Early in my career I was tasked with collecting information about the characteristics of people lacking health insurance. As an eager graduate student intern, I worked diligently to gather, analyze, and share data on this vulnerable population.
That research included visiting libraries, requesting and waiting days or weeks for journal articles (hard copies only!), and using microfiche (younger readers can Google that).
The most current available data was almost always several years old. It was a rarity to encounter information that could easily be disaggregated by socio-economic, racial, or ethnic factors. Facts at the national or state level were plentiful, while facts at the county or municipal level were harder to come by. And, most information was presented in what now seem to be simple tables and mundane graphs.
The current capacity to access and analyze data would have been difficult to imagine in my intern days.
It’s exciting to be working in an information environment where the ability to solve questions about health needs has improved dramatically, because the answers can guide us in targeting intervention to people and places in greatest need more effectively.
Today, timely information about community health is readily available through many reliable sources. That includes kchealthmatters.org, a one-stop, regionally focused site that offers data on health determinants, disease incidence, socio-economic indicators and demographics. Tools for examining health disparities — by race/ethnicity, age and gender — are available for counties and some municipalities in a dedicated dashboard. Health Forward is proud to support this resource for grant applicants, community partners, and anyone who is curious about health in our area.
We can also answer more detailed questions about how health varies within a county. ExploreMOhealth.org (created in 2018 through a partnership between the Missouri Foundation for Health and the Missouri Hospital Association Health Institute) provides data by zip code, giving users unique insight into neighborhood-level needs.
While county-level data can mask geographic disparities, the availability of this sub-county-level information enriches our understanding of which communities are most deeply affected by health determinants and conditions.
An example illustrates this clearly. In Jackson County, Missouri, the median rate for diabetes diagnoses is 194 per 1000 people, but that county-level figure doesn’t tell a complete story. Data at the zip code level reveal that the rate for diabetes diagnoses varies widely by neighborhood, ranging from a low of 48/1000 to a high of 564/1000 (data from the highest and lowest ranking zip codes are in the table below).
|Zip Code||Neighborhood Name||Diabetes Diagnoses per 1000 (2016)|
|64112||Kansas City–Sunset Hill||48.07|
|64147||Kansas City–Eagles Landing||48.85|
|64113||Kansas City–Greenway Fields||53.46|
|Jackson County Median||194.43|
|64109||Kansas City–Center City||437.84|
|64130||Kansas City–MT Cleveland||493.91|
|64128||Kansas City–Palestine East||536.64|
And, four zip codes have diabetes rates that are more than double the median. Exploring zip code-level information highlights the areas of greatest need.
In addition to having greatly improved access to more timely and detailed data, contemporary technology tools enrich our ability to visualize data in new and informative ways. No longer just data analysts, we have the capability to tell data stories like the one below. In a single graphic about teen birth rates, we can learn about trends over time, areas of greatest need and how local and state level data compare.
There’s no doubt that data has become an increasingly accessible and powerful tool to inform health strategy. We can now tell our community health story more completely and leverage the power of data to inform choices about intervention. I’m already wondering what the future will bring!