An Updated Look at Applications Submitted During the Pandemic

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In a previous post, we looked at the gender distribution of designated principal investigators (PI’s) of R01 and Research Project Grant (RPG) applications submitted before and after the onset of the COVID-19 pandemic. Since that time, we have paid close attention to the well-being of the extramural biomedical research workforce, in part through our survey of institutional leaders and scientists. Others have followed preprint postings and publications, finding evidence of the pandemic’s disproportionate effects. Here we look at NIH R01 and RPG application patterns for January 1 through April 8 over the past 6 years; these applications patterns may well reflect longer-term pandemic effects.

R01 Equivalent Applications

Table 1 shows the percentage of R01-equivalent applications submitted by men only, women only, and both men and women (as would be possible for multi-PI applications). The overall number of application submissions continues to increase, up 8% compared to last year, just before the pandemic hit. The proportion of applications from men only has declined somewhat, while the proportion of multi-gender applications has increased. The proportion of women-only applications has remained stable.

Table 2 shows corresponding data for race. The proportion of applications coming from Whites only has decreased slightly, while the proportion of mixed applications has increased. Only a small proportion of applications come from under-represented minorities (URM) only.

Table 1: R01-equivalent applications by gender received between January 1 and April 8 in 6 consecutive years

Year Number All Men(%) All Women(%) Both(%) Unknown(%)
2016 10835 62.8 24.8 11.1 1.4
2017 10433 61.2 25.5 11.8 1.5
2018 10876 61.1 24.5 12.7 1.8
2019 11184 59.6 25.5 12.8 2.1
2020 10735 57.6 26.3 13.8 2.3
2021 11564 56.3 25.8 15.3 2.6

Table 2: R01-equivalent applications by race received between January 1 and April 8 in 6 consecutive years

Year Number White Only(%) Asian Only(%) Mixed(%) More Than One(%) Unknown Only(%) URM Only(%)
2016 10835 57.6 23.5 11.1 0.8 5.6 1.4
2017 10433 56.7 23.7 11.9 0.9 5.4 1.4
2018 10876 55.5 23.4 12.9 0.9 5.7 1.5
2019 11184 54.4 23.8 13 0.8 6.2 1.7
2020 10735 54.5 23.5 13.6 0.8 6.1 1.4
2021 11564 51.2 23.9 16.1 0.9 6.2 1.8

Research Project Grant (RPG) Applications

Tables 3 and 4 show corresponding data for RPG applications. The trends (or lack thereof) are similar to those of R01-equivalent applications.

Table 3: RPG applications by gender received between January 1 and April 8 in 6 consecutive years

Year Number All Men(%) All Women(%) Both(%) Unknown(%)
2016 17829 60.5 26.7 10.2 2.7
2017 17830 58.7 27.3 11.3 2.7
2018 17559 59.1 26.3 11.7 3
2019 17796 57.4 27 12.1 3.5
2020 17099 56.1 27.3 12.7 3.9
2021 17980 55.4 26.9 13.8 3.9

Table 4: RPG applications by race received between January 1 and April 8 in 6 consecutive years. URM = Under-represented Minorities.

Year Number White Only(%) Asian Only(%) Mixed(%) More Than One(%) Unknown Only(%) URM Only(%)
2016 17829 56.3 23.5 10.2 0.9 7.2 1.9
2017 17830 55.4 24 11 1 6.8 1.8
2018 17559 54.6 23.5 11.9 1 7.1 1.9
2019 17796 53.5 23.8 12.2 1 7.5 2
2020 17099 53.4 23.7 12.4 1 7.8 1.8
2021 17980 50.9 24.2 14.4 0.9 7.5 2.1

Summary

In these descriptive analyses, we find no particularly marked changes in the high-level demographics of designated PI’s on R01-equivalent and RPG applications. We plan to examine these data in greater depth, particularly once peer review outcomes and funding decisions are known.

I am grateful to my colleagues in the NIH Office of Extramural Research (OER) Division of Biomedical Research Workforce and Division of Statistical Analysis and Reporting for their help collecting these data and conducting these analyses.

20 Comments

  1. Thank you for this interesting information. However, I would be also very much interested about how many women did receive the funding.

  2. Two comments:
    (1) Applications data is nice, but doesn’t reflect reality. Award data is the reality and I would like to see how awards are apportioned.
    (2) Don’t use a dark background for a webpage.

  3. Thank you for sharing the data and analyses. I echo Jana’s request. In addition, I’m also interested in the award amount for different gender and ethnic groups.

