that the age-adjusted incidence rate is higher than in Region X. This shows that the difference in cancer incidence between the two regions is influenced by the fact that the population in Region X is older.

Some locations in Delaware, like zip codes or census tracts, have very few people in them. This means that if we report the number of cases of a disease in that small area, it might be easier for that person to be identified.

To help prevent this release of sensitive information, we use something called Data Suppression. Data suppression means that numbers for some areas of the state cannot be released. Data suppression in public health is carried out to protect privacy and prevent the identification of individual people, release of sensitive information, and uphold public trust.

We also suppress data to maintain statistical reliability, because when we calculate statistics for very small numbers, they can sometimes be wrong. Data suppression It is a balancing act to provide accurate and meaningful data while safeguarding individual privacy and public interests.