How values, attention, economics, and power influence scientific priorities, and why culturally intelligent research funding may be one of the most important public health tools we have.

Research is often described as a process that begins with a question and ends with data.

In reality, something comes before the question.

A decision about what is worth asking.

That decision is influenced by scientific opportunity, disease burden, available expertise, public need, institutional history, economics, political priorities, advocacy, and culture. Researchers use rigorous methods precisely because science needs protection from bias. Yet the larger research enterprise still exists within society, and society brings its values into the rooms where priorities are established.

Within every culture are values. Within those values are ideas about what deserves attention, what feels urgent, what inspires fear, what generates hope, what attracts investment, and ultimately what people become passionate enough to study.

This means data can influence research priorities enormously without being the only force that determines them.

And the consequences reach far beyond universities.

Research priorities help determine which interventions are developed, which health problems receive sustained attention, which preventive strategies acquire an evidence base, which communities become visible in the scientific literature, and which solutions eventually become available to the people who need them.

A research budget, therefore, is more than a financial document.

It is a map of what a society has decided is worth discovering.

Data Is Powerful. Priority Setting Is Human.

Some of the strongest public health institutions in the United States are deliberately building systems around data driven decision making. The Centers for Disease Control and Prevention, for example, describes its Public Health Data Strategy as a means of providing timely, actionable information that can support decisions about where resources should be prioritized (Centers for Disease Control and Prevention [CDC], 2026).

This is exactly the direction modern public health should be heading.

Yet evidence from federal research funding demonstrates why data alone cannot completely determine priorities.

Ballreich et al. (2021) examined NIH funding across 46 disease categories and compared funding with changes in disease burden. The strongest predictor of NIH funding in 2019 was remarkably simple: how much funding the disease had received in 2008. The correlation between funding in the two periods was 0.88. Once prior funding was incorporated into the statistical model, disease burden and changes in disease burden were no longer statistically significant predictors of 2019 funding.

That finding deserves attention.

It suggests that research systems can develop institutional memory. Once an area has laboratories, investigators, review expertise, advocacy networks, professional societies, congressional familiarity, established programs, and a history of awards, future investment can become easier to sustain.

This is understandable. Continuity matters in science. Researchers need stable careers. Laboratories cannot be assembled and dismantled every time a population health indicator moves. Basic science can generate discoveries whose importance is impossible to predict in advance.

But stability can also create inertia.

A dataset can tell us where disease burden is changing. It cannot issue a funding announcement. It cannot appropriate money. It cannot assemble a review panel. It cannot persuade a decision maker that a quiet problem deserves urgent attention.

People and institutions do those things.

And people live inside cultures.

Culture Enters Research Through Values, Advocacy, and Attention

The social influence on research funding has been studied directly.

Best (2012) examined 53 diseases across 19 years and found that diseases with highly organized patient advocacy communities secured substantial increases in federal medical research funding. Advocacy did more than help particular diseases. It reshaped the broader culture of medical research priority setting by influencing how policymakers compared diseases and thought about who deserved research investment.

Hegde and Sampat (2015) similarly found evidence that disease advocacy and lobbying can influence congressional support and portions of federal biomedical research funding.

This should not lead us to conclude that advocacy is a problem. Advocacy has transformed health care for the better. Patients and families have forced institutions to recognize neglected diseases, challenged stigma, accelerated discovery, and made scientists aware of needs that traditional systems overlooked.

The deeper issue is unequal cultural visibility.

Some health problems have recognizable symbols. Some have celebrity advocates. Some affect large voting constituencies. Some generate emotionally powerful stories. Some possess well financed nonprofit ecosystems. Some immediately activate fear.

Others unfold quietly.

Their consequences may accumulate across years. Their prevention may depend on better conversations, social support, healthier environments, earlier screening, trust, education, behavior change, community health workers, better coordination, or improvements in the way people navigate systems.

Those subjects are harder to dramatize.

And culture has a spotlight.

Research shows that the spotlight is uneven. Maggio et al. (2019) examined 11,436 federally funded cancer research articles and found that media attention was not proportional to cancer burden. Research involving cancer prevention and control received less media attention than work in other portions of the cancer continuum.