    1. 100% agree. While it is good to know the submission stats, the real story is in the awards.
      Also, it would be good to divide the categories more — first author gender and ethnicity, separate out “Mixed,” etc.

  4. Bravo to the parents and others in difficult circumstances who persisted. The NIH’s attention to this issue is appreciated. I echo the interest in funding outcomes, including delayed outcomes, and I am also curious about data from the second half of 2020 encompassing the October deadline.

  5. These data are interesting and seem to suggest the pandemic did not negatively impact the productivity of those who submit grant applications to the NIH. While this may be true, in general, there was no analysis stratifying by investigator status. I suspect, there was a reduction in submitted applications by ESI, who are more likely to have struggled with childcare-related issues this year.

    1. Agree with this – it could be far more illuminating to break down these data by age (and/or career stage which often tracks with age). The investigators I know who experienced the most negative impacts on productivity this year were both women and men with kids at home. Investigators I know without kids at home actually *increased* their productivity this year including grant submissions. I would love to know whether an increase in RPG applications from a subset of investigators has masked a decrease in applications from ESI?

  6. We don’t have information yet about award rates since the applications recently arrived. But we will certainly be monitoring that information and posting the result.

  7. Data confirms how little Women and URMs are part of the current NIH funding process. This is a picture of the greater biomedical sciences where women and individuals from URM groups are continuously excluded from the system. URMs not even applying for grants is a picture of the larger overall structural racist system we have in place. I don’t believe these data are anything to be proud of. I agree with others that award data would be more insightful; however even if 100% of women and URMs are being funded, it will do nothing to close this gaping hole that our racist and male-dominated society has created in the biomedical sciences, which then leads to the perpetuation of the growing health disparities we see in our society. Unless we change how we fund the biomedical sciences and the current NIH funding system, these numbers will remain the same over the next 10, 20, 30 years.

    1. Would you mind citing your sources that show racism is the cause of discrepancy in NIH funding? Thanks.

  8. I am curious to see the stats for R15 submissions, categorized by career stage, by childcare responsibilities, by other dependants responsibilities (aging parents), and by health conditions. I hope that such a description will help in understanding the effect of the pandemic and in the design of policies to support equitable funding processes.

  9. Yes, I’d be curious to see awards data corresponding to these submissions; particularly as I developed Best Practices Tools for documenting impact of COVID on faculty research, teaching, and service.

  10. What should be compared are the number of grants from men with school-aged children and the women with school-aged children….and the number of grants from anyone vs. from women with school-aged children. It’s almost like this study was trying to NOT see gender differences…by ignoring the population most affected.

  11. These data seem to demonstrate that most investigators are willing to share information on their demographic characteristics with the NIH.
    Is it possible to clarify the following:
    1. Who is allowed to access these data and what process/policy governs the access to these data?
    2. Can analyses (by IC or disease area) be requested? If so, how?
    3. What is the definition of URM for these analyses?
    Thank you!

    1. 1) Only certain NIH staff have access to these self-reported demographic data. For more on what we collect and who may access the data, please see this NIH Open Mike post: https://nexus.od.nih.gov/all/2018/06/29/when-to-share-or-when-not-to-share-that-is-the-privacy-question/
      2) We currently publicly report some demographic data in the NIH Data Book, see the report here: https://report.nih.gov/sites/report/files/docs/NIH_Principal_Investigators_by_Gender_Race_Ethnicity_and_Disability_2016-2020_02_23_2021_PDF.pdf. Additional data and publications are available on the NIH Scientific Workforce Diversity website at https://diversity.nih.gov/building-evidence/racial-disparities-nih-funding. Other analyses may be requested via a Freedom of Information Act request, but please be advised that due to provisions within the FOIA and the specific request itself, we may not create new records to answer the specific request.
      3) For more on how URMs are defined, please visit: https://diversity.nih.gov/about-us/population-underrepresented

  12. Thank you for the breakdown. I echo most of the previous comments regarding award rate – the real bottom line. Are there similar analyses for SBIR/STTR grants? In Science yesterday, there was a commentary and article on the connection of the inventor gender gap to the gender healthcare gap. That is, “all female-inventor teams were more likely than all-male teams to focus on women’s health”. This ongoing gender gap also drives the direction of technology and invention across the broader society. I would be unsurprised to see similar trends with regards to racial or economic diversity. See: https://pubmed.ncbi.nlm.nih.gov/34140388/

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