medical research funded by government

U.S. Government Accountability Office

Federal Research and Development: Funding Has Grown since 2012 and Is Concentrated within a Few Agencies

Innovation is critical to U.S. competitiveness, prosperity, and security. In the last 10 years, the federal government has increased funding for research and development (R&D)—investing $179.5 billion in FY 2021.

DOD and the Department of Health and Human Services received 77% of the FY 2021 funding. COVID-19 stimulus funding led to large R&D increases for HHS. For example, an HHS agency that helps develop vaccines saw increased spending from $736 million in FY 2019 to $16 billion in FY 2020.

Some funding supports multi-agency initiatives in complex areas of strategic national importance—such as nanotechnology and artificial intelligence.

Federal Research and Development Investments, FYs 2012-2021

What GAO Found

Federal research and development (R&D) funding has increased since 2012—most recently because of COVID-19 stimulus funding. Five agencies obligated the majority of federal R&D funding with the Departments of Defense (DOD) and Health and Human Services (HHS) accounting for nearly 80 percent in fiscal year 2021 (see figure). HHS has mainly funded research, while DOD mainly funds development. However, HHS has become a major funder of development in recent years because of COVID-19 stimulus funding. HHS averaged less than 1 percent in development funding through fiscal year 2019 but reported 37 percent of its R&D obligations were for development in fiscal year 2021. Of the estimated $179.5 billion in federal R&D obligations in fiscal year 2021, about two-thirds went to organizations outside the federal government. In fiscal year 2021, industry, universities, and colleges received the majority of these external R&D obligations—almost $90 billion.

Federal Research and Development Obligations, Fiscal Year 2021

Note: FY 2021 data are estimates provided by federal agencies to the National Science Foundation.

Federal funding also includes four multi-agency initiatives in areas identified as having long-term national importance, such as quantum information science and nanotechnology. These initiatives coordinate activities in areas that are too broad or complex to be addressed by one agency alone. For example, more than 60 agencies participate in an initiative on network and information technology, which includes investments in artificial intelligence and machine learning. Not all participating agencies contribute funding to such initiatives. Funding for these initiatives increased over the previous decade, and accounted for roughly $14 billion in fiscal year 2020, just under 9 percent of the total federal R&D budget.

Why GAO Did This Study

Scientific and technological innovation are critical to long-term U.S. economic competitiveness, prosperity, and national security. The U.S. has long been a global leader in advancing the frontiers of science and technology. Increased competition from other countries has led some experts to express concern that the U.S. may be losing its competitive edge in certain technologies. Agencies are investing in various R&D initiatives, including those that are of strategic national importance, such as network and information technology, nanotechnology, quantum information science, and global environmental changes.

This report describes (1) trends in federal R&D funding over the last 10 years and (2) the funding and organization for selected multi-agency R&D initiatives, among other objectives.

To address these objectives, GAO analyzed data published by the National Science Foundation on annual R&D expenditures and examined Office of Management and Budget (OMB) data. GAO also reviewed agency documentation and collected written responses to structured questions on federal R&D from the Chief Financial Officer or budget office from the five agencies that fund most R&D.

In addition, GAO interviewed officials from OMB and the Office of Science and Technology Policy, including the Directors of the National Coordination Offices for selected multi-agency R&D initiatives, which are coordinated under the auspices of the National Science and Technology Council.

For more information, contact Candice N. Wright at (202) 512-6888 or [email protected] .

Full Report

Gao contacts.

Candice N. Wright Director [email protected] (202) 512-6888

Office of Public Affairs

Sarah Kaczmarek Acting Managing Director [email protected] (202) 512-4800

Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC)

Table Published: May  14, 2024

The table below displays the annual support level for various research, condition, and disease categories based on grants, contracts, and other funding mechanisms used across the National Institutes of Health (NIH), as well as disease burden data published by the  National Center for Health Statistics (NCHS)  at the  Centers for Disease Control & Prevention (CDC).

At the request of Congress, the NIH embarked on a process to provide better consistency and transparency in the reporting of its funded research. This new process, implemented in 2008 through the Research, Condition, and Disease Categorization (RCDC) system, uses sophisticated text data mining (categorizing and clustering using words and multiword phrases) in conjunction with NIH-wide definitions used to match projects to categories. RCDC use of data mining improves consistency and eliminates the wide variability in defining the research categories reported. The definitions are a list of terms and concepts selected by NIH scientific experts to define a research category. The research category levels represent the NIH's best estimates based on the category definitions. These definitions include all aspects of the topic, such as basic, pre-clinical, clinical, biomedical, health services, behavioral, and social research.

In 2016, the NIH added mortality and prevalence data from two sources of consistent and nationally representative disease statistics provided by NCHS/CDC. These data are reported alongside the budgeting categories to provide the public and policymakers with information that is helpful for understanding the NIH research portfolio and its relationship to public health needs. However, NIH believes that the best way to understand disease burdens is by examining patterns in the larger context of multiple methods and measurements, chosen on a case-by-case basis as appropriate for each disease or condition. Further descriptions of these disease statistics can be found  here .

The NIH does not expressly budget by category. The annual estimates reflect amounts that change because of science, actual research projects funded, and the NIH budget. The research categories are not mutually exclusive. Individual research projects can be included in multiple categories so amounts depicted within each column of this table do not add up to 100 percent of NIH-funded research.

The table shows historical data for FY 2008 through FY 2023. Estimates for FY 2024 and FY 2025 are based on RCDC actual data and are usually posted when the President’s Budget is released.

Total Number of Research/Disease Areas: 324  

NIH… Turning Discovery Into Health ®

• U.S. Department of Health & Human Services

• Virtual Tour
• Staff Directory
• En Español

NIH Research Festival

A Celebration of Intramural Science, featuring lectures, exhibits, poster sessions and more.

Learn more »

Give your heart some TLC!

Learn how the Therapeutic Lifestyle Changes (TLC) program can help you lower your cholesterol.

August Is Gastroparesis Awareness Month

Gastroparesis is a disorder that slows or stops the movement of food to the stomach or small intestine.

College Drinking Prevention

Get resources to help prevent harmful and underage drinking at NIAAA's College Drinking Prevention website.

NIH STEM Teaching Resources

Free, innovative NIH-funded content to engage pre-K-12 students in health science.

In the News

CPR for Cardiac Arrest

Black people and women less likely to survive bystanders provide aid.

Vision Care

Study finds gaps in Medicaid coverage of routine vision care.

Age-Related Macular Degeneration

AREDS2 formula slows progression of late-stage “dry” AMD.

Addiction Treatment Barriers

Doctors report top reasons why they may be reluctant to intervene.

NIH at a Glance

Virtual-tour-screenshot-square.jpg.

Take the Virtual Tour

Explore the Bethesda campus and how NIH turns discovery into health.

dr-monica-bertagnolli-thumbnail.jpg

The NIH Director

Monica M. Bertagnolli, M.D., is the NIH Director and provides leadership for the 27 Institutes and Centers that make up NIH.

nih-at-a-glance-funding.jpg

Funding for Research

NIH is the largest source of funding for medical research in the world, creating hundreds of thousands of high-quality jobs.

nih-at-a-glance-labs.jpg

Labs at NIH

Scientists conduct research on NIH campuses across the U.S., as part of our Intramural Research Program.

improving-health-collage.jpg

Impact of NIH Research

NIH-supported research has had a major positive impact on nearly all of our lives.

researcher-holding-petri-dish.jpg

Jobs at NIH

The central recruitment point of access to all NIH jobs and training opportunities

Featured Resources & Initiatives

A new science agency proposed by President Joseph Biden as part of NIH to drive biomedical breakthroughs and provide transformative solutions for all patients.

Anti-Sexual Harassment

NIH does not tolerate pervasive or severe harassment of any kind, including sexual harassment.

Ending Structural Racism

Learn more about NIH’s efforts to end structural racism in biomedical research through the UNITE initiative.

All of Us Research Program

A research effort to revolutionize how we improve health and treat disease.

NIH HEAL Initiative

Trans-agency effort to speed scientific solutions to stem the national opioid crisis.

Clinical Trials

Learn about participating in clinical trials and where to find them.

Accelerating Medicines Partnership

A bold venture to help identify new treatments and cures for diseases.

Medical Research Initiatives

Important initiatives aimed at improving medical research.

Training at NIH

NIH provides training opportunities internally, as well as at universities and other institutions across the U.S.

A new research initiative to understand, prevent, and treat the long-term effects of COVID-19.

COVID-19 Research information from NIH

NIH supports research in COVID-19 testing, treatments, and vaccines. También disponible en español.

Climate Change and Health Initiative

Research to reduce health threats from climate change.

Connect with Us

• More Social Media from NIH

• Open access
• Published: 18 February 2016

The 10 largest public and philanthropic funders of health research in the world: what they fund and how they distribute their funds

• Roderik F. Viergever 1 &
• Thom C. C. Hendriks 2  

Health Research Policy and Systems volume  14 , Article number:  12 ( 2016 ) Cite this article

110k Accesses

99 Citations

196 Altmetric

Metrics details

Little is known about who the main public and philanthropic funders of health research are globally, what they fund and how they decide what gets funded. This study aims to identify the 10 largest public and philanthropic health research funding organizations in the world, to report on what they fund, and on how they distribute their funds.

The world’s key health research funding organizations were identified through a search strategy aimed at identifying different types of funding organizations. Organizations were ranked by their reported total annual health research expenditures. For the 10 largest funding organizations, data were collected on (1) funding amounts allocated towards 20 health areas, and (2) schemes employed for distributing funding (intramural/extramural, project/‘people’/organizational and targeted/untargeted funding). Data collection consisted of a review of reports and websites and interviews with representatives of funding organizations. Data collection was challenging; data were often not reported or reported using different classification systems.

Overall, 55 key health research funding organizations were identified. The 10 largest funding organizations together funded research for $37.1 billion, constituting 40% of all public and philanthropic health research spending globally. The largest funder was the United States National Institutes of Health ($26.1 billion), followed by the European Commission ($3.7 billion), and the United Kingdom Medical Research Council ($1.3 billion). The largest philanthropic funder was the Wellcome Trust ($909.1 million), the largest funder of health research through official development assistance was USAID ($186.4 million), and the largest multilateral funder was the World Health Organization ($135.0 million). Funding distribution mechanisms and funding patterns varied substantially between the 10 largest funders.

Conclusions

There is a need for increased transparency about who the main funders of health research are globally, what they fund and how they decide on what gets funded, and for improving the evidence base for various funding models. Data on organizations’ funding patterns and funding distribution mechanisms are often not available, and when they are, they are reported using different classification systems. To start increasing transparency in health research funding, we have established www.healthresearchfunders.org that lists health research funding organizations worldwide and their health research expenditures.

Peer Review reports

Approximately 40% of all health research in high-income countries is funded by public and philanthropic funding organizations [ 1 ]. These organizations play a central role in the development of new knowledge and products, particularly in areas that are not sufficiently profitable [ 2 ]. For example, the involvement of public and philanthropic funding organizations has been key in the development of new medical products to combat neglected diseases [ 1 , 2 ] and, since recently, these organizations are increasingly taking action to address the lack of development of new antibiotics [ 3 – 5 ].

Transparency on who the main funding organizations of health research are, on what they fund (their funding patterns) and on how they decide on what gets funded (their priority setting mechanisms and funding distribution mechanisms) can help funding organizations to synchronize their efforts, potentially preventing the duplication of research and improving collaboration on research priorities, and has various other strategic and practical benefits for funders [ 2 , 6 – 12 ]. Such transparency also allows for external evaluation of funding organizations’ portfolios and decision-making processes [ 7 , 13 ]. This is particularly important for public funding organizations, since they distribute public funds. For philanthropic funders, such accountabilities are less clear, but given the substantial impact of some of these funders on the global landscape for health research, it might be reasonable to make similar demands from this group of funders [ 14 , 15 ].

Although substantial insight has been created in recent years into countries’ expenditures on health research [ 1 , 16 – 20 ], there has been relatively little scrutiny of the funding patterns and mechanisms of individual funding organizations. Mappings of individual funding organizations’ expenditures on health research are often limited to one or several countries [ 7 , 10 , 21 – 26 ] or to a select group of diseases [ 25 , 27 – 29 ]. To increase the available information on major public and philanthropic funders of health research, we present a mapping in this article that had a simple target: to identify the 10 largest public and philanthropic funders of health research in the world, to report on what they fund, and on their mechanisms for distributing these funds (funding organizations’ priority setting mechanisms were beyond the scope of this study – see Limitations section for more detail).

Here, we outline the methods used to identify the 10 largest funding organizations of health research in the world, and to assess the funding patterns and funding distribution mechanisms of these organizations. A more detailed description of these methods is provided in Additional file 1 . All data were collected from November 4, 2013, to August 12, 2014.

Identifying the 10 largest funders of health research

Search strategy.

This study distinguished between four types of public and philanthropic health research funders: (1) public national or regional funders (excluding funders of official development assistance (ODA) and multilateral funders), (2) philanthropic funders, (3) ODA funders, and (4) multilateral funders. The mandate of the funding body did not need to be limited to funding health research. Funding organizations were identified through a search strategy that had several components: key funding organizations in the 20 countries with the highest spending on health research [ 1 ] were identified, membership lists of collaborative groups of funders (i.e. groups where major funders of health research collaborate on a global or regional level) were reviewed, publicly available lists of funding organizations that included annual spending on health research were searched, and data on Development Assistance for Health were used to identify key ODA funders. For every funder type, a specific search strategy was used to identify the largest funders of health research (Additional file 1 ). Private for-profit funding organizations were not included in our analysis; we only aimed to map public and philanthropic funders (private for-profit health research funders are mapped elsewhere [ 30 ]). Product development partnerships (PDPs) and other public private partnerships (PPPs) were also excluded because they are intermediate funding organizations, who are funded in turn by governments, philanthropies and the for-profit sector. Furthermore, we excluded single disease funders; although the majority of philanthropic funders of health research focuses on one disease [ 21 ], the largest philanthropic funders of health research tend to fund across multiple disease areas (with some exceptions [ 31 , 32 ]). We note that the annual health research expenditures of the largest PDP, PPP and single-disease funders that we are aware of are lower than the annual expenditures of the 10 largest public and philanthropic funders reported in this study (see Additional file 1 ). Finally, in two cases (the United States Department of Defense (US DoD) and the European Commission (EC)) we included both the overarching organization at its largest sub-organizations or sub-programmes, because of the substantial differences between the funding distribution mechanisms of these sub-organizations and sub-programmes.

To aid future analyses of this kind, we provide an overview of various sources that helped us identify the main public and philanthropic funders of health research globally in Additional file 2 .

Assessing health research expenditures

For all the funding organizations that followed from our search, publicly available data were collected on the organizations’ annual health research expenditures (from annual reports and websites). Data were collected for the most recent year available. When we were not able to find data on organizations’ annual expenditures in the public domain, we contacted funders to ask if they could provide us with their annual expenditures on health research.

Funding organizations differ on at least three aspects in terms of how they report their annual health research expenditures. First, expenditures can be reported as actual expenditures, commitments or budgets. Second, there can be differences in terms of what the expenditures cover. They can cover the organization’s total expenditures on health research excluding operational costs (for managing the funding organization), its total expenditures including operational costs, or its total overall turnover over a single fiscal year (this was only collected if the funding organization exclusively funded health research). Third, there can be differences in terms of the research areas that the reported expenditures pertain to: only health research, or broader categories such as health and biological research or life sciences research. For each funder we extracted data on annual health research expenditures in a step-wise manner, always reporting the actual expenditures excluding operational costs in the area of health research when possible. When these numbers were not available, we reported the next best available number, following the order in the categories provided above. We note that the data from the funding organizations in the top 10 all relate only to health research, all concern actual expenditures or commitments, and for all, except one, operational costs were excluded.

Training support and research education were not included in the overall amount for health research expenditures. In addition, for government ministries, we excluded two types of funding flows. First, when funding was provided by ministries to funding agencies for distribution, we included the funding for the funding agencies, but not for the ministries. Second, for government ministries, such as ministries of education or health, we excluded block funding to universities or hospitals (similar to other initiatives that have reported on health research funding flows [ 24 ]). For funding agencies, we did include institutional funding.

Finally, organizations’ expenditures were made comparable using methods by Young et al. [ 17 , 20 ]. To do so, we first deflated organizations’ expenditures in the national currency to the year 2013 using Gross Domestic Product deflators from the International Monetary Fund World Economic Outlook Database of April 2014 [ 33 ]. Second, we converted the inflation-corrected expenditures to US dollars using the World Bank Official exchange rates for the year 2013. As a secondary outcome, we calculated funding organizations’ health research expenditures as 2013 purchasing power parity-adjusted US dollars (these are not reported in this article, but are available on www.healthresearchfunders.org ) [ 17 , 20 ].

Assessing the funding patterns and funding distribution mechanisms of the 10 largest funders of health research

After the 10 largest funding organizations of health research were identified, data were collected on their funding patterns and funding distribution mechanisms. For each organization, data were collected on:

The funding mechanisms used to distribute funding: intramural funding or extramural funding. For extramural funding, we distinguished between project grants, ‘people grants’, programme grants, funding distributed to organizations and other extramural research funding. For project grants, data were collected to assess if the distribution was untargeted, targeted or highly targeted (for definitions see Additional file 1 ).

The amount of funding allocated to a list of 20 key health areas from the Global Burden of Disease classification [ 34 ].

Funding for operational expenditures was excluded.

Finally, we denoted whether funding organizations used a classification system to classify funding to various health areas and whether they reported statistics on funding for various research types (e.g. biomedical research, clinical research, epidemiological research or health systems research [ 35 ]) and recipient countries or regions.

All data were collected from online reporting databases, annual reports, official websites, or other information sources. After this, each funder was invited to participate in an interview. Before the interview, a document with collected data was made available to a representative of the funder. Before and during the interviews, representatives were asked to add, amend or confirm the data.

Identifying the 10 largest funding organizations of health research

Public and philanthropic funding organizations.

Our search identified 55 public and philanthropic funders that were candidates for being one of the 10 largest funders of health research in the world (Table  1 ), excluding ODA funders and multilaterals (we searched separately for these and report on them later). For 41 organizations, data on the organizations’ annual health research expenditures were available. For five of these organizations, this information was received through personal communications (not publicly reported). Fourteen funders did not provide figures about their annual health research expenditures. Often, these organizations were general funders of research and did provide overall expenditure data but not for health research specifically.

For the 10 largest funders, health research funding totalled to $ 37.1 billion, approximately 40% of all spending on health research globally by public and philanthropic sources [ 1 ]. The United States National Institutes of Health (NIH) contributed the largest part of this amount, with $ 26.1 billion in health research funding in 2013. The largest philanthropic funder was the Wellcome Trust ($ 909.1 million). The Wellcome Trust and the Howard Hughes Medical Institute (HHMI) were the only two philanthropic funders among the 10 largest funders of health research; the other eight organizations were public funding bodies. All 10 funders came from Northern America, Europe or Oceania. The largest Asian funding organization identified was the National Natural Science Foundation of China (NSFC) ($ 621.3 million), the largest funder from Latin America and the Caribbean was Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) from Argentina ($ 184.4 million), and the largest African funder was the South African Medical Research Council (SA MRC) ($ 63.2 million).

ODA-agencies and multilaterals

The expenditures of ODA-agencies and multilaterals on health research were substantially smaller than the expenditures of the largest public and philanthropic funding organizations (Tables  2 and 3 ). The largest funder of health research through ODA was USAID ($ 186.4 million) and the largest multilateral funder was WHO ($ 135.0 million).

Assessing the funding patterns and funding distribution mechanisms of the 10 largest funding organizations of health research

Funding mechanisms used to distribute funding.

There was considerable diversity in organizations’ funding distribution mechanisms (Table  4 ). Five funders funded research fully extramurally, five allocated at least a proportion of their funding to intramural research institutes, and one funder, the Institut national de la santé et de la recherche médicale (Inserm), funded research (almost) exclusively intramurally (total is 11 because for the EC and the US DoD we analysed the sub-organizations or sub-programmes: the US Congressionally Directed Medical Research Program (CDMRP), the Health theme of the EC FP7 Cooperation programme and the European Research Council (ERC)).

Of the 10 funding organizations that provided extramural funding, for six, the main mechanism for extramural funding distribution was the allocation of funding through untargeted competitive project or investigator grants (often, there were also some smaller programmes that used a more targeted distribution). Two funders, the Health theme of the European Commission FP7 Cooperation programme and the US CDMRP, used a more targeted approach and issued calls under prioritized areas. Funders also made use, in varying degrees, of highly targeted funding schemes, such as research contracts, tenders or prizes, but this was never the dominant form of funding distribution. The last two funders, the United Kingdom Medical Research Council (MRC) and the Deutsche Forschungsgemeinschaft (DFG), used a mixed approach to allocate funding, with substantial contributions made through different funding distribution mechanisms. Lastly, the funding model of the NIH and the untargeted part of the MRC deserve separate mentioning because, although they adhered largely to an untargeted model and research funding was available for all areas of health research, the amounts of funding available for various broad research areas were earmarked (in the case of the NIH, for example, through budgets for the NIH institutes). This differs from targeted approaches, where not all areas have to be funded and the prioritization is often more specific, but it is also not completely untargeted.