This does not establish that media attention causes funding decisions. It reveals something more fundamental: scientific importance and cultural visibility are different currencies.

A society capable of distinguishing between them can make better investments.

Economics Creates Another Gravity Field

Culture is only one influence. Economics matters as well.

Biomedical innovation takes place within systems where governments, universities, health systems, foundations, insurers, pharmaceutical companies, technology companies, investors, and patients all operate under different incentives.

Barrenho et al. (2019), examining hundreds of therapeutic indications, found that pharmaceutical innovation reflected both disease burden and market characteristics. In other words, scientific activity responds partly to need and partly to the economic environment surrounding that need.

This is hardly surprising.

A treatment that can generate a proprietary product, reimbursement pathway, patent portfolio, or large commercial market has an economic mechanism capable of pulling capital toward it.

Many preventive interventions create value differently.

Their economic achievement may be a hospitalization that never happens.

A complication avoided.

A person who remains independent.

A medication error prevented.

A fall that never occurs.

A conversation that leads someone to seek treatment six months earlier.

A community health worker who helps someone control diabetes before renal failure develops.

A caregiver who receives support before reaching crisis.

A lonely older adult who becomes socially engaged before deterioration accelerates.

These outcomes can create enormous human and economic value. Yet the financial return may be distributed across patients, families, insurers, employers, hospitals, government programs, and society rather than concentrated inside a single product.

The result is an important mismatch: some of the interventions with the greatest potential social return may have weaker natural mechanisms for attracting commercial investment.

This is exactly where public research funding becomes indispensable.

Communication Is Clinical Infrastructure

Consider communication.

Communication is routinely described as a “soft skill” in health care. The evidence suggests something much more consequential.

A review published by the Agency for Healthcare Research and Quality reported that communication failures had been implicated at the root of more than 70 percent of Joint Commission sentinel events. The same review described associations between communication and patient harm, length of stay, resource utilization, mortality, satisfaction, and readmissions (Dingley et al., 2008).

More contemporary evidence points in the same direction. Humphrey et al. (2022) examined medical malpractice claims and identified communication failures in approximately 49 percent of claims. Among cases involving failed handoffs, the researchers concluded that a substantial majority potentially could have been averted through improved handoff practices.

So the strongest evidence based version of the “50 percent” argument is powerful: in serious patient safety and liability datasets, communication failures repeatedly approach or exceed the 50 percent threshold.

That makes communication a clinical issue.

Communication determines whether the right information reaches the right person before a decision. It affects whether a patient understands instructions, whether concerns are voiced, whether social barriers are discovered, whether goals are understood, whether a clinician recognizes a change in condition, whether one professional knows what another has already done, and whether a patient leaves an encounter capable of acting on the care plan.

Communication, viewed this way, is preventive infrastructure.

Yet imagine two research proposals competing for cultural attention.

One promises a new technology capable of treating an advanced disease.

The other proposes to test a communication intervention designed to identify risk earlier, improve adherence, strengthen coordination, reduce preventable complications, and help patients act before their health deteriorates.

Which one sounds more dramatic?

Which one produces the more exciting headline?

Now ask a more important question:

Which one prevents more suffering per dollar invested?

We cannot know until we study them.

That is precisely why culturally quieter interventions need rigorous research.

The Prevention Paradox: Success Often Looks Like Nothing Happened

There is a communications problem embedded inside prevention itself.

Treatment gives us a patient we can see.

Prevention may give us a patient who never has to become one.

When medicine saves someone during a dramatic crisis, the result is immediate and emotionally understandable. When prevention succeeds, the event disappears. The heart attack never occurs. The infection never spreads. The fall never happens. The diabetes never progresses. The emergency department visit never takes place.

Prevention frequently produces its greatest result by eliminating the story that would have made people pay attention.

This creates a cultural disadvantage.

Evidence suggests that there is meaningful room to improve how preventive research portfolios correspond with population health priorities. Vargas et al. (2019) examined NIH supported prevention research from 2012 through 2017. Only 25.9 percent of prevention research projects measured one of the ten leading causes of death, even though those causes accounted for approximately 74 percent of deaths. Just 34 percent of projects measured a leading risk factor for death, while those risk factors collectively accounted for an estimated 57.3 percent of mortality.

