On adjusting the funding disparity in NIH grant awards
June 16, 2020
I’ve been tweeting a lot of stuff lately that is related to Ginther et al., 2011, Ginther et al., 2018 and most especially the Hoppe et al. 2019 publication. This has been somewhat related to the national conversation we’re having about racial disparity in the wake of the white woman dogwalker attempted murder by cop, the George Floyd murder by cop and the ensuing peaceful protests and cop counter-protest violence that ensued.
I’ve had quite a bit to say about the original Ginther and the dismal NIH response to it. I was particularly unhappy with the NIH (ok, Director Francis Collin’s) response to the Hoppe et al paper.
These papers and findings are tops on my mind, especially as I fielded reactions both direct and indirect from my colleagues. Everybody is really dismayed by the George Floyd murder. Everybody is taking a moment, maybe because they are home with the Corona virus restrictions, but taking a moment to be be really bothered. And really keen to UNDERSTAND. And to DO something. Well, doing things kinda starts in our own house, eh?
The Hoppe paper is mostly about topic words and the way that the types of research interests that Black PIs have may set them at a disadvantage. Nevermind the fact that even within topic word clusters Black PIs still are at a disadvantage, the NIH is really keen to discuss the glass being half not-racist instead of the fact it’s also still half racist. But for me this was an opportunity to grapple with the numbers and revisit my old topics about how few grants it would actually take to even up the hit rate for Black PIs. This is because Black applicants are only in the low single digits in terms of percentages. The Hoppe data looks at R01 applications submitted for FY2011-2015, taking only the ones with identified Black or white PIs. We’re going jump into the middle a bit here so that I can download my recent tweet storm into a post. First, a poll I put up.
The question came from my thinking about Hoppe, but I waited to see the votes before returning to a theme I’d been on before. It will help you to open both Hoppe and the Supplement and look at Figure 1 from the former and Table S1 of the latter. Figure 1 confuses applicants (left side) with applications (top right) so it can be good to refer to the Table S1.
There were 2403 R01 applications from Black PIs. 1346 (or 56%) were triaged and 1057 (44%) were discussed. Of the discussed applications 256 (10.7%) were funded and 801 of the discussed apps were not funded. (Note there’s some rounding error here so don’t hold me to one app one way or the other. That 10.7% was rounded up because 10.7% of 2403 is 257, not 256.) This was for applications submitted across five Fiscal Years, so we’re talking ~269 apps triaged (not discussed) per year and ~160 discussed but not funded per year. There are 25 NIH ICs that fund grants, if I have it right. (I’m pulling the relative allocation per-IC below from a spreadsheet that lists 25.)
So that’s 11 (triaged) and 6 (discussed) Black PI applications per year per IC that do not get funded. For reference, NIMH (which is the 9th biggest IC by budget) has 256 new R01 and 37 Type 2 renewal R01s on the books right now. That’s right, you say, ICs are different in size and so therefore we need to adjust the unfunded applications from Black PIs to the size of the IC. Yes, I realize we probably have large differences in % Black PIs seeking funding across the ICs but it’s all we have to go on without better information. ok, so lets look at the unfunded apps by IC share. This analysis to follow will be selected ICs.
The biggest NIH institute, NCI, receives 15.5% of the entire NIH allocation (which is $41.64 Billion). If we allocate the unfunded applications from Black PIs proportionally then NCI applications account for 42 NDs and 25 discussed-unfunded. But that institute is so large it is hard to really grasp. Lets look at NIGMS (5th by $)- 19 NDs and 11 unfunded. MH? 13/8; DA? 10/6; AA? 4/2. and I’m rounding up for the last two ICs. so. what percentage of their funded (type 1, type 2) would this be? I’m basing off current FY Type 1 and 2 because we’re talking forward policy. If these ICs picked up the discussed-not-funded by %NIH$ share? NIGMS- 2%, NIMH- 2.7%, NIDA- 5.2%, NIAAA- 2.5%.
For completeness the share of the triaged/ND apps would be: NIGMS- 3.3%, NIMH- 4.5%, NIDA- 8.7%, NIAAA- 4.2%. again as a fraction of their current new grants. I mention this because one of the consistent findings of Gither et al 2011 and Hoppe et al. 2019 is that applications from Black PIs are more likely to be triaged. The difference in the Hoppe data set was 56% of applications from Black PIs went un-discussed versus only 42.6% of white PI applications.
So. Those numbers of discussed-but-unfunded applications from Black PIs are low, but it seems high enough to be relevant. A couple to five percentage of the portfolio for a year? This is not unimportant to the IC portfolio. But to YOU, my friend… remember the population size. If we took those 801 apps from the Hoppe data set and funded them, while subtracting 801 apps funded to white PIs (remember, they ignored all other categories of PI race), this would make the success rate for white PI applications go from 17.7% to…wait for it…16.9%. Recall, the funding rate for Black PI applications was 10.7%. So yes, that would push the success rate for Black PI applications to 44%ile if NIH funded all of the discussed applications. Which sounds totally unfair. But before you get too amped about that, recall your history.
Those people we think of as the current luminaries spend a good chunk of the middle of their careers enjoying >30% success. Look at those rates in the 1980s…you may not be aware of this but the early 80s was time remembered as simply terrible in the grant getting. Oh, the older folks would tell me tales of their woes even in the mid 2000s. Well I eventually realized why. Some of them had a few years in there, prior to the 1980s, of 40% or better. And this particular data set (it’s RPG, not just R01 btw) isn’t even broken out by established/new PI or continuation/new grant! So I’m sure the hit rate for established PI applications was higher as was the rate for competing renewal applications.
Why yes, we ARE coming back to the establishment of generational accumulated wealth. From a certain point of view. but not right now. we’re not ready to talk about the R word.
Instead, let’s come at this the other way. We kinda got into this a few days ago talking about the white PI grants that were funded at lower scores than *any* funded app with a Black PI (this is in Table 1 of Hoppe et al). There were 2403 Black PI applications in the dataset used in Hoppe et al.. 17.7% of this is 425. Subtract the 256 that were funded and we are at 169 applications (as a reminder this is NIH wide, over 5 years) to reach parity with the white PI rate. Of course subtracting those 169 from the white PI pool would plunge their success. *plunge* I tell you.
From 17.7% to…..17.5%. which would obviously be totally unfair so I’ll let you do the math to get them to meet in the middle. Just remember NIH prefers if the Black PI apps are juuuuuust under. Statistically indistinguishable tho. Like for gender. Getting this to meet in the middle means that something less than a 0.2% change in the success rate of grants submitted by white PIs would fix the 7.0% deficit in success rates that applications from Black PIs suffer.
If instead of just matching success rate, NIH were to fund every single discussed application submitted by Black PIs, this would only change white PI success rates by 0.8%, down from 17.7% to 16.9% as outlind above. Again, we need to compare that 0.8% drop to the 7% deficit suffered by applications with a Black PIs that is currently NBD according to the NIH. and many of our science peers.
I feel confident there are many who are contemplating these analyses and the implied questions thinking “wait, I’m not exchanging my grant for their grant“. But that’s not the right way to think about this. You would be exchanging your current 17.7% success rate for a 17.5% success rate.
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