In his widely acclaimed book “The Difference” (1), Scott Page, a Professor at the University of Michigan, described a computer modeling experiment designed to test the “Diversity Trumps Ability Theorem.” The theorem postulates that “collections of diverse individuals outperform collections of more individually capable individuals” (1). The computer model showed that diversity enhanced the ability to solve problems or make accurate predictions (2), but only when 4 conditions were met: (1) the problems were difficult, (2) all problem solvers were “smart” (but not the smartest), (3) diversity was sufficient to ensure that different problem solvers could exploit the solutions of others, and (4) the populations of problem solvers and collections of problem solvers were large (1). As all 4 of these conditions are clearly met in heart, lung, and blood (HLB) research, we were stimulated to examine the diversity of topics and mechanisms in the National Heart, Lung, and Blood Institute (NHLBI) portfolio.
To further support his argument for the benefits of diversity, Page cited a number of empirical examples, including cities (3), policy-making agencies, management teams, and groups of scientists (1). Other authors have cited examples supporting the importance of diversity in science: multidisciplinary interactions have repeatedly been shown to generate greater degrees of rigor, creativity, evolution of ideas, academic productivity (4), and innovation (5, 6). Page argues that when faced with difficult problems, different people (or more generally different agents) can bring different “toolboxes.” Diverse toolboxes offer varying perspectives, interpretations, heuristics, and prediction models. Diversity works, both theoretically and empirically, because application of many different toolboxes reframes confusing data into eminently solvable problems and because diverse agents naturally build upon each others’ work (1, 2).
Science is one of society’s most valuable diverse “toolboxes” for improving health and for serving as a sound economic investment (7). Over the past 60 years, diverse groups of government-funded researchers in basic, translational, clinical, and epidemiological sciences played pivotal roles in enabling dramatic reductions in cardiovascular mortality and morbidity (8). Research America reports that every million dollars invested by the National Institutes of Health (NIH) generates 2 millions of new state business activity (9). Yet, the value of a diverse biomedical science toolbox, funded through an investment that is relatively small given the nation’s total health expenditures (about 1 penny for each dollar) (9–11), is questioned by some, with concerns being raised about what constitutes “worthy” science or wasteful projects (12). Others criticize funding agencies for being too conservative or spending too much in specific areas (13). Some scientific thought leaders have even called into question the value of funding research projects outside their own spheres or expertise (13–16). Unfortunately, there is no simple and universally accepted approach for allocating finite research resources (17). In fact, many of the arguments being heard today were also raised 20 years ago in another period of budgetary constraint (18).
Not surprisingly, whenever the economic landscape forces limitations on research funding, organizations that support science engage in a cyclic “soul searching” exercise. They face choices regarding types of science to fund (basic, clinical/applied), levels of risk to assume (“sure bet” versus high risk/ high reward), sizes of awards, and distributions among different types of applicants (individuals and/or large teams). The NIH recently invited the scientific community to provide input on “How Do You Think We Should Manage Science in Fiscally Challenging Times?” (19).
The NIH has long supported a diverse, balanced mix of basic and applied research. The basic-to-applied funding ratio has remained remarkably constant: in 1994, 57% of NIH research funding supported basic research, whereas 43% supported applied and development research. In 2004, the corresponding values were 55% and 45% (20), whereas more recently they were 56% and 41% (21). Even with the formation of the new National Center for Advancing Translational Sciences (NCATS), NIH remains publicly committed to maintaining that traditional balance (22, 23).
Just as in other fields, scientific diversity has been and continues to be critical for the success of HLB research. What do we mean by scientific diversity? Stirling cites three parameters: variety, which refers to the number of categories; balance, which indicates “how many of each”; and disparity, which describes how well categories can be distinguished (5). As with other natural or man-made environments, the survival, evolution, and eventual success of scientific ecosystems depend on their ability to capitalize on diversity in variety, balance, and disparity, especially under challenging conditions. For instance, it has been argued that the driving forces in the growth and development of cities and regions can be found in the productivity gains associated with the clustering of a diversity of talented people (human capital) (3).