Finally, most funders mainly dispensed funding via project grants, with smaller programmes that provide grants to excellent individual researchers. However, others put more focus on individual excellence. The HHMI has traditionally been a proponent of such people-focused funding. Since recently, other funders, such as the Wellcome Trust and the NIH, are increasingly making use of ‘people grants’ as well [ 36 ].

Funding patterns towards diseases

The funding organizations’ research expenditures towards 20 specific health areas are shown in Table  5 . We could report data for at least one health area for seven funders. However, as the table makes clear, these data were often not available.

Funding patterns varied, with some funders showing preferences for investing in non-communicable over communicable diseases and others showing the opposite. For example, the NIH spent less on infectious disease research in total than on cancer research alone, while the Wellcome Trust spent 14 times more on infectious disease research than on cancer research. Similar variations arose when comparing more specific disease areas within the non-communicable or communicable diseases. For example, the NIH spent almost three times more on cancer research than on cardiovascular research while the EC under the FP7 programme spent roughly equal amounts on both, and while HIV/AIDS funding comprised more than half of the infectious disease research funding at the US NIH, it comprised less than 10% of that funding at the Australian National Health and Medical Research Council (NHMRC).

Six funders used classification systems to classify their funding to health areas (Table  6 ); five different classification systems were used by these funders (the two funders from the United Kingdom used the same system). Besides using different categories for health problems, these systems also varied on other aspects, such as who enters the data (e.g. the researcher or a specialist employed by the funder) and whether grants can be indexed as belonging to one or multiple health problems. Seven funders reported amounts of funding allocated to various research types and the same seven reported how much funding was allocated to various recipient countries or regions.

In this article, we have identified the 10 largest funding organizations of health research globally and shed more light on their funding distribution mechanisms and funding patterns. Two main conclusions can be drawn from this mapping of influential funders of health research.

Differences between funding organizations: the need for more evaluation of funding distribution models

First, there is considerable diversity between funding organizations in terms of what they fund and how they distribute those funds. This begs the question: do some funding distribution models have more impact than others? The impact of different approaches to funding health research is regularly discussed in the literature, for example, for intramural versus extramural funding [ 23 ], for targeted versus untargeted funding [ 37 ], for ‘people grants’ versus project grants [ 36 , 38 ], for small grants versus large grants [ 10 ], and for competitive versus non-competitive research funding [ 39 ]. However, comparative evaluations of the impact of various funding models are scarce [ 10 , 23 , 38 ], even though approaches to measure the impact of health research are available [ 40 ]. An exception has been the recent comparisons of ‘people grants’ versus projects grants in the United States, which compared HHMI with NIH researchers and NIH Pioneer Awards with NIH project grants [ 36 , 41 – 43 ]. These comparisons have led the NIH to consider a broad shift toward ‘people grants’, demonstrating the value and potential impact of such evaluations [ 36 ]. Evaluations of this kind provide new insights when comparing funding models across funding organizations, but given the different contexts in which funders operate, comparing the impact of different models within one funding organization is perhaps particularly valuable and should become more common practice.

There is also a need for more debate about where the power to decide priorities for publicly funded health research should lie (with parliaments, ministries, funding agencies, or independent committees of experts). Such debate is needed because there are finite resources for investing in health research and thus priorities need to be set using fair and legitimate methods and using the best possible evidence [ 44 ]. In practice, public sector health research funding decisions are not only made on the basis of what research is needed, but are regularly influenced by other factors, such as political interests, advocacy and lobbying [ 2 ]. Thus, there is a need for transparency on who makes those decisions and to debate who should make them [ 2 , 13 , 45 – 47 ]. Analysis of funding organizations’ priority setting processes was not part of this study (see Limitations) but deserves to be a more frequent subject of research studies in the future.

Improving publicly available data on health research funding

Second, to enable evaluation and debates as noted above, it is necessary to have a map of the health research funding landscape: to know who the main funders of health research are, what they fund, and how they decide what gets funded [ 2 , 6 – 11 , 13 ]. Yet, this study shows that these data are often not available. Through our study, we did not find a list of all public or philanthropic health research funders worldwide that included their annual health research expenditures (Additional file 1 ). Therefore, we have now established such a list ourselves at www.healthresearchfunders.org . On this website, we provide access to the data collected for this article and to information on more than 200 other public and philanthropic funders of health research that we have added to this website since the mapping for this article was completed.

Besides the absence of a global listing of funding organizations, we found that data on organizations’ funding patterns and funding distribution mechanisms are often not available, and when they are, they are difficult to aggregate, owing to differences in funders’ data classification systems. Notably, we only collected these data for the 10 largest funding organizations of health research. The absence of such information, and the difficulties in aggregating the data across funders, are likely to be more prominent when smaller funders are also included. There is currently no consensus on a framework for producing descriptive data on funders’ funding patterns (both in terms of health areas and research types) nor on a framework for describing their funding distribution mechanisms [ 6 , 8 , 37 ]. In this article, we have proposed three frameworks for reporting data on health research funding: for reporting data on funding distribution mechanisms (Table  4 ), for reporting data on funding patterns in terms of health problems (the Global Burden of Disease classification [ 34 ]), and for reporting data on funding patterns in terms of research types (biomedical research, clinical research, epidemiological research or health systems research, as proposed by Frenk [ 35 ]). The adoption of standards for reporting funding data, including guidance on what data classification systems to use, by funding organizations, for example through collaborative initiatives such as the Heads of International Research Organizations, would substantially improve the quality and comparability of reported funding data [ 9 ].

Funding organizations are starting to support the goal of transparency and are increasingly recognizing the problems noted above and addressing them. At the 2014 World Health Summit in Berlin, several major funders of health research expressed interest to work together toward developing a common approach for mapping health research funding flows [ 12 ]. Another good example of a multi-funder collaboration to increase insight in health research investments is the World RePORT website [ 48 ]. On a national level, the United Kingdom has led the way in terms of harmonized reporting by showing it is feasible to collect comparable data on health research funding from all major public funding bodies and charities in a country [ 22 ]. Besides initiatives from funders themselves, there are also several promising initiatives from other parties to address the lack of data on global health research funding [ 1 , 16 , 49 – 51 ]. The recent decision to establish a Global Observatory on Health R&D at WHO in particular may help to improve transparency in this area [ 1 ].

Limitations

Finally, we note that the mapping conducted for this article has had several limitations. First, we have excluded funding organizations in the private for-profit sector (these are listed elsewhere [ 30 ]). Second, national systems for funding health research vary. In many countries, a large amount of health funding is dispersed directly from governments to universities or research institutes via block grants. We excluded these block grants and therefore the public funding organizations that we report on do not all contribute the same share of all health research that is publicly funded in a country. Third, we had to make several generalizations in order to be able to report data across funders that were diverse in their funding distribution mechanisms and reporting systems. For instance, what we have termed ‘targeted’ research funding, is a grey area that ranges from broad prioritized research areas to specific research topics or questions [ 52 ]. Also, funders reported on their expenditures on health research in various formats. Although we have kept track of these varying reporting formats, they decrease comparability across funders. Fourth, we would have liked to exclude overhead costs within project funding (not operational costs of the funder, which we did exclude where possible, but overhead costs of the research organization), to measure only the amount of funding that went to research, but this was not feasible because it was mostly not reported. Fifth, our proposed framework for reporting on funders’ funding distribution mechanisms (Table  4 ) lacks detail. It would have been interesting to also report on more detailed mechanisms, such as funders’ grants for businesses and PDPs/PPPs, but we did not include such analyses because of a lack of comparable data across funders. Sixth, funding organizations frequently make adaptations to their funding strategies, and therefore our findings should be viewed as a snapshot of funders’ expenditures, funding distribution mechanisms and funding patterns during the time of our data collection [ 53 ]. Seventh, in addition to reporting about funding organizations’ funding distribution mechanisms and patterns, we would have liked to report on funding organizations’ priority setting processes as part of this work (another important aspect of how funders decide what gets funded). However, we found that priority setting processes were generally not well-described and highly variable across funders, making it difficult to analyse and report our data. It deserves recommendation that such an analysis is conducted in the future, but the development of a framework for assessing priority setting processes at funders is needed first, potentially based on existing guidance for health research priority setting [ 44 ]. Lastly, and most importantly, our search strategy was limited in scope (see for more detail Additional file 1 ), was aimed only at finding the 10 largest funding organizations of health research in the world, and detailed data were only collected for those 10 organizations.

This study identified the 10 largest funding organizations of health research in the world and showed that these organizations together fund research for $37.1 billion, 40% of all public and philanthropic health research spending globally. It also mapped the funding patterns and funding distributions mechanisms of these funders and showed that there is considerable diversity between organizations in terms of what they fund and how they distribute those funds, highlighting the need for comparative evaluations of the impact of different funding distribution models. Moreover, because many of the data we tried to collect were not available, our study demonstrates that there is a need for increased transparency on who the largest funding organizations of health research are, what they fund, and how they decide what gets funded. As a first step in improving transparency in this area, we have proposed frameworks for reporting on funding patterns (in terms of health problems and research types) and for reporting on funding distribution mechanisms in this article and have established www.healthresearchfunders.org , where we list more than 250 public and philanthropic funders of health research and their annual health research expenditures. We will further expand and update this list of funding organizations in the future and welcome both suggestions and data from all who wish to help us make this database more accurate and more inclusive.

Røttingen J-A, Regmi S, Eide M, Young AJ, Viergever RF, Årdal C, et al. Mapping available health R&D data: what’s there, what’s missing and what role for a Global Observatory. Lancet. 2013;382:1286–307.

Article   PubMed   Google Scholar  

Viergever RF. The mismatch between the health research and development (R&D) that is needed and the R&D that is undertaken: an overview of the problem, the causes, and solutions. Glob Health Action. 2013;6:22450.

PubMed   Google Scholar  

Power E. Impact of antibiotic restrictions: the pharmaceutical perspective. Clin Microbiol Infect. 2006;12 Suppl 5:25–34.

Spellberg B, Bartlett JG, Gilbert DN. The future of antibiotics and resistance. N Engl J Med. 2013;368:299–302.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Head MG, Fitchett JR, Cooke MK, Wurie FB, Atun R, Hayward AC, et al. Systematic analysis of funding awarded for antimicrobial resistance research to institutions in the UK, 1997–2010. J Antimicrob Chemother. 2014;69:548–54.

Article   CAS   PubMed   Google Scholar  

Chalmers I, Bracken MB, Djulbegovic B, Garattini S, Grant J, Gülmezoglu AM, et al. How to increase value and reduce waste when research priorities are set. Lancet. 2014;383:156–65.

Sampat BN, Buterbaugh K, Perl M. New evidence on the allocation of NIH funds across diseases. Milbank Q. 2013;91:163–85.

Article   PubMed   PubMed Central   Google Scholar  

Terry RF, Allen L, Gardner C, Guzman J, Moran M, Viergever RF. Mapping global health research investments, time for new thinking – A Babel Fish for research data. Health Res Policy Syst. 2012;10:28.

Viergever RF. Aid alignment for global health research: the role of HIROs. Health Res Policy Syst. 2011;9:12.

Couzin-Frankel J. Chasing the money. Science. 2014;344:24–5.

Track and trace. Nature. 2014;507:8.

World Health Summit. Workshop: global health research & development: mapping funding flows – working towards a common approach. Geneva: WHO; 2014.

Google Scholar  

Gillum LA, Gouveia C, Dorsey ER, Pletcher M, Mathers CD, McCulloch CE, et al. NIH disease funding levels and burden of disease. PLoS One. 2011;6:e16837.

What has the Gates Foundation done for global health? Lancet. 2009;373:1577.

Matthews KR, Ho V. The grand impact of the Gates Foundation. Sixty billion dollars and one famous person can affect the spending and research focus of public agencies. EMBO Rep. 2008;9:409–12.

World Health Organization. WHO informal workshop – monitoring financial flows in support of health research & development. Geneva: WHO; 2013.

Young AJ, Terry RF, Røttingen J-A, Viergever RF. Global biomedical R&D expenditures. N Engl J Med. 2014;370:2451.

Chakma J, Sun GH, Steinberg JD, Sammut SM, Jagsi R. Asia’s ascent--global trends in biomedical R&D expenditures. N Engl J Med. 2014;370:3–6.

Sun GH, Steinberg JD, Jagsi R. The calculus of national medical research policy--the United States versus Asia. N Engl J Med. 2012;367:687–90.

Young AJ, Terry RF, Røttingen J-A, Viergever RF. Global trends in health research and development R&D expenditures – the challenge of making reliable estimates for international comparison. Health Res Policy Syst. 2015;13.

Myers ER, Alciati MH, Ahlport KN, Sung NS. Similarities and differences in philanthropic and federal support for medical research in the United States: an analysis of funding by nonprofits in 2006–2008. Acad Med. 2012;87:1574–81.

Reports & Downloads: Health Research Analysis Data. http://www.hrcsonline.net/pages/data . Accessed 13 January 2016.

Braun D. Structure and dynamics of health research and public funding: an international institutional comparison. Dordrecht: Kluwer Academic Publishers; 1994.

UK Clinical Research Collaboration. UK health research analysis 2009/2010. London: UKCRC; 2010.

Head MG, Fitchett JR, Cooke MK, Wurie FB, Hayward AC, Atun R. UK investments in global infectious disease research 1997–2010: a case study. Lancet Infect Dis. 2013;2013(13):55–64.

Article   Google Scholar  

Dorsey ER, de Roulet J, Thompson JP, Reminick JI, Thai A, White-Stellato Z, et al. Funding of US biomedical research, 2003–2008. JAMA. 2010;303:137–43.

Moran M, Guzman J, Henderson K, Liyanage R, Wu L, Chin E, et al. G-FINDER 2012 – neglected disease research & development: a five year review. Sydney: Policy Cures; 2012.

Frick M, Jiménez-Levi E. Tuberculosis Research and Development. Report on tuberculosis research funding trends, 2005–2012. New York: Treatment Action Group; 2013.

Head MG, Fitchett JR, Cooke GS, Foster GR, Atun R. Systematic analysis of funding awarded for viral hepatitis-related research to institutions in the United Kingdom. J Viral Hepat. 2015;22(3):230–7. doi: 10.1111/jvh.12300 .

The EU Industrial R&D Investment Scoreboard. http://iri.jrc.ec.europa.eu/scoreboard.html . Accessed 13 January 2016.

Saving Lives through Research: Annual Report and Accounts 2012/13. London: Cancer Research UK; 2013.

Research Facts 2012–13. American Heart Association; 2014. https://my.americanheart.org/idc/groups/ahamahpublic/@wcm/@sop/@rsch/documents/downloadable/ucm_317600.pdf . Accessed 13 January 2016.

International Monetary Fund (IMF) World Economic Outlook Database. https://www.imf.org/external/pubs/ft/weo/2014/01/weodata/index.aspx . Accessed 13 January 2016.

World Health Organization: Global Health Estimates. http://www.who.int/healthinfo/global_burden_disease/en/ . Accessed 13 January 2016.

Frenk J. The new public health. Annu Rev Public Health. 1993;14:469–90.

Kaiser J. Funding. NIH institute considers broad shift to “people” awards. Science. 2014;345:366–7.

Callahan D. Shaping biomedical research priorities: the case of the National Institutes of Health. Health Care Anal. 1999;7:115–29.

Ioannidis JPA. More time for research: fund people not projects. Nature. 2011;477:529–31.

Research funding needs overhaul. Science (80-) 2014, 345:122.

Banzi R, Moja L, Pistotti V, Facchini A, Liberati A. Conceptual frameworks and empirical approaches used to assess the impact of health research: an overview of reviews. Health Res Policy Syst. 2011;9:26.

Lal B, Wilson A, Jonas S, Lee E, Richards A, Peña V. An outcome evaluation of the national institutes of health (NIH) Director’s pioneer award (NDPA) program, FY 2004–2006. Washington: Ida Science & Technology Policy Institute; 2012.

Collins FS, Wilder EL, Zerhouni E. NIH roadmap/common fund at 10 years. Science. 2014;345:274–6.

Azoulay P, Graff Zivin JS, Manso G. Incentives and creativity: evidence from the academic life sciences. RAND J Econ. 2011;42:527–54.

Viergever RF, Olifson S, Ghaffar A, Terry RF. A checklist for health research priority setting: nine common themes of good practice. Health Res Policy Syst. 2010;8:36.

Hegde D, Sampat BN. Can private money buy public science? Disease group lobbying and federal funding for biomedical research. https://www8.gsb.columbia.edu/faculty-research/sites/faculty-research/files/canprivate.pdf . Accessed 13 January 2016.

Reardon S. Lobbying sways NIH grants. Nature. 2014;515:19.

Viergever RF, Hendriks TCC. Targeted public funding for health research in the Netherlands. Ned Tijdschr Geneeskd. 2014;159:A8174.

Collins F, Beaudet A, Draghia-Akli R, Gruss P, Savill J, Syrota A, et al. A database on global health research in Africa. Lancet Glob Heal. 2013;1:e64–5.

Rani M, Bekedam H, Buckley BS. Improving health research governance and management in the Western Pacific: a WHO expert consultation. J Evid Based Med. 2011;4:204–13.

Research classification in practice: Stamping or Understanding! http://www.uberresearch.com/uberresWP/wp-content/uploads/CASRAI-ReConnect-Research-Classification-2-UberResearch_March-2014.pdf . Accessed 13 January 2016.

Erawatch: Platform on Research and Innovation policies and systems. http://erawatch.jrc.ec.europa.eu/ . Accessed 15 August 2014.

The National Institute of Allergy and Infectious Diseases (NIAID): Choose Approach and Find FOAs. http://www.niaid.nih.gov/researchfunding/grant/strategy/pages/2choosefoa.aspx . Accessed 13 January 2016.

Wilkinson E. Wellcome Trust overhauls its funding framework. Lancet. 2014;384:1913.

G-FINDER public search tool. http://g-finder.policycures.org/gfinder_report/ . Accessed 13 January 2016.

Terry RF, van der Rijt T. Overview of research activities associated with the World Health Organization: results of a survey covering 2006/07. Health Res Policy Syst. 2010;8:25.

Download references

Acknowledgements

We would like to thank Alison Young, Koos van der Velden, Rob Terry, Noor Tromp, Leon Bijlmakers, Sanne van Kampen and Eric Budgell for reviewing drafts of this article.

Author information

Authors and affiliations.

Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands

Roderik F. Viergever

Department of Metamedica, VUmc, Amsterdam, The Netherlands

Thom C. C. Hendriks

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Roderik F. Viergever .

Additional information

Competing interests.

The authors declare that they have no competing interests. No specific funding was received for conducting this project.

Authors’ contributions

RV conceived the idea for the study, RV and TH developed the study methods, TH conducted most data collection and analysis, RV conducted additional data collection and analysis, and RV and TH wrote the article. Both authors read and approved the final manuscript.

Additional files

Additional file 1:.

More detailed description of methods. (DOCX 46 kb)

Additional file 2:

Sources for identification of health research funding organizations. (DOCX 45 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Viergever, R.F., Hendriks, T.C.C. The 10 largest public and philanthropic funders of health research in the world: what they fund and how they distribute their funds. Health Res Policy Sys 14 , 12 (2016). https://doi.org/10.1186/s12961-015-0074-z

Download citation

Received : 09 May 2015

Accepted : 18 December 2015

Published : 18 February 2016

DOI : https://doi.org/10.1186/s12961-015-0074-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Globalization
• Health funding
• Health policy
• Health research
• Priority setting
• Research and development (R&D)
• Research funding
• Research governance
• Research grants
• Research policy

Health Research Policy and Systems

ISSN: 1478-4505

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Americans broadly favor government funding for medical and science research

Americans are strongly supportive of the government investing in research in medicine and science, according to a new Pew Research Center survey.

Around eight-in-ten U.S. adults say government investments in medical research (80%), engineering and technology (80%) or basic scientific research (77%) usually pay off in the long run. Only about two-in-ten believe government funding in each of these areas is not worth it (19% for medical research, 19% for engineering and technology and 22% for basic scientific research).

Pew Research Center surveys in 2014 and 2009 also found broad public support for government investments in basic scientific research and engineering and technology, though those surveys used different polling methods and somewhat different question wording. (Views on funding for medical research were not included in the past surveys.)

Majorities across the political spectrum agree that government investments in medical research, engineering and technology or basic scientific research pay off in the long run, although there are political differences on these questions. For instance, 92% of liberal Democrats say government investments in basic scientific research ultimately pay off. By comparison, 61% of conservative Republicans say government investments in basic research pay off, while a sizable minority (38%) says government investments in basic scientific research “aren’t worth it.”

A 2017 Pew Research Center survey found a wide and growing political divide over increasing federal spending on scientific research. Democrats and Democratic-leaning independents were 27 percentage points more likely than Republicans and Republican leaners to say they would increase spending on scientific research. In 2001, by contrast, there was no significant divide between Republicans and Democrats over increasing federal spending for scientific research.

The new survey also asked Americans to weigh the importance of government versus private investment in research funding. A majority of Americans (57%) say government funding is essential for scientific progress, while 42% say private funding will ensure enough progress even without government funding. The share saying government investment is essential is broadly consistent with previous Pew Research Center surveys that again used different survey methods and somewhat different question wording.

