Tag Archives: Data Mining

Finding and Using Phantom Data in the Service Expansion in Mental Health/Substance Services, Oral Health and Comprehensive Pharmacy Services Under the Health Center Program

RFP needs assessments will sometimes request data that aren’t readily available or just don’t exist. The question then becomes for you, the grant writer, what to do when caught between an RFP’s instructions and the reality of phantom data. When you can’t find it, you’ll have to get creative.

The Service Expansion in Mental Health/Substance Services, Oral Health and Comprehensive Pharmacy Services Under the Health Center Program (see the RFP in a Word file here) presents a good example of this problem. The narrative section for “B. Oral Health Review Criteria” begins on page 44. Under “Review Criterion 1: Need,” “Subsection 2,” the RFP says “Applicant clearly describes the target population for the proposed oral health service, including […]” which goes to “Subsection c,” which says, “The oral health status and treatment needs of the target population (e.g., caries rate, edentulism, periodontal disease, fluoridation in community water, oral cancer).” Such data are not tracked nationally. To the extent anyone keeps data, they do on a county-by-county or state-by-state basis, which can make finding the data hard—particularly for a service area that may not match up with a county or other jurisdictional boundary. But I had to answer the RFP and so looked for information about oral health status online but could find little if anything through state, county, or city websites.

If you can’t find important data online, your next step is to contact whichever public officials might be able to have it. The organization we worked for was in a state with dental health responsibility rolled into the Department of Health. Contact information was listed on the Department of Health’s website. So I called and e-mailed both the person at the state office and the person responsible for the organization’s county. Neither answered. I skipped the rest of 1.2.c until one of the state representatives replied—promptly, too!—with a Word file containing what data they had. While the information helped, and I cited what they offered, the statistics didn’t answer all of the named examples of the health status and treatment needs. I still had an unfilled data gap.

This left two choices: say the data isn’t available or write in generalities about the problems, extrapolating from what data are available. If you say the data isn’t there, you might score lower for the section compared to the people who have data or write in generalities. If you obfuscate and explain, there’s a chance you’ll receive some points. Therefore, the latter is almost always the better choice: this can be done by discussing what data you have in generalities, telling anecdotes, appealing to organization experience, and alluding to known local health problems that don’t have specific studies backing them up. This usually means saying something to the effect of, “While specific data are not available for the target area, it can be assumed that…” and then continuing. I used a combination of these strategies by citing what data I had from the state and filling the gaps with generalities.

When you find requests for data, do everything you can to seek it, and don’t be afraid to contact public officials. If you still can’t find the data, summon whatever construct as artful an explanation as you can and then move on. Chances are that if the RFP wants data so unusual that you can’t find it after a concerted effort, many other people won’t be able to find it either. You should also remember The Perils of Perfectionism: every hour you spend searching for data is an hour you’re not spending on other parts of the proposal. You shouldn’t invest hours and hours of time in finding trivial data, and after you’ve made a reasonably strong effort to search the Internet and contact whoever you can, stop and move on. This is especially important because you might be searching for data that simply does not exist, in which case it will never be found and you’re wasting time trying to find it. While it is fun to search for the last unicorn, you are most likely to find a horse with a cardboard horn strapped to her forehead than a mythical and elusive creature.