Understanding Rates

On the City Health Dashboard, we present metrics related to deaths (mortality) as rates. Other metrics are presented as percentages or averages. In this blog, we’ll break down why certain data are most effectively communicated using rates, and how to make sense of rates.

In our daily lives we interact with different types of rates all the time – for example, if the speed limit is 50 miles per hour, that’s a rate! Or, if someone making deliveries earns $20/hour, that’s also a rate . Technically, percentages are a type of rate. If 60% of people have diabetes in a city, you could communicate the same information as a rate: “There are 60 people with diabetes in this city per 100 city residents .” When we present mortality rates on the City Health Dashboard, we use “per 100,000 population.” For example, in Dayton, OH in 2021, there were an estimated 24 firearm homicide deaths per 100,000 population. Why are these metrics presented as rates, when others use averages or percentages?

Averages preset the most typical value for an area, time, or population in a single variable, like PM2.5 or 3rd grade reading scores. We could consider providing the number of deaths in a city as an average over time (e.g. per year), but a critical benefit of rates is their ability to accommodate the fact that different places have different population sizes. If we were to compare the average colorectal cancer deaths in Casper, WY from 2019-2021, it might be somewhere around 10 deaths per year. The average in New York City for the same period would be around 945 deaths per year. These numbers are misleading though, because New York City has a far larger population than Casper (~8.5 million in NYC vs. 60,000 in Casper). We need to ‘standardize’ these numbers - adjust them so they are calculated relative to a common population size - to be able to determine which city has a higher “burden” of colorectal cancer deaths occurring among their residents.

Why not use a percentage, then? Percentages, as noted above, are a type of rate calculating an outcome per 100. The percentage of colorectal cancer deaths in Casper would be ~0.0168%, and in New York City would be ~0.0111%. In Casper, the percentage of the population that die from colorectal cancer is higher than in New York City! But it’s hard to interpret and compare such small decimals: numbers so small can feel easy to brush aside. And presenting percentages this small runs the risk of understating the importance of different public health problems. When calculating metrics for rarer outcomes (for example, colorectal cancer deaths are much less common than cases of diabetes), we strive to present numbers that more clearly and accurately communicate the burden. For instance, on the Dashboard you can find the numbers 16.8 colorectal cancer deaths per 100,000 in Casper and 11.1 in New York City. Presenting our colorectal cancer death metric as a rate provides comparable, easy to understand numbers. You could interpret these numbers as: “If 100,000 people lived in Casper, WY in 2021, then 16 people would have died of colorectal cancer .”

(Important note: These are simplified example calculations that don’t incorporate the age-adjustment approaches also used in mortality metrics . Please see the Dashboard technical document for more information.)

How can you interpret the data presented as rates for your city? How do we know if 16.8 colorectal cancer deaths per 100,000 in Casper is high, low, or average? Most of us won’t intuitively know what that number means. This is where the Dashboard’s scalebars come in handy.

In this screenshot from the Dashboard, the smallest number on the left side of the scalebar (4) represents the lowest city-level colorectal cancer death rate in 2021, and the largest number on the right (71) represents the largest. Casper is on the lower end of the scale bar, but it’s near the Dashboard average of 14.8 deaths per 100,000 - denoted by the caret symbol (^) at the bottom of the scale bar labeled ‘Average of Dashboard Cities’. This tells us that Casper’s mortality rate is about average.

The Dashboard’s mortality data can be useful for highlighting disease burden and disparities. When sharing these rates, we recommend the following strategies to help people understand them better:

• Include a brief explanation of rates and “per 100,000 population”- feel free to use this blog as a source!

• Compare rates between groups or cities to help people understand if rates are low, high, or similar to the average of rates.

Now that you’ve learned why mortality metrics are presented as rates, and how to interpret these data, we hope that you feel more resourced to utilize mortality data in your work.