Liberal Democrats and conservative Republicans have different views on this question. About eight-in-ten liberal Democrats (78%) say government funding of research is essential for scientific progress. By contrast, 31% of conservative Republicans say government investment is essential, while two-thirds (67%) say private investment will ensure enough progress is made, even without government funding.

Note: See topline results and methodology here . 

• Federal Government
• Health Policy
• Medicine & Health
• Science Funding & Policy

Brian Kennedy is a senior researcher focusing on science and society research at Pew Research Center .

Americans see many federal agencies favorably, but Republicans grow more critical of Justice Department

Americans’ views of government’s role: persistent divisions and areas of agreement, americans rate their federal, state and local governments less positively than a few years ago, nearly three-quarters of americans say it would be ‘too risky’ to give presidents more power, the changing face of america’s veteran population, most popular.

901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

Warning: The NCBI web site requires JavaScript to function. more...

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

National Academies (US) Committee on Measuring Economic and Other Returns on Federal Research Investments. Measuring the Impacts of Federal Investments in Research: A Workshop Summary. Washington (DC): National Academies Press (US); 2011.

Measuring the Impacts of Federal Investments in Research: A Workshop Summary.

• Hardcopy Version at National Academies Press

APPENDIX D THE IMPACT OF PUBLICLY FUNDED BIOMEDICAL AND HEALTH RESEARCH: A REVIEW 1

Bhaven N. Sampat

Department of Health Policy and Management

Columbia University

I. INTRODUCTION AND BACKGROUND

New biomedical technologies trigger a number of major challenges and opportunities in health policy. Among economists, there is widespread consensus that new technologies are the major drivers of increased healthcare costs but at the same time a major source of health and welfare improvements ( Murphy and Topel 2003 ). This has led to discussion about whether technological change in medicine is “worth it” ( Cutler and McClellan 2001 ). The impact of new technologies on the health care system has also been the subject of much debate among health policy scholars more generally ( Callahan 2009 ).

Public sector research agencies have an important role in the U.S. biomedical innovation system. In 2004, federal agencies funded roughly one-third of all U.S. biomedical R and D ( Moses et al. 2005 ). The National Institutes of Health (NIH) accounted for three-quarters of this amount. Private sector drug, biotechnology, and medical device companies provide the majority of U.S. biomedical R and D funding (about 58 percent). This private sector research is, in general, focused more downstream and tends to be closer to commercial application than NIH-funded research.

Donald Stokes (1997) observes that the public values science “not for what it is but what it is for.” A perennial question in U.S. science and technology policy is what benefits taxpayers obtain from publicly funded biomedical research. Recent concerns about the clinical and economic returns to NIH funding in the post-doubling era reflect this emphasis.

In this paper, we review the evidence on the effects of publicly funded biomedical research. Reflecting Stokes’s observation above, the review will focus on the health and economic effects of public research, rather than measures of scientific outcomes. Given the prominence of the NIH in funding this research, many of the published articles and research focus on this agency. The evidence examined includes quantitative analyses, and qualitative case studies, published by scholars from a range of fields. While we have made efforts to be broad, the references discussed should be viewed as representative rather than exhaustive. This review takes stock of the empirical methodologies employed and the types of data used; it also highlights common research and evaluation challenges, and emphasizes where existing evidence is more, or less, robust.

We proceed as follows. In Section II, below, we discuss a stylized model of how public research funding affects health, economic, and intermediate outcomes. As Kline and Rosenberg (1986) , Gelijns and Rosenberg (1994) , and others have emphasized, the research process cannot be reduced to a neat, linear model. While we recognize this fact (and highlight it in our literature review) the simple model is still useful in helping to organize our discussion of theory and data on the effects of publicly funded research. In Section III, we discuss the empirical evidence. In Section IV, we discuss common evaluation difficulties. In Section V, we conclude. The empirical approaches, data sources, and findings of many of the studies reviewed are also summarized in Tables D1 - D3 .

Public Funding and Health Outcomes: Summary of Selected Studies.

Public Funding and New Drugs, Devices: Summary of Selected Studies.

Public Funding and Private R and D, Patenting: Summary of Selected Studies.

II. PUBLIC SECTOR RESEARCH AND OUTCOMES: AN OVERVIEW

Figure D-1 is a simple model illustrating how the literature has conceptualized the health and economic effects of publicly funded biomedical research (and publicly funded research more generally):

Publicly Funded R and D and Outcomes, Logic Model. SOURCE: Sampat, 2011

The top arm of the model illustrates one important relationship: publicly funded R and D yields fundamental knowledge, which then improves the R and D efficiency of private sector firms, yielding new technologies (drugs and devices) that improve health outcomes. 2 This conceptualization has been the essential raison-d’ e tre for the public funding of science since Vannevar Bush’s celebrated postwar report, Science, The Endless Frontier . For example, Bush asserted in 1945 that “discovery of new therapeutic agents and methods usually results from basic studies in medicine and the underlying sciences” ( Bush 1945 ). It is also the essential mechanism in several important economic models of R and D (e.g. Nelson 1984). Importantly, this conceptualization generally views publicly funded research as “basic” research that is not oriented at particular goals, and thus yields benefits across fields. The influential “market failure” argument for public funding of basic research is that profit-maximizing, private-sector firms will tend to underinvest in this type of fundamental, curiosity driven research, since they cannot appropriate its benefits fully ( Nelson 1959 , Arrow 1962 ).

The channels through which publicly funded basic research might influence private sector innovation are diverse, including dissemination via publications, presentations and conferences, as well as through informal networks ( Cohen et al. 2002 ). Labor markets are another channel, since public agencies may also be important in training doctoral and post-doctoral students who move on to work for private sector firms ( Scherer 2000 ).

The second arrow illustrates another relationship. New instruments and techniques that are by-products of “basic” research can also improve private sector R and D ( Rosenberg 2000 ). Prominent examples of instruments and research tools emanating from academic research include the scanning electron microscope, the computer, and the Cohen-Boyer recombinant DNA technique.

Third, publicly-funded researchers sometimes develop prototypes for new products and processes. Some of these are indistinguishable from the informational outputs of basic research discussed above. For example, when academic researchers learned that specific prostaglandins can help reduce intraocular pressure this discovery immediately suggested a drug candidate based on those prostaglandins, though the candidate required significant additional testing and development. (This academic discovery later became the blockbuster glaucoma drug, Xalatan .) The public sector has also been important in developing prototypes ( Gelijns and Rosenberg 1995 ). Roughly since the passage of the Bayh-Dole Act, in 1980, publicly funded researchers have become more active in taking out patents on these inventions and prototypes for new products and processes, and licensing them to private firms ( Mowery et al. 2004 . Azoulay et al. 2007 ).

While much of the discussion of publicly funded biomedical research focuses on this more “basic” or fundamental research the public sector also funds more “applied” research and development. 3 For example, about one-third of the NIH budget is for clinical research, including patient oriented research, clinical trials, epidemiological and behavioral studies, as well as outcomes and health services research. Such research can be a useful input into the development of prototypes, and may also directly inform private sector R and D. Clinical research may also directly affect health behaviors. For example, knowledge from epidemiological research about cardiovascular health risk factors contributed to reductions in smoking and better diets ( Cutler and Kadiyala 2003 ). New applied knowledge can also influence physicians: for example, by changing their prescribing habits (e.g. “beta-blockers after heart attacks improve outcomes”) or routines (e.g. “this type of device works best in this type of patient”). Importantly, as various studies we review below will emphasize, negative results from clinical trials—showing that particular interventions do not work — can also be important for clinical practice and in shaping health behaviors.

While the discussion above assumes that new biomedical knowledge and technologies improve health outcomes, this is a topic of debate. The conventional wisdom is that while other factors (e.g. better diet, nutrition, and economic factors) were more important for health outcomes historically ( McKeown 1976 ), improvements in American health in the post-World War II era have been driven largely by new medical knowledge and technologies ( Cutler, Deaton, and Lleras-Muney 2006 ). The contribution of publicly funded research to these developments is an open empirical question, discussed below.

At the same time, some scholars suggest that we may have entered an era of diminishing returns, where new technologies are yielding increasingly less value ( Callahan 2009 ; Deyo and Patrick 2004). The effect of new biomedical technologies on healthcare costs is a related concern. There is general agreement among health economists that new medical technologies are the single biggest contributor to the increase in long-run health costs, accounting for roughly half of cost growth (Newhouse 1992). Rising health costs strain the budgets of public and private insurers as well as employers, and may also contribute to generate health inequalities. The dynamic that exists between new medical technologies and health costs in the U.S. may reflect a “technological imperative,” which creates strong incentives for the healthcare system to adopt new technologies once they exist (Fuchs 1995; Cutler 1995 ). It may also reflect positive feedbacks between demand for insurance and incentives for innovation ( Weisbrod 1991 ).

Concern about the effects of technology on health costs has fueled empirical work on whether technological change in medicine is “worth it.” Long ago, Mushkin (1979) noted (though did not share) “widespread doubt about the worth of biomedical research given the cost impacts.”

A large literature in health economics suggests that new biomedical technologies are indeed, in the aggregate, worth it. Cutler (1995) and others suggest that, given the high value of improved health (current estimates suggest the value of one additional life year is $100,000 or more), even very costly medical technologies pass the cost-benefit test. 4 Nordhaus (2003) estimates that the value of improvements in health over the past half century are equal in the magnitude to measured improvements in all non-health sectors combined. Others ( Callahan 2009 ) view these health cost increases as unaffordable, even if they deliver significant value, and therefore ultimately unsustainable.

At the same time, not all medical technologies necessarily increase costs. As Cutler (1995) and Weisbrod (1991) indicate, technologies that make a disease treatable but do not cure it - moving from non-treatment to “halfway” technology in Lewis Thomas’s characterization-are likely to increase costs. The iron-lung to treat polio is an example of this. However, technologies that make possible prevention or cure (“high technology”) can be cost-reducing, especially relative to halfway technologies. Thus the polio vaccine was much cheaper than the iron lung. Consistent with this, Lichtenberg (2001) shows that while new drugs are more expensive than old drugs, they reduce other health expenditures (e.g. hospitalizations). Overall, he argues, they result in net decreases in health costs (and improve health outcomes). 5

As Weisbrod (1991) notes, “The aggregate effect of technological change on health care costs will depend on the relative degree to which halfway technologies are replacing lower, less costly technologies, or are being replaced by new, higher technologies. ” 6 One way to think about the effects of public sector spending on costs would be to assess the propensity of publicly funded research to generate (or facilitate the creation of) these different types of technologies. However, since the effects of these new technologies are mediated by various facets of the health care and delivery system, it may be difficult conceptually (and empirically) to isolate and measure the effects of public sector spending on overall health costs ( Cutler 1995 ). 7

II. THE EFFECT OF PUBLICLY FUNDED RESEARCH: A REVIEW OF THE EVIDENCE

Measuring the health returns to publicly funded medical research has been a topic of interest to policymakers for decades. In an early influential study, Comroe and Dripps (1976) consider what types of research (basic or clinical) are more important to the advance of clinical practice and health. The authors rely on interviews and expert opinion to determine the top ten clinical advances in the cardiovascular and pulmonary arena, and identified 529 key articles associated with these advances. They coded each of the key articles into six categories: (1) Basic research unrelated to clinical problems; (2) Basic research related to clinical problems (what Stokes later termed “use-oriented” basic research); (3) Research not aimed at understanding of basic biological mechanisms; (4) Reviews or syntheses; (5) Development of techniques or apparatuses for research; and (6) Development of techniques or apparatuses for clinical use. The authors find that 40 percent of the articles were in category 1, and 62 percent in categories 1 or 2. Based on this, the authors assert “a generous portion of the nation’s biomedical research dollars should be used to identify and then to provide long-term support for creative scientists whose main goal is to learn how living organisms function, without regard to the immediate relation of their research to specific human diseases.” Comroe and Dripps also note “that basic research, as we have defined it, pays off in terms of key discoveries almost twice as handsomely as other types of research and development combined” (1976).

A more recent set of studies examines the effects of publicly funded research on health outcomes. Operationalizing the concept of “health” is notoriously difficult. Common measures employed to account for both the morbidity and mortality effects of disease include quality adjusted life years (QALYs) and disability adjusted life years (DALYs) ( Gold et al, 2002 ). However, it is difficult to get longitudinal information on these measures by disease. As a result, most of the analyses of the effects of public funding on health examine more blunt outcomes, including the number of deaths and mortality rates for particular diseases.

Numerous prominent academic studies ( Weisbrod 1983 , Mushkin 1979 ) aim to examine the health effects of biomedical research, and the economic value of this impact, in a cost-benefit framework. One important recent study in this tradition, Cutler and Kadiyala (2003) , focuses on cardiovascular disease—the disease area where there has been the strongest improvement in health outcomes over the past sixty years. Since 1950 mortality from cardiovascular disease decreased by two- thirds, as Figure D-2 (reprinted from their paper) shows:

Mortality by cause of death 1950–1994. SOURCE: Cutler and Kadiyala 2003

Cutler and Kadiyala, through a detailed review of the causes of this advance (relying on epidemiological and clinical data, medical textbooks, and other sources), estimate that roughly one third of this cardiovascular improvement is due to high-tech treatments, one third to low tech treatments, and one third to behavioral changes. Assuming one additional life year gained is valued at $100,000, the authors compute a rate of return of 4-to-1 for investments in treatments and 30-to-1 for investments in behavioral changes. These investments include costs borne by consumers and insurers, and estimates of public sector R and D for cardiovascular disease.

Based on these figures, the authors argue that the rate of return to public funding is high, though they don’t directly trace public funding to changes in outcomes in their quantitative analyses. Interestingly, in their qualitative account, the major public sector research activities highlighted have an “applied” orientation, including the NIH’s role in sponsoring large epidemiological trials and holding consensus conferences. This may reflect a traceability and attribution problem, which is common to the evaluation of fundamental research: It is difficult to directly link improvements in outcome indicators to public sector investments in basic research, even in a study as detailed as this one.

A paper by Heidenreich and McClellan (2003) is similarly ambitious, looking at sources of advance in the treatment of heart attacks. The authors focus on this treatment area, not only because of the large improvements, but also because it is a “best case” for attributing health outcomes to particular biomedical investments. Specifically, these authors go further than Cutler and Kadiyala by attempting to link changes in clinical practice to changes in specific R and D inputs. The authors focus here on clinical trials, not basic research. This is not because they believe that basic research is unimportant, “but because it is much easier to identify connections between these applied studies and changes in medical care and health.”

Based on detailed analyses of MEDLINE-listed trials and health outcomes, the authors argue that medical treatments studied in these trials account for the bulk of improvement in AMI outcomes. The authors associate changes in clinical practice and outcomes to research results reported in trials through analysis of timing of events, and detailed clinical knowledge of how the trial results, clinical practices, and health outcomes relate.

One interesting result from this paper is that clinical practice often “leads” formal trials, challenging the “linear” model embodied in Figure D-1 (above). The authors also emphasize that an important role for trials is negative: telling clinicians what doesn’t work, and stopping the diffusion of ineffective technologies. While the sample they examine represents a mix of publicly funded and privately funded trials, the authors do emphasize a particularly important role for the public sector in funding trials on drugs off patent, where private firms have fewer incentives to do so.

Philipson and Jena’s (2005) study of HIV-AIDS drugs is another paper that examines the value of increases in health from new medical technologies. Though this study does not explicitly focus on the role of the public sector, it estimates that HIV-AIDS drugs introduced in the 1990s generated a social value of $1.4 trillion, based on the value of the increments to life expectancy created from these drugs (here again, using the estimate of $100,000 per life year). This study is relevant because of the important role of public sector research in the development of HIV‐AIDS drugs, which is observed in several of the empirical studies discussed below.

A recent paper by Lakdawalla et al (2011) employs a similar approach to assess the benefits from cancer research. The authors find these benefits to be large, estimating the social value of improvements from improvements in life expectancy during the 1988–2000 period to be nearly $2 trillion. The authors note that this compares to investments of about $80 billion dollars in total funding for the National Cancer Institute between 1971 and 2000. As with the HIV studies discussed above, the authors do not calculate a rate-of-return on publicly funded research explicitly, but do argue that the social benefits from cancer research in general far exceed research investments and treatment costs.

A large share of the benefits in the cancer arena, according to this work, results from better treatments. Lichtenberg (2004) also suggests that new drug development has been extremely important in progress against cancer. 8 Public sector research may have been important to the development of these drugs: various studies ( Stevens et al. 2011 , Chabner and Shoemaker 1989) suggest an important role for the public sector in cancer drug development. 9

Each of the studies discussed so far focuses on particular disease areas. In a more “macro” approach Manton (2009) and colleagues relate mortality rates in four disease areas to lagged NIH funding by the relevant Institute, over the period 1950–2004. They find that for two of the four diseases (heart disease, stroke) there is a strong negative correlation, but find weaker evidence for cancer and diabetes. Several issues arise here that will re-emerge in other quantitative analyses discussed below. First, linking funds to disease areas is difficult. As with other studies we will consider below, the authors here rely on the disease foci of Institutes within the NIH. More importantly, the counterfactual is hard to prove: It is difficult to make the case that the relationships estimated are causal, since Institute-specific funding is not exogenous. In particular, diseases where there is highest expectation of progress (even absent funding) may be more likely to get funds. Finally, competing risks also complicate interpretation of health outcomes. For example, part of the reason cancer mortality has increased rather than decreased over the period studied is that people no longer die of heart attacks, due to advances in the cardiovascular arena.

Private Sector R and D

Another set of studies relates publicly funded research to private sector R and D and productivity. These include econometric analyses relating public sector and private sector funding, surveys of firm R and D managers, and studies examining the geographic dimension of spillovers from public sector researchers.

Several papers relate NIH funding by disease area to later private sector funding. One motivation in these studies is to assess if public and private sector R and D are substitutes or complements, an issue of perennial interest in science and technology policy ( David, Hall, and Toole 2000 ). The econometric analyses generally find a positive association between public sector and private sector funding. Toole (2007) uses data from the NIH’S Computerized Retrieval of Information on Scientific Projects (CRISP) database, covering NIH basic and clinical research funding across seven therapeutic classes (between 1972 and 1996), and data from the Pharmaceutical Manufacturers of America (PhRMA) on private sector R and D in these same areas (between 1980 and 1999) to examine the relationships between the two. This study finds a 1 percent increase in basic research funding associated with a 1.7 percent increase in private sector funding, though the elasticity for clinical research is much smaller (.4 percent). In a similar analysis, Ward and Dranove (1995) , using PhRMA data on R and D spending and NIH data on funding by Institute (similar to that used in the Manton et al 2009 study discussed above) find that a 1 percent increase in NIH research support in a disease area is associated with a .76 percent increase in private sector R and D within that same disease area over the next seven years.

Surveys of firm R and D managers have also been used to gauge how public sector research affects private sector R and D. Cohen, Nelson, and Walsh (2002) report on the 1994 Carnegie Mellon Survey of Industrial R and D managers, which examined (among other issues) the roles of the public sector in industrial R and D, and channels through which public research affects industrial R and D. This survey is particularly interesting since it has data on both the drug and device sectors, and allows for comparison of these sectors to others. The authors find that the pharmaceutical industry is an outlier in its reliance on public sector R and D. In the pharmaceutical industry, according to respondents, public research was the most important source of new project ideas and contributor to project completion. By contrast, in the medical instruments industry R and D projects less frequently rely on public research than other industries. There are also some differences in the fields of science relied upon across these different industries. Thus the top three fields of science important to R and D projects in the pharmaceutical industry are medicine, biology, and chemistry. In medical instruments sector, the top three fields are medicine, materials science, and biology. Although much of the literature on the effects of public sector funding tend to focus on the NIH, the bulk of funding for materials science R and D comes from other agencies (including the National Science Foundation, Department of Energy, and the Department of Defense).

Another set of studies, examining how interactions between public and private sector scientists affects the productivity of private sector R and D, generally finds a strong relationship between the two. Cockburn and Henderson (1996) examine how private sector co-authorship with public sector scientists affects firm level R and D and productivity. The authors bring together several novel datasets, including MEDLINE data on firm publication activity and USPTO data on firm patenting activity. Using panel regression models (with firm fixed effects to control for time-invariant firm characteristics), they find a positive and statistically significant association between their productivity measure (based on important patents per R and D dollar) and collaboration with public sector scientists.

Research by Zucker, Darby, and Brewer (1998) examines the importance of academic science in the creation of new biotechnology firms in the 1980s. In this work, the authors relate new biotechnology firm formations by area to the number of academic “star scientists” (as measured by publications and other measures of scientific productivity) working in that area. The authors find that the presence of academic stars and their collaborators— intellectual capital”—within a geographic area has a statistically significant and positive relationship with the number of new biotechnology enterprises later formed in that area. This research suggests that public sector science has an important, though geographically mediated, effect on private sector research.

