Researchers Behaving Badly: Known Frauds Are "the Tip of the Iceberg"
Last week, the whistleblowers in the Paolo Macchiarini affair at Sweden's Karolinska Institutet went on the record here to detail the retaliation they suffered for trying to expose a star surgeon's appalling research misconduct.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise.
The whistleblowers had discovered that in six published papers, Macchiarini falsified data, lied about the condition of patients and circumvented ethical approvals. As a result, multiple patients suffered and died. But Karolinska turned a blind eye for years.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise. Just this week, for example, Retraction Watch and STAT together broke the news that a Harvard Medical School cardiologist and stem cell researcher, Piero Anversa, falsified data in a whopping 31 papers, which now have to be retracted. Anversa had claimed that he could regenerate heart muscle by injecting bone marrow cells into damaged hearts, a result that no one has been able to duplicate.
A 2009 study published in the Public Library of Science (PLOS) found that about two percent of scientists admitted to committing fabrication, falsification or plagiarism in their work. That's a small number, but up to one third of scientists admit to committing "questionable research practices" that fall into a gray area between rigorous accuracy and outright fraud.
These dubious practices may include misrepresentations, research bias, and inaccurate interpretations of data. One common questionable research practice entails formulating a hypothesis after the research is done in order to claim a successful premise. Another highly questionable practice that can shape research is ghost-authoring by representatives of the pharmaceutical industry and other for-profit fields. Still another is gifting co-authorship to unqualified but powerful individuals who can advance one's career. Such practices can unfairly bolster a scientist's reputation and increase the likelihood of getting the work published.
The above percentages represent what scientists admit to doing themselves; when they evaluate the practices of their colleagues, the numbers jump dramatically. In a 2012 study published in the Journal of Research in Medical Sciences, researchers estimated that 14 percent of other scientists commit serious misconduct, while up to 72 percent engage in questionable practices. While these are only estimates, the problem is clearly not one of just a few bad apples.
In the PLOS study, Daniele Fanelli says that increasing evidence suggests the known frauds are "just the 'tip of the iceberg,' and that many cases are never discovered" because fraud is extremely hard to detect.
Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
In addition, it's likely that most cases of scientific misconduct go unreported because of the high price of whistleblowing. Those in the Macchiarini case showed extraordinary persistence in their multi-year campaign to stop his deadly trachea implants, while suffering serious damage to their careers. Such heroic efforts to unmask fraud are probably rare.
To make matters worse, there are numerous players in the scientific world who may be complicit in either committing misconduct or covering it up. These include not only primary researchers but co-authors, institutional executives, journal editors, and industry leaders. Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
Another part of the problem is that it's rare for students in science and medicine to receive an education in ethics. And studies have shown that older, more experienced and possibly jaded researchers are more likely to fudge results than their younger, more idealistic colleagues.
So, given the steep price that individuals and institutions pay for scientific misconduct, what compels them to go down that road in the first place? According to the JRMS study, individuals face intense pressures to publish and to attract grant money in order to secure teaching positions at universities. Once they have acquired positions, the pressure is on to keep the grants and publishing credits coming in order to obtain tenure, be appointed to positions on boards, and recruit flocks of graduate students to assist in research. And not to be underestimated is the human ego.
Paolo Macchiarini is an especially vivid example of a scientist seeking not only fortune, but fame. He liberally (and falsely) claimed powerful politicians and celebrities, even the Pope, as patients or admirers. He may be an extreme example, but we live in an age of celebrity scientists who bring huge amounts of grant money and high prestige to the institutions that employ them.
The media plays a significant role in both glorifying stars and unmasking frauds. In the Macchiarini scandal, the media first lifted him up, as in NBC's laudatory documentary, "A Leap of Faith," which painted him as a kind of miracle-worker, and then brought him down, as in the January 2016 documentary, "The Experiments," which chronicled the agonizing death of one of his patients.
Institutions can also play a crucial role in scientific fraud by putting more emphasis on the number and frequency of papers published than on their quality. The whole course of a scientist's career is profoundly affected by something called the h-index. This is a number based on both the frequency of papers published and how many times the papers are cited by other researchers. Raising one's ranking on the h-index becomes an overriding goal, sometimes eclipsing the kind of patient, time-consuming research that leads to true breakthroughs based on reliable results.
