Toolkit in real life

These are examples of the Local health data analysis and communication toolkit approaches being used in real life.

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Summarizing key findings

Posted 6/23/2026


The situation

A few years into my first county epidemiologist job, I was tasked with managing a Community Health Assessment report.    

It was a large undertaking: 39 indicators analyzed by comparing them to the state, trends over time, and disparities by age, gender, race/ethnicity, sub-county region, and level of education.   The report was 78 pages long, with a two-page spread for each indicator, and a 59-page appendix with detailed data tables.  I also included a 4-page summary table with one row per indicator.

Most people probably wouldn't have taken the time to read the whole thing.


What analysts often get wrong

Epidemiologists are very excited about data.  (Which is great, because that's our job!)

At the same time, for many audiences, providing a lot of material can be overwhelming.  

That overwhelm can make it harder for communities to engage with our reports and use the data in their work.


What to do instead

Step 6 of the "Producing meaningful reports" section of the toolkit, on page 11, encourages analysts to summarize their key findings.  Ideally, that summary will align with their audiences' interests and priorities.

On a whim, near the end of the project, I decided to pull together one additional summary table.  Because our management was interested in health disparities, I grouped indicators by demographic group. Below is a handful of categories and indicators to illustrate:

For older adults:

For our Hispanic population:

For females:

And so on for all of the disparity groups I analyzed.

My director loved it.  He printed out just this summary sheet and referred to it often in his conversations with colleagues across our county.  That summary sheet often piqued folks' interest, which led them to ask deeper questions.


Why it matters

When we overwhelm people with data, they often disengage.

In this situation, my director didn't reach for the report; he reached for the digested summary that told him what the data meant.  The summary worked because it was a doorway into the report.

As epidemiologists, we often believe that the most complete report is the most valuable one. In many cases, it's not.  People will engage with our data; we just need to frame it around what matters to them.

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Building out context

Posted 3/26/2026


The situation

A few years ago, I was looking at mortality data for a county with a large older population.  I told a colleague that the county’s death rate was about the same as the state average. 

They said that was great news — because the county is older, they’d expect a higher death rate, so this must mean the population is healthier.

I then felt awkward when I had to backtrack and explain that the statement already took age into account.  


What analysts often get wrong

Analysts are trained to account for confounders in our analysis, which is what age-adjusting does.  It's a good practice because age is a strong predictor of many health outcomes.  

At the same time, for many audiences, leading with an adjusted figure can skip a step and create confusion about what the numbers actually mean.  

That confusion can create distance between analysts and their communities.  I've had this happen many times in my career.


What to do instead

The "Producing meaningful reports" section of the toolkit, starting on page 6, suggests that analysts can:

Start with the count (number of deaths) to ground the conversation:

Then start building out context for what that number means. First, we can calculate a crude rate to help folks understand how much the health issue is impacting our population:

Now we can build out context a step further by comparing ourselves to a benchmark.  I like to do this for both crude and age-adjusted rates. In this example, I found that our county's crude death rate was 50% higher than the state average, but after adjusting for age, the rates were almost identical. Here's how I explained this:


Why it matters

Starting with something concrete helps people follow what's happening, which reduces misinterpretation and builds trust in both the data and the analyst.

In this situation, this approach was very well-received and helped build a trusting and mutually respectful relationship with my colleague.  In my experience, this step-by-step approach helps people understand what they’re seeing without getting lost.

As epidemiologists, we often assume the issue is data literacy. In many cases, it’s not. People can follow the logic - we just need to do a better job explaining it, starting with meeting them where they are.