The question of whether spillovers from public research to firms are geographically mediated has also been examined through studies using patent citation data ( Jaffe et al. 1993 ). When patents are granted they include citations to prior art: earlier publications and patents that were deemed (by either the applicant or the patent examiner) as relevant to an invention. Economists and others have interpreted patent citations as evidence of knowledge flows or spillovers: thus if a firm patent cites a public sector publication or patent, this is considered evidence that the firm benefited from public funding. While there is some skepticism about this measure, given the prominence of patent examiners in generating citations ( Alcacer et al. 2009 ; Cohen and Roach 2010 ), it remains commonly employed. Moreover, as it turns out, examiner-added citation are less common within the biomedical arena ( Sampat 2010 ) and for citations to scientific publications ( Lemley and Sampat 2011 ) suggesting that citations in biomedical patents to scientific publications may be less subject to the concerns cited above.

Azoulay, Graff Zivin, Sampat (2011) collected data on 10,450 elite life science researchers (most of them publicly funded), historical information on productivity, employment locations of each scientist, MEDLINE data on their publications, ISI data on citations to their publications, and USPTO data on their patents and citations to their patents and publications. The authors assess the effects of geography on spillovers by examining how citation patterns change after the scientists move. Overall, they find some evidence that geography matters for spillovers, though weaker than in previous analyses. They also find the results on geography are sensitive to whether spillovers are measured through paper-to-paper citations, patent-to-patent citations, or patent-to‐paper citations.

Private Sector Innovation

Numerous studies also consider the public sector role in the development of marketed innovations. Survey work by Mansfield (1998) examines the importance of academic research for industrial innovation for firms across a range fields. In this work, as in the Carnegie Mellon Survey discussed above, the biomedical industries are outliers. The share of products developed over the late 1980s and early 1990s that could not have been developed (without substantial delay) absent recent academic research is nearly twice as high in drugs and medical products than in other industries.

Various recent studies examine the roles of the public sector in drug development using patent and “bibliometric” data. In addition to providing an indicator of returns to public R and D, this work may also be relevant to current policy proposals that aim to exploit public sector ownership of drugs to help reduce downstream drug prices and expand access ( Sampat and Lichtenberg 2011 ).

Sampat (2007) uses data on all drugs approved by the Food and Drug Administration (FDA) between 1988 and 2005 (and listed on the FDA’s Orange Book ), and USPTO data on patents associated with these drugs, to examine the share of drugs on which academic institutions (including public sector laboratories) own patents. Overall, a small number of new molecular entities (NMEs), about 10 percent, have academic patents. However, this share is larger for new molecular entities that received priority review (arguably the most innovative new drugs), where about 1-in-5 drugs have academic ownership. He also finds that public sector ownership of drugs is more pronounced for HIV‐AIDS drugs than for other drug classes.

Stevens et al. (2011) expand on this research to include vaccines and biologicals (not always listed on the Orange Book), and construct measures based not only on publicly available patent data but also propriety data on drug licenses. They find 153 FDA-approved drugs were discovered by the public sector over the past 40 years (102 NMEs, 36 biologics, and 15 vaccines.) The authors show that about 13 percent of NMEs (and 21 percent of priority NMEs) were licensed from public sector institutions, consistent with the numbers reported in Sampat (2007). Strikingly, the authors also show that virtually all the important vaccines introduced over the past quarter century came from the public sector. The authors also show broad correlations between NIH Institute budgets and the therapeutic classes where there are numerous public-sector based drugs, similar in spirit to econometric analyses we will review below.

Kneller (2010) takes a different approach, relying not on patent assignment records but instead on information related to the inventors’ places of employment, and applies his analysis to 252 drugs approved by the FDA between 1998 and 2007. Using these measures, Kneller finds a larger public sector influence than the previous studies. Overall, about a quarter of drugs are from university inventors, and a third of priority review drugs are from academic inventors.

The Sampat, Stevens et al, and Kneller studies rely on direct academic involvement in developing the molecules (resulting in academic ownership of the key patents or academic inventors listed on those patents). However, as discussed in Section II, in addition to the development prototypes, the public sector can facilitate or enhance industrial innovation in other ways as well. Thus Keyhani et al (2005) , using data from the Federal Register, government clinical trials databases, and documents from the FDA, finds the government was active in supporting clinical trials for nearly 7 percent of a sample of drugs approved between 1992 and 2002. Here again, the government role was more pronounced for HIV-AIDS drugs than for others.

Sampat and Lichtenberg (2011) distinguish between the direct effects of public sector research on drug development, where academic institutions are involved in discovering the molecule, and the indirect effects, where other knowledge spillovers from academic work increase private sector productivity. The authors measure the direct effect of public sector funding using information on “government interest” statements in Orange Book listed patents. And they use citations in Orange Book listed patents to academic patents or academic publications as a measure of this indirect effect. Consistent with the various studies cited above, this study suggests the direct effect is small overall: about 9 percent of drugs, and about 17 percent of priority review drugs, have public sector owned patents. However the indirect effect is much larger: about 48 percent of drugs have patents that cite public sector patents and publications. Among priority drugs, this indirect influence rises to nearly two-thirds. This finding is broadly consistent with the qualitative results from Cockburn and Henderson’s (1996) study of fifteen drugs, which shows the public sector made key enabling discovery for the majority (11 of the 15), but was involved in synthesis of the compound for only 2 of the 15.

The studies discussed above are accounting exercises. Others also have attempted to relate variation in funding by disease area to drug development patterns, econometrically. Dorsey et al. (2009) relate NIH funding by therapeutic area to later drug approvals across nine disease areas between 1995 and 2000. The authors allocate funding to specific diseases based on funding Institute using information in Congressional budget requests for those institutes. They find that despite a sharp rise in NIH funding over this time period, drug approvals remained flat overall. And their cross-therapeutic area analyses show little correlation between NIH funding and subsequent drug approvals.

Blume-Kohut (2009) also explores these issues, using panel regression models. She constructs data on NIH funding by disease area between 1975 and 2004 from the agency’s CRISP and RePORTER databases, based on parsing of abstracts and keywords of grants for disease keywords. She also examines information on drugs in development by class using data from a private data vendor, PharmaProjects. Her results show little evidence of responsiveness between the number of drugs in Phase III trials (late stage) and NIH funding, but evidence of a positive relationship for the number of drugs in earlier stage Phase I trials. The author notes these results may suggest that factors other than NIH funding (or the state of knowledge) may be important for Phase III trials, including commercial considerations such as the size of the market. In a similar approach, using a different outcome measure, Ward and Dranove (1995) relate MEDLINE publications tagged as “drug” articles to NIH R and D funding by disease area, here again categorized based on funding institute. They find a strong relationship between the two.

Most of the studies we have discussed thus far, examining public sector research and product development, focus on drugs and involve quantitative analysis. By contrast, Morlacchi and Nelson (2011) examine the sources of innovation in the development of the left ventricular assist device (LVAD), a medical device used for patients with end-stage heart failure. While the device originally was developed as a “bridge” solution until a heart became available for transplant, it is increasingly used as destination therapy, as a substitute for a heart transplant. Morlacchi and Nelson draw on interviews, primary and secondary articles, and patents to develop a longitudinal history of the development of the LVAD. They consider, among other questions, the importance of public sector funding in this development. Echoing some of the themes in Heidenreich and McClellan’s study of heart attack treatment, they find that in this field application led scientific understanding. The development of the device occurred even as basic understanding of heart failure remained weak, once again challenging the linear model of innovation portrayed in Figure D-1 . They also find that the applied and diffusion oriented activities of public sector funders were important in the development of this device, including the NIH’s sponsorship of conferences and centers to spread best-practice, funding of trials and development of important component technologies, and contracts to spur firm formation.

Health Costs

Despite longstanding concerns about the effects of new biomedical technologies on healthcare costs, and speculation that public sector research may be implicated in spurring this cost spiral, there has been surprisingly little empirical research on this topic. For example, there is a paucity of academic work relating funding patterns by disease area to subsequent cost growth, analogous to the work relating funding to private sector R and D, drug development, and health outcomes discussed above.

In 1993, the NIH prepared studies on the cost savings from a non‐random sample of 34 health technologies resulting from NIH support, demonstrating substantial cost savings ( NIH 1993 ). This study examined NIH funding for new technologies, as well as cost savings that accrued to patients, based on conservative assumptions on reductions in disease attributable to those same technologies. An NIH summary (NIH 2005) of this work notes that,“[t]aken together, the 34 technologies were estimated to reduce health care costs by about $8.3 billion to $12.0 billion annually.” As with several studies discussed earlier, difficulty in tracing the effects of “basic” research to particular technologies may complicate such calculations. Moreover, as the agency’s summary emphasizes “because the 34 new health care technologies studies were not chosen to be representative of all health advances resulting from NIH support, the results of these case studies cannot be generalized.”

While there has been little work, beyond this NIH study, on the effects of public sector funding on the direct costs of disease (i.e. health expenditures), the various studies discussed above that address the value of new biomedical technologies, can be interpreted as evidence that public sector funding reduces the total cost of disease, to the extent that the estimated improvements in health are viewed as reductions in the social costs associated with disease.

III. MEASUREMENT AND EVALUATION ISSUES

The diverse set of studies reviewed here illustrates a number of common measurement and evaluation issues that complicate efforts to estimate the health and economic effects of publicly funded biomedical research. Here, we will highlight several that stand out.

Several of the studies reviewed relate public sector funding by disease area to outputs. All of these focus on the NIH, since for other agencies publicly available data on funding by disease area is not readily available. Even for the studies focused on the NIH, however, there are measurement issues. While many studies construct funding stocks based on which Institutes fund the research, Institutes fund numerous diseases, introducing considerable noise into these measures.

The NIH’s CRISP database includes disease keywords, which can also be used to construct disease specific funding, but these are not collected in a standard way across the NIH ( Sampat 2011 ). In 2008, the NIH launched the “Research, Condition, and Disease Categorization” (RCDC) database, which uses standard methodologies to classify funds by area. Whereas, previously, each NIH Institute had linked its grants to diseases in an ad hoc and non-standard way, the RCDC employs standard category definitions to classify grants, developed with input from disease groups, the scientific community, and outside consulting groups. Before the RCDC, the NIH had provided disease-specific funding figures tentatively and with many caveats. Today, with the existence of the RCDC database, the agency has exhibited a more firm commitment to its own data sources and tracking. The NIH website thus affirms: “RCDC provides consistent and transparent information to the public about NIH-funded research. For the first time, a complete list of all NIH-funded projects related to each category is available.” This database may prove a boon for future researchers. However, its time frame and scope (covering only diseases and conditions “of historical interest to Congress”) may limit the types of analyses that can be conducted using these data.

A more fundamental issue is difficulty in categorizing “basic” research in these studies. Thus in the CRISP funding database, 49% of grants awarded in 1996 (accounting for 46% of NIH allocations) listed no disease terms, and only about 45% of grants map to a disease category in the RCDC ( Sampat 2011 ). It is difficult to incorporate these grants into disease level associations of funding and outputs. Basic research is also difficult to trace to outcomes even in a case study context, given lags and diffuse channels of impact. Thus it is not surprising that several of the evaluation studies discussed above (including the study of heart attack treatment, and the studies of NIH research and costs) focus on the effects of applied research.

The bibliometric approaches discussed above, linking grants to publications to citations to patents to drugs may overcome these traceability challenges, relying on paper trails between research and outcomes, and avoiding the need to associate public sector funding with particular diseases. However, the validity of these analyses rest on a number of assumptions, e.g. the extent to which patent-paper citations reflect real knowledge flows from public sector research.

Thus, measurement of inputs and intermediate steps is difficult. Measuring outcomes is conceptually easier, at least relative to evaluation of research outputs in non-biomedical contexts. Though the right output measures (e.g. morbidity or mortality, direct or indirect costs) or desiderata (should the NIH be mainly focused on advancing health? science? competitiveness? something else?) are the subject of debate, there is a wealth of data available to examine changes in health-related outcomes. Similarly, the research community has exploited numerous useful measures of relevant economic outcomes (e.g. patents, drug development, publications), again more readily available in the biomedical context than other arenas.

Causal evaluation of the effects of publicly funded research on these outcomes is difficult however, in this context and in S and T policy more generally. Simply put, funding choices are not random, making it difficult to attribute observed changes in outcomes to specific policies. As just one example, if public sector funding targets disease areas with high scientific opportunity, it is difficult to untangle whether subsequent improvements in health (or changes in private sector R and D, or drug development) reflect the effects of the funding or of the scientific opportunity. Several of the studies discussed attempt to address this problem econometrically, including through panel regression models with disease fixed effects, to absorb the effects of disease-specific characteristics that do not change over time. Going forward, quasi-experimental techniques may also prove useful. For example, it may be possible to exploit random shocks to funding in particular areas that are unrelated to scientific opportunity and disease burden could (e.g. those introduced through political influence on the allocation process, or changes in agencies’ funding rules) to assess the effects of public research.

There is also a need for more qualitative work. A number of the case studies surveyed above relied on detailed knowledge of the institutions at play, in depth clinical knowledge, and information on the timing of relevant events, to make credible arguments that the relationships they observed were causal. These too represent promising research approaches going forward.

IV. CONCLUSIONS

The measurement evaluation challenges highlighted above are endemic to science and technology policy in general (Jaffe 1998). A main output of science and technology policy is knowledge, which is difficult to measure and link to downstream outcomes. This exacerbates traditional difficulties with attributing causal effects to policy interventions, common to evaluation in most public policy domains.

Notwithstanding these challenges, at least on several issues various studies point in the same direction. First, there is consistent evidence across on the importance of public sector biomedical R and D for the efficiency of private sector R and D. The evidence is compelling since it is based on a range of studies using different techniques and samples, including surveys, case studies, and econometric analyses.

Second, the accounting studies on sources of innovation in drugs suggest that the public sector was directly involved in the development of a small share of drugs overall, but that the public sector role is more pronounced for more “important” drugs, and that the indirect effect of public sector research on drug development is larger than the direct effect. On the other hand, the studies that relate patterns of funding by disease area to drug development show less consistent results.

Third, a number of the studies suggest the importance of the applied and clinical public research activities on product development, patient behaviors, and health outcomes. This is striking, since much of the discussion about publicly funded biomedical research focuses on (and most of the funding is for) “basic” research. Whether the importance of applied activities reflects that their effects are easier to measure and trace, or that they are really very important, is an open empirical question. 10

Overall, there is strong evidence that new biomedical technologies have created significant value, as measured through the economic value of health improvements. Some scholars believe that even if public sector research was responsible for only a small share of this gain, it delivers high returns on investment ( Murphy and Topel 2003 ). 11

More work is needed directly examining the role of the public sector per se, and especially public sector basic research, in affecting these health outcomes. Similarly, very little is known about the effects of public sector research on health expenditures. Detailed longitudinal case studies of trends in public and private sector research activity, technology utilization, health outcomes, and health expenditures across a number of disease areas would be useful for promoting understanding on each of these issues. To the extent possible, it would be useful for these studies to employ common methods and measures, and to examine both disease areas where there has been considerable advance, and those where there has been less progress.

Finally, the bulk of the academic work in this area focuses on the NIH and pharmaceuticals. Much more research is needed on the effects of other funding agencies, and on the effects of public funding on the device sector.

• Alcacer J, Gittleman M, Sampat BN. Applicant and Examiner Citations in U.S. Patents: An Overview and Analysis. Research Policy. 2009; 38 (2):415–427.
• Arrow K. Economic Welfare and the Allocation of Resources for Invention. In: Nelson Richard., editor. The Rate and Direction of Inventive Activity. Princeton, NJ: Princeton University Press; 1962.
• Azoulay P, Michigan R, Sampat BN. The Anatomy of Medical School Patenting. The New England Journal of Medicine. 2007; 20 (357):2049–2056. [ PubMed : 18003961 ]
• Azoulay P, Zivin JG, Sampat BN. The Diffusion of Scientific Knowledge across Time and Space: Evidence from Professional Transitions for the Superstars of Medicine. NBER Working Paper 16683. 2011
• Ballar J, Gornick H. Cancer Undefeated. New England Journal of Medicine. 1997; 336 :1569–1574. [ PubMed : 9164814 ]
• Blume-Kohout ME. Drug Development and Public Research Funding: Evidence of Lagged Effects. Waterloo, ON, Canada: University of Waterloo; 2009. pp. 1–35.
• Bush V. Washington, DC: United States Government Printing Office; Science, The Endless Frontier. 1945
• Callahan D. Princeton: Princeton University Press; Taming the Beloved Beast: How Medical Technology Costs Are Destroying Our Health Care System. 2009
• Chabner BA, Shoemaker D. Drug Development for Cancer: Implications for Chemical Modifiers. International Journal of Radiation Oncology Biology Physics. 1988; 16 :907–909. [ PubMed : 2703396 ]
• Chandra A, Skinner J. Technology Growth and Expenditure Growth in Health Care. NBER Working Paper 16953. 2011 [ PubMed : 21957511 ]
• Cockburn IHR. Public-Private Interaction in Pharmaceutical Research. Proceedings National Academy of Science USA. 1996; 93 (23):12725–12730. [ PMC free article : PMC34128 ] [ PubMed : 8917485 ]
• Cohen WM, Nelson R, Walsh J. Links and Impacts: The Influence of Public Research on Industrial R and D. Management Science. 2002; 48 (1):1–23.
• Cohen W, Roach M. Patent Citations As Indicators of Knowledge Flows From Public Research. Working Paper. 2010 [ PMC free article : PMC3901515 ] [ PubMed : 24470690 ]
• Comroe J, Dripps RD. Scientific Basis for the Support of Biomedical Science. Science. 1976; 192 (4235):105–111. [ PubMed : 769161 ]
• Cutler D. Technology, Health Costs, and the NIH. Paper prepared for the National Institutes of Health Economic Roundtable on Biomedical Research. 1995.
• Cutler DM. Are We Finally Winning the War on Cancer? Journal of Economic Perspectives. 2008; 22 (4):3–26. [ PubMed : 19768842 ]
• Cutler D, McClellan M. Is Technological Change in Medicine Worth It? Heath Affairs. 2001; 20 (5):11–29. [ PubMed : 11558696 ]
• Cutler D, Kadiyala S. The Returns to Biomedical Research: Treatment and Behavioral Effects. In: Murphy Kevin, Topel Robert., editors. Measuring the Gains from Medical Research: An Economic Approach. Chicago: University of Chicago Press; 2003. pp. 110–162.
• Cutler D, Deaton A, Lleras-Muney A. The Determinants of Mortality. The Journal of Economic Perspectives. 2006; 20 (3)
• David PA, Hall BA, Toole AA. Is Public R and D a Complement or Substitute for Private R and D? A Review of the Econometric Evidence. Research Policy. 2000; 29 :497–529.
• Dorsey ER, Thompson JP, Carrasco M, de Roulet J, Vitticore P, Nicholson S, Johnston SC, Holloway RG, Moses H III. Financing of U.S. Biomedical Research and New Drug Approvals across Therapeutic Areas. PloS One. 2009; 4 (9):e7015. [ PMC free article : PMC2735780 ] [ PubMed : 19750225 ]
• Dorsey ER, Vitticore P, De Roulet J, Thompson JP, Carrasco M, Johnston SC, Holloway RG, Moses H III. Financial Anatomy of Neuroscience Research. Annals of Neurology. 2006; 60 (6):652–659. [ PubMed : 17192926 ]
• Fuchs V. Cambridge, MA: Harvard University Press; The Health Economy. 1986
• Gelijns A, Rosenberg N. The Dynamics of Technological Change in Medicine. Health Affairs. 1994; 13 (3):28–46. [ PubMed : 7927160 ]
• Gelijns A, Rosenberg N. The Changing Nature of Medical Technology Development. In: Rosenberg N, Gelijsn A, Dawkins H, editors. Sources of Medical Technology: Universities and Industry. Washington National Academies Press; 1995. [ PubMed : 25121227 ]
• Gold M, Stevenson D, Fryback D. HALYS and QALYS and DALYS, OH MY: Similarities and Differences in Summary Measures of Population Health. Annual Review of Public Health. 2002; 23 :115–134. [ PubMed : 11910057 ]
• Heidenreich P, McClellan M. Biomedical Research and Then Some: The Causes of Technological Change in Heart Attack Treatment. In: Murphy Kevin, Topel Robert., editors. Measuring the Gains from Medical Research: An Economic Approach. Chicago: University of Chicago Press; 2003. pp. 163–205.
• Jaffe A, Trajtenberg M, Henderson R. Geographic Localization of Spillovers as Evidenced By Patent Citations. The Quarterly Journal of Economics. 1993; 108 (3):577–598.
• Keyhani S, Diener-West M, Powe N. Do Drug Prices Reflect Development Time and Government Investment? Medical Care. 2005; 43 (8):753–762. [ PubMed : 16034288 ]
• Kline S, Rosenberg N. An Overview of Innovation. In: Landau Ralph, Rosenberg Nathan., editors. The Positive Sum Strategy: Harnessing Technology for Economic Growth. 1986.
• Kneller R. The Importance of New Companies for Drug Discovery: Origins of a Decade of New Drugs. Nature Reviews Drug Discovery. 2010; 9 (11):867–882. [ PubMed : 21031002 ]
• Lemley M, Sampat BN. Examiner Characteristics and Patent Office Outcomes. Forthcoming. Review of Economics and Statistics. 2011
• Lichtenberg F. Are the Benefits of New Drugs Worth Their Cost? Health Affairs. 2001;(20):241–251. [ PubMed : 11558710 ]
• Mansfield E. Academic Research and Industrial Innovation: An Update of Empirical Findings. Research Policy. 1998; 7–8 (26):773–776.
• Manton K, Gu X, Lowrimore G, Ullian A, Tolley H. NIH Funding Trajectories and Their Correlations with Us Health Dynamics from 1950 to 2004. Proceedings National Academy of Science USA. 2009; 106 (27):10981–10986. [ PMC free article : PMC2700155 ] [ PubMed : 19549852 ]
• McKeown T. The Role of Medicine: Dream, Mirage, or Nemesis? London: Nuffield Provincial Hospitals Trust; 1976.
• Morlacchi P, Nelson RR. How Medical Practice Evolves: The Case of the Left Ventricular Assist Device. Research Policy. 2011; 40 (4):511–525.
• Moses H III, Dorsey ER, Matheson DHM, Thier SO. Financial Anatomy of Biomedical Research. Journal of the American Medical Association. 2005; 294 (11):1333–1342. [ PubMed : 16174691 ]
• Mowery D, Nelson RR, Sampat BN, Ziedonis AA. Ivory Tower and Industrial Innovation: University–Industry Technology Transfer Before and After Bayh-Dole. Stanford, CA: Stanford University Press; 2004.
• Murphy KM. Measuring the Gains from Medical Research: An Economic Approach. 1. University of Chicago Press; 2003.
• Murphy K, Topel R, editors. Measuring the Gains from Medical Research: An Economic Approach. Chicago, IL: University of Chicago Press; 2003.
• Mushkin S. Biomedical Research: Costs and Benefits. Cambridge, MA: Ballinger Publishing; 1979.
• National Institutes of Health. Cost savings resulting from NIH research support: a periodic evaluation of the cost-benefits of biomedical research. 1993.
• Nelson RR. The Simple Economics of Basic Scientific Research. Journal of Political Economy. 1959;(67):297–306.
• Nelson RR. The Role of Knowledge in R and D Efficiency. The Quarterly Journal of Economics. 1982; 97 (3):453–470.
• Nordhaus. The Health of Nations: The Contribution of Improved Health to Living Standards. In: Murphy Kevin, Topel Robert., editors. Measuring the Gains from Medical Research: An Economic Approach. 2003.
• Philipson T, Jena AB. Who Benefits from New Medical Technologies? Estimates of Consumer and Producer Surpluses for HIV/AIDS Drugs. Forum for Health Economics and Policy. 2006; 9 (2) Biomedical Research and the Economy), Article 3.
• Rosenberg N. Schumpeter and the Endogeneity of Technology: Some American Perspectives. London: Psychology Press; 2000.
• Sampat BN. Academic Patents and Access to Medicines in Developing Countries. American Journal of Public Health. 2009; 99 (1):9–17. [ PMC free article : PMC2636619 ] [ PubMed : 19008514 ]
• Sampat BN. When Do Patent Applicants Search for Prior Art? Journal of Law and Economics. 2010; 53 :399–416.
• Sampat BN. The Allocation of NIH Funds Across Diseases and the Political Economy of Mission-Oriented Biomedical Research, Working paper. 2011.
• Sampat BN, Lichtenberg F. What Are The Respective Roles Of The Public And Private Sectors In Pharmaceutical Innovation? Health Affairs. 2011; 30 (2):332–339. [ PubMed : 21289355 ]
• Scherer FM. The pharmaceutical industry, Chapter 25. In: Culyer Anthony, Newhouse Joseph., editors. Handbook of Health Economics. Amsterdam: North Holland; 2000.
• Sporn MB. The War on Cancer: A Review. Journal of the New York Academy of Sciences. 1997; 833 (1)
• Stevens AJ, Jensen JJ, Wyller K, Kilgore PC, Chatterjee S, Rohrbaugh ML. The Role of Public-Sector Research in the Discovery of Drugs and Vaccines. The New England Journal of Medicine. 2011; 364 (6):535–541. [ PubMed : 21306239 ]
• Stokes D. Pasteur’s Quadrant: Basic Science and Technological Innovation. Washingon Brookings Institution Press. 1997
• Toole AA. Does Public Scientific Research Complement Private Investment in Research and Development in the Pharmaceutical Industry? The Journal of Law and Economics. 2007; 50 (1):81–104.
• Ward MR, Dranove D. The Vertical Chain of Research and Development in the Pharmaceutical Industry. Economic Inquiry. 1995; 33 :70–87.
• Weisbrod BA. Economics and Medical Research. AEI Press; 1983.
• Weisbrod BA. The health care quadrilemma: an essay on technological, change, insurance, quality, and cost containment. Journal of Economic Literature. 1991:523–552.
• Zhang Y, Soumerai S. Do Newer Prescription Drugs Pay for Themselves? A Reassessment of the Evidence. Health Affairs. 2007; 26 (3):880–886. [ PubMed : 17485770 ]
• Zucker L, Brewer M, Darby M. Intellectual Human Capital and The Birth of U.S. Biotechnology Enterprises. American Economic Review. 1998; 88 (1):290–306.

