Cross-posted from Datahound

To continue my series of posts on the NIH HIgh Risk Research programs, I submitted a FOIA request to NIH for data regarding the race and ethnicity of the applicant pools for the four High Risk Research programs and recently received the results.

These data represent voluntarily self-identified race and ethnicity data that are collected when initiating an NIH Commons account (and used to be collected as part of an application). The categories listed are African American, American Indian, Asian, Multiple Races, Native Hawaiian, Unknown, White, and Withheld. While the data are presented by year (as I had requested), data are redacted for cells that represent fewer than 11 individuals. Since this limitation applies to many cells, I will focus on the data aggregated over all years of each program. It is important to note that these are not unique applicants, that is, applicants who apply to a given program multiple years will be counted multiple times.

The results for the African Americans, Asians, White, Unknown, and Withheld categories are shown below. The results for American Indians, Multiple Races, and Native Hawaiians are not included in this graph because of the redacted data.

Application pools

These data show that the percentage of White applicants ranged from 53% to 68% with the highest percentages for the Pioneer and Transformative R01 programs (68% and 63%) and lower percentages for the New Innovator (53%) and Early Independence (56%). This correlates with the likely career stage of the applicants with the Pioneer and Transformative R01 programs attracting established, more senior, investigators. The percentage of African American applicants ranged from 1.4% to 2.7% with the opposite trend (higher percentages for the New Innovator and Early Independence programs). The percentage of Asian applicants ranged from 16% for the Early Independence program to 30% for the New Innovator program with those for the Pioneer (18%) and Transformative R01 (20%) at intermediate values.

How do the applicant pools compared with the awardee pools? Because of the relatively low number of awardees for these programs, data about the race and ethnicities of these individuals were inferred by examining each awardee individually. It is important to note the difference in methodology between self-reported race and ethnicity for the applicant pool and inferred race (based on available biographical information, appearance, and name) for the awardee pool.

With this important disclaimer, I estimated the numbers of African American, Asian (primarily Chinese, Indian, and Japanese), and Other awardees in each program. These were compared with the corresponding numbers from the applicant pools. The ratios of Awardees to Applicants for these three racial groups for each program are shown below. The numbers of applicants in each group is also shown since these bear on the interpretation of the results.


For the Pioneer program, the ratio of awardees to applicants is 8/64 = .125. This ratio is higher than the ratio for Other (non-African American, non-Asian) at 107/3053 = 0.035. This difference is statistically significant with a p value of 0.002. Similarly, the ratio for Asians of 40/693 = 0.058 is significantly larger than that for Other with a p value of 0.009. The difference between the ratios for African Americans and Asians is not statistically significant (p = 0.054).

These results suggest that the success rates for African Americans and Asians are higher than that for Other applicants. Interpretation of these observations must be done with considerable caution. First, the number of African American applicants and awardees is quite small, an average of less than 1 awardee per year. While the result is statistically significant, it is not very robust to small changes in the number of awardees. Second, there is considerable selection bias in this program, based on my direct experience trying to encourage individuals to apply. Some are not aware of the program (or the timeline) while others feel that they are so unlikely to succeed that they are reluctant to apply even when encouraged. This self-selection could apply somewhat differently to different racial groups although I have no data that bear of this.

For the New Innovator program, the observed ratio of awardees to applicants is lowest for African Americans, slightly higher for Asians, and highest for Other, but these differences are not statistically significant. The numbers of applicants in all three groups are relatively large so that these findings are more robust than those for the Pioneer program.

For the Early Independence program, there appear to have been no African American awardees. However, there have been only 10 self-identified African American applicants (this value was not redacted in the materials I received) and the difference between the ratio of 0/10 is not statistically significantly different from 59/381 for Other (p value 0.37). The ratio for Asians is slightly higher than that for Other, but this difference is also not statistically significant.

The analysis for the Transformative R01 program is slightly more complicated because this program allows multiple principal investigators (PIs). I have included all PIs in my analysis of the awardees and believe this is also true for the applicant data that I received from NIH (although I am working to confirm this). With those caveats (and the disclaimer above about methodology), the ratio of awardees to applicants is 2/75 for African Americans. This ratio is slightly lower than the ratio of 154/4099 for Other, but this difference is not statistically significant (p = 1.00). The ratio for Asian applicants is also not statistically different from the other ratios.

As a final note, there is some overlap between the awardees in the Pioneer, New Innovator, and Transformative R01 programs with awardees from one program going on to receive additional awards in the same or other programs. Indeed, both African American Transformative R01 recipients had previously received Pioneer awards. This overlap will be the subject of a future post.

About The Author

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Jeremy Berg is a scientist and science administrator who firmly believes that, just as in science, discussions of science policy are most productive when reasonable data relevant to the topic are available to analyze, criticize, and interpret. He received his Ph.D. in Chemistry, did a post-doc in Biophysics, spent 4 years on the Chemistry faculty at Johns Hopkins University School of Arts and Sciences, 13 years as Director of Biophysics and Biophysical Chemistry at Johns Hopkins University School of Medicine, 8 years as Director of the National Institute of General Medical Sciences at NIH, and is now at the University of Pittsburgh in Computational and Systems Biology and the Institute for Personalized Medicine.