The New York State Department of Health recently launched a new online tool for researching the prevalence of certain medical conditions by zip code. It has a terribly boring name—Prevention Quality Indicators in New York State—but what they’re providing is very exciting.
Prevention Quality Indicators or PQIs are a set of measures developed by a federal health agency. They count the number of people admitted to hospitals for a specific list of twelve conditions, some of which include various complications from diabetes, hypertension, asthma, and urinary tract infections. All of these are conditions in which good preventative care can help avoid hospitalization or the development of more severe conditions. As the Department explains, “The PQIs can be used as a starting point for evaluating the overall quality of primary and preventive care in an area. They are sometimes characterized as ‘avoidable hospitalizations,’ but this does not mean that the hospitalizations were unnecessary or inappropriate at the time they occurred.”
It’s not the kind of data that would normally get your average New York resident excited. Even though it’s personal information—it doesn’t get more personal than health—it’s unlikely to feel very personal to anyone.
That’s what makes numbers and data off-putting for so many people. Even when the numbers include people like us, we don’t see ourselves in them, so it’s hard to feel like those numbers have anything to say to us personally. At the same time, so many decisions are being made based on data, huge decisions that affect all of us. It’s important for democracy that ordinary citizens have a stake in the data, that they not only have access to the data but that they also have an interest in reviewing the data themselves.
What’s interesting to me about this website, then, is that is its potential for making this obscure piece of government health data much more immediate and personal for ordinary citizens, and not just public health data geeks. As soon as I heard about this website, the first thing I did was look up my zip code, “11205” in the county of Kings (Brooklyn). I could then see racial disparities in the admission rate for these conditions in my neighborhood, and even see data on specific hospitals in my area. Whenever there is a way to organize and access data in a way that is personal to the user, it’s immediately more compelling.
There’s no particular reason for me to wonder what asthma admission rates were in my zip code in 2006. But I can imagine a mother of a child with asthma coming upon this site, wondering what asthma rates are in her zip code and the ones around it, and maybe seeing patterns that lead her to talk to other parents and elected officials. And I can imagine other data sets of personal information being made truly relevant and personal in similar ways.