I thank Pierre Azoulay, and participants in the National Academies’ 2011 Workshop on Measuring the Impacts of Federal Investments in Research, for useful comments and suggestions.

Stokes (1997) and others have challenged this definition of “basic” research.

Stokes (1997) provides a thoughtful critique of conventional distinctions between “basic” and “applied” research. Since much of the literature before and since Stokes uses this terminology, we employ it in our review of this literature, even while recognizing the importance of his argument.

Cutler (1998) observes “Common wisdom suggests that rapid cost increases are necessarily bad. This view, however, is incorrect. Cost increases are justified if things that they buy (increases in health) are worth the price paid.” (2)

See however, Zhang and Sourmerai (2007) for a critique of this finding.

The cost-effectiveness of these technologies also depends on the populations on which they are used, as Chandra and Skinner (2011) emphasize.

There is also some discussion about whether the public sector should be paying attention to the cost-side consequences of its investment decisions. Weisbrod (1991) notes: “With respect to the NIH, it would be useful to learn more about the way the size and allocation of the scientific research budget are influenced, perhaps quite indirectly, by the health insurance system, through its impact on the eventual market for new technologies of various types” (535).

Cutler (2008) also emphasizes progress in the “war on cancer” – though highlights the role of screening and personal behavior changes, and notes the high costs of treatment. Sporn (2006) and Balilar and Gonik (1997) offer less sanguine assessments, emphasizing that progress against cancer has been highly uneven. Long-standing debates in assessments of the War on Cancer include the disagreements on the relative importance of treatment versus prevention, and of basic versus applied research. The literature also suggests it is difficult to evaluate the extent of progress in cancer, for two main reasons. First, advances in screening increase incidence. The second is competing risks: for example, the reduction in mortality from cardiovascular disease, discussed above, increased cancer cases. See Cutler (2008) for a review.

A National Cancer Institute (NCI) “Fact Sheet” asserts that “approximately one half of the chemotherapeutic drugs currently used by oncologists for cancer treatment were discovered and/or developed at NCI.” http://www ​.cancer.gov ​/cancertopics/factsheet ​/NCI/drugdiscovery

However, recall that the Toole (2007) study shows that basic research funding by the public sector has a stronger effect on private R and D than clinical research funding.

Heidenreich and McClellan (2003) summarize this point of view in the introduction to their study (discussed above), noting that while previous analyses “have generally not provided direct evidence of the impact on health of specific research studies, or on the likely value of additional research funding” these previous studies tend to conclude “recent gains in health are extraordinarily valuable in comparison with the relatively modest past funding.”

• Cite this Page National Academies (US) Committee on Measuring Economic and Other Returns on Federal Research Investments. Measuring the Impacts of Federal Investments in Research: A Workshop Summary. Washington (DC): National Academies Press (US); 2011. APPENDIX D, THE IMPACT OF PUBLICLY FUNDED BIOMEDICAL AND HEALTH RESEARCH: A REVIEW.
• PDF version of this title (1.6M)

In this Page

• INTRODUCTION AND BACKGROUND
• PUBLIC SECTOR RESEARCH AND OUTCOMES: AN OVERVIEW
• THE EFFECT OF PUBLICLY FUNDED RESEARCH: A REVIEW OF THE EVIDENCE
• MEASUREMENT AND EVALUATION ISSUES
• CONCLUSIONS

Other titles in this collection

• The National Academies Collection: Reports funded by National Institutes of Health

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Recent Activity

• THE IMPACT OF PUBLICLY FUNDED BIOMEDICAL AND HEALTH RESEARCH: A REVIEW - Measuri... THE IMPACT OF PUBLICLY FUNDED BIOMEDICAL AND HEALTH RESEARCH: A REVIEW - Measuring the Impacts of Federal Investments in Research

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

Medical Research: Sustainable Funding for Tomorrow's Cures

New section.

Some physicians, after or during medical school, choose a career path that combines the practice of medicine with a career in medical or scientific research. These physicians are a crucial bridge between the bench and the bedside — between research and patient care. 

Medical research is funded by various entities, including the federal government, patient and disease groups, and industry. A primary source of federal funding for tomorrow’s cures comes from the National Institutes of Health (NIH). AAMC-member institutions conduct over 60 percent of the extramural research with these NIH funds.  

The NIH is the nation’s primary funder of the medical research behind just about every test, treatment, and cure. The research NIH funds today leads to improved health tomorrow, including almost 3.8 million lives saved by cancer research since 1991, and a 56% decrease in the rate of heart attack deaths per 100,000 people between 1999 and 2020. Additionally, NIH research has led to cutting-edge and life-saving innovations in care and treatment, including cell-based gene therapies for treatment of sickle cell disease, immunotherapies for lung cancer and leukemia, ways to determine the effectiveness of chemotherapy on breast cancer, and advancements in cochlear implants and liver transplants. 

The AAMC and other science, research, and medical organizations have been advocating for increases in NIH funding to grow the U.S. research enterprise and maintain the country’s standing as the world leader in medical research. Sustained, predictable growth in funding for NIH is vital to developing the cures and treatments many Americans need. 

Learn more on the AAMC’s policy and advocacy pages

Following the U.S. Supreme Court ruling concerning the consideration of an applicant's racial and ethnic background in higher education admissions, David Skorton, MD, addressed future physicians and researchers to reassure them of the AAMC's commitment to supporting a diverse medical school community and health care workforce.

GME funding, health equity, and investment in medical research and workforce programs are critical to your future improving the health of patients and communities. Raise your voice with others who are helping to advocate for federal priorities vital to academic medicine.

The AAMC focuses on a number of key issues including strengthening the physician workforce, supporting medical research and clinical care, and investing in healthier communities.

Washington Highlights is a weekly electronic newsletter that features brief updates on the latest legislative and regulatory activities affecting medical schools and teaching hospitals.

• Introduction
• Conclusions
• Article Information

Publications (PMIDs), NIH-funded project years, and project year costs for basic research on 87 novel drug targets with first-to-target drugs (A) and for applied research on 356 drugs (B) approved from 2010 to 2019. Data are shown without applied discount rates.

All costs were calculated to the year of drug approval with no discount rate or discount rates of 3% or 7% for years before approval. A, Costs for the NIH for basic research on 87 novel targets before the launch of a first-to-target product identified by searching for a drug target, but not the drug itself. B, Costs for the NIH for published applied research on 356 drugs identified by searching for the drug. C, Costs for the NIH for basic and applied research associated with 87 first-to-target drugs.

eTable 1. PubMed drug and target search terms

eTable 2. NIH costs associated with 86 novel drug targets with quartile and 95% percentile values

eTable 3. NIH costs for basic and applied research related to NMEs approved 2010-2019 without outlier elimination

eTable 4. NIH costs associated with 356 drugs approved 2010-2019 with quartile and 95% percentile values

eTable 5. Calculation of the NIH contribution to phased clinical trials of failed clinical compounds for each product approval

eFigure 1. NIH funding for basic and applied research related to drugs approved 2010-2019 by Project Activity Code

eFigure 2. Number of approved FDA drugs (through June 2015) associated with 515 drug targets

eReferences.

Data sharing statement

See More About

Select your interests.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Others Also Liked

• Download PDF
• X Facebook More LinkedIn

Galkina Cleary E , Jackson MJ , Zhou EW , Ledley FD. Comparison of Research Spending on New Drug Approvals by the National Institutes of Health vs the Pharmaceutical Industry, 2010-2019. JAMA Health Forum. 2023;4(4):e230511. doi:10.1001/jamahealthforum.2023.0511

Manage citations:

© 2024

• Permissions

Comparison of Research Spending on New Drug Approvals by the National Institutes of Health vs the Pharmaceutical Industry, 2010-2019

• 1 Center for Integration of Science and Industry, Bentley University, Waltham, Massachusetts
• 2 Exponent, Inc
• 3 Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts
• 4 Department of Natural and Applied Sciences, Bentley University, Waltham, Massachusetts
• 5 Department of Management, Bentley University, Waltham, Massachusetts

Question   How does National Institutes of Health (NIH) investment in pharmaceutical innovation compare with investment by the pharmaceutical industry?

Findings   In this cross-sectional study of 356 drugs approved by the US Food and Drug Administration from 2010 to 2019, the NIH spent $1.44 billion per approval on basic or applied research for products with novel targets or $599 million per approval considering applications of basic research to multiple products. Spending from the NIH was not less than industry spending, with full costs of these investments calculated with comparable accounting.

Meaning   The results of this cross-sectional study suggest that the relative scale of NIH and industry investment in new drugs may provide a basis for calibrating the balance of social and private returns from these products.

Importance   Government and the pharmaceutical industry make substantive contributions to pharmaceutical innovation. This study compared the investments by the National Institutes of Health (NIH) and industry and estimated the cost basis for assessing the balance of social and private returns.

Objectives   To compare NIH and industry investments in recent drug approvals.

Design, Setting, and Participants   This cross-sectional study of NIH funding associated with drugs approved by the FDA from 2010 to 2019 was conducted from May 2020 to July 2022 and accounted for basic and applied research, failed clinical candidates, and discount rates for government spending compared with analogous estimates of industry investment.

Main Outcomes and Measures   Costs from the NIH for research associated with drug approvals.

Results   Funding from the NIH was contributed to 354 of 356 drugs (99.4%) approved from 2010 to 2019 totaling $187 billion, with a mean (SD) $1344.6 ($1433.1) million per target for basic research on drug targets and $51.8 ($96.8) million per drug for applied research on products. Including costs for failed clinical candidates, mean (SD) NIH costs were $1441.5 ($1372.0) million per approval or $1730.3 ($1657.6) million per approval, estimated with a 3% discount rate. The mean (SD) NIH spending was $2956.0 ($3106.3) million per approval with a 10.5% cost of capital, which estimates the cost savings to industry from NIH spending. Spending and approval by NIH for 81 first-to-target drugs was greater than reported industry spending on 63 drugs approved from 2010 to 2019 (difference, −$1998.4 million; 95% CI, −$3302.1 million to −$694.6 million; P  = .003). Spending from the NIH was not less than industry spending considering clinical failures, a 3% discount rate for NIH spending, and a 10.5% cost of capital for the industry (difference, −$1435.3 million; 95% CI, −$3114.6 million to $244.0 million; P  = .09) or when industry spending included prehuman research (difference, −$1394.8 million; 95% CI, −$3774.8 million to $985.2 million; P  = .25). Accounting for spillovers of NIH-funded basic research on drug targets to multiple products, NIH costs were $711.3 million with a 3% discount rate, which was less than the range of reported industry costs with 10.5% cost of capital.

Conclusions and Relevance   The results of this cross-sectional study found that NIH investment in drugs approved from 2010 to 2019 was not less than investment by the pharmaceutical industry, with comparable accounting for basic and applied research, failed clinical trials, and cost of capital or discount rates. The relative scale of NIH and industry investment may provide a cost basis for calibrating the balance of social and private returns from investments in pharmaceutical innovation.

Private sector investment and returns are classically viewed as the primary driving force for innovation. Evidence also shows that public sector investments in basic and applied biomedical research, including those from the National Institutes of Health (NIH), contribute substantively to the emergence of new drugs 1 - 7 and drug-related patents. 2 , 4 , 8 , 9 Recent economic studies have recognized the government’s contributions to pharmaceutical innovation by contextualizing government as an “early-stage investor and government funding for research as an investment.” 10 - 17 As such, these studies argued that there should be an equitable balance of investment risk and return between the public and private sectors 15 , 16 and framed policy regarding the pharmaceutical industry’s drug pricing practices and profits as shaping this balance. 17 , 18

The objective of this study was to compare NIH investment in the products approved by the US Food and Drug Administration (FDA) from 2010 to 2019 with reported levels of investment by the industry. 19 - 21 This comparison required an accounting for NIH spending comparable with that used to estimate total industry investment. This typically includes not only costs associated with approved products, but also costs associated with products that fail in clinical development and the cost of capital, or opportunity cost, associated with these investments. 19 , 20 , 22 , 23

Funding from the NIH for pharmaceutical innovation has been estimated from total NIH budget allocations 24 or categorical funding from the Research, Condition, and Disease Categories or Research Portfolio Online Reporting Tools (REPORTER). 25 , 26 These methods do not delineate spending associated with individual products. Case study methods have been used to identify NIH contributions associated with specific patents 2 , 8 or products. 5 These methods may not capture funding for basic research, which represents half of NIH funding and is classically undertaken “without specific applications towards processes or products in mind.” 27

Other studies have focused on NIH funding for published research associated with approved drugs or their targets. 6 , 28 , 29 In these studies, the costs of NIH-funded projects (grants) supporting research on a drug or its target were used as a measure of the NIH contribution to that product. In this method, drug-related publications represent applied research, and those associated with the drug’s target, but not the drug, represent basic research. Initial studies using this method identified NIH funding for research underlying each of the 210 drugs approved from 2010 to 2016, with total NIH costs of more than $100 billion and funding for each first-in-class drug of more than $800 million. 28 These studies also demonstrated spillover effects in which NIH spending for basic research in immunology or endocrinology contributed to the development of products for treating cancer. 29

This study extended these methods by developing an accounting for NIH spending that was comparable with reported investments by the industry. Using a data set of drugs approved from 2010 to 2019 (before the COVID-19 pandemic), this analysis estimated the NIH investment in these drugs, including the cost of published basic and applied research associated with these products, cost of phased clinical trials of failed product candidates, and opportunity cost, using discount rates recommended for government spending. 30 , 31 These estimates were used to compare NIH and industry investments in new drug approvals, the cost savings to the industry provided by NIH spending, and the economic efficiencies created through spillovers of NIH-funded basic research on drug targets to multiple products. These results are discussed in the context of policy regarding drug pricing and corporate profit that affects the balance of investment risk and reward between the public and private sectors.

This cross-sectional study analyzed NIH-funding for published research related to drugs approved from 2010 to 2019 or their biological targets that was conducted from May 2020 to July 2022. This study did not involve human participants and was not subject to institutional review board review. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines.

The core data collection of PubMed publications, NIH-funded projects and project costs associated with drugs approved from 2010 to 2019 has been previously described. 6 Products approved by the FDA from 2010 to 2019 (new drug application or biologics license application [type 1]), excluding those derived from blood or tissue, diagnostic agents, vaccines, and antimicrobials, and dates of first approval were identified from annual FDA reports. 32 , 33 Drug targets were identified from published literature 34 , 35 or the Therapeutic Targets Database. 36

Publications from 1960 to 2020 were identified in PubMed. Projects funded by the NIH from 2000 to 2020 were identified using the NIH REPORTER application programming interface. Projects were identified by NIH project number comprising the activity code, awarding institute, and number. Data on each project included the start year, end year, and costs for each fiscal year, subproject, or supplemental award. The analysis included phase-specific clinical success rates, 19 average NIH costs for phased clinical trials, 37 average industry investments, 19 , 20 and drug-specific industry costs. 20

Funding from the NIH for publications (PMIDs) associated with study drugs or their targets was identified in NIH REPORTER using methods described previously 6 , 28 (eMethods in Supplement 1 ). Briefly, PMIDs were identified in PubMed using optimized search terms for drugs (eTable 1A in Supplement 1 ) or targets (eTable 1B in Supplement 1 ) as well as automatic term mapping protocols, including medical subject heading terms and Boolean modifiers. The PMIDs were indexed by PubMed Identifier, publication year, and search terms. Projects funded by the NIH that were associated with PMIDs were identified using the REPORTER publication link tables. The PMIDs were further associated with 1 fiscal year of project funding (project year) and total project costs for the year corresponding to the publication year. Project years and costs were not assigned to PMIDs published after the product’s first FDA approval, before the project start year, or more than 4 years after the project end year. Drug-specific costs were calculated from 2000 through the date of first FDA approval. To account for lags between funding and publication, 38 PMIDs with publication dates 1 to 4 years after the project end year were associated with the project end year. The PMIDs identified by drug search were categorized as applied research, which included development. The PMIDs identified by target search, but not a drug search, were categorized as basic research. Project years and costs were categorized as applied research if 1 or more PMIDs associated with that project year were identified by drug search and categorized as basic research if none of the associated PMIDs were identified in drug searches. Duplicate PMIDs, project years, and costs were eliminated independently for each calculation.

