Tag Archives: Needs Assessments

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.

The Census During Hard Times: A Gift That Keeps On Giving

One of the best things that can happen to a grant writer is to have the Census roll around during a time of economic crisis, because decennial Census data hangs around for about ten years. It takes the Census Bureau around two years or so to publish the latest data, which then gets used until the next turn of the census screw. The “2010 Census” will really be used as the 2012–2022 Census.

While the Census Bureau and other data miners produce interim data, such data are mostly a hodgepodge of extrapolations, which is another word for educated guesses. It’s possible for a city or county to request a special mid-decade census, but it’s doubtful that many have the money for it, so grant writers are pretty much stuck with whatever the Census produces. It’s our job to craft compelling Needs Assessments, whether the data is good, bad or indifferent. The task becomes a lot easier when the data shows economic calamity.

Given the recent economic collapse, incomes will be down, poverty up, etc., in the 2010 Census for the kinds of target areas we usually write about. When the Census coincides with better times, such as the 2000 Census, it’s much harder to make the case that things are tough because incomes and so forth will be relatively high, but a good grant writer will make this case anyway, pointing out the lingering effects of the last recession, the coming recession, or the ever popular refrain, “the target area is an island of misery in a sea of prosperity.” But lousy census data means happy times for grant writers. The 2010 Census will be a case in point, as we will be using the dismal economic data to good effect until the year 2022 or so!

Being as old as mud, I started using census data from the 1970 Census. In 1978, I was hired as the Grant Writing Coordinator for the City of Lynwood, CA, which is located next to Compton and Watts in LA County. By the time I got to Lynwood, most residents were African American and very low-income, but one would never know it by looking at the 1970 Census data. The 1970 Census painted Lynwood as a largely middle class, white community, which it was when the Census was taken. Like its much better known neighbor, Compton, which has been immortalized in endless rap songs like N.W.A.’s “Straight Outta Compton,” Lynwood was the victim of blockbusting and turned almost overnight from white to Black. It’s just that Compton metamorphosed immediately after the Watts Rebellion and before the Census was taken. In contrast, Lynwood changed demographically just after the Census was taken. I left Lynwood before the 1980 Census data was taken, so I spent three years writing proposals in which I had to explain away the available census data. While annoying, this helped hone my grant writing skills.

One interesting factoid about the census is that, and as reported, albeit obliquely, by the Pew Research Center in Census History: Counting Hispanics, Hispanics were not actually counted until the 1980 Census and the questions relating to Hispanic status change each census cycle, making it very challenging to make the kind of comparisons that are the stuff of needs assessments. This is compounded by the fact that the Census Bureau does not consider “Hispanic” to be a race. One can be counted as a Hispanic of any number of races and, if all are added up, this can easily total more than 100% of the population. There are various work arounds, the easiest of which is to check with the local city or county to see if they have sorted out what the percentage of “Hispanics” is in their jurisdiction.

We are currently writing an OJJDP FY 2010 Youth Gang Prevention and Intervention Program proposal for a nonprofit in Southern California. The target area was largely middle class and white at the time of the 2000 Census but is now Hispanic and low-income. So, for me it’s 1978 again and I am struggling with same data issues I was in Lynwood.

As the wheel of time turns and grant writers must use out-of-date census data for at least two more years. Look on the bright side of things––data from the 2010 Census will be absolutely awful and you can use it to your advantage for many years to come.

On the Subject of Crystal Balls and Magic Beans in Writing FIP, SGIG, BTOP and Other Fun-Filled Proposals

I’ve noticed a not-too-subtle change in RFPs lately—largely, I think, due to the Stimulus Bill—that requires us to drag out our trusty Crystal Ball, which is an essential tool of grant writing. Like Bullwinkle J. Moose, we gaze into our Crystal Ball and say,”Eenie meenie chili beanie, the spirits are about to speak,” as we try to answer imponderable questions. For example, our old friend the HUD Neighborhood Stabilization Program 2 (NSP2) wants:

A reasonable projection of the extent to which the market(s) in your target geography is likely to absorb abandoned and foreclosed properties through increased housing demand during the next three years, if you do not receive this funding.

