The HRSA Uniform Data Source (UDS) Mapper: A complement to Census data

By now you’re familiar with writing needs assessments and you’re familiar with using Census data in the needs assessment. While Census data is useful for economic, language, and many other socioeconomic indicators, it’s not very useful for most health surveillance data—and most health-related data is hard to get. This is because it’s collected in weird ways, by county or state entities, and often compiled into reports for health districts and other non-standard sub-geographies that don’t match up with census tracks or even municipal boundaries. The collection and reporting mess often makes it to compare various areas. Enter HRSA’s Uniform Data Source (USD) Mapper tool.

I don’t know the specifics about the UDS Mapper’s genesis, but I’ll guess that HRSA got tired of receiving proposals that used a hodgepodge of non-comparable data sources derived from a byzantine collection of sources, some likely reliable and some likely less than reliable. To take one example we’re intimately familiar with, the five Service Planning Areas (SPAs) for which LA Country aggregates most data. If you’ve written proposals to LA City or LA County, you’ve likely encountered SPA data. While SPA data is very useful, it doesn’t contain much, if any, health care data. Healthcare data is largely maintained by the LA County Health Department and doesn’t correspond to SPAs, leaving applicants frustrated.

(As an aside, school data is yet another wrinkle in this, since it’s usually collected by school or by district, and those sources usually don’t match up with census tracks or political sub-divisions. There’s also Kids Count data, but that is usually sorted by state or county—not that helpful for a target area in the huge LA County with a population of 10 million.)

The UDS Mapper combines Census data with reports from Section 330 providers, then sorts that information by zip code, city, county, and state levels. It’s not perfect and should probably not be your only data source. But it’s a surprisingly rich and robust data source that most non-FQHCs don’t yet know about.

Everyone knows about Census data. Most know about Google Scholar, which can be used to improve the citations and scholarly framework of your proposal (and this is a grant proposal, so no one checks the cites, but they do notice if they’re there or not). HRSA hasn’t done much to promote UDS data outside the cloistered confines of FQHCs. So we’re doing our part to make sure you know about the new data goldmine.