The first drug associated with a novel biological target was classified as first to target. 34 , 35 , 39 Applied research costs were estimated from costs identified through the drug search. Basic research costs were estimated from costs identified in searches for targets of first-to-target drugs. Averages were calculated after 95th percentile outlier elimination to account for searches with poor specificity. The average number of drugs per target was recalculated from Santos et al 35 after excluding products derived from blood or tissue, diagnostic agents, vaccines, and antimicrobials. Spending from the NIH on failed clinical trials was estimated from phase transition rates 19 and phase-specific NIH costs. 37 Compounded 3% or 7% discount rates 30 , 31 or a 10.5% cost of capital 19 were calculated from the project year to first FDA approval.

Product-specific costs were compared for 81 first-in-class drugs with NIH costs estimated in this analysis and 63 drugs with industry costs described by Wouters 20 using univariate regression in which Cost i  =   β 0   +   β 1 Source i in which Cost i is the estimated NIH cost for research associated with the product or reported industry costs; Source i is an indicator variable with a value of 0 for NIH costs and 1 for industry costs; β 0 estimates the median and 95% CI for NIH spending; and β 1 estimates the median and 95% CI for the difference between NIH and industry spending. Costs were inflation-adjusted to 2018. Analyses were performed in Excel (Microsoft), PostgreSQL (PostgreSQL Global Development Group), or Python. All tests were 2 tailed. A 2-sided P  < .05 was considered statistically significant.

The FDA approved 356 drugs from 2010 to 2019, including 336 associated with 217 known targets. PubMed searches for drug names identified 229 000 PMIDs, while searches for known drug targets identified 1.9 million publications, of which 21.4% had NIH funding ( Table 1 ). Funding from the NIH funding was identified in 310 of 356 drug searches (87%) and in all 217 target searches ( Table 1 ). Overall, this analysis identified NIH-funded research associated with 354 of 356 products (99.4%) approved from 2010 to 2019. The products without NIH funding were a chelating agent and osmotic laxative.

Funding from the NIH totaled $187 billion; $31 billion (17%) represented applied research on approved drugs, and $156 billion (83%) represented basic research on drug targets ( Table 1 ). Figure 1 shows annual publications, NIH project (funding) years, and costs leading to first FDA approval.

Research projects and research-related programs, which typically support investigator-initiated research, provided 40.2% of NIH funding, including 43.2% of basic research costs and 24.8% of applied research costs. However, research program projects and centers as well as cooperative agreements (including clinical translational science awards), which typically contribute infrastructure or shared research capabilities, comprised 46.2% of total costs, 42.4% of basic research costs, and 65.6% of applied research costs (eFigure 1 in Supplement 1 ).

Of the 356 approvals, 86 (24.2%) were first-to-target products. Figure 1 A shows NIH-funded publications, project years, and NIH costs associated with these targets leading to first-to-target product launch. Funding from the NIH was identified for all 86 targets (eTable 2 in Supplement 1 ).

Figure 2 A shows NIH costs per novel drug target with no discount rate or 3% and 7% discount rates. After 95th percentile outlier elimination, the mean (SD) NIH cost for research on a novel drug target before a first-to-target product approval was $1.34 ($1.43) billion (3% discount, $1.63 [$1.74] billion; 7% discount, $2.15 [$1.66] billion; 10.5% cost of capital, $2.85 [$3.15] billion) ( Table 2 ). Outliers included searches for CD-4, B-cell lymphoma 2, and epidermal growth factor receptor, which returned publications not explicitly associated with the drug target. Calculations without outlier elimination are shown in eTable 3 in Supplement 1 .

Figure 1 B shows NIH-funded publications, project years, and NIH costs associated with applied research on 356 drugs through the year of approval. Before first approval, 301 of 356 products (84.5%) had NIH research funding (eTable 4 in Supplement 1 ). Figure 2 B shows cumulative NIH costs for applied research with no discount rate or 3% and 7% discount rates. After 95th percentile outlier elimination, the mean (SD) NIH cost for applied research before approval was $51.8 ($96.8) million (3% discount, $58.5 [$111.9] million; 7% discount, $69.4 [$137.8] million; 10.5% cost of capital, $81.4 [$168.3] million) ( Table 2 ). Outliers included searches failing to distinguish applied research on the approved product from basic research on the corresponding natural compound (ie, clotting factors, hormones, α-1 antitrypsin). Results without outlier elimination are shown in eTable 3 in Supplement 1 .

The NIH costs calculation associated with failed clinical trials is shown in eTable 5 in Supplement 1 . Based on reported phase transition rates, 19 8.53 phase 1 trials, 5.08 phase 2 trials, and 1.79 phase 3 trials were conducted for each product approved. With NIH costs of $5.7 million for phase 1, $7.2 million for phase 2, and $3.9 million for phase 3, 37 estimated NIH costs for clinical trials of failed candidates were $75.4 million for each product approval (3% discount, $80.6 million; 7% discount, $88.6 million; 10.5% cost of capital, $96.8 million) ( Table 2 ).

Total NIH costs were calculated for 86 first-to-target products as the sum of NIH costs for basic research on the target, applied research on the drug, and phased clinical trials of failed compounds. The distribution of costs is shown in Figure 2 C. After 95th percentile outlier elimination, mean (SD) NIH costs before a first-to-target product launch was $1.44 ($1.37) billion (3% discount, $1.73 [$1.66] billion; 7% discount, $2.24 [$2.18] billion; 10.5% cost of capital, $2.96 [$3.11] billion) ( Table 2 ). Data without outlier elimination are shown in eTables 2 and 4 in Supplement 1 .

DiMasi et al 19 estimated average industry spending on 106 drugs approved from 1990 to 2010 at $1.5 billion or $2.8 billion with a 10.5% cost of capital (inflation-adjusted to 2018). Using different methods, Wouters et al 20 reported an average industry spending on 63 drugs approved from 2009 to 2018 of $374.1 million, (95% CI, $301.9 million to $464.2 million) or $1.6 billion (95% CI, $1.27 billion to $1.89 billion) with a 10.5% cost of capital.

Spending from the NIH per approval for 81 first-to-target products was significantly greater than reported industry spending on 63 drugs 20 before accounting for clinical failures, cost of capital, or discount rates (difference, −$1998.4 million; 95% CI, −$3302.1 million to −$694.6 million; P  = .003) or with accounting for clinical failures (difference, −$1415.8 million; 95% CI, −$2731.4 million to $100.2 million; P  = .04) ( Table 3 ). Spending from the NIH was not less than industry spending when industry costs were estimated with clinical failures and a 10.5% cost of capital, and NIH spending was estimated with clinical failures and either a 3% discount rate (difference, −$1435.3 million; 95% CI, −$3114.6 million to $244.0 million; P  = .09) or a 7% discount rate (difference, −$2436.3 million; 95% CI, −$4782.1 million to −$90.5 million; P  = .04) ( Table 3 ). Investment from the NIH and the industry was not significantly different when industry spending was estimated with clinical failures, prehuman costs 19 (30.8% real costs), and a 10.5% cost of capital, and when NIH costs were estimated with clinical failures and either a 3% discount rate (difference, −$393.8 million; 95% CI, −$2120.5 million to $1332.9 million; P  = .65) or 7% discount rate (difference, −$1394.8 million; 95% CI, −$3774.8 million to $985.2 million; P  = .25) ( Table 3 ).

Santos et al 40 cataloged 893 biological targets for FDA-approved products (1578) through June 2015, of which 1467 (93.0%) met inclusion criteria for this study. These products were associated with 515 biological targets, an average of 2.85 products per target (eFigure 2 in Supplement 1 ).

Accounting for spillovers of basic research on novel drug targets to 2.85 product approvals, the NIH cost for basic research per approval was $471.8 million (3% discount, $572.2 million; 7% discount, $753.4 million; 10.5% cost of capital, $1.0 billion) ( Table 2 ). Accounting for spillover effects from basic research on drug targets, costs of applied research, product failures, and discount rates or cost of capital, the estimated NIH investment per approval was $599.0 million (3% discount, $711.3 million; 7% discount, $911.4 million; 10.5% cost of capital, $1179 million). Estimated NIH spending was lower than the reported average industry spending 19 but within the 95% CI of per drug spending. 20

In this cross-sectional study, evidence suggests the public sector makes substantial contributions to the foundational knowledge on which drug approvals are based, 1 , 2 , 4 , 6 - 8 , 41 , 42 but less to patents 6 , 9 or development. 2 , 3 , 37 , 43 Conversely, the industry is primarily responsible for product development and sponsored more than 99% of the product launches in this data set. 6

The objective of this work was to compare NIH investments in recent drug approvals with reported investment by the industry. This required an accounting for NIH spending with costs for basic research on the targets for these drugs, applied research on the approved products, phased clinical trials of failed products, and the recommended discount rates for government spending. 30 , 31 This accounting adheres closely to methods used to estimate industry investment, 19 , 20 while also recognizing fundamental differences in the nature of public and private sector investment in prevailing economic theories. 10

These analyses suggest that NIH project costs for basic or applied research associated with the products approved from 2010 to 2019 were significantly greater than reported industry spending. Costs for the NIH were also higher than industry costs when both included spending on failed clinical trials of candidate products. Including clinical failures, NIH investment (calculated with either a 3% or 7% discount rate) was not less than industry investment calculated with a 10.5% cost of capital. Investment from the NIH calculated with clinical failures and a 3% or 7% discount rate was also not less than industry investment calculated with clinical failures, additional costs of prehuman research, and 10.5% cost of capital. These results suggest that NIH investments in pharmaceutical innovation are comparable with those made by industry.

While including the cost of capital in estimates of the industry’s investment in pharmaceutical innovation is controversial 44 and estimates of this rate vary, 45 , 46 consideration of the cost of capital is normative in finance theory and practice. These calculations are also consistent with prevailing economic theories that view private sector investment as inherently productive in that it typically generates a return on investment. In this context, the cost of capital represents the opportunity cost or financial risk that long-term capital investments in drug discovery and development may not achieve normal returns on investment.

There is no theoretical basis for applying an equivalent cost of capital to government spending. Prevailing economic theories treat government funding as nonproductive in that it is not expected to provide a return on investment. The 3% and 7% discount rates recommended by the US Office of Management and Budget for government spending 30 , 31 have distinct theoretical foundations. The 3% discount rate on federal spending approximates the historical cost of government borrowing and, consequently, the full cost of government spending. 31 , 47 The 7% discount rate represents the average productivity of private sector investments and is interpreted as a measure of the opportunity cost to the economy if public sector spending crowds out and reduces private sector investment. 30 , 48 Given evidence that NIH funding for biomedical research stimulates, rather than reduces, private sector investment, 49 estimating NIH investment with the 3% discount rate may be most consistent with prevailing economic principles.

This analysis did include an NIH spending calculation with the 10.5% cost of capital. This value provides an estimate of the additional costs that the industry would incur in the absence of NIH spending. Comparing these estimated cost savings with those of DiMasi et al 19 or Wouters et al 20 of industry investment suggests that industry costs would be more than double in the absence of the NIH contributions.

This work also recognizes that economic efficiencies may arise through spillover of knowledge or capabilities gained from NIH-funded basic research to applications by multiple firms or multiple products. 8 , 29 , 35 , 43 , 50 Such spillovers would reduce the estimated NIH cost per approval. Considering only potential spillovers from NIH-funded basic research on drug targets to multiple products directed at the same targets, NIH spending per drug was within the range of actual industry spending. 20 Spending from the NIH was estimated with either a 3% or 7% discount rate was lower than industry spending calculated with the 10.5% cost of capital.

Science and innovation policy remains grounded in a model in which government investments in basic research generate scientific capital that can be commercialized by industry for social and economic benefit. This model is exemplified by NIH spending for basic biomedical science, which plays an enabling role in pharmaceutical innovation. 1 , 3 , 6 , 7 , 28 , 49 Emerging economic theory formalizes this model by contextualizing government funding for research as an early-stage investment in innovation. 10 - 13 , 15 - 17 These theories further posit that, as early-stage investors, government or the public sector it represents could expect social or economic returns commensurate with those of comparable investments by the private sector. 10 , 15 , 16

The present study was predicated on this concept that NIH spending represents an investment that can be meaningfully compared with investment by the industry. In this context, the finding that the magnitude of NIH investment in new drugs is comparable with that of the industry suggests that returns to the public and private sector should also be comparable. To achieve this, public policy associated with drug pricing, 51 corporate profit, 52 and commercial applications of government-funded invention 53 should be calibrated to provide an equitable distribution of returns between the public and private sectors. 10 , 15 , 16 The present results may provide a cost basis for considering not only the private rate of return to industry or the economy, but also the social return on investments, 40 including the multiplex elements associated with health. 54 , 55

First, this analysis is limited by the sensitivity and specificity of PubMed searches, right censoring of the data collection, and reported false-positive and false-negative associations between PMIDs and NIH projects in REPORTER. 38 Search terms may not identify NIH funding for research tools, pharmaceutical modalities, or process development, which may underestimate total NIH costs.

Second, NIH costs for each publication were estimated as 1 fiscal year of project funding. This is consistent with evidence that 5-year NIH grants produce a median of 5 publications 56 but may underestimate NIH costs for studies spanning multiple years.

Third, NIH funding in REPORTER represents a fraction of public sector funding for biomedical research and does not include funding from other agencies or governments, nongovernment organizations, academic institutions, or nonprofit organizations. This analysis also did not include contract funding, research and development tax credits, or vouchers. This would underestimate the public sector contribution to pharmaceutical innovation.

Fourth, this study considered only spillovers from basic research on drug targets. Spillovers may also emerge from NIH funding for research training, infrastructure, or capabilities. This would not affect the total NIH costs but would underestimate the gain from economic efficiencies.

This cross-sectional study found that NIH investment in drugs approved from 2010 to 2019 was not less than investment by industry, with comparable accounting for basic and applied research, failed clinical trials, and cost of capital or discount rates. The relative scale of NIH and industry investment may provide a cost basis for calibrating the balance of social and private returns from investments in pharmaceutical innovation.

Accepted for Publication: February 18, 2023.

Published: April 28, 2023. doi:10.1001/jamahealthforum.2023.0511

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Galkina Cleary E et al. JAMA Health Forum .

Corresponding Author: Fred D. Ledley, MD, Center for Integration of Science and Industry, Bentley University, Jennison Hall 144, 175 Forest St, Waltham, MA 02452 ( [email protected] ).

Author Contributions: Dr Ledley had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Ledley.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Ledley.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Obtained funding: Ledley.

Administrative, technical, or material support: Jackson.

Supervision: Ledley.

Conflict of Interest Disclosures: Drs Jackson, Galkina Cleary, and Ledley reported grants from the National Biomedical Research Foundation and Institute for New Economic Thinking during the conduct of the study as well as grants from the West Health Policy Center and National Pharmaceutical Council outside the submitted work. Dr Zhou reported grants from the National Biomedical Research Foundation during the conduct of the study as well as a grant from the National Pharmaceutical Council outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by grants from the Institute for New Economic Thinking and the National Biomedical Research Foundation.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank Juliana Harrison, MBA, Bentley University for assistance preparing the manuscript; Zoë Folchman-Wagner, PhD, and Vineeta Tanwar, PhD, Bentley University, for contributions to data collection; John Overington, PhD, VP Discovery Informatics, Exscientia and Olivier Wouters, PhD, London School of Economics, for consultation concerning their published works; and Michael Boss, PhD, Nancy Hsiung, PhD, and Bruce Leicher, JD, Bentley University, for their critical analysis. Drs Boss and Hsiung and Mr Leicher received no compensation for work at Bentley University or their role in this study.

Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Federally Funded Research

Health literacy research, literacy research, grant writing.

The federal government supports and encourages health literacy research in several ways. Below you can find funding opportunities, research findings, and training initiatives from several federal government agencies.

• The Network of the National Library of Medicine’s Regional Medical Libraries offer grant funding in their  respective regions . Funded projects often address health literacy by linking members of the community with quality health information resources and providing training on their use. Other projects address health literacy by offering training to information professionals, healthcare providers, or other health professionals about how to support and address health literacy in their communities. You can also find information about  previously funded projects , and filter the list of projects by multiple parameters.
• The Agency for Healthcare Research and Quality (AHRQ) lists Funding Opportunities , including a Special Emphasis Notice (SEN) announcing interest in research on improving organizational health literacy to prevent and manage chronic disease .
• AHRQ has  tools and data  to help health services researchers conduct health literacy research.
• The National Institutes of Health (NIH) provides key research findings on health literacy .

Successful research may lead to changes in process, behavior, and attitudes or to better health outcomes.

• Adult Literacy Research – The U.S. Department of Education’s Division of Adult Education and Literacy summarizes recent and emerging research on adult literacy. The division also provides online access to evidence-based materials to help adult education practitioners and state and local staff improve adult education programs, services, instruction, and teacher quality.
• AHRQ Research Studies is a monthly compilation of research articles funded by AHRQ or authored by AHRQ researchers and recently published in journals or newsletters. You can search by key word, year, or topic (e.g., health literacy).
• Discretionary Grants and Cooperative Agreements – Find U.S. Department of Education programs by title and office. You can also search by key word (e.g., literacy).
• Early Learning and School Readiness
• Language, Bilingualism and Biliteracy
• Reading, Writing and Related Learning Disabilities
• The  Network of the National Library of Medicine  provides resources for writing a proposal, submitting an application, submitting project reports, and more.
• The NIH Grants & Funding office provides the following guidance on grants.
• Write Your Application is a resource that may help you develop a strong application that allows reviewers to better evaluate the science and merit of your proposal.
• Communicating Research Intent and Value in NIH Applications: Plain Language Examples illustrates ways to reword titles, abstracts, and public health relevance statements to better communicate the value and intent of your application to nonscientists.

To receive email updates about this page, enter your email address:

What role does private sector R&D play compared to the National Institutes of Health?

While the National Institutes of Health (NIH) funds vital basic academic research, globally, it’s the private sector driving applied research and development of new medications.

Private companies and investors spend five times what the U.S. federal government does every year on basic medical research.

Drug innovators use government research to spur applied R&D to create actual treatments for patients in need.

In fact, the U.S. biopharmaceutical industry puts more of its revenues back into researching and developing the next generation of innovative products than any other industry.

Related Questions

• How are prescription drug costs really determined?

Related Debates

• International Drug Pricing

About Grants

Did you know that NIH is the largest public funder of biomedical research in the world, investing more than $32 billion a year to enhance life, and reduce illness and disability? NIH funded research has led to breakthroughs and new treatments, helping people live longer, healthier lives, and building the  research foundation that drives discovery. Read on for an orientation to NIH funding, grant programs, how the grants process works, and how to apply.

Grants Process Overview

Learn the steps needed for an application to proceed from planning and submission to award and close out. Drill down on each step for guidance that can deepen your understanding of the grants process and help you submit a grant application and manage your grant award. 

Get Started

Before getting started, learn the basics like why it is important to understand the structure of NIH and how we approach grant funding, what types of organizations and people are eligible to apply, what we look for in a research project, and the types of grant programs we offer. Once you have the big picture move on to learn about planning your application.

How to Apply

How to Apply serves as our comprehensive application guide, providing step-by-step instructions to get you through the grant application process, from completing required registrations, finding a funding opportunity to accessing the application forms and instructions, formatting your application, finding due dates and submission policies, and more. 

Application Referral and Review

Once you submit your application to NIH, we assign your application to a specific study section for review and to a specific NIH Institute or Center for funding consideration. After assignment, the application undergoes a two level peer review process. Explore this page to learn more.

Pre-Award and Post-Award Processes

Applications that do well in review begin the pre-award process. Learn what happens during this process and what types of information you will be expected to provide. Once awarded, grantees must follow the requirements in the NIH Grants Policy statement and provide periodic reports to NIH that help NIH monitor the award.

Forms Directory

Essential NIH forms, instructions and format pages you need to apply for, manage, and close out grant awards. 

This page last updated on: March 17, 2017

• Bookmark & Share
• E-mail Updates
• Privacy Notice
• Accessibility
• National Institutes of Health (NIH), 9000 Rockville Pike, Bethesda, Maryland 20892
• NIH... Turning Discovery Into Health

• Skip to primary navigation
• Skip to main content
• Skip to footer

Understanding Science

How science REALLY works...

• Much scientific research is funded by government grants, private companies, and non-profit organizations.
• Though funding sources may occasionally introduce bias to scientific research, science has safeguards in place to detect such biases.

Who pays for science?

Today, we all do. Most scientific research is funded by government grants (e.g., from the National Science Foundation, the National Institutes of Health, etc.), companies doing research and development, and non-profit foundations (e.g., the Breast Cancer Research Foundation, the David and Lucile Packard Foundation, etc.). As a society, we reap the rewards from this ​​ science  in the form of technological innovations and advanced knowledge, but we also help pay for it. You indirectly support science everyday through taxes you pay, products and services you purchase from companies, and donations you make to charities. Something as simple as buying a bottle of aspirin may help foot the bill for multiple sclerosis research.