Universities also create a high-pressured environment that encourages scientists to cut corners. They, too, place a heavy emphasis on attracting large monetary grants and accruing fame and prestige. This can lead them, just as it led Karolinska, to protect a star scientist's sloppy or questionable research. According to Dr. Andrew Rosenberg, who is director of the Center for Science and Democracy at the U.S.-based Union of Concerned Scientists, "Karolinska defended its investment in an individual as opposed to the long-term health of the institution. People were dying, and they should have outsourced the investigation from the very beginning."
Having institutions investigate their own practices is a conflict of interest from the get-go, says Rosenberg.
Scientists, universities, and research institutions are also not immune to fads. "Hot" subjects attract grant money and confer prestige, incentivizing scientists to shift their research priorities in a direction that garners more grants. This can mean neglecting the scientist's true area of expertise and interests in favor of a subject that's more likely to attract grant money. In Macchiarini's case, he was allegedly at the forefront of the currently sexy field of regenerative medicine -- a field in which Karolinska was making a huge investment.
The relative scarcity of resources intensifies the already significant pressure on scientists. They may want to publish results rapidly, since they face many competitors for limited grant money, academic positions, students, and influence. The scarcity means that a great many researchers will fail while only a few succeed. Once again, the temptation may be to rush research and to show it in the most positive light possible, even if it means fudging or exaggerating results.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable.
Intense competition can have a perverse effect on researchers, according to a 2007 study in the journal Science of Engineering and Ethics. Not only does it place undue pressure on scientists to succeed, it frequently leads to the withholding of information from colleagues, which undermines a system in which new discoveries build on the previous work of others. Researchers may feel compelled to withhold their results because of the pressure to be the first to publish. The study's authors propose that more investment in basic research from governments could alleviate some of these competitive pressures.
Scientific journals, although they play a part in publishing flawed science, can't be expected to investigate cases of suspected fraud, says the German science blogger Leonid Schneider. Schneider's writings helped to expose the Macchiarini affair.
"They just basically wait for someone to retract problematic papers," he says.
He also notes that, while American scientists can go to the Office of Research Integrity to report misconduct, whistleblowers in Europe have no external authority to whom they can appeal to investigate cases of fraud.
"They have to go to their employer, who has a vested interest in covering up cases of misconduct," he says.
Science is increasingly international. Major studies can include collaborators from several different countries, and he suggests there should be an international body accessible to all researchers that will investigate suspected fraud.
Ultimately, says Rosenberg, the scientific system must incorporate trust. "You trust co-authors when you write a paper, and peer reviewers at journals trust that scientists at research institutions like Karolinska are acting with integrity."
Without trust, the whole system falls apart. It's the trust of the public, an elusive asset once it has been betrayed, that science depends upon for its very existence. Scientific research is overwhelmingly financed by tax dollars, and the need for the goodwill of the public is more than an abstraction.
The Macchiarini affair raises a profound question of trust and responsibility: Should multiple co-authors be held responsible for a lead author's misconduct?
Karolinska apparently believes so. When the institution at last owned up to the scandal, it vindictively found Karl Henrik-Grinnemo, one of the whistleblowers, guilty of scientific misconduct as well. It also designated two other whistleblowers as "blameworthy" for their roles as co-authors of the papers on which Macchiarini was the lead author.
As a result, the whistleblowers' reputations and employment prospects have become collateral damage. Accusations of research misconduct can be a career killer. Research grants dry up, employment opportunities evaporate, publishing becomes next to impossible, and collaborators vanish into thin air.
Grinnemo contends that co-authors should only be responsible for their discrete contributions, not for the data supplied by others.
"Different aspects of a paper are highly specialized," he says, "and that's why you have multiple authors. You cannot go through every single bit of data because you don't understand all the parts of the article."
This is especially true in multidisciplinary, translational research, where there are sometimes 20 or more authors. "You have to trust co-authors, and if you find something wrong you have to notify all co-authors. But you couldn't go through everything or it would take years to publish an article," says Grinnemo.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable. Along with increased support from governments and industry, a change in academic culture that emphasizes quality over quantity of published studies could help encourage meritorious research.
But beyond that, trust will always play a role when numerous specialists unite to achieve a common goal: the accumulation of knowledge that will promote human health, wealth, and well-being.
[Correction: An earlier version of this story mistakenly credited The New York Times with breaking the news of the Anversa retractions, rather than Retraction Watch and STAT, which jointly published the exclusive on October 14th. The piece in the Times ran on October 15th. We regret the error.]
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers