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Schaffer women's health congress 2012 draft 4 mar 2012

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  • Only includes applications reviewed by CSR standing study sections.The same pattern observed for applied and clinical sciences is observed for basic sciences.African Asian White Total American (includes all races)2000 15 368 1,722 2,307 2001 20 375 1,637 2,269 2002 19 411 1,613 2,264 2003 22 394 1,633 2,226 2004 38 741 2,719 3,875 2005 45 1,020 3,506 5,075 2006 70 1,363 4,312 6,483 2007 58 1,511 4,145 6,604 2008 56 1,485 3,643 6,058 2009 66 1,376 3,339 5,795 2010 68 1,711 4,080 7,151
  • Conducted by Discovery Logic and Kansas UniversityMultivariate regression models to investigate award probability differencesSample restricted to Type 1 R01 applications submitted by PhD applicants between FY2000-06. Related or revised submissions received within 2 years of the original application were collapsed into one grant application.Information about an application was derived from the last funded or unfunded application.
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    • 1. WOMEN’S HEALTH CONGRESS March 15, 2012Walter T. Schaffer, PhDSenior Scientific Advisor for Extramural ResearchNational Institutes of Health
    • 2. Importance of Workforce Diversity• NIH Programs in place since the 70s, MARC, MBRS, RCMI• The NIH has an unique and compelling need to promote diversity in the biomedical, behavioral, clinical and social sciences research workforce. – Involvement of the most talented researchers from all groups in order to: • improve the quality of the educational and training environment • balance and broaden perspectives in setting research priorities • improve the ability to recruit subjects from diverse backgrounds into clinical research protocols • improve the Nations capacity to address and eliminate health disparities.
    • 3. Representation of Women, by NIH Award Mechanism NIH RePort: http://report.nih.gov/nihdatabook/Default.aspx?catid=15
    • 4. R01-Equivalent Grants: Success rates, by the Reported Sex of the Applicant and Type of Application NIH Report: http://report.nih.gov/nihdatabook/Default.aspx?catid=15
    • 5. Diversity of the NIH-Funded Workforce NIH has had a less than impressive impact on the diversity of the NIH- funded scientific workforce over the past 30+ years 0.1% 0.1% 6.6% Hispanic or Latino (of any race) 12.5% 0.7% 3.6% American Indian and Alaska Native 17.2% Asian 4.1% 10.2% 0.1% Black or African American 12.7% White 62.9% 61.5% Native Hawaiian and Other Pacific Islander 2.9% Other, unknown, not reported and more than one race 2010 US Full-Time Medical School Faculty2008 US Census Bureau Report 0.2% 3.4% 11.2% 0.4% 16.7% 71.9% 1.2% 2009 NIH Principal Investigators on RPGs 5
    • 6. Participation of the Indicated Racial and Ethnic Groups asAwardees on NIH Research Project Grants (FY 2000 - 2010) 20% Participation of the Indicated Racial and Ethnic Groups as Awardees on NIH Research Project Grants (FY 2000 - 2010) 18% 16% 14% Asian 12% Black or African American 10% American Indian/Alaska Native Native Hawaiian or other Pacific 8% Islander Hispanic 6% 4% 2% 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
    • 7. Acad Med 86:759-767, 2011
    • 8. Career Transitions – Race/Ethnicity Change in Percent Representation vs. Previous Milestone, *significantly different from previous milestone p<0.0510% * *8% *6% *4% *2% White * Black0% Hispanic *-2% Asian * * * * Native American-4% * * *-6% *-8% College Graduate School Medical School Grad School to Medical School to (1996) (2001) (2001) Asst Prof Asst Prof (2006; SDR) (2006; SDR) Diversity in Academic Biomedicine: An Evaluation of Education and Career Outcomes with Implications for Policy, Donna K. Ginther, Walter T. Schaffer, Joshua Schnell, Beth Masimore, Faye Liu, Laurel L. Haak, Raynard S. Kington , http://ssrn.com/abstract=1677993
    • 9. Major Finding: Award ProbabilityThere is a significant difference in R01 award probability by race and ethnicity. 30% Black or African 25% ‡ American R01 Award Probability Asian 20% Hispanic 15% ‡ White 10% Full Sample 5% 0% * p<0.05, ** p<0.01, ‡ p<0.001 Race, Ethnicity, and NIH Research Awards Donna K. Ginther, Walter T. Schaffer, Joshua Schnell, Beth Masimore, Faye Liu, Laurel L. Haak, Raynard Kington, Science 333: 1015, 2011 9
    • 10. Success Rates by Field of Science and Race: Type 1 RPG What are the Success Rate Trends in Basic Sciences by Race? Type 1 RPG Applications Fiscal Years 2000-2010 50.0% 45.0% 40.0% 35.0% 30.0%Success Rate Overall Success Rate* 25.0% African American 20.0% Asian White 15.0% *Overall Success Rate includes applications and 10.0% awards contributed by American Indians and Alasksa Natives, Native 5.0% Hawaiians and Other Pacific Islanders, persons reporting multiple 0.0% races, as well as those 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 whose race is unknown or who choose to withhold Fiscal Year their race.