Schneider (24) offers a different construct, proposing that scientists come in 4 “flavors,” all of which are essential for moving any scientific field forward. The scientists of the first flavor excel at being able to visualize the “fuzzy front end.” Their out-of-the box ideas are then translated into doable experiments designed and executed by scientists of the second and third flavors. Their experiments allow the new ideas to be methodically tested and then synthesized and further developed into new hypotheses by the fourth flavor of scientists who collect, categorize, interpret, and pass on large amounts of data. Schneider’s categorization may be oversimplified, but it illustrates how biomedical science is a relay exercise, better, a collection of relay exercises, by which scientists (better groups of scientists), interact to solve the many complex problems presented by human health and disease. As a community, we are most successful when we achieve active engagement of diverse problem solvers, including basic scientists, engineers, translational and clinical researchers, clinical practitioners, statisticians, policy experts, patients, communities, and indeed all those who can at some level understand and translate along the way.
To illustrate the intellectual diversity of the NHLBI’s HLB portfolio, we used the NIH funding database publicly available on the NIH RePORT website (25), “Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC).” The RCDC system uses sophisticated text data mining (categorizing and clustering using words and multiword phrases) in conjunction with NIH-wide definitions used to assign projects to categories. We extracted and reported here funding data only about those projects that were (1) active in fiscal year 2010, (2) performed in an extramural location (outside the NIH), and (3) were funded or primarily administered by the NHLBI. We designated projects as clinical if they were categorized as “clinical research” or “clinical trials.” We report funding levels according to research mechanism (Table 1), to clinical status, and to RCDC categories that include the great majority of HLB research (Tables 2 and 3); it should be noted that the categories are not mutually exclusive. We calculated values for numbers of projects, total funding, and quartile costs per project according to mechanism or topic using the SAS version 9.2 “Proc Tabulate” procedure. The data indicate a generally well-balanced distribution of NHLBI funds among clinical and nonclinical projects across topics and funding mechanisms. The individual research project grant (R01) mechanism predominates funding both for the clinical and nonclinical awards, being by far the most prevalent for the latter, whereas the cooperative agreement (U01) mechanism is used by many more clinical projects. Using another NIH tool that tracks published acknowledgments to NHLBI awards, we estimated that the grant portion of the portfolio illustrated here has generated more than 45,000 publications garnering approximately 2.4 million citations to date. (We are assuming that all NHLBI grantees comply with the obligation in their grant awards to include an acknowledgment of NIH funding in all manuscripts resulting from their NIH-supported research [26].)
TABLE 1.
FISCAL YEAR 2010 EXTRAMURAL NHLBI PROJECTS BY SELECTED MECHANISMS THAT TOGETHER ACCOUNTED FOR 87% OF ALL EXTRAMURAL FUNDS
| Nonclinical Projects |
Clinical Projects |
|||||
| Mechanism | No. | Total Funding (% Extramural Funding) | Median Cost per Project (25th, 75th Percentiles) | No. | Total Funding (% Extramural Funding) | Median Cost per Project (25th and 75th Percentiles) |
| Contract | 86 | 155,399 (6%) | 1,717 (522, 2,274) | 68 | 65,479 (3%) | 378 (109, 1,018) |
| P01 | 120 | 187,232 (7%) | 1,752 (1,018, 2,116) | 33 | 57,204 (2%) | 1945 (1,570, 2,423) |
| P50 | 1 | 1734 (<1%) | — | 21 | 42,449 (2%) | 2247 (1,619, 2,594) |
| R01 | 2475 | 804,079 (33%) | 381 (364, 410) | 995 | 515,239 (21%) | 431 (375, 672) |
| R21 | 263 | 37,610 (2%) | 213 (191, 231) | 71 | 14,664 (<1%) | 199 (189, 228) |
| R44 | 50 | 27,958 (1%) | 500 (373, 681) | 34 | 26,727 (1%) | 647 (420, 998) |
| U01 | 37 | 31,756 (1%) | 1,092 (345, 1,311) | 236 | 181,620 (7%) | 472 (159, 948) |
Definition of abbreviations: P01 = Program Project Grant; P50 = Research Center Grant; R01 = Research Project Grant; R21 = Exploratory/Developmental Research Grant; R44 = Small Business Innovation Research Grant; U01 = Research Project Cooperative Agreement.
All costs are in units of $1,000. All data were obtained from the publicly available National Institutes of Health Research Portfolio Online Reporting Tools (RePORT). Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC) are found at http://report.nih.gov/categorical_spending.aspx. Clinical projects are those categorized in RCDC as “Clinical Research” or “Clinical Trials.” Total NHLBI extramural funding in fiscal year 2010 was $2,441,772,050. Values for numbers of projects, total funding for each mechanism, and median (25th, 75th percentiles) costs per project were calculated using the SAS version 9.2 “Proc Tabulate” procedure.
