Learn how to layer your understanding of chronic diseases and prevention in communities

A growing body of evidence has linked oral health, particularly periodontal or gum disease, to several chronic diseases like diabetes, heart disease, and stroke. In pregnant women, poor oral health has also been associated with premature births and low birth weight.

The Centers for Disease Control and Prevention (CDC) released the PLACES dataset late last year that provides a more granular way to look at 33 leading health indicators and chronic diseases.  Oral health is one of them.

Using Metopio’s scatterplot, we can see that oral health – as measured by annual visits to see the dentist – are in fact highly correlated with diagnosed diabetes. When dental visits go up, diagnosed diabetes goes down.

Remember, the scatterplot shows correlation not causation.

If we run this analysis across coronary heart disease and diagnosed stroke we see similar correlations. The scatterplot helps narrow the scope of related variables so you are able to concentrate on those that have the greatest impact.

Now that we know oral health is correlated with other health outcomes, how do we understand the communities where the numbers are the lowest?

A shortcoming of the PLACES dataset is the lack of stratifications. It is based on survey data from adults age 18 and older, and modeled using small area estimates. The CDC advice is that “the PLACES Project does not include any stratifications by race/ethnicity. However, to the extent that populations within census tracts or ZCTAs tend to be relatively homogeneous, geographic disparities by census tract or ZCTA may be somewhat reflective of racial/ethnic disparities.”

Metopio can do just that with a filtered maps.

With Metopio filtered maps and the City of Cincinnati as an example, you can examine the percent of adults who visited the dentist in the last year at the Census tract level.

You will note we labeled Census tract 86.01 and Census tract 77 as they fall far below Cincinnati and Ohio for annual visits to the dentist. By adding a second demographic filter, we can now reflect on the racial and ethnic make-up of the communities one at a time.

Both of these communities are predominantly non-Hispanic black. You can do the same analysis by ZIP Code. Once you have identified the areas that meet your criteria, you can also benchmark them across geographies.

Visit Metopio’s Oral Health project to explore all the insights.

Metopio aggregates high-quality, verified data that has some geographic component – think address or any part thereof – to develop insights that inform your business and policy decisions. If you have data that would enhance this analysis let us know! Contact us