Funding for science has changed with the times. Historically, science has been largely supported through private patronage (the backing of a prominent person or family), church sponsorship, or simply paying for the research yourself. Galileo’s work in the 16th and 17th centuries, for example, was supported mainly by wealthy individuals, including the Pope. Darwin’s  Beagle  voyage in the 19th century was, on the other hand, funded by the British government — the vessel was testing clocks and drawing maps for the navy — and his family’s private assets financed the rest of his scientific work. Today, researchers are likely to be funded by a mix of grants from various government ​​ agencies , institutions, and foundations. For example, a 2007 study of the movement of carbon in the ocean was funded by the National Science Foundation, the U.S. Department of Energy, the Australian Cooperative Research Centre, and the Australian Antarctic Division. 1  Other research is funded by private companies — such as the pharmaceutical company that financed a recent study comparing different drugs administered after heart failure. 2  Such corporate sponsorship is widespread in some fields. Almost 75% of U.S. ​​ clinical trials  in medicine are paid for by private companies. 3  And, of course, some researchers today still fund small-scale studies out of their own pockets. Most of us can’t afford to do cyclotron research as a private hobby, but birdwatchers, scuba divers, rockhounds, and others can do real research on a limited budget.

An imperfect world

In a perfect world, money wouldn’t matter — all scientific studies (regardless of funding source) would be completely ​​ objective . But of course, in the real world, funding may introduce biases — for example, when the backer has a stake in the study’s outcome. A pharmaceutical company paying for a study of a new depression medication, for example, might influence the study’s design or interpretation in ways that subtly favor the drug that they’d like to market. There is ​​ evidence  that some biases like this do occur. Drug research sponsored by the pharmaceutical industry is more likely to end up favoring the drug under consideration than studies sponsored by government grants or charitable organizations. 4  Similarly, nutrition research sponsored by the food industry is more likely to end up favoring the food under consideration than independently funded research. 5

Take a sidetrip

Find out more about  the tobacco industry’s manipulation of scientific research .

So what should we make of all this? Should we ignore any research funded by companies or special interest groups? Certainly not. These groups provide invaluable funding for scientific research. Furthermore, science has many safeguards in place to catch instances of bias that affect research outcomes. Ultimately, misleading results will be corrected as science proceeds; however, this process takes time. Meanwhile, it pays to scrutinize studies funded by industry or special interest groups with extra care. So don’t, for example, brush off a study of cell phone safety just because it was funded by a cell phone manufacturer — but do ask some careful questions about the research before jumping on the bandwagon. Are the results consistent with other independently funded studies? Does the study seem fairly designed? What do other scientists have to say about this research? A little scrutiny can go a long way towards identifying bias associated with funding source.

1 Buesseler, K.O., C.H. Lamborg, P.W. Boyd, P.J. Lam, T.W. Trull, R.R. Bidigare, J.K.B. Bishop, K.L. Casciotti, F. Dehairs, M. Elskens, M. Honda, D.M. Karl, D.A. Siegel, M.W. Silver, D.K. Steinberg, J. Valdes, B. Van Mooy, and S. Wilson. 2007. Revisiting carbon flux through the ocean's twilight zone.  Science  316:567. 2 Mebazaa, A., M.S. Nieminen, M. Packer, A. Cohen-Solal, F.X. Kleber, S.J. Pocock, R. Thakkar, R.J. Padley, P. Poder, and M. Kivikko. 2007. Levosimendan vs dobutamine for patients with acute decompensated heart failure: The SURVIVE randomized trial.  Journal of the American Medical Association  297:1883-1891. 3 Bodenheimer, T. 2000. Uneasy alliance: Clinical investigators and the pharmaceutical industry.  New England Journal of Medicine  342:1539-1544. 4 Als-nielson, B., W. Chen, C. Gluud, and L.L. Kjaergard. 2003. Association of funding and conclusions in randomized drug trails: A reflection of treatment effect or adverse events?  Journal of the American Medical Association  290:921-928. 5 This research focused on studies of soft drinks, juice, and milk. Lesser, L.I., C.B. Ebbeling, M. Goozner, D. Wypij, and D.S. Ludwig. 2007. Relationship between funding source and conclusion among nutrition-related scientific articles.  Public Library of Science Medicine  4:41-46.

Subscribe to our newsletter

• Understanding Science 101
• The science flowchart
• Science stories
• Grade-level teaching guides
• Teaching resource database
• Journaling tool
• Misconceptions

• Report PDF (759 KB)
• Report - All Formats .ZIP (4.6 MB)
• Share on X/Twitter
• Share on Facebook
• Share on LinkedIn
• Send as Email

Analysis of Federal Funding for Research and Development in 2022: Basic Research

August 15, 2024

Funding for U.S. domestic research and development (R&D) performance, estimated to reach $885.6 billion in 2022, comes from a number of sectors, including businesses, government, higher education, and nonprofit organizations. U.S. R&D Increased by \$72 Billion in 2021 to \$789 Billion; Estimate for 2022 Indicates Further Increase to \$886 Billion. NSF 24-317. Alexandria, VA: U.S. National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24317/ ." data-bs-content="Anderson G; National Center for Science and Engineering Statistics (NCSES). 2024. U.S. R&D Increased by \$72 Billion in 2021 to \$789 Billion; Estimate for 2022 Indicates Further Increase to \$886 Billion. NSF 24-317. Alexandria, VA: U.S. National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24317/ ." data-endnote-uuid="9f8e4978-3bf6-4fc6-bca6-7368f3e4be59">​ Anderson G; National Center for Science and Engineering Statistics (NCSES). 2024. U.S. R&D Increased by $72 Billion in 2021 to $789 Billion; Estimate for 2022 Indicates Further Increase to $886 Billion. NSF 24-317. Alexandria, VA: U.S. National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24317/ . The National Patterns of R&D Resources ( National Patterns ) publication series compiles data from surveys of organizations that perform R&D and documents trends in U.S. R&D funding and performance. In the 1980s, the business sector passed the federal government as the largest overall funder of domestic R&D performance. National Patterns of R&D Resources (annual series): table 6. Available at https://ncses.nsf.gov/pubs/nsf24318/table/6 ." data-bs-content="National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series): table 6. Available at https://ncses.nsf.gov/pubs/nsf24318/table/6 ." data-endnote-uuid="30c1cb86-05b9-46b1-846c-f431e4823b87">​ National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series): table 6. Available at https://ncses.nsf.gov/pubs/nsf24318/table/6 . The most recent National Patterns publication made it clear that a similar milestone is approaching with respect to funding of basic research. At the turn of the century, the federal government funded about 60% of basic research. In 2022, the federal government is estimated to fund 40% of basic research ( figure 1 ). National Patterns data are presented on a calendar year approximations based on fiscal year data, and all other data in this InfoBrief show fiscal years." data-bs-content=" National Patterns data are presented on a calendar year approximations based on fiscal year data, and all other data in this InfoBrief show fiscal years." data-endnote-uuid="409f1052-5e0c-4369-8809-8d76701b399d">​ National Patterns data are presented on a calendar year approximations based on fiscal year data, and all other data in this InfoBrief show fiscal years. As the share of federal funding for basic research has decreased, the share funded by business has increased. National Patterns estimates show that in 2022 40% and 37% of basic research is funded by the federal government and businesses, respectively.

• For grouped bar charts, Tab to the first data element (bar/line data point) which will bring up a pop-up with the data details
• To read the data in all groups Arrow-Down will go back and forth
• For bar/line chart data points are linear and not grouped, Arrow-Down will read each bar/line data points in order
• For line charts, Arrow-Left and Arrow-Right will move to the next set of data points after Tabbing to the first data point
• For stacked bars use the Arrow-Down key again after Tabbing to the first data bar
• Then use Arrow-Right and Arrow-Left to navigate the stacked bars within that stack
• Arrow-Down to advance to the next stack. Arrow-Up reverses

U.S. basic research expenditures, by source of funds: 1953–2022

FFRDCs = federally funded research and development centers.

Detail may not add to total because of rounding. This figure reaggregates the R&D performer data according to major categories of R&D funding: federal government, nonfederal government (state and local), business, higher education, and nonprofit. Business sources of R&D funding in this figure include the own funds of domestic R&D-performing businesses, funds from other domestic businesses, and funds from foreign businesses. For trend comparisons, use only the historical data reported in this figure because some back-year data may have been revised. Data are based on reports by performers in the National Center for Science and Engineering Statistics annual surveys on R&D expenditures: Business Enterprise Research and Development Survey, Annual Business Survey, Higher Education Research and Development Survey, Survey of Federal Funds for Research and Development, FFRDC Research and Development Survey, and Survey of State Government Research and Development. R&D expenditures by business performers—and, before 2001, also industry-administered FFRDCs—were collected on a calendar year basis. Expenditures for other performers are calendar year approximations based on fiscal year data. Some data for 2021 are preliminary and may be revised in future iterations of the National Patterns for R&D Resources report. The data for 2022 include estimates and are likely to be revised in the next iteration of the National Patterns for R&D Resources report.

National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series).

However, data from the Federal Funding for R&D by Budget Function ( Budget Function ) show that federal funding for basic research increased from 14% of total R&D budget authority in FY 1978 to a high of over 27% in FY 2017 and has remained relatively stable ever since, holding at approximately 25% in FY 2022 ( figure 2 ). In order to understand whether federal funding for basic research is in decline and to understand what constitutes the federal government’s portfolio of basic research funding, this InfoBrief will examine federal funding for basic research using three data sources: National Patterns , Budget Function , and the Survey of Federal Funds for Research and Development ( Federal Funds for R&D Survey ), all sponsored by the National Center for Science and Engineering Statistics (NCSES) within the U.S. National Science Foundation (NSF).

Federal budget authority for basic research as a percentage of total R&D budget authority: FYs 1978–2024

Detail may not add to total because of rounding. Agencies in several functions received emergency COVID-19 pandemic-related funding for R&D and R&D plant in FYs 2020–22. The data for FY 2017 and onward reflect application of the narrowed definition of development described by the Office of Management and Budget (OMB) in its Circular A-11 of July 2016. The previous years' numbers reflect use of the former development definition. FYs 2009–10 include American Recovery and Reinvestment Act of 2009. In FY 2007, the Department of Homeland Security (DHS) changed its R&D portfolio to reclassify funding in National defense and most Administration of justice as General science and basic research; in FY 2013, this was reclassified back to Administration of justice. In FY 2004, DHS changed its R&D portfolio to reclassify funding in General science and basic research and in Agriculture as Administration of justice. In FY 2000, the National Aeronautics and Space Administration (NASA) transferred funding for the International Space Station program from R&D to R&D plant; the change is reflected in the budget authority for Space flight, research, and supporting activities. In FYs 2009 and 2012, NASA revised the classification of its R&D and non-R&D funding. In FY 2000, the National Institutes of Health in the Department of Health and Human Services classified all of its previous development activities as basic research or applied research, with a resulting increase in Health basic research. In FY 1998, many Department of Energy programs were reclassified from Energy to General science and basic research. In FY 1998, the Department of Veterans Affairs began reporting medical care support funds as a part of its total research budgetary resources; the change is reflected in the budget authority for Veterans benefits and services.

Data from FYs 1955–94 are from agencies' submissions to OMB, Circular A-11, exhibit 44A, "Research and development activities," and from supplemental data obtained from agencies' budget offices. Data from FYs 1995–2024 are from agencies' submissions to OMB per MAX Schedule C, agencies' budget justification documents, supplemental data obtained from agencies' budget offices, and Executive Office of the President, OMB, Budget of the United States Government, Fiscal Year 2024.

Basic Research by Funding Source

The federal government funds R&D, including basic research, that is performed in all sectors of the economy (i.e., government, business, higher education, and nonprofit). Although the federal government’s share of basic research across all performing sectors of the economy has been declining since the late 1970s, data from National Patterns shows federal funding for basic research as a percentage of total federal funds for R&D performance has increased from 10% in 1953 to 32% in 2022. Federal funds for basic research as a percentage of total federal funds for R&D reached an all-time high of 39% in 2003. The increases in federal funding for basic and applied research in the late 1990s and early 2000s can be attributed to the doubling of the budget for the National Institutes of Health (NIH), commonly referred to as “the NIH doubling”—a 5-year plan (1998 to 2003) between Congress and the executive branch to stimulate research through NIH, which is within the Department of Health and Human Services (HHS). https://crsreports.congress.gov/product/pdf/R/R43341 ." data-bs-content="For more information on the NIH doubling, see https://crsreports.congress.gov/product/pdf/R/R43341 ." data-endnote-uuid="9c275636-85d4-42ac-89f6-0de7038e4ee9">​ For more information on the NIH doubling, see https://crsreports.congress.gov/product/pdf/R/R43341 . Since 2008, federal funding for basic research as a percentage of all federal funding for R&D has remained relatively stable, hovering between 30% and 33% through 2022 ( figure 3 ).

U.S. federally funded R&D expenditures, by type of R&D: 1953–2022

Detail may not add to total because of rounding. This figure reaggregates the R&D performer data according to major categories of R&D funding: federal government, nonfederal government (state and local), business, higher education, and nonprofit. Business sources of R&D funding in this figure include the own funds of domestic R&D-performing businesses, funds from other domestic businesses, and funds from foreign businesses. For trend comparisons, use only the historical data reported in this figure because some back-year data may have been revised. Data are based on reports by performers in the National Center for Science and Engineering Statistics annual surveys on R&D expenditures: Business Enterprise Research and Development Survey, Annual Business Survey, Higher Education Research and Development Survey, Survey of Federal Funds for Research and Development, FFRDC Research and Development Survey, and Survey of State Government Research and Development. R&D expenditures by business performers—and, before 2001, also industry-administered FFRDCs—were collected on a calendar year basis. Some data for 2021 are preliminary and may be revised in future iterations of the National Patterns for R&D Resources report. The data for 2022 include estimates and are likely to be revised in the next iteration of the National Patterns for R&D Resources report.

If the share of federal funds for basic research as a percentage of all federal R&D funding has been stable at about 32% since the late 2000s, why does National Patterns data show that the percentage of total basic research supported by the federal government has been in decline? National Patterns data also demonstrate a trend in private sector R&D. For example, between 2000 and 2022, the amount of R&D funded by the business sector has more than doubled when measured in constant dollars ( figure 4 ). In 2000, businesses funded $256 billion in domestic R&D, which increased 123% to an estimated $570 billion in 2022. In contrast, federally funded R&D increased from $92 billion to $135 billion over the same time period, an increase of nearly 47%. Similarly, business funding for basic research increased from $11 billion in 2000 to $41 billion in 2022, whereas federal funding for basic research increased from $33 billion to $43 billion over the same period. (All dollar amounts in this paragraph are in constant dollars). National Patterns of R&D Resources (annual series): table 7, U.S. basic research expenditures by source of funds and performing section: 1953–2022 . Gross domestic product implicit price deflators (2017 = 1.00000) were used to adjust current dollars for inflation." data-bs-content="Data are adjusted to 2017 constant dollars from the National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series): table 7, U.S. basic research expenditures by source of funds and performing section: 1953–2022 . Gross domestic product implicit price deflators (2017 = 1.00000) were used to adjust current dollars for inflation." data-endnote-uuid="c22c0fdb-a2ad-4266-8a9a-afeb48e82ddc">​ Data are adjusted to 2017 constant dollars from the National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series): table 7, U.S. basic research expenditures by source of funds and performing section: 1953–2022 . Gross domestic product implicit price deflators (2017 = 1.00000) were used to adjust current dollars for inflation. Therefore, although federal funding for basic research as a percentage of all federal funding for R&D has been relatively stable in recent years, the growth in private sector funding for basic research has reduced the federal share of basic research across the economy overall.

U.S. R&D expenditures, by source of funds: 1953–2022

Detail may not add to total because of rounding. Gross domestic product price deflators (2017 = 1.00000) were used to adjust current dollars for inflation. This figure reaggregates the R&D performer data according to major categories of R&D funding: federal government, nonfederal government (state and local), business, higher education, and nonprofit. Business sources of R&D funding in this figure include the own funds of domestic R&D-performing businesses, funds from other domestic businesses, and funds from foreign businesses. Constant-dollar estimates are derived from unrounded data. For trend comparisons, use only the historical data reported in this figure because some back-year data may have been revised. Data are based on reports by performers in the National Center for Science and Engineering Statistics annual surveys on R&D expenditures: Business Enterprise Research and Development Survey, Annual Business Survey, Higher Education Research and Development Survey, Survey of Federal Funds for Research and Development, FFRDC Research and Development Survey, and Survey of State Government Research and Development. R&D expenditures by business performers—and, before 2001, also industry-administered FFRDCs—were collected on a calendar year basis. Expenditures for other performers are calendar year approximations based on fiscal year data. Some data for 2021 are preliminary and may be revised in future iterations of the National Patterns for R&D Resources report. The data for 2022 include estimates and are likely to be revised in the next iteration of the National Patterns for R&D Resources report.

Federal Budget Authority for Basic Research by Function

The NCSES-sponsored Budget Function report provides information on budget authority for R&D, the congressionally set ceiling on obligations and outlays within the federal government, by congressional budget functions. Circular A-11: Preparation, Submission, and Execution of the Budget, Section 20.4; Office of Management and Budget, 2023. Available at https://www.whitehouse.gov/wp-content/uploads/2018/06/a11_web_toc.pdf ." data-bs-content="For more information, see Circular A-11: Preparation, Submission, and Execution of the Budget, Section 20.4; Office of Management and Budget, 2023. Available at https://www.whitehouse.gov/wp-content/uploads/2018/06/a11_web_toc.pdf ." data-endnote-uuid="1a828c44-6e91-49db-9eb3-1478c8847866">​ For more information, see Circular A-11: Preparation, Submission, and Execution of the Budget, Section 20.4; Office of Management and Budget, 2023. Available at https://www.whitehouse.gov/wp-content/uploads/2018/06/a11_web_toc.pdf . , Budget Function Classifications: Origins, Trends, and Implications for Current Uses , U.S. General Accounting Office, February 1998, GAO/AIMD-98-67 ." data-bs-content="Budget function classifications are intended to provide a means of arraying budget data according to the major purposes served. These functions include all spending for a given topic, regardless of the federal agency that oversees the individual federal program. For more information, see Budget Function Classifications: Origins, Trends, and Implications for Current Uses , U.S. General Accounting Office, February 1998, GAO/AIMD-98-67 ." data-endnote-uuid="54c28b5e-3696-46be-aa19-56b74c864cc9">​ Budget function classifications are intended to provide a means of arraying budget data according to the major purposes served. These functions include all spending for a given topic, regardless of the federal agency that oversees the individual federal program. For more information, see Budget Function Classifications: Origins, Trends, and Implications for Current Uses , U.S. General Accounting Office, February 1998, GAO/AIMD-98-67 . Before federal agencies can issue obligations and incur outlays, they must receive authorization from Congress. Authorization is often made in the form of budget authority. Along with data presented earlier in figure 3 on federally funded R&D expenditures, federal budget authority for basic research has increased as a percentage of total R&D budget authority, from 14% in FY 1978 to a high of 27% in FY 2020 ( figure 2 ). Although the share of budget authority for basic research declined since FY 2020, the current dollar value has actually increased to $45.2 billion in FY 2022 and is expected to increase to $47.1 billion and $48.3 billion in FY 2023 and FY 2024, respectively ( figure 5 ). Budget Function : table 23 in the full set of tables at https://ncses.nsf.gov/data-collections/federal-budget-function/2022-2024#data ." data-bs-content="For details, see Budget Function : table 23 in the full set of tables at https://ncses.nsf.gov/data-collections/federal-budget-function/2022-2024#data ." data-endnote-uuid="43195897-9890-4a99-91a9-0b36f4e644dd">​ For details, see Budget Function : table 23 in the full set of tables at https://ncses.nsf.gov/data-collections/federal-budget-function/2022-2024#data . The increased amount of total R&D budget authority during the COVID-19 pandemic had the effect of reducing the share of basic research even though the amount in current dollars is expected to increase. ​ Congress took a number of legislative steps from March 2020 through March 2021 to provide added appropriations in response to the COVID-19 pandemic, some of which increased funding for related R&D. These include the Coronavirus Preparedness and Response Supplemental Appropriations Act, 2020 (H.R. 6074, 6 March 2020); the Families First Coronavirus Response Act (H.R. 6201, 18 March 2020); the Coronavirus Aid, Relief, and Economic Security Act (H.R. 748, 27 March 2020); and the Paycheck Protection Program and Health Care Enhancement Act (H.R. 266, 24 April 2020). Additional funding for R&D related to COVID-19 was also provided by the Consolidated Appropriations Act, 2021 (H.R. 133, 27 December 2020) and by the American Rescue Plan Act of 2021 (H.R. 1319, 10 March 2021).

Federal budget authority for basic research: FYs 1978–2024

Detail may not add to total because of rounding. Gross domestic product implicit price deflators (2017 = 1.00000) were used to adjust current dollars for inflation. FY 2009 includes funds from the American Recovery and Reinvestment Act. In FYs 2020–22, agencies in several functions received emergency COVID-19 pandemic-related funding for R&D or R&D plant.

Data from FYs 1978–94 are from agencies' submissions to the Office of Management and Budget (OMB), Circular A-11, exhibit 44A, "Research and development activities," and from supplemental data obtained from agencies' budget offices. Data from FYs 1995–2024 are from agencies' submissions to OMB per MAX Schedule C, agencies' budget justification documents, supplemental data obtained from agencies' budget offices, and Executive Office of the President, OMB, Budget of the United States Government, Fiscal Year 2024.