TABLE 2.
FISCAL YEAR 2010 EXTRAMURAL NHLBI PROJECTS (INCLUDING ALL MECHANISMS) BY RCDC TOPICS THAT TRANSCEND DISEASE CATEGORIES
| Nonclinical Projects |
Clinical Projects |
|||||
| RCDC Topic | No. | Total Funding (% Extramural Funding) | Median Cost per Project (25th, 75th Percentiles) | No. | Total Funding (% Extramural Funding) | Median Cost per Project (25th and 75th Percentiles) |
| All NHLBI | 4,279 | 1,402,315 (57%) | 373 (249, 410) | 2,110 | 1,039,457 (43%) | 375 (138, 620) |
| Aging | 194 | 126,773 (5%) | 384 (328, 471) | 225 | 158,832 (7%) | 441 (234, 738) |
| BBSS | 27 | 10,644 (<1%) | 347 (137, 394) | 91 | 43,760 (2%) | 370 (140, 636) |
| BSS | 62 | 23,336 (1%) | 333 (180, 394) | 328 | 165,222 (7%) | 430 (146, 724) |
| Bioengineering | 487 | 312,832 (13%) | 374 (212, 568) | 214 | 128,412 (5%) | 393 (197, 661) |
| Biotechnology | 685 | 344,337 (14%) | 379 (249, 449) | 229 | 132,881 (6%) | 399 (135, 719) |
| CER | 2 | 328 (<1%) | 164 (—) | 88 | 76,779 (3%) | 454 (50, 760) |
| Gene therapy | 83 | 54,126 (2%) | 386 (228, 557) | 22 | 20,779 (1%) | 437 (325, 1,868) |
| Genomics | 92 | 85,165 (3%) | 414 (371, 1,598) | 134 | 92,047 (4%) | 619 (285, 781) |
| Nanotechnology | 43 | 83,237 (3%) | 393 (212, 707) | 6 | 4,314 (<1%) | 350 (133, 1,609) |
| Pediatrics | 263 | 128,799 (5%) | 370 (233, 415) | 320 | 182,147 (7%) | 384 (143, 719) |
| Prevention | 292 | 135,839 (6%) | 369 (229, 419) | 570 | 317,302 (13%) | 436 (149, 733) |
| Stem cells | 411 | 190,552 (8%) | 378 (233, 415) | 140 | 74,160 (3%) | 371 (136, 544) |
| Trials | — | — | — | 444 | 323,579 (13%) | 429 (164, 729) |
Definition of abbreviations: BBSS = basic behavioral and social sciences; BSS = behavioral and social sciences; CER = comparative effectiveness research.
All costs are in units of $1,000. All data were obtained from the NIH Research Portfolio Online Reporting Tools (RePORT). Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC) are found at http://report.nih.gov/categorical_spending.aspx. RCDC topics are not necessarily exclusive of one another (hence total percentages exceed 100). Clinical projects are those categorized in RCDC as “Clinical Research” or “Clinical Trials.” Total NHLBI extramural funding in fiscal year 2010 was $2,441,772,050. Numbers of projects, total funding in each RCDC area, and median (25th, 75th percentiles) costs per project were calculated using the SAS 9.2 “Proc Tabulate” procedure.
TABLE 3.