This does not mean every research dollar should mechanically follow mortality statistics. Scientific opportunity, emerging threats, rare diseases, fundamental discovery, disability, quality of life, health disparities, and long range innovation all matter.

It does mean we should continually ask whether the research portfolio is aligned with the outcomes we say we want.

The economic case for doing so is compelling.

A systematic review of 52 studies found a median return on investment of 14.3 to 1 across evaluated public health interventions, although returns varied widely by intervention and study quality (Masters et al., 2017).

And the evidence continues to develop. A 2026 systematic review of 41 community health worker programs across 23 states found median annual savings of approximately $403,000 against median annual program costs of approximately $155,000, producing a median return of $2.12 for every dollar invested (Rashid et al., 2026).

Those are examples of something public health understands especially well:

The most valuable health intervention may be the one that reaches a person before the expensive part begins.

When Culture Becomes Policy, Research Can Change Quickly

The relationship between culture and research becomes particularly visible when social priorities are converted into government policy.

Presidents, legislators, agency leaders, advisory bodies, foundations, university leaders, and other decision makers influence funding priorities through budgets, strategic plans, regulatory decisions, program announcements, and administrative policies.

Recent history provides an unusually clear illustration.

Between February 28 and April 8, 2025, researchers documented the termination of 694 NIH grants across 24 NIH institutes and centers, representing approximately $1.81 billion in awarded funding. The authors described the terminations as focusing on topics that were no longer aligned with agency priorities (Liu et al., 2025).

Whatever one’s political perspective, the structural lesson is larger than a single administration.

Scientific priority setting is connected to governing priority setting.

Elections matter. Agency leadership matters. Congressional appropriations matter. Institutional leadership matters. Foundations matter. Advocacy matters. Economic incentives matter.

Data remain essential throughout this process, but data operate inside decision systems controlled by human beings.

The goal, therefore, should be to make those systems more intelligent.

We Need Cultural Intelligence in Research Funding

The answer is not to remove human judgment from science. Human judgment is necessary.

The opportunity is to make the forces surrounding that judgment more visible.

A mature research system should be able to examine disease burden, preventability, scientific opportunity, existing investment, disparities, cost, implementation feasibility, commercial incentives, public attention, and advocacy intensity simultaneously.

Imagine a funding dashboard capable of showing decision makers that a health problem carries a high population burden, possesses realistic preventive interventions, receives comparatively little research investment, attracts limited media attention, and has weak commercial incentives.

That combination should tell us something.

It may identify precisely the area where public investment can create the greatest additional value.

Similarly, unusually high cultural attention should not disqualify an important research area. It should simply become another variable we understand. The objective is awareness.

A culturally intelligent research system would ask whether we are funding an issue because its burden has increased, because a major scientific opportunity has emerged, because prevention is possible, because affected communities have identified an unmet need, because an established funding structure already exists, because commercial incentives are strong, because public attention has intensified, or because several of those forces are operating simultaneously.

Once those influences are visible, decision makers can become more intentional.

This also creates a compelling argument for protecting a meaningful portion of health research funding for prevention, implementation science, communication, community based interventions, and other approaches whose benefits may be substantial even when their cultural visibility or commercial potential is limited.

Treatment and prevention belong in the same health strategy.

Research funding should recognize the value of keeping people healthy with the same seriousness it applies to restoring health after disease has progressed.

Research Should Study the Forces That Shape Research

There is another opportunity here.

Researchers study human behavior, organizational behavior, economics, implementation, political systems, communication, culture, and decision making.

We should apply that same scientific curiosity to the research enterprise itself.

Why do certain health problems acquire momentum?

Why do others remain scientifically underdeveloped despite measurable burden?

How much does media visibility influence public demand?

How does advocacy alter funding trajectories?

Which preventive interventions remain underinvestigated because their economic benefits are dispersed?

Where does institutional funding history overpower changing population need?

Which communities have enough organizational infrastructure to make themselves heard, and which populations experience equally serious problems without the same ability to command attention?

These are research questions.

And answering them could improve the return on billions of dollars in future health investment.

The Next Frontier Is Smarter Attention

Scientific rigor requires us to follow evidence wherever it leads.

