06. Gender, racial, and ethnic identity representation in National Institutes of Health study sections: Impact of the COVID-19 pandemic, 2019-2021

Disparities in biomedical research hinder the professional advancement of underrepresented researchers. Before the COVID-19 pandemic, women were underrepresented in National Institutes of Health (NIH) study sections, panels of scientists that influence allocation of grant funding. Little is known about representation of racial/ethnic or sexual/gender minorities. This retrospective study seeks to elucidate the effect of the COVID-19 pandemic on NIH study section representation. Data were extracted from public study section lists during the May to July 2019, 2020 and 2021 NIH review cycles. For all reviewers (N=16,980), gender was identified using pronouns or photos from reputable websites. To confirm gender identity and collect data on race/ethnicity and sexual orientation, an electronic REDCap survey was distributed to all reviewers in fall 2022.
We assessed the demographic composition of NIH study sections from 2019-2021 under different methods to address missing data. Complete case analysis was conducted with the 42% of reviewers who completed the survey (n=7189). To address missing race/ethnicity data, we propose random imputation with proportional constraints. Expected proportions for racial/ethnic groups were obtained from the NIH Data Book, which reports demographics for research grant principal investigators. Sensitivity analysis was conducted with 2020 and 2021 US Census data, which included reference proportions for race/ethnicity and sexual orientation. Lastly, for each missing data approach, we used generalized estimating equations to model the effects of gender, institute, race, ethnicity and review cycle on study section membership. This analysis leverages novel data and existing reference information to assess representation in NIH study sections. Understanding representation of the scientists who influence NIH grant decisions is an important first step to ensure biomedical workforce diversity and innovative science that addresses the needs of the US.

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Women in Statistics and Data Science 2024