FISCAL YEAR 2010 EXTRAMURAL NHLBI PROJECTS (INCLUDING ALL MECHANISMS) BY RCDC TOPICS THAT RELATE TO SPECIFIC DISEASE CATEGORIES
| Nonclinical Projects |
Clinical Projects |
|||||
| RCDC Topic | No. | Total Funding (% Extramural Funding) | Median Cost per Project (25th, 75th Percentiles) | No. | Total Funding (% Extramural Funding) | Median Cost per Project (25th and 75th Percentiles) |
| Cardiovascular | 1,913 | 865,499 (35%) | 374 (270, 414) | 1,005 | 539,545 (22%) | 379 (142, 658) |
| Atherosclerosis | 393 | 243,991 (10%) | 375 (303, 414) | 271 | 160,713 (7%) | 401 (198, 722) |
| CAD | 440 | 221,791 (9%) | 380 (323, 420) | 267 | 156,696 (6%) | 422 (200, 721) |
| Heart disease | 1,258 | 595,073 (24%) | 375 (286,417) | 727 | 410,409 (17%) | 386 (147, 685) |
| Hypertension | 231 | 105,746 (4%) | 370 (312, 401) | 111 | 50,652 (2%) | 371 (151, 594) |
| Lung | 697 | 325,329 (13%) | 376 (293, 410) | 605 | 283,649 (12%) | 375 (137, 610) |
| COPD | 55 | 58,142 (2%) | 410 (346, 1,730) | 84 | 44,767 (2%) | 372 (142, 703) |
| Asthma | 94 | 40,732 (2%) | 373 (249, 400) | 144 | 79,092 (3%) | 407 (160, 750) |
| ARDS | 110 | 61,255 (3%) | 384 (335, 410) | 82 | 29,665 (1%) | 326 (135, 415) |
| Cystic fibrosis | 42 | 21,253 (1%) | 371 (326, 475) | 37 | 17,928 (1%) | 385 (173, 648) |
| Sleep | 55 | 16,969 (1%) | 367 (230, 393) | 97 | 51,527 (2%) | 405 (189, 607) |
| Hematology | 406 | 165,228 (7%) | 370 (191, 410) | 295 | 139,989 (6%) | 375 (140, 464) |
| Sickle cell | 21 | 9,325 (<1%) | 338 (139, 458) | 60 | 28,774 (1%) | 303 (125, 451) |
| Cooley anemia | 9 | 4,949 (<1%) | 394 (373, 429) | 9 | 8,112 (<1%) | 411 (390, 1,327) |
Definition of abbreviations: ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; COPD = chronic obstructive pulmonary disease.
All costs are in units of $1,000. All data were obtained from the NIH Research Portfolio Online Reporting Tools (RePORT). Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC) are found at http://report.nih.gov/categorical_spending.aspx. RCDC topics are not necessarily exclusive of one another (hence total percentages exceed 100). Clinical projects are those categorized in RCDC as “Clinical Research” or “Clinical Trials Total NHLBI extramural funding in fiscal year 2010 was $2,441,772,050. Numbers of projects, total funding in each RCDC area, and median (25th, 75th percentiles) costs per project were calculated using the SAS 9.2 “Proc Tabulate” procedure.
When times are tough, it is tempting to retreat into a conservative, short-sighted investment stance. However, just as with any other long-term investment portfolio expected to weather various conditions, the NHLBI must maintain a strong commitment to investing in a diverse science portfolio that balances risk and long- versus short-time pay-offs. Diversity, like any other investment strategy (5), has downsides, including increased transaction costs, losses of economies of scale, difficult standardization, and internecine conflicts that arise from fundamental preference differences (1) about ultimate goals. Even knowing that we oversee a diverse portfolio still begs a number of other critical issues, such as identifying “hot,” potentially transformative, gaps; assessing whether the balances between topics are optimal; considering trade-offs between relatively conservative (“blue chip”) and innovative (“high-risk”) investments; and applying the concepts of diversity to “big science” infrastructure projects such as NHLBI-funded population cohorts. Nonetheless, in HLB research, the 4 criteria listed by Scott Page (1) for the “Diversity Trumps Ability Theorem” are met: heart, lung, and blood diseases are complex problems; NHLBI funding is highly competitive in all areas, meaning that only high-quality proposals are being funded; the NHLBI funds a widely diverse set of researchers and research groups who are increasingly collaborating with one another; and the research community is large. By recognizing and leveraging each others’ strengths and working together (18) to discover, implement, and educate, the diverse HLB research community as a whole will be better poised to continue to improve human health toward achieving the important goals set forward by health initiatives such the “Healthy People 2020” (27) and “The Million Hearts” (28). A united HLB research community will also be a stronger voice, a voice that can better inform public opinion about the value of publicly funded biomedical science: it takes diversity of vision, teamwork, time, and money to back the best science.
Supplementary Material
Acknowledgments
The authors thank Drs. Carl Roth and Melissa Antman for their constructive criticisms of an earlier draft of the manuscript and Ebyan Addou for her valuable contribution to data analysis.
Footnotes
This article is being published jointly by invitation and consent in Circulation Research, Blood, and the American Journal of Respiratory and Critical Care Medicine.
Author disclosures are available with the text of this article at www.atsjournals.org.
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