Scientific stewardship requires something else: the wisdom to decide where we should look in the first place.

Culture will always influence research because research is a human enterprise. Values determine which outcomes people care about. Emotion influences urgency. Economics influences investment. Advocacy influences visibility. Institutions preserve previous priorities. Political leadership can accelerate, redirect, or interrupt funding.

Understanding these forces gives us the ability to manage them more intelligently.

The goal is a research culture capable of hearing both the alarm and the whisper.

It should respond when a devastating disease commands national attention. It should also recognize the quieter opportunities to prevent suffering before a crisis exists to command attention.

Because the ultimate measure of research is not how dramatic the question sounds.

It is what becomes possible because we answered it.

The next frontier of evidence based health policy may therefore be evidence about the forces that determine which evidence gets produced.

If we understand those forces, we can become more deliberate about where research dollars go, more sophisticated about the cultural pressures surrounding those decisions, and more capable of investing in solutions before human suffering makes them impossible to ignore.

And that may be one of the most powerful preventive interventions of all.

References

Ballreich, J. M., Gross, C. P., Powe, N. R., & Anderson, G. F. (2021). Allocation of National Institutes of Health funding by disease category in 2008 and 2019. JAMA Network Open, 4(1), e2034890. doi:10.1001/jamanetworkopen.2020.34890

Barrenho, E., Miraldo, M., & Smith, P. C. (2019). Does global drug innovation correspond to burden of disease? The neglected diseases in developed and developing countries. Health Economics, 28(1), 123–143. doi:10.1002/hec.3833

Best, R. K. (2012). Disease politics and medical research funding: Three ways advocacy shapes policy. American Sociological Review, 77(5), 780–803. doi:10.1177/0003122412458509

Centers for Disease Control and Prevention. (2026). About the Public Health Data Strategy. U.S. Department of Health and Human Services.

Dingley, C., Daugherty, K., Derieg, M. K., & Persing, R. (2008). Improving patient safety through provider communication strategy enhancements. In K. Henriksen, J. B. Battles, M. A. Keyes, et al. (Eds.), Advances in patient safety: New directions and alternative approaches: Vol. 3. Performance and tools. Agency for Healthcare Research and Quality.

Hegde, D., & Sampat, B. N. (2015). Can private money buy public science? Disease group lobbying and federal funding for biomedical research. Management Science, 61(10), 2281–2298. doi:10.1287/mnsc.2014.2107

Humphrey, K. E., Sundberg, M., Milliren, C. E., Graham, D. A., & Landrigan, C. P. (2022). Frequency and nature of communication and handoff failures in medical malpractice claims. Journal of Patient Safety, 18(2), 130–137. doi:10.1097/PTS.0000000000000937

Liu, M., Kadakia, K. T., Patel, V. R., & Krumholz, H. M. (2025). Characterization of research grant terminations at the National Institutes of Health. JAMA, 334(6), 534–536. doi:10.1001/jama.2025.7707

Maggio, L. A., Ratcliff, C. L., Krakow, M., Moorhead, L. L., Enkhbayar, A., & Alperin, J. P. (2019). Making headlines: An analysis of US government funded cancer research mentioned in online media. BMJ Open, 9(2), e025783. doi:10.1136/bmjopen-2018-025783

Masters, R., Anwar, E., Collins, B., Cookson, R., & Capewell, S. (2017). Return on investment of public health interventions: A systematic review. Journal of Epidemiology and Community Health, 71(8), 827–834. doi:10.1136/jech-2016-208141

Rashid, M., Fu, M., Jitareewong, P., Cho, J. Y., Nelson, R. E., Ceballos, R. M., Saokaew, S., & Chaiyakunapruk, N. (2026). Return on investment of community health workers in the United States: A systematic review. The Lancet Regional Health – Americas, 58, 101469. doi:10.1016/j.lana.2026.101469

Vargas, A. J., Schully, S. D., Villani, J., Ganoza Caballero, L., & Murray, D. M. (2019). Assessment of prevention research measuring leading risk factors and causes of mortality and disability supported by the US National Institutes of Health. JAMA Network Open, 2(11), e1914718. doi:10.1001/jamanetworkopen.2019.14718