1. WOMEN’S HEALTH CONGRESS
March 15, 2012
Walter T. Schaffer, PhD
Senior Scientific Advisor for Extramural Research
National 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 Nation's 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 Faculty
2008 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 as
Awardees 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<0.05
10%
* *
8%
*
6% *
4%
*
2% White
* Black
0%
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 Probability
There 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 -0.012
Unknown 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