However, when adjusted for inflation, federal R&D budget authority has declined since FY 2020 and is estimated to be relatively unchanged between FYs 2022 and 2024 ( figure 5 ). Health is the predominant budget function for budget authority in basic research and has been since the late 1970s ( figure 6 ). Circular A-11: Preparation, Submission, and Execution of the Budget, Exhibit 79A; Office of Management and Budget, 2023. Available at https://www.whitehouse.gov/wp-content/uploads/2018/06/a11_web_toc.pdf ." data-bs-content="As part of the annual budget process, the federal government designates funds for R&D to help foster knowledge and innovation within the nation. This funding is classified into 20 functional categories. For more information on federal budget functional categories see Circular A-11: Preparation, Submission, and Execution of the Budget, Exhibit 79A; Office of Management and Budget, 2023. Available at https://www.whitehouse.gov/wp-content/uploads/2018/06/a11_web_toc.pdf ." data-endnote-uuid="4e89f648-286d-4d62-966e-483f155fe369">​ As part of the annual budget process, the federal government designates funds for R&D to help foster knowledge and innovation within the nation. This funding is classified into 20 functional categories. For more information on federal budget functional categories see Circular A-11: Preparation, Submission, and Execution of the Budget, Exhibit 79A; Office of Management and Budget, 2023. Available at https://www.whitehouse.gov/wp-content/uploads/2018/06/a11_web_toc.pdf . Once again, the increases in the Health budget function from the late 1990s through the early 2000s are indicative of the effect of the NIH doubling. Further federal stimulus investments in 2009 from the American Recovery and Reinvestment Act and in 2020 from funding related to the COVID-19 pandemic have contributed to specific increases totaling $23.7 billion and $20.7 billion, respectively. However, these periodic stimulus funds have not had the effect of setting new levels of sustained budget authority for basic research.

Federal budget authority for basic research, by selected budget function: FYs 1978–2024

Detail may not add to total because of rounding. Not all basic research funded by the federal government falls into the General science and basic research (251) category. Federal funding for basic research arises in other functional categories—such as National defense or Health—and is included in the category funding totals there. In FYs 2020–22, agencies in several functions received emergency COVID-19 pandemic-related funding for R&D or R&D plant. FY 2009 includes funds from the American Recovery and Reinvestment Act. In FY 2007, the Department of Homeland Security (DHS) changed its R&D portfolio to reclassify funding in National defense and most Administration of justice as General science and basic research; in FY 2013, this was reclassified back to Administration of justice. In FY 2004, DHS changed its R&D portfolio to reclassify funding in General science and basic research and in Agriculture as Administration of justice. In FY 2000, the National Aeronautics and Space Administration (NASA) transferred funding for the International Space Station program from R&D to R&D plant; the change is reflected in the budget authority for Space flight, research, and supporting activities. In FYs 2009 and 2012, NASA revised the classification of its R&D and non-R&D funding. In FY 2000, the National Institutes of Health in the Department of Health and Human Services classified all of its previous development activities as basic research or applied research, with a resulting increase in Health basic research. In FY 1998, many Department of Energy programs were reclassified from Energy to General science and basic research. In FY 1998, the Department of Veterans Affairs began reporting medical care support funds as a part of its total research budgetary resources; the change is reflected in the budget authority for Veterans benefits and services.

Data from FYs 1955–94 are from agencies' submissions to the Office of Management and Budget (OMB), Circular A-11, exhibit 44A, "Research and development activities," and from supplemental data obtained from agencies' budget offices. Data from FYs 1995–2024 are from agencies' submissions to OMB per MAX Schedule C, agencies' budget justification documents, supplemental data obtained from agencies' budget offices, and Executive Office of the President, OMB, Budget of the United States Government, Fiscal Year 2024.

Federal Obligations for Basic Research by Agency

In FY 2022, federal obligations for basic research totaled $45.4 billion in current dollars, or about 24% of the total federal R&D portfolio ($190.4 billion). https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data ." data-bs-content="For details, see table 1 in the full set of Federal Funds for R&D Survey tables at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data ." data-endnote-uuid="67a2875c-949d-42f8-9e98-9310fbc34d17">​ For details, see table 1 in the full set of Federal Funds for R&D Survey tables at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data . From a science policy perspective, obligations for R&D are an important measure because they indicate an agency’s funding priorities at a given point in time. ​ Obligations represent the amount for orders placed, contracts awarded, services received, and similar transactions during a given period, regardless of when the funds were appropriated or when future payment of money is required. Data from the NCSES-sponsored Federal Funds for R&D Survey show that HHS (previously called the Department of Health, Education, and Welfare) has consistently been the largest funder of basic research by the federal government since at least FY 1967 when it accounted for 27% of all federal obligations for basic research ( figure 7 ). ​ The Department of Health, Education, and Welfare was re-organized in 1979, creating the new Department of Education and the Department of Health and Human Services. In FY 2008, HHS peaked as the largest funder of basic research with 59% of all federal obligations for basic research. In FY 2023, HHS alone accounted for 49% of all federal funding for basic research.

Federal obligations for basic research, by agency: FYs 1967–2023

USDA = Department of Agriculture, DOD = Department of Defense, DOE = Department of Energy, HHS = Department of Health and Human Services, NASA = National Aeronautics and Space Administration, NSF = National Science Foundation.

Because of rounding, detail may not add to total. FYs 2020, 2021, and 2022 obligations include additional funding provided by supplemental COVID-19 pandemic-related appropriations (e.g., Coronavirus Aid, Relief, and Economic Security [CARES] Act). FYs 2009 and 2010 obligations include additional funding provided by the American Recovery and Reinvestment Act of 2009. Beginning with FY 2016, the totals reported for development obligations represent a refinement to this category by more narrowly defining it to be "experimental development." Most notably, totals for development do not include the Department of Defense (DOD) Budget Activity 7 (Operational System Development) obligations. Those funds, previously included in DOD's development obligation totals, support the development efforts to upgrade systems that have been fielded or have received approval for full-rate production and anticipate production funding in the current or subsequent fiscal year. Therefore, the data are not directly comparable with totals reported in previous years. Prior to FY 1979, the Department of Health and Human Services was the Department of Health, Education, and Welfare; these data include the predecessor organization. The federal fiscal year cycle changed in FY 1977, from 1 July–30 June to the current 1 October–30 September cycle; no data were collected for the 3-month transition period of July–September 1976. Prior to FY 1977, the Department of Energy R&D programs were under the Energy Research and Development Administration from FYs 1974–76, and prior to FY 1974, R&D was under the Atomic Energy Commission; these data include the predecessor organizations.

National Center for Science and Engineering Statistics, Survey of Federal Funds for Research and Development.

Agencies’ shares of obligations for basic research have changed over time as agency missions have grown and developed with national needs. For example, in FY 1967, the National Aeronautics and Space Administration (NASA) was the second-largest funder of basic research, accounting for nearly 18% of federal obligations, followed by the Department of Energy (DOE) (previously called the Atomic Energy Commission) (16%), the Department of Defense (DOD) (15%), NSF (13%), and the Department of Agriculture (USDA) (5%). By FY 2023, DOE and NSF were the second- and third-largest funders of basic research, respectively, accounting for 13% each, followed by NASA (11%), DOD (7%), and USDA (3%). In FY 2023, all other agencies combined accounted for 3% of federal obligations for basic research.

Federal Obligations for Basic Research by Fields of R&D

Federal Funds for R&D also measures federal agency obligations by type of R&D (i.e., basic research, applied research, and experimental development) and by field of R&D, formerly known as field of science and engineering. https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#technical-tables ." data-bs-content="Effective with volume 71 (FYs 2021 and 2022), the Federal Funds for R&D Survey was redesigned and the fields of science and engineering were revised to the fields of R&D. For a crosswalk of changes from fields of science and engineering to fields of R&D, see technical table A-3, Crosswalk for the Survey of Federal Funds for Research and Development: Volume 70 to Volume 71 at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#technical-tables ." data-endnote-uuid="89ad1195-442a-4be0-a244-6aca3f465ffe">​ Effective with volume 71 (FYs 2021 and 2022), the Federal Funds for R&D Survey was redesigned and the fields of science and engineering were revised to the fields of R&D. For a crosswalk of changes from fields of science and engineering to fields of R&D, see technical table A-3, Crosswalk for the Survey of Federal Funds for Research and Development: Volume 70 to Volume 71 at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#technical-tables . Federal funding for fields of R&D is often driven by agency mission and need. For example, given that HHS is the predominate funder of basic research among all agencies, it should come as little surprise that the majority of federal funding for basic research is in the field of life sciences, which accounted for 42% of all fields of R&D for basic research in FY 2022 ( figure 8 ). https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data ." data-bs-content="In FY 2022, HHS accounted for 81% of all basic research funding for life sciences. For details, see table 29 in the full set of Federal Funds for R&D Survey tables at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data ." data-endnote-uuid="6d476dda-9842-4f60-9e2c-c4d07fa84fd1">​ In FY 2022, HHS accounted for 81% of all basic research funding for life sciences. For details, see table 29 in the full set of Federal Funds for R&D Survey tables at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data . Life sciences reached a peak of nearly 60% of all fields of R&D for basic research in FY 2003 at the end of the NIH doubling process. Life sciences includes five subcategories: agricultural sciences, biological and biomedical sciences, health sciences, natural resources and conservation, and all other life sciences. https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data ." data-bs-content="For details, see table 29 in the full set of Federal Funds for R&D Survey tables at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data ." data-endnote-uuid="f50a55ed-8f00-49a2-af8b-346d054980eb">​ For details, see table 29 in the full set of Federal Funds for R&D Survey tables at https://ncses.nsf.gov/surveys/federal-funds-research-development/2022-2023#data .

Federal obligations for basic research, by field of R&D: FYs 1967–2023

Because of rounding, detail may not add to total. As of volume 71 (FYs 2021–22) computer science and mathematics are collected as two separate categories: computer and information science, and mathematics and statistics. For ease of reference, they have been combined for presentation based on previous classification. Prior to volume 71, geosciences, atmospheric sciences, and ocean sciences were titled environmental sciences. As of volume 71, social sciences no longer includes education research or law; those categories were modified to other fields. FYs 2020–22 obligations include additional funding provided by supplemental COVID-19 pandemic-related appropriations (e.g., Coronavirus Aid, Relief, and Economic Security [CARES] Act). Beginning with FY 2016, the totals reported for development obligations represent a refinement to this category by more narrowly defining it to be "experimental development." Most notably, totals for development do not include the Department of Defense (DOD) Budget Activity 7 (Operational System Development) obligations. Those funds, previously included in DOD's development obligation totals, support the development efforts to upgrade systems that have been fielded or have received approval for full-rate production and anticipate production funding in the current or subsequent fiscal year. Therefore, the data are not directly comparable with totals reported in previous years. FYs 2009 and 2010 obligations include additional funding provided by the American Recovery and Reinvestment Act of 2009. Prior to FY 1979, the Department of Health and Human Services was the Department of Health, Education, and Welfare; these data include the predecessor organization. The federal fiscal year cycle changed in FY 1977, from 1 July–30 June to the current 1 October–30 September cycle; no data were collected for the 3-month transition period of July–September 1976. Prior to FY 1977, the Department of Energy R&D programs were under the Energy Research and Development Administration from FYs 1974–76, and prior to FY 1974, R&D was under the Atomic Energy Commission; these data include the predecessor organizations.

Although HHS accounted for nearly 81% of all federal basic research funding for life sciences in FY 2022, USDA was the second-largest funder, accounting for 6% ($1.2 billion), followed by NSF and the Department of Veterans Affairs at 4% ($787.0 million) and 3% ($665.1 million), respectively ( table 1 ). Although federal obligations for basic research in psychology amounts to only 5% ($2.3 billion) of all fields of R&D, it is highly concentrated within HHS, which accounted for 97% ($2.2 billion) of all basic research in psychology in FY 2022.

Federal obligations for basic research, by agency and field of R&D: FY 2022

Because of rounding, detail may not add to total. Only those agencies and subdivisions that had obligations in variables represented by this table appear in the table. FY 2022 obligations include additional funding provided by supplemental COVID-19 pandemic-related appropriations (e.g., Coronavirus Aid, Relief, and Economic Security [CARES] Act).

National Center for Science and Engineering Statistics, Survey of Federal Funds for Research and Development, FYs 2022–23.

Self-adjusting brain pacemaker may help reduce Parkinson’s disease symptoms

Small NIH-funded trial shows the promise of personalized medicine 

A small feasibility study funded by the National Institutes of Health ( NIH ) found that an implanted device regulated by the body’s brain activity could provide continual and improved treatment for the symptoms of Parkinson’s disease (PD) in certain people with the disorder. This type of treatment, called adaptive deep brain stimulation (aDBS), is an improvement on a technique that has been used for PD and other brain disorders for many years. The study found aDBS was markedly more effective at controlling PD symptoms compared to conventional DBS treatments.

“This study marks a big step forward towards developing a DBS system that adapts to what the individual patient needs at a given time,” said Megan Frankowski, Ph.D., program director for NIH ’s Brain Research Through Advancing Innovative Neurotechnologies®  Initiative, or   The BRAIN Initiative ® , which helped fund this project. “By helping to control residual symptoms while not exacerbating others, adaptive DBS has the potential to improve the quality of life for some people living with Parkinson’s disease.”

DBS involves implanting fine wires called electrodes into the brain at specific locations. These wires then deliver electrical signals that can help mitigate the symptoms of brain disorders such as PD. Conventional DBS provides a constant level of stimulation and can also lead to unwanted side effects, because the brain does not always need the same strength of treatment. Therefore, aDBS uses data taken directly from a person’s brain and uses machine learning to adjust the level of stimulation in real time as the person’s needs change over time.

Four people already receiving conventional DBS were first asked what they felt was their most bothersome symptom that had persisted despite treatment. In many instances this was either involuntary movements or difficulty in initiating movement. The participants were then set up to receive aDBS treatment alongside their existing DBS therapy. After training the aDBS algorithm for several months, the participants were sent home, where the comparison test was performed by alternating between conventional and aDBS treatments. Changes occurred every two to seven days.

aDBS improved each participant’s most bothersome symptom roughly 50% compared to conventional DBS. Notably, even though they were not told which type of treatment they were receiving at any one time, three of the four participants were often able to correctly guess when they were on aDBS due to noticeable symptom improvement. 

This project is a continuation of several years of work led by Philip Starr, M.D., Ph.D., and colleagues at the University of California, San Francisco. Previously, in 2018, they reported the development of an  adaptive DBS system , referred to as a “closed loop” system, that adjusted based on feedback from the brain itself. Later, in 2021, they described their ability to  record brain activity in people as they went about their daily lives. 

Here, those two findings were combined to use brain activity recorded during normal life activities to drive the aDBS system. However, DBS treatment changed brain activity so much that the signal that had been expected to control the aDBS system was no longer detectable. This required researchers to take a computational and data-driven approach to identify a different signal within the brains of people with PD who were receiving conventional DBS therapy.

Conventional treatment for Parkinson’s disease often involves the drug levodopa, which is used to replace dopamine in the brain that has been lost because of the disorder. Because the amount of the drug in the brain fluctuates, peaking shortly after administration of the drug and gradually decreasing as it is metabolized by the body, aDBS could help smooth out the fluctuations by providing increased stimulation when drug levels are high and vice versa, making it an attractive option for patients requiring high doses of levodopa.

While these findings are promising, there remain significant challenges to overcome for this therapy to be more widely available. The initial setup of the device requires considerable input from highly trained clinicians. Researchers envision a future where most of the work would be managed by the device itself, greatly reducing the need for repeat visits to the clinic for fine tuning.

This type of automation is also necessary for other groups to test and eventually offer aDBS therapy in a clinical setting.

“One of the big issues facing DBS, even in approved indications like Parkinson’s, is access, both for patients in terms of where they can get it and also the physicians who need special training to program these devices,” said Frankowski. “If there were a way for a system to find the most optimal settings at the press of a button, that would really increase the availability of this treatment for more people.” 

This study was supported by NINDS and NIH ’s The BRAIN Initiative (NS10054, NS129627, NS080680, NS120037, NS131405, NS113637), Thiemann Foundation, and the TUYF Charitable Trust Fund. Article:  Oehrn CR, Cernera S, Hammer LH, et al. “Chronic adaptive deep brain stimulation is superior to conventional stimulation in Parkinson’s disease: a blinded randomized feasibility trial.” Nature Medicine  August 19, 2024. DOI:  10.1038/s41591-024-03196-z

About the National Institute of Neurological Disorders and Stroke (NINDS) :  NINDS   is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease.

NIH ’s  The BRAIN Initiative , a multidisciplinary collaboration across  10 NIH Institutes and Centers , is uniquely positioned for cross-cutting discoveries in neuroscience to revolutionize our understanding of the human brain. By accelerating the development and application of innovative neurotechnologies, The BRAIN Initiative® is enabling researchers to understand the brain at unprecedented levels of detail in both health and disease, improving how we treat, prevent, and cure brain disorders. The BRAIN Initiative involves a multidisciplinary network of federal and non-federal partners whose missions and current research portfolios complement the goals of The BRAIN Initiative. 

About the National Institutes of Health (NIH) :  NIH , the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases.

• Share full article

Advertisement

Supported by

Biden Awards $150 Million in Research Grants as Part of Cancer ‘Moonshot’

President Biden has had a deep personal interest in cancer research since his son Beau died of an aggressive brain cancer in 2015.

President Biden Announces $150 Million in Cancer Research Grants

President biden said eight research centers would receive research awards aimed at pioneering new methods of precision cancer surgery as part of his administration’s cancer “moonshoot” initiative..

As all of you know, cancer surgery is an incredibly challenging procedure. It takes the best surgeons in the world, and it takes its toll on families. As Jill and I — as Jill says, it steals time. It steals away hope. Our family knows the feeling, as many here do. Today, we’re announcing $150 million ARPA-H funding for some of the nation’s cutting-edge cancer research institutions. That includes, right here, Tulane University. [cheers] And we’re moving quickly because we know all families touched by cancers are in a race against time. It’s all part of our goal, of our cancer “moonshot,” to end cancer as we know it. Even cure some cancers. We’re mobilizing the whole of country effort to cut American cancer deaths in half by — within 25 years, and boost support for patients and their families. I’m confident in our capacity to do that.

By Zach Montague

Reporting from New Orleans

Freed from the campaign trail and the grinding pursuit of another term, President Biden traveled to New Orleans on Tuesday to focus on a project close to his heart: the “moonshot” effort to sharply cut cancer deaths in the United States that he carried over from his time as vice president and has become a hallmark of his presidency.

Speaking at Tulane University, Mr. Biden and the first lady, Jill Biden, announced eight research centers, including one at Tulane, that will collectively receive $150 million in research awards aimed at pioneering new methods of precision cancer surgery.

Before addressing a crowd on campus, the president and the first lady met with a team of researchers who demonstrated the technology under development at Tulane. It uses imaging of cells on tumor sites to verify for surgeons that cancer cells have been fully removed and to reduce the need for follow-up surgeries.

Standing in front of a sign reading “curing cancer faster,” Mr. Biden described touring cancer centers in Australia and Ireland, and being frustrated by a lack of international collaboration.

“We don’t want to keep information — we want to share it,” he said.

The awards announced on Tuesday are to be made through the Advanced Research Projects Agency for Health , or ARPA-H, which was founded in 2022 and is aimed at driving biomedical innovation.

The other award recipients were Dartmouth College; Johns Hopkins University; Rice University; the University of California, San Francisco; the University of Illinois Urbana-Champaign; the University of Washington; and Cision Vision in Mountain View, Calif.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Entire Site
• Research & Funding
• Health Information
• About NIDDK
• Current Funding Opportunities

Single Source for the Continuation of the Type 1 Diabetes in Acute Pancreatitis Consortium - Data Coordinating Center (T1DAPC-DCC) (U01 Clinical Trial Optional)

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) is announcing its intent to request a single source cooperative agreement application from the Pennsylvania State University Hershey Medical Center for the continuation of the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC), a clinical consortium composed of one Data Coordinating Center (DCC) and up to ten Clinical Centers (CC) to continue a prospective longitudinal observational study of the incidence of diabetes that occurs during or after an acute pancreatitis (AP) episode, with an emphasis on type 1 diabetes (T1D) [the Diabetes Related to Acute Pancreatitis and its Mechanisms (DREAM) study] (NCT05197920). The study has been designed to gain insight into the incidence, clinical evolution, etiology, type, and pathophysiology of T1D and other forms of diabetes that occur during, or after one or more episodes of AP. The Pennsylvania State University Hershey Medical Center serves as the DCC of the currently funded consortium and has been instrumental in providing all the administrative, regulatory, managerial, logistic, analytic and financial functions to establish and pursue the research objectives of the T1DAPC. Applications for the CCs can be submitted in response to a separate NOFO, RFA-DK-25-017: Limited Competition for the Continuation of the Type 1 Diabetes in Acute Pancreatitis Consortium Clinical Centers (T1DAPC-CC) (U01 Clinical Trial Optional).

Funding Opportunity Details

Full Announcement: RFA-DK-25-018 Related Notices or Announcements: None

Program Contact: Aynur Unalp-Arida, M.D., Ph.D.

Open Date: 10/21/2024

Letter of Intent Due Date: October 21, 2024

Application Due Date: November 19, 2024