How many houses will be foreclosed upon, but also absorbed, in our little slice of heaven target area in 2012? If I was smart enough to figure this out, I’d be buying just the right foreclosed houses in just the right places, instead of grant writing. People much smarter than us who were predicting in 2005 how many houses they’d need to absorb in 2009 were tremendously, catastrophically wrong, which is why we’re in this financial mess in the first place: you fundamentally can’t predict what will happen to any market, including real estate markets. Consequently, HUD’s question is so silly as to demand the Crystal Ball approach, so we nailed together available data, plastered it over with academic sounding metric mumbo jumbo, and voila! we had the precise numbers we needed. In other words, we used the S.W.A.G. method (“silly” or “scientific wild assed guess,” depending on your point of view). I have no idea why HUD would ask applicants a question that Warren Buffett (or, Jimmy Buffet for that matter, who may or may not be a cousin of Warren) could not answer, but answer we did.

You can find another example of Crystal Ball grant writing in the brand new and charmingly named Facility Investment Program (FIP), brought to us by HRSA, which are for Section 330 providers (e.g. nonprofit Community Health Centers (CHCs)). We’re writing a couple of these, which requires us to drag out the ‘ol Crystal Ball again, since the applicant is supposed to keep track of the “number of construction jobs” and “projected number of health center jobs created or retained.”

I just lean back, imagine some numbers and start typing, since there is neither a way to accurately predict any of this nor a way to verify it after project completion. HRSA is new to the game of estimating and tracking jobs, so they make it easy for us overworked grant writers and applicants by not requiring job creation certifications. Other agencies, like the Economic Development Administration (EDA), which has been about the business of handing out construction bucks for 40 years, are much craftier. For instance, the ever popular Public Works and Economic Development Program requires applicants to produce iron-clad letters from private sector partners to confirm that at least one permanent job be created for every $5,000 of assistance. We’ve written lots of funded EDA grants over the years, and the inevitable job generation issue is always the most challenging part of the application. HRSA will eventually wise up when they are unable to prove that the ephemeral construction and created/retained jobs ever existed. Alternately, they might wise up when they realize the futility of the endeavor in which they’re engaged, but I’m not betting on it.

This tendency to ask for impossible metrics is always true in grant writing, as Jake discussed in Finding and Using Phantom Data, but sometimes it’s more true than others. I ascribe the recent flurry to the Stimulus Bill because more RFPs than usual are being extruded faster than usual, resulting in even less thought going into them than usual, forcing grant writers to spend even more time pondering what our Crystal Balls might be telling us.

Since the term “Crystal Ball” began popping up whenever I scoped a new proposal with a client, I got to thinking of other shorthand ways of explaining some of the more curious aspects of the federal grant making process to the uninitiated and came up with “Magic Beans,” like Jack and the Beanstalk. We’re writing many proposals these days for businesses, who have never before applied for federal funds, for programs like the Department of Energy’s Smart Grid Investment Grant (SGIG) Program, and the Broadband Technology Opportunities Program (BTOP) of the National Telecommunications & Information Agency.

When scoping such projects, I am invariably on a conference call with a combination of marketing and engineer types. The marketing folks speak in marketing-speak platitudes (“We make the best stuff,” even if they don’t know what the stuff is) and the engineers don’t speak at all. So, to move the process along, and to get answers to the essential “what” and “how” of the project concept, I’ve taken to asking them to, in 20 words or less, describe the “Magic Beans” they will be using and what will happen when the magic beans are geminated after that long golden stream of Stimulus Bucks arcs out of Washington onto their project. This elicits a succinct reply, I can conclude the scoping call, and we can fire up the proposal extruding machine.

So use your Magic Beans to climb the federal beanstalk and reach the ultimate Golden Goose, keeping your Crystal Ball close at hand.