    • 11. Institutional Characteristics 40% — R01 Award Probability 30% — — Asian Black — 20% Hispanic White 10% Unknown — Average 0% Top 30 31-100 101-200 >201• Award probabilities are correlated with NIH Funding Rank of applicant’s institution.• In each Rank group, Black applicants have the lowest award probability. Note: These results are from the full sample n = 106, 368 Race, Ethnicity, and NIH Research Awards Donna K. Ginther, Walter T. Schaffer, Joshua Schnell, Beth Masimore, Faye 11 Liu, Laurel L. Haak, Raynard Kington, Science 333: 1015, 2011
    • 12. Future Efforts• MDs, Medical Schools, and NIH R01 Awards, Donna K. Ginther, PhD, Laurel L. Haak, PhD, Walter T. Schaffer, PhD, and Raynard Kington, MD, PhD, Submitted to Academic Medicine – Explores Racial/Ethnic Differences in Success for MD and MD/PhD Applicants • Differences smaller than for PhDs • Applicants that work in medical schools have better outcomes• Extended Studies: Race, Ethnicity, and NIH Research Awards, Donna K. Ginther, Walter T. Schaffer, Laurel L. Haak, Raynard Kington, in progress – Explores Differences in Success for Applicants examining variables that were not present in structured data • Training • Networks• Activities of the Diversity Workgroup of the Directors Advisory Committee – Chaired by Larry Tabak, Reed Tucson, and John Ruffin – Use experimental techniques to assess the benefits of pre-application mentoring – Use experimental techniques to determine possible contribution of bias in peer review setting.
    • 13. Supplemental Slides
    • 14. REGRESSION MODELS: VARIABLES NIH R01 Applications FY2000-06 from PhDs (n=83,188) MODEL 1: Demographic Characteristics: Gender, Race, Ethnicity, Age, Foreign Born, Foreign PhD MODEL 2: Education and Training: MODEL1 + Degree Type, Previous NIH Training Support, PhD field, PhD Institution Funding Rank MODEL 3: Institutional Characteristics. MODEL 2 + Employer Characteristics (organization type), Employer Region, NIH Funding Rank, Employer Carnegie Rank MODEL 4: NIH Resources. MODEL 3 + NIH Institute, FY Funding, Human Subjects, Prior Grants, Review Committee MODEL 5: Research Record. MODEL 4 + Prior Publications, % Last Author and Single Author Publications, Citations, Impact of Publications Applicants missing >1 demographic variable, such as race and gender, were excluded from the analysis. Race, Ethnicity, and NIH Research Awards Donna K. Ginther, Walter T. Schaffer, Joshua Schnell, Beth Masimore, Faye Liu, Laurel L. Haak, Raynard Kington, Science 333: 1015, 2011 14
    • 15. MODEL COEFFICIENTS PhD Sample R01 Award Model N % Probability 1 2 3 4 5 White 58,124 69.9% 29.3% Asian 13,481 16.2% 25.4%‡ -0.054‡ -0.054‡ -0.051‡ -0.040‡ -0.042‡ Black 1,149 1.4% 16.1%‡ -0.131‡ -0.131‡ -0.119‡ -0.110‡ -0.104‡Hispanic 2,657 3.2% 28.1% -0.027* -0.027* -0.023 -0.014 -0.012Unknown 7,637 9.2% 25.7%‡ -0.049‡ -0.044‡ -0.040‡ 0.012 0.016 * p<0.5, ** p<0.01, ‡ p<0.001, p-values corrected for multiple comparisons  Model 3 (Institution Characteristics) explains the difference in R01 award probability for Hispanic applicants.  Model 5 (Research Impact) explains 3 percentage points of the difference for Black applicants.  None of the models explain the difference for Asian applicants. Race, Ethnicity, and NIH Research Awards Donna K. Ginther, Walter T. Schaffer, Joshua Schnell, Beth Masimore, Faye Liu, Laurel L. Haak, Raynard Kington, Science 333: 1015, 2011 15